diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..08ae74117cd54402e260369ed4cbf761e09cc283
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,40 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+ - repo: https://github.com/timothycrosley/isort
+ rev: 4.3.21-2
+ hooks:
+ - id: isort
+ args: [-sl]
+ - repo: https://github.com/psf/black
+ rev: stable
+ hooks:
+ - id: black
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v2.0.0
+ hooks:
+ - id: check-ast
+ - id: check-case-conflict
+ - id: trailing-whitespace
+ - id: end-of-file-fixer
+ - id: debug-statements
+ - id: check-added-large-files
+ - id: flake8
+ - repo: local
+ hooks:
+ - id: sphinx-build
+ name: sphinx build
+ entry: python -m sphinx.cmd.build
+ args: [-a, -E, -W, doc, sphinx]
+ language: system
+ files: ^doc/
+ types: [file]
+ pass_filenames: false
+ - id: sphinx-doctest
+ name: sphinx doctest
+ entry: python -m sphinx.cmd.build
+ args: [-a, -E, -b, doctest, doc, sphinx]
+ language: system
+ files: ^doc/
+ types: [file]
+ pass_filenames: false
diff --git a/bob/__init__.py b/bob/__init__.py
index 2ca5e07cb73f0bdddcb863ef497955964087e301..edbb4090fca046b19d22d3982711084621bff3be 100644
--- a/bob/__init__.py
+++ b/bob/__init__.py
@@ -1,3 +1,4 @@
# see https://docs.python.org/3/library/pkgutil.html
from pkgutil import extend_path
-__path__ = extend_path(__path__, __name__)
\ No newline at end of file
+
+__path__ = extend_path(__path__, __name__)
diff --git a/bob/learn/__init__.py b/bob/learn/__init__.py
index 2ab1e28b150f0549def9963e9e87de3fdd6b2579..edbb4090fca046b19d22d3982711084621bff3be 100644
--- a/bob/learn/__init__.py
+++ b/bob/learn/__init__.py
@@ -1,3 +1,4 @@
# see https://docs.python.org/3/library/pkgutil.html
from pkgutil import extend_path
+
__path__ = extend_path(__path__, __name__)
diff --git a/bob/learn/tensorflow/__init__.py b/bob/learn/tensorflow/__init__.py
index b17348644c602f4d16633e7c392b19a37c59086a..1b0e2db6061b8fcd5e328762aadf66d77a398193 100644
--- a/bob/learn/tensorflow/__init__.py
+++ b/bob/learn/tensorflow/__init__.py
@@ -3,4 +3,5 @@ def get_config():
Returns a string containing the configuration information.
"""
import bob.extension
+
return bob.extension.get_config(__name__)
diff --git a/bob/learn/tensorflow/callbacks.py b/bob/learn/tensorflow/callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..efd97e09bc8ddb67495f6f7f69d82b80feaf2afc
--- /dev/null
+++ b/bob/learn/tensorflow/callbacks.py
@@ -0,0 +1,100 @@
+import json
+import os
+
+import tensorflow as tf
+from tensorflow.keras import callbacks
+
+
+class CustomBackupAndRestore(tf.keras.callbacks.experimental.BackupAndRestore):
+ """This callback is experimental and might be removed in future.
+ See :any:`add_backup_callback`
+ """
+
+ def __init__(self, callbacks, backup_dir, **kwargs):
+ super().__init__(backup_dir=backup_dir, **kwargs)
+ self.callbacks = callbacks
+ self.callbacks_backup_path = os.path.join(self.backup_dir, "callbacks.json")
+
+ def backup(self):
+ variables = {}
+ for cb_name, cb in self.callbacks.items():
+ variables[cb_name] = {}
+ for k, v in cb.__dict__.items():
+ if not isinstance(v, (int, float)):
+ continue
+ variables[cb_name][k] = v
+ with open(self.callbacks_backup_path, "w") as f:
+ json.dump(variables, f, indent=4, sort_keys=True)
+
+ def restore(self):
+ if not os.path.isfile(self.callbacks_backup_path):
+ return False
+
+ with open(self.callbacks_backup_path, "r") as f:
+ variables = json.load(f)
+
+ for cb_name, cb in self.callbacks.items():
+ if cb_name not in variables:
+ continue
+ for k, v in cb.__dict__.items():
+ if k in variables[cb_name]:
+ cb.__dict__[k] = variables[cb_name][k]
+
+ return True
+
+ def on_train_begin(self, logs=None):
+ super().on_train_begin(logs=logs)
+ if self.restore():
+ print(f"Restored callbacks from {self.callbacks_backup_path}")
+ else:
+ print("Did not restore callbacks")
+
+ def on_epoch_end(self, epoch, logs=None):
+ super().on_epoch_end(epoch, logs=logs)
+ self.backup()
+
+ def on_train_end(self, logs=None):
+ # do not delete backups
+ pass
+
+
+def add_backup_callback(callbacks, backup_dir, **kwargs):
+ """Adds a backup callback to your callbacks to restore the training process
+ if it is interrupted.
+
+ .. warning::
+
+ This function is experimental and may be removed or changed in future.
+
+ Examples
+ --------
+
+ >>> CHECKPOINT = "checkpoints"
+ >>> callbacks = {
+ ... "best": tf.keras.callbacks.ModelCheckpoint(
+ ... f"{CHECKPOINT}/best",
+ ... monitor="val_acc",
+ ... save_best_only=True,
+ ... mode="max",
+ ... verbose=1,
+ ... ),
+ ... "tensorboard": tf.keras.callbacks.TensorBoard(
+ ... log_dir=f"{CHECKPOINT}/logs",
+ ... update_freq=15,
+ ... write_graph=False,
+ ... ),
+ ... }
+ >>> callbacks = add_backup_callback(callbacks, f"{CHECKPOINT}/backup")
+ >>> # callbacks will be a list that can be given to model.fit
+ >>> isinstance(callbacks, list)
+ True
+ """
+ if not isinstance(callbacks, dict):
+ raise ValueError(
+ "Please provide a dictionary of callbacks where "
+ "keys are simple names for your callbacks!"
+ )
+ cb = CustomBackupAndRestore(callbacks=callbacks, backup_dir=backup_dir, **kwargs)
+ callbacks = list(callbacks.values())
+ callbacks.append(cb)
+ return callbacks
diff --git a/bob/learn/tensorflow/configs/style_transfer/inception_v1_example.py b/bob/learn/tensorflow/configs/style_transfer/inception_v1_example.py
deleted file mode 100644
index 00bfa1e0628534802d47bba19ad27b20b04ef236..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/configs/style_transfer/inception_v1_example.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""
-Example using inception resnet v1
-
-"""
-
-import tensorflow as tf
-
-# -- architecture
-from bob.learn.tensorflow.network import inception_resnet_v1_batch_norm
-architecture = inception_resnet_v1_batch_norm
-
-# --checkpoint-dir
-from bob.extension import rc
-checkpoint_dir = rc['bob.bio.face_ongoing.inception-v1_batchnorm_rgb']
-
-# --style-end-points and -- content-end-points
-style_end_points = ["Conv2d_1a_3x3", "Conv2d_2b_3x3"]
-content_end_points = ["Block8"]
-
-scopes = {"InceptionResnetV1/":"InceptionResnetV1/"}
-
-# --style-image-paths
-style_image_paths = ["vincent_van_gogh.jpg",
- "vincent_van_gogh2.jpg"]
-
-# --preprocess-fn
-preprocess_fn = tf.image.per_image_standardization
diff --git a/bob/learn/tensorflow/configs/style_transfer/inception_v2_example.py b/bob/learn/tensorflow/configs/style_transfer/inception_v2_example.py
deleted file mode 100644
index 54eb3f2931f65e78fb6a1e201b65d119f172d0be..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/configs/style_transfer/inception_v2_example.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
-Example using inception resnet v2
-"""
-
-import tensorflow as tf
-
-# -- architecture
-from bob.learn.tensorflow.network import inception_resnet_v2_batch_norm
-architecture = inception_resnet_v2_batch_norm
-
-# --checkpoint-dir
-from bob.extension import rc
-checkpoint_dir = rc['bob.bio.face_ongoing.inception-v2_batchnorm_rgb']
-
-# --style-end-points and -- content-end-points
-style_end_points = ["Conv2d_1a_3x3", "Conv2d_2b_3x3"]
-content_end_points = ["Block8"]
-
-scopes = {"InceptionResnetV2/":"InceptionResnetV2/"}
-
-# --style-image-paths
-style_image_paths = ["vincent_van_gogh.jpg",
- "vincent_van_gogh2.jpg"]
-
-# --preprocess-fn
-preprocess_fn = tf.image.per_image_standardization
diff --git a/bob/learn/tensorflow/configs/style_transfer/vgg19_example.py b/bob/learn/tensorflow/configs/style_transfer/vgg19_example.py
deleted file mode 100644
index 4d31888882eb98570549b4015f83a6089074ae6a..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/configs/style_transfer/vgg19_example.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""
-Example using VGG19
-"""
-
-from bob.learn.tensorflow.network import vgg_19
-# --architecture
-architecture = vgg_19
-
-
-import numpy
-
-# -- checkpoint-dir
-# YOU CAN DOWNLOAD THE CHECKPOINTS FROM HERE
-# https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models
-checkpoint_dir = "/PATH-TO/vgg_19.ckpt"
-
-# --style-end-points and -- content-end-points
-content_end_points = ['vgg_19/conv4/conv4_2', 'vgg_19/conv5/conv5_2']
-style_end_points = ['vgg_19/conv1/conv1_2',
- 'vgg_19/conv2/conv2_1',
- 'vgg_19/conv3/conv3_1',
- 'vgg_19/conv4/conv4_1',
- 'vgg_19/conv5/conv5_1'
- ]
-
-
-scopes = {"vgg_19/":"vgg_19/"}
-
-style_image_paths = ["/PATH/TO/vincent_van_gogh.jpg"]
-
-
-# --preprocess-fn and --un-preprocess-fn
-# Taken from VGG19
-def mean_norm(tensor):
- return tensor - numpy.array([ 123.68 , 116.779, 103.939])
-
-def un_mean_norm(tensor):
- return tensor + numpy.array([ 123.68 , 116.779, 103.939])
-
-preprocess_fn = mean_norm
-
-un_preprocess_fn = un_mean_norm
-
diff --git a/bob/learn/tensorflow/configs/style_transfer/vincent_van_gogh.jpg b/bob/learn/tensorflow/configs/style_transfer/vincent_van_gogh.jpg
deleted file mode 100644
index c17534eca0469abfa043146da04f82eab014ba99..0000000000000000000000000000000000000000
Binary files a/bob/learn/tensorflow/configs/style_transfer/vincent_van_gogh.jpg and /dev/null differ
diff --git a/bob/learn/tensorflow/configs/style_transfer/vincent_van_gogh2.jpg b/bob/learn/tensorflow/configs/style_transfer/vincent_van_gogh2.jpg
deleted file mode 100644
index 9a74d8588e73b044c3f8a51e8bb3bcf1e950174a..0000000000000000000000000000000000000000
Binary files a/bob/learn/tensorflow/configs/style_transfer/vincent_van_gogh2.jpg and /dev/null differ
diff --git a/bob/learn/tensorflow/extractors/__init__.py b/bob/learn/tensorflow/data/__init__.py
similarity index 58%
rename from bob/learn/tensorflow/extractors/__init__.py
rename to bob/learn/tensorflow/data/__init__.py
index 05492a8e524ac60164f5be396ab9597ddcc2ec88..3d31417512b35396459e77db40c3448d4372a34e 100644
--- a/bob/learn/tensorflow/extractors/__init__.py
+++ b/bob/learn/tensorflow/data/__init__.py
@@ -1,7 +1,5 @@
-from .Base import Base, normalize_checkpoint_path
-from .Generic import Generic
-from .Estimator import Estimator
-
+from .generator import Generator, dataset_using_generator
+from .tfrecords import dataset_to_tfrecord, dataset_from_tfrecord, TFRECORDS_EXT
# gets sphinx autodoc done right - don't remove it
def __appropriate__(*args):
@@ -9,7 +7,7 @@ def __appropriate__(*args):
Parameters:
- *args: An iterable of objects to modify
+ *args: An iterable of objects to modify
Resolves `Sphinx referencing issues
<https://github.com/sphinx-doc/sphinx/issues/3048>`
@@ -20,8 +18,6 @@ def __appropriate__(*args):
__appropriate__(
- Base,
- Generic,
- Estimator,
+ Generator,
)
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/dataset/generator.py b/bob/learn/tensorflow/data/generator.py
similarity index 92%
rename from bob/learn/tensorflow/dataset/generator.py
rename to bob/learn/tensorflow/data/generator.py
index cf2798ae941db9669a1f1369eff854458b7fd33f..7e68c0246eb227862f6d79214c1000caaa6a8561 100644
--- a/bob/learn/tensorflow/dataset/generator.py
+++ b/bob/learn/tensorflow/data/generator.py
@@ -1,6 +1,7 @@
-import tensorflow as tf
-import random
import logging
+import random
+
+import tensorflow as tf
logger = logging.getLogger(__name__)
@@ -30,7 +31,14 @@ class Generator:
The shapes of the returned samples.
"""
- def __init__(self, samples, reader, multiple_samples=False, shuffle_on_epoch_end=False, **kwargs):
+ def __init__(
+ self,
+ samples,
+ reader,
+ multiple_samples=False,
+ shuffle_on_epoch_end=False,
+ **kwargs
+ ):
super().__init__(**kwargs)
self.reader = reader
self.samples = list(samples)
@@ -55,8 +63,8 @@ class Generator:
break
# Creating a "fake" dataset just to get the types and shapes
dataset = tf.data.Dataset.from_tensors(dlk)
- self._output_types = dataset.output_types
- self._output_shapes = dataset.output_shapes
+ self._output_types = tf.compat.v1.data.get_output_types(dataset)
+ self._output_shapes = tf.compat.v1.data.get_output_shapes(dataset)
logger.info(
"Initializing a dataset with %d %s and %s types and %s shapes",
diff --git a/bob/learn/tensorflow/data/tfrecords.py b/bob/learn/tensorflow/data/tfrecords.py
new file mode 100644
index 0000000000000000000000000000000000000000..bacf49cf8c10bbecd8f613a52033c104c212290a
--- /dev/null
+++ b/bob/learn/tensorflow/data/tfrecords.py
@@ -0,0 +1,137 @@
+"""Utilities for TFRecords
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+
+import tensorflow as tf
+
+
+TFRECORDS_EXT = ".tfrecords"
+
+
+def tfrecord_name_and_json_name(output):
+ output = normalize_tfrecords_path(output)
+ json_output = output[: -len(TFRECORDS_EXT)] + ".json"
+ return output, json_output
+
+
+def normalize_tfrecords_path(output):
+ if not output.endswith(TFRECORDS_EXT):
+ output += TFRECORDS_EXT
+ return output
+
+
+def bytes_feature(value):
+ if isinstance(value, type(tf.constant(0))):
+ value = value.numpy() # BytesList won't unpack a string from an EagerTensor.
+ return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+
+def int64_feature(value):
+ return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
+
+
+def dataset_to_tfrecord(dataset, output):
+ """Writes a tf.data.Dataset into a TFRecord file.
+
+ Parameters
+ ----------
+ dataset : ``tf.data.Dataset``
+ The tf.data.Dataset that you want to write into a TFRecord file.
+ output : str
+ Path to the TFRecord file. Besides this file, a .json file is also created.
+ This json file is needed when you want to convert the TFRecord file back into
+ a dataset.
+
+ Returns
+ -------
+ ``tf.Operation``
+ A tf.Operation that, when run, writes contents of dataset to a file. When
+ running in eager mode, calling this function will write the file. Otherwise, you
+ have to call session.run() on the returned operation.
+ """
+ output, json_output = tfrecord_name_and_json_name(output)
+ # dump the structure so that we can read it back
+ meta = {
+ "output_types": repr(tf.compat.v1.data.get_output_types(dataset)),
+ "output_shapes": repr(tf.compat.v1.data.get_output_shapes(dataset)),
+ }
+ with open(json_output, "w") as f:
+ json.dump(meta, f)
+
+ # create a custom map function that serializes the dataset
+ def serialize_example_pyfunction(*args):
+ feature = {}
+ for i, f in enumerate(args):
+ key = f"feature{i}"
+ feature[key] = bytes_feature(f)
+ example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
+ return example_proto.SerializeToString()
+
+ def tf_serialize_example(*args):
+ args = tf.nest.flatten(args)
+ args = [tf.io.serialize_tensor(f) for f in args]
+ tf_string = tf.py_function(serialize_example_pyfunction, args, tf.string)
+ return tf.reshape(tf_string, ()) # The result is a scalar
+
+ dataset = dataset.map(tf_serialize_example)
+ writer = tf.data.experimental.TFRecordWriter(output)
+ return writer.write(dataset)
+
+
+def dataset_from_tfrecord(tfrecord, num_parallel_reads=None):
+ """Reads TFRecords and returns a dataset.
+ The TFRecord file must have been created using the :any:`dataset_to_tfrecord`
+ function.
+
+ Parameters
+ ----------
+ tfrecord : str or list
+ Path to the TFRecord file. Pass a list if you are sure several tfrecords need
+ the same map function.
+ num_parallel_reads: int
+ A `tf.int64` scalar representing the number of files to read in parallel.
+ Defaults to reading files sequentially.
+
+ Returns
+ -------
+ ``tf.data.Dataset``
+ A dataset that contains the data from the TFRecord file.
+ """
+ # these imports are needed so that eval can work
+ from tensorflow import TensorShape
+
+ if isinstance(tfrecord, str):
+ tfrecord = [tfrecord]
+ tfrecord = [tfrecord_name_and_json_name(path) for path in tfrecord]
+ json_output = tfrecord[0][1]
+ tfrecord = [path[0] for path in tfrecord]
+ raw_dataset = tf.data.TFRecordDataset(
+ tfrecord, num_parallel_reads=num_parallel_reads
+ )
+
+ with open(json_output) as f:
+ meta = json.load(f)
+ for k, v in meta.items():
+ meta[k] = eval(v)
+ output_types = tf.nest.flatten(meta["output_types"])
+ output_shapes = tf.nest.flatten(meta["output_shapes"])
+ feature_description = {}
+ for i in range(len(output_types)):
+ key = f"feature{i}"
+ feature_description[key] = tf.io.FixedLenFeature([], tf.string)
+
+ def _parse_function(example_proto):
+ # Parse the input tf.Example proto using the dictionary above.
+ args = tf.io.parse_single_example(
+ serialized=example_proto, features=feature_description
+ )
+ args = tf.nest.flatten(args)
+ args = [tf.io.parse_tensor(v, t) for v, t in zip(args, output_types)]
+ args = [tf.reshape(v, s) for v, s in zip(args, output_shapes)]
+ return tf.nest.pack_sequence_as(meta["output_types"], args)
+
+ return raw_dataset.map(_parse_function)
diff --git a/bob/learn/tensorflow/dataset/__init__.py b/bob/learn/tensorflow/dataset/__init__.py
deleted file mode 100644
index 93eb960fa720b1cfb93b96f9b0bfefc8cf6fc6f2..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/dataset/__init__.py
+++ /dev/null
@@ -1,419 +0,0 @@
-import tensorflow as tf
-import numpy
-import os
-import bob.io.base
-
-DEFAULT_FEATURE = {
- "data": tf.FixedLenFeature([], tf.string),
- "label": tf.FixedLenFeature([], tf.int64),
- "key": tf.FixedLenFeature([], tf.string),
-}
-
-
-def from_hdf5file_to_tensor(filename):
- import bob.io.image
-
- data = bob.io.image.to_matplotlib(bob.io.base.load(filename))
-
- # reshaping to ndim == 3
- if data.ndim == 2:
- data = numpy.reshape(data, (data.shape[0], data.shape[1], 1))
- data = data.astype("float32")
-
- return data
-
-
-def from_filename_to_tensor(filename, extension=None):
- """
- Read a file and it convert it to tensor.
-
- If the file extension is something that tensorflow understands (.jpg, .bmp, .tif,...),
- it uses the `tf.image.decode_image` otherwise it uses `bob.io.base.load`
- """
-
- if extension == "hdf5":
- return tf.py_func(from_hdf5file_to_tensor, [filename], [tf.float32])
- else:
- return tf.cast(tf.image.decode_image(tf.read_file(filename)), tf.float32)
-
-
-def append_image_augmentation(
- image,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- random_gamma=False,
- random_crop=False,
-):
- """
- Append to the current tensor some random image augmentation operation
-
- **Parameters**
- gray_scale:
- Convert to gray scale?
-
- output_shape:
- If set, will randomly crop the image given the output shape
-
- random_flip:
- Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
-
- random_brightness:
- Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
-
- random_contrast:
- Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
-
- random_saturation:
- Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
-
- random_rotate:
- Randomly rotate face images between -5 and 5 degrees
-
- per_image_normalization:
- Linearly scales image to have zero mean and unit norm.
-
- """
-
- # Changing the range from 0-255 to 0-1
- image = tf.cast(image, tf.float32) / 255
- # FORCING A SEED FOR THE RANDOM OPERATIONS
- tf.set_random_seed(0)
-
- if output_shape is not None:
- assert len(output_shape) == 2
- if random_crop:
- image = tf.random_crop(image, size=list(output_shape) + [3])
- else:
- image = tf.image.resize_image_with_crop_or_pad(
- image, output_shape[0], output_shape[1]
- )
-
- if random_flip:
- image = tf.image.random_flip_left_right(image)
-
- if random_brightness:
- image = tf.image.random_brightness(image, max_delta=0.15)
- image = tf.clip_by_value(image, 0, 1)
-
- if random_contrast:
- image = tf.image.random_contrast(image, lower=0.85, upper=1.15)
- image = tf.clip_by_value(image, 0, 1)
-
- if random_saturation:
- image = tf.image.random_saturation(image, lower=0.85, upper=1.15)
- image = tf.clip_by_value(image, 0, 1)
-
- if random_gamma:
- image = tf.image.adjust_gamma(
- image, gamma=tf.random.uniform(shape=[], minval=0.85, maxval=1.15)
- )
- image = tf.clip_by_value(image, 0, 1)
-
- if random_rotate:
- # from https://stackoverflow.com/a/53855704/1286165
- degree = 0.08726646259971647 # math.pi * 5 /180
- random_angles = tf.random.uniform(shape=(1,), minval=-degree, maxval=degree)
- image = tf.contrib.image.transform(
- image,
- tf.contrib.image.angles_to_projective_transforms(
- random_angles,
- tf.cast(tf.shape(image)[-3], tf.float32),
- tf.cast(tf.shape(image)[-2], tf.float32),
- ),
- )
-
- if gray_scale:
- image = tf.image.rgb_to_grayscale(image, name="rgb_to_gray")
-
- # normalizing data
- if per_image_normalization:
- image = tf.image.per_image_standardization(image)
-
- return image
-
-
-def arrange_indexes_by_label(input_labels, possible_labels):
-
- # Shuffling all the indexes
- indexes_per_labels = dict()
- for l in possible_labels:
- indexes_per_labels[l] = numpy.where(input_labels == l)[0]
- numpy.random.shuffle(indexes_per_labels[l])
- return indexes_per_labels
-
-
-def triplets_random_generator(input_data, input_labels):
- """
- Giving a list of samples and a list of labels, it dumps a series of
- triplets for triple nets.
-
- **Parameters**
-
- input_data: List of whatever representing the data samples
-
- input_labels: List of the labels (needs to be in EXACT same order as input_data)
- """
- anchor = []
- positive = []
- negative = []
-
- def append(anchor_sample, positive_sample, negative_sample):
- """
- Just appending one element in each list
- """
- anchor.append(anchor_sample)
- positive.append(positive_sample)
- negative.append(negative_sample)
-
- possible_labels = list(set(input_labels))
- input_data = numpy.array(input_data)
- input_labels = numpy.array(input_labels)
- total_samples = input_data.shape[0]
-
- indexes_per_labels = arrange_indexes_by_label(input_labels, possible_labels)
-
- # searching for random triplets
- offset_class = 0
- for i in range(total_samples):
-
- anchor_sample = input_data[
- indexes_per_labels[possible_labels[offset_class]][
- numpy.random.randint(
- len(indexes_per_labels[possible_labels[offset_class]])
- )
- ],
- ...,
- ]
-
- positive_sample = input_data[
- indexes_per_labels[possible_labels[offset_class]][
- numpy.random.randint(
- len(indexes_per_labels[possible_labels[offset_class]])
- )
- ],
- ...,
- ]
-
- # Changing the class
- offset_class += 1
-
- if offset_class == len(possible_labels):
- offset_class = 0
-
- negative_sample = input_data[
- indexes_per_labels[possible_labels[offset_class]][
- numpy.random.randint(
- len(indexes_per_labels[possible_labels[offset_class]])
- )
- ],
- ...,
- ]
-
- append(str(anchor_sample), str(positive_sample), str(negative_sample))
- # yield anchor, positive, negative
- return anchor, positive, negative
-
-
-def siamease_pairs_generator(input_data, input_labels):
- """
- Giving a list of samples and a list of labels, it dumps a series of
- pairs for siamese nets.
-
- **Parameters**
-
- input_data: List of whatever representing the data samples
-
- input_labels: List of the labels (needs to be in EXACT same order as input_data)
- """
-
- # Lists that will be returned
- left_data = []
- right_data = []
- labels = []
-
- def append(left, right, label):
- """
- Just appending one element in each list
- """
- left_data.append(left)
- right_data.append(right)
- labels.append(label)
-
- possible_labels = list(set(input_labels))
- input_data = numpy.array(input_data)
- input_labels = numpy.array(input_labels)
- total_samples = input_data.shape[0]
-
- # Filtering the samples by label and shuffling all the indexes
- # indexes_per_labels = dict()
- # for l in possible_labels:
- # indexes_per_labels[l] = numpy.where(input_labels == l)[0]
- # numpy.random.shuffle(indexes_per_labels[l])
- indexes_per_labels = arrange_indexes_by_label(input_labels, possible_labels)
-
- left_possible_indexes = numpy.random.choice(
- possible_labels, total_samples, replace=True
- )
- right_possible_indexes = numpy.random.choice(
- possible_labels, total_samples, replace=True
- )
-
- genuine = True
- for i in range(total_samples):
-
- if genuine:
- # Selecting the class
- class_index = left_possible_indexes[i]
-
- # Now selecting the samples for the pair
- left = input_data[
- indexes_per_labels[class_index][
- numpy.random.randint(len(indexes_per_labels[class_index]))
- ]
- ]
- right = input_data[
- indexes_per_labels[class_index][
- numpy.random.randint(len(indexes_per_labels[class_index]))
- ]
- ]
- append(left, right, 0)
- # yield left, right, 0
- else:
- # Selecting the 2 classes
- class_index = list()
- class_index.append(left_possible_indexes[i])
-
- # Finding the right pair
- j = i
- # TODO: Lame solution. Fix this
- while (
- j < total_samples
- ): # Here is an unidiretinal search for the negative pair
- if left_possible_indexes[i] != right_possible_indexes[j]:
- class_index.append(right_possible_indexes[j])
- break
- j += 1
-
- if j < total_samples:
- # Now selecting the samples for the pair
- left = input_data[
- indexes_per_labels[class_index[0]][
- numpy.random.randint(len(indexes_per_labels[class_index[0]]))
- ]
- ]
- right = input_data[
- indexes_per_labels[class_index[1]][
- numpy.random.randint(len(indexes_per_labels[class_index[1]]))
- ]
- ]
- append(left, right, 1)
-
- genuine = not genuine
- return left_data, right_data, labels
-
-
-def blocks_tensorflow(images, block_size):
- """Return all non-overlapping blocks of an image using tensorflow
- operations.
-
- Parameters
- ----------
- images : `tf.Tensor`
- The input color images. It is assumed that the image has a shape of
- [?, H, W, C].
- block_size : (int, int)
- A tuple of two integers indicating the block size.
-
- Returns
- -------
- blocks : `tf.Tensor`
- All the blocks in the batch dimension. The output will be of
- size [?, block_size[0], block_size[1], C].
- n_blocks : int
- The number of blocks that was obtained per image.
- """
- # normalize block_size
- block_size = [1] + list(block_size) + [1]
- output_size = list(block_size)
- output_size[0] = -1
- output_size[-1] = images.shape[-1]
- blocks = tf.extract_image_patches(
- images, block_size, block_size, [1, 1, 1, 1], "VALID"
- )
- n_blocks = int(numpy.prod(blocks.shape[1:3]))
- output = tf.reshape(blocks, output_size)
- return output, n_blocks
-
-
-def tf_repeat(tensor, repeats):
- """
- Parameters
- ----------
- tensor
- A Tensor. 1-D or higher.
- repeats
- A list. Number of repeat for each dimension, length must be the same as
- the number of dimensions in input
-
- Returns
- -------
- A Tensor. Has the same type as input. Has the shape of tensor.shape *
- repeats
- """
- with tf.variable_scope("repeat"):
- expanded_tensor = tf.expand_dims(tensor, -1)
- multiples = [1] + repeats
- tiled_tensor = tf.tile(expanded_tensor, multiples=multiples)
- repeated_tesnor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats)
- return repeated_tesnor
-
-
-def all_patches(image, label, key, size):
- """Extracts all patches of an image
-
- Parameters
- ----------
- image:
- The image should be channels_last format and already batched.
-
- label:
- The label for the image
-
- key:
- The key for the image
-
- size: (int, int)
- The height and width of the blocks.
-
- Returns
- -------
- blocks:
- The non-overlapping blocks of size from image and labels and keys are
- repeated.
-
- label:
-
- key:
- """
- blocks, n_blocks = blocks_tensorflow(image, size)
-
- # duplicate label and key as n_blocks
- def repeats(shape):
- r = shape.as_list()
- for i in range(len(r)):
- if i == 0:
- r[i] = n_blocks
- else:
- r[i] = 1
- return r
-
- label = tf_repeat(label, repeats(label.shape))
- key = tf_repeat(key, repeats(key.shape))
-
- return blocks, label, key
diff --git a/bob/learn/tensorflow/dataset/bio.py b/bob/learn/tensorflow/dataset/bio.py
deleted file mode 100644
index 4a9b66bcc91f8656367739b59bfaf580a355c381..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/dataset/bio.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from bob.bio.base import read_original_data
-from .generator import Generator
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class BioGenerator(Generator):
- """A generator class which wraps bob.bio.base databases so that they can
- be used with tf.data.Dataset.from_generator
-
- Attributes
- ----------
- biofile_to_label : :obj:`object`, optional
- A callable with the signature of ``label = biofile_to_label(biofile)``.
- By default -1 is returned as label.
- database : :any:`bob.bio.base.database.BioDatabase`
- The database that you want to use.
- load_data : :obj:`object`, optional
- A callable with the signature of
- ``data = load_data(database, biofile)``.
- :any:`bob.bio.base.read_original_data` is wrapped to be used by
- default.
- biofiles : [:any:`bob.bio.base.database.BioFile`]
- The list of the bio files .
- keys : [str]
- The keys of samples obtained by calling ``biofile.make_path("", "")``
- labels : [int]
- The labels obtained by calling ``label = biofile_to_label(biofile)``
- """
-
- def __init__(
- self,
- database,
- biofiles,
- load_data=None,
- biofile_to_label=None,
- multiple_samples=False,
- **kwargs
- ):
-
- if load_data is None:
-
- def load_data(database, biofile):
- data = read_original_data(
- biofile, database.original_directory, database.original_extension
- )
- return data
-
- if biofile_to_label is None:
-
- def biofile_to_label(biofile):
- return -1
-
- self.database = database
- self.load_data = load_data
- self.biofile_to_label = biofile_to_label
-
- def _reader(f):
- label = int(self.biofile_to_label(f))
- data = self.load_data(self.database, f)
- key = str(f.make_path("", "")).encode("utf-8")
- return data, label, key
-
- if multiple_samples:
- def reader(f):
- data, label, key = _reader(f)
- for d in data:
- yield (d, label, key)
- else:
- def reader(f):
- return _reader(f)
-
- super(BioGenerator, self).__init__(
- biofiles, reader, multiple_samples=multiple_samples, **kwargs
- )
-
- @property
- def labels(self):
- for f in self.biofiles:
- yield int(self.biofile_to_label(f))
-
- @property
- def keys(self):
- for f in self.biofiles:
- yield str(f.make_path("", "")).encode("utf-8")
-
- @property
- def biofiles(self):
- return self.samples
-
- def __len__(self):
- return len(self.biofiles)
diff --git a/bob/learn/tensorflow/dataset/image.py b/bob/learn/tensorflow/dataset/image.py
deleted file mode 100644
index b731e55260cac42d6e56edec37f4f6cfb65627f7..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/dataset/image.py
+++ /dev/null
@@ -1,221 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from functools import partial
-from . import append_image_augmentation, from_filename_to_tensor
-
-
-def shuffle_data_and_labels_image_augmentation(filenames,
- labels,
- data_shape,
- data_type,
- batch_size,
- epochs=None,
- buffer_size=10**3,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Dump random batches from a list of image paths and labels:
-
- The list of files and labels should be in the same order e.g.
- filenames = ['class_1_img1', 'class_1_img2', 'class_2_img1']
- labels = [0, 0, 1]
-
- **Parameters**
-
- filenames:
- List containing the path of the images
-
- labels:
- List containing the labels (needs to be in EXACT same order as filenames)
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- batch_size:
- Size of the batch
-
- epochs:
- Number of epochs to be batched
-
- buffer_size:
- Size of the shuffle bucket
-
- gray_scale:
- Convert to gray scale?
-
- output_shape:
- If set, will randomly crop the image given the output shape
-
- random_flip:
- Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
-
- random_brightness:
- Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
-
- random_contrast:
- Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
-
- random_saturation:
- Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
-
- random_rotate:
- Randomly rotate face images between -5 and 5 degrees
-
- per_image_normalization:
- Linearly scales image to have zero mean and unit norm.
-
- extension:
- If None, will load files using `tf.image.decode..` if set to `hdf5`, will load with `bob.io.base.load`
-
- """
-
- dataset = create_dataset_from_path_augmentation(
- filenames,
- labels,
- data_shape,
- data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- extension=extension)
-
- dataset = dataset.shuffle(buffer_size).batch(batch_size).repeat(epochs)
-
- data, labels = dataset.make_one_shot_iterator().get_next()
- return data, labels
-
-
-def create_dataset_from_path_augmentation(filenames,
- labels,
- data_shape,
- data_type,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Create dataset from a list of tf-record files
-
- **Parameters**
-
- filenames:
- List containing the path of the images
-
- labels:
- List containing the labels (needs to be in EXACT same order as filenames)
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- feature:
-
- """
-
- parser = partial(
- image_augmentation_parser,
- data_shape=data_shape,
- data_type=data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- extension=extension)
-
- dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
- dataset = dataset.map(parser)
- return dataset
-
-
-def image_augmentation_parser(filename,
- label,
- data_shape,
- data_type,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Parses a single tf.Example into image and label tensors.
- """
-
- # Convert the image data from string back to the numbers
- image = from_filename_to_tensor(filename, extension=extension)
-
- # Reshape image data into the original shape
- image = tf.reshape(image, data_shape)
-
- #Applying image augmentation
- image = append_image_augmentation(
- image,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization)
-
- label = tf.cast(label, tf.int64)
- features = dict()
- features['data'] = image
- features['key'] = filename
-
- return features, label
-
-
-def load_pngs(img_path, img_shape):
- """Read png files using tensorflow API
- You must know the shape of the image beforehand to use this function.
-
- Parameters
- ----------
- img_path : str
- Path to the image
- img_shape : list
- A list or tuple that contains image's shape in channels_last format
-
- Returns
- -------
- object
- The loaded png file
- """
- img_raw = tf.read_file(img_path)
- img_tensor = tf.image.decode_png(img_raw, channels=img_shape[-1])
- img_final = tf.reshape(img_tensor, img_shape)
- return img_final
diff --git a/bob/learn/tensorflow/dataset/siamese_image.py b/bob/learn/tensorflow/dataset/siamese_image.py
deleted file mode 100644
index 51a56a64b5290b3d853a3be849cefa261e4e4266..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/dataset/siamese_image.py
+++ /dev/null
@@ -1,254 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from functools import partial
-from . import append_image_augmentation, siamease_pairs_generator, from_filename_to_tensor
-
-
-def shuffle_data_and_labels_image_augmentation(filenames,
- labels,
- data_shape,
- data_type,
- batch_size,
- epochs=None,
- buffer_size=10**3,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Dump random batches for siamese networks from a list of image paths and labels:
-
- The list of files and labels should be in the same order e.g.
- filenames = ['class_1_img1', 'class_1_img2', 'class_2_img1']
- labels = [0, 0, 1]
-
- The batches returned with tf.Session.run() with be in the following format:
- **data** a dictionary containing the keys ['left', 'right'], each one representing
- one element of the pair and **labels** which is [0, 1] where 0 is the genuine pair
- and 1 is the impostor pair.
-
-
- **Parameters**
-
- filenames:
- List containing the path of the images
-
- labels:
- List containing the labels (needs to be in EXACT same order as filenames)
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- batch_size:
- Size of the batch
-
- epochs:
- Number of epochs to be batched
-
- buffer_size:
- Size of the shuffle bucket
-
- gray_scale:
- Convert to gray scale?
-
- output_shape:
- If set, will randomly crop the image given the output shape
-
- random_flip:
- Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
-
- random_brightness:
- Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
-
- random_contrast:
- Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
-
- random_saturation:
- Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
-
- random_rotate:
- Randomly rotate face images between -5 and 5 degrees
-
- per_image_normalization:
- Linearly scales image to have zero mean and unit norm.
-
- extension:
- If None, will load files using `tf.image.decode..` if set to `hdf5`, will load with `bob.io.base.load`
- """
-
- dataset = create_dataset_from_path_augmentation(
- filenames,
- labels,
- data_shape,
- data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- extension=extension)
-
- dataset = dataset.shuffle(buffer_size).batch(batch_size).repeat(epochs)
- data, labels = dataset.make_one_shot_iterator().get_next()
- return data, labels
-
-
-def create_dataset_from_path_augmentation(filenames,
- labels,
- data_shape,
- data_type,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Create dataset from a list of tf-record files
-
- **Parameters**
-
- filenames:
- List containing the path of the images
-
- labels:
- List containing the labels (needs to be in EXACT same order as filenames)
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- batch_size:
- Size of the batch
-
- epochs:
- Number of epochs to be batched
-
- buffer_size:
- Size of the shuffle bucket
-
- gray_scale:
- Convert to gray scale?
-
- output_shape:
- If set, will randomly crop the image given the output shape
-
- random_flip:
- Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
-
- random_brightness:
- Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
-
- random_contrast:
- Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
-
- random_saturation:
- Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
-
- random_rotate:
- Randomly rotate face images between -10 and 10 degrees
-
- per_image_normalization:
- Linearly scales image to have zero mean and unit norm.
-
- extension:
- If None, will load files using `tf.image.decode..` if set to `hdf5`, will load with `bob.io.base.load`
-
- """
-
- parser = partial(
- image_augmentation_parser,
- data_shape=data_shape,
- data_type=data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- extension=extension)
-
- left_data, right_data, siamese_labels = siamease_pairs_generator(
- filenames, labels)
- dataset = tf.data.Dataset.from_tensor_slices((left_data, right_data,
- siamese_labels))
- dataset = dataset.map(parser)
- return dataset
-
-
-def image_augmentation_parser(filename_left,
- filename_right,
- label,
- data_shape,
- data_type,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Parses a single tf.Example into image and label tensors.
- """
-
- # Convert the image data from string back to the numbers
- image_left = from_filename_to_tensor(filename_left, extension=extension)
- image_right = from_filename_to_tensor(filename_right, extension=extension)
-
- # Reshape image data into the original shape
- image_left = tf.reshape(image_left, data_shape)
- image_right = tf.reshape(image_right, data_shape)
-
- #Applying image augmentation
- image_left = append_image_augmentation(
- image_left,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization)
-
- image_right = append_image_augmentation(
- image_right,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization)
-
- image = dict()
- image['left'] = image_left
- image['right'] = image_right
- label = tf.cast(label, tf.int64)
-
- return image, label
diff --git a/bob/learn/tensorflow/dataset/tfrecords.py b/bob/learn/tensorflow/dataset/tfrecords.py
deleted file mode 100644
index 45201b88c0b732de17d87c8510f158cf653c4af9..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/dataset/tfrecords.py
+++ /dev/null
@@ -1,666 +0,0 @@
-"""Utilities for TFRecords
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from functools import partial
-import json
-import logging
-import os
-import sys
-
-import tensorflow as tf
-
-from . import append_image_augmentation, DEFAULT_FEATURE
-
-
-logger = logging.getLogger(__name__)
-TFRECORDS_EXT = ".tfrecords"
-
-
-def tfrecord_name_and_json_name(output):
- output = normalize_tfrecords_path(output)
- json_output = output[: -len(TFRECORDS_EXT)] + ".json"
- return output, json_output
-
-
-def normalize_tfrecords_path(output):
- if not output.endswith(TFRECORDS_EXT):
- output += TFRECORDS_EXT
- return output
-
-
-def bytes_feature(value):
- return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
-
-
-def int64_feature(value):
- return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
-
-
-def dataset_to_tfrecord(dataset, output):
- """Writes a tf.data.Dataset into a TFRecord file.
-
- Parameters
- ----------
- dataset : ``tf.data.Dataset``
- The tf.data.Dataset that you want to write into a TFRecord file.
- output : str
- Path to the TFRecord file. Besides this file, a .json file is also created.
- This json file is needed when you want to convert the TFRecord file back into
- a dataset.
-
- Returns
- -------
- ``tf.Operation``
- A tf.Operation that, when run, writes contents of dataset to a file. When
- running in eager mode, calling this function will write the file. Otherwise, you
- have to call session.run() on the returned operation.
- """
- output, json_output = tfrecord_name_and_json_name(output)
- # dump the structure so that we can read it back
- meta = {
- "output_types": repr(dataset.output_types),
- "output_shapes": repr(dataset.output_shapes),
- }
- with open(json_output, "w") as f:
- json.dump(meta, f)
-
- # create a custom map function that serializes the dataset
- def serialize_example_pyfunction(*args):
- feature = {}
- for i, f in enumerate(args):
- key = f"feature{i}"
- feature[key] = bytes_feature(f)
- example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
- return example_proto.SerializeToString()
-
- def tf_serialize_example(*args):
- args = tf.contrib.framework.nest.flatten(args)
- args = [tf.serialize_tensor(f) for f in args]
- tf_string = tf.py_func(serialize_example_pyfunction, args, tf.string)
- return tf.reshape(tf_string, ()) # The result is a scalar
-
- dataset = dataset.map(tf_serialize_example)
- writer = tf.data.experimental.TFRecordWriter(output)
- return writer.write(dataset)
-
-
-def dataset_from_tfrecord(tfrecord, num_parallel_reads=None):
- """Reads TFRecords and returns a dataset.
- The TFRecord file must have been created using the :any:`dataset_to_tfrecord`
- function.
-
- Parameters
- ----------
- tfrecord : str or list
- Path to the TFRecord file. Pass a list if you are sure several tfrecords need
- the same map function.
- num_parallel_reads: int
- A `tf.int64` scalar representing the number of files to read in parallel.
- Defaults to reading files sequentially.
-
- Returns
- -------
- ``tf.data.Dataset``
- A dataset that contains the data from the TFRecord file.
- """
- # these imports are needed so that eval can work
- from tensorflow import TensorShape, Dimension
-
- if isinstance(tfrecord, str):
- tfrecord = [tfrecord]
- tfrecord = [tfrecord_name_and_json_name(path) for path in tfrecord]
- json_output = tfrecord[0][1]
- tfrecord = [path[0] for path in tfrecord]
- raw_dataset = tf.data.TFRecordDataset(
- tfrecord, num_parallel_reads=num_parallel_reads
- )
-
- with open(json_output) as f:
- meta = json.load(f)
- for k, v in meta.items():
- meta[k] = eval(v)
- output_types = tf.contrib.framework.nest.flatten(meta["output_types"])
- output_shapes = tf.contrib.framework.nest.flatten(meta["output_shapes"])
- feature_description = {}
- for i in range(len(output_types)):
- key = f"feature{i}"
- feature_description[key] = tf.FixedLenFeature([], tf.string)
-
- def _parse_function(example_proto):
- # Parse the input tf.Example proto using the dictionary above.
- args = tf.parse_single_example(example_proto, feature_description)
- args = tf.contrib.framework.nest.flatten(args)
- args = [tf.parse_tensor(v, t) for v, t in zip(args, output_types)]
- args = [tf.reshape(v, s) for v, s in zip(args, output_shapes)]
- return tf.contrib.framework.nest.pack_sequence_as(meta["output_types"], args)
-
- return raw_dataset.map(_parse_function)
-
-
-def write_a_sample(writer, data, label, key, feature=None, size_estimate=False):
- if feature is None:
- feature = {
- "data": bytes_feature(data.tostring()),
- "label": int64_feature(label),
- "key": bytes_feature(key),
- }
-
- example = tf.train.Example(features=tf.train.Features(feature=feature))
- example = example.SerializeToString()
- if not size_estimate:
- writer.write(example)
- return sys.getsizeof(example)
-
-
-def example_parser(serialized_example, feature, data_shape, data_type):
- """
- Parses a single tf.Example into image and label tensors.
-
- """
- # Decode the record read by the reader
- features = tf.parse_single_example(serialized_example, features=feature)
- # Convert the image data from string back to the numbers
- image = tf.decode_raw(features["data"], data_type)
- # Cast label data into int64
- label = tf.cast(features["label"], tf.int64)
- # Reshape image data into the original shape
- image = tf.reshape(image, data_shape)
- key = tf.cast(features["key"], tf.string)
- return image, label, key
-
-
-def image_augmentation_parser(
- serialized_example,
- feature,
- data_shape,
- data_type,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- random_gamma=False,
- random_crop=False,
-):
- """
- Parses a single tf.Example into image and label tensors.
-
- """
- # Decode the record read by the reader
- features = tf.parse_single_example(serialized_example, features=feature)
- # Convert the image data from string back to the numbers
- image = tf.decode_raw(features["data"], data_type)
-
- # Reshape image data into the original shape
- image = tf.reshape(image, data_shape)
-
- # Applying image augmentation
- image = append_image_augmentation(
- image,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- random_gamma=random_gamma,
- random_crop=random_crop,
- )
-
- # Cast label data into int64
- label = tf.cast(features["label"], tf.int64)
- key = tf.cast(features["key"], tf.string)
-
- return image, label, key
-
-
-def read_and_decode(filename_queue, data_shape, data_type=tf.float32, feature=None):
- """
- Simples parse possible for a tfrecord.
- It assumes that you have the pair **train/data** and **train/label**
- """
-
- if feature is None:
- feature = DEFAULT_FEATURE
- # Define a reader and read the next record
- reader = tf.TFRecordReader()
- _, serialized_example = reader.read(filename_queue)
- return example_parser(serialized_example, feature, data_shape, data_type)
-
-
-def create_dataset_from_records(
- tfrecord_filenames, data_shape, data_type, feature=None
-):
- """
- Create dataset from a list of tf-record files
-
- **Parameters**
-
- tfrecord_filenames:
- List containing the tf-record paths
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- feature:
-
- """
-
- if feature is None:
- feature = DEFAULT_FEATURE
- dataset = tf.data.TFRecordDataset(tfrecord_filenames)
- parser = partial(
- example_parser, feature=feature, data_shape=data_shape, data_type=data_type
- )
- dataset = dataset.map(parser)
- return dataset
-
-
-def create_dataset_from_records_with_augmentation(
- tfrecord_filenames,
- data_shape,
- data_type,
- feature=None,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- random_gamma=False,
- random_crop=False,
-):
- """
- Create dataset from a list of tf-record files
-
- **Parameters**
-
- tfrecord_filenames:
- List containing the tf-record paths
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- feature:
-
- """
-
- if feature is None:
- feature = DEFAULT_FEATURE
- if isinstance(tfrecord_filenames, str) and os.path.isdir(tfrecord_filenames):
- tfrecord_filenames = [
- os.path.join(tfrecord_filenames, f) for f in os.listdir(tfrecord_filenames)
- ]
- dataset = tf.data.TFRecordDataset(tfrecord_filenames)
- parser = partial(
- image_augmentation_parser,
- feature=feature,
- data_shape=data_shape,
- data_type=data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- random_gamma=random_gamma,
- random_crop=random_crop,
- )
- dataset = dataset.map(parser)
- return dataset
-
-
-def shuffle_data_and_labels_image_augmentation(
- tfrecord_filenames,
- data_shape,
- data_type,
- batch_size,
- epochs=None,
- buffer_size=10 ** 3,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- random_gamma=False,
- random_crop=False,
- drop_remainder=False,
-):
- """Dump random batches from a list of tf-record files and applies some image augmentation
-
- Parameters
- ----------
-
- tfrecord_filenames:
- List containing the tf-record paths
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- batch_size:
- Size of the batch
-
- epochs:
- Number of epochs to be batched
-
- buffer_size:
- Size of the shuffle bucket
-
- gray_scale:
- Convert to gray scale?
-
- output_shape:
- If set, will randomly crop the image given the output shape
-
- random_flip:
- Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
-
- random_brightness:
- Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
-
- random_contrast:
- Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
-
- random_saturation:
- Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
-
- random_rotate:
- Randomly rotate face images between -5 and 5 degrees
-
- per_image_normalization:
- Linearly scales image to have zero mean and unit norm.
-
- drop_remainder:
- If True, the last remaining batch that has smaller size than batch_size will be dropped.
- """
-
- dataset = create_dataset_from_records_with_augmentation(
- tfrecord_filenames,
- data_shape,
- data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- random_gamma=random_gamma,
- random_crop=random_crop,
- )
-
- dataset = dataset.shuffle(buffer_size)
- dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
- dataset = dataset.repeat(epochs)
-
- dataset = dataset.map(lambda d, l, k: ({"data": d, "key": k}, l))
-
- return dataset
-
-
-def shuffle_data_and_labels(
- tfrecord_filenames,
- data_shape,
- data_type,
- batch_size,
- epochs=None,
- buffer_size=10 ** 3,
-):
- """
- Dump random batches from a list of tf-record files
-
- **Parameters**
-
- tfrecord_filenames:
- List containing the tf-record paths
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- batch_size:
- Size of the batch
-
- epochs:
- Number of epochs to be batched
-
- buffer_size:
- Size of the shuffle bucket
-
- """
-
- dataset = create_dataset_from_records(tfrecord_filenames, data_shape, data_type)
- dataset = dataset.shuffle(buffer_size).batch(batch_size).repeat(epochs)
-
- data, labels, key = dataset.make_one_shot_iterator().get_next()
- features = dict()
- features["data"] = data
- features["key"] = key
-
- return features, labels
-
-
-def batch_data_and_labels(
- tfrecord_filenames, data_shape, data_type, batch_size, epochs=1
-):
- """
- Dump in order batches from a list of tf-record files
-
- Parameters
- ----------
-
- tfrecord_filenames:
- List containing the tf-record paths
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- batch_size:
- Size of the batch
-
- epochs:
- Number of epochs to be batched
-
- """
- dataset = create_dataset_from_records(tfrecord_filenames, data_shape, data_type)
- dataset = dataset.batch(batch_size).repeat(epochs)
-
- data, labels, key = dataset.make_one_shot_iterator().get_next()
- features = dict()
- features["data"] = data
- features["key"] = key
-
- return features, labels
-
-
-def batch_data_and_labels_image_augmentation(
- tfrecord_filenames,
- data_shape,
- data_type,
- batch_size,
- epochs=1,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- random_gamma=False,
- random_crop=False,
- drop_remainder=False,
-):
- """
- Dump in order batches from a list of tf-record files
-
- Parameters
- ----------
-
- tfrecord_filenames:
- List containing the tf-record paths
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- batch_size:
- Size of the batch
-
- epochs:
- Number of epochs to be batched
-
- drop_remainder:
- If True, the last remaining batch that has smaller size than batch_size will be dropped.
- """
-
- dataset = create_dataset_from_records_with_augmentation(
- tfrecord_filenames,
- data_shape,
- data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- random_gamma=random_gamma,
- random_crop=random_crop,
- )
-
- dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
- dataset = dataset.repeat(epochs)
-
- data, labels, key = dataset.make_one_shot_iterator().get_next()
- features = dict()
- features["data"] = data
- features["key"] = key
-
- return features, labels
-
-
-def describe_tf_record(tf_record_path, shape, batch_size=1):
- """
- Describe the number of samples and the number of classes of a tf-record
-
- Parameters
- ----------
-
- tf_record_path: str
- Base path containing your tf-record files
-
- shape: tuple
- Shape inside of the tf-record
-
- batch_size: int
- Well, batch size
-
-
- Returns
- -------
-
- n_samples: int
- Total number of samples
-
- n_classes: int
- Total number of classes
-
- """
-
- tf_records = [os.path.join(tf_record_path, f) for f in os.listdir(tf_record_path)]
- filename_queue = tf.train.string_input_producer(
- tf_records, num_epochs=1, name="input"
- )
-
- feature = {
- "data": tf.FixedLenFeature([], tf.string),
- "label": tf.FixedLenFeature([], tf.int64),
- "key": tf.FixedLenFeature([], tf.string),
- }
-
- # Define a reader and read the next record
- reader = tf.TFRecordReader()
-
- _, serialized_example = reader.read(filename_queue)
-
- # Decode the record read by the reader
- features = tf.parse_single_example(serialized_example, features=feature)
-
- # Convert the image data from string back to the numbers
- image = tf.decode_raw(features["data"], tf.uint8)
-
- # Cast label data into int32
- label = tf.cast(features["label"], tf.int64)
- img_name = tf.cast(features["key"], tf.string)
-
- # Reshape image data into the original shape
- image = tf.reshape(image, shape)
-
- # Getting the batches in order
- data_ph, label_ph, img_name_ph = tf.train.batch(
- [image, label, img_name],
- batch_size=batch_size,
- capacity=1000,
- num_threads=5,
- name="shuffle_batch",
- )
-
- # Start the reading
- session = tf.Session()
- tf.local_variables_initializer().run(session=session)
- tf.global_variables_initializer().run(session=session)
-
- # Preparing the batches
- thread_pool = tf.train.Coordinator()
- threads = tf.train.start_queue_runners(coord=thread_pool, sess=session)
-
- logger.info("Counting in %s", tf_record_path)
- labels = set()
- counter = 0
- try:
- while True:
- _, label, _ = session.run([data_ph, label_ph, img_name_ph])
- counter += len(label)
-
- for i in set(label):
- labels.add(i)
-
- except tf.errors.OutOfRangeError:
- pass
-
- thread_pool.request_stop()
- return counter, len(labels)
diff --git a/bob/learn/tensorflow/dataset/triplet_image.py b/bob/learn/tensorflow/dataset/triplet_image.py
deleted file mode 100644
index 944641100a8a2da2791b363e957beea0dac65a51..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/dataset/triplet_image.py
+++ /dev/null
@@ -1,210 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from functools import partial
-from . import append_image_augmentation, triplets_random_generator, from_filename_to_tensor
-
-
-def shuffle_data_and_labels_image_augmentation(filenames,
- labels,
- data_shape,
- data_type,
- batch_size,
- epochs=None,
- buffer_size=10**3,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Dump random batches for triplee networks from a list of image paths and labels:
-
- The list of files and labels should be in the same order e.g.
- filenames = ['class_1_img1', 'class_1_img2', 'class_2_img1']
- labels = [0, 0, 1]
-
- The batches returned with tf.Session.run() with be in the following format:
- **data** a dictionary containing the keys ['anchor', 'positive', 'negative'].
-
-
- **Parameters**
-
- filenames:
- List containing the path of the images
-
- labels:
- List containing the labels (needs to be in EXACT same order as filenames)
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- batch_size:
- Size of the batch
-
- epochs:
- Number of epochs to be batched
-
- buffer_size:
- Size of the shuffle bucket
-
- gray_scale:
- Convert to gray scale?
-
- output_shape:
- If set, will randomly crop the image given the output shape
-
- random_flip:
- Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
-
- random_brightness:
- Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
-
- random_contrast:
- Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
-
- random_saturation:
- Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
-
- random_rotate:
- Randomly rotate face images between -5 and 5 degrees
-
- per_image_normalization:
- Linearly scales image to have zero mean and unit norm.
-
- extension:
- If None, will load files using `tf.image.decode..` if set to `hdf5`, will load with `bob.io.base.load`
-
- """
-
- dataset = create_dataset_from_path_augmentation(
- filenames,
- labels,
- data_shape,
- data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- extension=extension)
-
- dataset = dataset.shuffle(buffer_size).batch(batch_size).repeat(epochs)
- #dataset = dataset.batch(buffer_size).batch(batch_size).repeat(epochs)
-
- data = dataset.make_one_shot_iterator().get_next()
- return data
-
-
-def create_dataset_from_path_augmentation(filenames,
- labels,
- data_shape,
- data_type=tf.float32,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Create dataset from a list of tf-record files
-
- **Parameters**
-
- filenames:
- List containing the path of the images
-
- labels:
- List containing the labels (needs to be in EXACT same order as filenames)
-
- data_shape:
- Samples shape saved in the tf-record
-
- data_type:
- tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
-
- feature:
-
- """
-
- parser = partial(
- image_augmentation_parser,
- data_shape=data_shape,
- data_type=data_type,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization,
- extension=extension)
-
- anchor_data, positive_data, negative_data = triplets_random_generator(
- filenames, labels)
-
- dataset = tf.data.Dataset.from_tensor_slices((anchor_data, positive_data,
- negative_data))
- dataset = dataset.map(parser)
- return dataset
-
-
-def image_augmentation_parser(anchor,
- positive,
- negative,
- data_shape,
- data_type=tf.float32,
- gray_scale=False,
- output_shape=None,
- random_flip=False,
- random_brightness=False,
- random_contrast=False,
- random_saturation=False,
- random_rotate=False,
- per_image_normalization=True,
- extension=None):
- """
- Parses a single tf.Example into image and label tensors.
- """
-
- triplet = dict()
- for n, v in zip(['anchor', 'positive', 'negative'],
- [anchor, positive, negative]):
-
- # Convert the image data from string back to the numbers
- image = from_filename_to_tensor(v, extension=extension)
-
- # Reshape image data into the original shape
- image = tf.reshape(image, data_shape)
-
- # Applying image augmentation
- image = append_image_augmentation(
- image,
- gray_scale=gray_scale,
- output_shape=output_shape,
- random_flip=random_flip,
- random_brightness=random_brightness,
- random_contrast=random_contrast,
- random_saturation=random_saturation,
- random_rotate=random_rotate,
- per_image_normalization=per_image_normalization)
-
- triplet[n] = image
-
- return triplet
diff --git a/bob/learn/tensorflow/estimators/EPSC.py b/bob/learn/tensorflow/estimators/EPSC.py
deleted file mode 100644
index 8665830bd1ccf73808892853c1e3bee7f360f74b..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/estimators/EPSC.py
+++ /dev/null
@@ -1,489 +0,0 @@
-# vim: set fileencoding=utf-8 :
-# @author: Amir Mohammadi <amir.mohammadi@idiap.ch>
-
-from . import check_features, get_trainable_variables
-from .Logits import moving_average_scaffold
-from ..network.utils import append_logits
-from ..utils import predict_using_tensors
-from ..loss.epsc import epsc_metric, siamese_loss
-from tensorflow.python.estimator import estimator
-import tensorflow as tf
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class EPSCBase:
- """A base class for EPSC based estimators"""
-
- def _get_loss(self, bio_logits, pad_logits, bio_labels, pad_labels, mode):
- main_loss = self.loss_op(
- bio_logits=bio_logits,
- pad_logits=pad_logits,
- bio_labels=bio_labels,
- pad_labels=pad_labels,
- )
- total_loss = main_loss
-
- if self.add_regularization_losses:
-
- regularization_losses = tf.get_collection(
- tf.GraphKeys.REGULARIZATION_LOSSES
- )
- regularization_losses = [
- tf.cast(l, main_loss.dtype) for l in regularization_losses
- ]
-
- regularization_losses = tf.add_n(
- regularization_losses, name="regularization_losses"
- )
- tf.summary.scalar("regularization_losses", regularization_losses)
-
- total_loss = tf.add_n([main_loss, regularization_losses], name="total_loss")
-
- if self.vat_loss is not None:
- vat_loss = self.vat_loss(
- self.end_points["features"],
- self.end_points["Logits/PAD"],
- self.pad_architecture,
- mode,
- )
- total_loss = tf.add_n([main_loss, vat_loss], name="total_loss")
-
- return total_loss
-
-
-class EPSCLogits(EPSCBase, estimator.Estimator):
- """An logits estimator for epsc problems"""
-
- def __init__(
- self,
- architecture,
- optimizer,
- loss_op,
- n_classes,
- config=None,
- embedding_validation=False,
- model_dir="",
- validation_batch_size=None,
- extra_checkpoint=None,
- apply_moving_averages=True,
- add_histograms="train",
- add_regularization_losses=True,
- vat_loss=None,
- optimize_loss=tf.contrib.layers.optimize_loss,
- optimize_loss_learning_rate=None,
- ):
-
- self.architecture = architecture
- self.n_classes = n_classes
- self.loss_op = loss_op
- self.loss = None
- self.embedding_validation = embedding_validation
- self.extra_checkpoint = extra_checkpoint
- self.add_regularization_losses = add_regularization_losses
- self.apply_moving_averages = apply_moving_averages
- self.vat_loss = vat_loss
- self.optimize_loss = optimize_loss
- self.optimize_loss_learning_rate = optimize_loss_learning_rate
-
- if apply_moving_averages and isinstance(optimizer, tf.train.Optimizer):
- logger.info(
- "Encapsulating the optimizer with " "the MovingAverageOptimizer"
- )
- optimizer = tf.contrib.opt.MovingAverageOptimizer(optimizer)
-
- self.optimizer = optimizer
-
- def _model_fn(features, labels, mode):
-
- check_features(features)
- data = features["data"]
- key = features["key"]
-
- # Checking if we have some variables/scope that we may want to shut
- # down
- trainable_variables = get_trainable_variables(
- self.extra_checkpoint, mode=mode
- )
- prelogits, end_points = self.architecture(
- data, mode=mode, trainable_variables=trainable_variables
- )
-
- name = "Logits/Bio"
- bio_logits = append_logits(
- prelogits, n_classes, trainable_variables=trainable_variables, name=name
- )
- end_points[name] = bio_logits
-
- name = "Logits/PAD"
- pad_logits = append_logits(
- prelogits, 2, trainable_variables=trainable_variables, name=name
- )
- end_points[name] = pad_logits
-
- self.end_points = end_points
-
- # for vat_loss
- self.end_points["features"] = data
-
- def pad_architecture(features, mode, reuse):
- prelogits, end_points = self.architecture(
- features,
- mode=mode,
- trainable_variables=trainable_variables,
- reuse=reuse,
- )
- pad_logits = append_logits(
- prelogits,
- 2,
- reuse=reuse,
- trainable_variables=trainable_variables,
- name="Logits/PAD",
- )
- return pad_logits, end_points
-
- self.pad_architecture = pad_architecture
-
- if self.embedding_validation and mode != tf.estimator.ModeKeys.TRAIN:
-
- # Compute the embeddings
- embeddings = tf.nn.l2_normalize(prelogits, 1)
- predictions = {"embeddings": embeddings}
- else:
- predictions = {
- # Generate predictions (for PREDICT and EVAL mode)
- "bio_classes": tf.argmax(input=bio_logits, axis=1),
- # Add `softmax_tensor` to the graph. It is used for PREDICT
- # and by the `logging_hook`.
- "bio_probabilities": tf.nn.softmax(
- bio_logits, name="bio_softmax_tensor"
- ),
- }
-
- predictions.update(
- {
- "pad_classes": tf.argmax(input=pad_logits, axis=1),
- "pad_probabilities": tf.nn.softmax(
- pad_logits, name="pad_softmax_tensor"
- ),
- "key": key,
- }
- )
-
- # add predictions to end_points
- self.end_points.update(predictions)
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- bio_labels = labels["bio"]
- pad_labels = labels["pad"]
-
- if self.embedding_validation and mode != tf.estimator.ModeKeys.TRAIN:
- bio_predictions_op = predict_using_tensors(
- predictions["embeddings"], bio_labels, num=validation_batch_size
- )
- else:
- bio_predictions_op = predictions["bio_classes"]
-
- pad_predictions_op = predictions["pad_classes"]
-
- metrics = {
- "bio_accuracy": tf.metrics.accuracy(
- labels=bio_labels, predictions=bio_predictions_op
- ),
- "pad_accuracy": tf.metrics.accuracy(
- labels=pad_labels, predictions=pad_predictions_op
- ),
- }
-
- if mode == tf.estimator.ModeKeys.EVAL:
- self.loss = self._get_loss(
- bio_logits, pad_logits, bio_labels, pad_labels, mode=mode
- )
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=self.loss,
- train_op=None,
- eval_metric_ops=metrics,
- )
-
- # restore the model from an extra_checkpoint
- if self.extra_checkpoint is not None:
- if "Logits/" not in self.extra_checkpoint["scopes"]:
- logger.warning(
- '"Logits/" (which are automatically added by this '
- "Logits class are not in the scopes of "
- "extra_checkpoint). Did you mean to restore the "
- "Logits variables as well?"
- )
-
- logger.info(
- "Restoring model from %s in scopes %s",
- self.extra_checkpoint["checkpoint_path"],
- self.extra_checkpoint["scopes"],
- )
- tf.train.init_from_checkpoint(
- ckpt_dir_or_file=self.extra_checkpoint["checkpoint_path"],
- assignment_map=self.extra_checkpoint["scopes"],
- )
-
- # Calculate Loss
- self.loss = self._get_loss(
- bio_logits, pad_logits, bio_labels, pad_labels, mode=mode
- )
-
- # Compute the moving average of all individual losses and the total
- # loss.
- loss_averages = tf.train.ExponentialMovingAverage(0.9, name="avg")
- loss_averages_op = loss_averages.apply(
- tf.get_collection(tf.GraphKeys.LOSSES)
- )
- tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, loss_averages_op)
-
- with tf.name_scope("train"):
- train_op = self.optimize_loss(
- loss=self.loss,
- global_step=tf.train.get_or_create_global_step(),
- optimizer=self.optimizer,
- learning_rate=self.optimize_loss_learning_rate,
- )
-
- # Get the moving average saver after optimizer.minimize is called
- if self.apply_moving_averages:
- self.saver, self.scaffold = moving_average_scaffold(
- self.optimizer.optimizer
- if hasattr(self.optimizer, "optimizer")
- else self.optimizer,
- config,
- )
- else:
- self.saver, self.scaffold = None, None
-
- # Log accuracy and loss
- with tf.name_scope("train_metrics"):
- tf.summary.scalar("bio_accuracy", metrics["bio_accuracy"][1])
- tf.summary.scalar("pad_accuracy", metrics["pad_accuracy"][1])
- for l in tf.get_collection(tf.GraphKeys.LOSSES):
- tf.summary.scalar(
- l.op.name + "_averaged", loss_averages.average(l)
- )
-
- # add histograms summaries
- if add_histograms == "all":
- for v in tf.all_variables():
- tf.summary.histogram(v.name, v)
- elif add_histograms == "train":
- for v in tf.trainable_variables():
- tf.summary.histogram(v.name, v)
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=self.loss,
- train_op=train_op,
- eval_metric_ops=metrics,
- scaffold=self.scaffold,
- )
-
- super().__init__(model_fn=_model_fn, model_dir=model_dir, config=config)
-
-
-class EPSCSiamese(EPSCBase, estimator.Estimator):
- """An siamese estimator for epsc problems"""
-
- def __init__(
- self,
- architecture,
- optimizer,
- loss_op=siamese_loss,
- config=None,
- model_dir="",
- validation_batch_size=None,
- extra_checkpoint=None,
- apply_moving_averages=True,
- add_histograms="train",
- add_regularization_losses=True,
- vat_loss=None,
- optimize_loss=tf.contrib.layers.optimize_loss,
- optimize_loss_learning_rate=None,
- ):
-
- self.architecture = architecture
- self.loss_op = loss_op
- self.loss = None
- self.extra_checkpoint = extra_checkpoint
- self.add_regularization_losses = add_regularization_losses
- self.apply_moving_averages = apply_moving_averages
- self.vat_loss = vat_loss
- self.optimize_loss = optimize_loss
- self.optimize_loss_learning_rate = optimize_loss_learning_rate
-
- if self.apply_moving_averages and isinstance(optimizer, tf.train.Optimizer):
- logger.info(
- "Encapsulating the optimizer with " "the MovingAverageOptimizer"
- )
- optimizer = tf.contrib.opt.MovingAverageOptimizer(optimizer)
-
- self.optimizer = optimizer
-
- def _model_fn(features, labels, mode):
-
- if mode != tf.estimator.ModeKeys.TRAIN:
- check_features(features)
- data = features["data"]
- key = features["key"]
- else:
- if "left" not in features or "right" not in features:
- raise ValueError(
- "The input features needs to be a dictionary "
- "with the keys `left` and `right`"
- )
- data_right = features["right"]["data"]
- labels_right = labels["right"]
- data = features["left"]["data"]
- labels = labels_left = labels["left"]
-
- # Checking if we have some variables/scope that we may want to shut
- # down
- trainable_variables = get_trainable_variables(
- self.extra_checkpoint, mode=mode
- )
-
- prelogits, end_points = self.architecture(
- data, mode=mode, trainable_variables=trainable_variables
- )
-
- self.end_points = end_points
-
- predictions = dict(
- bio_embeddings=tf.nn.l2_normalize(prelogits, 1),
- pad_probabilities=tf.math.exp(-tf.norm(prelogits, ord=2, axis=-1)),
- )
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- predictions["key"] = key
-
- # add predictions to end_points
- self.end_points.update(predictions)
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- metrics = None
- if mode != tf.estimator.ModeKeys.TRAIN:
- assert validation_batch_size is not None
- bio_labels = labels["bio"]
- pad_labels = labels["pad"]
-
- metrics = epsc_metric(
- predictions["bio_embeddings"],
- predictions["pad_probabilities"],
- bio_labels,
- pad_labels,
- validation_batch_size,
- )
-
- if mode == tf.estimator.ModeKeys.EVAL:
- self.loss = tf.reduce_mean(0)
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=self.loss,
- train_op=None,
- eval_metric_ops=metrics,
- )
-
- # now that we are in TRAIN mode, build the right graph too
- prelogits_left = prelogits
- prelogits_right, _ = self.architecture(
- data_right,
- mode=mode,
- reuse=True,
- trainable_variables=trainable_variables,
- )
-
- bio_logits = {"left": prelogits_left, "right": prelogits_right}
- pad_logits = bio_logits
-
- bio_labels = {"left": labels_left["bio"], "right": labels_right["bio"]}
-
- pad_labels = {"left": labels_left["pad"], "right": labels_right["pad"]}
-
- # restore the model from an extra_checkpoint
- if self.extra_checkpoint is not None:
- logger.info(
- "Restoring model from %s in scopes %s",
- self.extra_checkpoint["checkpoint_path"],
- self.extra_checkpoint["scopes"],
- )
- tf.train.init_from_checkpoint(
- ckpt_dir_or_file=self.extra_checkpoint["checkpoint_path"],
- assignment_map=self.extra_checkpoint["scopes"],
- )
-
- global_step = tf.train.get_or_create_global_step()
-
- # Some layer like tf.layers.batch_norm need this:
- update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
-
- with tf.control_dependencies(update_ops), tf.name_scope("train"):
-
- # Calculate Loss
- self.loss = self._get_loss(
- bio_logits, pad_logits, bio_labels, pad_labels, mode=mode
- )
-
- # Compute the moving average of all individual losses
- # and the total loss.
- loss_averages = tf.train.ExponentialMovingAverage(0.9, name="avg")
- loss_averages_op = loss_averages.apply(
- tf.get_collection(tf.GraphKeys.LOSSES)
- )
-
- train_op = tf.group(
- self.optimize_loss(
- loss=self.loss,
- global_step=tf.train.get_or_create_global_step(),
- optimizer=self.optimizer,
- learning_rate=self.optimize_loss_learning_rate,
- ),
- loss_averages_op,
- )
-
- # Get the moving average saver after optimizer.minimize is called
- if apply_moving_averages:
- self.saver, self.scaffold = moving_average_scaffold(
- self.optimizer.optimizer
- if hasattr(self.optimizer, "optimizer")
- else self.optimizer,
- config,
- )
- else:
- self.saver, self.scaffold = None, None
-
- # Log moving average of losses
- with tf.name_scope("train_metrics"):
- for l in tf.get_collection(tf.GraphKeys.LOSSES):
- tf.summary.scalar(l.op.name + "_averaged", loss_averages.average(l))
-
- # add histograms summaries
- if add_histograms == "all":
- for v in tf.all_variables():
- tf.summary.histogram(v.name, v)
- elif add_histograms == "train":
- for v in tf.trainable_variables():
- tf.summary.histogram(v.name, v)
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=self.loss,
- train_op=train_op,
- eval_metric_ops=metrics,
- scaffold=self.scaffold,
- )
-
- super().__init__(model_fn=_model_fn, model_dir=model_dir, config=config)
diff --git a/bob/learn/tensorflow/estimators/Logits.py b/bob/learn/tensorflow/estimators/Logits.py
deleted file mode 100755
index 7a7570bf4a935533900573eb9cb5d31d55061eb6..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/estimators/Logits.py
+++ /dev/null
@@ -1,545 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from bob.learn.tensorflow.network.utils import append_logits
-from tensorflow.python.estimator import estimator
-from bob.learn.tensorflow.utils import predict_using_tensors
-from bob.learn.tensorflow.loss import (
- mean_cross_entropy_center_loss,
- balanced_softmax_cross_entropy_loss_weights,
- balanced_sigmoid_cross_entropy_loss_weights,
-)
-
-from . import check_features, get_trainable_variables
-
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class Logits(estimator.Estimator):
- """Logits estimator.
-
- NN estimator with `Cross entropy loss
- <https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits>`_
- in the hot-encoded layer
- :py:class:`bob.learn.tensorflow.estimators.Logits`.
-
- The architecture function should follow the following pattern::
-
- def my_beautiful_architecture(placeholder, **kwargs):
-
- end_points = dict()
- graph = convXX(placeholder)
- end_points['conv'] = graph
-
- return graph, end_points
-
-
- The **loss** function should follow the following pattern::
-
- def my_beautiful_loss(logits, labels, **kwargs):
- return loss_set_of_ops(logits, labels)
-
-
- Attributes
- ----------
-
- architecture:
- Pointer to a function that builds the graph.
-
- optimizer:
- One of the tensorflow solvers
-
- config:
-
- n_classes:
- Number of classes of your problem. The logits will be appended in this
- class
-
- loss_op:
- Pointer to a function that computes the loss.
-
- embedding_validation:
- Run the validation using embeddings?? [default: False]
-
- model_dir:
- Model path
-
- validation_batch_size:
- Size of the batch for validation. This value is used when the
- validation with embeddings is used. This is a hack.
-
- params:
- Extra params for the model function (please see
- https://www.tensorflow.org/extend/estimators for more info)
-
- extra_checkpoint: dict
- In case you want to use other model to initialize some variables.
- This argument should be in the following format::
-
- extra_checkpoint = {
- "checkpoint_path": <YOUR_CHECKPOINT>,
- "scopes": dict({"<SOURCE_SCOPE>/": "<TARGET_SCOPE>/"}),
- "trainable_variables": [<LIST OF VARIABLES OR SCOPES THAT YOU WANT TO RETRAIN>]
- }
-
- apply_moving_averages: bool
- Apply exponential moving average in the training variables and in the loss.
- https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
- By default the decay for the variable averages is 0.9999 and for the loss is 0.9
- """
-
- def __init__(
- self,
- architecture,
- optimizer,
- loss_op,
- n_classes,
- config=None,
- embedding_validation=False,
- model_dir="",
- validation_batch_size=None,
- params=None,
- extra_checkpoint=None,
- apply_moving_averages=True,
- add_histograms=None,
- vat_loss=None,
- architecture_has_logits=False,
- balanced_loss_weight=False,
- use_sigmoid=False,
- labels_are_one_hot=False,
- optimize_loss=tf.contrib.layers.optimize_loss,
- optimize_loss_learning_rate=None,
- ):
-
- self.architecture = architecture
- self.n_classes = n_classes
- self.loss_op = loss_op
- self.loss = None
- self.embedding_validation = embedding_validation
- self.extra_checkpoint = extra_checkpoint
- self.apply_moving_averages = apply_moving_averages
-
- if self.apply_moving_averages and isinstance(optimizer, tf.train.Optimizer):
- logger.info("Encapsulating the optimizer with the MovingAverageOptimizer")
- optimizer = tf.contrib.opt.MovingAverageOptimizer(optimizer)
-
- self.optimizer = optimizer
- self.vat_loss = vat_loss
- self.balanced_loss_weight = balanced_loss_weight
- self.use_sigmoid = use_sigmoid
- self.labels_are_one_hot = labels_are_one_hot
- self.optimize_loss = optimize_loss
- self.optimize_loss_learning_rate = optimize_loss_learning_rate
-
- if self.n_classes < 3 and not self.use_sigmoid:
- logger.error(
- "n_classes is less than 3 and use_sigmoid is False. Are you sure? "
- "It's better to use sigmoid activation on your logits for binary "
- "classification tasks. Don't forget to change your loss to a sigmoid "
- "loss too! Change n_classes to 1 too if not already."
- )
-
- def _model_fn(features, labels, mode, config):
-
- check_features(features)
- data = features["data"]
- key = features["key"]
-
- # Checking if we have some variables/scope that we may want to shut
- # down
- trainable_variables = get_trainable_variables(
- self.extra_checkpoint, mode=mode
- )
- prelogits, end_points = self.architecture(
- data, mode=mode, trainable_variables=trainable_variables
- )
- if architecture_has_logits:
- logits, prelogits = prelogits, end_points["prelogits"]
- else:
- logits = append_logits(
- prelogits, n_classes, trainable_variables=trainable_variables
- )
-
- # for vat_loss
- def whole_architecture(features, mode, reuse):
- prelogits, end_points = self.architecture(
- features,
- mode=mode,
- trainable_variables=trainable_variables,
- reuse=reuse,
- )
- logits = append_logits(
- prelogits,
- n_classes,
- reuse=reuse,
- trainable_variables=trainable_variables,
- )
- return logits, end_points
-
- if architecture_has_logits:
- whole_architecture = self.architecture
-
- if self.embedding_validation and mode != tf.estimator.ModeKeys.TRAIN:
-
- # Compute the embeddings
- embeddings = tf.nn.l2_normalize(prelogits, 1)
- predictions = {"embeddings": embeddings, "key": key}
- else:
- predictions = {
- # Generate predictions (for PREDICT and EVAL mode)
- "classes": tf.argmax(input=logits, axis=1)
- if self.n_classes > 1
- else logits >= 0.5,
- # Add `softmax_tensor` to the graph. It is used for PREDICT
- # and by the `logging_hook`.
- "probabilities": tf.nn.sigmoid(logits, name="sigmoid_tensor")
- if self.use_sigmoid
- else tf.nn.softmax(logits, name="softmax_tensor"),
- "key": key,
- }
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- if self.embedding_validation and mode != tf.estimator.ModeKeys.TRAIN:
- predictions_op = predict_using_tensors(
- predictions["embeddings"], labels, num=validation_batch_size
- )
- else:
- predictions_op = predictions["classes"]
-
- accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions_op)
- metrics = {"accuracy": accuracy}
-
- loss_kwargs = {}
- loss_kwargs["logits"] = logits
- if self.balanced_loss_weight:
- weights_fn = (
- balanced_sigmoid_cross_entropy_loss_weights
- if self.use_sigmoid
- else balanced_softmax_cross_entropy_loss_weights
- )
- labels_one_hot = labels
- if (not self.labels_are_one_hot) and (not self.use_sigmoid):
- labels_one_hot = tf.one_hot(labels, depth=self.n_classes)
- weights = weights_fn(labels_one_hot, logits.dtype)
- loss_kwargs["weights"] = weights
-
- if self.use_sigmoid:
- loss_kwargs["multi_class_labels"] = labels
- else:
- loss_kwargs["labels"] = labels
-
- if mode == tf.estimator.ModeKeys.EVAL:
- self.loss = self.loss_op(**loss_kwargs)
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=self.loss,
- train_op=None,
- eval_metric_ops=metrics,
- )
-
- # restore the model from an extra_checkpoint
- if extra_checkpoint is not None:
- if (
- "Logits/" not in extra_checkpoint["scopes"]
- and not architecture_has_logits
- ):
- logger.warning(
- '"Logits/" (which are automatically added by this '
- "Logits class are not in the scopes of "
- "extra_checkpoint). Did you mean to restore the "
- "Logits variables as well?"
- )
- tf.train.init_from_checkpoint(
- ckpt_dir_or_file=extra_checkpoint["checkpoint_path"],
- assignment_map=extra_checkpoint["scopes"],
- )
-
- # Calculate Loss
- self.loss = self.loss_op(**loss_kwargs)
-
- if self.vat_loss is not None:
- vat_loss = self.vat_loss(data, logits, whole_architecture, mode)
- self.loss = tf.add_n([self.loss, vat_loss], name="total_loss_with_vat")
-
- # Compute the moving average of all individual losses and the total loss.
- loss_averages = tf.train.ExponentialMovingAverage(0.9, name="avg")
- loss_averages_op = loss_averages.apply(
- tf.get_collection(tf.GraphKeys.LOSSES)
- )
-
- train_op = tf.group(
- self.optimize_loss(
- loss=self.loss,
- global_step=tf.train.get_or_create_global_step(),
- optimizer=self.optimizer,
- learning_rate=self.optimize_loss_learning_rate,
- ),
- loss_averages_op,
- )
-
- # Get the moving average saver after optimizer.minimize is called
- if self.apply_moving_averages:
- self.saver, self.scaffold = moving_average_scaffold(
- self.optimizer.optimizer
- if hasattr(self.optimizer, "optimizer")
- else self.optimizer,
- config,
- )
- else:
- self.saver, self.scaffold = None, None
-
- # Log accuracy and loss
- with tf.name_scope("train_metrics"):
- tf.summary.scalar("accuracy", accuracy[1])
- for l in tf.get_collection(tf.GraphKeys.LOSSES):
- tf.summary.scalar(l.op.name + "_averaged", loss_averages.average(l))
-
- # add histograms summaries
- if add_histograms == "all":
- for v in tf.all_variables():
- tf.summary.histogram(v.name, v)
- elif add_histograms == "train":
- for v in tf.trainable_variables():
- tf.summary.histogram(v.name, v)
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=self.loss,
- train_op=train_op,
- eval_metric_ops=metrics,
- scaffold=self.scaffold,
- )
-
- super(Logits, self).__init__(
- model_fn=_model_fn, model_dir=model_dir, params=params, config=config
- )
-
-
-class LogitsCenterLoss(estimator.Estimator):
- """Logits estimator with center loss.
-
- NN estimator with `Cross entropy loss
- <https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits>`_
- in the hot-encoded layer :py:class:`bob.learn.tensorflow.estimators.Logits`
- plus the center loss implemented in: "Wen, Yandong, et al. "A
- discriminative feature learning approach for deep face recognition."
- European Conference on Computer Vision. Springer, Cham, 2016."
-
- See :any:`Logits` for the description of parameters.
- """
-
- def __init__(
- self,
- architecture=None,
- optimizer=None,
- config=None,
- n_classes=0,
- embedding_validation=False,
- model_dir="",
- alpha=0.9,
- factor=0.01,
- validation_batch_size=None,
- params=None,
- extra_checkpoint=None,
- apply_moving_averages=True,
- optimize_loss=tf.contrib.layers.optimize_loss,
- optimize_loss_learning_rate=None,
- ):
-
- self.architecture = architecture
- self.n_classes = n_classes
- self.alpha = alpha
- self.factor = factor
- self.loss = None
- self.embedding_validation = embedding_validation
- self.extra_checkpoint = extra_checkpoint
- self.optimize_loss = optimize_loss
- self.optimize_loss_learning_rate = optimize_loss_learning_rate
- self.apply_moving_averages = apply_moving_averages
-
- if self.architecture is None:
- raise ValueError("Please specify a function to build the architecture !!")
-
- if optimizer is None:
- raise ValueError(
- "Please specify a optimizer (https://www.tensorflow.org/"
- "api_guides/python/train) !!"
- )
-
- if self.n_classes <= 0:
- raise ValueError("Number of classes must be greated than 0")
-
- if self.apply_moving_averages and isinstance(optimizer, tf.train.Optimizer):
- logger.info("Encapsulating the optimizer with the MovingAverageOptimizer")
- optimizer = tf.contrib.opt.MovingAverageOptimizer(optimizer)
- self.optimizer = optimizer
-
- def _model_fn(features, labels, mode, config):
-
- check_features(features)
- data = features["data"]
- key = features["key"]
-
- # Configure the Training Op (for TRAIN mode)
- if mode == tf.estimator.ModeKeys.TRAIN:
- # Building the training graph
-
- # Checking if we have some variables/scope that we may want to shut down
- trainable_variables = get_trainable_variables(self.extra_checkpoint)
- prelogits = self.architecture(
- data, mode=mode, trainable_variables=trainable_variables
- )[0]
- logits = append_logits(prelogits, n_classes)
-
- global_step = tf.train.get_or_create_global_step()
-
- # Compute the moving average of all individual losses and the total loss.
- if self.apply_moving_averages:
- variable_averages = tf.train.ExponentialMovingAverage(
- 0.9999, global_step
- )
- variable_averages_op = variable_averages.apply(
- tf.trainable_variables()
- )
- else:
- variable_averages_op = tf.no_op(name="noop")
-
- update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
-
- # Compute Loss (for TRAIN mode)
- loss_dict = mean_cross_entropy_center_loss(
- logits,
- prelogits,
- labels,
- self.n_classes,
- alpha=self.alpha,
- factor=self.factor,
- )
-
- self.loss = loss_dict["loss"]
- centers = loss_dict["centers"]
-
- # Compute the moving average of all individual losses and the total loss.
- loss_averages = tf.train.ExponentialMovingAverage(0.9, name="avg")
- loss_averages_op = loss_averages.apply(
- tf.get_collection(tf.GraphKeys.LOSSES)
- )
-
- for l in tf.get_collection(tf.GraphKeys.LOSSES):
- tf.summary.scalar(l.op.name, loss_averages.average(l))
-
- if self.extra_checkpoint is not None:
- tf.contrib.framework.init_from_checkpoint(
- self.extra_checkpoint["checkpoint_path"],
- self.extra_checkpoint["scopes"],
- )
-
- train_op = self.optimize_loss(
- self.loss,
- global_step,
- self.optimize_loss_learning_rate,
- optimizer,
- update_ops=[variable_averages_op] + update_ops,
- )
-
- # Get the moving average saver after optimizer.minimize is called
- if self.apply_moving_averages:
- self.saver, self.scaffold = moving_average_scaffold(
- self.optimizer.optimizer
- if hasattr(self.optimizer, "optimizer")
- else self.optimizer,
- config,
- )
- else:
- self.saver, self.scaffold = None, None
-
- train_op = tf.group(
- train_op, centers, variable_averages_op, loss_averages_op
- )
- return tf.estimator.EstimatorSpec(
- mode=mode, loss=self.loss, train_op=train_op
- )
-
- # Building the training graph for PREDICTION OR VALIDATION
- prelogits, end_points = self.architecture(data, mode=mode)
- logits = append_logits(prelogits, n_classes)
- end_points["logits"] = logits
-
- if self.embedding_validation:
- # Compute the embeddings
- embeddings = tf.nn.l2_normalize(prelogits, 1)
- predictions = {"embeddings": embeddings, "key": key}
- else:
- predictions = {
- # Generate predictions (for PREDICT and EVAL mode)
- "classes": tf.argmax(input=logits, axis=1),
- # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
- # `logging_hook`.
- "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
- "key": key,
- }
- end_points.update(predictions)
- self.end_points = end_points
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- # IF Validation
- loss_dict = mean_cross_entropy_center_loss(
- logits,
- prelogits,
- labels,
- self.n_classes,
- alpha=self.alpha,
- factor=self.factor,
- )
- self.loss = loss_dict["loss"]
-
- if self.embedding_validation:
- predictions_op = predict_using_tensors(
- predictions["embeddings"], labels, num=validation_batch_size
- )
- eval_metric_ops = {
- "accuracy": tf.metrics.accuracy(
- labels=labels, predictions=predictions_op
- )
- }
- return tf.estimator.EstimatorSpec(
- mode=mode, loss=self.loss, eval_metric_ops=eval_metric_ops
- )
-
- else:
- # Add evaluation metrics (for EVAL mode)
- eval_metric_ops = {
- "accuracy": tf.metrics.accuracy(
- labels=labels, predictions=predictions["classes"]
- )
- }
- return tf.estimator.EstimatorSpec(
- mode=mode, loss=self.loss, eval_metric_ops=eval_metric_ops
- )
-
- super(LogitsCenterLoss, self).__init__(
- model_fn=_model_fn, model_dir=model_dir, config=config
- )
-
-
-def moving_average_scaffold(optimizer, config):
- max_to_keep = 5 if config is None else config.keep_checkpoint_max
- keep_checkpoint_every_n_hours = (
- 10000.0 if config is None else config.keep_checkpoint_every_n_hours
- )
- saver = optimizer.swapping_saver(
- max_to_keep=max_to_keep,
- keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
- )
- scaffold = tf.train.Scaffold(saver=saver)
- return saver, scaffold
diff --git a/bob/learn/tensorflow/estimators/Regressor.py b/bob/learn/tensorflow/estimators/Regressor.py
deleted file mode 100644
index 7788def1d969e10270eff18650ef23813e8faa6e..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/estimators/Regressor.py
+++ /dev/null
@@ -1,171 +0,0 @@
-from . import check_features, get_trainable_variables
-from .Logits import moving_average_scaffold
-from bob.learn.tensorflow.network.utils import append_logits
-from tensorflow.python.estimator import estimator
-import tensorflow as tf
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class Regressor(estimator.Estimator):
- """An estimator for regression problems"""
-
- def __init__(
- self,
- architecture,
- optimizer=tf.train.AdamOptimizer(),
- loss_op=tf.losses.mean_squared_error,
- label_dimension=1,
- config=None,
- model_dir=None,
- apply_moving_averages=True,
- add_regularization_losses=True,
- extra_checkpoint=None,
- add_histograms=None,
- optimize_loss=tf.contrib.layers.optimize_loss,
- optimize_loss_learning_rate=None,
- architecture_has_logits=False,
- ):
- self.architecture = architecture
- self.label_dimension = label_dimension
- self.loss_op = loss_op
- self.add_regularization_losses = add_regularization_losses
- self.apply_moving_averages = apply_moving_averages
-
- if self.apply_moving_averages and isinstance(optimizer, tf.train.Optimizer):
- logger.info(
- "Encapsulating the optimizer with " "the MovingAverageOptimizer"
- )
- optimizer = tf.contrib.opt.MovingAverageOptimizer(optimizer)
-
- self.optimizer = optimizer
- self.optimize_loss = optimize_loss
- self.optimize_loss_learning_rate = optimize_loss_learning_rate
-
- def _model_fn(features, labels, mode, config):
-
- check_features(features)
- data = features["data"]
- key = features["key"]
-
- # Checking if we have some variables/scope that we may want to shut
- # down
- trainable_variables = get_trainable_variables(extra_checkpoint, mode=mode)
- prelogits, end_points = self.architecture(
- data, mode=mode, trainable_variables=trainable_variables
- )
- if architecture_has_logits:
- logits, prelogits = prelogits, end_points["prelogits"]
- else:
- logits = append_logits(
- prelogits, label_dimension, trainable_variables=trainable_variables
- )
-
- predictions = {"predictions": logits, "key": key}
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- # in PREDICT mode logits rank must be 2 but in EVAL and TRAIN the
- # rank should be 1 for the loss function!
- predictions["predictions"] = tf.squeeze(logits)
-
- predictions_op = predictions["predictions"]
-
- # Calculate root mean squared error
- rmse = tf.metrics.root_mean_squared_error(labels, predictions_op)
- metrics = {"rmse": rmse}
-
- if mode == tf.estimator.ModeKeys.EVAL:
- self.loss = self._get_loss(predictions=predictions_op, labels=labels)
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=self.loss,
- train_op=None,
- eval_metric_ops=metrics,
- )
-
- # restore the model from an extra_checkpoint
- if extra_checkpoint is not None:
- if "Logits/" not in extra_checkpoint["scopes"]:
- logger.warning(
- '"Logits/" (which are automatically added by this '
- "Regressor class are not in the scopes of "
- "extra_checkpoint). Did you mean to restore the "
- "Logits variables as well?"
- )
- tf.train.init_from_checkpoint(
- ckpt_dir_or_file=extra_checkpoint["checkpoint_path"],
- assignment_map=extra_checkpoint["scopes"],
- )
-
- # Calculate Loss
- self.loss = self._get_loss(predictions=predictions_op, labels=labels)
-
- # Compute the moving average of all individual losses
- # and the total loss.
- loss_averages = tf.train.ExponentialMovingAverage(0.9, name="avg")
- loss_averages_op = loss_averages.apply(
- tf.get_collection(tf.GraphKeys.LOSSES)
- )
-
- train_op = tf.group(
- self.optimize_loss(
- loss=self.loss,
- global_step=tf.train.get_or_create_global_step(),
- optimizer=self.optimizer,
- learning_rate=self.optimize_loss_learning_rate,
- ),
- loss_averages_op,
- )
-
- # Get the moving average saver after optimizer.minimize is called
- if self.apply_moving_averages:
- self.saver, self.scaffold = moving_average_scaffold(
- self.optimizer.optimizer
- if hasattr(self.optimizer, "optimizer")
- else self.optimizer,
- config,
- )
- else:
- self.saver, self.scaffold = None, None
-
- # Log rmse and loss
- with tf.name_scope("train_metrics"):
- tf.summary.scalar("rmse", rmse[1])
- for l in tf.get_collection(tf.GraphKeys.LOSSES):
- tf.summary.scalar(l.op.name + "_averaged", loss_averages.average(l))
-
- # add histograms summaries
- if add_histograms == "all":
- for v in tf.all_variables():
- tf.summary.histogram(v.name, v)
- elif add_histograms == "train":
- for v in tf.trainable_variables():
- tf.summary.histogram(v.name, v)
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=self.loss,
- train_op=train_op,
- eval_metric_ops=metrics,
- scaffold=self.scaffold,
- )
-
- super(Regressor, self).__init__(
- model_fn=_model_fn, model_dir=model_dir, config=config
- )
-
- def _get_loss(self, predictions, labels):
- main_loss = self.loss_op(predictions=predictions, labels=labels)
- if not self.add_regularization_losses:
- return main_loss
- regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
- regularization_losses = [
- tf.cast(l, main_loss.dtype) for l in regularization_losses
- ]
- total_loss = tf.add_n([main_loss] + regularization_losses, name="total_loss")
- return total_loss
diff --git a/bob/learn/tensorflow/estimators/Siamese.py b/bob/learn/tensorflow/estimators/Siamese.py
deleted file mode 100755
index f9be6e084eb1dc157ae3f72793835ee51266226d..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/estimators/Siamese.py
+++ /dev/null
@@ -1,161 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from tensorflow.python.estimator import estimator
-from bob.learn.tensorflow.utils import predict_using_tensors
-from . import check_features, get_trainable_variables
-
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class Siamese(estimator.Estimator):
- """NN estimator for Siamese Networks.
- Proposed in: "Chopra, Sumit, Raia Hadsell, and Yann LeCun. "Learning a
- similarity metric discriminatively, with application to face verification."
- Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer
- Society Conference on. Vol. 1. IEEE, 2005."
-
- See :any:`Logits` for the description of parameters.
- """
-
- def __init__(
- self,
- architecture=None,
- optimizer=None,
- config=None,
- loss_op=None,
- model_dir="",
- validation_batch_size=None,
- params=None,
- extra_checkpoint=None,
- add_histograms=None,
- add_regularization_losses=True,
- optimize_loss=tf.contrib.layers.optimize_loss,
- optimize_loss_learning_rate=None,
- ):
-
- self.architecture = architecture
- self.optimizer = optimizer
- self.loss_op = loss_op
- self.loss = None
- self.extra_checkpoint = extra_checkpoint
- self.add_regularization_losses = add_regularization_losses
-
- self.optimize_loss = optimize_loss
- self.optimize_loss_learning_rate = optimize_loss_learning_rate
-
- if self.architecture is None:
- raise ValueError("Please specify a function to build the architecture !!")
-
- if self.optimizer is None:
- raise ValueError(
- "Please specify a optimizer (https://www.tensorflow.org/api_guides/python/train) !!"
- )
-
- if self.loss_op is None:
- raise ValueError("Please specify a function to build the loss !!")
-
- def _model_fn(features, labels, mode):
- if mode == tf.estimator.ModeKeys.TRAIN:
-
- # Building one graph, by default everything is trainable
- # The input function needs to have dictionary pair with the `left` and `right` keys
- if "left" not in features.keys() or "right" not in features.keys():
- raise ValueError(
- "The input function needs to contain a dictionary with the keys `left` and `right` "
- )
-
- # Building one graph
- trainable_variables = get_trainable_variables(self.extra_checkpoint)
- data_left = features["left"]
- data_left = (
- data_left["data"] if isinstance(data_left, dict) else data_left
- )
- data_right = features["right"]
- data_right = (
- data_right["data"] if isinstance(data_right, dict) else data_right
- )
- prelogits_left, end_points_left = self.architecture(
- data_left, mode=mode, trainable_variables=trainable_variables
- )
- prelogits_right, end_points_right = self.architecture(
- data_right,
- reuse=True,
- mode=mode,
- trainable_variables=trainable_variables,
- )
-
- if self.extra_checkpoint is not None:
- tf.contrib.framework.init_from_checkpoint(
- self.extra_checkpoint["checkpoint_path"],
- self.extra_checkpoint["scopes"],
- )
-
- # Compute Loss (for both TRAIN and EVAL modes)
- labels = (
- tf.not_equal(labels["left"], labels["right"])
- if isinstance(labels, dict)
- else labels
- )
- self.loss = self.loss_op(prelogits_left, prelogits_right, labels)
- if self.add_regularization_losses:
- regularization_losses = tf.get_collection(
- tf.GraphKeys.REGULARIZATION_LOSSES
- )
- regularization_losses = [
- tf.cast(l, self.loss.dtype) for l in regularization_losses
- ]
- self.loss = tf.add_n(
- [self.loss] + regularization_losses, name="total_loss"
- )
- train_op = self.optimize_loss(
- loss=self.loss,
- global_step=tf.train.get_or_create_global_step(),
- optimizer=self.optimizer,
- learning_rate=self.optimize_loss_learning_rate,
- )
-
- # add histograms summaries
- if add_histograms == "all":
- for v in tf.all_variables():
- tf.summary.histogram(v.name, v)
- elif add_histograms == "train":
- for v in tf.trainable_variables():
- tf.summary.histogram(v.name, v)
-
- return tf.estimator.EstimatorSpec(
- mode=mode, loss=self.loss, train_op=train_op
- )
-
- check_features(features)
- data = features["data"]
- key = features["key"]
-
- # Compute the embeddings
- prelogits = self.architecture(data, mode=mode)[0]
- embeddings = tf.nn.l2_normalize(prelogits, 1)
- predictions = {"embeddings": embeddings, "key": key}
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- predictions_op = predict_using_tensors(
- predictions["embeddings"], labels, num=validation_batch_size
- )
- eval_metric_ops = {
- "accuracy": tf.metrics.accuracy(
- labels=labels, predictions=predictions_op
- )
- }
-
- return tf.estimator.EstimatorSpec(
- mode=mode, loss=tf.reduce_mean(1), eval_metric_ops=eval_metric_ops
- )
-
- super(Siamese, self).__init__(
- model_fn=_model_fn, model_dir=model_dir, params=params, config=config
- )
diff --git a/bob/learn/tensorflow/estimators/Triplet.py b/bob/learn/tensorflow/estimators/Triplet.py
deleted file mode 100644
index c34a964652af7236aea6c853fca61acae511317e..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/estimators/Triplet.py
+++ /dev/null
@@ -1,141 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from tensorflow.python.estimator import estimator
-from bob.learn.tensorflow.utils import predict_using_tensors
-from bob.learn.tensorflow.loss import triplet_loss
-from . import check_features, get_trainable_variables
-
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class Triplet(estimator.Estimator):
- """NN estimator for Triplet networks.
-
- Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A
- unified embedding for face recognition and clustering." Proceedings of the
- IEEE Conference on Computer Vision and Pattern Recognition. 2015.
-
- See :any:`Logits` for the description of parameters.
- """
-
- def __init__(
- self,
- architecture=None,
- optimizer=None,
- config=None,
- loss_op=triplet_loss,
- model_dir="",
- validation_batch_size=None,
- extra_checkpoint=None,
- optimize_loss=tf.contrib.layers.optimize_loss,
- optimize_loss_learning_rate=None,
- ):
-
- self.architecture = architecture
- self.optimizer = optimizer
- self.loss_op = loss_op
- self.loss = None
- self.extra_checkpoint = extra_checkpoint
- self.optimize_loss = optimize_loss
- self.optimize_loss_learning_rate = optimize_loss_learning_rate
-
- if self.architecture is None:
- raise ValueError("Please specify a function to build the architecture !!")
-
- if self.optimizer is None:
- raise ValueError(
- "Please specify a optimizer (https://www.tensorflow.org/api_guides/python/train) !!"
- )
-
- if self.loss_op is None:
- raise ValueError("Please specify a function to build the loss !!")
-
- def _model_fn(features, labels, mode, params, config):
-
- if mode == tf.estimator.ModeKeys.TRAIN:
-
- # The input function needs to have dictionary pair with the `left` and `right` keys
- if (
- "anchor" not in features.keys()
- or "positive" not in features.keys()
- or "negative" not in features.keys()
- ):
- raise ValueError(
- "The input function needs to contain a dictionary with the "
- "keys `anchor`, `positive` and `negative` "
- )
-
- # Building one graph
- trainable_variables = get_trainable_variables(self.extra_checkpoint)
- prelogits_anchor = self.architecture(
- features["anchor"],
- mode=mode,
- trainable_variables=trainable_variables,
- )[0]
- prelogits_positive = self.architecture(
- features["positive"],
- reuse=True,
- mode=mode,
- trainable_variables=trainable_variables,
- )[0]
- prelogits_negative = self.architecture(
- features["negative"],
- reuse=True,
- mode=mode,
- trainable_variables=trainable_variables,
- )[0]
-
- if self.extra_checkpoint is not None:
- tf.contrib.framework.init_from_checkpoint(
- self.extra_checkpoint["checkpoint_path"],
- self.extra_checkpoint["scopes"],
- )
-
- # Compute Loss (for both TRAIN and EVAL modes)
- self.loss = self.loss_op(
- prelogits_anchor, prelogits_positive, prelogits_negative
- )
- # Configure the Training Op (for TRAIN mode)
- global_step = tf.train.get_or_create_global_step()
- train_op = self.optimize_loss(
- loss=self.loss,
- global_step=global_step,
- optimizer=self.optimizer,
- learning_rate=self.optimize_loss_learning_rate,
- )
- return tf.estimator.EstimatorSpec(
- mode=mode, loss=self.loss, train_op=train_op
- )
-
- check_features(features)
- data = features["data"]
-
- # Compute the embeddings
- prelogits = self.architecture(data, mode=mode)[0]
- embeddings = tf.nn.l2_normalize(prelogits, 1)
- predictions = {"embeddings": embeddings}
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- predictions_op = predict_using_tensors(
- predictions["embeddings"], labels, num=validation_batch_size
- )
- eval_metric_ops = {
- "accuracy": tf.metrics.accuracy(
- labels=labels, predictions=predictions_op
- )
- }
-
- return tf.estimator.EstimatorSpec(
- mode=mode, loss=tf.reduce_mean(1), eval_metric_ops=eval_metric_ops
- )
-
- super(Triplet, self).__init__(
- model_fn=_model_fn, model_dir=model_dir, config=config
- )
diff --git a/bob/learn/tensorflow/estimators/__init__.py b/bob/learn/tensorflow/estimators/__init__.py
deleted file mode 100644
index 10f500c531d9c10e19204272d5dd1a9fb27355a8..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/estimators/__init__.py
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-
-from ..utils import get_trainable_variables, check_features
-from .utils import MovingAverageOptimizer, learning_rate_decay_fn
-from .Logits import Logits, LogitsCenterLoss
-from .Siamese import Siamese
-from .Triplet import Triplet
-from .Regressor import Regressor
-
-
-# gets sphinx autodoc done right - don't remove it
-def __appropriate__(*args):
- """Says object was actually declared here, an not on the import module.
-
- Parameters:
-
- *args: An iterable of objects to modify
-
- Resolves `Sphinx referencing issues
- <https://github.com/sphinx-doc/sphinx/issues/3048>`
- """
-
- for obj in args:
- obj.__module__ = __name__
-
-
-__appropriate__(
- Logits, LogitsCenterLoss, Siamese, Triplet, Regressor, MovingAverageOptimizer
-)
-__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/estimators/utils.py b/bob/learn/tensorflow/estimators/utils.py
deleted file mode 100644
index fc202a06f70f10055cdcab2f3fa651ee325ba768..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/estimators/utils.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import tensorflow as tf
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class MovingAverageOptimizer:
-
- """Creates a callable that can be given to bob.learn.tensorflow.estimators
-
- This class is useful when you want to have a learning_rate_decay_fn **and** a moving
- average optimizer **and** use bob.learn.tensorflow.estimators
-
- Attributes
- ----------
- optimizer : object
- A tf.train.Optimizer that is created and wrapped with
- tf.contrib.opt.MovingAverageOptimizer.
-
- Example
- -------
- >>> import tensorflow as tf
- >>> from bob.learn.tensorflow.estimators import MovingAverageOptimizer
- >>> optimizer = MovingAverageOptimizer("adam")
- >>> actual_optimizer = optimizer(lr=1e-3)
- >>> isinstance(actual_optimizer, tf.train.Optimizer)
- True
- >>> actual_optimizer is optimizer.optimizer
- True
- """
-
- def __init__(self, optimizer, **kwargs):
- super().__init__(**kwargs)
- assert isinstance(optimizer, str), optimizer
- self._optimizer = optimizer
-
- def __call__(self, lr):
- logger.info("Encapsulating the optimizer with the MovingAverageOptimizer")
-
- if self._optimizer == "sgd":
- optimizer = tf.train.GradientDescentOptimizer(learning_rate=lr)
- elif self._optimizer == "adam":
- optimizer = tf.train.AdamOptimizer(learning_rate=lr)
-
- self.optimizer = tf.contrib.opt.MovingAverageOptimizer(optimizer)
-
- return self.optimizer
-
-
-def learning_rate_decay_fn(
- learning_rate, global_step, decay_steps, decay_rate, staircase=False
-):
- """A simple learning_rate_decay_fn.
-
- To use it with ``tf.contrib.layer.optimize_loss``:
-
- >>> from bob.learn.tensorflow.estimators import learning_rate_decay_fn
- >>> from functools import partial
- >>> learning_rate_decay_fn = partial(
- ... learning_rate_decay_fn,
- ... decay_steps=1000,
- ... decay_rate=0.9,
- ... staircase=True,
- ... )
- """
- return tf.train.exponential_decay(
- learning_rate,
- global_step=global_step,
- decay_steps=decay_steps,
- decay_rate=decay_rate,
- staircase=staircase,
- )
diff --git a/bob/learn/tensorflow/examples/mnist/mnist_config.py b/bob/learn/tensorflow/examples/mnist/mnist_config.py
deleted file mode 100644
index 38e8da80cd299e97e80acb1843462e0a1fa3b120..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/examples/mnist/mnist_config.py
+++ /dev/null
@@ -1,233 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-# create reproducible nets:
-from bob.learn.tensorflow.utils.reproducible import run_config
-import tensorflow as tf
-from bob.db.mnist import Database
-
-model_dir = '/tmp/mnist_model'
-train_tfrecords = ['/tmp/mnist_data/train.tfrecords']
-eval_tfrecords = ['/tmp/mnist_data/test.tfrecords']
-
-run_config = run_config.replace(keep_checkpoint_max=10**3)
-run_config = run_config.replace(save_checkpoints_secs=60)
-
-
-def input_fn(mode, batch_size=1):
- """A simple input_fn using the contrib.data input pipeline."""
-
- def example_parser(serialized_example):
- """Parses a single tf.Example into image and label tensors."""
- features = tf.parse_single_example(
- serialized_example,
- features={
- 'data': tf.FixedLenFeature([], tf.string),
- 'label': tf.FixedLenFeature([], tf.int64),
- 'key': tf.FixedLenFeature([], tf.string),
- })
- image = tf.decode_raw(features['data'], tf.uint8)
- image.set_shape([28 * 28])
-
- # Normalize the values of the image from the range
- # [0, 255] to [-0.5, 0.5]
- image = tf.cast(image, tf.float32) / 255 - 0.5
- label = tf.cast(features['label'], tf.int32)
-
- key = tf.cast(features['key'], tf.string)
- return image, tf.one_hot(label, 10), key
-
- if mode == tf.estimator.ModeKeys.TRAIN:
- tfrecords_files = train_tfrecords
- elif mode == tf.estimator.ModeKeys.EVAL:
- tfrecords_files = eval_tfrecords
- else:
- assert mode == tf.estimator.ModeKeys.PREDICT, 'invalid mode'
- tfrecords_files = eval_tfrecords
-
- for tfrecords_file in tfrecords_files:
- assert tf.gfile.Exists(tfrecords_file), (
- 'Run github.com:tensorflow/models/official/mnist/'
- 'convert_to_records.py first to convert the MNIST data to '
- 'TFRecord file format.')
-
- dataset = tf.data.TFRecordDataset(tfrecords_files)
-
- # For training, repeat the dataset forever
- if mode == tf.estimator.ModeKeys.TRAIN:
- dataset = dataset.repeat()
-
- # Map example_parser over dataset, and batch results by up to batch_size
- dataset = dataset.map(
- example_parser, num_parallel_calls=1).prefetch(batch_size)
- dataset = dataset.batch(batch_size)
- images, labels, keys = dataset.make_one_shot_iterator().get_next()
-
- return {'images': images, 'keys': keys}, labels
-
-
-def train_input_fn():
- return input_fn(tf.estimator.ModeKeys.TRAIN)
-
-
-def eval_input_fn():
- return input_fn(tf.estimator.ModeKeys.EVAL)
-
-
-def predict_input_fn():
- return input_fn(tf.estimator.ModeKeys.PREDICT)
-
-
-def mnist_model(inputs, mode):
- """Takes the MNIST inputs and mode and outputs a tensor of logits."""
- # Input Layer
- # Reshape X to 4-D tensor: [batch_size, width, height, channels]
- # MNIST images are 28x28 pixels, and have one color channel
- inputs = tf.reshape(inputs, [-1, 28, 28, 1])
- data_format = 'channels_last'
-
- if tf.test.is_built_with_cuda():
- # When running on GPU, transpose the data from channels_last (NHWC) to
- # channels_first (NCHW) to improve performance. See
- # https://www.tensorflow.org/performance/performance_guide#data_formats
- data_format = 'channels_first'
- inputs = tf.transpose(inputs, [0, 3, 1, 2])
-
- # Convolutional Layer #1
- # Computes 32 features using a 5x5 filter with ReLU activation.
- # Padding is added to preserve width and height.
- # Input Tensor Shape: [batch_size, 28, 28, 1]
- # Output Tensor Shape: [batch_size, 28, 28, 32]
- conv1 = tf.layers.conv2d(
- inputs=inputs,
- filters=32,
- kernel_size=[5, 5],
- padding='same',
- activation=tf.nn.relu,
- data_format=data_format)
-
- # Pooling Layer #1
- # First max pooling layer with a 2x2 filter and stride of 2
- # Input Tensor Shape: [batch_size, 28, 28, 32]
- # Output Tensor Shape: [batch_size, 14, 14, 32]
- pool1 = tf.layers.max_pooling2d(
- inputs=conv1, pool_size=[2, 2], strides=2, data_format=data_format)
-
- # Convolutional Layer #2
- # Computes 64 features using a 5x5 filter.
- # Padding is added to preserve width and height.
- # Input Tensor Shape: [batch_size, 14, 14, 32]
- # Output Tensor Shape: [batch_size, 14, 14, 64]
- conv2 = tf.layers.conv2d(
- inputs=pool1,
- filters=64,
- kernel_size=[5, 5],
- padding='same',
- activation=tf.nn.relu,
- data_format=data_format)
-
- # Pooling Layer #2
- # Second max pooling layer with a 2x2 filter and stride of 2
- # Input Tensor Shape: [batch_size, 14, 14, 64]
- # Output Tensor Shape: [batch_size, 7, 7, 64]
- pool2 = tf.layers.max_pooling2d(
- inputs=conv2, pool_size=[2, 2], strides=2, data_format=data_format)
-
- # Flatten tensor into a batch of vectors
- # Input Tensor Shape: [batch_size, 7, 7, 64]
- # Output Tensor Shape: [batch_size, 7 * 7 * 64]
- pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
-
- # Dense Layer
- # Densely connected layer with 1024 neurons
- # Input Tensor Shape: [batch_size, 7 * 7 * 64]
- # Output Tensor Shape: [batch_size, 1024]
- dense = tf.layers.dense(
- inputs=pool2_flat, units=1024, activation=tf.nn.relu)
-
- # Add dropout operation; 0.6 probability that element will be kept
- dropout = tf.layers.dropout(
- inputs=dense, rate=0.4, training=(mode == tf.estimator.ModeKeys.TRAIN))
-
- # Logits layer
- # Input Tensor Shape: [batch_size, 1024]
- # Output Tensor Shape: [batch_size, 10]
- logits = tf.layers.dense(inputs=dropout, units=10)
- return logits
-
-
-def model_fn(features, labels=None, mode=tf.estimator.ModeKeys.TRAIN):
- """Model function for MNIST."""
- keys = features['keys']
- features = features['images']
- logits = mnist_model(features, mode)
-
- predictions = {
- 'classes': tf.argmax(input=logits, axis=1),
- 'probabilities': tf.nn.softmax(logits, name='softmax_tensor'),
- 'keys': keys,
- }
-
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
-
- # Configure the training op
- if mode == tf.estimator.ModeKeys.TRAIN:
- optimizer = tf.train.AdamOptimizer(learning_rate=1e-4)
- train_op = optimizer.minimize(loss,
- tf.train.get_or_create_global_step())
- else:
- train_op = None
-
- accuracy = tf.metrics.accuracy(
- tf.argmax(labels, axis=1), predictions['classes'])
- metrics = {'accuracy': accuracy}
-
- with tf.name_scope('train_metrics'):
- # Create a tensor named train_accuracy for logging purposes
- tf.summary.scalar('train_accuracy', accuracy[1])
-
- tf.summary.scalar('train_loss', loss)
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=loss,
- train_op=train_op,
- eval_metric_ops=metrics)
-
-
-estimator = tf.estimator.Estimator(
- model_fn=model_fn, model_dir=model_dir, params=None, config=run_config)
-
-output = train_tfrecords[0]
-db = Database()
-data, labels = db.data(groups='train')
-
-# output = eval_tfrecords[0]
-# db = Database()
-# data, labels = db.data(groups='test')
-
-samples = zip(data, labels, (str(i) for i in range(len(data))))
-
-
-def reader(sample):
- return sample
diff --git a/bob/learn/tensorflow/examples/mnist/tfrecords.py b/bob/learn/tensorflow/examples/mnist/tfrecords.py
deleted file mode 100644
index 2a4eb216d7e4275ee339a961a1667c6705086bda..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/examples/mnist/tfrecords.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Required objects:
-
-# you need a database object that inherits from
-# bob.bio.base.database.BioDatabase (PAD dbs work too)
-database = Database()
-
-# the directory pointing to where the processed data is:
-data_dir = '/idiap/temp/user/database_name/sub_directory/preprocessed'
-
-# the directory to save the tfrecords in:
-output_dir = '/idiap/temp/user/database_name/sub_directory'
-
-
-# A function that converts a BioFile or a PadFile to a label:
-# Example for PAD
-def file_to_label(f):
- return f.attack_type is None
-
-
-# Example for Bio (You may want to run this script for groups=['world'] only
-# in biometric recognition experiments.)
-CLIENT_IDS = (str(f.client_id) for f in db.all_files(groups=groups))
-CLIENT_IDS = list(set(CLIENT_IDS))
-CLIENT_IDS = dict(zip(CLIENT_IDS, range(len(CLIENT_IDS))))
-
-
-def file_to_label(f):
- return CLIENT_IDS[str(f.client_id)]
-
-
-# Optional objects:
-
-# The groups that you want to create tfrecords for. It should be a list of
-# 'world' ('train' in bob.pad.base), 'dev', and 'eval' values. [default:
-# 'world']
-groups = ['world']
-
-# you need a reader function that reads the preprocessed files. [default:
-# bob.bio.base.utils.load]
-reader = Preprocessor().read_data
-reader = Extractor().read_feature
-# or
-from bob.bio.base.utils import load as reader
-
-# or a reader that casts images to uint8:
-
-
-def reader(path):
- data = bob.bio.base.utils.load(path)
- return data.astype("uint8")
-
-
-# extension of the preprocessed files. [default: '.hdf5']
-data_extension = '.hdf5'
-
-# Shuffle the files before writing them into a tfrecords. [default: False]
-shuffle = True
-
-# Whether the each file contains one sample or more. [default: True] If
-# this is False, the loaded samples from a file are iterated over and each
-# of them is saved as an independent feature.
-one_file_one_sample = True
diff --git a/bob/learn/tensorflow/extractors/Base.py b/bob/learn/tensorflow/extractors/Base.py
deleted file mode 100644
index c4e1064ff9278b9db778149dc4d2717f70d0ca06..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/extractors/Base.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import tensorflow as tf
-import os
-import numpy as np
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-def normalize_checkpoint_path(path):
- if os.path.splitext(path)[1] == ".meta":
- filename = os.path.splitext(path)[0]
- elif os.path.isdir(path):
- filename = tf.train.latest_checkpoint(path)
- else:
- filename = path
-
- return filename
-
-
-class Base:
- def __init__(self, output_name, input_shape, checkpoint, scopes,
- input_transform=None, output_transform=None,
- input_dtype='float32', extra_feed=None, **kwargs):
-
- self.output_name = output_name
- self.input_shape = input_shape
- self.checkpoint = normalize_checkpoint_path(checkpoint)
- self.scopes = scopes
- self.input_transform = input_transform
- self.output_transform = output_transform
- self.input_dtype = input_dtype
- self.extra_feed = extra_feed
- self.session = None
- super().__init__(**kwargs)
-
- def load(self):
- self.session = tf.Session(graph=tf.Graph())
-
- with self.session.as_default(), self.session.graph.as_default():
-
- self.input = data = tf.placeholder(self.input_dtype, self.input_shape)
-
- if self.input_transform is not None:
- data = self.input_transform(data)
-
- self.output = self.get_output(data, tf.estimator.ModeKeys.PREDICT)
-
- if self.output_transform is not None:
- self.output = self.output_transform(self.output)
-
- tf.train.init_from_checkpoint(
- ckpt_dir_or_file=self.checkpoint,
- assignment_map=self.scopes,
- )
- # global_variables_initializer must run after init_from_checkpoint
- self.session.run(tf.global_variables_initializer())
- logger.info('Restored the model from %s', self.checkpoint)
-
- def __call__(self, data):
- if self.session is None:
- self.load()
-
- data = np.ascontiguousarray(data, dtype=self.input_dtype)
- feed_dict = {self.input: data}
- if self.extra_feed is not None:
- feed_dict.update(self.extra_feed)
-
- return self.session.run(self.output, feed_dict=feed_dict)
-
- def get_output(self, data, mode):
- raise NotImplementedError()
diff --git a/bob/learn/tensorflow/extractors/Estimator.py b/bob/learn/tensorflow/extractors/Estimator.py
deleted file mode 100644
index 768b539f1906d2a70f5b8a71537d16a1924ac472..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/extractors/Estimator.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import tensorflow as tf
-from .Base import Base
-
-
-class Estimator(Base):
- def __init__(self, estimator, **kwargs):
- self.estimator = estimator
- kwargs['checkpoint'] = kwargs.get('checkpoint', estimator.model_dir)
- super().__init__(**kwargs)
-
- def get_output(self, data, mode):
- features = {'data': data, 'key': tf.constant(['key'])}
- self.estimator_spec = self.estimator._call_model_fn(
- features, None, mode, None)
- self.end_points = self.estimator.end_points
- return self.end_points[self.output_name]
diff --git a/bob/learn/tensorflow/extractors/Generic.py b/bob/learn/tensorflow/extractors/Generic.py
deleted file mode 100644
index 3aab2573317c0916531eea2a748329147e543f87..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/extractors/Generic.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .Base import Base
-
-
-class Generic(Base):
- def __init__(self, architecture, **kwargs):
-
- self.architecture = architecture
- super().__init__(**kwargs)
-
- def get_output(self, data, mode):
- self.end_points = self.architecture(data, mode=mode)[1]
- return self.end_points[self.output_name]
diff --git a/bob/learn/tensorflow/gan/__init__.py b/bob/learn/tensorflow/gan/__init__.py
deleted file mode 100644
index 502898e8d514775462a0e9de662ffab6c6cc4c25..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/gan/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from . import spectral_normalization
-from . import losses
diff --git a/bob/learn/tensorflow/gan/losses.py b/bob/learn/tensorflow/gan/losses.py
deleted file mode 100644
index ec378245953d60c96d4634eb797d6d1c30e2dec0..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/gan/losses.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import tensorflow as tf
-
-
-def relativistic_discriminator_loss(
- discriminator_real_outputs,
- discriminator_gen_outputs,
- label_smoothing=0.25,
- real_weights=1.0,
- generated_weights=1.0,
- scope=None,
- loss_collection=tf.GraphKeys.LOSSES,
- reduction=tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
- add_summaries=False,
-):
- """Relativistic (average) loss
-
- Args:
- discriminator_real_outputs: Discriminator output on real data.
- discriminator_gen_outputs: Discriminator output on generated data. Expected
- to be in the range of (-inf, inf).
- label_smoothing: The amount of smoothing for positive labels. This technique
- is taken from `Improved Techniques for Training GANs`
- (https://arxiv.org/abs/1606.03498). `0.0` means no smoothing.
- real_weights: Optional `Tensor` whose rank is either 0, or the same rank as
- `real_data`, and must be broadcastable to `real_data` (i.e., all
- dimensions must be either `1`, or the same as the corresponding
- dimension).
- generated_weights: Same as `real_weights`, but for `generated_data`.
- scope: The scope for the operations performed in computing the loss.
- loss_collection: collection to which this loss will be added.
- reduction: A `tf.compat.v1.losses.Reduction` to apply to loss.
- add_summaries: Whether or not to add summaries for the loss.
-
- Returns:
- A loss Tensor. The shape depends on `reduction`.
- """
- with tf.name_scope(
- scope,
- "discriminator_relativistic_loss",
- (
- discriminator_real_outputs,
- discriminator_gen_outputs,
- real_weights,
- generated_weights,
- label_smoothing,
- ),
- ) as scope:
-
- real_logit = discriminator_real_outputs - tf.reduce_mean(
- discriminator_gen_outputs
- )
- fake_logit = discriminator_gen_outputs - tf.reduce_mean(
- discriminator_real_outputs
- )
-
- loss_on_real = tf.losses.sigmoid_cross_entropy(
- tf.ones_like(real_logit),
- real_logit,
- real_weights,
- label_smoothing,
- scope,
- loss_collection=None,
- reduction=reduction,
- )
- loss_on_generated = tf.losses.sigmoid_cross_entropy(
- tf.zeros_like(fake_logit),
- fake_logit,
- generated_weights,
- scope=scope,
- loss_collection=None,
- reduction=reduction,
- )
-
- loss = loss_on_real + loss_on_generated
- tf.losses.add_loss(loss, loss_collection)
-
- if add_summaries:
- tf.summary.scalar("discriminator_gen_relativistic_loss", loss_on_generated)
- tf.summary.scalar("discriminator_real_relativistic_loss", loss_on_real)
- tf.summary.scalar("discriminator_relativistic_loss", loss)
-
- return loss
-
-
-def relativistic_generator_loss(
- discriminator_real_outputs,
- discriminator_gen_outputs,
- label_smoothing=0.0,
- real_weights=1.0,
- generated_weights=1.0,
- scope=None,
- loss_collection=tf.GraphKeys.LOSSES,
- reduction=tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
- add_summaries=False,
- confusion_labels=False,
-):
- """Relativistic (average) loss
-
- Args:
- discriminator_real_outputs: Discriminator output on real data.
- discriminator_gen_outputs: Discriminator output on generated data. Expected
- to be in the range of (-inf, inf).
- label_smoothing: The amount of smoothing for positive labels. This technique
- is taken from `Improved Techniques for Training GANs`
- (https://arxiv.org/abs/1606.03498). `0.0` means no smoothing.
- real_weights: Optional `Tensor` whose rank is either 0, or the same rank as
- `real_data`, and must be broadcastable to `real_data` (i.e., all
- dimensions must be either `1`, or the same as the corresponding
- dimension).
- generated_weights: Same as `real_weights`, but for `generated_data`.
- scope: The scope for the operations performed in computing the loss.
- loss_collection: collection to which this loss will be added.
- reduction: A `tf.compat.v1.losses.Reduction` to apply to loss.
- add_summaries: Whether or not to add summaries for the loss.
-
- Returns:
- A loss Tensor. The shape depends on `reduction`.
- """
- with tf.name_scope(
- scope,
- "generator_relativistic_loss",
- (
- discriminator_real_outputs,
- discriminator_gen_outputs,
- real_weights,
- generated_weights,
- label_smoothing,
- ),
- ) as scope:
-
- real_logit = discriminator_real_outputs - tf.reduce_mean(
- discriminator_gen_outputs
- )
- fake_logit = discriminator_gen_outputs - tf.reduce_mean(
- discriminator_real_outputs
- )
-
- if confusion_labels:
- real_labels = tf.ones_like(real_logit) / 2
- fake_labels = tf.ones_like(fake_logit) / 2
- else:
- real_labels = tf.zeros_like(real_logit)
- fake_labels = tf.ones_like(fake_logit)
-
- loss_on_real = tf.losses.sigmoid_cross_entropy(
- real_labels,
- real_logit,
- real_weights,
- label_smoothing,
- scope,
- loss_collection=None,
- reduction=reduction,
- )
- loss_on_generated = tf.losses.sigmoid_cross_entropy(
- fake_labels,
- fake_logit,
- generated_weights,
- scope=scope,
- loss_collection=None,
- reduction=reduction,
- )
-
- loss = loss_on_real + loss_on_generated
- tf.losses.add_loss(loss, loss_collection)
-
- if add_summaries:
- tf.summary.scalar("generator_gen_relativistic_loss", loss_on_generated)
- tf.summary.scalar("generator_real_relativistic_loss", loss_on_real)
- tf.summary.scalar("generator_relativistic_loss", loss)
-
- return loss
diff --git a/bob/learn/tensorflow/gan/spectral_normalization.py b/bob/learn/tensorflow/gan/spectral_normalization.py
deleted file mode 100644
index ad2ecfaa4487892592f733858c88f0c24b569912..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/gan/spectral_normalization.py
+++ /dev/null
@@ -1,316 +0,0 @@
-# Copied from: https://github.com/tensorflow/tensorflow/blob/c4f40aea1d4f916aa3dfeb79f024c495ac609106/tensorflow/contrib/gan/python/features/python/spectral_normalization_impl.py
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Keras-like layers and utilities that implement Spectral Normalization.
-
-Based on "Spectral Normalization for Generative Adversarial Networks" by Miyato,
-et al in ICLR 2018. https://openreview.net/pdf?id=B1QRgziT-
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import contextlib
-import numbers
-import re
-
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import ops
-from tensorflow.python.keras.engine import base_layer_utils as keras_base_layer_utils
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import init_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn
-from tensorflow.python.ops import variable_scope
-from tensorflow.python.platform import tf_logging as logging
-
-__all__ = [
- 'compute_spectral_norm', 'spectral_normalize', 'spectral_norm_regularizer',
- 'spectral_normalization_custom_getter', 'keras_spectral_normalization'
-]
-
-# tf.bfloat16 should work, but tf.matmul converts those to tf.float32 which then
-# can't directly be assigned back to the tf.bfloat16 variable.
-_OK_DTYPES_FOR_SPECTRAL_NORM = (dtypes.float16, dtypes.float32, dtypes.float64)
-_PERSISTED_U_VARIABLE_SUFFIX = 'spectral_norm_u'
-
-
-def compute_spectral_norm(w_tensor, power_iteration_rounds=1, name=None):
- """Estimates the largest singular value in the weight tensor.
-
- Args:
- w_tensor: The weight matrix whose spectral norm should be computed.
- power_iteration_rounds: The number of iterations of the power method to
- perform. A higher number yields a better approximation.
- name: An optional scope name.
-
- Returns:
- The largest singular value (the spectral norm) of w.
- """
- with variable_scope.variable_scope(name, 'spectral_norm'):
- # The paper says to flatten convnet kernel weights from
- # (C_out, C_in, KH, KW) to (C_out, C_in * KH * KW). But TensorFlow's Conv2D
- # kernel weight shape is (KH, KW, C_in, C_out), so it should be reshaped to
- # (KH * KW * C_in, C_out), and similarly for other layers that put output
- # channels as last dimension.
- # n.b. this means that w here is equivalent to w.T in the paper.
- w = array_ops.reshape(w_tensor, (-1, w_tensor.get_shape()[-1]))
-
- # Persisted approximation of first left singular vector of matrix `w`.
- u_var = variable_scope.get_variable(
- _PERSISTED_U_VARIABLE_SUFFIX,
- shape=(w.shape[0], 1),
- dtype=w.dtype,
- initializer=init_ops.random_normal_initializer(),
- trainable=False)
- u = u_var
-
- # Use power iteration method to approximate spectral norm.
- for _ in range(power_iteration_rounds):
- # `v` approximates the first right singular vector of matrix `w`.
- v = nn.l2_normalize(math_ops.matmul(array_ops.transpose(w), u))
- u = nn.l2_normalize(math_ops.matmul(w, v))
-
- # Update persisted approximation.
- with ops.control_dependencies([u_var.assign(u, name='update_u')]):
- u = array_ops.identity(u)
-
- u = array_ops.stop_gradient(u)
- v = array_ops.stop_gradient(v)
-
- # Largest singular value of `w`.
- spectral_norm = math_ops.matmul(
- math_ops.matmul(array_ops.transpose(u), w), v)
- spectral_norm.shape.assert_is_fully_defined()
- spectral_norm.shape.assert_is_compatible_with([1, 1])
-
- return spectral_norm[0][0]
-
-
-def spectral_normalize(w, power_iteration_rounds=1, name=None):
- """Normalizes a weight matrix by its spectral norm.
-
- Args:
- w: The weight matrix to be normalized.
- power_iteration_rounds: The number of iterations of the power method to
- perform. A higher number yields a better approximation.
- name: An optional scope name.
-
- Returns:
- A normalized weight matrix tensor.
- """
- with variable_scope.variable_scope(name, 'spectral_normalize'):
- w_normalized = w / compute_spectral_norm(
- w, power_iteration_rounds=power_iteration_rounds)
- return array_ops.reshape(w_normalized, w.get_shape())
-
-
-def spectral_norm_regularizer(scale, power_iteration_rounds=1, scope=None):
- """Returns a functions that can be used to apply spectral norm regularization.
-
- Small spectral norms enforce a small Lipschitz constant, which is necessary
- for Wasserstein GANs.
-
- Args:
- scale: A scalar multiplier. 0.0 disables the regularizer.
- power_iteration_rounds: The number of iterations of the power method to
- perform. A higher number yields a better approximation.
- scope: An optional scope name.
-
- Returns:
- A function with the signature `sn(weights)` that applies spectral norm
- regularization.
-
- Raises:
- ValueError: If scale is negative or if scale is not a float.
- """
- if isinstance(scale, numbers.Integral):
- raise ValueError('scale cannot be an integer: %s' % scale)
- if isinstance(scale, numbers.Real):
- if scale < 0.0:
- raise ValueError(
- 'Setting a scale less than 0 on a regularizer: %g' % scale)
- if scale == 0.0:
- logging.info('Scale of 0 disables regularizer.')
- return lambda _: None
-
- def sn(weights, name=None):
- """Applies spectral norm regularization to weights."""
- with ops.name_scope(scope, 'SpectralNormRegularizer', [weights]) as name:
- scale_t = ops.convert_to_tensor(
- scale, dtype=weights.dtype.base_dtype, name='scale')
- return math_ops.multiply(
- scale_t,
- compute_spectral_norm(
- weights, power_iteration_rounds=power_iteration_rounds),
- name=name)
-
- return sn
-
-
-def _default_name_filter(name):
- """A filter function to identify common names of weight variables.
-
- Args:
- name: The variable name.
-
- Returns:
- Whether `name` is a standard name for a weight/kernel variables used in the
- Keras, tf.layers, tf.contrib.layers or tf.contrib.slim libraries.
- """
- match = re.match(r'(.*\/)?(depthwise_|pointwise_)?(weights|kernel)$', name)
- return match is not None
-
-
-def spectral_normalization_custom_getter(name_filter=_default_name_filter,
- power_iteration_rounds=1):
- """Custom getter that performs Spectral Normalization on a weight tensor.
-
- Specifically it divides the weight tensor by its largest singular value. This
- is intended to stabilize GAN training, by making the discriminator satisfy a
- local 1-Lipschitz constraint.
-
- Based on [Spectral Normalization for Generative Adversarial Networks][sn-gan].
-
- [sn-gan]: https://openreview.net/forum?id=B1QRgziT-
-
- To reproduce an SN-GAN, apply this custom_getter to every weight tensor of
- your discriminator. The last dimension of the weight tensor must be the number
- of output channels.
-
- Apply this to layers by supplying this as the `custom_getter` of a
- `tf.compat.v1.variable_scope`. For example:
-
- with tf.compat.v1.variable_scope('discriminator',
- custom_getter=spectral_norm_getter()):
- net = discriminator_fn(net)
-
- IMPORTANT: Keras does not respect the custom_getter supplied by the
- VariableScope, so Keras users should use `keras_spectral_normalization`
- instead of (or in addition to) this approach.
-
- It is important to carefully select to which weights you want to apply
- Spectral Normalization. In general you want to normalize the kernels of
- convolution and dense layers, but you do not want to normalize biases. You
- also want to avoid normalizing batch normalization (and similar) variables,
- but in general such layers play poorly with Spectral Normalization, since the
- gamma can cancel out the normalization in other layers. By default we supply a
- filter that matches the kernel variable names of the dense and convolution
- layers of the tf.layers, tf.contrib.layers, tf.keras and tf.contrib.slim
- libraries. If you are using anything else you'll need a custom `name_filter`.
-
- This custom getter internally creates a variable used to compute the spectral
- norm by power iteration. It will update every time the variable is accessed,
- which means the normalized discriminator weights may change slightly whilst
- training the generator. Whilst unusual, this matches how the paper's authors
- implement it, and in general additional rounds of power iteration can't hurt.
-
- Args:
- name_filter: Optionally, a method that takes a Variable name as input and
- returns whether this Variable should be normalized.
- power_iteration_rounds: The number of iterations of the power method to
- perform per step. A higher number yields a better approximation of the
- true spectral norm.
-
- Returns:
- A custom getter function that applies Spectral Normalization to all
- Variables whose names match `name_filter`.
-
- Raises:
- ValueError: If name_filter is not callable.
- """
- if not callable(name_filter):
- raise ValueError('name_filter must be callable')
-
- def _internal_getter(getter, name, *args, **kwargs):
- """A custom getter function that applies Spectral Normalization.
-
- Args:
- getter: The true getter to call.
- name: Name of new/existing variable, in the same format as
- tf.get_variable.
- *args: Other positional arguments, in the same format as tf.get_variable.
- **kwargs: Keyword arguments, in the same format as tf.get_variable.
-
- Returns:
- The return value of `getter(name, *args, **kwargs)`, spectrally
- normalized.
-
- Raises:
- ValueError: If used incorrectly, or if `dtype` is not supported.
- """
- if not name_filter(name):
- return getter(name, *args, **kwargs)
-
- if name.endswith(_PERSISTED_U_VARIABLE_SUFFIX):
- raise ValueError(
- 'Cannot apply Spectral Normalization to internal variables created '
- 'for Spectral Normalization. Tried to normalized variable [%s]' %
- name)
-
- if kwargs['dtype'] not in _OK_DTYPES_FOR_SPECTRAL_NORM:
- raise ValueError('Disallowed data type {}'.format(kwargs['dtype']))
-
- # This layer's weight Variable/PartitionedVariable.
- w_tensor = getter(name, *args, **kwargs)
-
- if len(w_tensor.get_shape()) < 2:
- raise ValueError(
- 'Spectral norm can only be applied to multi-dimensional tensors')
-
- return spectral_normalize(
- w_tensor,
- power_iteration_rounds=power_iteration_rounds,
- name=(name + '/spectral_normalize'))
-
- return _internal_getter
-
-
-@contextlib.contextmanager
-def keras_spectral_normalization(name_filter=_default_name_filter,
- power_iteration_rounds=1):
- """A context manager that enables Spectral Normalization for Keras.
-
- Keras doesn't respect the `custom_getter` in the VariableScope, so this is a
- bit of a hack to make things work.
-
- Usage:
- with keras_spectral_normalization():
- net = discriminator_fn(net)
-
- Args:
- name_filter: Optionally, a method that takes a Variable name as input and
- returns whether this Variable should be normalized.
- power_iteration_rounds: The number of iterations of the power method to
- perform per step. A higher number yields a better approximation of the
- true spectral norm.
-
- Yields:
- A context manager that wraps the standard Keras variable creation method
- with the `spectral_normalization_custom_getter`.
- """
- original_make_variable = keras_base_layer_utils.make_variable
- sn_getter = spectral_normalization_custom_getter(
- name_filter=name_filter, power_iteration_rounds=power_iteration_rounds)
-
- def make_variable_wrapper(name, *args, **kwargs):
- return sn_getter(original_make_variable, name, *args, **kwargs)
-
- keras_base_layer_utils.make_variable = make_variable_wrapper
-
- yield
-
- keras_base_layer_utils.make_variable = original_make_variable
diff --git a/bob/learn/tensorflow/image/__init__.py b/bob/learn/tensorflow/image/__init__.py
deleted file mode 100644
index 9a8962844d659e6b1e13f6eeeb5e1d11dc698e8d..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/image/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from .filter import gaussian_kernel, GaussianFilter
-
-# gets sphinx autodoc done right - don't remove it
-def __appropriate__(*args):
- """Says object was actually declared here, an not on the import module.
-
- Parameters:
-
- *args: An iterable of objects to modify
-
- Resolves `Sphinx referencing issues
- <https://github.com/sphinx-doc/sphinx/issues/3048>`
- """
- for obj in args:
- obj.__module__ = __name__
-
-
-__appropriate__(GaussianFilter)
-__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/image/filter.py b/bob/learn/tensorflow/image/filter.py
deleted file mode 100644
index 3ac149db3113cf5166d46fe5c4ca80ed11052c2c..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/image/filter.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import tensorflow as tf
-
-
-def gaussian_kernel(size: int, mean: float, std: float):
- """Makes 2D gaussian Kernel for convolution.
- Code adapted from: https://stackoverflow.com/a/52012658/1286165"""
-
- d = tf.distributions.Normal(mean, std)
-
- vals = d.prob(tf.range(start=-size, limit=size + 1, dtype=tf.float32))
-
- gauss_kernel = tf.einsum("i,j->ij", vals, vals)
-
- return gauss_kernel / tf.reduce_sum(gauss_kernel)
-
-
-class GaussianFilter:
- """A class for blurring images"""
-
- def __init__(self, size=13, mean=0.0, std=3.0, **kwargs):
- super().__init__(**kwargs)
- self.size = size
- self.mean = mean
- self.std = std
- self.gauss_kernel = gaussian_kernel(size, mean, std)[:, :, None, None]
-
- def __call__(self, image):
- shape = tf.shape(image)
- image = tf.reshape(image, [-1, shape[-3], shape[-2], shape[-1]])
- input_channels = shape[-1]
- gauss_kernel = tf.tile(self.gauss_kernel, [1, 1, input_channels, 1])
- return tf.nn.depthwise_conv2d(
- image,
- gauss_kernel,
- strides=[1, 1, 1, 1],
- padding="SAME",
- data_format="NHWC",
- )
diff --git a/bob/learn/tensorflow/layers/Maxout.py b/bob/learn/tensorflow/layers/Maxout.py
deleted file mode 100644
index 69c05520a52e2ad31b5703bcee6d928bcb5fc2e2..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/layers/Maxout.py
+++ /dev/null
@@ -1,74 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# @date: Fri 04 Aug 2017 14:14:22 CEST
-
-# MAXOUT IMPLEMENTED FOR TENSORFLOW
-
-from tensorflow.python.layers import base
-import tensorflow as tf
-
-
-def maxout(inputs, num_units, axis=-1, name=None):
- return Maxout(num_units=num_units, axis=axis, name=name)(inputs)
-
-
-class Maxout(base.Layer):
- """
- Adds a maxout op from
-
- "Maxout Networks"
-
- Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua
- Bengio
-
- Usually the operation is performed in the filter/channel dimension. This can also be
- used after fully-connected layers to reduce number of features.
-
- **Parameters**
- inputs: Tensor input
-
- num_units: Specifies how many features will remain after maxout in the `axis` dimension (usually channel).
- This must be multiple of number of `axis`.
-
- axis: The dimension where max pooling will be performed. Default is the
- last dimension.
-
- name: Optional scope for name_scope.
- """
-
- def __init__(self, num_units, axis=-1, name=None, **kwargs):
- super(Maxout, self).__init__(name=name, trainable=False, **kwargs)
- self.axis = axis
- self.num_units = num_units
-
- def call(self, inputs, training=False):
- inputs = tf.convert_to_tensor(inputs)
- shape = inputs.get_shape().as_list()
- # Dealing with batches with arbitrary sizes
- for i in range(len(shape)):
- if shape[i] is None:
- shape[i] = tf.shape(inputs)[i]
-
- num_channels = shape[self.axis]
- if not isinstance(num_channels, tf.Tensor) and num_channels % self.num_units:
- raise ValueError(
- "number of features({}) is not "
- "a multiple of num_units({})".format(num_channels, self.num_units)
- )
-
- if self.axis < 0:
- axis = self.axis + len(shape)
- else:
- axis = self.axis
- assert axis >= 0, "Find invalid axis: {}".format(self.axis)
-
- expand_shape = shape[:]
- expand_shape[axis] = self.num_units
- k = num_channels // self.num_units
- expand_shape.insert(axis, k)
-
- outputs = tf.math.reduce_max(
- tf.reshape(inputs, expand_shape), axis, keepdims=False
- )
- return outputs
diff --git a/bob/learn/tensorflow/layers/__init__.py b/bob/learn/tensorflow/layers/__init__.py
deleted file mode 100644
index a392020c2c59553c47af419005a0e4b27aea5b6f..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/layers/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from .Maxout import Maxout, maxout
-
-
-# gets sphinx autodoc done right - don't remove it
-def __appropriate__(*args):
- """Says object was actually declared here, an not on the import module.
-
- Parameters:
-
- *args: An iterable of objects to modify
-
- Resolves `Sphinx referencing issues
- <https://github.com/sphinx-doc/sphinx/issues/3048>`
- """
-
- for obj in args:
- obj.__module__ = __name__
-
-
-__appropriate__(
- Maxout,
- maxout,
-)
-__all__ = [_ for _ in dir() if not _.startswith('_')]
diff --git a/bob/learn/tensorflow/loss/BaseLoss.py b/bob/learn/tensorflow/loss/BaseLoss.py
deleted file mode 100644
index 50e83e14a089466c25933301d56504a0ad66ce9b..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/BaseLoss.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import logging
-import tensorflow as tf
-logger = logging.getLogger(__name__)
-
-slim = tf.contrib.slim
-
-
-def mean_cross_entropy_loss(logits, labels, add_regularization_losses=True):
- """
- Simple CrossEntropy loss.
- Basically it wrapps the function tf.nn.sparse_softmax_cross_entropy_with_logits.
-
- **Parameters**
- logits:
- labels:
- add_regularization_losses: Regulize the loss???
-
- """
-
- with tf.variable_scope('cross_entropy_loss'):
- cross_loss = tf.reduce_mean(
- tf.nn.sparse_softmax_cross_entropy_with_logits(
- logits=logits, labels=labels),
- name="cross_entropy_loss")
-
- tf.summary.scalar('cross_entropy_loss', cross_loss)
- tf.add_to_collection(tf.GraphKeys.LOSSES, cross_loss)
-
- if add_regularization_losses:
- regularization_losses = tf.get_collection(
- tf.GraphKeys.REGULARIZATION_LOSSES)
-
- total_loss = tf.add_n(
- [cross_loss] + regularization_losses, name="total_loss")
- return total_loss
- else:
- return cross_loss
-
-
-def mean_cross_entropy_center_loss(logits,
- prelogits,
- labels,
- n_classes,
- alpha=0.9,
- factor=0.01):
- """
- Implementation of the CrossEntropy + Center Loss from the paper
- "A Discriminative Feature Learning Approach for Deep Face Recognition"(http://ydwen.github.io/papers/WenECCV16.pdf)
-
- **Parameters**
- logits:
- prelogits:
- labels:
- n_classes: Number of classes of your task
- alpha: Alpha factor ((1-alpha)*centers-prelogits)
- factor: Weight factor of the center loss
-
- """
- # Cross entropy
- with tf.variable_scope('cross_entropy_loss'):
- cross_loss = tf.reduce_mean(
- tf.nn.sparse_softmax_cross_entropy_with_logits(
- logits=logits, labels=labels),
- name="cross_entropy_loss")
- tf.add_to_collection(tf.GraphKeys.LOSSES, cross_loss)
- tf.summary.scalar('loss_cross_entropy', cross_loss)
-
- # Appending center loss
- with tf.variable_scope('center_loss'):
- n_features = prelogits.get_shape()[1]
-
- centers = tf.get_variable(
- 'centers', [n_classes, n_features],
- dtype=tf.float32,
- initializer=tf.constant_initializer(0),
- trainable=False)
-
- # label = tf.reshape(labels, [-1])
- centers_batch = tf.gather(centers, labels)
- diff = (1 - alpha) * (centers_batch - prelogits)
- centers = tf.scatter_sub(centers, labels, diff)
- center_loss = tf.reduce_mean(tf.square(prelogits - centers_batch))
- tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
- center_loss * factor)
- tf.summary.scalar('loss_center', center_loss)
-
- # Adding the regularizers in the loss
- with tf.variable_scope('total_loss'):
- regularization_losses = tf.get_collection(
- tf.GraphKeys.REGULARIZATION_LOSSES)
- total_loss = tf.add_n(
- [cross_loss] + regularization_losses, name="total_loss")
- tf.add_to_collection(tf.GraphKeys.LOSSES, total_loss)
- tf.summary.scalar('loss_total', total_loss)
-
- loss = dict()
- loss['loss'] = total_loss
- loss['centers'] = centers
-
- return loss
diff --git a/bob/learn/tensorflow/loss/ContrastiveLoss.py b/bob/learn/tensorflow/loss/ContrastiveLoss.py
deleted file mode 100644
index 6fa29f1aefe140d6e09de5c876958735f5e5508b..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/ContrastiveLoss.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import logging
-import tensorflow as tf
-from bob.learn.tensorflow.utils import compute_euclidean_distance
-
-logger = logging.getLogger(__name__)
-
-
-def contrastive_loss(left_embedding, right_embedding, labels, contrastive_margin=2.0):
- """
- Compute the contrastive loss as in
-
- http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
-
- :math:`L = 0.5 * (1-Y) * D^2 + 0.5 * (Y) * {max(0, margin - D)}^2`
-
- where, `0` are assign for pairs from the same class and `1` from pairs from different classes.
-
-
- **Parameters**
-
- left_feature:
- First element of the pair
-
- right_feature:
- Second element of the pair
-
- labels:
- Label of the pair (0 or 1)
-
- margin:
- Contrastive margin
-
- """
-
- with tf.name_scope("contrastive_loss"):
- labels = tf.to_float(labels)
-
- left_embedding = tf.nn.l2_normalize(left_embedding, 1)
- right_embedding = tf.nn.l2_normalize(right_embedding, 1)
-
- d = compute_euclidean_distance(left_embedding, right_embedding)
-
- with tf.name_scope("within_class"):
- one = tf.constant(1.0)
- within_class = tf.multiply(one - labels, tf.square(d)) # (1-Y)*(d^2)
- within_class_loss = tf.reduce_mean(within_class, name="within_class")
- tf.add_to_collection(tf.GraphKeys.LOSSES, within_class_loss)
-
- with tf.name_scope("between_class"):
- max_part = tf.square(tf.maximum(contrastive_margin - d, 0))
- between_class = tf.multiply(
- labels, max_part
- ) # (Y) * max((margin - d)^2, 0)
- between_class_loss = tf.reduce_mean(between_class, name="between_class")
- tf.add_to_collection(tf.GraphKeys.LOSSES, between_class_loss)
-
- with tf.name_scope("total_loss"):
- loss = 0.5 * (within_class + between_class)
- loss = tf.reduce_mean(loss, name="contrastive_loss")
- tf.add_to_collection(tf.GraphKeys.LOSSES, loss)
-
- tf.summary.scalar("contrastive_loss", loss)
- tf.summary.scalar("between_class", between_class_loss)
- tf.summary.scalar("within_class", within_class_loss)
-
- return loss
diff --git a/bob/learn/tensorflow/loss/StyleLoss.py b/bob/learn/tensorflow/loss/StyleLoss.py
deleted file mode 100644
index 43cc655e291d5634f4cbe2032f6ebcf154b2a465..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/StyleLoss.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import logging
-import tensorflow as tf
-import functools
-logger = logging.getLogger(__name__)
-
-
-def content_loss(noises, content_features):
- """
-
- Implements the content loss from:
-
- Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015).
-
- For a given noise signal :math:`n`, content image :math:`c` and convolved with the DCNN :math:`\phi` until the layer :math:`l` the content loss is defined as:
-
- :math:`L(n,c) = \sum_{l=?}^{?}({\phi^l(n) - \phi^l(c)})^2`
-
-
- Parameters
- ----------
-
- noises: :any:`list`
- A list of tf.Tensor containing all the noises convolved
-
- content_features: :any:`list`
- A list of numpy.array containing all the content_features convolved
-
- """
-
- content_losses = []
- for n,c in zip(noises, content_features):
- content_losses.append((2 * tf.nn.l2_loss(n - c) / c.size))
- return functools.reduce(tf.add, content_losses)
-
-
-def linear_gram_style_loss(noises, gram_style_features):
- """
-
- Implements the style loss from:
-
- Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015).
-
- For a given noise signal :math:`n`, content image :math:`c` and convolved with the DCNN :math:`\phi` until the layer :math:`l` the STYLE loss is defined as
-
- :math:`L(n,c) = \\sum_{l=?}^{?}\\frac{({\phi^l(n)^T*\\phi^l(n) - \\phi^l(c)^T*\\phi^l(c)})^2}{N*M}`
-
-
- Parameters
- ----------
-
- noises: :any:`list`
- A list of tf.Tensor containing all the noises convolved
-
- gram_style_features: :any:`list`
- A list of numpy.array containing all the content_features convolved
-
- """
-
- style_losses = []
- for n,s in zip(noises, gram_style_features):
- style_losses.append((2 * tf.nn.l2_loss(n - s)) / s.size)
-
- return functools.reduce(tf.add, style_losses)
-
-
-
-def denoising_loss(noise):
- """
- Computes the denoising loss as in:
-
- Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015).
-
- Parameters
- ----------
-
- noise:
- Input noise
-
- """
- def _tensor_size(tensor):
- from operator import mul
- return functools.reduce(mul, (d.value for d in tensor.get_shape()), 1)
-
- shape = noise.get_shape().as_list()
-
- noise_y_size = _tensor_size(noise[:,1:,:,:])
- noise_x_size = _tensor_size(noise[:,:,1:,:])
- denoise_loss = 2 * ( (tf.nn.l2_loss(noise[:,1:,:,:] - noise[:,:shape[1]-1,:,:]) / noise_y_size) +
- (tf.nn.l2_loss(noise[:,:,1:,:] - noise[:,:,:shape[2]-1,:]) / noise_x_size))
-
- return denoise_loss
-
diff --git a/bob/learn/tensorflow/loss/TripletLoss.py b/bob/learn/tensorflow/loss/TripletLoss.py
deleted file mode 100644
index d2616d6aa0394f1ea024b65b9199292221951ca4..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/TripletLoss.py
+++ /dev/null
@@ -1,180 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import logging
-logger = logging.getLogger(__name__)
-import tensorflow as tf
-
-from bob.learn.tensorflow.utils import compute_euclidean_distance
-
-
-def triplet_loss(anchor_embedding,
- positive_embedding,
- negative_embedding,
- margin=5.0):
- """
- Compute the triplet loss as in
-
- Schroff, Florian, Dmitry Kalenichenko, and James Philbin.
- "Facenet: A unified embedding for face recognition and clustering."
- Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
-
- :math:`L = sum( |f_a - f_p|^2 - |f_a - f_n|^2 + \lambda)`
-
- **Parameters**
-
- left_feature:
- First element of the pair
-
- right_feature:
- Second element of the pair
-
- label:
- Label of the pair (0 or 1)
-
- margin:
- Contrastive margin
-
- """
-
- with tf.name_scope("triplet_loss"):
- # Normalize
- anchor_embedding = tf.nn.l2_normalize(
- anchor_embedding, 1, 1e-10, name="anchor")
- positive_embedding = tf.nn.l2_normalize(
- positive_embedding, 1, 1e-10, name="positive")
- negative_embedding = tf.nn.l2_normalize(
- negative_embedding, 1, 1e-10, name="negative")
-
- d_positive = tf.reduce_sum(
- tf.square(tf.subtract(anchor_embedding, positive_embedding)), 1)
- d_negative = tf.reduce_sum(
- tf.square(tf.subtract(anchor_embedding, negative_embedding)), 1)
-
- basic_loss = tf.add(tf.subtract(d_positive, d_negative), margin)
-
- with tf.name_scope("TripletLoss"):
- # Between
- between_class_loss = tf.reduce_mean(d_negative)
- tf.summary.scalar('loss_between_class', between_class_loss)
- tf.add_to_collection(tf.GraphKeys.LOSSES, between_class_loss)
-
- # Within
- within_class_loss = tf.reduce_mean(d_positive)
- tf.summary.scalar('loss_within_class', within_class_loss)
- tf.add_to_collection(tf.GraphKeys.LOSSES, within_class_loss)
-
- # Total loss
- loss = tf.reduce_mean(
- tf.maximum(basic_loss, 0.0), 0, name="total_loss")
- tf.add_to_collection(tf.GraphKeys.LOSSES, loss)
- tf.summary.scalar('loss_triplet', loss)
-
- return loss
-
-
-def triplet_fisher_loss(anchor_embedding, positive_embedding,
- negative_embedding):
-
- with tf.name_scope("triplet_loss"):
- # Normalize
- anchor_embedding = tf.nn.l2_normalize(
- anchor_embedding, 1, 1e-10, name="anchor")
- positive_embedding = tf.nn.l2_normalize(
- positive_embedding, 1, 1e-10, name="positive")
- negative_embedding = tf.nn.l2_normalize(
- negative_embedding, 1, 1e-10, name="negative")
-
- average_class = tf.reduce_mean(anchor_embedding, 0)
- average_total = tf.div(tf.add(tf.reduce_mean(anchor_embedding, axis=0),\
- tf.reduce_mean(negative_embedding, axis=0)), 2)
-
- length = anchor_embedding.get_shape().as_list()[0]
- dim = anchor_embedding.get_shape().as_list()[1]
- split_positive = tf.unstack(positive_embedding, num=length, axis=0)
- split_negative = tf.unstack(negative_embedding, num=length, axis=0)
-
- Sw = None
- Sb = None
- for s in zip(split_positive, split_negative):
- positive = s[0]
- negative = s[1]
-
- buffer_sw = tf.reshape(
- tf.subtract(positive, average_class), shape=(dim, 1))
- buffer_sw = tf.matmul(buffer_sw,
- tf.reshape(buffer_sw, shape=(1, dim)))
-
- buffer_sb = tf.reshape(
- tf.subtract(negative, average_total), shape=(dim, 1))
- buffer_sb = tf.matmul(buffer_sb,
- tf.reshape(buffer_sb, shape=(1, dim)))
-
- if Sw is None:
- Sw = buffer_sw
- Sb = buffer_sb
- else:
- Sw = tf.add(Sw, buffer_sw)
- Sb = tf.add(Sb, buffer_sb)
-
- # Sw = tf.trace(Sw)
- # Sb = tf.trace(Sb)
- #loss = tf.trace(tf.div(Sb, Sw))
- loss = tf.trace(tf.div(Sw, Sb), name=tf.GraphKeys.LOSSES)
-
- return loss, tf.trace(Sb), tf.trace(Sw)
-
-
-def triplet_average_loss(anchor_embedding,
- positive_embedding,
- negative_embedding,
- margin=5.0):
- """
- Compute the triplet loss as in
-
- Schroff, Florian, Dmitry Kalenichenko, and James Philbin.
- "Facenet: A unified embedding for face recognition and clustering."
- Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
-
- :math:`L = sum( |f_a - f_p|^2 - |f_a - f_n|^2 + \lambda)`
-
- **Parameters**
-
- left_feature:
- First element of the pair
-
- right_feature:
- Second element of the pair
-
- label:
- Label of the pair (0 or 1)
-
- margin:
- Contrastive margin
-
- """
-
- with tf.name_scope("triplet_loss"):
- # Normalize
- anchor_embedding = tf.nn.l2_normalize(
- anchor_embedding, 1, 1e-10, name="anchor")
- positive_embedding = tf.nn.l2_normalize(
- positive_embedding, 1, 1e-10, name="positive")
- negative_embedding = tf.nn.l2_normalize(
- negative_embedding, 1, 1e-10, name="negative")
-
- anchor_mean = tf.reduce_mean(anchor_embedding, 0)
-
- d_positive = tf.reduce_sum(
- tf.square(tf.subtract(anchor_mean, positive_embedding)), 1)
- d_negative = tf.reduce_sum(
- tf.square(tf.subtract(anchor_mean, negative_embedding)), 1)
-
- basic_loss = tf.add(tf.subtract(d_positive, d_negative), margin)
- loss = tf.reduce_mean(
- tf.maximum(basic_loss, 0.0), 0, name=tf.GraphKeys.LOSSES)
-
- return loss, tf.reduce_mean(d_negative), tf.reduce_mean(d_positive)
-
-
diff --git a/bob/learn/tensorflow/loss/__init__.py b/bob/learn/tensorflow/loss/__init__.py
deleted file mode 100644
index 7d3937ffcf1364387480e5fdfba054543571f2ed..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/__init__.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from .BaseLoss import mean_cross_entropy_loss, mean_cross_entropy_center_loss
-from .ContrastiveLoss import contrastive_loss
-from .TripletLoss import triplet_loss, triplet_average_loss, triplet_fisher_loss
-from .StyleLoss import linear_gram_style_loss, content_loss, denoising_loss
-from .vat import VATLoss
-from .pixel_wise import PixelWise
-from .center_loss import CenterLoss
-from .mmd import *
-from .pairwise_confusion import total_pairwise_confusion
-from .utils import *
-
-
-# gets sphinx autodoc done right - don't remove it
-def __appropriate__(*args):
- """Says object was actually declared here, an not on the import module.
-
- Parameters:
-
- *args: An iterable of objects to modify
-
- Resolves `Sphinx referencing issues
- <https://github.com/sphinx-doc/sphinx/issues/3048>`
- """
-
- for obj in args:
- obj.__module__ = __name__
-
-
-__appropriate__(
- mean_cross_entropy_loss,
- mean_cross_entropy_center_loss,
- contrastive_loss,
- triplet_loss,
- triplet_average_loss,
- triplet_fisher_loss,
- VATLoss,
- PixelWise,
-)
-__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/loss/center_loss.py b/bob/learn/tensorflow/loss/center_loss.py
deleted file mode 100644
index 00494387c11fb4c13bacc0b4d43e374f34ae7b01..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/center_loss.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import tensorflow as tf
-
-# TODO(amir): replace parent class with tf.Module in tensorflow 1.14 and above.
-# * pass ``name`` to parent class
-# * replace get_variable with tf.Variable
-# * replace variable_scope with name_scope
-class CenterLoss:
- """Center loss."""
-
- def __init__(self, n_classes, n_features, alpha=0.9, name="center_loss", **kwargs):
- super().__init__(**kwargs)
- self.n_classes = n_classes
- self.n_features = n_features
- self.alpha = alpha
- self.name = name
- with tf.variable_scope(self.name):
- self.centers = tf.get_variable(
- "centers",
- [n_classes, n_features],
- dtype=tf.float32,
- initializer=tf.constant_initializer(0.),
- trainable=False,
- )
-
- def __call__(self, sparse_labels, prelogits):
- with tf.name_scope(self.name):
- centers_batch = tf.gather(self.centers, sparse_labels)
- diff = (1 - self.alpha) * (centers_batch - prelogits)
- self.centers_update_op = tf.scatter_sub(self.centers, sparse_labels, diff)
- center_loss = tf.reduce_mean(tf.square(prelogits - centers_batch))
- tf.summary.scalar("loss_center", center_loss)
- # Add histogram for all centers
- for i in range(self.n_classes):
- tf.summary.histogram(f"center_{i}", self.centers[i])
- return center_loss
-
- @property
- def update_ops(self):
- return [self.centers_update_op]
diff --git a/bob/learn/tensorflow/loss/epsc.py b/bob/learn/tensorflow/loss/epsc.py
deleted file mode 100644
index cfadb012ca54b73d92ffbab3ab63faaf4345db65..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/epsc.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import tensorflow as tf
-import bob.measure
-import numpy
-from tensorflow.python.ops.metrics_impl import metric_variable
-from ..utils import norm, predict_using_tensors
-from .ContrastiveLoss import contrastive_loss
-
-
-def logits_loss(
- bio_logits, pad_logits, bio_labels, pad_labels, bio_loss, pad_loss, alpha=0.5
-):
-
- with tf.name_scope("Bio_loss"):
- bio_loss_ = bio_loss(logits=bio_logits, labels=bio_labels)
-
- with tf.name_scope("PAD_loss"):
- pad_loss_ = pad_loss(
- logits=pad_logits, labels=tf.cast(pad_labels, dtype="int32")
- )
-
- with tf.name_scope("EPSC_loss"):
- total_loss = (1 - alpha) * bio_loss_ + alpha * pad_loss_
-
- tf.add_to_collection(tf.GraphKeys.LOSSES, bio_loss_)
- tf.add_to_collection(tf.GraphKeys.LOSSES, pad_loss_)
- tf.add_to_collection(tf.GraphKeys.LOSSES, total_loss)
-
- tf.summary.scalar("bio_loss", bio_loss_)
- tf.summary.scalar("pad_loss", pad_loss_)
- tf.summary.scalar("epsc_loss", total_loss)
-
- return total_loss
-
-
-def embedding_norm_loss(prelogits_left, prelogits_right, b, c, margin=10.0):
- with tf.name_scope("embedding_norm_loss"):
- prelogits_left = norm(prelogits_left)
- prelogits_right = norm(prelogits_right)
-
- loss = tf.add_n(
- [
- tf.reduce_mean(b * (tf.maximum(prelogits_left - margin, 0))),
- tf.reduce_mean((1 - b) * (tf.maximum(2 * margin - prelogits_left, 0))),
- tf.reduce_mean(c * (tf.maximum(prelogits_right - margin, 0))),
- tf.reduce_mean((1 - c) * (tf.maximum(2 * margin - prelogits_right, 0))),
- ],
- name="embedding_norm_loss",
- )
- tf.add_to_collection(tf.GraphKeys.LOSSES, loss)
- tf.summary.scalar("embedding_norm_loss", loss)
- # log norm of embeddings for BF and PA separately to see how their norm
- # evolves over time
- bf_norm = tf.concat(
- [
- tf.gather(prelogits_left, tf.where(b > 0.5)),
- tf.gather(prelogits_right, tf.where(c > 0.5)),
- ],
- axis=0,
- )
- pa_norm = tf.concat(
- [
- tf.gather(prelogits_left, tf.where(b < 0.5)),
- tf.gather(prelogits_right, tf.where(c < 0.5)),
- ],
- axis=0,
- )
- tf.summary.histogram("BF_embeddings_norm", bf_norm)
- tf.summary.histogram("PA_embeddings_norm", pa_norm)
- return loss
-
-
-def siamese_loss(bio_logits, pad_logits, bio_labels, pad_labels, alpha=0.1):
- # prepare a, b, c
- with tf.name_scope("epsc_labels"):
- a = tf.to_float(
- tf.math.equal(bio_labels["left"], bio_labels["right"]), name="a"
- )
- b = tf.to_float(tf.math.equal(pad_labels["left"], True), name="b")
- c = tf.to_float(tf.math.equal(pad_labels["right"], True), name="c")
- tf.summary.scalar("Mean_a", tf.reduce_mean(a))
- tf.summary.scalar("Mean_b", tf.reduce_mean(b))
- tf.summary.scalar("Mean_c", tf.reduce_mean(c))
-
- prelogits_left = bio_logits["left"]
- prelogits_right = bio_logits["right"]
-
- bio_loss = contrastive_loss(prelogits_left, prelogits_right, labels=1 - a)
-
- pad_loss = alpha * embedding_norm_loss(prelogits_left, prelogits_right, b, c)
-
- with tf.name_scope("epsc_loss"):
- epsc_loss = (1 - alpha) * bio_loss + alpha * pad_loss
- tf.add_to_collection(tf.GraphKeys.LOSSES, epsc_loss)
-
- tf.summary.scalar("epsc_loss", epsc_loss)
-
- return epsc_loss
-
-
-def py_eer(negatives, positives):
- def _eer(neg, pos):
- if neg.size == 0 or pos.size == 0:
- return numpy.array(0.0, dtype="float64")
- return bob.measure.eer(neg, pos)
-
- negatives = tf.reshape(tf.cast(negatives, "float64"), [-1])
- positives = tf.reshape(tf.cast(positives, "float64"), [-1])
-
- eer = tf.py_func(_eer, [negatives, positives], tf.float64, name="py_eer")
-
- return tf.cast(eer, "float32")
-
-
-def epsc_metric(
- bio_embeddings,
- pad_probabilities,
- bio_labels,
- pad_labels,
- batch_size,
- pad_threshold=numpy.exp(-15),
-):
- # math.exp(-2.0) = 0.1353352832366127
- # math.exp(-15.0) = 3.059023205018258e-07
- with tf.name_scope("epsc_metrics"):
- bio_predictions_op = predict_using_tensors(
- bio_embeddings, bio_labels, num=batch_size
- )
-
- # find the lowest value of bf and highest value of pa
- # their mean is the threshold
- # bf_probabilities = tf.gather(pad_probabilities, tf.where(pad_labels))
- # pa_probabilities = tf.gather(pad_probabilities, tf.where(tf.logical_not(pad_labels)))
-
- # eer = py_eer(pa_probabilities, bf_probabilities)
- # acc = 1 - eer
-
- # pad_threshold = (tf.reduce_max(pa_probabilities) + tf.reduce_min(bf_probabilities)) / 2
- # true_positives = tf.reduce_sum(tf.to_int32(bf_probabilities >= pad_threshold))
- # true_negatives = tf.reduce_sum(tf.to_int32(pa_probabilities < pad_threshold))
- # # pad_accuracy = metric_variable([], tf.float32, name='pad_accuracy')
- # acc = (true_positives + true_negatives) / batch_size
-
- # pad_accuracy, pad_update_ops = tf.metrics.mean(acc)
-
- # print_ops = [
- # tf.print(pad_probabilities),
- # tf.print(bf_probabilities, pa_probabilities),
- # tf.print(pad_threshold),
- # tf.print(true_positives, true_negatives),
- # tf.print(pad_probabilities.shape[0]),
- # tf.print(acc),
- # ]
- # update_op = tf.assign_add(pad_accuracy, tf.cast(acc, tf.float32))
- # update_op = tf.group([update_op] + print_ops)
-
- tp = tf.metrics.true_positives_at_thresholds(
- pad_labels, pad_probabilities, [pad_threshold]
- )
- fp = tf.metrics.false_positives_at_thresholds(
- pad_labels, pad_probabilities, [pad_threshold]
- )
- tn = tf.metrics.true_negatives_at_thresholds(
- pad_labels, pad_probabilities, [pad_threshold]
- )
- fn = tf.metrics.false_negatives_at_thresholds(
- pad_labels, pad_probabilities, [pad_threshold]
- )
- pad_accuracy = (tp[0] + tn[0]) / (tp[0] + tn[0] + fp[0] + fn[0])
- pad_accuracy = tf.reduce_mean(pad_accuracy)
- pad_update_ops = tf.group([x[1] for x in (tp, tn, fp, fn)])
-
- eval_metric_ops = {
- "bio_accuracy": tf.metrics.accuracy(
- labels=bio_labels, predictions=bio_predictions_op
- ),
- "pad_accuracy": (pad_accuracy, pad_update_ops),
- }
- return eval_metric_ops
diff --git a/bob/learn/tensorflow/loss/mmd.py b/bob/learn/tensorflow/loss/mmd.py
deleted file mode 100644
index 2933d7b1d3cd32b7533c2fa5213e38eeb7192965..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/mmd.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import tensorflow as tf
-
-
-def compute_kernel(x, y):
- """Gaussian kernel.
- """
- x_size = tf.shape(x)[0]
- y_size = tf.shape(y)[0]
- dim = tf.shape(x)[1]
- tiled_x = tf.tile(
- tf.reshape(x, tf.stack([x_size, 1, dim])), tf.stack([1, y_size, 1])
- )
- tiled_y = tf.tile(
- tf.reshape(y, tf.stack([1, y_size, dim])), tf.stack([x_size, 1, 1])
- )
- return tf.exp(
- -tf.reduce_mean(tf.square(tiled_x - tiled_y), axis=2) / tf.cast(dim, tf.float32)
- )
-
-
-def mmd(x, y):
- """Maximum Mean Discrepancy with Gaussian kernel.
- See: https://stats.stackexchange.com/a/276618/49433
- """
- x_kernel = compute_kernel(x, x)
- y_kernel = compute_kernel(y, y)
- xy_kernel = compute_kernel(x, y)
- return (
- tf.reduce_mean(x_kernel)
- + tf.reduce_mean(y_kernel)
- - 2 * tf.reduce_mean(xy_kernel)
- )
diff --git a/bob/learn/tensorflow/loss/pairwise_confusion.py b/bob/learn/tensorflow/loss/pairwise_confusion.py
deleted file mode 100644
index 155b1a299625283ade232af1c84d13e146cceff1..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/pairwise_confusion.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import tensorflow as tf
-from ..utils import pdist_safe, upper_triangle
-
-def total_pairwise_confusion(prelogits, name=None):
- """Total Pairwise Confusion Loss
-
- [1]X. Tu et al., “Learning Generalizable and Identity-Discriminative
- Representations for Face Anti-Spoofing,” arXiv preprint arXiv:1901.05602, 2019.
- """
- # compute L2 norm between all prelogits and sum them.
- with tf.name_scope(name, default_name="total_pairwise_confusion"):
- prelogits = tf.reshape(prelogits, (tf.shape(prelogits)[0], -1))
- loss_tpc = tf.reduce_mean(upper_triangle(pdist_safe(prelogits)))
-
- tf.summary.scalar("loss_tpc", loss_tpc)
- return loss_tpc
diff --git a/bob/learn/tensorflow/loss/pixel_wise.py b/bob/learn/tensorflow/loss/pixel_wise.py
deleted file mode 100644
index b34695045c20273bdc0063a928ceb723324eca6d..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/pixel_wise.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from ..dataset import tf_repeat
-from .utils import (
- balanced_softmax_cross_entropy_loss_weights,
- balanced_sigmoid_cross_entropy_loss_weights,
-)
-import tensorflow as tf
-
-
-class PixelWise:
- """A pixel wise loss which is just a cross entropy loss but applied to all pixels"""
-
- def __init__(
- self, balance_weights=True, n_one_hot_labels=None, label_smoothing=0.5, **kwargs
- ):
- super(PixelWise, self).__init__(**kwargs)
- self.balance_weights = balance_weights
- self.n_one_hot_labels = n_one_hot_labels
- self.label_smoothing = label_smoothing
-
- def __call__(self, labels, logits):
- with tf.name_scope("PixelWiseLoss"):
- flatten = tf.keras.layers.Flatten()
- logits = flatten(logits)
- n_pixels = logits.get_shape()[-1]
- weights = 1.0
- if self.balance_weights and self.n_one_hot_labels:
- # use labels to figure out the required loss
- weights = balanced_softmax_cross_entropy_loss_weights(
- labels, dtype=logits.dtype
- )
- # repeat weights for all pixels
- weights = tf_repeat(weights[:, None], [1, n_pixels])
- weights = tf.reshape(weights, (-1,))
- elif self.balance_weights and not self.n_one_hot_labels:
- # use labels to figure out the required loss
- weights = balanced_sigmoid_cross_entropy_loss_weights(
- labels, dtype=logits.dtype
- )
- # repeat weights for all pixels
- weights = tf_repeat(weights[:, None], [1, n_pixels])
-
- if self.n_one_hot_labels:
- labels = tf_repeat(labels, [n_pixels, 1])
- labels = tf.reshape(labels, (-1, self.n_one_hot_labels))
- # reshape logits too as softmax_cross_entropy is buggy and cannot really
- # handle higher dimensions
- logits = tf.reshape(logits, (-1, self.n_one_hot_labels))
- loss_fn = tf.losses.softmax_cross_entropy
- else:
- labels = tf.reshape(labels, (-1, 1))
- labels = tf_repeat(labels, [n_pixels, 1])
- labels = tf.reshape(labels, (-1, n_pixels))
- loss_fn = tf.losses.sigmoid_cross_entropy
-
- loss_pixel_wise = loss_fn(
- labels,
- logits=logits,
- weights=weights,
- label_smoothing=self.label_smoothing,
- reduction=tf.losses.Reduction.MEAN,
- )
- tf.summary.scalar("loss_pixel_wise", loss_pixel_wise)
- return loss_pixel_wise
diff --git a/bob/learn/tensorflow/loss/utils.py b/bob/learn/tensorflow/loss/utils.py
deleted file mode 100644
index 8c0e9eeaa7d1f059216f441017469c2701531426..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/utils.py
+++ /dev/null
@@ -1,144 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Amir Mohammadi <amir.mohammadi@idiap.ch>
-
-import tensorflow as tf
-
-
-def balanced_softmax_cross_entropy_loss_weights(labels, dtype="float32"):
- """Computes weights that normalizes your loss per class.
-
- Labels must be a batch of one-hot encoded labels. The function takes labels and
- computes the weights per batch. Weights will be smaller for classes that have more
- samples in this batch. This is useful if you unbalanced classes in your dataset or
- batch.
-
- Parameters
- ----------
- labels : ``tf.Tensor``
- Labels of your current input. The shape must be [batch_size, n_classes]. If your
- labels are not one-hot encoded, you can use ``tf.one_hot`` to convert them first
- before giving them to this function.
- dtype : ``tf.dtype``
- The dtype that weights will have. It should be float. Best is to provide
- logits.dtype as input.
-
- Returns
- -------
- ``tf.Tensor``
- Computed weights that will cancel your dataset imbalance per batch.
-
- Examples
- --------
- >>> import numpy
- >>> import tensorflow as tf
- >>> from bob.learn.tensorflow.loss import balanced_softmax_cross_entropy_loss_weights
- >>> labels = numpy.array([[1, 0, 0],
- ... [1, 0, 0],
- ... [0, 0, 1],
- ... [0, 1, 0],
- ... [0, 0, 1],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [0, 0, 1],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [0, 1, 0],
- ... [1, 0, 0],
- ... [0, 1, 0],
- ... [1, 0, 0],
- ... [0, 0, 1],
- ... [0, 0, 1],
- ... [1, 0, 0],
- ... [0, 0, 1],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [0, 1, 0],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [1, 0, 0],
- ... [0, 1, 0],
- ... [1, 0, 0],
- ... [0, 0, 1],
- ... [1, 0, 0]], dtype="int32")
- >>> session = tf.Session() # Eager execution is also possible check https://www.tensorflow.org/guide/eager
- >>> session.run(tf.reduce_sum(labels, axis=0))
- array([20, 5, 7], dtype=int32)
- >>> session.run(balanced_softmax_cross_entropy_loss_weights(labels, dtype='float32'))
- array([0.53333336, 0.53333336, 1.5238096 , 2.1333334 , 1.5238096 ,
- 0.53333336, 0.53333336, 1.5238096 , 0.53333336, 0.53333336,
- 0.53333336, 0.53333336, 0.53333336, 0.53333336, 2.1333334 ,
- 0.53333336, 2.1333334 , 0.53333336, 1.5238096 , 1.5238096 ,
- 0.53333336, 1.5238096 , 0.53333336, 0.53333336, 2.1333334 ,
- 0.53333336, 0.53333336, 0.53333336, 2.1333334 , 0.53333336,
- 1.5238096 , 0.53333336], dtype=float32)
-
- You would use it like this:
-
- >>> #weights = balanced_softmax_cross_entropy_loss_weights(labels, dtype=logits.dtype)
- >>> #loss = tf.losses.softmax_cross_entropy(logits=logits, labels=labels, weights=weights)
- """
- shape = tf.cast(tf.shape(labels), dtype=dtype)
- batch_size, n_classes = shape[0], shape[1]
- weights = tf.cast(tf.reduce_sum(labels, axis=0), dtype=dtype)
- weights = batch_size / weights / n_classes
- weights = tf.gather(weights, tf.argmax(labels, axis=1))
- return weights
-
-
-def balanced_sigmoid_cross_entropy_loss_weights(labels, dtype="float32"):
- """Computes weights that normalizes your loss per class.
-
- Labels must be a batch of binary labels. The function takes labels and
- computes the weights per batch. Weights will be smaller for the class that have more
- samples in this batch. This is useful if you unbalanced classes in your dataset or
- batch.
-
- Parameters
- ----------
- labels : ``tf.Tensor``
- Labels of your current input. The shape must be [batch_size] and values must be
- either 0 or 1.
- dtype : ``tf.dtype``
- The dtype that weights will have. It should be float. Best is to provide
- logits.dtype as input.
-
- Returns
- -------
- ``tf.Tensor``
- Computed weights that will cancel your dataset imbalance per batch.
-
- Examples
- --------
- >>> import numpy
- >>> import tensorflow as tf
- >>> from bob.learn.tensorflow.loss import balanced_sigmoid_cross_entropy_loss_weights
- >>> labels = numpy.array([1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
- ... 1, 1, 0, 1, 1, 1, 0, 1, 0, 1], dtype="int32")
- >>> sum(labels), len(labels)
- (20, 32)
- >>> session = tf.Session() # Eager execution is also possible check https://www.tensorflow.org/guide/eager
- >>> session.run(balanced_sigmoid_cross_entropy_loss_weights(labels, dtype='float32'))
- array([0.8 , 0.8 , 1.3333334, 1.3333334, 1.3333334, 0.8 ,
- 0.8 , 1.3333334, 0.8 , 0.8 , 0.8 , 0.8 ,
- 0.8 , 0.8 , 1.3333334, 0.8 , 1.3333334, 0.8 ,
- 1.3333334, 1.3333334, 0.8 , 1.3333334, 0.8 , 0.8 ,
- 1.3333334, 0.8 , 0.8 , 0.8 , 1.3333334, 0.8 ,
- 1.3333334, 0.8 ], dtype=float32)
-
- You would use it like this:
-
- >>> #weights = balanced_sigmoid_cross_entropy_loss_weights(labels, dtype=logits.dtype)
- >>> #loss = tf.losses.sigmoid_cross_entropy(logits=logits, labels=labels, weights=weights)
- """
- labels = tf.cast(labels, dtype='int32')
- batch_size = tf.cast(tf.shape(labels)[0], dtype=dtype)
- weights = tf.cast(tf.reduce_sum(labels), dtype=dtype)
- weights = tf.convert_to_tensor([batch_size - weights, weights])
- weights = batch_size / weights / 2
- weights = tf.gather(weights, labels)
- return weights
diff --git a/bob/learn/tensorflow/loss/vat.py b/bob/learn/tensorflow/loss/vat.py
deleted file mode 100644
index b48f4f8918287a68e71f1647d22cbe1a4a0d2c52..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/loss/vat.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Adapted from https://github.com/takerum/vat_tf Its license:
-#
-# MIT License
-#
-# Copyright (c) 2017 Takeru Miyato
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-
-import tensorflow as tf
-from functools import partial
-
-
-def get_normalized_vector(d):
- d /= (1e-12 + tf.reduce_max(tf.abs(d), list(range(1, len(d.get_shape()))), keepdims=True))
- d /= tf.sqrt(1e-6 + tf.reduce_sum(tf.pow(d, 2.0), list(range(1, len(d.get_shape()))), keepdims=True))
- return d
-
-
-def logsoftmax(x):
- xdev = x - tf.reduce_max(x, 1, keepdims=True)
- lsm = xdev - tf.log(tf.reduce_sum(tf.exp(xdev), 1, keepdims=True))
- return lsm
-
-
-def kl_divergence_with_logit(q_logit, p_logit):
- q = tf.nn.softmax(q_logit)
- qlogq = tf.reduce_mean(tf.reduce_sum(q * logsoftmax(q_logit), 1))
- qlogp = tf.reduce_mean(tf.reduce_sum(q * logsoftmax(p_logit), 1))
- return qlogq - qlogp
-
-
-def entropy_y_x(logit):
- p = tf.nn.softmax(logit)
- return -tf.reduce_mean(tf.reduce_sum(p * logsoftmax(logit), 1))
-
-
-class VATLoss:
- """A class to hold parameters for Virtual Adversarial Training (VAT) Loss
- and perform it.
-
- Attributes
- ----------
- epsilon : float
- norm length for (virtual) adversarial training
- method : str
- The method for calculating the loss: ``vatent`` for VAT loss + entropy
- and ``vat`` for only VAT loss.
- num_power_iterations : int
- the number of power iterations
- xi : float
- small constant for finite difference
- """
-
- def __init__(self, epsilon=8.0, xi=1e-6, num_power_iterations=1, method='vatent', **kwargs):
- super(VATLoss, self).__init__(**kwargs)
- self.epsilon = epsilon
- self.xi = xi
- self.num_power_iterations = num_power_iterations
- self.method = method
-
- def __call__(self, features, logits, architecture, mode):
- """Computes the VAT loss for unlabeled features.
- If you are doing semi-supervised learning, only pass the unlabeled
- features and their logits here.
-
- Parameters
- ----------
- features : object
- Tensor representing the (unlabeled) features
- logits : object
- Tensor representing the logits of (unlabeled) features.
- architecture : object
- A callable that constructs the model. It should accept ``mode`` and
- ``reuse`` as keyword arguments. The features will be given as the
- first input.
- mode : str
- One of tf.estimator.ModeKeys.{TRAIN,EVAL} strings.
-
- Returns
- -------
- object
- The loss.
-
- Raises
- ------
- NotImplementedError
- If self.method is not ``vat`` or ``vatent``.
- """
- if mode != tf.estimator.ModeKeys.TRAIN:
- return 0.
- architecture = partial(architecture, reuse=True)
- with tf.variable_scope(tf.get_variable_scope(), reuse=True):
- vat_loss = self.virtual_adversarial_loss(features, logits, architecture, mode)
- tf.summary.scalar("loss_VAT", vat_loss)
- tf.add_to_collection(tf.GraphKeys.LOSSES, vat_loss)
- if self.method == 'vat':
- loss = vat_loss
- elif self.method == 'vatent':
- ent_loss = entropy_y_x(logits)
- tf.summary.scalar("loss_entropy", ent_loss)
- tf.add_to_collection(tf.GraphKeys.LOSSES, ent_loss)
- loss = vat_loss + ent_loss
- else:
- raise ValueError
- return loss
-
- def virtual_adversarial_loss(self, features, logits, architecture, mode, name="vat_loss_op"):
- r_vadv = self.generate_virtual_adversarial_perturbation(features, logits, architecture, mode)
- logit_p = tf.stop_gradient(logits)
- adversarial_input = features + r_vadv
- tf.summary.image("Adversarial_Image", adversarial_input)
- logit_m = architecture(adversarial_input, mode=mode)[0]
- loss = kl_divergence_with_logit(logit_p, logit_m)
- return tf.identity(loss, name=name)
-
- def generate_virtual_adversarial_perturbation(self, features, logits, architecture, mode):
- d = tf.random_normal(shape=tf.shape(features))
-
- for _ in range(self.num_power_iterations):
- d = self.xi * get_normalized_vector(d)
- logit_p = logits
- logit_m = architecture(features + d, mode=mode)[0]
- dist = kl_divergence_with_logit(logit_p, logit_m)
- grad = tf.gradients(dist, [d], aggregation_method=2)[0]
- d = tf.stop_gradient(grad)
-
- return self.epsilon * get_normalized_vector(d)
diff --git a/bob/learn/tensorflow/style_transfer/__init__.py b/bob/learn/tensorflow/losses/__init__.py
similarity index 65%
rename from bob/learn/tensorflow/style_transfer/__init__.py
rename to bob/learn/tensorflow/losses/__init__.py
index 11f0c6a989d75fcae4804500619f8d980349e54d..65cdcab2e9e6b5ba4090b269eb5fc68e5544797c 100644
--- a/bob/learn/tensorflow/style_transfer/__init__.py
+++ b/bob/learn/tensorflow/losses/__init__.py
@@ -1,4 +1,4 @@
-from .neural_transfer import compute_features, compute_gram, do_style_transfer
+from .center_loss import CenterLoss, CenterLossLayer
# gets sphinx autodoc done right - don't remove it
def __appropriate__(*args):
@@ -6,7 +6,7 @@ def __appropriate__(*args):
Parameters:
- *args: An iterable of objects to modify
+ *args: An iterable of objects to modify
Resolves `Sphinx referencing issues
<https://github.com/sphinx-doc/sphinx/issues/3048>`
@@ -17,6 +17,7 @@ def __appropriate__(*args):
__appropriate__(
+ CenterLoss,
+ CenterLossLayer
)
-
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/losses/center_loss.py b/bob/learn/tensorflow/losses/center_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..894a461200639042094c6b9e29e1721eee1478cb
--- /dev/null
+++ b/bob/learn/tensorflow/losses/center_loss.py
@@ -0,0 +1,88 @@
+import tensorflow as tf
+
+
+class CenterLossLayer(tf.keras.layers.Layer):
+ """A layer to be added in the model if you want to use CenterLoss
+
+ Attributes
+ ----------
+ centers : tf.Variable
+ The variable that keeps track of centers.
+ n_classes : int
+ Number of classes of the task.
+ n_features : int
+ The size of prelogits.
+ """
+
+ def __init__(self, n_classes, n_features, **kwargs):
+ super().__init__(**kwargs)
+ self.n_classes = n_classes
+ self.n_features = n_features
+ self.centers = tf.Variable(
+ tf.zeros([n_classes, n_features]),
+ name="centers",
+ trainable=False,
+ # in a distributed strategy, we want updates to this variable to be summed.
+ aggregation=tf.VariableAggregation.SUM,
+ )
+
+ def call(self, x):
+ # pass through layer
+ return tf.identity(x)
+
+ def get_config(self):
+ config = super().get_config()
+ config.update({"n_classes": self.n_classes, "n_features": self.n_features})
+ return config
+
+
+class CenterLoss(tf.keras.losses.Loss):
+ """Center loss.
+ Introduced in: A Discriminative Feature Learning Approach for Deep Face Recognition
+ https://ydwen.github.io/papers/WenECCV16.pdf
+
+ .. warning::
+
+ This loss MUST NOT BE CALLED during evaluation as it will update the centers!
+ This loss only works with sparse labels.
+ This loss must be used with CenterLossLayer embedded into the model
+
+ Attributes
+ ----------
+ alpha : float
+ The moving average coefficient for updating centers in each batch.
+ centers : tf.Variable
+ The variable that keeps track of centers.
+ centers_layer
+ The layer that keeps track of centers.
+ """
+
+ def __init__(
+ self,
+ centers_layer,
+ alpha=0.9,
+ update_centers=True,
+ name="center_loss",
+ **kwargs
+ ):
+ super().__init__(name=name, **kwargs)
+ self.centers_layer = centers_layer
+ self.centers = self.centers_layer.centers
+ self.alpha = alpha
+ self.update_centers = update_centers
+
+ def call(self, sparse_labels, prelogits):
+ sparse_labels = tf.reshape(sparse_labels, (-1,))
+ centers_batch = tf.gather(self.centers, sparse_labels)
+ # the reduction of batch dimension will be done by the parent class
+ center_loss = tf.keras.losses.mean_squared_error(prelogits, centers_batch)
+
+ # update centers
+ if self.update_centers:
+ diff = (1 - self.alpha) * (centers_batch - prelogits)
+ updates = tf.scatter_nd(sparse_labels[:, None], diff, self.centers.shape)
+ # using assign_sub will make sure updates are added during distributed
+ # training
+ self.centers.assign_sub(updates)
+
+ return center_loss
diff --git a/bob/learn/tensorflow/metrics/__init__.py b/bob/learn/tensorflow/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..72ee7b5fce68569d2359176ed78f076030030d47
--- /dev/null
+++ b/bob/learn/tensorflow/metrics/__init__.py
@@ -0,0 +1,20 @@
+from .embedding_accuracy import EmbeddingAccuracy, predict_using_tensors
+
+# gets sphinx autodoc done right - don't remove it
+def __appropriate__(*args):
+ """Says object was actually declared here, an not on the import module.
+
+ Parameters:
+
+ *args: An iterable of objects to modify
+
+ Resolves `Sphinx referencing issues
+ <https://github.com/sphinx-doc/sphinx/issues/3048>`
+ """
+
+ for obj in args:
+ obj.__module__ = __name__
+
+
+__appropriate__(EmbeddingAccuracy)
+__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/metrics/embedding_accuracy.py b/bob/learn/tensorflow/metrics/embedding_accuracy.py
new file mode 100644
index 0000000000000000000000000000000000000000..20ac7294a1000ed09b688d08bbd4be9f1eb35b60
--- /dev/null
+++ b/bob/learn/tensorflow/metrics/embedding_accuracy.py
@@ -0,0 +1,39 @@
+import numpy as np
+import tensorflow as tf
+import tensorflow.keras.backend as K
+from tensorflow.python.keras.metrics import MeanMetricWrapper
+
+from ..utils import pdist
+
+
+def predict_using_tensors(embedding, labels):
+ """
+ Compute the predictions through exhaustive comparisons between
+ embeddings using tensors
+ """
+
+ # Fitting the main diagonal with infs (removing comparisons with the same
+ # sample)
+ inf = tf.cast(tf.ones_like(labels), tf.float32) * np.inf
+
+ distances = pdist(embedding)
+ distances = tf.linalg.set_diag(distances, inf)
+ indexes = tf.argmin(input=distances, axis=1)
+ return tf.gather(labels, indexes)
+
+
+def accuracy_from_embeddings(labels, prelogits):
+ labels = tf.reshape(labels, (-1,))
+ embeddings = tf.nn.l2_normalize(prelogits, 1)
+ predictions = predict_using_tensors(embeddings, labels)
+ return tf.cast(tf.math.equal(labels, predictions), K.floatx())
+
+
+class EmbeddingAccuracy(MeanMetricWrapper):
+ """Calculates accuracy from labels and prelogits.
+ This class relies on the fact that, in each batch, at least two images are
+ available from each class(identity).
+ """
+
+ def __init__(self, name="embedding_accuracy", dtype=None):
+ super().__init__(accuracy_from_embeddings, name, dtype=dtype)
diff --git a/bob/learn/tensorflow/models/__init__.py b/bob/learn/tensorflow/models/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..48804ec2338b14d47fe6e4e5c332b561d56daed9 100644
--- a/bob/learn/tensorflow/models/__init__.py
+++ b/bob/learn/tensorflow/models/__init__.py
@@ -0,0 +1,26 @@
+from .alexnet import AlexNet_simplified
+from .densenet import DenseNet
+from .mine import MineModel
+
+# gets sphinx autodoc done right - don't remove it
+def __appropriate__(*args):
+ """Says object was actually declared here, an not on the import module.
+
+ Parameters:
+
+ *args: An iterable of objects to modify
+
+ Resolves `Sphinx referencing issues
+ <https://github.com/sphinx-doc/sphinx/issues/3048>`
+ """
+
+ for obj in args:
+ obj.__module__ = __name__
+
+
+__appropriate__(
+ AlexNet_simplified,
+ DenseNet,
+ MineModel
+)
+__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/models/alexnet.py b/bob/learn/tensorflow/models/alexnet.py
index 202348034fdbbd299b1fb2f64c09e439e822d682..1ff0907775faa81c6ce5777f0065bf1ad49b3e42 100644
--- a/bob/learn/tensorflow/models/alexnet.py
+++ b/bob/learn/tensorflow/models/alexnet.py
@@ -9,20 +9,50 @@ def AlexNet_simplified(name="AlexNet", **kwargs):
model = tf.keras.Sequential(
[
tf.keras.Input(shape=(227, 227, 3)),
- tf.keras.layers.Conv2D(filters=96, kernel_size=11, strides=4, name="C1", activation="relu"),
+ tf.keras.layers.Conv2D(
+ filters=96, kernel_size=11, strides=4, name="C1", activation="relu"
+ ),
tf.keras.layers.MaxPool2D(pool_size=3, strides=2, name="P1"),
- tf.keras.layers.Conv2D(filters=256, kernel_size=5, strides=1, name="C2", activation="relu", padding="same"),
+ tf.keras.layers.Conv2D(
+ filters=256,
+ kernel_size=5,
+ strides=1,
+ name="C2",
+ activation="relu",
+ padding="same",
+ ),
tf.keras.layers.MaxPool2D(pool_size=3, strides=2, name="P2"),
- tf.keras.layers.Conv2D(filters=384, kernel_size=3, strides=1, name="C3", activation="relu", padding="same"),
- tf.keras.layers.Conv2D(filters=384, kernel_size=3, strides=1, name="C4", activation="relu", padding="same"),
- tf.keras.layers.Conv2D(filters=256, kernel_size=3, strides=1, name="C5", activation="relu", padding="same"),
+ tf.keras.layers.Conv2D(
+ filters=384,
+ kernel_size=3,
+ strides=1,
+ name="C3",
+ activation="relu",
+ padding="same",
+ ),
+ tf.keras.layers.Conv2D(
+ filters=384,
+ kernel_size=3,
+ strides=1,
+ name="C4",
+ activation="relu",
+ padding="same",
+ ),
+ tf.keras.layers.Conv2D(
+ filters=256,
+ kernel_size=3,
+ strides=1,
+ name="C5",
+ activation="relu",
+ padding="same",
+ ),
tf.keras.layers.MaxPool2D(pool_size=3, strides=2, name="P5"),
tf.keras.layers.Flatten(name="FLATTEN"),
tf.keras.layers.Dropout(rate=0.5, name="D6"),
tf.keras.layers.Dense(units=4096, activation="relu", name="F6"),
tf.keras.layers.Dropout(rate=0.5, name="D7"),
tf.keras.layers.Dense(units=4096, activation="relu", name="F7"),
- tf.keras.layers.Dense(units=1000, activation="softmax", name="OUTPUT"),
+ tf.keras.layers.Dense(units=1000, name="OUTPUT"),
],
name=name,
**kwargs
@@ -32,6 +62,7 @@ def AlexNet_simplified(name="AlexNet", **kwargs):
if __name__ == "__main__":
import pkg_resources
+
from bob.learn.tensorflow.utils import model_summary
model = AlexNet_simplified()
diff --git a/bob/learn/tensorflow/models/autoencoder_face.py b/bob/learn/tensorflow/models/autoencoder_face.py
index 7be56d7b056afc423e452963a4bb5278257f4a18..bb92f92c6eb0658e31e6881e38a5794fc9a5a581 100644
--- a/bob/learn/tensorflow/models/autoencoder_face.py
+++ b/bob/learn/tensorflow/models/autoencoder_face.py
@@ -4,6 +4,7 @@ Mohammadi, Amir and Bhattacharjee, Sushil and Marcel, Sebastien, ICASSP 2020
"""
import tensorflow as tf
+
from bob.learn.tensorflow.models.densenet import densenet161
@@ -114,6 +115,7 @@ def autoencoder_face(z_dim=256, weight_decay=1e-10, decoder_last_act="tanh"):
if __name__ == "__main__":
import pkg_resources
from tabulate import tabulate
+
from bob.learn.tensorflow.utils import model_summary
model = ConvDecoder(z_dim=256, weight_decay=1e-9, last_act="tanh", name="Decoder")
diff --git a/bob/learn/tensorflow/models/densenet.py b/bob/learn/tensorflow/models/densenet.py
index 7dfbc8aea0941ab86eb1fc3780d862578d3adf82..f17f5b860b2d3b92f06cd2b8586d5b590e0378e3 100644
--- a/bob/learn/tensorflow/models/densenet.py
+++ b/bob/learn/tensorflow/models/densenet.py
@@ -3,6 +3,7 @@ Reference [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.
"""
import tensorflow as tf
+
from bob.extension import rc
l2 = tf.keras.regularizers.l2
@@ -433,21 +434,21 @@ class DeepPixBiS(tf.keras.Model):
tf.keras.layers.Flatten(
data_format=data_format, name="Pixel_Logits_Flatten"
),
- tf.keras.layers.Activation("sigmoid", name="activation"),
]
def call(self, x, training=None):
- for l in self.sequential_layers:
+ for layer in self.sequential_layers:
try:
- x = l(x, training=training)
+ x = layer(x, training=training)
except TypeError:
- x = l(x)
+ x = layer(x)
return x
if __name__ == "__main__":
import pkg_resources
from tabulate import tabulate
+
from bob.learn.tensorflow.utils import model_summary
def print_model(inputs, outputs):
diff --git a/bob/learn/tensorflow/models/inception.py b/bob/learn/tensorflow/models/inception.py
index 3e25a59fa4ae9c6bb9d823ea7d6c20c311f37ca4..5a8314aa9ce82c1ddadcab47be0a16a16b5a0739 100644
--- a/bob/learn/tensorflow/models/inception.py
+++ b/bob/learn/tensorflow/models/inception.py
@@ -2,8 +2,7 @@ import tensorflow as tf
class LRN(tf.keras.layers.Lambda):
- """local response normalization with default parameters for GoogLeNet
- """
+ """local response normalization with default parameters for GoogLeNet"""
def __init__(self, alpha=0.0001, beta=0.75, depth_radius=5, **kwargs):
self.alpha = alpha
@@ -21,8 +20,8 @@ class LRN(tf.keras.layers.Lambda):
class InceptionModule(tf.keras.Model):
"""The inception module as it was introduced in:
- C. Szegedy et al., “Going deeper with convolutions,” in Proceedings of the IEEE
- Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.
+ C. Szegedy et al., “Going deeper with convolutions,” in Proceedings of the IEEE
+ Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.
"""
def __init__(
@@ -112,7 +111,9 @@ def GoogLeNet(*, num_classes=1000, name="GoogLeNet", **kwargs):
),
tf.keras.layers.MaxPool2D(3, 2, padding="same", name="pool1/3x3_s2"),
LRN(name="pool1/norm1"),
- tf.keras.layers.Conv2D(64, 1, padding="same", activation="relu", name="conv2/3x3_reduce"),
+ tf.keras.layers.Conv2D(
+ 64, 1, padding="same", activation="relu", name="conv2/3x3_reduce"
+ ),
tf.keras.layers.Conv2D(
192, 3, padding="same", activation="relu", name="conv2/3x3"
),
@@ -131,7 +132,7 @@ def GoogLeNet(*, num_classes=1000, name="GoogLeNet", **kwargs):
InceptionModule(384, 192, 384, 48, 128, 128, name="inception_5b"),
tf.keras.layers.GlobalAvgPool2D(name="pool5"),
tf.keras.layers.Dropout(rate=0.4, name="dropout"),
- tf.keras.layers.Dense(num_classes, name="output", activation="softmax"),
+ tf.keras.layers.Dense(num_classes, name="output"),
],
name=name,
**kwargs
@@ -143,6 +144,7 @@ def GoogLeNet(*, num_classes=1000, name="GoogLeNet", **kwargs):
if __name__ == "__main__":
import pkg_resources
from tabulate import tabulate
+
from bob.learn.tensorflow.utils import model_summary
inputs = tf.keras.Input((28, 28, 192), name="input")
diff --git a/bob/learn/tensorflow/models/inception_resnet_v2.py b/bob/learn/tensorflow/models/inception_resnet_v2.py
index 79b1a66d24f59a3788742fe843e08466e3a67bbd..ad5b8629f52b27496aa5c92aeaa2e3793965042a 100644
--- a/bob/learn/tensorflow/models/inception_resnet_v2.py
+++ b/bob/learn/tensorflow/models/inception_resnet_v2.py
@@ -1,29 +1,39 @@
# -*- coding: utf-8 -*-
"""Inception-ResNet-V2 MultiScale-Inception-ResNet-V2 models for Keras.
"""
-from tensorflow.keras.models import Model
-from tensorflow.keras.layers import (
- Activation,
- BatchNormalization,
- Concatenate,
- Conv2D,
- Dense,
- Dropout,
- Input,
- Lambda,
- MaxPool2D,
- AvgPool2D,
- GlobalAvgPool2D,
- GlobalMaxPool2D,
-)
-from tensorflow.keras import backend as K
-import tensorflow as tf
import logging
+import tensorflow as tf
+from tensorflow.keras import backend as K
+from tensorflow.keras.layers import Activation
+from tensorflow.keras.layers import AvgPool2D
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.layers import Concatenate
+from tensorflow.keras.layers import Conv2D
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.layers import GlobalAvgPool2D
+from tensorflow.keras.layers import GlobalMaxPool2D
+from tensorflow.keras.layers import Input
+from tensorflow.keras.layers import MaxPool2D
+from tensorflow.keras.models import Model
+from tensorflow.keras.models import Sequential
+
+from ..utils import SequentialLayer
+
logger = logging.getLogger(__name__)
-class Conv2D_BN(tf.keras.Sequential):
+def Conv2D_BN(
+ filters,
+ kernel_size,
+ strides=1,
+ padding="same",
+ activation="relu",
+ use_bias=False,
+ name=None,
+ **kwargs,
+):
"""Utility class to apply conv + BN.
# Arguments
@@ -54,50 +64,30 @@ class Conv2D_BN(tf.keras.Sequential):
and `name + '/BatchNorm'` for the batch norm layer.
"""
- def __init__(
- self,
- filters,
- kernel_size,
- strides=1,
- padding="same",
- activation="relu",
- use_bias=False,
- name=None,
- **kwargs,
- ):
-
- self.filters = filters
- self.kernel_size = kernel_size
- self.strides = strides
- self.padding = padding
- self.activation = activation
- self.use_bias = use_bias
-
- layers = [
- Conv2D(
- filters,
- kernel_size,
- strides=strides,
- padding=padding,
- use_bias=use_bias,
- name=name,
- )
- ]
+ layers = [
+ Conv2D(
+ filters,
+ kernel_size,
+ strides=strides,
+ padding=padding,
+ use_bias=use_bias,
+ name="Conv2D",
+ )
+ ]
- if not use_bias:
- bn_axis = 1 if K.image_data_format() == "channels_first" else 3
- bn_name = None if name is None else name + "/BatchNorm"
- layers += [BatchNormalization(axis=bn_axis, scale=False, name=bn_name)]
+ if not use_bias:
+ bn_axis = 1 if K.image_data_format() == "channels_first" else 3
+ layers += [BatchNormalization(axis=bn_axis, scale=False, name="BatchNorm")]
- if activation is not None:
- ac_name = None if name is None else name + "/Act"
- layers += [Activation(activation, name=ac_name)]
+ if activation is not None:
+ layers += [Activation(activation, name="Act")]
- super().__init__(layers, name=name, **kwargs)
+ return SequentialLayer(layers, name=name, **kwargs)
-class ScaledResidual(tf.keras.Model):
+class ScaledResidual(tf.keras.layers.Layer):
"""A scaled residual connection layer"""
+
def __init__(self, scale, name="scaled_residual", **kwargs):
super().__init__(name=name, **kwargs)
self.scale = scale
@@ -105,8 +95,13 @@ class ScaledResidual(tf.keras.Model):
def call(self, inputs, training=None):
return inputs[0] + inputs[1] * self.scale
+ def get_config(self):
+ config = super().get_config()
+ config.update({"scale": self.scale, "name": self.name})
+ return config
-class InceptionResnetBlock(tf.keras.Model):
+
+class InceptionResnetBlock(tf.keras.layers.Layer):
"""An Inception-ResNet block.
This class builds 3 types of Inception-ResNet blocks mentioned
@@ -164,32 +159,24 @@ class InceptionResnetBlock(tf.keras.Model):
self.n = n
if block_type == "block35":
- branch_0 = [Conv2D_BN(32 // n, 1, name="branch0_conv1")]
- branch_1 = [Conv2D_BN(32 // n, 1, name="branch1_conv1")]
- branch_1 += [Conv2D_BN(32 // n, 3, name="branch1_conv2")]
- branch_2 = [Conv2D_BN(32 // n, 1, name="branch2_conv1")]
- branch_2 += [Conv2D_BN(48 // n, 3, name="branch2_conv2")]
- branch_2 += [Conv2D_BN(64 // n, 3, name="branch2_conv3")]
+ branch_0 = [Conv2D_BN(32 // n, 1, name="Branch_0/Conv2d_1x1")]
+ branch_1 = [Conv2D_BN(32 // n, 1, name="Branch_1/Conv2d_0a_1x1")]
+ branch_1 += [Conv2D_BN(32 // n, 3, name="Branch_1/Conv2d_0b_3x3")]
+ branch_2 = [Conv2D_BN(32 // n, 1, name="Branch_2/Conv2d_0a_1x1")]
+ branch_2 += [Conv2D_BN(48 // n, 3, name="Branch_2/Conv2d_0b_3x3")]
+ branch_2 += [Conv2D_BN(64 // n, 3, name="Branch_2/Conv2d_0c_3x3")]
branches = [branch_0, branch_1, branch_2]
elif block_type == "block17":
- branch_0 = [Conv2D_BN(192 // n, 1, name="branch0_conv1")]
- branch_1 = [Conv2D_BN(128 // n, 1, name="branch1_conv1")]
- branch_1 += [
- Conv2D_BN(160 // n, (1, 7), name="branch1_conv2")
- ]
- branch_1 += [
- Conv2D_BN(192 // n, (7, 1), name="branch1_conv3")
- ]
+ branch_0 = [Conv2D_BN(192 // n, 1, name="Branch_0/Conv2d_1x1")]
+ branch_1 = [Conv2D_BN(128 // n, 1, name="Branch_1/Conv2d_0a_1x1")]
+ branch_1 += [Conv2D_BN(160 // n, (1, 7), name="Branch_1/Conv2d_0b_1x7")]
+ branch_1 += [Conv2D_BN(192 // n, (7, 1), name="Branch_1/Conv2d_0c_7x1")]
branches = [branch_0, branch_1]
elif block_type == "block8":
- branch_0 = [Conv2D_BN(192 // n, 1, name="branch0_conv1")]
- branch_1 = [Conv2D_BN(192 // n, 1, name="branch1_conv1")]
- branch_1 += [
- Conv2D_BN(224 // n, (1, 3), name="branch1_conv2")
- ]
- branch_1 += [
- Conv2D_BN(256 // n, (3, 1), name="branch1_conv3")
- ]
+ branch_0 = [Conv2D_BN(192 // n, 1, name="Branch_0/Conv2d_1x1")]
+ branch_1 = [Conv2D_BN(192 // n, 1, name="Branch_1/Conv2d_0a_1x1")]
+ branch_1 += [Conv2D_BN(224 // n, (1, 3), name="Branch_1/Conv2d_0b_1x3")]
+ branch_1 += [Conv2D_BN(256 // n, (3, 1), name="Branch_1/Conv2d_0c_3x1")]
branches = [branch_0, branch_1]
else:
raise ValueError(
@@ -203,18 +190,9 @@ class InceptionResnetBlock(tf.keras.Model):
channel_axis = 1 if K.image_data_format() == "channels_first" else 3
self.concat = Concatenate(axis=channel_axis, name="concatenate")
self.up_conv = Conv2D_BN(
- n_channels, 1, activation=None, use_bias=True, name="up_conv"
+ n_channels, 1, activation=None, use_bias=True, name="Conv2d_1x1"
)
- # output_shape = (None, None, n_channels)
- # if K.image_data_format() == "channels_first":
- # output_shape = (n_channels, None, None)
- # self.residual = Lambda(
- # lambda inputs, scale: inputs[0] + inputs[1] * scale,
- # output_shape=output_shape,
- # arguments={"scale": scale},
- # name="residual_scale",
- # )
self.residual = ScaledResidual(scale)
self.act = lambda x: x
if activation is not None:
@@ -236,8 +214,26 @@ class InceptionResnetBlock(tf.keras.Model):
return x
+ def get_config(self):
+ config = super().get_config()
+ config.update(
+ {
+ name: getattr(self, name)
+ for name in [
+ "n_channels",
+ "scale",
+ "block_type",
+ "block_idx",
+ "activation",
+ "n",
+ "name",
+ ]
+ }
+ )
+ return config
+
-class ReductionA(tf.keras.Model):
+class ReductionA(tf.keras.layers.Layer):
"""A Reduction A block for InceptionResnetV2"""
def __init__(
@@ -265,19 +261,19 @@ class ReductionA(tf.keras.Model):
3,
strides=1 if use_atrous else 2,
padding=padding,
- name="branch1_conv1",
+ name="Branch_0/Conv2d_1a_3x3",
)
]
branch_2 = [
- Conv2D_BN(k, 1, name="branch2_conv1"),
- Conv2D_BN(kl, 3, name="branch2_conv2"),
+ Conv2D_BN(k, 1, name="Branch_1/Conv2d_0a_1x1"),
+ Conv2D_BN(kl, 3, name="Branch_1/Conv2d_0b_3x3"),
Conv2D_BN(
km,
3,
strides=1 if use_atrous else 2,
padding=padding,
- name="branch2_conv3",
+ name="Branch_1/Conv2d_1a_3x3",
),
]
@@ -286,7 +282,7 @@ class ReductionA(tf.keras.Model):
3,
strides=1 if use_atrous else 2,
padding=padding,
- name="branch3_pool1",
+ name="Branch_2/MaxPool_1a_3x3",
)
]
self.branches = [branch_1, branch_2, branch_pool]
@@ -306,8 +302,18 @@ class ReductionA(tf.keras.Model):
return self.concat(branch_outputs)
+ def get_config(self):
+ config = super().get_config()
+ config.update(
+ {
+ name: getattr(self, name)
+ for name in ["padding", "k", "kl", "km", "n", "use_atrous", "name"]
+ }
+ )
+ return config
+
-class ReductionB(tf.keras.Model):
+class ReductionB(tf.keras.layers.Layer):
"""A Reduction B block for InceptionResnetV2"""
def __init__(
@@ -334,31 +340,23 @@ class ReductionB(tf.keras.Model):
self.pq = pq
branch_1 = [
- Conv2D_BN(n, 1, name="branch1_conv1"),
- Conv2D_BN(
- no, 3, strides=2, padding=padding, name="branch1_conv2"
- ),
+ Conv2D_BN(n, 1, name="Branch_0/Conv2d_0a_1x1"),
+ Conv2D_BN(no, 3, strides=2, padding=padding, name="Branch_0/Conv2d_1a_3x3"),
]
branch_2 = [
- Conv2D_BN(p, 1, name="branch2_conv1"),
- Conv2D_BN(
- pq, 3, strides=2, padding=padding, name="branch2_conv2"
- ),
+ Conv2D_BN(p, 1, name="Branch_1/Conv2d_0a_1x1"),
+ Conv2D_BN(pq, 3, strides=2, padding=padding, name="Branch_1/Conv2d_1a_3x3"),
]
branch_3 = [
- Conv2D_BN(k, 1, name="branch3_conv1"),
- Conv2D_BN(kl, 3, name="branch3_conv2"),
- Conv2D_BN(
- km, 3, strides=2, padding=padding, name="branch3_conv3"
- ),
+ Conv2D_BN(k, 1, name="Branch_2/Conv2d_0a_1x1"),
+ Conv2D_BN(kl, 3, name="Branch_2/Conv2d_0b_3x3"),
+ Conv2D_BN(km, 3, strides=2, padding=padding, name="Branch_2/Conv2d_1a_3x3"),
]
branch_pool = [
- MaxPool2D(
- 3, strides=2, padding=padding, name=f"branch4_pool1"
- )
+ MaxPool2D(3, strides=2, padding=padding, name="Branch_3/MaxPool_1a_3x3")
]
self.branches = [branch_1, branch_2, branch_3, branch_pool]
channel_axis = 1 if K.image_data_format() == "channels_first" else 3
@@ -377,23 +375,49 @@ class ReductionB(tf.keras.Model):
return self.concat(branch_outputs)
+ def get_config(self):
+ config = super().get_config()
+ config.update(
+ {
+ name: getattr(self, name)
+ for name in ["padding", "k", "kl", "km", "n", "no", "p", "pq", "name"]
+ }
+ )
+ return config
+
-class InceptionA(tf.keras.Model):
+class InceptionA(tf.keras.layers.Layer):
def __init__(self, pool_filters, name="inception_a", **kwargs):
super().__init__(name=name, **kwargs)
self.pool_filters = pool_filters
- self.branch1x1 = Conv2D_BN(96, kernel_size=1, padding="same", name="branch1_conv1")
+ self.branch1x1 = Conv2D_BN(
+ 96, kernel_size=1, padding="same", name="Branch_0/Conv2d_1x1"
+ )
- self.branch3x3dbl_1 = Conv2D_BN(64, kernel_size=1, padding="same", name="branch2_conv1")
- self.branch3x3dbl_2 = Conv2D_BN(96, kernel_size=3, padding="same", name="branch2_conv2")
- self.branch3x3dbl_3 = Conv2D_BN(96, kernel_size=3, padding="same", name="branch2_conv3")
+ self.branch3x3dbl_1 = Conv2D_BN(
+ 64, kernel_size=1, padding="same", name="Branch_2/Conv2d_0a_1x1"
+ )
+ self.branch3x3dbl_2 = Conv2D_BN(
+ 96, kernel_size=3, padding="same", name="Branch_2/Conv2d_0b_3x3"
+ )
+ self.branch3x3dbl_3 = Conv2D_BN(
+ 96, kernel_size=3, padding="same", name="Branch_2/Conv2d_0c_3x3"
+ )
- self.branch5x5_1 = Conv2D_BN(48, kernel_size=1, padding="same", name="branch3_conv1")
- self.branch5x5_2 = Conv2D_BN(64, kernel_size=5, padding="same", name="branch3_conv2")
+ self.branch5x5_1 = Conv2D_BN(
+ 48, kernel_size=1, padding="same", name="Branch_1/Conv2d_0a_1x1"
+ )
+ self.branch5x5_2 = Conv2D_BN(
+ 64, kernel_size=5, padding="same", name="Branch_1/Conv2d_0b_5x5"
+ )
- self.branch_pool_1 = AvgPool2D(pool_size=3, strides=1, padding="same", name="branch4_pool1")
- self.branch_pool_2 = Conv2D_BN(pool_filters, kernel_size=1, padding="same", name="branch4_conv1")
+ self.branch_pool_1 = AvgPool2D(
+ pool_size=3, strides=1, padding="same", name="Branch_3/AvgPool_0a_3x3"
+ )
+ self.branch_pool_2 = Conv2D_BN(
+ pool_filters, kernel_size=1, padding="same", name="Branch_3/Conv2d_0b_1x1"
+ )
channel_axis = 1 if K.image_data_format() == "channels_first" else 3
self.concat = Concatenate(axis=channel_axis)
@@ -414,6 +438,11 @@ class InceptionA(tf.keras.Model):
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return self.concat(outputs)
+ def get_config(self):
+ config = super().get_config()
+ config.update({"pool_filters": self.pool_filters, "name": self.name})
+ return config
+
def InceptionResNetV2(
include_top=True,
@@ -421,18 +450,17 @@ def InceptionResNetV2(
input_shape=None,
pooling=None,
classes=1000,
+ bottleneck=False,
+ dropout_rate=0.2,
+ name="InceptionResnetV2",
**kwargs,
):
"""Instantiates the Inception-ResNet v2 architecture.
- Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`.
# Arguments
include_top: whether to include the fully-connected
layer at the top of the network.
- weights: one of `None` (random initialization),
- 'imagenet' (pre-training on ImageNet),
- or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of `tf.keras.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
@@ -469,84 +497,118 @@ def InceptionResNetV2(
else:
img_input = input_tensor
- # Stem block: 35 x 35 x 192
- x = Conv2D_BN(32, 3, strides=2, padding="valid")(img_input)
- x = Conv2D_BN(32, 3, padding="valid")(x)
- x = Conv2D_BN(64, 3)(x)
- x = MaxPool2D(3, strides=2)(x)
- x = Conv2D_BN(80, 1, padding="valid")(x)
- x = Conv2D_BN(192, 3, padding="valid")(x)
- x = MaxPool2D(3, strides=2)(x)
-
- # Mixed 5b (Inception-A block): 35 x 35 x 320
- # branch_0 = Conv2D_BN(96, 1)(x)
- # branch_1 = Conv2D_BN(48, 1)(x)
- # branch_1 = Conv2D_BN(64, 5)(branch_1)
- # branch_2 = Conv2D_BN(64, 1)(x)
- # branch_2 = Conv2D_BN(96, 3)(branch_2)
- # branch_2 = Conv2D_BN(96, 3)(branch_2)
- # branch_pool = AvgPool2D(3, strides=1, padding="same")(x)
- # branch_pool = Conv2D_BN(64, 1)(branch_pool)
- # branches = [branch_0, branch_1, branch_2, branch_pool]
- # channel_axis = 1 if K.image_data_format() == "channels_first" else 3
- # x = Concatenate(axis=channel_axis, name="mixed_5b")(branches)
- x = InceptionA(pool_filters=64)(x)
+ layers = [
+ # Stem block: 35 x 35 x 192
+ Conv2D_BN(32, 3, strides=2, padding="valid", name="Conv2d_1a_3x3"),
+ Conv2D_BN(32, 3, padding="valid", name="Conv2d_2a_3x3"),
+ Conv2D_BN(64, 3, name="Conv2d_2b_3x3"),
+ MaxPool2D(3, strides=2, name="MaxPool_3a_3x3"),
+ Conv2D_BN(80, 1, padding="valid", name="Conv2d_3b_1x1"),
+ Conv2D_BN(192, 3, padding="valid", name="Conv2d_4a_3x3"),
+ MaxPool2D(3, strides=2, name="MaxPool_5a_3x3"),
+ # Mixed 5b (Inception-A block): 35 x 35 x 320
+ InceptionA(pool_filters=64, name="Mixed_5b"),
+ ]
# 10x block35 (Inception-ResNet-A block): 35 x 35 x 320
for block_idx in range(1, 11):
- x = InceptionResnetBlock(
- n_channels=320, scale=0.17, block_type="block35", block_idx=block_idx,
- name=f"block35_{block_idx}",
- )(x)
+ layers.append(
+ InceptionResnetBlock(
+ n_channels=320,
+ scale=0.17,
+ block_type="block35",
+ block_idx=block_idx,
+ name=f"block35_{block_idx}",
+ )
+ )
# Mixed 6a (Reduction-A block): 17 x 17 x 1088
- x = ReductionA(padding="valid", n=384, k=256, kl=256, km=384, use_atrous=False)(x)
+ layers.append(
+ ReductionA(
+ padding="valid",
+ n=384,
+ k=256,
+ kl=256,
+ km=384,
+ use_atrous=False,
+ name="Mixed_6a",
+ )
+ )
# 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088
for block_idx in range(1, 21):
- x = InceptionResnetBlock(
- n_channels=1088, scale=0.1, block_type="block17", block_idx=block_idx,
- name=f"block17_{block_idx}",
- )(x)
+ layers.append(
+ InceptionResnetBlock(
+ n_channels=1088,
+ scale=0.1,
+ block_type="block17",
+ block_idx=block_idx,
+ name=f"block17_{block_idx}",
+ )
+ )
# Mixed 7a (Reduction-B block): 8 x 8 x 2080
- x = ReductionB(
- padding="valid", n=256, no=384, p=256, pq=288, k=256, kl=288, km=320
- )(x)
+ layers.append(
+ ReductionB(
+ padding="valid",
+ n=256,
+ no=384,
+ p=256,
+ pq=288,
+ k=256,
+ kl=288,
+ km=320,
+ name="Mixed_7a",
+ )
+ )
# 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080
for block_idx in range(1, 10):
- x = InceptionResnetBlock(
- n_channels=2080, scale=0.2, block_type="block8", block_idx=block_idx,
- name=f"block8_{block_idx}",
- )(x)
- x = InceptionResnetBlock(
- n_channels=2080, scale=1.0, activation=None, block_type="block8", block_idx=10,
- name=f"block8_{block_idx+1}",
- )(x)
+ layers.append(
+ InceptionResnetBlock(
+ n_channels=2080,
+ scale=0.2,
+ block_type="block8",
+ block_idx=block_idx,
+ name=f"block8_{block_idx}",
+ )
+ )
+ layers.append(
+ InceptionResnetBlock(
+ n_channels=2080,
+ scale=1.0,
+ activation=None,
+ block_type="block8",
+ block_idx=10,
+ name=f"block8_{block_idx+1}",
+ )
+ )
# Final convolution block: 8 x 8 x 1536
- x = Conv2D_BN(1536, 1, name="conv_7b")(x)
+ layers.append(Conv2D_BN(1536, 1, name="Conv2d_7b_1x1"))
- if include_top:
- # Classification block
- x = GlobalAvgPool2D(name="avg_pool")(x)
- x = Dense(classes, activation="softmax", name="predictions")(x)
- else:
- if pooling == "avg":
- x = GlobalAvgPool2D()(x)
- elif pooling == "max":
- x = GlobalMaxPool2D()(x)
+ if (include_top and pooling is None) or (bottleneck):
+ pooling = "avg"
- # Ensure that the model takes into account
- # any potential predecessors of `input_tensor`.
- if input_tensor is not None:
- inputs = tf.keras.utils.get_source_inputs(input_tensor)
- else:
- inputs = img_input
+ if pooling == "avg":
+ layers.append(GlobalAvgPool2D())
+ elif pooling == "max":
+ layers.append(GlobalMaxPool2D())
+
+ if bottleneck:
+ layers.append(Dropout(dropout_rate, name="Dropout"))
+ layers.append(Dense(128, use_bias=False, name="Bottleneck"))
+ layers.append(
+ BatchNormalization(axis=-1, scale=False, name="Bottleneck/BatchNorm")
+ )
+
+ # Classification block
+ if include_top:
+ layers.append(Dense(classes, name="logits"))
- # Create model.
- model = Model(inputs, x, name="inception_resnet_v2")
+ # Create model and call it on input to create its variables.
+ model = Sequential(layers, name=name, **kwargs)
+ model(img_input)
return model
@@ -574,7 +636,7 @@ def MultiScaleInceptionResNetV2(
padding = "SAME" if align_feature_maps else "VALID"
name = name or "InceptionResnetV2"
- with tf.name_scope(name, "InceptionResnetV2", [img_input]):
+ with tf.compat.v1.name_scope(name, "InceptionResnetV2", [img_input]):
# convert colors from RGB to a learned color space and batch norm inputs
# 224, 224, 4
net = Conv2D_BN(
@@ -683,8 +745,8 @@ def MultiScaleInceptionResNetV2(
if __name__ == "__main__":
import pkg_resources
- from tabulate import tabulate
from bob.learn.tensorflow.utils import model_summary
+ from tabulate import tabulate
def print_model(inputs, outputs, name=None):
print("")
diff --git a/bob/learn/tensorflow/models/lenet5.py b/bob/learn/tensorflow/models/lenet5.py
index a868515b897fe2c010cf407b1af44d5fbe352649..425b337725b60b6dc471c572c80df1f83eded74e 100644
--- a/bob/learn/tensorflow/models/lenet5.py
+++ b/bob/learn/tensorflow/models/lenet5.py
@@ -22,7 +22,7 @@ def LeNet5_simplified(name="LeNet5", **kwargs):
),
tf.keras.layers.Flatten(name="FLATTEN"),
tf.keras.layers.Dense(units=84, activation="tanh", name="F6"),
- tf.keras.layers.Dense(units=10, activation="sigmoid", name="OUTPUT"),
+ tf.keras.layers.Dense(units=10, name="OUTPUT"),
],
name=name,
**kwargs
@@ -32,6 +32,7 @@ def LeNet5_simplified(name="LeNet5", **kwargs):
if __name__ == "__main__":
import pkg_resources
+
from bob.learn.tensorflow.utils import model_summary
model = LeNet5_simplified()
diff --git a/bob/learn/tensorflow/models/mcae.py b/bob/learn/tensorflow/models/mcae.py
index 07cbb529be878551cfd2efcf2e438f802c52557f..214307820df190b1eb6af376ad7af4a1654d9d2e 100644
--- a/bob/learn/tensorflow/models/mcae.py
+++ b/bob/learn/tensorflow/models/mcae.py
@@ -51,8 +51,8 @@ class ConvEncoder(tf.keras.Model):
self.sequential_layers = layers
def call(self, x, training=None):
- for l in self.sequential_layers:
- x = l(x)
+ for layer in self.sequential_layers:
+ x = layer(x)
return x
@@ -67,7 +67,7 @@ class ConvDecoder(tf.keras.Model):
name="Decoder",
**kwargs,
):
- super().__init__(name=name, ** kwargs)
+ super().__init__(name=name, **kwargs)
self.data_format = data_format
l2_kw = get_l2_kw(weight_decay)
layers = []
@@ -91,8 +91,8 @@ class ConvDecoder(tf.keras.Model):
self.sequential_layers = layers
def call(self, x, training=None):
- for l in self.sequential_layers:
- x = l(x)
+ for layer in self.sequential_layers:
+ x = layer(x)
return x
diff --git a/bob/learn/tensorflow/models/mine.py b/bob/learn/tensorflow/models/mine.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f766236f0cf33f6b1d73ffd14a2eafec636b1ad
--- /dev/null
+++ b/bob/learn/tensorflow/models/mine.py
@@ -0,0 +1,68 @@
+"""
+Implements the MINE loss from the paper:
+
+Mutual Information Neural Estimation (https://arxiv.org/pdf/1801.04062.pdf)
+
+"""
+
+import tensorflow as tf
+
+class MineModel(tf.keras.Model):
+ """
+
+ Parameters
+ **********
+
+ is_mine_f: bool
+ If true, will implement MINE-F (equation 6), otherwise will implement equation 5
+ """
+
+ def __init__(self, is_mine_f=False, name="MINE", units=10, **kwargs):
+ super().__init__(name=name, **kwargs)
+ self.units = units
+ self.is_mine_f = is_mine_f
+
+ self.transformer_x = tf.keras.layers.Dense(self.units)
+ self.transformer_z = tf.keras.layers.Dense(self.units)
+ self.transformer_xz = tf.keras.layers.Dense(self.units)
+ self.transformer_output = tf.keras.layers.Dense(1)
+
+ def call(self, inputs):
+ def compute(x, z):
+ h1_x = self.transformer_x(x)
+ h1_z = self.transformer_z(z)
+ h1 = tf.keras.layers.ReLU()(h1_x + h1_z)
+ h2 = self.transformer_output(tf.keras.layers.ReLU()(self.transformer_xz(h1)))
+
+ return h2
+
+ def compute_lower_bound(x, z):
+ t_xz = compute(x,z)
+ z_shuffle = tf.random.shuffle(z)
+ t_x_z = compute(x, z_shuffle)
+
+ if self.is_mine_f:
+ lb = -(
+ tf.reduce_mean(t_xz, axis=0)
+ - tf.reduce_mean(tf.math.exp(t_x_z-1))
+ )
+ else:
+ lb = -(
+ tf.reduce_mean(t_xz, axis=0)
+ - tf.math.log(tf.reduce_mean(tf.math.exp(t_x_z)))
+ )
+
+ self.add_loss(lb)
+ return -lb
+
+ x = inputs[0]
+ z = inputs[1]
+
+ return compute_lower_bound(x, z)
+
+
+ def get_config(self):
+ config = super().get_config()
+ config.update({"units": self.units})
+ return config
+
diff --git a/bob/learn/tensorflow/models/mlp.py b/bob/learn/tensorflow/models/mlp.py
index 3804c4e3222b2b0616e59f243bf8498f4f8f151f..71076818853e976063e64be108a3f27e3bd1a3d8 100644
--- a/bob/learn/tensorflow/models/mlp.py
+++ b/bob/learn/tensorflow/models/mlp.py
@@ -22,7 +22,9 @@ class MLP(tf.keras.Model):
for i, n in enumerate(hidden_layers, start=1):
sequential_layers.extend(
[
- tf.keras.layers.Dense(n, use_bias=False, name=f"dense_{i}", **dense_kw),
+ tf.keras.layers.Dense(
+ n, use_bias=False, name=f"dense_{i}", **dense_kw
+ ),
tf.keras.layers.BatchNormalization(scale=False, name=f"bn_{i}"),
tf.keras.layers.Activation("relu", name=f"relu_{i}"),
]
@@ -77,7 +79,9 @@ class MLPDropout(tf.keras.Model):
for i, n in enumerate(hidden_layers, start=1):
sequential_layers.extend(
[
- tf.keras.layers.Dense(n, use_bias=False, name=f"dense_{i}", **dense_kw),
+ tf.keras.layers.Dense(
+ n, use_bias=False, name=f"dense_{i}", **dense_kw
+ ),
tf.keras.layers.Activation("relu", name=f"relu_{i}"),
tf.keras.layers.Dropout(rate=drop_rate, name=f"drop_{i}"),
]
diff --git a/bob/learn/tensorflow/models/msu_patch.py b/bob/learn/tensorflow/models/msu_patch.py
index af1ad1da9a287d3cd55b2e616385a0145acc50d5..21ad7bf98ca0adc7bf2efab14177ff82b2c3531f 100644
--- a/bob/learn/tensorflow/models/msu_patch.py
+++ b/bob/learn/tensorflow/models/msu_patch.py
@@ -13,24 +13,39 @@ def MSUPatch(name="MSUPatch", **kwargs):
return tf.keras.Sequential(
[
tf.keras.layers.Conv2D(
- 50, (5, 5), padding="same", use_bias=False, name="Conv-1", input_shape=(96, 96, 3)
+ 50,
+ (5, 5),
+ padding="same",
+ use_bias=False,
+ name="Conv-1",
+ input_shape=(96, 96, 3),
),
tf.keras.layers.BatchNormalization(scale=False, name="BN-1"),
tf.keras.layers.Activation("relu", name="ReLU-1"),
tf.keras.layers.MaxPool2D(padding="same", name="MaxPool-1"),
- tf.keras.layers.Conv2D(100, (3, 3), padding="same", use_bias=False, name="Conv-2"),
+ tf.keras.layers.Conv2D(
+ 100, (3, 3), padding="same", use_bias=False, name="Conv-2"
+ ),
tf.keras.layers.BatchNormalization(scale=False, name="BN-2"),
tf.keras.layers.Activation("relu", name="ReLU-2"),
tf.keras.layers.MaxPool2D(padding="same", name="MaxPool-2"),
- tf.keras.layers.Conv2D(150, (3, 3), padding="same", use_bias=False, name="Conv-3"),
+ tf.keras.layers.Conv2D(
+ 150, (3, 3), padding="same", use_bias=False, name="Conv-3"
+ ),
tf.keras.layers.BatchNormalization(scale=False, name="BN-3"),
tf.keras.layers.Activation("relu", name="ReLU-3"),
- tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding="same", name="MaxPool-3"),
- tf.keras.layers.Conv2D(200, (3, 3), padding="same", use_bias=False, name="Conv-4"),
+ tf.keras.layers.MaxPool2D(
+ pool_size=3, strides=2, padding="same", name="MaxPool-3"
+ ),
+ tf.keras.layers.Conv2D(
+ 200, (3, 3), padding="same", use_bias=False, name="Conv-4"
+ ),
tf.keras.layers.BatchNormalization(scale=False, name="BN-4"),
tf.keras.layers.Activation("relu", name="ReLU-4"),
tf.keras.layers.MaxPool2D(padding="same", name="MaxPool-4"),
- tf.keras.layers.Conv2D(250, (3, 3), padding="same", use_bias=False, name="Conv-5"),
+ tf.keras.layers.Conv2D(
+ 250, (3, 3), padding="same", use_bias=False, name="Conv-5"
+ ),
tf.keras.layers.BatchNormalization(scale=False, name="BN-5"),
tf.keras.layers.Activation("relu", name="ReLU-5"),
tf.keras.layers.MaxPool2D(padding="same", name="MaxPool-5"),
@@ -52,6 +67,7 @@ def MSUPatch(name="MSUPatch", **kwargs):
if __name__ == "__main__":
import pkg_resources
from tabulate import tabulate
+
from bob.learn.tensorflow.utils import model_summary
model = MSUPatch()
diff --git a/bob/learn/tensorflow/models/simple_cnn.py b/bob/learn/tensorflow/models/simple_cnn.py
index 82ae65a68e12325d727a6ae9e2e024d12ca7eba7..f4c4ec56f71dbaecec83b0ddf23a0010fb62539a 100644
--- a/bob/learn/tensorflow/models/simple_cnn.py
+++ b/bob/learn/tensorflow/models/simple_cnn.py
@@ -1,15 +1,14 @@
"""A small CNN used for patch-based Face PAD"""
-from tensorflow.python.keras import Sequential, Input
-from tensorflow.python.keras.layers import (
- Conv2D,
- BatchNormalization,
- Activation,
- MaxPool2D,
- Flatten,
- Dense,
- Dropout,
-)
+from tensorflow.python.keras import Input
+from tensorflow.python.keras import Sequential
+from tensorflow.python.keras.layers import Activation
+from tensorflow.python.keras.layers import BatchNormalization
+from tensorflow.python.keras.layers import Conv2D
+from tensorflow.python.keras.layers import Dense
+from tensorflow.python.keras.layers import Dropout
+from tensorflow.python.keras.layers import Flatten
+from tensorflow.python.keras.layers import MaxPool2D
def SimpleCNN(input_shape=(28, 28, 3), inputs=None, name="SimpleCNN", **kwargs):
@@ -44,6 +43,7 @@ def SimpleCNN(input_shape=(28, 28, 3), inputs=None, name="SimpleCNN", **kwargs):
if __name__ == "__main__":
import pkg_resources
from tabulate import tabulate
+
from bob.learn.tensorflow.utils import model_summary
model = SimpleCNN()
diff --git a/bob/learn/tensorflow/network/Chopra.py b/bob/learn/tensorflow/network/Chopra.py
deleted file mode 100644
index a3242e40b3a925cdd5a4a4a228beb1c4726003d6..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/Chopra.py
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-
-
-def chopra(
- inputs,
- conv1_kernel_size=[7, 7],
- conv1_output=15,
- pooling1_size=[2, 2],
- conv2_kernel_size=[6, 6],
- conv2_output=45,
- pooling2_size=[4, 3],
- fc1_output=250,
- seed=10,
- reuse=False,
-):
- """Class that creates the architecture presented in the paper:
-
- Chopra, Sumit, Raia Hadsell, and Yann LeCun. "Learning a similarity metric discriminatively, with application to
- face verification." 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Vol. 1. IEEE, 2005.
-
- This is modifield version of the original architecture.
- It is inspired on https://gitlab.idiap.ch/bob/xfacereclib.cnn/blob/master/lua/network.lua
-
- -- C1 : Convolutional, kernel = 7x7 pixels, 15 feature maps
-
- -- M2 : MaxPooling, 2x2
-
- -- HT : Hard Hyperbolic Tangent
-
- -- C3 : Convolutional, kernel = 6x6 pixels, 45 feature maps
-
- -- M4 : MaxPooling, 4x3
-
- -- HT : Hard Hyperbolic Tangent
-
- -- R : Reshaping layer HT 5x5 => 25 (45 times; once for each feature map)
-
- -- L5 : Linear 25 => 250
-
-
- **Parameters**
-
- conv1_kernel_size:
-
- conv1_output:
-
- pooling1_size:
-
- conv2_kernel_size:
-
- conv2_output:
-
- pooling2_size
-
- fc1_output:
-
- seed:
- """
- slim = tf.contrib.slim
-
- end_points = dict()
-
- initializer = tf.contrib.layers.xavier_initializer(
- uniform=False, dtype=tf.float32, seed=seed)
-
- graph = slim.conv2d(
- inputs,
- conv1_output,
- conv1_kernel_size,
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope='conv1',
- reuse=reuse)
- end_points['conv1'] = graph
-
- graph = slim.max_pool2d(graph, pooling1_size, scope='pool1')
- end_points['pool1'] = graph
-
- graph = slim.conv2d(
- graph,
- conv2_output,
- conv2_kernel_size,
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope='conv2',
- reuse=reuse)
- end_points['conv2'] = graph
- graph = slim.max_pool2d(graph, pooling2_size, scope='pool2')
- end_points['pool2'] = graph
-
- graph = slim.flatten(graph, scope='flatten1')
- end_points['flatten1'] = graph
-
- graph = slim.fully_connected(
- graph,
- fc1_output,
- weights_initializer=initializer,
- activation_fn=None,
- scope='fc1',
- reuse=reuse)
- end_points['fc1'] = graph
-
- return graph, end_points
diff --git a/bob/learn/tensorflow/network/Dummy.py b/bob/learn/tensorflow/network/Dummy.py
deleted file mode 100644
index 917f30863a6fffafe6b590b44bdc8d4a785da587..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/Dummy.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from .utils import is_trainable
-
-
-def dummy(inputs,
- reuse=False,
- mode=tf.estimator.ModeKeys.TRAIN,
- trainable_variables=None,
- **kwargs):
- """
- Create all the necessary variables for this CNN
-
- Parameters
- ----------
- inputs:
-
- reuse:
-
- mode:
-
- trainable_variables:
-
- """
-
- slim = tf.contrib.slim
- end_points = dict()
-
- # Here is my choice to shutdown the whole scope
- trainable = is_trainable("Dummy", trainable_variables)
- with tf.variable_scope('Dummy', reuse=reuse):
-
- initializer = tf.contrib.layers.xavier_initializer()
- name = 'conv1'
- graph = slim.conv2d(
- inputs,
- 10, [3, 3],
- activation_fn=tf.nn.relu,
- stride=1,
- scope=name,
- weights_initializer=initializer,
- trainable=trainable)
- end_points[name] = graph
-
- graph = slim.max_pool2d(graph, [4, 4], scope='pool1')
- end_points['pool1'] = graph
-
- graph = slim.flatten(graph, scope='flatten1')
- end_points['flatten1'] = graph
-
- name = 'fc1'
- graph = slim.fully_connected(
- graph,
- 50,
- weights_initializer=initializer,
- activation_fn=None,
- scope=name,
- trainable=trainable)
- end_points[name] = graph
-
- return graph, end_points
diff --git a/bob/learn/tensorflow/network/InceptionResnetV1.py b/bob/learn/tensorflow/network/InceptionResnetV1.py
deleted file mode 100644
index 4d5cfda1492c858d6e8948d6ede7e2b7994378f9..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/InceptionResnetV1.py
+++ /dev/null
@@ -1,674 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Contains the definition of the Inception Resnet V1 architecture.
-As described in http://arxiv.org/abs/1602.07261.
- Inception-v4, Inception-ResNet and the Impact of Residual Connections
- on Learning
- Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import tensorflow.contrib.slim as slim
-from .utils import is_trainable
-
-
-# Inception-Renset-A
-def block35(net,
- scale=1.0,
- activation_fn=tf.nn.relu,
- scope=None,
- reuse=None,
- trainable_variables=True):
- """Builds the 35x35 resnet block."""
- with tf.variable_scope(scope, 'Block35', [net]):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net, 32, 1, scope='Conv2d_1x1',
- reuse=reuse,
- trainable=trainable_variables)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net,
- 32,
- 1,
- scope='Conv2d_0a_1x1',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- 32,
- 3,
- scope='Conv2d_0b_3x3',
- reuse=reuse,
- trainable=trainable_variables)
- with tf.variable_scope('Branch_2'):
- tower_conv2_0 = slim.conv2d(
- net,
- 32,
- 1,
- scope='Conv2d_0a_1x1',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv2_1 = slim.conv2d(
- tower_conv2_0,
- 32,
- 3,
- scope='Conv2d_0b_3x3',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv2_2 = slim.conv2d(
- tower_conv2_1,
- 32,
- 3,
- scope='Conv2d_0c_3x3',
- reuse=reuse,
- trainable=trainable_variables)
- mixed = tf.concat([tower_conv, tower_conv1_1, tower_conv2_2], 3)
- up = slim.conv2d(
- mixed,
- net.get_shape()[3],
- 1,
- normalizer_fn=None,
- activation_fn=None,
- scope='Conv2d_1x1',
- reuse=reuse,
- trainable=trainable_variables)
- net += scale * up
- if activation_fn:
- net = activation_fn(net)
- return net
-
-
-# Inception-Renset-B
-def block17(net,
- scale=1.0,
- activation_fn=tf.nn.relu,
- scope=None,
- reuse=None,
- trainable_variables=True):
- """Builds the 17x17 resnet block."""
- with tf.variable_scope(scope, 'Block17', [net]):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net, 128, 1, scope='Conv2d_1x1', trainable=trainable_variables, reuse=reuse)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net,
- 128,
- 1,
- scope='Conv2d_0a_1x1',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- 128, [1, 7],
- scope='Conv2d_0b_1x7',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv1_2 = slim.conv2d(
- tower_conv1_1,
- 128, [7, 1],
- scope='Conv2d_0c_7x1',
- reuse=reuse,
- trainable=trainable_variables)
- mixed = tf.concat([tower_conv, tower_conv1_2], 3)
- up = slim.conv2d(
- mixed,
- net.get_shape()[3],
- 1,
- normalizer_fn=None,
- activation_fn=None,
- scope='Conv2d_1x1',
- reuse=reuse,
- trainable=trainable_variables)
- net += scale * up
- if activation_fn:
- net = activation_fn(net)
- return net
-
-
-# Inception-Resnet-C
-def block8(net,
- scale=1.0,
- activation_fn=tf.nn.relu,
- scope=None,
- reuse=None,
- trainable_variables=True):
- """Builds the 8x8 resnet block."""
- with tf.variable_scope(scope, 'Block8', [net]):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net, 192, 1, scope='Conv2d_1x1', trainable=trainable_variables,reuse=reuse)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net,
- 192,
- 1,
- scope='Conv2d_0a_1x1',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- 192, [1, 3],
- scope='Conv2d_0b_1x3',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv1_2 = slim.conv2d(
- tower_conv1_1,
- 192, [3, 1],
- scope='Conv2d_0c_3x1',
- reuse=reuse,
- trainable=trainable_variables)
- mixed = tf.concat([tower_conv, tower_conv1_2], 3)
- up = slim.conv2d(
- mixed,
- net.get_shape()[3],
- 1,
- normalizer_fn=None,
- activation_fn=None,
- scope='Conv2d_1x1',
- reuse=reuse,
- trainable=trainable_variables)
- net += scale * up
- if activation_fn:
- net = activation_fn(net)
- return net
-
-
-def reduction_a(net, k, l, m, n, trainable_variables=True, reuse=None):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net,
- n,
- 3,
- stride=2,
- padding='VALID',
- scope='Conv2d_1a_3x3',
- reuse=reuse,
- trainable=trainable_variables)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net, k, 1, scope='Conv2d_0a_1x1', trainable=trainable_variables, reuse=reuse)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- l,
- 3,
- scope='Conv2d_0b_3x3',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv1_2 = slim.conv2d(
- tower_conv1_1,
- m,
- 3,
- stride=2,
- padding='VALID',
- reuse=reuse,
- scope='Conv2d_1a_3x3',
- trainable=trainable_variables)
- with tf.variable_scope('Branch_2'):
- tower_pool = slim.max_pool2d(
- net, 3, stride=2, padding='VALID', scope='MaxPool_1a_3x3')
- net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
- return net
-
-
-def reduction_b(net, trainable_variables=True, reuse=None):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net, 256, 1, scope='Conv2d_0a_1x1', trainable=trainable_variables, reuse=reuse)
- tower_conv_1 = slim.conv2d(
- tower_conv,
- 384,
- 3,
- stride=2,
- padding='VALID',
- reuse=reuse,
- scope='Conv2d_1a_3x3',
- trainable=trainable_variables)
- with tf.variable_scope('Branch_1'):
- tower_conv1 = slim.conv2d(
- net, 256, 1, scope='Conv2d_0a_1x1', trainable=trainable_variables, reuse=reuse)
- tower_conv1_1 = slim.conv2d(
- tower_conv1,
- 256,
- 3,
- stride=2,
- padding='VALID',
- scope='Conv2d_1a_3x3',
- reuse=reuse,
- trainable=trainable_variables)
- with tf.variable_scope('Branch_2'):
- tower_conv2 = slim.conv2d(
- net, 256, 1, scope='Conv2d_0a_1x1', trainable=trainable_variables, reuse=reuse)
- tower_conv2_1 = slim.conv2d(
- tower_conv2,
- 256,
- 3,
- scope='Conv2d_0b_3x3',
- reuse=reuse,
- trainable=trainable_variables)
- tower_conv2_2 = slim.conv2d(
- tower_conv2_1,
- 256,
- 3,
- stride=2,
- padding='VALID',
- scope='Conv2d_1a_3x3',
- reuse=reuse,
- trainable=trainable_variables)
- with tf.variable_scope('Branch_3'):
- tower_pool = slim.max_pool2d(
- net, 3, stride=2, padding='VALID', scope='MaxPool_1a_3x3')
- net = tf.concat([tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool],
- 3)
- return net
-
-
-def inception_resnet_v1_batch_norm(inputs,
- dropout_keep_prob=0.8,
- bottleneck_layer_size=128,
- reuse=None,
- scope='InceptionResnetV1',
- mode=tf.estimator.ModeKeys.TRAIN,
- trainable_variables=None,
- weight_decay=1e-5,
- **kwargs):
- """
- Creates the Inception Resnet V1 model applying batch not to each
- Convolutional and FullyConnected layer.
-
- Parameters
- ----------
-
- inputs:
- 4-D tensor of size [batch_size, height, width, 3].
-
- num_classes:
- number of predicted classes.
-
- is_training:
- whether is training or not.
-
- dropout_keep_prob: float
- the fraction to keep before final layer.
-
- reuse:
- whether or not the network and its variables should be reused. To be
- able to reuse 'scope' must be given.
-
- scope:
- Optional variable_scope.
-
- trainable_variables: :any:`list`
- List of variables to be trainable=True
-
- Returns
- -------
- logits:
- the logits outputs of the model.
-
- end_points:
- the set of end_points from the inception model.
-
- """
-
- batch_norm_params = {
- # Decay for the moving averages.
- 'decay': 0.995,
- # epsilon to prevent 0s in variance.
- 'epsilon': 0.001,
- # force in-place updates of mean and variance estimates
- 'updates_collections': None,
- }
-
- with slim.arg_scope(
- [slim.conv2d, slim.fully_connected],
- weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
- weights_regularizer=slim.l2_regularizer(weight_decay),
- normalizer_fn=slim.batch_norm,
- normalizer_params=batch_norm_params):
- return inception_resnet_v1(
- inputs,
- dropout_keep_prob=dropout_keep_prob,
- bottleneck_layer_size=bottleneck_layer_size,
- reuse=reuse,
- scope=scope,
- mode=mode,
- trainable_variables=trainable_variables,
- )
-
-
-def inception_resnet_v1(inputs,
- dropout_keep_prob=0.8,
- bottleneck_layer_size=128,
- reuse=None,
- scope='InceptionResnetV1',
- mode=tf.estimator.ModeKeys.TRAIN,
- trainable_variables=None,
- **kwargs):
- """
- Creates the Inception Resnet V1 model.
-
- Parameters
- ----------
-
- inputs:
- 4-D tensor of size [batch_size, height, width, 3].
-
- num_classes:
- number of predicted classes.
-
- is_training:
- whether is training or not.
-
- dropout_keep_prob: float
- the fraction to keep before final layer.
-
- reuse:
- whether or not the network and its variables should be reused. To be
- able to reuse 'scope' must be given.
-
- scope:
- Optional variable_scope.
-
- trainable_variables: :any:`list`
- List of variables to be trainable=True
-
- Returns
- -------
- logits:
- the logits outputs of the model.
-
- end_points:
- the set of end_points from the inception model.
-
- """
- end_points = {}
-
- with tf.variable_scope(scope, 'InceptionResnetV1', [inputs], reuse=reuse):
- with slim.arg_scope(
- [slim.dropout],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN)):
-
- with slim.arg_scope(
- [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
- stride=1,
- padding='SAME'):
-
- # 149 x 149 x 32
- name = "Conv2d_1a_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Conv2d_1a_3x3"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.conv2d(
- inputs,
- 32,
- 3,
- stride=2,
- padding='VALID',
- reuse=reuse,
- scope=name,
- trainable=trainable)
- end_points[name] = net
-
- # 147 x 147 x 32
- name = "Conv2d_2a_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
- name = "Conv2d_2a_3x3"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net,
- 32,
- 3,
- padding='VALID',
- scope=name,
- reuse=reuse,
- trainable=trainable)
- end_points[name] = net
-
- # 147 x 147 x 64
- name = "Conv2d_2b_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Conv2d_2b_3x3"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net, 64, 3, scope=name, trainable=trainable, reuse=reuse)
- end_points[name] = net
-
- # 73 x 73 x 64
- net = slim.max_pool2d(
- net, 3, stride=2, padding='VALID', scope='MaxPool_3a_3x3')
- end_points['MaxPool_3a_3x3'] = net
-
- # 73 x 73 x 80
- name = "Conv2d_3b_1x1_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Conv2d_3b_1x1"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net,
- 80,
- 1,
- padding='VALID',
- scope=name,
- reuse=reuse,
- trainable=trainable)
- end_points[name] = net
-
- # 71 x 71 x 192
- name = "Conv2d_4a_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Conv2d_4a_3x3"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net,
- 192,
- 3,
- padding='VALID',
- scope=name,
- reuse=reuse,
- trainable=trainable)
- end_points[name] = net
-
- # 35 x 35 x 256
- name = "Conv2d_4b_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Conv2d_4b_3x3"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net,
- 256,
- 3,
- stride=2,
- padding='VALID',
- scope=name,
- reuse=reuse,
- trainable=trainable)
- end_points[name] = net
-
- # 5 x Inception-resnet-A
- name = "block35_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "block35"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.repeat(
- net,
- 5,
- block35,
- scale=0.17,
- reuse=reuse,
- trainable_variables=trainable)
- end_points[name] = net
-
- # Reduction-A
- name = "Mixed_6a_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Mixed_6a"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with tf.variable_scope(name):
- net = reduction_a(
- net,
- 192,
- 192,
- 256,
- 384,
- trainable_variables=trainable)
- end_points[name] = net
-
- # 10 x Inception-Resnet-B
- name = "block17_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "block17"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.repeat(
- net,
- 10,
- block17,
- scale=0.10,
- trainable_variables=trainable)
- end_points[name] = net
-
- # Reduction-B
- name = "Mixed_7a_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Mixed_7a"
- trainable = is_trainable(name, trainable_variables, mode=mode)
-
- with tf.variable_scope(name):
- net = reduction_b(
- net, trainable_variables=trainable, reuse=reuse)
- end_points[name] = net
-
- # 5 x Inception-Resnet-C
- name = "block8_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "block8"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.repeat(
- net,
- 5,
- block8,
- scale=0.20,
- trainable_variables=trainable)
- end_points[name] = net
-
- name = "Mixed_8b_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Mixed_8b"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = block8(
- net,
- activation_fn=None,
- trainable_variables=trainable)
- end_points[name] = net
-
- with tf.variable_scope('Logits'):
- end_points['PrePool'] = net
- #pylint: disable=no-member
- net = slim.avg_pool2d(
- net,
- net.get_shape()[1:3],
- padding='VALID',
- scope='AvgPool_1a_8x8')
- net = slim.flatten(net)
-
- net = slim.dropout(
- net,
- dropout_keep_prob,
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- scope='Dropout')
-
- end_points['PreLogitsFlatten'] = net
-
- name = "Bottleneck_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- trainable=trainable):
-
- name = "Bottleneck"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = slim.fully_connected(
- net,
- bottleneck_layer_size,
- activation_fn=None,
- scope=name,
- trainable=trainable)
- end_points[name] = net
-
- return net, end_points
diff --git a/bob/learn/tensorflow/network/InceptionResnetV2.py b/bob/learn/tensorflow/network/InceptionResnetV2.py
deleted file mode 100644
index 3daf573feee3d6c210e642a92c5461acc69e6c32..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/InceptionResnetV2.py
+++ /dev/null
@@ -1,763 +0,0 @@
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Contains the definition of the Inception Resnet V2 architecture.
-As described in http://arxiv.org/abs/1602.07261.
- Inception-v4, Inception-ResNet and the Impact of Residual Connections
- on Learning
- Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-import tensorflow.contrib.slim as slim
-from .utils import is_trainable
-
-
-# Inception-Renset-A
-def block35(net,
- scale=1.0,
- activation_fn=tf.nn.relu,
- scope=None,
- reuse=None,
- trainable_variables=True):
- """Builds the 35x35 resnet block."""
- with tf.variable_scope(scope, 'Block35', [net]):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net,
- 32,
- 1,
- scope='Conv2d_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net,
- 32,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- 32,
- 3,
- scope='Conv2d_0b_3x3',
- trainable=trainable_variables,
- reuse=reuse)
- with tf.variable_scope('Branch_2'):
- tower_conv2_0 = slim.conv2d(
- net,
- 32,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- tower_conv2_1 = slim.conv2d(
- tower_conv2_0,
- 48,
- 3,
- scope='Conv2d_0b_3x3',
- trainable=trainable_variables,
- reuse=reuse)
- tower_conv2_2 = slim.conv2d(
- tower_conv2_1,
- 64,
- 3,
- scope='Conv2d_0c_3x3',
- trainable=trainable_variables,
- reuse=reuse)
- mixed = tf.concat([tower_conv, tower_conv1_1, tower_conv2_2], 3)
- up = slim.conv2d(
- mixed,
- net.get_shape()[3],
- 1,
- normalizer_fn=None,
- activation_fn=None,
- scope='Conv2d_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- net += scale * up
- if activation_fn:
- net = activation_fn(net)
- return net
-
-
-# Inception-Renset-B
-def block17(net,
- scale=1.0,
- activation_fn=tf.nn.relu,
- scope=None,
- reuse=None,
- trainable_variables=True):
- """Builds the 17x17 resnet block."""
- with tf.variable_scope(scope, 'Block17', [net]):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net,
- 192,
- 1,
- scope='Conv2d_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net,
- 128,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- 160, [1, 7],
- scope='Conv2d_0b_1x7',
- trainable=trainable_variables,
- reuse=reuse)
- tower_conv1_2 = slim.conv2d(
- tower_conv1_1,
- 192, [7, 1],
- scope='Conv2d_0c_7x1',
- trainable=trainable_variables,
- reuse=reuse)
- mixed = tf.concat([tower_conv, tower_conv1_2], 3)
- up = slim.conv2d(
- mixed,
- net.get_shape()[3],
- 1,
- normalizer_fn=None,
- activation_fn=None,
- scope='Conv2d_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- net += scale * up
- if activation_fn:
- net = activation_fn(net)
- return net
-
-
-# Inception-Resnet-C
-def block8(net,
- scale=1.0,
- activation_fn=tf.nn.relu,
- scope=None,
- reuse=None,
- trainable_variables=True):
- """Builds the 8x8 resnet block."""
- with tf.variable_scope(scope, 'Block8', [net]):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net,
- 192,
- 1,
- scope='Conv2d_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net,
- 192,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- 224, [1, 3],
- scope='Conv2d_0b_1x3',
- trainable=trainable_variables,
- reuse=reuse)
- tower_conv1_2 = slim.conv2d(
- tower_conv1_1,
- 256, [3, 1],
- scope='Conv2d_0c_3x1',
- trainable=trainable_variables,
- reuse=reuse)
- mixed = tf.concat([tower_conv, tower_conv1_2], 3)
- up = slim.conv2d(
- mixed,
- net.get_shape()[3],
- 1,
- normalizer_fn=None,
- activation_fn=None,
- scope='Conv2d_1x1',
- trainable=trainable_variables,
- reuse=reuse)
- net += scale * up
- if activation_fn:
- net = activation_fn(net)
- return net
-
-
-def inception_resnet_v2_batch_norm(inputs,
- dropout_keep_prob=0.8,
- bottleneck_layer_size=128,
- reuse=None,
- scope='InceptionResnetV2',
- mode=tf.estimator.ModeKeys.TRAIN,
- trainable_variables=None,
- weight_decay=5e-5,
- **kwargs):
- """
- Creates the Inception Resnet V2 model applying batch not to each
- Convolutional and FullyConnected layer.
-
-
- **Parameters**:
-
- inputs:
- 4-D tensor of size [batch_size, height, width, 3].
-
- num_classes:
- number of predicted classes.
-
- is_training:
- whether is training or not.
-
- dropout_keep_prob: float
- the fraction to keep before final layer.
-
- reuse:
- whether or not the network and its variables should be reused. To be
- able to reuse 'scope' must be given.
-
- scope:
- Optional variable_scope.
-
- trainable_variables: list
- List of variables to be trainable=True
-
- **Returns**:
-
- logits:
- the logits outputs of the model.
-
- end_points:
- the set of end_points from the inception model.
- """
-
- batch_norm_params = {
- # Decay for the moving averages.
- 'decay': 0.995,
- # epsilon to prevent 0s in variance.
- 'epsilon': 0.001,
- # force in-place updates of mean and variance estimates
- 'updates_collections': None,
- }
-
- with slim.arg_scope(
- [slim.conv2d, slim.fully_connected],
- weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
- weights_regularizer=slim.l2_regularizer(weight_decay),
- normalizer_fn=slim.batch_norm,
- normalizer_params=batch_norm_params):
- return inception_resnet_v2(
- inputs,
- dropout_keep_prob=dropout_keep_prob,
- bottleneck_layer_size=bottleneck_layer_size,
- reuse=reuse,
- scope=scope,
- mode=mode,
- trainable_variables=trainable_variables,
- **kwargs)
-
-
-def inception_resnet_v2(inputs,
- dropout_keep_prob=0.8,
- bottleneck_layer_size=128,
- reuse=None,
- scope='InceptionResnetV2',
- mode=tf.estimator.ModeKeys.TRAIN,
- trainable_variables=None,
- **kwargs):
- """Creates the Inception Resnet V2 model.
-
- Parameters
- ----------
-
- inputs:
- 4-D tensor of size [batch_size, height, width, 3].
-
- num_classes:
- number of predicted classes.
-
- is_training:
- whether is training or not.
-
- dropout_keep_prob: float
- the fraction to keep before final layer.
-
- reuse:
- whether or not the network and its variables should be reused. To be
- able to reuse 'scope' must be given.
-
- scope:
- Optional variable_scope.
-
- trainable_variables: :any:`list`
- List of variables to be trainable=True
-
- Returns
- -------
- logits:
- the logits outputs of the model.
-
- end_points:
- the set of end_points from the inception model.
- """
- end_points = {}
-
- with tf.variable_scope(scope, 'InceptionResnetV2', [inputs], reuse=reuse):
- with slim.arg_scope([slim.dropout],
- is_training=(mode == tf.estimator.ModeKeys.TRAIN)):
-
- with slim.arg_scope(
- [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
- stride=1,
- padding='SAME'):
- # 149 x 149 x 32
- name = "Conv2d_1a_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
- name = "Conv2d_1a_3x3"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.conv2d(
- inputs,
- 32,
- 3,
- stride=2,
- padding='VALID',
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = net
-
- # 147 x 147 x 32
- name = "Conv2d_2a_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
- name = "Conv2d_2a_3x3"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net,
- 32,
- 3,
- padding='VALID',
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = net
-
- # 147 x 147 x 64
- name = "Conv2d_2b_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
- name = "Conv2d_2b_3x3"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net, 64, 3, scope=name, trainable=trainable, reuse=reuse)
- end_points[name] = net
-
- # 73 x 73 x 64
- net = slim.max_pool2d(
- net, 3, stride=2, padding='VALID', scope='MaxPool_3a_3x3')
- end_points['MaxPool_3a_3x3'] = net
-
- # 73 x 73 x 80
- name = "Conv2d_3b_1x1_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
- name = "Conv2d_3b_1x1"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net,
- 80,
- 1,
- padding='VALID',
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = net
-
- # 71 x 71 x 192
- name = "Conv2d_4a_3x3_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
- name = "Conv2d_4a_3x3"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net,
- 192,
- 3,
- padding='VALID',
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = net
-
- # 35 x 35 x 192
- net = slim.max_pool2d(
- net, 3, stride=2, padding='VALID', scope='MaxPool_5a_3x3')
- end_points['MaxPool_5a_3x3'] = net
-
- # 35 x 35 x 320
- name = "Mixed_5b_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
-
- name = "Mixed_5b"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- with tf.variable_scope(name):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net,
- 96,
- 1,
- scope='Conv2d_1x1',
- trainable=trainable,
- reuse=reuse)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net,
- 48,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable,
- reuse=reuse)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- 64,
- 5,
- scope='Conv2d_0b_5x5',
- trainable=trainable,
- reuse=reuse)
- with tf.variable_scope('Branch_2'):
- tower_conv2_0 = slim.conv2d(
- net,
- 64,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable,
- reuse=reuse)
- tower_conv2_1 = slim.conv2d(
- tower_conv2_0,
- 96,
- 3,
- scope='Conv2d_0b_3x3',
- trainable=trainable,
- reuse=reuse)
- tower_conv2_2 = slim.conv2d(
- tower_conv2_1,
- 96,
- 3,
- scope='Conv2d_0c_3x3',
- trainable=trainable,
- reuse=reuse)
- with tf.variable_scope('Branch_3'):
- tower_pool = slim.avg_pool2d(
- net,
- 3,
- stride=1,
- padding='SAME',
- scope='AvgPool_0a_3x3')
- tower_pool_1 = slim.conv2d(
- tower_pool,
- 64,
- 1,
- scope='Conv2d_0b_1x1',
- trainable=trainable,
- reuse=reuse)
- net = tf.concat([
- tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1
- ], 3)
- end_points[name] = net
-
- # BLOCK 35
- name = "Block35_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
-
- name = "Block35"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.repeat(
- net,
- 10,
- block35,
- scale=0.17,
- trainable_variables=trainable,
- reuse=reuse)
- end_points[name] = net
-
- # 17 x 17 x 1024
- name = "Mixed_6a_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
-
- name = "Mixed_6a"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- with tf.variable_scope(name):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net,
- 384,
- 3,
- stride=2,
- padding='VALID',
- scope='Conv2d_1a_3x3',
- trainable=trainable,
- reuse=reuse)
- with tf.variable_scope('Branch_1'):
- tower_conv1_0 = slim.conv2d(
- net,
- 256,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable,
- reuse=reuse)
- tower_conv1_1 = slim.conv2d(
- tower_conv1_0,
- 256,
- 3,
- scope='Conv2d_0b_3x3',
- trainable=trainable,
- reuse=reuse)
- tower_conv1_2 = slim.conv2d(
- tower_conv1_1,
- 384,
- 3,
- stride=2,
- padding='VALID',
- scope='Conv2d_1a_3x3',
- trainable=trainable,
- reuse=reuse)
- with tf.variable_scope('Branch_2'):
- tower_pool = slim.max_pool2d(
- net,
- 3,
- stride=2,
- padding='VALID',
- scope='MaxPool_1a_3x3')
- net = tf.concat(
- [tower_conv, tower_conv1_2, tower_pool], 3)
- end_points[name] = net
-
- # BLOCK 17
- name = "Block17_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
-
- name = "Block17"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.repeat(
- net,
- 20,
- block17,
- scale=0.10,
- trainable_variables=trainable,
- reuse=reuse)
- end_points[name] = net
-
- name = "Mixed_7a_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
-
- name = "Mixed_7a"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- with tf.variable_scope(name):
- with tf.variable_scope('Branch_0'):
- tower_conv = slim.conv2d(
- net,
- 256,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable,
- reuse=reuse)
- tower_conv_1 = slim.conv2d(
- tower_conv,
- 384,
- 3,
- stride=2,
- padding='VALID',
- scope='Conv2d_1a_3x3',
- trainable=trainable,
- reuse=reuse)
- with tf.variable_scope('Branch_1'):
- tower_conv1 = slim.conv2d(
- net,
- 256,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable,
- reuse=reuse)
- tower_conv1_1 = slim.conv2d(
- tower_conv1,
- 288,
- 3,
- stride=2,
- padding='VALID',
- scope='Conv2d_1a_3x3',
- trainable=trainable,
- reuse=reuse)
- with tf.variable_scope('Branch_2'):
- tower_conv2 = slim.conv2d(
- net,
- 256,
- 1,
- scope='Conv2d_0a_1x1',
- trainable=trainable,
- reuse=reuse)
- tower_conv2_1 = slim.conv2d(
- tower_conv2,
- 288,
- 3,
- scope='Conv2d_0b_3x3',
- trainable=trainable,
- reuse=reuse)
- tower_conv2_2 = slim.conv2d(
- tower_conv2_1,
- 320,
- 3,
- stride=2,
- padding='VALID',
- scope='Conv2d_1a_3x3',
- trainable=trainable,
- reuse=reuse)
- with tf.variable_scope('Branch_3'):
- tower_pool = slim.max_pool2d(
- net,
- 3,
- stride=2,
- padding='VALID',
- scope='MaxPool_1a_3x3')
- net = tf.concat([
- tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool
- ], 3)
- end_points[name] = net
-
- # Block 8
- name = "Block8_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
-
- name = "Block8"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.repeat(
- net,
- 9,
- block8,
- scale=0.20,
- trainable_variables=trainable,
- reuse=reuse)
- net = block8(
- net,
- activation_fn=None,
- trainable_variables=trainable,
- reuse=reuse)
- end_points[name] = net
-
- name = "Conv2d_7b_1x1_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
-
- name = "Conv2d_7b_1x1"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.conv2d(
- net, 1536, 1, scope=name, trainable=trainable, reuse=reuse)
- end_points[name] = net
-
- with tf.variable_scope('Logits'):
- end_points['PrePool'] = net
- # pylint: disable=no-member
- net = slim.avg_pool2d(
- net,
- net.get_shape()[1:3],
- padding='VALID',
- scope='AvgPool_1a_8x8')
- net = slim.flatten(net)
-
- net = slim.dropout(net, dropout_keep_prob, scope='Dropout')
-
- end_points['PreLogitsFlatten'] = net
-
- name = "Bottleneck_BN"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm],
- is_training=trainable,
- trainable=trainable):
-
- name = "Bottleneck"
- trainable = is_trainable(
- name, trainable_variables, mode=mode)
- net = slim.fully_connected(
- net,
- bottleneck_layer_size,
- activation_fn=None,
- scope=name,
- reuse=reuse,
- trainable=trainable)
- end_points[name] = net
-
- return net, end_points
diff --git a/bob/learn/tensorflow/network/JointIncResV2Simple.py b/bob/learn/tensorflow/network/JointIncResV2Simple.py
deleted file mode 100644
index fcbd6da85d7903b9d42a931a7d82545f90052c32..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/JointIncResV2Simple.py
+++ /dev/null
@@ -1,171 +0,0 @@
-from .InceptionResnetV2 import inception_resnet_v2_batch_norm
-from .InceptionResnetV1 import inception_resnet_v1_batch_norm
-from .SimpleCNN import base_architecture as simplecnn_arch
-import numpy as np
-import tensorflow as tf
-
-
-def architecture(faces, mode, face_arch='InceptionResnetV2', **kwargs):
- # construct patches inside the model
- ksizes = strides = [1, 28, 28, 1]
- rates = [1, 1, 1, 1]
- patches = tf.extract_image_patches(faces, ksizes, strides, rates, 'VALID')
- n_blocks = int(np.prod(patches.shape[1:3]))
- # n_blocks should be 25 for 160x160 faces
- patches = tf.reshape(patches, [-1, n_blocks, 28, 28, 3])
-
- simplecnn_kwargs = {
- 'kernerl_size': (3, 3),
- 'data_format': 'channels_last',
- 'add_batch_norm': True,
- 'use_bias_with_batch_norm': False,
- }
- simplecnn_kwargs.update(kwargs)
- endpoints = {}
- # construct simplecnn from patches
- for i in range(n_blocks):
- if i == 0:
- reuse = False
- else:
- reuse = True
- with tf.variable_scope('SimpleCNN', reuse=reuse):
- net, temp = simplecnn_arch(patches[:, i], mode, **simplecnn_kwargs)
- if i == 0:
- simplecnn_embeddings = net
- endpoints.update(temp)
- else:
- simplecnn_embeddings += net
- # average the embeddings of patches
- simplecnn_embeddings /= n_blocks
-
- # construct inception_resnet_v1 or 2 from faces
- if face_arch == 'InceptionResnetV2':
- face_embeddings, temp = inception_resnet_v2_batch_norm(
- faces, mode=mode, **kwargs)
- elif face_arch == 'InceptionResnetV1':
- face_embeddings, temp = inception_resnet_v1_batch_norm(
- faces, mode=mode, **kwargs)
- endpoints.update(temp)
-
- embeddings = tf.concat([simplecnn_embeddings, face_embeddings], 1)
-
- endpoints['final_embeddings'] = embeddings
-
- return embeddings, endpoints
-
-
-def model_fn(features, labels, mode, params, config):
- """The model function for join face and patch PAD. The input to the model
- is 160x160 faces."""
-
- faces = features['data']
- key = features['key']
-
- # organize the parameters
- params = params or {}
- learning_rate = params.get('learning_rate', 1e-4)
- apply_moving_averages = params.get('apply_moving_averages', True)
- n_classes = params.get('n_classes', 2)
- add_histograms = params.get('add_histograms')
- face_arch = params.get('face_arch', 'InceptionResnetV2')
-
- embeddings, _ = architecture(faces, mode, face_arch=face_arch)
-
- # Logits layer
- logits = tf.layers.dense(inputs=embeddings, units=n_classes, name='logits')
-
- # # restore the model from an extra_checkpoint
- # if extra_checkpoint is not None and mode == tf.estimator.ModeKeys.TRAIN:
- # tf.train.init_from_checkpoint(
- # ckpt_dir_or_file=extra_checkpoint["checkpoint_path"],
- # assignment_map=extra_checkpoint["scopes"],
- # )
-
- predictions = {
- # Generate predictions (for PREDICT and EVAL mode)
- "classes": tf.argmax(input=logits, axis=1),
- # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
- # `logging_hook`.
- "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
- 'key': key,
- }
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- accuracy = tf.metrics.accuracy(
- labels=labels, predictions=predictions["classes"])
- metrics = {'accuracy': accuracy}
-
- global_step = tf.train.get_or_create_global_step()
-
- # Compute the moving average of all individual losses and the total loss.
- if apply_moving_averages and mode == tf.estimator.ModeKeys.TRAIN:
- variable_averages = tf.train.ExponentialMovingAverage(
- 0.9999, global_step)
- variable_averages_op = variable_averages.apply(
- tf.trainable_variables())
- else:
- variable_averages_op = tf.no_op(name='noop')
-
- if mode == tf.estimator.ModeKeys.TRAIN:
- # for batch normalization to be updated as well:
- update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
- else:
- update_ops = []
-
- with tf.control_dependencies([variable_averages_op] + update_ops):
-
- # Calculate Loss (for both TRAIN and EVAL modes)
- cross_loss = tf.losses.sparse_softmax_cross_entropy(
- logits=logits, labels=labels)
-
- regularization_losses = tf.get_collection(
- tf.GraphKeys.REGULARIZATION_LOSSES)
-
- loss = tf.add_n(
- [cross_loss] + regularization_losses, name="total_loss")
-
- if apply_moving_averages and mode == tf.estimator.ModeKeys.TRAIN:
- # Compute the moving average of all individual losses and the total
- # loss.
- loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
- loss_averages_op = loss_averages.apply(
- tf.get_collection(tf.GraphKeys.LOSSES))
- else:
- loss_averages_op = tf.no_op(name='noop')
-
- if mode == tf.estimator.ModeKeys.TRAIN:
-
- optimizer = tf.train.GradientDescentOptimizer(
- learning_rate=learning_rate)
- train_op = tf.group(
- optimizer.minimize(loss, global_step=global_step),
- variable_averages_op, loss_averages_op)
-
- # Log accuracy and loss
- with tf.name_scope('train_metrics'):
- tf.summary.scalar('accuracy', accuracy[1])
- tf.summary.scalar('cross_entropy_loss', cross_loss)
- tf.summary.scalar('loss', loss)
- if apply_moving_averages:
- for l in tf.get_collection(tf.GraphKeys.LOSSES):
- tf.summary.scalar(l.op.name + "_averaged",
- loss_averages.average(l))
-
- # add histograms summaries
- if add_histograms == 'all':
- for v in tf.all_variables():
- tf.summary.histogram(v.name, v)
- elif add_histograms == 'train':
- for v in tf.trainable_variables():
- tf.summary.histogram(v.name, v)
-
- else:
- train_op = None
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=loss,
- train_op=train_op,
- eval_metric_ops=metrics)
diff --git a/bob/learn/tensorflow/network/LightCNN9.py b/bob/learn/tensorflow/network/LightCNN9.py
deleted file mode 100644
index 6d102ca22129e4703aeec66b03dedea086de0e28..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/LightCNN9.py
+++ /dev/null
@@ -1,189 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from bob.learn.tensorflow.layers import maxout
-from .utils import is_trainable
-
-
-def light_cnn9(inputs,
- seed=10,
- reuse=False,
- trainable_variables=None,
- **kwargs):
- """Creates the graph for the Light CNN-9 in
-
- Wu, Xiang, et al. "A light CNN for deep face representation with noisy labels." arXiv preprint arXiv:1511.02683 (2015).
- """
- slim = tf.contrib.slim
-
- with tf.variable_scope('LightCNN9', reuse=reuse):
- initializer = tf.contrib.layers.xavier_initializer(
- uniform=False, dtype=tf.float32, seed=seed)
- end_points = dict()
- name = "Conv1"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- inputs,
- 96, [5, 5],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = graph
-
- graph = maxout(graph, num_units=48, name='Maxout1')
-
- graph = slim.max_pool2d(
- graph, [2, 2], stride=2, padding="SAME", scope='Pool1')
-
- ####
- name = "Conv2a"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 96, [1, 1],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
-
- graph = maxout(graph, num_units=48, name='Maxout2a')
-
- name = "Conv2"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 192, [3, 3],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = graph
-
- graph = maxout(graph, num_units=96, name='Maxout2')
-
- graph = slim.max_pool2d(
- graph, [2, 2], stride=2, padding="SAME", scope='Pool2')
-
- #####
- name = "Conv3a"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 192, [1, 1],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
-
- graph = maxout(graph, num_units=96, name='Maxout3a')
-
- name = "Conv3"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 384, [3, 3],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = graph
-
- graph = maxout(graph, num_units=192, name='Maxout3')
-
- graph = slim.max_pool2d(
- graph, [2, 2], stride=2, padding="SAME", scope='Pool3')
-
- #####
- name = "Conv4a"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 384, [1, 1],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
-
- graph = maxout(graph, num_units=192, name='Maxout4a')
-
- name = "Conv4"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 256, [3, 3],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = graph
-
- graph = maxout(graph, num_units=128, name='Maxout4')
-
- #####
- name = "Conv5a"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 256, [1, 1],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
-
- graph = maxout(graph, num_units=128, name='Maxout5a')
-
- name = "Conv5"
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 256, [3, 3],
- activation_fn=tf.nn.relu,
- stride=1,
- weights_initializer=initializer,
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points[name] = graph
-
- graph = maxout(graph, num_units=128, name='Maxout5')
-
- graph = slim.max_pool2d(
- graph, [2, 2], stride=2, padding="SAME", scope='Pool4')
-
- graph = slim.flatten(graph, scope='flatten1')
- end_points['flatten1'] = graph
-
- graph = slim.dropout(graph, keep_prob=0.5, scope='dropout1')
-
- name = "fc1"
- trainable = is_trainable(name, trainable_variables)
- prelogits = slim.fully_connected(
- graph,
- 512,
- weights_initializer=initializer,
- activation_fn=tf.nn.relu,
- scope=name,
- trainable=trainable,
- reuse=reuse)
- end_points['fc1'] = prelogits
-
- return prelogits, end_points
diff --git a/bob/learn/tensorflow/network/MLP.py b/bob/learn/tensorflow/network/MLP.py
deleted file mode 100644
index 8225724984d0d688b6c348c0b957d3f228e6521b..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/MLP.py
+++ /dev/null
@@ -1,126 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from bob.learn.tensorflow.network.utils import is_trainable
-
-slim = tf.contrib.slim
-
-
-def mlp(
- inputs,
- output_shape,
- hidden_layers=[10],
- hidden_activation=tf.nn.tanh,
- output_activation=None,
- seed=10,
- **kwargs
-):
- """An MLP is a representation of a Multi-Layer Perceptron.
-
- This implementation is feed-forward and fully-connected.
- The implementation allows setting a global and the output activation functions.
- References to fully-connected feed-forward networks: Bishop's Pattern Recognition and Machine Learning, Chapter 5. Figure 5.1 shows what is programmed.
-
- MLPs normally are multi-layered systems, with 1 or more hidden layers.
-
- **Parameters**
-
- output_shape: number of neurons in the output.
-
- hidden_layers: :py:class:`list` that contains the amount of hidden layers, where each element is the number of neurons
-
- hidden_activation: Activation function of the hidden layers. Possible values can be seen
- `here <https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#activation-functions>`_.
- If you set to ``None``, the activation will be linear.
-
- output_activation: Activation of the output layer. If you set to `None`, the activation will be linear
-
- seed:
- """
-
- initializer = tf.contrib.layers.xavier_initializer(
- uniform=False, dtype=tf.float32, seed=seed
- )
-
- graph = inputs
- for i in range(len(hidden_layers)):
-
- weights = hidden_layers[i]
- graph = slim.fully_connected(
- graph,
- weights,
- weights_initializer=initializer,
- activation_fn=hidden_activation,
- scope="fc_{0}".format(i),
- )
-
- graph = slim.fully_connected(
- graph,
- output_shape,
- weights_initializer=initializer,
- activation_fn=output_activation,
- scope="fc_output",
- )
-
- return graph
-
-
-def mlp_with_batchnorm_and_dropout(
- inputs,
- fully_connected_layers,
- mode=tf.estimator.ModeKeys.TRAIN,
- trainable_variables=None,
- **kwargs
-):
-
- if trainable_variables is not None:
- raise ValueError(
- "The batch_norm layers selectable training is not implemented!"
- )
-
- end_points = {}
- net = slim.flatten(inputs)
-
- weight_decay = 1e-5
- dropout_keep_prob = 0.5
- batch_norm_params = {
- # Decay for the moving averages.
- "decay": 0.995,
- # epsilon to prevent 0s in variance.
- "epsilon": 0.001,
- # force in-place updates of mean and variance estimates
- "updates_collections": None,
- "is_training": (mode == tf.estimator.ModeKeys.TRAIN),
- }
-
- with slim.arg_scope(
- [slim.fully_connected],
- weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
- weights_regularizer=slim.l2_regularizer(weight_decay),
- normalizer_fn=slim.batch_norm,
- normalizer_params=batch_norm_params,
- ), tf.name_scope("MLP"):
-
- # hidden layers
- for i, n in enumerate(fully_connected_layers):
- name = "fc_{:0d}".format(i)
- trainable = is_trainable(name, trainable_variables, mode=mode)
- with slim.arg_scope(
- [slim.batch_norm], is_training=trainable, trainable=trainable
- ):
-
- net = slim.fully_connected(net, n, scope=name, trainable=trainable)
- end_points[name] = net
-
- name = "dropout_{:0d}".format(i)
- net = slim.dropout(
- net,
- dropout_keep_prob,
- is_training=(mode == tf.estimator.ModeKeys.TRAIN),
- scope=name,
- )
- end_points[name] = net
-
- return net, end_points
diff --git a/bob/learn/tensorflow/network/PatchCNN.py b/bob/learn/tensorflow/network/PatchCNN.py
deleted file mode 100644
index 98db7e52ccffbf1addd9d3fb139a5bb5afdc4541..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/PatchCNN.py
+++ /dev/null
@@ -1,434 +0,0 @@
-"""Path-based CNN Estimator for "Face Anti-spoofing Using Patch and Depth-based
-CNNs".
-
-The architecture is:
-
-+--------------+---------------+---------------+
-| Layer | Filter/Stride | Output Size |
-+--------------+---------------+---------------+
-| Conv-1 | 5 x 5/1 | 96 x 96 x 50 |
-| BN-1 | | 96 x 96 x 50 |
-| MaxPooling-1 | 2 x 2/2 | 48 x 48 x 50 |
-+--------------+---------------+---------------+
-| Conv-2 | 3 x 3/1 | 48 x 48 x 100 |
-| BN-2 | | 48 x 48 x 100 |
-| MaxPooling-2 | 2 x 2/2 | 24 x 24 x 100 |
-+--------------+---------------+---------------+
-| Conv-3 | 3 x 3/1 | 24 x 24 x 150 |
-| BN-3 | | 24 x 24 x 150 |
-| MaxPooling-3 | 3 x 3/2 | 12 x 12 x 150 |
-+--------------+---------------+---------------+
-| Conv-4 | 3 x 3/1 | 12 x 12 x 200 |
-| BN-4 | | 12 x 12 x 200 |
-| MaxPooling-4 | 2 x 2/2 | 6 x 6 x 200 |
-+--------------+---------------+---------------+
-| Conv-5 | 3 x 3/1 | 6 x 6 x 250 |
-| BN-5 | | 6 x 6 x 250 |
-| MaxPooling-5 | 2 x 2/2 | 3 x 3 x 250 |
-+--------------+---------------+---------------+
-| FC-1 | 3 x 3/1 | 1 x 1 x 1000 |
-| BN-6 | | 1 x 1 x 1000 |
-| Dropout | 0.5 | 1 x 1 x 1000 |
-+--------------+---------------+---------------+
-| FC-2 | 1 x 1/1 | 1 x 1 x 400 |
-| BN-7 | | 1 x 1 x 400 |
-| FC-3 | 1 x 1/1 | 1 x 1 x 2 |
-+--------------+---------------+---------------+
-
-```
-from tensorflow.python.keras import *
-from tensorflow.python.keras.layers import *
-patch = Sequential([
- Conv2D(50, (5, 5), padding='same', use_bias=False, input_shape=(96,96,3)),
- BatchNormalization(scale=False),
- Activation('relu'),
- MaxPool2D(padding='same'),
-
- Conv2D(100, (3, 3), padding='same', use_bias=False),
- BatchNormalization(scale=False),
- Activation('relu'),
- MaxPool2D(padding='same'),
-
- Conv2D(150, (3, 3), padding='same', use_bias=False),
- BatchNormalization(scale=False),
- Activation('relu'),
- MaxPool2D(pool_size=3, strides=2, padding='same'),
-
- Conv2D(200, (3, 3), padding='same', use_bias=False),
- BatchNormalization(scale=False),
- Activation('relu'),
- MaxPool2D(padding='same'),
-
- Conv2D(250, (3, 3), padding='same', use_bias=False),
- BatchNormalization(scale=False),
- Activation('relu'),
- MaxPool2D(padding='same'),
-
- Flatten(),
- Dense(1000, use_bias=False),
- BatchNormalization(scale=False),
- Activation('relu'),
- Dropout(rate=0.5),
-
- Dense(400, use_bias=False),
- BatchNormalization(scale=False),
- Activation('relu'),
-
- Dense(2),
-])
-patch.summary()
-```
-_________________________________________________________________
-Layer (type) Output Shape Param #
-=================================================================
-conv2d_1 (Conv2D) (None, 96, 96, 50) 3750
-_________________________________________________________________
-batch_normalization_1 (Batch (None, 96, 96, 50) 150
-_________________________________________________________________
-activation_1 (Activation) (None, 96, 96, 50) 0
-_________________________________________________________________
-max_pooling2d_1 (MaxPooling2 (None, 48, 48, 50) 0
-_________________________________________________________________
-conv2d_2 (Conv2D) (None, 48, 48, 100) 45000
-_________________________________________________________________
-batch_normalization_2 (Batch (None, 48, 48, 100) 300
-_________________________________________________________________
-activation_2 (Activation) (None, 48, 48, 100) 0
-_________________________________________________________________
-max_pooling2d_2 (MaxPooling2 (None, 24, 24, 100) 0
-_________________________________________________________________
-conv2d_3 (Conv2D) (None, 24, 24, 150) 135000
-_________________________________________________________________
-batch_normalization_3 (Batch (None, 24, 24, 150) 450
-_________________________________________________________________
-activation_3 (Activation) (None, 24, 24, 150) 0
-_________________________________________________________________
-max_pooling2d_3 (MaxPooling2 (None, 12, 12, 150) 0
-_________________________________________________________________
-conv2d_4 (Conv2D) (None, 12, 12, 200) 270000
-_________________________________________________________________
-batch_normalization_4 (Batch (None, 12, 12, 200) 600
-_________________________________________________________________
-activation_4 (Activation) (None, 12, 12, 200) 0
-_________________________________________________________________
-max_pooling2d_4 (MaxPooling2 (None, 6, 6, 200) 0
-_________________________________________________________________
-conv2d_5 (Conv2D) (None, 6, 6, 250) 450000
-_________________________________________________________________
-batch_normalization_5 (Batch (None, 6, 6, 250) 750
-_________________________________________________________________
-activation_5 (Activation) (None, 6, 6, 250) 0
-_________________________________________________________________
-max_pooling2d_5 (MaxPooling2 (None, 3, 3, 250) 0
-_________________________________________________________________
-flatten_1 (Flatten) (None, 2250) 0
-_________________________________________________________________
-dense_1 (Dense) (None, 1000) 2250000
-_________________________________________________________________
-batch_normalization_6 (Batch (None, 1000) 3000
-_________________________________________________________________
-activation_6 (Activation) (None, 1000) 0
-_________________________________________________________________
-dropout_1 (Dropout) (None, 1000) 0
-_________________________________________________________________
-dense_2 (Dense) (None, 400) 400000
-_________________________________________________________________
-batch_normalization_7 (Batch (None, 400) 1200
-_________________________________________________________________
-activation_7 (Activation) (None, 400) 0
-_________________________________________________________________
-dense_3 (Dense) (None, 2) 802
-=================================================================
-Total params: 3,561,002
-Trainable params: 3,556,702
-Non-trainable params: 4,300
-_________________________________________________________________
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-
-def create_conv_layer(inputs,
- mode,
- data_format,
- endpoints,
- number,
- filters,
- kernel_size,
- pool_size,
- pool_strides,
- skip_pool=False):
- bn_axis = 1 if data_format.lower() == 'channels_first' else 3
- training = mode == tf.estimator.ModeKeys.TRAIN
-
- name = 'Conv-{}'.format(number)
- conv = tf.layers.conv2d(
- inputs=inputs,
- filters=filters,
- kernel_size=kernel_size,
- padding="same",
- activation=None,
- data_format=data_format,
- name=name)
- endpoints[name] = conv
-
- name = 'BN-{}'.format(number)
- bn = tf.layers.batch_normalization(
- conv, axis=bn_axis, training=training, name=name)
- endpoints[name] = bn
-
- name = 'Activation-{}'.format(number)
- bn_act = tf.nn.relu(bn, name=name)
- endpoints[name] = bn_act
-
- name = 'MaxPooling-{}'.format(number)
- if skip_pool:
- pool = bn_act
- else:
- pool = tf.layers.max_pooling2d(
- inputs=bn_act,
- pool_size=pool_size,
- strides=pool_strides,
- padding='same',
- data_format=data_format,
- name=name)
- endpoints[name] = pool
-
- return pool
-
-
-def create_dense_layer(inputs, mode, endpoints, number, units):
- training = mode == tf.estimator.ModeKeys.TRAIN
-
- name = 'FC-{}'.format(number)
- fc = tf.layers.dense(
- inputs=inputs, units=units, activation=None, name=name)
- endpoints[name] = fc
-
- name = 'BN-{}'.format(number + 5)
- bn = tf.layers.batch_normalization(
- fc, axis=1, training=training, name=name)
- endpoints[name] = bn
-
- name = 'Activation-{}'.format(number + 5)
- bn_act = tf.nn.relu(bn, name=name)
- endpoints[name] = bn_act
-
- return bn_act
-
-
-def base_architecture(input_layer,
- mode,
- data_format,
- skip_first_two_pool=False,
- **kwargs):
- training = mode == tf.estimator.ModeKeys.TRAIN
- # Keep track of all the endpoints
- endpoints = {}
-
- # ======================
- # Convolutional Layer #1
- pool1 = create_conv_layer(
- inputs=input_layer,
- mode=mode,
- data_format=data_format,
- endpoints=endpoints,
- number=1,
- filters=50,
- kernel_size=(5, 5),
- pool_size=(2, 2),
- pool_strides=2,
- skip_pool=skip_first_two_pool)
-
- # ======================
- # Convolutional Layer #2
- pool2 = create_conv_layer(
- inputs=pool1,
- mode=mode,
- data_format=data_format,
- endpoints=endpoints,
- number=2,
- filters=100,
- kernel_size=(3, 3),
- pool_size=(2, 2),
- pool_strides=2,
- skip_pool=skip_first_two_pool)
-
- # ======================
- # Convolutional Layer #3
- pool3 = create_conv_layer(
- inputs=pool2,
- mode=mode,
- data_format=data_format,
- endpoints=endpoints,
- number=3,
- filters=150,
- kernel_size=(3, 3),
- pool_size=(3, 3),
- pool_strides=2)
-
- # ======================
- # Convolutional Layer #4
- pool4 = create_conv_layer(
- inputs=pool3,
- mode=mode,
- data_format=data_format,
- endpoints=endpoints,
- number=4,
- filters=200,
- kernel_size=(3, 3),
- pool_size=(2, 2),
- pool_strides=2)
-
- # ======================
- # Convolutional Layer #5
- pool5 = create_conv_layer(
- inputs=pool4,
- mode=mode,
- data_format=data_format,
- endpoints=endpoints,
- number=5,
- filters=250,
- kernel_size=(3, 3),
- pool_size=(2, 2),
- pool_strides=2)
-
- # ========================
- # Flatten tensor into a batch of vectors
- name = 'MaxPooling-5-Flat'
- pool5_flat = tf.layers.flatten(pool5, name=name)
- endpoints[name] = pool5_flat
-
- # ========================
- # Fully Connected Layer #1
- fc1 = create_dense_layer(
- inputs=pool5_flat,
- mode=mode,
- endpoints=endpoints,
- number=1,
- units=1000)
-
- # ========================
- # Dropout
- name = 'dropout'
- dropout = tf.layers.dropout(
- inputs=fc1, rate=0.5, training=training, name=name)
- endpoints[name] = dropout
-
- # ========================
- # Fully Connected Layer #2
- fc2 = create_dense_layer(
- inputs=dropout, mode=mode, endpoints=endpoints, number=2, units=400)
-
- return fc2, endpoints
-
-
-def architecture(input_layer,
- mode=tf.estimator.ModeKeys.TRAIN,
- skip_first_two_pool=False,
- n_classes=2,
- data_format='channels_last',
- reuse=False,
- regularizer=None,
- **kwargs):
-
- with tf.variable_scope('PatchCNN', reuse=reuse, regularizer=regularizer):
-
- fc2, endpoints = base_architecture(
- input_layer=input_layer,
- mode=mode,
- data_format=data_format,
- skip_first_two_pool=skip_first_two_pool)
- # Logits layer
- logits = tf.layers.dense(inputs=fc2, units=n_classes)
- endpoints['FC-3'] = logits
- endpoints['logits'] = logits
-
- return logits, endpoints
-
-
-def model_fn(features, labels, mode, params=None, config=None):
- """Model function for CNN."""
- data = features['data']
- key = features['key']
-
- params = params or {}
- params = {k: v for k, v in params.items() if v is not None}
-
- initial_learning_rate = params.get('learning_rate', 1e-3)
- momentum = params.get('momentum', 0.99)
- decay_steps = params.get('decay_steps', 1e5)
- decay_rate = params.get('decay_rate', 1e-4)
- staircase = params.get('staircase', True)
- regularization_rate = params.get('regularization_rate', 0)
-
- arch_kwargs = {
- 'skip_first_two_pool': params.get('skip_first_two_pool'),
- 'n_classes': params.get('n_classes'),
- 'data_format': params.get('data_format'),
- 'regularizer': params.get('regularizer')
- }
- arch_kwargs = {k: v for k, v in arch_kwargs.items() if v is not None}
-
- logits, _ = architecture(data, mode=mode, **arch_kwargs)
-
- predictions = {
- # Generate predictions (for PREDICT and EVAL mode)
- "classes": tf.argmax(input=logits, axis=1),
- # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
- # `logging_hook`.
- "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
- 'key': key,
- }
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- # Calculate Loss (for both TRAIN and EVAL modes)
- labels = tf.cast(labels, dtype="int32")
- loss = tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)
- # Add the regularization terms to the loss
- if regularization_rate:
- loss += regularization_rate * \
- tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
-
- accuracy = tf.metrics.accuracy(
- labels=labels, predictions=predictions["classes"])
- metrics = {'accuracy': accuracy}
-
- # Configure the training op
- if mode == tf.estimator.ModeKeys.TRAIN:
- global_step = tf.train.get_or_create_global_step()
-
- learning_rate = tf.train.exponential_decay(
- learning_rate=initial_learning_rate,
- global_step=global_step,
- decay_steps=decay_steps,
- decay_rate=decay_rate,
- staircase=staircase)
-
- optimizer = tf.train.MomentumOptimizer(
- learning_rate=learning_rate, momentum=momentum)
-
- # for batch normalization to be updated as well:
- update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
- with tf.control_dependencies(update_ops):
- train_op = optimizer.minimize(loss=loss, global_step=global_step)
-
- # Log accuracy and loss
- with tf.name_scope('train_metrics'):
- tf.summary.scalar('accuracy', accuracy[1])
- tf.summary.scalar('loss', loss)
- tf.summary.scalar('learning_rate', learning_rate)
- else:
- train_op = None
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=loss,
- train_op=train_op,
- eval_metric_ops=metrics)
diff --git a/bob/learn/tensorflow/network/SimpleCNN.py b/bob/learn/tensorflow/network/SimpleCNN.py
deleted file mode 100644
index 1a3b2a084720f39c8fc1f80db092ea3098081ab0..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/SimpleCNN.py
+++ /dev/null
@@ -1,483 +0,0 @@
-"""
-The network using keras (same as new_architecture function below)::
-
- from tensorflow.python.keras import *
- from tensorflow.python.keras.layers import *
- simplecnn = Sequential([
- Conv2D(32,(3,3),padding='same',use_bias=False, input_shape=(28,28,3)),
- BatchNormalization(scale=False),
- Activation('relu'),
- MaxPool2D(padding='same'),
- Conv2D(64,(3,3),padding='same',use_bias=False),
- BatchNormalization(scale=False),
- Activation('relu'),
- MaxPool2D(padding='same'),
- Flatten(),
- Dense(1024, use_bias=False),
- BatchNormalization(scale=False),
- Activation('relu'),
- Dropout(rate=0.4),
- Dense(2),
- ])
- simplecnn.summary()
- _________________________________________________________________
- Layer (type) Output Shape Param #
- =================================================================
- conv2d_1 (Conv2D) (None, 28, 28, 32) 864
- _________________________________________________________________
- batch_normalization_1 (Batch (None, 28, 28, 32) 96
- _________________________________________________________________
- activation_1 (Activation) (None, 28, 28, 32) 0
- _________________________________________________________________
- max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32) 0
- _________________________________________________________________
- conv2d_2 (Conv2D) (None, 14, 14, 64) 18432
- _________________________________________________________________
- batch_normalization_2 (Batch (None, 14, 14, 64) 192
- _________________________________________________________________
- activation_2 (Activation) (None, 14, 14, 64) 0
- _________________________________________________________________
- max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64) 0
- _________________________________________________________________
- flatten_1 (Flatten) (None, 3136) 0
- _________________________________________________________________
- dense_1 (Dense) (None, 1024) 3211264
- _________________________________________________________________
- batch_normalization_3 (Batch (None, 1024) 3072
- _________________________________________________________________
- activation_3 (Activation) (None, 1024) 0
- _________________________________________________________________
- dropout_1 (Dropout) (None, 1024) 0
- _________________________________________________________________
- dense_2 (Dense) (None, 2) 2050
- =================================================================
- Total params: 3,235,970
- Trainable params: 3,233,730
- Non-trainable params: 2,240
- _________________________________________________________________
-"""
-
-
-import collections
-import tensorflow as tf
-from .utils import is_trainable
-from ..estimators import get_trainable_variables
-
-
-def create_conv_layer(inputs,
- mode,
- data_format,
- endpoints,
- number,
- filters,
- kernel_size,
- pool_size,
- pool_strides,
- add_batch_norm=False,
- trainable_variables=None,
- use_bias_with_batch_norm=True):
- bn_axis = 1 if data_format.lower() == 'channels_first' else 3
- training = mode == tf.estimator.ModeKeys.TRAIN
-
- if add_batch_norm:
- activation = None
- else:
- activation = tf.nn.relu
-
- name = 'conv{}'.format(number)
- trainable = is_trainable(name, trainable_variables)
- conv = tf.layers.conv2d(
- inputs=inputs,
- filters=filters,
- kernel_size=kernel_size,
- padding="same",
- activation=activation,
- data_format=data_format,
- trainable=trainable,
- use_bias=((not add_batch_norm) or use_bias_with_batch_norm),
- )
- endpoints[name] = conv
-
- if add_batch_norm:
- name = 'bn{}'.format(number)
- trainable = is_trainable(name, trainable_variables)
- bn = tf.layers.batch_normalization(
- conv, axis=bn_axis, training=training, trainable=trainable,
- scale=use_bias_with_batch_norm)
- endpoints[name] = bn
-
- name = 'activation{}'.format(number)
- bn_act = tf.nn.relu(bn)
- endpoints[name] = bn_act
- else:
- bn_act = conv
-
- name = 'pool{}'.format(number)
- pool = tf.layers.max_pooling2d(
- inputs=bn_act,
- pool_size=pool_size,
- strides=pool_strides,
- padding='same',
- data_format=data_format)
- endpoints[name] = pool
-
- return pool
-
-
-def base_architecture(input_layer,
- mode=tf.estimator.ModeKeys.TRAIN,
- kernerl_size=(3, 3),
- data_format='channels_last',
- add_batch_norm=False,
- trainable_variables=None,
- use_bias_with_batch_norm=True,
- **kwargs):
- training = mode == tf.estimator.ModeKeys.TRAIN
- # Keep track of all the endpoints
- endpoints = {}
-
- # Convolutional Layer #1
- # Computes 32 features using a kernerl_size filter with ReLU
- # activation.
- # Padding is added to preserve width and height.
- pool1 = create_conv_layer(
- inputs=input_layer,
- mode=mode,
- data_format=data_format,
- endpoints=endpoints,
- number=1,
- filters=32,
- kernel_size=kernerl_size,
- pool_size=(2, 2),
- pool_strides=2,
- add_batch_norm=add_batch_norm,
- trainable_variables=trainable_variables,
- use_bias_with_batch_norm=use_bias_with_batch_norm,
- )
-
- # Convolutional Layer #2
- # Computes 64 features using a kernerl_size filter.
- # Padding is added to preserve width and height.
- pool2 = create_conv_layer(
- inputs=pool1,
- mode=mode,
- data_format=data_format,
- endpoints=endpoints,
- number=2,
- filters=64,
- kernel_size=kernerl_size,
- pool_size=(2, 2),
- pool_strides=2,
- add_batch_norm=add_batch_norm,
- trainable_variables=trainable_variables,
- use_bias_with_batch_norm=use_bias_with_batch_norm,
- )
-
- # Flatten tensor into a batch of vectors
- pool2_flat = tf.layers.flatten(pool2)
- endpoints['pool2_flat'] = pool2_flat
-
- # Dense Layer
- # Densely connected layer with 1024 neurons
- if add_batch_norm:
- activation = None
- else:
- activation = tf.nn.relu
-
- name = 'dense'
- trainable = is_trainable(name, trainable_variables)
- dense = tf.layers.dense(
- inputs=pool2_flat,
- units=1024,
- activation=activation,
- trainable=trainable,
- use_bias=((not add_batch_norm) or use_bias_with_batch_norm),
- )
- endpoints[name] = dense
-
- if add_batch_norm:
- name = 'bn{}'.format(3)
- trainable = is_trainable(name, trainable_variables)
- bn = tf.layers.batch_normalization(
- dense, axis=1, training=training, trainable=trainable,
- scale=use_bias_with_batch_norm)
- endpoints[name] = bn
-
- name = 'activation{}'.format(3)
- bn_act = tf.nn.relu(bn)
- endpoints[name] = bn_act
- else:
- bn_act = dense
-
- # Add dropout operation; 0.6 probability that element will be kept
- dropout = tf.layers.dropout(
- inputs=bn_act, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
- endpoints['dropout'] = dropout
-
- return dropout, endpoints
-
-
-def new_architecture(
- input_layer,
- mode=tf.estimator.ModeKeys.TRAIN,
- kernerl_size=(3, 3),
- data_format='channels_last',
- add_batch_norm=True,
- trainable_variables=None,
- use_bias_with_batch_norm=False,
- reuse=False,
- **kwargs):
- with tf.variable_scope('SimpleCNN', reuse=reuse):
- return base_architecture(
- input_layer=input_layer,
- mode=mode,
- kernerl_size=kernerl_size,
- data_format=data_format,
- add_batch_norm=add_batch_norm,
- trainable_variables=trainable_variables,
- use_bias_with_batch_norm=use_bias_with_batch_norm,
- **kwargs)
-
-
-def slim_architecture(
- input_layer,
- mode=tf.estimator.ModeKeys.TRAIN,
- kernerl_size=(3, 3),
- data_format='channels_last',
- add_batch_norm=True,
- trainable_variables=None,
- use_bias_with_batch_norm=False,
- reuse=False,
- **kwargs):
- if data_format != 'channels_last':
- raise ValueError("Only channels_last data_format is implemented!")
- if (not add_batch_norm) or use_bias_with_batch_norm:
- raise NotImplementedError()
- slim = tf.contrib.slim
- batch_norm_params = {
- # Decay for the moving averages.
- 'decay': 0.995,
- # epsilon to prevent 0s in variance.
- 'epsilon': 0.001,
- # force in-place updates of mean and variance estimates
- 'updates_collections': None,
- }
-
- weight_decay = 5e-5
- end_points = {}
- with slim.arg_scope(
- [slim.conv2d, slim.fully_connected],
- weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
- weights_regularizer=slim.l2_regularizer(weight_decay),
- normalizer_fn=slim.batch_norm,
- normalizer_params=batch_norm_params):
- with tf.variable_scope('SimpleCNN', reuse=reuse), slim.arg_scope(
- [slim.batch_norm, slim.dropout],
- is_training=mode == tf.estimator.ModeKeys.TRAIN):
-
- initializer = tf.contrib.layers.xavier_initializer()
- name = 'conv1'
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- input_layer,
- 32, kernerl_size,
- activation_fn=tf.nn.relu,
- stride=1,
- scope=name,
- weights_initializer=initializer,
- trainable=trainable)
- end_points[name] = graph
-
- graph = slim.max_pool2d(graph, [2, 2], scope='pool1')
- end_points['pool1'] = graph
-
- name = 'conv2'
- trainable = is_trainable(name, trainable_variables)
- graph = slim.conv2d(
- graph,
- 64, kernerl_size,
- activation_fn=tf.nn.relu,
- stride=1,
- scope=name,
- weights_initializer=initializer,
- trainable=trainable)
- end_points[name] = graph
-
- graph = slim.max_pool2d(graph, [2, 2], scope='pool2')
- end_points['pool2'] = graph
-
- graph = slim.flatten(graph, scope='flatten')
- end_points['flatten'] = graph
-
- name = 'dense'
- trainable = is_trainable(name, trainable_variables)
- graph = slim.fully_connected(
- graph,
- 1024,
- weights_initializer=initializer,
- activation_fn=tf.nn.relu,
- scope=name,
- trainable=trainable)
- end_points[name] = graph
-
- name = 'dropout'
- graph = slim.dropout(graph, 0.6, scope='Dropout')
- end_points[name] = graph
-
- return graph, end_points
-
-
-def architecture(input_layer,
- mode=tf.estimator.ModeKeys.TRAIN,
- kernerl_size=(3, 3),
- n_classes=2,
- data_format='channels_last',
- reuse=False,
- add_batch_norm=False,
- trainable_variables=None,
- **kwargs):
-
- with tf.variable_scope('SimpleCNN', reuse=reuse):
-
- dropout, endpoints = base_architecture(
- input_layer,
- mode,
- kernerl_size,
- data_format,
- add_batch_norm=add_batch_norm,
- trainable_variables=trainable_variables)
- # Logits layer
- # Input Tensor Shape: [batch_size, 1024]
- # Output Tensor Shape: [batch_size, n_classes]
- name = 'logits'
- trainable = is_trainable(name, trainable_variables)
- logits = tf.layers.dense(
- inputs=dropout, units=n_classes, trainable=trainable)
- endpoints[name] = logits
-
- return logits, endpoints
-
-
-def model_fn(features, labels, mode, params=None, config=None):
- """Model function for CNN."""
- data = features['data']
- key = features['key']
-
- params = params or {}
- learning_rate = params.get('learning_rate', 1e-5)
- apply_moving_averages = params.get('apply_moving_averages', False)
- extra_checkpoint = params.get('extra_checkpoint')
- trainable_variables = get_trainable_variables(extra_checkpoint)
- loss_weights = params.get('loss_weights', 1.0)
- add_histograms = params.get('add_histograms')
- nnet_optimizer = params.get('nnet_optimizer') or 'sgd'
-
- arch_kwargs = {
- 'kernerl_size': params.get('kernerl_size', None),
- 'n_classes': params.get('n_classes', None),
- 'data_format': params.get('data_format', None),
- 'add_batch_norm': params.get('add_batch_norm', None),
- 'trainable_variables': trainable_variables,
- }
- arch_kwargs = {k: v for k, v in arch_kwargs.items() if v is not None}
-
- logits, _ = architecture(data, mode, **arch_kwargs)
-
- # restore the model from an extra_checkpoint
- if extra_checkpoint is not None and mode == tf.estimator.ModeKeys.TRAIN:
- tf.train.init_from_checkpoint(
- ckpt_dir_or_file=extra_checkpoint["checkpoint_path"],
- assignment_map=extra_checkpoint["scopes"],
- )
-
- predictions = {
- # Generate predictions (for PREDICT and EVAL mode)
- "classes": tf.argmax(input=logits, axis=1),
- # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
- # `logging_hook`.
- "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
- 'key': key,
- }
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- # convert labels to the expected int32 format
- labels = tf.cast(labels, dtype="int32")
-
- accuracy = tf.metrics.accuracy(
- labels=labels, predictions=predictions["classes"])
- metrics = {'accuracy': accuracy}
-
- global_step = tf.train.get_or_create_global_step()
-
- # Compute the moving average of all individual losses and the total loss.
- if apply_moving_averages and mode == tf.estimator.ModeKeys.TRAIN:
- variable_averages = tf.train.ExponentialMovingAverage(
- 0.9999, global_step)
- variable_averages_op = variable_averages.apply(
- tf.trainable_variables())
- else:
- variable_averages_op = tf.no_op(name='noop')
-
- if mode == tf.estimator.ModeKeys.TRAIN:
- # for batch normalization to be updated as well:
- update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
- else:
- update_ops = []
-
- with tf.control_dependencies([variable_averages_op] + update_ops):
-
- # convert weights of per sample to weights per class
- if isinstance(loss_weights, collections.Iterable):
- loss_weights = tf.gather(loss_weights, labels)
-
- # Calculate Loss (for both TRAIN and EVAL modes)
- loss = tf.losses.sparse_softmax_cross_entropy(
- logits=logits, labels=labels, weights=loss_weights)
-
- if apply_moving_averages and mode == tf.estimator.ModeKeys.TRAIN:
- # Compute the moving average of all individual losses and the total
- # loss.
- loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
- loss_averages_op = loss_averages.apply(
- tf.get_collection(tf.GraphKeys.LOSSES))
- else:
- loss_averages_op = tf.no_op(name='noop')
-
- if mode == tf.estimator.ModeKeys.TRAIN:
-
- if nnet_optimizer == 'sgd':
- optimizer = tf.train.GradientDescentOptimizer(
- learning_rate=learning_rate)
- else:
- optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
- train_op = tf.group(
- optimizer.minimize(loss, global_step=global_step),
- variable_averages_op, loss_averages_op)
-
- # Log accuracy and loss
- with tf.name_scope('train_metrics'):
- tf.summary.scalar('accuracy', accuracy[1])
- tf.summary.scalar('loss', loss)
- if apply_moving_averages:
- for l in tf.get_collection(tf.GraphKeys.LOSSES):
- tf.summary.scalar(l.op.name + "_averaged",
- loss_averages.average(l))
-
- # add histograms summaries
- if add_histograms == 'all':
- for v in tf.all_variables():
- tf.summary.histogram(v.name, v)
- elif add_histograms == 'train':
- for v in tf.trainable_variables():
- tf.summary.histogram(v.name, v)
-
- else:
- train_op = None
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=loss,
- train_op=train_op,
- eval_metric_ops=metrics)
diff --git a/bob/learn/tensorflow/network/Vgg.py b/bob/learn/tensorflow/network/Vgg.py
deleted file mode 100644
index 732c6a42de2764e9c818e7e3c13efb9f71a8f371..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/Vgg.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-
-"""
-VGG16 and VGG19 wrappers
-"""
-
-import tensorflow as tf
-from tensorflow.contrib.slim.python.slim.nets import vgg
-from tensorflow.contrib.layers.python.layers import layers as layers_lib
-from tensorflow.contrib import layers
-import tensorflow.contrib.slim as slim
-from tensorflow.python.ops import variable_scope
-from .utils import is_trainable
-
-
-def vgg_19(inputs,
- reuse=None,
- mode=tf.estimator.ModeKeys.TRAIN, **kwargs):
- """
- Oxford Net VGG 19-Layers version E Example from tf-slim
-
- https://raw.githubusercontent.com/tensorflow/models/master/research/slim/nets/vgg.py
-
- **Parameters**:
-
- inputs: a 4-D tensor of size [batch_size, height, width, 3].
-
- reuse: whether or not the network and its variables should be reused. To be
- able to reuse 'scope' must be given.
-
- mode:
- Estimator mode keys
- """
-
- with slim.arg_scope(
- [slim.conv2d],
- trainable=mode==tf.estimator.ModeKeys.TRAIN):
-
- return vgg.vgg_19(inputs, spatial_squeeze=False)
-
-
-def vgg_16(inputs,
- reuse=None,
- mode=tf.estimator.ModeKeys.TRAIN,
- trainable_variables=None,
- scope="vgg_16",
- **kwargs):
- """
- Oxford Net VGG 16-Layers version E Example from tf-slim
-
- https://raw.githubusercontent.com/tensorflow/models/master/research/slim/nets/vgg.py
-
- **Parameters**:
-
- inputs: a 4-D tensor of size [batch_size, height, width, 3].
-
- reuse: whether or not the network and its variables should be reused. To be
- able to reuse 'scope' must be given.
-
- mode:
- Estimator mode keys
- """
-
- dropout_keep_prob = 0.5
- end_points = {}
-
-
- with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
- end_points_collection = sc.original_name_scope + '_end_points'
- # Collect outputs for conv2d, fully_connected and max_pool2d.
-
- with slim.arg_scope(
- [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d], outputs_collections=end_points_collection):
-
- with slim.arg_scope(
- [slim.conv2d],
- trainable=mode==tf.estimator.ModeKeys.TRAIN):
-
- name = "conv1"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = layers_lib.repeat(
- inputs, 2, layers.conv2d, 64, [3, 3], scope=name, trainable=trainable)
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
- end_points[name] = net
-
- name = "conv2"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = layers_lib.repeat(net, 2, layers.conv2d, 128, [3, 3], scope=name, trainable=trainable)
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
- end_points[name] = net
-
- name = "conv3"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = layers_lib.repeat(net, 3, layers.conv2d, 256, [3, 3], scope=name, trainable=trainable)
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
- end_points[name] = net
-
- name = "conv4"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = layers_lib.repeat(net, 3, layers.conv2d, 512, [3, 3], scope=name, trainable=trainable)
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
- end_points[name] = net
-
- name = "conv5"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = layers_lib.repeat(net, 3, layers.conv2d, 512, [3, 3], scope=name, trainable=trainable)
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
- end_points[name] = net
-
- net = layers.flatten(net)
-
- # Use conv2d instead of fully_connected layers.
- name = "fc6"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = layers.fully_connected(net, 4096, scope=name, trainable=trainable)
- net = layers_lib.dropout(
- net, dropout_keep_prob, is_training=mode==tf.estimator.ModeKeys.TRAIN, scope='dropout6')
- end_points[name] = net
-
- name = "fc7"
- trainable = is_trainable(name, trainable_variables, mode=mode)
- net = layers.fully_connected(net, 4096, scope=name, trainable=trainable)
- net = layers_lib.dropout(
- net, dropout_keep_prob, is_training=mode==tf.estimator.ModeKeys.TRAIN, scope='dropout7')
-
- end_points[name] = net
-
- # Convert end_points_collection into a end_point dict.
- return net, end_points
- #return vgg.vgg_16(inputs, spatial_squeeze=False)
-
diff --git a/bob/learn/tensorflow/network/__init__.py b/bob/learn/tensorflow/network/__init__.py
deleted file mode 100644
index 9497048c267c0c4f16cc244525217482b04c013b..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/__init__.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from .Chopra import chopra
-from .LightCNN9 import light_cnn9
-from .Dummy import dummy
-from .MLP import mlp, mlp_with_batchnorm_and_dropout
-from .InceptionResnetV2 import inception_resnet_v2, inception_resnet_v2_batch_norm
-from .InceptionResnetV1 import inception_resnet_v1, inception_resnet_v1_batch_norm
-from . import SimpleCNN
-from .Vgg import vgg_19, vgg_16
-
-
-# gets sphinx autodoc done right - don't remove it
-def __appropriate__(*args):
- """Says object was actually declared here, an not on the import module.
-
- Parameters:
-
- *args: An iterable of objects to modify
-
- Resolves `Sphinx referencing issues
- <https://github.com/sphinx-doc/sphinx/issues/3048>`
- """
-
- for obj in args:
- obj.__module__ = __name__
-
-
-__appropriate__(
- chopra,
- light_cnn9,
- dummy,
- mlp,
- inception_resnet_v2, inception_resnet_v2_batch_norm,
- inception_resnet_v1, inception_resnet_v1_batch_norm,
- SimpleCNN, vgg_19, vgg_16
-)
-
-__all__ = [_ for _ in dir() if not _.startswith('_')]
diff --git a/bob/learn/tensorflow/network/utils.py b/bob/learn/tensorflow/network/utils.py
deleted file mode 100644
index 0c5c855f023be38dcc3b4d130f1117cb0ccfa307..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/network/utils.py
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-# functions were moved
-from ..utils.network import append_logits, is_trainable
diff --git a/bob/learn/tensorflow/script/__init__.py b/bob/learn/tensorflow/script/__init__.py
deleted file mode 100644
index b156cdd2398fd5af2b88db8279fd84c85b767b36..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# gets sphinx autodoc done right - don't remove it
-__all__ = [_ for _ in dir() if not _.startswith('_')]
diff --git a/bob/learn/tensorflow/script/cache_dataset.py b/bob/learn/tensorflow/script/cache_dataset.py
deleted file mode 100644
index 7f8c04f39dceaf9a312ed655d1a17882431058e2..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/cache_dataset.py
+++ /dev/null
@@ -1,61 +0,0 @@
-#!/usr/bin/env python
-"""Trains networks using Tensorflow estimators.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import logging
-import click
-import tensorflow as tf
-from bob.extension.scripts.click_helper import (
- verbosity_option, ConfigCommand, ResourceOption, log_parameters)
-from bob.bio.base import is_argument_available
-
-logger = logging.getLogger(__name__)
-
-
-@click.command(
- entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand)
-@click.option(
- '--input-fn',
- '-i',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.input_fn',
- help='The ``input_fn`` that will return the features and labels. '
- 'You should call the dataset.cache(...) yourself in the input '
- 'function. If the ``input_fn`` accepts a ``cache_only`` argument, '
- 'it will be given as True.')
-@click.option(
- '--mode',
- cls=ResourceOption,
- default=tf.estimator.ModeKeys.TRAIN,
- show_default=True,
- type=click.Choice((tf.estimator.ModeKeys.TRAIN,
- tf.estimator.ModeKeys.EVAL,
- tf.estimator.ModeKeys.PREDICT)),
- help='mode value to be given to the input_fn.')
-@verbosity_option(cls=ResourceOption)
-def cache_dataset(input_fn, mode, **kwargs):
- """Trains networks using Tensorflow estimators."""
- log_parameters(logger)
-
- kwargs = {}
- if is_argument_available('cache_only', input_fn):
- kwargs['cache_only'] = True
- logger.info("cache_only as True will be passed to input_fn.")
-
- # call the input function manually
- with tf.Session() as sess:
- data = input_fn(mode, **kwargs)
- if isinstance(data, tf.data.Dataset):
- iterator = data.make_initializable_iterator()
- data = iterator.get_next()
- sess.run(iterator.initializer)
- sess.run(tf.initializers.global_variables())
- try:
- while True:
- sess.run(data)
- except tf.errors.OutOfRangeError:
- click.echo("Finished reading the dataset.")
- return
diff --git a/bob/learn/tensorflow/script/compute_statistics.py b/bob/learn/tensorflow/script/compute_statistics.py
deleted file mode 100644
index b876c5e4ae5be9f3df6f4f18c34aaaf50ef2395e..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/compute_statistics.py
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/usr/bin/env python
-"""Computes statistics on a BioGenerator.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import logging
-import click
-import numpy as np
-from bob.extension.scripts.click_helper import (
- verbosity_option, ConfigCommand, ResourceOption, log_parameters)
-from bob.learn.tensorflow.dataset.bio import BioGenerator
-
-logger = logging.getLogger(__name__)
-
-
-@click.command(
- entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand,
- epilog="""\b
-An example configuration could be::
- # define the database:
- from bob.bio.base.test.dummy.database import database
- groups = ['dev']
- biofiles = database.all_files(groups)
-"""
-)
-@click.option(
- '--database',
- '-d',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.bio.database',
- help='A bio database. Its original_directory must point to the correct '
- 'path.')
-@click.option(
- '--biofiles',
- required=True,
- cls=ResourceOption,
- help='The list of the bio files. You can only provide this through '
- 'config files.')
-@click.option(
- '--load-data',
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.load_data',
- help='A callable with the signature of '
- '``data = load_data(database, biofile)``. '
- ':any:`bob.bio.base.read_original_data` is used by default.')
-@click.option(
- '--multiple-samples',
- is_flag=True,
- cls=ResourceOption,
- help='If provided, it means that the data provided by reader contains '
- 'multiple samples with same label and path.')
-@verbosity_option(cls=ResourceOption)
-def compute_statistics(database, biofiles, load_data, multiple_samples,
- **kwargs):
- """Computes statistics on a BioGenerator.
-
- This script works with bob.bio.base databases. It will load all the samples
- and print their mean.
- """
- log_parameters(logger, ignore=('biofiles', ))
- logger.debug("len(biofiles): %d", len(biofiles))
-
- assert len(biofiles), "biofiles are empty!"
- logger.info('Calculating the mean for %d files', len(biofiles))
-
- generator = BioGenerator(
- database,
- biofiles,
- load_data=load_data,
- multiple_samples=multiple_samples)
-
- for i, (data, _, _) in enumerate(generator()):
- if i == 0:
- mean = np.cast['float'](data)
- else:
- mean += data
-
- mean = mean.reshape(mean.shape[0], -1)
- mean = np.mean(mean, axis=1)
- click.echo(mean / (i + 1.))
diff --git a/bob/learn/tensorflow/script/db_to_tfrecords.py b/bob/learn/tensorflow/script/db_to_tfrecords.py
deleted file mode 100644
index 7460f7c664ec2726bdeae2bec5ca4bcfc0e11d4a..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/db_to_tfrecords.py
+++ /dev/null
@@ -1,337 +0,0 @@
-#!/usr/bin/env python
-"""Converts Bio and PAD datasets to TFRecords file formats.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import logging
-import os
-import random
-import tempfile
-import click
-import tensorflow as tf
-from bob.io.base import create_directories_safe, HDF5File
-from bob.extension.scripts.click_helper import (
- verbosity_option,
- ConfigCommand,
- ResourceOption,
- log_parameters,
-)
-from bob.learn.tensorflow.dataset.tfrecords import (
- describe_tf_record,
- write_a_sample,
- normalize_tfrecords_path,
- tfrecord_name_and_json_name,
- dataset_to_tfrecord,
-)
-from bob.learn.tensorflow.utils import bytes2human
-
-
-logger = logging.getLogger(__name__)
-
-
-@click.command(entry_point_group="bob.learn.tensorflow.config", cls=ConfigCommand)
-@click.option(
- "--samples",
- required=True,
- cls=ResourceOption,
- help="A list of all samples that you want to write in the "
- "tfrecords file. Whatever is inside this list is passed to "
- "the reader.",
-)
-@click.option(
- "--reader",
- required=True,
- cls=ResourceOption,
- help="a function with the signature of ``data, label, key = "
- "reader(sample)`` which takes a sample and returns the "
- "loaded data, the label of the data, and a key which is "
- "unique for every sample.",
-)
-@click.option(
- "--output", "-o", required=True, cls=ResourceOption, help="Name of the output file."
-)
-@click.option(
- "--shuffle",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, it will shuffle the samples.",
-)
-@click.option(
- "--allow-failures",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, the samples which fail to load are ignored.",
-)
-@click.option(
- "--multiple-samples",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, it means that the data provided by reader contains "
- "multiple samples with same label and path.",
-)
-@click.option(
- "--size-estimate",
- is_flag=True,
- cls=ResourceOption,
- help="If given, will print the estimated file size instead of creating "
- "the final tfrecord file.",
-)
-@verbosity_option(cls=ResourceOption)
-def db_to_tfrecords(
- samples,
- reader,
- output,
- shuffle,
- allow_failures,
- multiple_samples,
- size_estimate,
- **kwargs,
-):
- """Converts Bio and PAD datasets to TFRecords file formats.
-
- The best way to use this script is to send it to the io-big queue if you
- are at Idiap::
-
- $ jman submit -i -q q1d -- %(prog)s <config_files>...
-
- An example for mnist would be::
-
- from bob.db.mnist import Database
- db = Database()
- data, labels = db.data(groups='train')
-
- samples = zip(data, labels, (str(i) for i in range(len(data))))
-
- def reader(sample):
- return sample
-
- allow_failures = True
- output = '/tmp/mnist_train.tfrecords'
- shuffle = True
-
- An example for bob.bio.base would be::
-
- from bob.bio.base.test.dummy.database import database
- from bob.bio.base.utils import read_original_data
-
- groups = 'dev'
-
- samples = database.all_files(groups=groups)
-
- CLIENT_IDS = (str(f.client_id) for f
- in database.all_files(groups=groups))
- CLIENT_IDS = list(set(CLIENT_IDS))
- CLIENT_IDS = dict(zip(CLIENT_IDS, range(len(CLIENT_IDS))))
-
-
- def file_to_label(f):
- return CLIENT_IDS[str(f.client_id)]
-
-
- def reader(biofile):
- data = read_original_data(
- biofile, database.original_directory,
- database.original_extension)
- label = file_to_label(biofile)
- key = biofile.path
- return (data, label, key)
- """
- log_parameters(logger, ignore=("samples",))
- logger.debug("len(samples): %d", len(samples))
-
- if size_estimate:
- output = tempfile.NamedTemporaryFile(suffix=".tfrecords").name
-
- output = normalize_tfrecords_path(output)
-
- if not size_estimate:
- logger.info("Writing samples to `{}'".format(output))
-
- total_size = 0
-
- create_directories_safe(os.path.dirname(output))
-
- n_samples = len(samples)
- sample_count = 0
- with tf.python_io.TFRecordWriter(output) as writer:
- if shuffle:
- logger.info("Shuffling the samples before writing ...")
- random.shuffle(samples)
- for i, sample in enumerate(samples):
- logger.info("Processing file %d out of %d", i + 1, n_samples)
-
- data, label, key = reader(sample)
-
- if data is None:
- if allow_failures:
- logger.debug("... Skipping `{0}`.".format(sample))
- continue
- else:
- raise RuntimeError("Reading failed for `{0}`".format(sample))
-
- if multiple_samples:
- for sample in data:
- total_size += write_a_sample(
- writer, sample, label, key, size_estimate=size_estimate
- )
- sample_count += 1
- else:
- total_size += write_a_sample(
- writer, data, label, key, size_estimate=size_estimate
- )
- sample_count += 1
-
- if not size_estimate:
- click.echo("Wrote {} samples into the tfrecords file.".format(sample_count))
- else:
- # delete the empty tfrecords file
- try:
- os.remove(output)
- except Exception:
- pass
- click.echo(
- "The total size of the tfrecords file will be roughly "
- "{} bytes".format(bytes2human(total_size))
- )
-
-
-@click.command()
-@click.argument("tf-record-path", nargs=1)
-@click.argument("shape", type=int, nargs=-1)
-@click.option(
- "--batch-size", help="Batch size", show_default=True, required=True, default=1000
-)
-@verbosity_option(cls=ResourceOption)
-def describe_tfrecord(tf_record_path, shape, batch_size, **kwargs):
- """
- Very often you have a tf-record file, or a set of them, and you have no
- idea how many samples you have there. Even worse, you have no idea how many
- classes you have.
-
- This click command will solve this thing for you by doing the following::
-
- $ %(prog)s <tf-record-path> 182 182 3
-
- """
- n_samples, n_labels = describe_tf_record(tf_record_path, shape, batch_size)
- click.echo("#############################################")
- click.echo("Number of samples {0}".format(n_samples))
- click.echo("Number of labels {0}".format(n_labels))
- click.echo("#############################################")
-
-
-@click.command(entry_point_group="bob.learn.tensorflow.config", cls=ConfigCommand)
-@click.option(
- "--dataset",
- required=True,
- cls=ResourceOption,
- entry_point_group="bob.learn.tensorflow.dataset",
- help="A tf.data.Dataset to be used.",
-)
-@click.option(
- "--output", "-o", required=True, cls=ResourceOption, help="Name of the output file."
-)
-@click.option(
- "--force",
- "-f",
- is_flag=True,
- cls=ResourceOption,
- help="Whether to overwrite existing files.",
-)
-@verbosity_option(cls=ResourceOption)
-def datasets_to_tfrecords(dataset, output, force, **kwargs):
- """Converts tensorflow datasets into TFRecords.
- Takes a list of datasets and outputs and writes each dataset into its output.
- ``datasets`` and ``outputs`` variables must be lists.
- You can convert the written TFRecord files back to datasets using
- :any:`bob.learn.tensorflow.dataset.tfrecords.dataset_from_tfrecord`.
-
- To use this script with SGE, change your dataset and output based on the SGE_TASK_ID
- environment variable in your config file.
- """
- log_parameters(logger)
-
- output, json_output = tfrecord_name_and_json_name(output)
- if not force and os.path.isfile(output):
- click.echo("Output file already exists: {}".format(output))
- return
-
- click.echo("Writing tfrecod to: {}".format(output))
- with tf.Session() as sess:
- os.makedirs(os.path.dirname(output), exist_ok=True)
- try:
- sess.run(dataset_to_tfrecord(dataset, output))
- except Exception:
- click.echo("Something failed. Deleting unfinished files.")
- os.remove(output)
- os.remove(json_output)
- raise
- click.echo("Successfully wrote all files.")
-
-
-@click.command(entry_point_group="bob.learn.tensorflow.config", cls=ConfigCommand)
-@click.option(
- "--dataset",
- required=True,
- cls=ResourceOption,
- entry_point_group="bob.learn.tensorflow.dataset",
- help="A tf.data.Dataset to be used.",
-)
-@click.option(
- "--output", "-o", required=True, cls=ResourceOption, help="Name of the output file."
-)
-@click.option(
- "--mean",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, the mean of data and labels will be saved in the hdf5 "
- 'file as well. You can access them in the "mean" groups.',
-)
-@verbosity_option(cls=ResourceOption)
-def dataset_to_hdf5(dataset, output, mean, **kwargs):
- """Saves a tensorflow dataset into an HDF5 file
-
- It is assumed that the dataset returns a tuple of (data, label, key) and
- the dataset is not batched.
- """
- log_parameters(logger)
-
- data, label, key = dataset.make_one_shot_iterator().get_next()
-
- sess = tf.Session()
-
- extension = ".hdf5"
-
- if not output.endswith(extension):
- output += extension
-
- create_directories_safe(os.path.dirname(output))
-
- sample_count = 0
- data_mean = 0.0
- label_mean = 0.0
-
- with HDF5File(output, "w") as f:
- while True:
- try:
- d, l, k = sess.run([data, label, key])
- group = "/{}".format(sample_count)
- f.create_group(group)
- f.cd(group)
- f["data"] = d
- f["label"] = l
- f["key"] = k
- sample_count += 1
- if mean:
- data_mean += (d - data_mean) / sample_count
- label_mean += (l - label_mean) / sample_count
- except tf.errors.OutOfRangeError:
- break
- if mean:
- f.create_group("/mean")
- f.cd("/mean")
- f["data_mean"] = data_mean
- f["label_mean"] = label_mean
-
- click.echo(f"Wrote {sample_count} samples into the hdf5 file.")
diff --git a/bob/learn/tensorflow/script/eval.py b/bob/learn/tensorflow/script/eval.py
deleted file mode 100644
index 0ff48972f05cf70aa80c849ac7f6e85f0c9508b1..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/eval.py
+++ /dev/null
@@ -1,295 +0,0 @@
-#!/usr/bin/env python
-"""Evaluates networks using Tensorflow estimators.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import click
-import logging
-import os
-import shutil
-import sys
-import tensorflow as tf
-import time
-from glob import glob
-from collections import defaultdict, OrderedDict
-from ..utils.eval import get_global_step
-from bob.extension.scripts.click_helper import (
- verbosity_option, ConfigCommand, ResourceOption, log_parameters)
-from bob.io.base import create_directories_safe
-
-logger = logging.getLogger(__name__)
-
-
-def copy_one_step(train_dir, global_step, save_dir, fail_on_error=False):
- for path in glob('{}/model.ckpt-{}.*'.format(train_dir, global_step)):
- dst = os.path.join(save_dir, os.path.basename(path))
- if os.path.isfile(dst):
- continue
- try:
- shutil.copy(path, dst)
- logger.info("Copied `%s' over to `%s'", path, dst)
- except OSError:
- logger.warning(
- "Failed to copy `%s' over to `%s'", path, dst,
- exc_info=True)
- if fail_on_error:
- raise
-
-
-def save_n_best_models(train_dir, save_dir, evaluated_file,
- keep_n_best_models, sort_by, exceptions=tuple()):
- logger.debug(
- "save_n_best_models was called with %s, %s, %s, %s, %s, %s",
- train_dir, save_dir, evaluated_file, keep_n_best_models, sort_by,
- exceptions)
-
- create_directories_safe(save_dir)
- evaluated = read_evaluated_file(evaluated_file)
-
- def _key(x):
- x = x[1][sort_by]
- if 'loss' in sort_by:
- return x
- else:
- return -x
-
- best_models = OrderedDict(
- sorted(evaluated.items(), key=_key)[:keep_n_best_models])
- logger.info("Best models: %s", best_models)
-
- # delete the old saved models that are not in top N best anymore
- saved_models = defaultdict(list)
- for path in glob('{}/model.ckpt-*'.format(save_dir)):
- global_step = path.split('model.ckpt-')[1].split('.')[0]
- saved_models[global_step].append(path)
-
- for global_step, paths in saved_models.items():
- if global_step not in best_models and global_step not in exceptions:
- for path in paths:
- logger.info("Deleting `%s'", path)
- os.remove(path)
-
- # copy over the best models if not already there
- for global_step in best_models:
- copy_one_step(train_dir, global_step, save_dir)
-
- # create a checkpoint file indicating to the best existing model:
- # 1. filter non-existing models first
- def _filter(x):
- return len(glob('{}/model.ckpt-{}.*'.format(save_dir, x[0]))) > 0
-
- best_models = OrderedDict(filter(_filter, best_models.items()))
-
- # 2. create the checkpoint file
- with open(os.path.join(save_dir, 'checkpoint'), 'wt') as f:
- if not len(best_models):
- return
- the_best_global_step = list(best_models)[0]
- f.write('model_checkpoint_path: "model.ckpt-{}"\n'.format(
- the_best_global_step))
- # reverse the models before saving since the last ones in checkpoints
- # are usually more important. This aligns with the bob tf trim script.
- for i, global_step in enumerate(reversed(best_models)):
- f.write('all_model_checkpoint_paths: "model.ckpt-{}"\n'.format(
- global_step))
-
-
-def read_evaluated_file(path):
- evaluated = {}
- with open(path) as f:
- for line in f:
- global_step, line = line.split(' ', 1)
- temp = {}
- for k_v in line.strip().split(', '):
- k, v = k_v.split(' = ')
- try:
- v = float(v)
- except ValueError: # not all values could be floats
- pass
- if 'global_step' in k:
- v = int(v)
- temp[k] = v
- evaluated[global_step] = temp
- return evaluated
-
-
-def append_evaluated_file(path, evaluations):
- str_evaluations = ', '.join(
- '%s = %s' % (k, v) for k, v in sorted(evaluations.items()))
- with open(path, 'a') as f:
- f.write('{} {}\n'.format(evaluations['global_step'], str_evaluations))
- return str_evaluations
-
-
-@click.command(
- entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand)
-@click.option(
- '--estimator',
- '-e',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.estimator',
- help='The estimator that will be evaluated.')
-@click.option(
- '--eval-input-fn',
- '-i',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.input_fn',
- help='The ``input_fn`` that will be given to '
- ':any:`tf.estimator.Estimator.eval`.')
-@click.option(
- '--hooks',
- cls=ResourceOption,
- multiple=True,
- entry_point_group='bob.learn.tensorflow.hook',
- help='List of SessionRunHook subclass instances. Used for callbacks '
- 'inside the evaluation loop.')
-@click.option(
- '--run-once',
- cls=ResourceOption,
- default=False,
- show_default=True,
- is_flag=True,
- help='If given, the model will be evaluated only once.')
-@click.option(
- '--eval-interval-secs',
- cls=ResourceOption,
- type=click.INT,
- default=60,
- show_default=True,
- help='The seconds to wait for the next evaluation.')
-@click.option('--name', cls=ResourceOption, help='Name of the evaluation')
-@click.option(
- '--keep-n-best-models',
- '-K',
- type=click.INT,
- cls=ResourceOption,
- default=1,
- show_default=True,
- help='If more than 0, will keep the best N models in the evaluation folder'
-)
-@click.option(
- '--sort-by',
- cls=ResourceOption,
- default="loss",
- show_default=True,
- help='The metric for sorting the N best models.')
-@click.option(
- '--max-wait-intervals',
- cls=ResourceOption,
- type=click.INT,
- default=-1,
- show_default=True,
- help='If given, the maximum number of intervals waiting for new training checkpoint.')
-@click.option(
- '--force-re-run',
- is_flag=True,
- default=False,
- help='A debugging flag. Do not use!')
-@verbosity_option(cls=ResourceOption)
-def eval(estimator, eval_input_fn, hooks, run_once, eval_interval_secs, name,
- keep_n_best_models, sort_by, max_wait_intervals, force_re_run,
- **kwargs):
- """Evaluates networks using Tensorflow estimators."""
- log_parameters(logger)
-
- real_name = 'eval_' + name if name else 'eval'
- eval_dir = os.path.join(estimator.model_dir, real_name)
- os.makedirs(eval_dir, exist_ok=True)
- evaluated_file = os.path.join(eval_dir, 'evaluated')
- wait_interval_count = 0
- evaluated_steps_count = 0
- while True:
- evaluated_steps = {}
- ckpt = tf.train.get_checkpoint_state(estimator.model_dir)
-
- if os.path.exists(evaluated_file):
- evaluated_steps = read_evaluated_file(evaluated_file)
-
- # create exceptions so we don't delete them
- exceptions = []
- if ckpt and ckpt.model_checkpoint_path:
- for checkpoint_path in ckpt.all_model_checkpoint_paths:
- try:
- global_step = str(get_global_step(checkpoint_path))
- except Exception:
- logger.warning("Failed to find global_step.", exc_info=True)
- continue
- if global_step not in evaluated_steps:
- exceptions.append(global_step)
-
- if max_wait_intervals > 0:
- new_evaluated_count = len(evaluated_steps.keys())
- if new_evaluated_count > 0:
- if new_evaluated_count == evaluated_steps_count:
- wait_interval_count += 1
- if wait_interval_count > max_wait_intervals:
- click.echo("Reached maximum wait interval!")
- break
- else:
- evaluated_steps_count = new_evaluated_count
- wait_interval_count = 0
-
- # Save the best N models into the eval directory
- save_n_best_models(estimator.model_dir, eval_dir, evaluated_file,
- keep_n_best_models, sort_by, exceptions)
-
- if (not ckpt) or (not ckpt.model_checkpoint_path):
- if max_wait_intervals > 0:
- wait_interval_count += 1
- if wait_interval_count > max_wait_intervals:
- break
- time.sleep(eval_interval_secs)
- continue
-
- for checkpoint_path in ckpt.all_model_checkpoint_paths:
- try:
- global_step = str(get_global_step(checkpoint_path))
- except Exception:
- logger.warning(
- 'Failed to find global_step for checkpoint_path {}, '
- 'skipping ...'.format(checkpoint_path), exc_info=True)
- continue
- if global_step in evaluated_steps and not force_re_run:
- continue
-
- # copy over the checkpoint before evaluating since it might
- # disappear after evaluation.
- try:
- copy_one_step(estimator.model_dir, global_step, eval_dir, fail_on_error=True)
- except Exception:
- # skip testing this checkpoint
- continue
-
- # evaluate based on the just copied checkpoint_path
- checkpoint_path = checkpoint_path.replace(estimator.model_dir, eval_dir + os.sep)
- checkpoint_path = os.path.abspath(checkpoint_path)
- logger.debug("Evaluating the model from %s", checkpoint_path)
-
- # Evaluate
- try:
- evaluations = estimator.evaluate(
- input_fn=eval_input_fn,
- steps=None,
- hooks=hooks,
- checkpoint_path=checkpoint_path,
- name=name,
- )
- except Exception:
- logger.info("Something went wrong in evaluation.")
- raise
-
- str_evaluations = append_evaluated_file(evaluated_file,
- evaluations)
- click.echo(str_evaluations)
- sys.stdout.flush()
-
- # Save the best N models into the eval directory
- save_n_best_models(estimator.model_dir, eval_dir, evaluated_file,
- keep_n_best_models, sort_by)
-
- if run_once:
- break
- time.sleep(eval_interval_secs)
diff --git a/bob/learn/tensorflow/script/fit.py b/bob/learn/tensorflow/script/fit.py
deleted file mode 100644
index f19776c5da551fa9dd611d0279eab99d0eee6469..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/fit.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-"""Trains networks using Keras Models.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import click
-import json
-import logging
-import os
-import tensorflow as tf
-from bob.extension.scripts.click_helper import (
- verbosity_option, ConfigCommand, ResourceOption, log_parameters)
-
-logger = logging.getLogger(__name__)
-
-
-@click.command(
- entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand)
-@click.option(
- '--model',
- '-m',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.model',
- help='The keras model that will be trained.')
-@click.option(
- '--train-input-fn',
- '-i',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.input_fn',
- help='A function that will return the training data as a tf.data.Dataset '
- 'or tf.data.Iterator. This will be given as `x` to '
- 'tf.keras.Model.fit.')
-@click.option(
- '--epochs',
- '-e',
- default=1,
- type=click.types.INT,
- cls=ResourceOption,
- help='Number of epochs to train model. See '
- 'tf.keras.Model.fit.')
-@click.option(
- '--callbacks',
- cls=ResourceOption,
- multiple=True,
- entry_point_group='bob.learn.tensorflow.callback',
- help='List of tf.keras.callbacks. Used for callbacks '
- 'inside the training loop.')
-@click.option(
- '--eval-input-fn',
- '-i',
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.input_fn',
- help='A function that will return the validation data as a tf.data.Dataset'
- ' or tf.data.Iterator. This will be given as `validation_data` to '
- 'tf.keras.Model.fit.')
-@click.option(
- '--class-weight',
- '-c',
- cls=ResourceOption,
- help='See tf.keras.Model.fit.')
-@click.option(
- '--initial-epoch',
- default=0,
- type=click.types.INT,
- cls=ResourceOption,
- help='See tf.keras.Model.fit.')
-@click.option(
- '--steps-per-epoch',
- type=click.types.INT,
- cls=ResourceOption,
- help='See tf.keras.Model.fit.')
-@click.option(
- '--validation-steps',
- type=click.types.INT,
- cls=ResourceOption,
- help='See tf.keras.Model.fit.')
-@verbosity_option(cls=ResourceOption)
-def fit(model, train_input_fn, epochs, verbose, callbacks, eval_input_fn,
- class_weight, initial_epoch, steps_per_epoch, validation_steps,
- **kwargs):
- """Trains networks using Keras models."""
- log_parameters(logger)
-
- # Train
- save_callback = [c for c in callbacks if isinstance(c, tf.keras.callbacks.ModelCheckpoint)]
- model_dir = None
- if save_callback:
- model_dir = save_callback[0].filepath
- logger.info("Training a model in %s", model_dir)
- history = model.fit(
- x=train_input_fn(),
- epochs=epochs,
- verbose=max(verbose, 2),
- callbacks=list(callbacks) if callbacks else None,
- validation_data=None if eval_input_fn is None else eval_input_fn(),
- class_weight=class_weight,
- initial_epoch=initial_epoch,
- steps_per_epoch=steps_per_epoch,
- validation_steps=validation_steps,
- )
- click.echo(history.history)
- if model_dir is not None:
- with open(os.path.join(model_dir, 'keras_fit_history.json'), 'w') as f:
- json.dump(history.history, f)
diff --git a/bob/learn/tensorflow/script/predict_bio.py b/bob/learn/tensorflow/script/predict_bio.py
deleted file mode 100644
index 54c5bb2c12cacac2c87416f4ded0df7a4140cfdf..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/predict_bio.py
+++ /dev/null
@@ -1,451 +0,0 @@
-#!/usr/bin/env python
-"""Saves predictions or embeddings of tf.estimators.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import os
-import sys
-import logging
-import click
-from bob.extension.scripts.click_helper import (
- verbosity_option,
- ConfigCommand,
- ResourceOption,
- log_parameters,
-)
-from collections import defaultdict
-import numpy as np
-import tensorflow as tf
-from bob.io.base import create_directories_safe
-from bob.bio.base.utils import save
-from bob.bio.base.tools.grid import indices
-from bob.learn.tensorflow.dataset.bio import BioGenerator
-
-try:
- import bob.bio.video
-except ModuleNotFoundError:
- pass
-
-logger = logging.getLogger(__name__)
-
-
-def make_output_path(output_dir, key):
- """Returns an output path used for saving keys. You need to make sure the
- directories leading to this output path exist.
-
- Parameters
- ----------
- output_dir : str
- The root directory to save the results
- key : str
- The key of the sample. Usually biofile.make_path("", "")
-
- Returns
- -------
- str
- The path for the provided key.
- """
- return os.path.join(output_dir, key + ".hdf5")
-
-
-def non_existing_files(paths, force=False):
- if force:
- for i in range(len(paths)):
- yield i
- return
- for i, path in enumerate(paths):
- if not os.path.isfile(path):
- yield i
-
-
-def save_predictions(output_dir, key, pred_buffer, video_container, remove_nan=False):
- outpath = make_output_path(output_dir, key)
- create_directories_safe(os.path.dirname(outpath))
- logger.debug("Saving predictions for %s", key)
- if video_container:
- fc = bob.bio.video.FrameContainer()
- for i, v in enumerate(pred_buffer[key]):
- if remove_nan and np.isnan(v):
- continue
- fc.add(i, v)
- data = fc
- else:
- if remove_nan:
- pred_buffer[key] = np.array(pred_buffer[key])
- pred_buffer[key] = pred_buffer[key][~np.isnan(pred_buffer[key])]
- data = np.mean(pred_buffer[key], axis=0)
- save(data, outpath)
-
-
-@click.command(entry_point_group="bob.learn.tensorflow.config", cls=ConfigCommand)
-@click.option(
- "--estimator",
- "-e",
- required=True,
- cls=ResourceOption,
- entry_point_group="bob.learn.tensorflow.estimator",
- help="The estimator that will be evaluated.",
-)
-@click.option(
- "--database",
- "-d",
- required=True,
- cls=ResourceOption,
- entry_point_group="bob.bio.database",
- help="A bio database. Its original_directory must point to the correct " "path.",
-)
-@click.option(
- "--biofiles",
- required=True,
- cls=ResourceOption,
- help="The list of the bio files. You can only provide this through config "
- "files.",
-)
-@click.option(
- "--bio-predict-input-fn",
- required=True,
- cls=ResourceOption,
- entry_point_group="bob.learn.tensorflow.biogenerator_input",
- help="A callable with the signature of "
- "`input_fn = bio_predict_input_fn(generator, output_types, output_shapes)`"
- " The inputs are documented in :any:`tf.data.Dataset.from_generator`"
- " and the output should be a function with no arguments and is passed"
- " to :any:`tf.estimator.Estimator.predict`.",
-)
-@click.option(
- "--output-dir",
- "-o",
- required=True,
- cls=ResourceOption,
- help="The directory to save the predictions.",
-)
-@click.option(
- "--load-data",
- cls=ResourceOption,
- entry_point_group="bob.learn.tensorflow.load_data",
- help="A callable with the signature of "
- "``data = load_data(database, biofile)``. "
- ":any:`bob.bio.base.read_original_data` is used by default.",
-)
-@click.option(
- "--hooks",
- cls=ResourceOption,
- multiple=True,
- entry_point_group="bob.learn.tensorflow.hook",
- help="List of SessionRunHook subclass instances.",
-)
-@click.option(
- "--predict-keys",
- "-k",
- multiple=True,
- default=None,
- cls=ResourceOption,
- help="List of `str`, name of the keys to predict. It is used if the "
- "`EstimatorSpec.predictions` is a `dict`. If `predict_keys` is used "
- "then rest of the predictions will be filtered from the dictionary. "
- "If `None`, returns all.",
-)
-@click.option(
- "--checkpoint-path",
- "-c",
- cls=ResourceOption,
- help="Path of a specific checkpoint to predict. If `None`, the "
- "latest checkpoint in `model_dir` is used. This can also "
- 'be a folder which contains a "checkpoint" file where the '
- "latest checkpoint from inside this file will be used as "
- "checkpoint_path.",
-)
-@click.option(
- "--multiple-samples",
- "-m",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, it assumes that the db interface returns "
- "several samples from a biofile. This option can be used "
- "when you are working with videos.",
-)
-@click.option(
- "--array",
- "-t",
- type=click.INT,
- default=1,
- cls=ResourceOption,
- help="Use this option alongside gridtk to submit this script as " "an array job.",
-)
-@click.option(
- "--force",
- "-f",
- is_flag=True,
- cls=ResourceOption,
- help="Whether to overwrite existing predictions.",
-)
-@click.option(
- "--video-container",
- "-vc",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, the predictions will be written in FrameContainers from"
- " bob.bio.video. You need to install bob.bio.video as well.",
-)
-@click.option(
- "--remove-nan",
- "-rn",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, will remove nans before computing the mean or remove nans "
- "from the frame container.",
-)
-@verbosity_option(cls=ResourceOption)
-def predict_bio(
- estimator,
- database,
- biofiles,
- bio_predict_input_fn,
- output_dir,
- load_data,
- hooks,
- predict_keys,
- checkpoint_path,
- multiple_samples,
- array,
- force,
- video_container,
- remove_nan,
- **kwargs
-):
- """Saves predictions or embeddings of tf.estimators.
-
- This script works with bob.bio.base databases. This script works with
- tensorflow 1.4 and above.
-
- An example configuration for a trained model and its evaluation could be::
-
- import tensorflow as tf
-
- # define the database:
- from bob.bio.base.test.dummy.database import database
-
- # load the estimator model
- estimator = tf.estimator.Estimator(model_fn, model_dir)
-
- groups = ['dev']
- biofiles = database.all_files(groups)
-
-
- # the ``dataset = tf.data.Dataset.from_generator(generator,
- # output_types, output_shapes)`` line is mandatory in the function
- # below. You have to create it in your configuration file since you
- # want it to be created in the same graph as your model.
- def bio_predict_input_fn(generator, output_types, output_shapes):
- def input_fn():
- dataset = tf.data.Dataset.from_generator(
- generator, output_types, output_shapes)
- # apply all kinds of transformations here, process the data
- # even further if you want.
- dataset = dataset.prefetch(1)
- dataset = dataset.batch(10**3)
- images, labels, keys = dataset.make_one_shot_iterator().get_next()
-
- return {'data': images, 'key': keys}, labels
- return input_fn
- """
- log_parameters(logger, ignore=("biofiles",))
- logger.debug("len(biofiles): %d", len(biofiles))
-
- assert len(biofiles), "biofiles are empty!"
-
- if array > 1:
- start, end = indices(biofiles, array)
- biofiles = biofiles[start:end]
-
- # filter the existing files
- paths = [make_output_path(output_dir, f.make_path("", "")) for f in biofiles]
- indexes = non_existing_files(paths, force)
- biofiles = [biofiles[i] for i in indexes]
-
- if len(biofiles) == 0:
- logger.warning("The biofiles are empty after checking for existing files.")
- return
-
- generator = BioGenerator(
- database, biofiles, load_data=load_data, multiple_samples=multiple_samples
- )
-
- predict_input_fn = bio_predict_input_fn(
- generator, generator.output_types, generator.output_shapes
- )
-
- logger.info("Saving the predictions of %d files in %s", len(generator), output_dir)
- generic_predict(
- estimator=estimator,
- predict_input_fn=predict_input_fn,
- output_dir=output_dir,
- predict_keys=predict_keys,
- checkpoint_path=checkpoint_path,
- hooks=hooks,
- video_container=video_container,
- remove_nan=remove_nan,
- )
-
-
-@click.command(entry_point_group="bob.learn.tensorflow.config", cls=ConfigCommand)
-@click.option(
- "--estimator",
- "-e",
- required=True,
- cls=ResourceOption,
- entry_point_group="bob.learn.tensorflow.estimator",
- help="The estimator that will be evaluated.",
-)
-@click.option(
- "--predict-input-fn",
- required=True,
- cls=ResourceOption,
- entry_point_group="bob.learn.tensorflow.input_fn",
- help="A callable with no arguments which will be used in estimator.predict.",
-)
-@click.option(
- "--output-dir",
- "-o",
- required=True,
- cls=ResourceOption,
- help="The directory to save the predictions.",
-)
-@click.option(
- "--predict-keys",
- "-k",
- multiple=True,
- default=None,
- cls=ResourceOption,
- help="List of `str`, name of the keys to predict. It is used if the "
- "`EstimatorSpec.predictions` is a `dict`. If `predict_keys` is used "
- "then rest of the predictions will be filtered from the dictionary. "
- "If `None`, returns all.",
-)
-@click.option(
- "--checkpoint-path",
- "-c",
- cls=ResourceOption,
- help="Path of a specific checkpoint to predict. If `None`, the "
- "latest checkpoint in `model_dir` is used. This can also "
- 'be a folder which contains a "checkpoint" file where the '
- "latest checkpoint from inside this file will be used as "
- "checkpoint_path.",
-)
-@click.option(
- "--hooks",
- cls=ResourceOption,
- multiple=True,
- entry_point_group="bob.learn.tensorflow.hook",
- help="List of SessionRunHook subclass instances.",
-)
-@click.option(
- "--video-container",
- "-vc",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, the predictions will be written in FrameContainers from"
- " bob.bio.video. You need to install bob.bio.video as well.",
-)
-@click.option(
- "--remove-nan",
- "-rn",
- is_flag=True,
- cls=ResourceOption,
- help="If provided, will remove nans before computing the mean or remove nans "
- "from the frame container.",
-)
-@verbosity_option(cls=ResourceOption)
-def predict(
- estimator,
- predict_input_fn,
- output_dir,
- predict_keys,
- checkpoint_path,
- hooks,
- video_container,
- remove_nan,
- **kwargs
-):
-
- generic_predict(
- estimator=estimator,
- predict_input_fn=predict_input_fn,
- output_dir=output_dir,
- predict_keys=predict_keys,
- checkpoint_path=checkpoint_path,
- hooks=hooks,
- video_container=video_container,
- remove_nan=remove_nan,
- )
-
-
-def generic_predict(
- estimator,
- predict_input_fn,
- output_dir,
- predict_keys=None,
- checkpoint_path=None,
- hooks=None,
- video_container=False,
- remove_nan=False,
-):
- # if the checkpoint_path is a directory, pick the latest checkpoint from
- # that directory
- if checkpoint_path:
- if os.path.isdir(checkpoint_path):
- ckpt = tf.train.get_checkpoint_state(checkpoint_path)
- if ckpt and ckpt.model_checkpoint_path:
- checkpoint_path = ckpt.model_checkpoint_path
-
- logger.info("Restoring the model from %s", checkpoint_path)
-
- predictions = estimator.predict(
- predict_input_fn,
- predict_keys=predict_keys,
- hooks=hooks,
- checkpoint_path=checkpoint_path,
- )
-
- if video_container:
- try:
- import bob.bio.video
- except ModuleNotFoundError:
- raise click.ClickException(
- "Could not import bob.bio.video. Have you installed it?"
- )
-
- pred_buffer = defaultdict(list)
- for i, pred in enumerate(predictions):
- key = pred["key"]
- # key is in bytes format in Python 3
- if sys.version_info >= (3,):
- key = key.decode(errors="replace")
- if predict_keys:
- prob = pred[predict_keys[0]]
- else:
- prob = pred.get(
- "probabilities", pred.get("embeddings", pred.get("predictions"))
- )
- assert prob is not None
- pred_buffer[key].append(prob)
- if i == 0:
- last_key = key
- if last_key == key:
- continue
- else:
- save_predictions(output_dir, last_key, pred_buffer, video_container, remove_nan)
- # delete saved data so we don't run out of RAM
- del pred_buffer[last_key]
- # start saving this new key
- last_key = key
-
- try:
- key
- # save the final returned key as well:
- save_predictions(output_dir, key, pred_buffer, video_container, remove_nan)
- except UnboundLocalError:
- # if the input_fn was empty and hence key is not defined
- click.echo("predict_input_fn returned no samples.")
- pass
diff --git a/bob/learn/tensorflow/script/style_transfer.py b/bob/learn/tensorflow/script/style_transfer.py
deleted file mode 100644
index 85c1db77b6e58d24fed3aa942c2dbbab7a17ec3f..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/style_transfer.py
+++ /dev/null
@@ -1,199 +0,0 @@
-#!/usr/bin/env python
-"""Trains networks using Tensorflow estimators.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import logging
-import click
-import tensorflow as tf
-from bob.extension.scripts.click_helper import (verbosity_option,
- ConfigCommand, ResourceOption)
-import bob.io.image
-import bob.io.base
-import numpy
-import bob.ip.base
-import bob.ip.color
-import sys
-import os
-from bob.learn.tensorflow.style_transfer import do_style_transfer
-
-
-logger = logging.getLogger(__name__)
-
-
-@click.command(
- entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand)
-@click.argument('content_image_path', required=True)
-@click.argument('output_path', required=True)
-@click.option('--style-image-paths',
- cls=ResourceOption,
- required=True,
- multiple=True,
- entry_point_group='bob.learn.tensorflow.style_images',
- help='List of images that encodes the style.')
-@click.option('--architecture',
- '-a',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.architecture',
- help='The base architecure.')
-@click.option('--checkpoint-dir',
- '-c',
- required=True,
- cls=ResourceOption,
- help='CNN checkpoint path')
-@click.option('--iterations',
- '-i',
- type=click.types.INT,
- help='Number of iterations to generate the image',
- default=1000)
-@click.option('--learning_rate',
- '-r',
- type=click.types.FLOAT,
- help='Learning rate.',
- default=1.)
-@click.option('--content-weight',
- type=click.types.FLOAT,
- help='Weight of the content loss.',
- default=5.)
-@click.option('--style-weight',
- type=click.types.FLOAT,
- help='Weight of the style loss.',
- default=100.)
-@click.option('--denoise-weight',
- type=click.types.FLOAT,
- help='Weight denoising loss.',
- default=100.)
-@click.option('--content-end-points',
- cls=ResourceOption,
- multiple=True,
- entry_point_group='bob.learn.tensorflow.end_points',
- help='List of end_points for the used to encode the content')
-@click.option('--style-end-points',
- cls=ResourceOption,
- multiple=True,
- entry_point_group='bob.learn.tensorflow.end_points',
- help='List of end_points for the used to encode the style')
-@click.option('--scopes',
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.scopes',
- help='Dictionary containing the mapping scores',
- required=True)
-@click.option('--pure-noise',
- is_flag=True,
- help="If set will save the raw noisy generated image."
- "If not set, the output will be RGB = stylizedYUV.Y, originalYUV.U, originalYUV.V"
- )
-@click.option('--preprocess-fn',
- '-pr',
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.preprocess_fn',
- help='Preprocess function. Pointer to a function that preprocess the INPUT signal')
-@click.option('--un-preprocess-fn',
- '-un',
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.preprocess_fn',
- help='Un preprocess function. Pointer to a function that preprocess the OUTPUT signal')
-@click.option(
- '--start-from',
- '-sf',
- cls=ResourceOption,
- default="noise",
- type=click.Choice(["noise", "content", "style"]),
- help="Starts from this image for reconstruction",
-)
-@verbosity_option(cls=ResourceOption)
-def style_transfer(content_image_path, output_path, style_image_paths,
- architecture, checkpoint_dir,
- iterations, learning_rate,
- content_weight, style_weight, denoise_weight, content_end_points,
- style_end_points, scopes, pure_noise, preprocess_fn,
- un_preprocess_fn, start_from, **kwargs):
- """
- Trains neural style transfer using the approach presented in:
-
- Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015).
-
- \b
-
- If you want run a style transfer using InceptionV2 as basis, use the following template
-
- Below follow a CONFIG template
-
- CONFIG.PY
- ```
-
- from bob.extension import rc
-
- from bob.learn.tensorflow.network import inception_resnet_v2_batch_norm
- architecture = inception_resnet_v2_batch_norm
-
- checkpoint_dir = rc["bob.bio.face_ongoing.idiap_casia_inception_v2_centerloss_rgb"]
-
- style_end_points = ["Conv2d_1a_3x3", "Conv2d_2b_3x3", "Conv2d_3b_1x1", "Conv2d_4a_3x3"]
-
- content_end_points = ["Bottleneck", "PreLogitsFlatten"]
-
- scopes = {"InceptionResnetV2/":"InceptionResnetV2/"}
-
- ```
- \b
-
- Then run::
-
- $ bob tf style <content-image> <output-image> --style-image-paths <style-image> CONFIG.py
-
-
- You can also provide a list of images to encode the style using the config file as in the example below.
-
- CONFIG.PY
- ```
-
- from bob.extension import rc
-
- from bob.learn.tensorflow.network import inception_resnet_v2_batch_norm
- architecture = inception_resnet_v2_batch_norm
-
- checkpoint_dir = rc["bob.bio.face_ongoing.idiap_casia_inception_v2_centerloss_rgb"]
-
- style_end_points = ["Conv2d_1a_3x3", "Conv2d_2b_3x3", "Conv2d_3b_1x1", "Conv2d_4a_3x3"]
-
- content_end_points = ["Bottleneck", "PreLogitsFlatten"]
-
- scopes = {"InceptionResnetV2/":"InceptionResnetV2/"}
-
- style_image_paths = ["STYLE_1.png",
- "STYLE_2.png"]
-
- ```
-
- Then run::
-
- $ bob tf style <content-image> <output-image> CONFIG.py
-
- \b \b
-
- """
-
- logger.info("Style transfer, content_image={0}, style_image={1}".format(content_image_path, style_image_paths))
-
- # Loading content image
- content_image = bob.io.base.load(content_image_path)
-
- # Reading and converting to the tensorflow format
- style_images = []
- for path in style_image_paths:
- style_images.append(bob.io.base.load(path))
-
- output = do_style_transfer(content_image, style_images,
- architecture, checkpoint_dir, scopes,
- content_end_points, style_end_points,
- preprocess_fn=preprocess_fn, un_preprocess_fn=un_preprocess_fn,
- pure_noise=pure_noise,
- iterations=iterations, learning_rate=learning_rate,
- content_weight=content_weight, style_weight=style_weight,
- denoise_weight=denoise_weight, start_from=start_from)
-
- os.makedirs(os.path.dirname(output_path), exist_ok=True)
- bob.io.base.save(output, output_path)
diff --git a/bob/learn/tensorflow/script/train.py b/bob/learn/tensorflow/script/train.py
deleted file mode 100644
index d33d5767abb82a3c012805df4117cce227df3bf5..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/train.py
+++ /dev/null
@@ -1,61 +0,0 @@
-#!/usr/bin/env python
-"""Trains networks using Tensorflow estimators.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import logging
-import click
-from bob.extension.scripts.click_helper import (
- verbosity_option, ConfigCommand, ResourceOption, log_parameters)
-
-logger = logging.getLogger(__name__)
-
-
-@click.command(
- entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand)
-@click.option(
- '--estimator',
- '-e',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.estimator',
- help='The estimator that will be trained.')
-@click.option(
- '--train-input-fn',
- '-i',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.input_fn',
- help='The ``input_fn`` that will be given to '
- ':any:`tf.estimator.Estimator.train`.')
-@click.option(
- '--hooks',
- cls=ResourceOption,
- multiple=True,
- entry_point_group='bob.learn.tensorflow.hook',
- help='List of SessionRunHook subclass instances. Used for callbacks '
- 'inside the training loop.')
-@click.option(
- '--steps',
- '-s',
- cls=ResourceOption,
- type=click.types.INT,
- help='Number of steps for which to train model. See '
- ':any:`tf.estimator.Estimator.train`.')
-@click.option(
- '--max-steps',
- '-m',
- cls=ResourceOption,
- type=click.types.INT,
- help='Number of total steps for which to train model. See '
- ':any:`tf.estimator.Estimator.train`.')
-@verbosity_option(cls=ResourceOption)
-def train(estimator, train_input_fn, hooks, steps, max_steps, **kwargs):
- """Trains networks using Tensorflow estimators."""
- log_parameters(logger)
-
- # Train
- logger.info("Training a model in %s", estimator.model_dir)
- estimator.train(
- input_fn=train_input_fn, hooks=hooks, steps=steps, max_steps=max_steps)
diff --git a/bob/learn/tensorflow/script/train_and_evaluate.py b/bob/learn/tensorflow/script/train_and_evaluate.py
deleted file mode 100644
index 3ba4ebde0c874750ef97ea6ec8a942c1a3d5bc85..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/train_and_evaluate.py
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/usr/bin/env python
-"""Trains and evaluates a network using Tensorflow estimators.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import tensorflow as tf
-from bob.learn.tensorflow.utils.hooks import EarlyStopException
-import logging
-import click
-from bob.extension.scripts.click_helper import (verbosity_option,
- ConfigCommand, ResourceOption)
-
-logger = logging.getLogger(__name__)
-
-
-@click.command(
- entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand)
-@click.option(
- '--estimator',
- '-e',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.estimator',
- help='The estimator that will be trained and evaluated.')
-@click.option(
- '--train-spec',
- '-it',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.trainspec',
- help='See :any:`tf.estimator.Estimator.train_and_evaluate`.')
-@click.option(
- '--eval-spec',
- '-ie',
- required=True,
- cls=ResourceOption,
- entry_point_group='bob.learn.tensorflow.evalspec',
- help='See :any:`tf.estimator.Estimator.train_and_evaluate`.')
-@click.option(
- '--exit-ok-exceptions',
- cls=ResourceOption,
- multiple=True,
- default=(EarlyStopException, ),
- show_default=True,
- entry_point_group='bob.learn.tensorflow.exception',
- help='A list of exceptions to exit properly if they occur. If nothing is '
- 'provided, the EarlyStopException is handled by default.')
-@verbosity_option(cls=ResourceOption)
-def train_and_evaluate(estimator, train_spec, eval_spec, exit_ok_exceptions,
- **kwargs):
- """Trains and evaluates a network using Tensorflow estimators.
-
- This script calls the estimator.train_and_evaluate function. Please see:
- https://www.tensorflow.org/api_docs/python/tf/estimator/train_and_evaluate
- https://www.tensorflow.org/api_docs/python/tf/estimator/TrainSpec
- https://www.tensorflow.org/api_docs/python/tf/estimator/EvalSpec
- for more details.
- """
- logger.debug('estimator: %s', estimator)
- logger.debug('train_spec: %s', train_spec)
- logger.debug('eval_spec: %s', eval_spec)
- logger.debug('exit_ok_exceptions: %s', exit_ok_exceptions)
- logger.debug('kwargs: %s', kwargs)
-
- # Train and evaluate
- try:
- tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
- except exit_ok_exceptions as e:
- logger.exception(e)
- return
diff --git a/bob/learn/tensorflow/script/trim.py b/bob/learn/tensorflow/script/trim.py
deleted file mode 100644
index d89bb292a75e75b191efc7e9542a083d4e9d9c45..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/trim.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/usr/bin/env python
-"""Deletes extra tensorflow checkpoints.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import click
-import logging
-import os
-import glob
-import tensorflow as tf
-from bob.extension.scripts.click_helper import verbosity_option, log_parameters
-
-logger = logging.getLogger(__name__)
-
-
-def delete_extra_checkpoints(directory, keep_last_n, dry_run):
- try:
- ckpt = tf.train.get_checkpoint_state(directory)
- except Exception:
- return
- if (not ckpt) or (not ckpt.model_checkpoint_path):
- logger.debug('Could not find a checkpoint in %s', directory)
- return
- for checkpoint_path in ckpt.all_model_checkpoint_paths[:-keep_last_n]:
- if checkpoint_path == ckpt.model_checkpoint_path:
- continue
- if dry_run:
- click.echo('Would delete {}.*'.format(checkpoint_path))
- else:
- logger.info('Deleting %s.*', checkpoint_path)
- for path in glob.glob('{}.*'.format(checkpoint_path)):
- os.remove(path)
-
- def _existing(x):
- return glob.glob('{}.*'.format(x))
-
- # update the checkpoint file
- all_paths = filter(_existing, ckpt.all_model_checkpoint_paths)
- all_paths = list(map(os.path.basename, all_paths))
- model_checkpoint_path = os.path.basename(ckpt.model_checkpoint_path)
- tf.train.update_checkpoint_state(
- directory, model_checkpoint_path, all_paths)
-
-
-@click.command(epilog='''\b
-Examples:
-$ bob tf trim -vv ~/my_models/model_dir
-$ bob tf trim -vv ~/my_models/model_dir1 ~/my_models/model_dir2
-$ bob tf trim -vvr ~/my_models
-$ bob tf trim -vvrn ~/my_models
-$ bob tf trim -vvrK 2 ~/my_models
-''')
-@click.argument(
- 'root_dirs',
- nargs=-1,
- type=click.Path(exists=True, file_okay=False, dir_okay=True),
-)
-@click.option(
- '--keep-last-n-models',
- '-K',
- type=click.INT,
- default=1,
- show_default=True,
- help='The number of recent checkpoints to keep.')
-@click.option(
- '--recurse',
- '-r',
- is_flag=True,
- help='If given, it will delete checkpoints recursively.')
-@click.option(
- '--dry-run',
- '-n',
- is_flag=True,
- help='If given, will only print what will be deleted.')
-@verbosity_option()
-def trim(root_dirs, keep_last_n_models, recurse, dry_run, **kwargs):
- """Deletes extra tensorflow checkpoints."""
- log_parameters(logger)
-
- for root_dir in root_dirs:
- if recurse:
- for directory, _, _ in os.walk(root_dir):
- delete_extra_checkpoints(directory, keep_last_n_models,
- dry_run)
- else:
- delete_extra_checkpoints(root_dir, keep_last_n_models, dry_run)
diff --git a/bob/learn/tensorflow/script/utils.py b/bob/learn/tensorflow/script/utils.py
deleted file mode 100644
index 12b360c00dff65ca02d8825ce95fcd68d5b14da3..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/utils.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import click
-
-
-def eager_execution_option(**kwargs):
- """Adds an option to your command to enable eager execution of tensorflow
-
- Returns
- -------
- callable
- A decorator to be used for adding this option to click commands
- """
- def custom_eager_execution_option(func):
- def callback(ctx, param, value):
- if not value or ctx.resilient_parsing:
- return
- import tensorflow as tf
- tf.enable_eager_execution()
- if not tf.executing_eagerly():
- raise click.ClickException(
- "Could not enable tensorflow eager execution mode!")
- else:
- click.echo("Executing tensorflow operations eagerly!")
-
- return click.option(
- '-e', '--eager', is_flag=True, callback=callback,
- expose_value=False, is_eager=True, **kwargs)(func)
- return custom_eager_execution_option
diff --git a/bob/learn/tensorflow/scripts/__init__.py b/bob/learn/tensorflow/scripts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..435eb712f6b140c26c5402f125616b558e7e223d
--- /dev/null
+++ b/bob/learn/tensorflow/scripts/__init__.py
@@ -0,0 +1,2 @@
+# gets sphinx autodoc done right - don't remove it
+__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/scripts/datasets_to_tfrecords.py b/bob/learn/tensorflow/scripts/datasets_to_tfrecords.py
new file mode 100644
index 0000000000000000000000000000000000000000..96f16e7ae1e0e93752e0c383a4c769665de3e6ad
--- /dev/null
+++ b/bob/learn/tensorflow/scripts/datasets_to_tfrecords.py
@@ -0,0 +1,69 @@
+"""Convert datasets to TFRecords
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import logging
+
+import click
+
+from bob.extension.scripts.click_helper import ConfigCommand
+from bob.extension.scripts.click_helper import ResourceOption
+from bob.extension.scripts.click_helper import verbosity_option
+
+
+logger = logging.getLogger(__name__)
+
+
+@click.command(entry_point_group="bob.learn.tensorflow.config", cls=ConfigCommand)
+@click.option(
+ "--dataset",
+ required=True,
+ cls=ResourceOption,
+ entry_point_group="bob.learn.tensorflow.dataset",
+ help="A tf.data.Dataset to be used.",
+)
+@click.option(
+ "--output", "-o", required=True, cls=ResourceOption, help="Name of the output file."
+)
+@click.option(
+ "--force",
+ "-f",
+ is_flag=True,
+ cls=ResourceOption,
+ help="Whether to overwrite existing files.",
+)
+@verbosity_option(cls=ResourceOption)
+def datasets_to_tfrecords(dataset, output, force, **kwargs):
+ """Converts tensorflow datasets into TFRecords.
+ Takes a list of datasets and outputs and writes each dataset into its output.
+ ``datasets`` and ``outputs`` variables must be lists.
+ You can convert the written TFRecord files back to datasets using
+ :any:`bob.learn.tensorflow.dataset.tfrecords.dataset_from_tfrecord`.
+
+ To use this script with SGE, change your dataset (like shard it) and output a part
+ of the dataset based on the SGE_TASK_ID environment variable in your config file.
+ """
+ from bob.extension.scripts.click_helper import log_parameters
+ import os
+ from bob.learn.tensorflow.data.tfrecords import dataset_to_tfrecord
+ from bob.learn.tensorflow.data.tfrecords import tfrecord_name_and_json_name
+
+ log_parameters(logger)
+
+ output, json_output = tfrecord_name_and_json_name(output)
+ if not force and os.path.isfile(output):
+ click.echo("Output file already exists: {}".format(output))
+ return
+
+ click.echo("Writing tfrecod to: {}".format(output))
+ os.makedirs(os.path.dirname(output), exist_ok=True)
+ try:
+ dataset_to_tfrecord(dataset, output)
+ except Exception:
+ click.echo("Something failed. Deleting unfinished files.")
+ os.remove(output)
+ os.remove(json_output)
+ raise
+ click.echo("Successfully wrote all files.")
diff --git a/bob/learn/tensorflow/scripts/fit.py b/bob/learn/tensorflow/scripts/fit.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb3085924b3d9c7f5ff768c06f22d03f6d3b505a
--- /dev/null
+++ b/bob/learn/tensorflow/scripts/fit.py
@@ -0,0 +1,120 @@
+#!/usr/bin/env python
+"""Trains networks using Keras Models.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+import logging
+import os
+
+import click
+import tensorflow as tf
+
+from bob.extension.scripts.click_helper import ConfigCommand
+from bob.extension.scripts.click_helper import ResourceOption
+from bob.extension.scripts.click_helper import log_parameters
+from bob.extension.scripts.click_helper import verbosity_option
+
+logger = logging.getLogger(__name__)
+
+
+@click.command(entry_point_group="bob.learn.tensorflow.config", cls=ConfigCommand)
+@click.option(
+ "--model-fn",
+ "-m",
+ required=True,
+ cls=ResourceOption,
+ entry_point_group="bob.learn.tensorflow.model",
+ help="The keras model that will be trained.",
+)
+@click.option(
+ "--train-input-fn",
+ "-i",
+ required=True,
+ cls=ResourceOption,
+ entry_point_group="bob.learn.tensorflow.input_fn",
+ help="A function that will return the training data as a tf.data.Dataset "
+ "or tf.data.Iterator. This will be given as `x` to "
+ "tf.keras.Model.fit.",
+)
+@click.option(
+ "--epochs",
+ "-e",
+ default=10,
+ type=click.types.INT,
+ cls=ResourceOption,
+ help="Number of epochs to train model. See " "tf.keras.Model.fit.",
+)
+@click.option(
+ "--callbacks",
+ cls=ResourceOption,
+ multiple=True,
+ entry_point_group="bob.learn.tensorflow.callback",
+ help="List of tf.keras.callbacks. Used for callbacks " "inside the training loop.",
+)
+@click.option(
+ "--eval-input-fn",
+ "-i",
+ cls=ResourceOption,
+ entry_point_group="bob.learn.tensorflow.input_fn",
+ help="A function that will return the validation data as a tf.data.Dataset"
+ " or tf.data.Iterator. This will be given as `validation_data` to "
+ "tf.keras.Model.fit.",
+)
+@click.option(
+ "--class-weight", "-c", cls=ResourceOption, help="See tf.keras.Model.fit."
+)
+@click.option(
+ "--steps-per-epoch",
+ type=click.types.INT,
+ cls=ResourceOption,
+ help="See tf.keras.Model.fit.",
+)
+@click.option(
+ "--validation-steps",
+ type=click.types.INT,
+ cls=ResourceOption,
+ help="See tf.keras.Model.fit.",
+)
+@verbosity_option(cls=ResourceOption)
+def fit(
+ model_fn,
+ train_input_fn,
+ epochs,
+ verbose,
+ callbacks,
+ eval_input_fn,
+ class_weight,
+ steps_per_epoch,
+ validation_steps,
+ **kwargs
+):
+ """Trains networks using Keras models."""
+ log_parameters(logger)
+
+ # Train
+ save_callback = [
+ c for c in callbacks if isinstance(c, tf.keras.callbacks.ModelCheckpoint)
+ ]
+ model_dir = None
+ if save_callback:
+ model_dir = save_callback[0].filepath
+ logger.info("Training a model in %s", model_dir)
+ model = model_fn()
+
+ history = model.fit(
+ x=train_input_fn(),
+ epochs=epochs,
+ verbose=max(verbose, 2),
+ callbacks=list(callbacks) if callbacks else None,
+ validation_data=None if eval_input_fn is None else eval_input_fn(),
+ class_weight=class_weight,
+ steps_per_epoch=steps_per_epoch,
+ validation_steps=validation_steps,
+ )
+ click.echo(history.history)
+ if model_dir is not None:
+ with open(os.path.join(model_dir, "keras_fit_history.json"), "w") as f:
+ json.dump(history.history, f)
diff --git a/bob/learn/tensorflow/script/keras.py b/bob/learn/tensorflow/scripts/keras.py
similarity index 65%
rename from bob/learn/tensorflow/script/keras.py
rename to bob/learn/tensorflow/scripts/keras.py
index 9f6ee9c24b09f174ceba366b98e4d17cf0ebbdd0..3670421eaeba52bf2f40db2384f8c0f6e5e7c198 100644
--- a/bob/learn/tensorflow/script/keras.py
+++ b/bob/learn/tensorflow/scripts/keras.py
@@ -3,13 +3,12 @@
import click
import pkg_resources
from click_plugins import with_plugins
+
from bob.extension.scripts.click_helper import AliasedGroup
-from .utils import eager_execution_option
-@with_plugins(pkg_resources.iter_entry_points('bob.learn.tensorflow.keras_cli'))
+@with_plugins(pkg_resources.iter_entry_points("bob.learn.tensorflow.keras_cli"))
@click.group(cls=AliasedGroup)
-@eager_execution_option()
def keras():
"""Keras-related commands."""
pass
diff --git a/bob/learn/tensorflow/script/tf.py b/bob/learn/tensorflow/scripts/tf.py
similarity index 66%
rename from bob/learn/tensorflow/script/tf.py
rename to bob/learn/tensorflow/scripts/tf.py
index f460df4333a100a9809b89d84012dec9ba0e3eb3..c7ccc63e485408d3a3683026b69ce3813f8b4d44 100644
--- a/bob/learn/tensorflow/script/tf.py
+++ b/bob/learn/tensorflow/scripts/tf.py
@@ -3,13 +3,12 @@
import click
import pkg_resources
from click_plugins import with_plugins
+
from bob.extension.scripts.click_helper import AliasedGroup
-from .utils import eager_execution_option
-@with_plugins(pkg_resources.iter_entry_points('bob.learn.tensorflow.cli'))
+@with_plugins(pkg_resources.iter_entry_points("bob.learn.tensorflow.cli"))
@click.group(cls=AliasedGroup)
-@eager_execution_option()
def tf():
"""Tensorflow-related commands."""
pass
diff --git a/bob/learn/tensorflow/style_transfer/neural_transfer.py b/bob/learn/tensorflow/style_transfer/neural_transfer.py
deleted file mode 100644
index b65bd853273959a73f1499d34ab464ab26adc066..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/style_transfer/neural_transfer.py
+++ /dev/null
@@ -1,276 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-
-import tensorflow as tf
-import numpy
-import os
-from bob.learn.tensorflow.loss import linear_gram_style_loss, content_loss, denoising_loss
-import bob.io.image
-import bob.ip.color
-
-import logging
-logger = logging.getLogger(__name__)
-
-
-def compute_features(input_image, architecture, checkpoint_dir, target_end_points, preprocess_fn=None):
- """
- For a given set of end_points, convolve the input image until these points
-
- Parameters
- ----------
-
- input_image: :any:`numpy.array`
- Input image in the format WxHxC
-
- architecture:
- Pointer to the architecture function
-
- checkpoint_dir: str
- DCNN checkpoint directory
-
- end_points: dict
- Dictionary containing the end point tensors
-
- preprocess_fn:
- Pointer to a preprocess function
-
- """
-
- input_pl = tf.placeholder('float32', shape=(1, input_image.shape[1],
- input_image.shape[2],
- input_image.shape[3]))
-
- if preprocess_fn is None:
- _, end_points = architecture(input_pl, mode=tf.estimator.ModeKeys.PREDICT, trainable_variables=None)
- else:
- _, end_points = architecture(tf.stack([preprocess_fn(i) for i in tf.unstack(input_pl)]), mode=tf.estimator.ModeKeys.PREDICT, trainable_variables=None)
- with tf.Session() as sess:
- # Restoring the checkpoint for the given architecture
- sess.run(tf.global_variables_initializer())
- saver = tf.train.Saver()
-
- if os.path.isdir(checkpoint_dir):
- saver.restore(sess, tf.train.latest_checkpoint(checkpoint_dir))
- else:
- saver.restore(sess, checkpoint_dir)
-
- #content_feature = sess.run(end_points[CONTENT_END_POINTS], feed_dict={input_image: content_image})
- features = sess.run([end_points[ep] for ep in target_end_points], feed_dict={input_pl: input_image})
-
- # Killing the graph
- tf.reset_default_graph()
- return features
-
-
-def compute_gram(features):
- """
- Given a list of features (as numpy.arrays) comput the gram matrices of each
- pinning the channel as in:
-
- Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015).
-
- Parameters
- ----------
-
- features: :any:`numpy.array`
- Convolved features in the format NxWxHxC
-
- """
-
- grams = []
- for f in features:
- f = numpy.reshape(f, (-1, f.shape[3]))
- grams.append(numpy.matmul(f.T, f) / f.size)
-
- return grams
-
-
-def do_style_transfer(content_image, style_images,
- architecture, checkpoint_dir, scopes,
- content_end_points, style_end_points,
- preprocess_fn=None, un_preprocess_fn=None, pure_noise=False,
- iterations=1000, learning_rate=0.1,
- content_weight=5., style_weight=500., denoise_weight=500., start_from="noise"):
-
- """
- Trains neural style transfer using the approach presented in:
-
- Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015).
-
- Parameters
- ----------
-
- content_image: :any:`numpy.array`
- Content image in the Bob format (C x W x H)
-
- style_images: :any:`list`
- List of numpy.array (Bob format (C x W x H)) that encodes the style
-
- architecture:
- Point to a function with the base architecture
-
- checkpoint_dir:
- CNN checkpoint path
-
- scopes:
- Dictionary containing the mapping scores
-
- content_end_points:
- List of end_points (from the architecture) for the used to encode the content
-
- style_end_points:
- List of end_points (from the architecture) for the used to encode the style
-
- preprocess_fn:
- Preprocess function. Pointer to a function that preprocess the INPUT signal
-
- unpreprocess_fn:
- Un preprocess function. Pointer to a function that preprocess the OUTPUT signal
-
- pure_noise:
- If set will save the raw noisy generated image.
- If not set, the output will be RGB = stylizedYUV.Y, originalYUV.U, originalYUV.V
-
- iterations:
- Number of iterations to generate the image
-
- learning_rate:
- Adam learning rate
-
- content_weight:
- Weight of the content loss
-
- style_weight:
- Weight of the style loss
-
- denoise_weight:
- Weight denoising loss
- """
-
- def wise_shape(shape):
- if len(shape)==2:
- return (1, shape[0], shape[1], 1)
- else:
- return (1, shape[0], shape[1], shape[2])
-
- def normalize4save(img):
- return (255 * ((img - numpy.min(img)) / (numpy.max(img)-numpy.min(img)))).astype("uint8")
-
- # Reshaping to NxWxHxC and converting to the tensorflow format
- # content
- original_image = content_image
- content_image = bob.io.image.to_matplotlib(content_image).astype("float32")
- content_image = numpy.reshape(content_image, wise_shape(content_image.shape))
-
- # and style
- for i in range(len(style_images)):
- image = bob.io.image.to_matplotlib(style_images[i])
- image = numpy.reshape(image, wise_shape(image.shape))
- style_images[i] = image
-
- # Base content features
- logger.info("Computing content features")
- content_features = compute_features(content_image, architecture, checkpoint_dir,
- content_end_points, preprocess_fn)
-
- # Base style features
- logger.info("Computing style features")
- style_grams = []
- for image in style_images:
- style_features = compute_features(image, architecture, checkpoint_dir,
- style_end_points, preprocess_fn)
- style_grams.append(compute_gram(style_features))
-
- # Organizing the trainer
- logger.info("Training.....")
- with tf.Graph().as_default():
- tf.set_random_seed(0)
-
- # Random noise
- if start_from == "noise":
- starting_image = tf.random_normal(shape=content_image.shape) * 0.256
- elif start_from == "content":
- starting_image = preprocess_fn(content_image)
- elif start_from == "style":
- starting_image = preprocess_fn(style_images[0])
- else:
- raise ValueError(f"Unknown starting image: {start_from}")
-
- noise = tf.Variable(starting_image, dtype="float32", trainable=True)
- _, end_points = architecture(noise,
- mode=tf.estimator.ModeKeys.PREDICT,
- trainable_variables=[])
-
- # Computing content loss
- content_noises = []
- for c in content_end_points:
- content_noises.append(end_points[c])
- c_loss = content_loss(content_noises, content_features)
-
- # Computing style_loss
- style_gram_noises = []
- s_loss = 0
-
- for grams_per_image in style_grams:
-
- for c in style_end_points:
- layer = end_points[c]
- _, height, width, number = map(lambda i: i.value, layer.get_shape())
- size = height * width * number
- features = tf.reshape(layer, (-1, number))
- style_gram_noises.append(tf.matmul(tf.transpose(features), features) / size)
- s_loss += linear_gram_style_loss(style_gram_noises, grams_per_image)
-
- # Variation denoise
- d_loss = denoising_loss(noise)
-
- #Total loss
- total_loss = content_weight*c_loss + style_weight*s_loss + denoise_weight*d_loss
-
- solver = tf.train.AdamOptimizer(learning_rate).minimize(total_loss)
-
- tf.contrib.framework.init_from_checkpoint(tf.train.latest_checkpoint(checkpoint_dir) if os.path.isdir(checkpoint_dir) else checkpoint_dir, scopes)
- # Training
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
-
- for i in range(iterations):
- _, loss = sess.run([solver, total_loss])
- logger.info("Iteration {0}, loss {1}".format(i, loss))
-
- # Saving generated image
- raw_style_image = sess.run(noise)[0, :, :,:]
- # Unpreprocessing the signal
- if un_preprocess_fn is not None:
- raw_style_image = un_preprocess_fn(raw_style_image)
-
- raw_style_image = bob.io.image.to_bob(raw_style_image)
- normalized_style_image = normalize4save(raw_style_image)
-
- if pure_noise:
- if normalized_style_image.shape[0] == 1:
- return normalized_style_image[0, :, :]
- else:
- return normalized_style_image
- else:
- # Original output
- if normalized_style_image.shape[0] == 1:
- normalized_style_image_yuv = bob.ip.color.rgb_to_yuv(bob.ip.color.gray_to_rgb(normalized_style_image[0,:,:]))
- # Loading the content image and clipping from 0-255 in case is in another scale
- #scaled_content_image = normalize4save(bob.io.base.load(content_image_path).astype("float32")).astype("float64")
- scaled_content_image = original_image.astype("float64")
- content_image_yuv = bob.ip.color.rgb_to_yuv(bob.ip.color.gray_to_rgb(scaled_content_image))
- else:
- normalized_style_image_yuv = bob.ip.color.rgb_to_yuv(bob.ip.color.gray_to_rgb(bob.ip.color.rgb_to_gray(normalized_style_image)))
- content_image_yuv = bob.ip.color.rgb_to_yuv(original_image)
-
- output_image = numpy.zeros(shape=content_image_yuv.shape, dtype="uint8")
- output_image[0,:,:] = normalized_style_image_yuv[0,:,:]
- output_image[1,:,:] = content_image_yuv[1,:,:]
- output_image[2,:,:] = content_image_yuv[2,:,:]
-
- output_image = bob.ip.color.yuv_to_rgb(output_image)
- return output_image
-
diff --git a/bob/learn/tensorflow/test/data/cnn_mnist/checkpoint b/bob/learn/tensorflow/test/data/cnn_mnist/checkpoint
deleted file mode 100644
index b12413de4ce3b1d66ef87edc2c45f1025ade150a..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/cnn_mnist/checkpoint
+++ /dev/null
@@ -1,2 +0,0 @@
-model_checkpoint_path: "model.ckp"
-all_model_checkpoint_paths: "model.ckp"
diff --git a/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp b/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp
deleted file mode 100644
index 2ebd4543b072d46406c33cf840cf3e8609d3a1a7..0000000000000000000000000000000000000000
Binary files a/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp and /dev/null differ
diff --git a/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp.meta b/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp.meta
deleted file mode 100644
index b81dee07f766e28d90741d0f04c48603bb99a1e7..0000000000000000000000000000000000000000
Binary files a/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp.meta and /dev/null differ
diff --git a/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp_sequence_net.pickle b/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp_sequence_net.pickle
deleted file mode 100644
index fedb9de4d16da446d0bea13e0744528135ada209..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/cnn_mnist/model.ckp_sequence_net.pickle
+++ /dev/null
@@ -1,147 +0,0 @@
-ccollections
-OrderedDict
-p0
-((lp1
-(lp2
-S'conv1'
-p3
-accopy_reg
-_reconstructor
-p4
-(cbob.learn.tensorflow.layers
-Conv2D
-p5
-c__builtin__
-object
-p6
-Ntp7
-Rp8
-(dp9
-S'batch_var'
-p10
-NsS'name'
-p11
-g3
-sS'filters'
-p12
-I10
-sS'use_gpu'
-p13
-I00
-sS'activation'
-p14
-ctensorflow.python.ops.math_ops
-tanh
-p15
-sS'W'
-p16
-NsS'stride'
-p17
-(lp18
-I1
-aI1
-aI1
-aI1
-asS'beta'
-p19
-NsS'b'
-p20
-NsS'weights_initialization'
-p21
-g4
-(cbob.learn.tensorflow.initialization
-Xavier
-p22
-g6
-Ntp23
-Rp24
-(dp25
-g13
-I00
-sS'seed'
-p26
-F10.0
-sbsS'input_layer'
-p27
-NsS'batch_mean'
-p28
-NsS'bias_initialization'
-p29
-g4
-(cbob.learn.tensorflow.initialization
-Constant
-p30
-g6
-Ntp31
-Rp32
-(dp33
-g13
-I00
-sg26
-NsS'constant_value'
-p34
-F0.1
-sbsS'kernel_size'
-p35
-I3
-sS'gamma'
-p36
-NsS'batch_norm'
-p37
-I00
-sbaa(lp38
-S'fc1'
-p39
-ag4
-(cbob.learn.tensorflow.layers
-FullyConnected
-p40
-g6
-Ntp41
-Rp42
-(dp43
-g10
-Nsg11
-g39
-sg13
-I00
-sg14
-NsS'shape'
-p44
-Nsg16
-Nsg19
-Nsg20
-Nsg21
-g4
-(g22
-g6
-Ntp45
-Rp46
-(dp47
-g13
-I00
-sg26
-F10.0
-sbsg27
-Nsg28
-Nsg29
-g4
-(g30
-g6
-Ntp48
-Rp49
-(dp50
-g13
-I00
-sg26
-Nsg34
-F0.1
-sbsS'output_dim'
-p51
-I10
-sg36
-Nsg37
-I00
-sbaatp52
-Rp53
-.
\ No newline at end of file
diff --git a/bob/learn/tensorflow/test/data/db_to_tfrecords_config.py b/bob/learn/tensorflow/test/data/db_to_tfrecords_config.py
deleted file mode 100644
index 6fa669656cafc38cf2f17d57df7389be6baa519c..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/db_to_tfrecords_config.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from bob.bio.base.test.dummy.database import database
-from bob.bio.base.utils import read_original_data
-from bob.learn.tensorflow.dataset.generator import dataset_using_generator
-
-groups = ["dev"]
-
-samples = database.all_files(groups=groups)
-
-CLIENT_IDS = (str(f.client_id) for f in database.all_files(groups=groups))
-CLIENT_IDS = list(set(CLIENT_IDS))
-CLIENT_IDS = dict(zip(CLIENT_IDS, range(len(CLIENT_IDS))))
-
-
-def file_to_label(f):
- return CLIENT_IDS[str(f.client_id)]
-
-
-def reader(biofile):
- data = read_original_data(
- biofile, database.original_directory, database.original_extension
- )
- label = file_to_label(biofile)
- key = str(biofile.path).encode("utf-8")
- return (data, label, key)
-
-
-dataset = dataset_using_generator(samples, reader)
-datasets = [dataset]
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diff --git a/bob/learn/tensorflow/test/data/estimator_atnt_faces_config.py b/bob/learn/tensorflow/test/data/estimator_atnt_faces_config.py
deleted file mode 100644
index 3dbcc3d2aef3140239c0d3d5beafb09a30906a4b..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/estimator_atnt_faces_config.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import tensorflow as tf
-
-model_dir = "%(model_dir)s"
-learning_rate = 0.00001
-
-
-def architecture(images):
- images = tf.cast(images, tf.float32)
- logits = tf.reshape(images, [-1, 92 * 112])
- logits = tf.contrib.slim.fully_connected(inputs=logits, num_outputs=20)
- return logits
-
-
-def model_fn(features, labels, mode, config):
- key = features['key']
- features = features['data']
-
- logits = architecture(features)
-
- predictions = {
- # Generate predictions (for PREDICT and EVAL mode)
- "classes": tf.argmax(input=logits, axis=1),
- # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
- # `logging_hook`.
- "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
- "key": key,
- }
- if mode == tf.estimator.ModeKeys.PREDICT:
- return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
-
- # Calculate Loss (for both TRAIN and EVAL modes)
- loss = tf.losses.sparse_softmax_cross_entropy(
- logits=logits, labels=labels)
- accuracy = tf.metrics.accuracy(
- labels=labels, predictions=predictions["classes"])
- metrics = {'accuracy': accuracy}
-
- # Configure the training op
- if mode == tf.estimator.ModeKeys.TRAIN:
- optimizer = tf.train.GradientDescentOptimizer(
- learning_rate=learning_rate)
- train_op = optimizer.minimize(
- loss=loss, global_step=tf.train.get_or_create_global_step())
- # Log accuracy and loss
- with tf.name_scope('train_metrics'):
- tf.summary.scalar('accuracy', accuracy[1])
- tf.summary.scalar('loss', loss)
- else:
- train_op = None
-
- return tf.estimator.EstimatorSpec(
- mode=mode,
- predictions=predictions,
- loss=loss,
- train_op=train_op,
- eval_metric_ops=metrics)
-
-
-estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir)
diff --git a/bob/learn/tensorflow/test/data/input_biogenerator_config.py b/bob/learn/tensorflow/test/data/input_biogenerator_config.py
deleted file mode 100644
index e4516be98af48040233f26bf42f61c11deec3899..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/input_biogenerator_config.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from bob.learn.tensorflow.dataset.bio import BioGenerator
-from bob.learn.tensorflow.utils import to_channels_last
-import tensorflow as tf
-
-batch_size = 2
-epochs = 2
-
-
-def input_fn(mode):
- from bob.bio.base.test.dummy.database import database as db
-
- if mode == tf.estimator.ModeKeys.TRAIN:
- groups = 'world'
- elif mode == tf.estimator.ModeKeys.EVAL:
- groups = 'dev'
-
- files = db.objects(groups=groups)
-
- # construct integer labels for each identity in the database
- CLIENT_IDS = (str(f.client_id) for f in files)
- CLIENT_IDS = list(set(CLIENT_IDS))
- CLIENT_IDS = dict(zip(CLIENT_IDS, range(len(CLIENT_IDS))))
-
- def biofile_to_label(f):
- return CLIENT_IDS[str(f.client_id)]
-
- def load_data(database, f):
- img = f.load(database.original_directory, database.original_extension)
- # make a channels_first image (bob format) with 1 channel
- img = img.reshape(1, 112, 92)
- return img
-
- generator = BioGenerator(db, files, load_data, biofile_to_label)
-
- dataset = tf.data.Dataset.from_generator(
- generator, generator.output_types, generator.output_shapes)
-
- def transform(image, label, key):
- # convert to channels last
- image = to_channels_last(image)
-
- # per_image_standardization
- image = tf.image.per_image_standardization(image)
- return (image, label, key)
-
- dataset = dataset.map(transform)
- if mode == tf.estimator.ModeKeys.TRAIN:
- # since we are caching to memory, caching only in training makes sense.
- dataset = dataset.cache()
- dataset = dataset.repeat(epochs)
- dataset = dataset.batch(batch_size)
-
- data, label, key = dataset.make_one_shot_iterator().get_next()
- return {'data': data, 'key': key}, label
-
-
-def train_input_fn():
- return input_fn(tf.estimator.ModeKeys.TRAIN)
-
-
-def eval_input_fn():
- return input_fn(tf.estimator.ModeKeys.EVAL)
-
-
-train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=50)
-eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn)
diff --git a/bob/learn/tensorflow/test/data/input_predict_bio_config.py b/bob/learn/tensorflow/test/data/input_predict_bio_config.py
deleted file mode 100644
index 2b8687f77bde2f024b213df0a1b49bc2231d5867..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/input_predict_bio_config.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import tensorflow as tf
-from bob.bio.base.test.dummy.database import database
-biofiles = database.all_files(['dev'])
-
-
-def bio_predict_input_fn(generator, output_types, output_shapes):
- def input_fn():
- dataset = tf.data.Dataset.from_generator(
- generator, output_types, output_shapes)
- # apply all kinds of transformations here, process the data
- # even further if you want.
- dataset = dataset.prefetch(1)
- dataset = dataset.batch(10**3)
- images, labels, keys = dataset.make_one_shot_iterator().get_next()
-
- return {'data': images, 'key': keys}, labels
- return input_fn
diff --git a/bob/learn/tensorflow/test/data/input_tfrecords_config.py b/bob/learn/tensorflow/test/data/input_tfrecords_config.py
deleted file mode 100644
index 2c5ec8f02e8f547bc31de2e062f2240ab044d0e5..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/input_tfrecords_config.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import tensorflow as tf
-from bob.learn.tensorflow.dataset.tfrecords import shuffle_data_and_labels, \
- batch_data_and_labels
-
-tfrecord_filenames = ['%(tfrecord_filenames)s']
-data_shape = (1, 112, 92) # size of atnt images
-data_type = tf.uint8
-batch_size = 2
-epochs = 2
-
-
-def train_input_fn():
- return shuffle_data_and_labels(tfrecord_filenames, data_shape, data_type,
- batch_size, epochs=epochs)
-
-
-def eval_input_fn():
- return batch_data_and_labels(tfrecord_filenames, data_shape, data_type,
- batch_size, epochs=1)
-
-
-# config for train_and_evaluate
-train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=200)
-eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn)
diff --git a/bob/learn/tensorflow/test/data/mnist_estimator.py b/bob/learn/tensorflow/test/data/mnist_estimator.py
deleted file mode 100644
index 224e974c2d8dc9627fd38e0a8fead61318f3b425..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/mnist_estimator.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import tensorflow as tf
-data = tf.feature_column.numeric_column('data', shape=[784])
-estimator = tf.estimator.LinearClassifier(feature_columns=[data], n_classes=10)
diff --git a/bob/learn/tensorflow/test/data/mnist_input_fn.py b/bob/learn/tensorflow/test/data/mnist_input_fn.py
deleted file mode 100644
index e5bf1f4a058a96b529613a879e396c66a410afa6..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/mnist_input_fn.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from bob.db.mnist import Database
-import tensorflow as tf
-
-database = Database()
-
-
-def input_fn(mode):
- if mode == tf.estimator.ModeKeys.TRAIN:
- groups = 'train'
- num_epochs = None
- shuffle = True
- else:
- groups = 'test'
- num_epochs = 1
- shuffle = True
- data, labels = database.data(groups=groups)
- return tf.estimator.inputs.numpy_input_fn(
- x={
- "data": data.astype('float32'),
- 'key': labels.astype('float32')
- },
- y=labels.astype('int32'),
- batch_size=128,
- num_epochs=num_epochs,
- shuffle=shuffle)
-
-
-train_input_fn = input_fn(tf.estimator.ModeKeys.TRAIN)
-eval_input_fn = input_fn(tf.estimator.ModeKeys.EVAL)
diff --git a/bob/learn/tensorflow/test/data/train_scripts/siamese.py b/bob/learn/tensorflow/test/data/train_scripts/siamese.py
deleted file mode 100644
index 3658e71f5103b4a4f7e8f7603dcf51413165edca..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/train_scripts/siamese.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from bob.learn.tensorflow.datashuffler import SiameseMemory, ScaleFactor
-from bob.learn.tensorflow.network import Chopra
-from bob.learn.tensorflow.trainers import SiameseTrainer as Trainer
-from bob.learn.tensorflow.trainers import constant
-from bob.learn.tensorflow.loss import ContrastiveLoss
-from bob.learn.tensorflow.utils import load_mnist
-import tensorflow as tf
-import numpy
-
-BATCH_SIZE = 32
-INPUT_SHAPE = [None, 28, 28, 1]
-SEED = 10
-
-### PREPARING DATASHUFFLER ###
-train_data, train_labels, validation_data, validation_labels = \
- load_mnist()
-train_data = numpy.reshape(train_data, (train_data.shape[0], 28, 28, 1))
-
-train_data_shuffler = SiameseMemory(
- train_data,
- train_labels,
- input_shape=INPUT_SHAPE,
- batch_size=BATCH_SIZE,
- normalizer=ScaleFactor())
-
-### ARCHITECTURE ###
-architecture = Chopra(seed=SEED, n_classes=10)
-
-### LOSS ###
-loss = ContrastiveLoss(contrastive_margin=4.)
-
-### LEARNING RATE ###
-learning_rate = constant(base_learning_rate=0.01)
-
-### SOLVER ###
-optimizer = tf.train.GradientDescentOptimizer(learning_rate)
-
-### Trainer ###
-trainer = Trainer
diff --git a/bob/learn/tensorflow/test/data/train_scripts/softmax.py b/bob/learn/tensorflow/test/data/train_scripts/softmax.py
deleted file mode 100644
index ed48ececfe66b19d7d5d83dbed6166de0d75f90e..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/train_scripts/softmax.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from bob.learn.tensorflow.datashuffler import Memory, ScaleFactor
-from bob.learn.tensorflow.network import chopra
-from bob.learn.tensorflow.trainers import Trainer, constant
-from bob.learn.tensorflow.loss import MeanSoftMaxLoss
-from bob.learn.tensorflow.utils import load_mnist
-import tensorflow as tf
-import numpy
-
-BATCH_SIZE = 32
-INPUT_SHAPE = [None, 28, 28, 1]
-SEED = 10
-USE_GPU = False
-
-### PREPARING DATASHUFFLER ###
-train_data, train_labels, validation_data, validation_labels = load_mnist()
-train_data = numpy.reshape(train_data, (train_data.shape[0], 28, 28, 1))
-
-train_data_shuffler = Memory(
- train_data,
- train_labels,
- input_shape=INPUT_SHAPE,
- batch_size=BATCH_SIZE,
- normalizer=ScaleFactor())
-
-### ARCHITECTURE ###
-architecture = chopra(seed=SEED, n_classes=10)
-
-### LOSS ###
-loss = MeanSoftMaxLoss()
-
-### LEARNING RATE ###
-learning_rate = constant(base_learning_rate=0.01)
-
-### SOLVER ###
-optimizer = tf.train.GradientDescentOptimizer(learning_rate)
-
-### Trainer ###
-trainer = Trainer
diff --git a/bob/learn/tensorflow/test/data/train_scripts/triplet.py b/bob/learn/tensorflow/test/data/train_scripts/triplet.py
deleted file mode 100644
index 35908c8e305eb64359f183d3fd74afee541a8a01..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/data/train_scripts/triplet.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from bob.learn.tensorflow.datashuffler import TripletMemory
-from bob.learn.tensorflow.network import Chopra
-from bob.learn.tensorflow.trainers import TripletTrainer as Trainer
-from bob.learn.tensorflow.trainers import constant
-from bob.learn.tensorflow.loss import TripletLoss
-from bob.learn.tensorflow.utils import load_mnist
-import tensorflow as tf
-import numpy
-
-BATCH_SIZE = 32
-INPUT_SHAPE = [None, 28, 28, 1]
-SEED = 10
-
-### PREPARING DATASHUFFLER ###
-train_data, train_labels, validation_data, validation_labels = \
- load_mnist()
-train_data = numpy.reshape(train_data, (train_data.shape[0], 28, 28, 1))
-
-train_data_shuffler = TripletMemory(
- train_data, train_labels, input_shape=INPUT_SHAPE, batch_size=BATCH_SIZE)
-
-### ARCHITECTURE ###
-architecture = Chopra(seed=SEED, n_classes=10)
-
-### LOSS ###
-loss = TripletLoss(margin=4.)
-
-### LEARNING RATE ###
-learning_rate = constant(base_learning_rate=0.01)
-
-### SOLVER ###
-optimizer = tf.train.GradientDescentOptimizer(learning_rate)
-
-### Trainer ###
-trainer = Trainer
diff --git a/bob/learn/tensorflow/test/test_architectures.py b/bob/learn/tensorflow/test/test_architectures.py
deleted file mode 100644
index 740768d10588265fadd4c0843fdbbd8a7b221506..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_architectures.py
+++ /dev/null
@@ -1,152 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-from nose.plugins.attrib import attr
-import tensorflow as tf
-from bob.learn.tensorflow.network import inception_resnet_v2, inception_resnet_v2_batch_norm,\
- inception_resnet_v1, inception_resnet_v1_batch_norm,\
- vgg_19, vgg_16, mlp_with_batchnorm_and_dropout
-
-# @attr('slow')
-# def test_inceptionv2():
-
-# tf.reset_default_graph()
-# # Testing WITHOUT batch norm
-# inputs = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-# graph, _ = inception_resnet_v2(inputs)
-# assert len(tf.trainable_variables()) == 490
-
-# tf.reset_default_graph()
-# assert len(tf.global_variables()) == 0
-
-# # Testing WITH batch norm
-# inputs = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-# graph, _ = inception_resnet_v2_batch_norm(inputs)
-# assert len(tf.trainable_variables()) == 490, len(tf.trainable_variables())
-
-# tf.reset_default_graph()
-# assert len(tf.global_variables()) == 0
-
-# @attr('slow')
-# def test_inceptionv2_adaptation():
-
-# tf.reset_default_graph()
-# for n, trainable_variables in [
-# (490, None),
-# (0, []),
-# (2, ['Conv2d_1a_3x3', 'Conv2d_1a_3x3_BN']),
-# (4, ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_1a_3x3_BN',
-# 'Conv2d_2a_3x3_BN']),
-# (6, ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
-# 'Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN', 'Conv2d_2b_3x3_BN']),
-# (1, ['Conv2d_1a_3x3_BN']),
-# (2, ['Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN']),
-# (3, ['Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN', 'Conv2d_2b_3x3_BN']),
-# ]:
-# input = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-# net, end_points = inception_resnet_v2_batch_norm(
-# input, trainable_variables=trainable_variables)
-# l = len(tf.trainable_variables())
-# assert l == n, (l, n)
-# tf.reset_default_graph()
-# tf.reset_default_graph()
-# assert len(tf.global_variables()) == 0
-
-# @attr('slow')
-# def test_inceptionv1():
-
-# tf.reset_default_graph()
-# # Testing WITHOUT batch norm
-# inputs = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-# graph, _ = inception_resnet_v1(inputs)
-# assert len(tf.trainable_variables()) == 266
-
-# tf.reset_default_graph()
-# assert len(tf.global_variables()) == 0
-
-# # Testing WITH batch norm
-# inputs = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-# graph, _ = inception_resnet_v1_batch_norm(inputs)
-# assert len(tf.trainable_variables()) == 266
-
-# tf.reset_default_graph()
-# assert len(tf.global_variables()) == 0
-
-# @attr('slow')
-# def test_inceptionv1_adaptation():
-
-# tf.reset_default_graph()
-# for n, trainable_variables in [
-# (266, None),
-# (0, []),
-# (2, ['Conv2d_1a_3x3', 'Conv2d_1a_3x3_BN']),
-# (4, ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_1a_3x3_BN',
-# 'Conv2d_2a_3x3_BN']),
-# (6, ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
-# 'Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN', 'Conv2d_2b_3x3_BN']),
-# (1, ['Conv2d_1a_3x3_BN']),
-# (2, ['Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN']),
-# (3, ['Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN', 'Conv2d_2b_3x3_BN']),
-# ]:
-# input = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-# net, end_points = inception_resnet_v1_batch_norm(
-# input, trainable_variables=trainable_variables)
-# l = len(tf.trainable_variables())
-# assert l == n, (l, n)
-# tf.reset_default_graph()
-# tf.reset_default_graph()
-# assert len(tf.global_variables()) == 0
-
-
-def test_vgg():
- tf.reset_default_graph()
-
- # Testing VGG19 Training mode
- inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
- graph, _ = vgg_19(inputs)
- assert len(tf.trainable_variables()) == 38
-
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
- # Testing VGG19 predicting mode
- inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
- graph, _ = vgg_19(inputs, mode=tf.estimator.ModeKeys.PREDICT)
- assert len(tf.trainable_variables()) == 0
-
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
-
- # Testing VGG 16 training mode
- inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
- graph, _ = vgg_16(inputs)
- assert len(tf.trainable_variables()) == 30
-
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
- # Testing VGG 16 predicting mode
- inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
- graph, _ = vgg_16(inputs, mode=tf.estimator.ModeKeys.PREDICT)
- assert len(tf.trainable_variables()) == 0
-
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
-
-def test_mlp():
-
- tf.reset_default_graph()
- # Testing MLP Training mode
- inputs = tf.placeholder(tf.float32, shape=(1, 10, 10, 3))
- graph, _ = mlp_with_batchnorm_and_dropout(inputs, [6, 5])
- assert len(tf.trainable_variables()) == 4
-
- tf.reset_default_graph()
- # Testing MLP Predicting mode
- inputs = tf.placeholder(tf.float32, shape=(1, 10, 10, 3))
- graph, _ = mlp_with_batchnorm_and_dropout(inputs, [6, 5], mode=tf.estimator.ModeKeys.PREDICT)
- assert len(tf.trainable_variables()) == 0
-
diff --git a/bob/learn/tensorflow/test/test_dataset.py b/bob/learn/tensorflow/test/test_dataset.py
deleted file mode 100644
index 0c5fe8cf4458669e7be26d26b74d70e45cd778ee..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_dataset.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import pkg_resources
-import numpy
-import tensorflow as tf
-from bob.learn.tensorflow.dataset.siamese_image import shuffle_data_and_labels_image_augmentation as siamese_batch
-from bob.learn.tensorflow.dataset.triplet_image import shuffle_data_and_labels_image_augmentation as triplet_batch
-from bob.learn.tensorflow.dataset.generator import dataset_using_generator
-
-data_shape = (250, 250, 3)
-output_shape = (50, 50)
-data_type = tf.float32
-batch_size = 2
-validation_batch_size = 250
-epochs = 1
-
-# Trainer logits
-filenames = [
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png')
-]
-labels = [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
-
-
-def test_siamese_dataset():
- data, label = siamese_batch(
- filenames,
- labels,
- data_shape,
- data_type,
- 2,
- per_image_normalization=False,
- output_shape=output_shape)
-
- with tf.Session() as session:
- d, l = session.run([data, label])
- assert len(l) == 2
- assert d['left'].shape == (2, 50, 50, 3)
- assert d['right'].shape == (2, 50, 50, 3)
-
-
-def test_triplet_dataset():
- data = triplet_batch(
- filenames,
- labels,
- data_shape,
- data_type,
- 2,
- per_image_normalization=False,
- output_shape=output_shape)
- with tf.Session() as session:
- d = session.run([data])[0]
- assert len(d.keys()) == 3
- assert d['anchor'].shape == (2, 50, 50, 3)
- assert d['positive'].shape == (2, 50, 50, 3)
- assert d['negative'].shape == (2, 50, 50, 3)
-
-
-def test_dataset_using_generator():
-
- def reader(f):
- key = 0
- label = 0
- yield {'data': f, 'key': key}, label
-
- shape = (2, 2, 1)
- samples = [numpy.ones(shape, dtype="float32")*i for i in range(10)]
-
- with tf.Session() as session:
- dataset = dataset_using_generator(samples,\
- reader,\
- multiple_samples=True)
- iterator = dataset.make_one_shot_iterator().get_next()
- for i in range(11):
- try:
- sample = session.run(iterator)
- assert sample[0]["data"].shape == shape
- assert numpy.allclose(sample[0]["data"], samples[i])
- except tf.errors.OutOfRangeError:
- break
-
diff --git a/bob/learn/tensorflow/test/test_db_to_tfrecords.py b/bob/learn/tensorflow/test/test_db_to_tfrecords.py
deleted file mode 100644
index db5491d08066aa5e5138b447d7e32677576e1900..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_db_to_tfrecords.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import os
-import shutil
-import pkg_resources
-import tempfile
-import tensorflow as tf
-import numpy as np
-from click.testing import CliRunner
-from bob.io.base import create_directories_safe
-from bob.learn.tensorflow.script.db_to_tfrecords import (
- db_to_tfrecords, describe_tf_record, datasets_to_tfrecords)
-from bob.learn.tensorflow.utils import load_mnist, create_mnist_tfrecord
-from bob.extension.scripts.click_helper import assert_click_runner_result
-from bob.extension.config import load
-from bob.learn.tensorflow.dataset.tfrecords import dataset_from_tfrecord
-
-regenerate_reference = False
-
-dummy_config = pkg_resources.resource_filename(
- 'bob.learn.tensorflow', 'test/data/db_to_tfrecords_config.py')
-
-
-def compare_datasets(ds1, ds2, sess=None):
- if tf.executing_eagerly():
- for values1, values2 in zip(ds1, ds2):
- values1 = tf.contrib.framework.nest.flatten(values1)
- values2 = tf.contrib.framework.nest.flatten(values2)
- for v1, v2 in zip(values1, values2):
- if not tf.reduce_all(tf.math.equal(v1, v2)):
- return False
- else:
- ds1 = ds1.make_one_shot_iterator().get_next()
- ds2 = ds2.make_one_shot_iterator().get_next()
- while True:
- try:
- values1, values2 = sess.run([ds1, ds2])
- except tf.errors.OutOfRangeError:
- break
- values1 = tf.contrib.framework.nest.flatten(values1)
- values2 = tf.contrib.framework.nest.flatten(values2)
- for v1, v2 in zip(values1, values2):
- v1, v2 = np.asarray(v1), np.asarray(v2)
- if not np.all(v1 == v2):
- return False
- return True
-
-
-def test_db_to_tfrecords():
- test_dir = tempfile.mkdtemp(prefix='bobtest_')
- output_path = os.path.join(test_dir, 'dev.tfrecords')
-
- try:
- runner = CliRunner()
- result = runner.invoke(
- db_to_tfrecords,
- args=(dummy_config, '--output', output_path),
- standalone_mode=False)
- assert result.exit_code == 0, '%s\n%s\n%s' % (
- result.exc_info, result.output, result.exception)
-
- # TODO: test if the generated tfrecords file is equal with a reference
- # file
-
- finally:
- shutil.rmtree(test_dir)
-
-
-def test_db_to_tfrecords_size_estimate():
- test_dir = tempfile.mkdtemp(prefix='bobtest_')
- output_path = os.path.join(test_dir, 'dev.tfrecords')
-
- try:
- args = (dummy_config, '--size-estimate', '--output', output_path)
- runner = CliRunner()
- result = runner.invoke(
- db_to_tfrecords, args=args, standalone_mode=False)
- assert result.exit_code == 0, '%s\n%s\n%s' % (
- result.exc_info, result.output, result.exception)
- assert '2.0 M bytes' in result.output, result.output
-
- finally:
- shutil.rmtree(test_dir)
-
-
-def test_tfrecord_counter():
- tfrecord_train = "./tf-train-test/train_mnist.tfrecord"
- shape = (3136,) # I'm saving the thing as float
- batch_size = 1000
-
- try:
- train_data, train_labels, validation_data, validation_labels = \
- load_mnist()
- create_directories_safe(os.path.dirname(tfrecord_train))
- create_mnist_tfrecord(
- tfrecord_train, train_data, train_labels, n_samples=6000)
-
- n_samples, n_labels = describe_tf_record(
- os.path.dirname(tfrecord_train), shape, batch_size)
-
- assert n_samples == 6000
- assert n_labels == 10
-
- finally:
- shutil.rmtree(os.path.dirname(tfrecord_train))
-
-
-def test_datasets_to_tfrecords():
- runner = CliRunner()
- with runner.isolated_filesystem():
- output_path = './test'
- args = (dummy_config, '--output', output_path)
- result = runner.invoke(
- datasets_to_tfrecords, args=args, standalone_mode=False)
- assert_click_runner_result(result)
- # read back the tfrecod
- with tf.Session() as sess:
- dataset2 = dataset_from_tfrecord(output_path)
- dataset1 = load(
- [dummy_config], attribute_name='dataset', entry_point_group='bob')
- assert compare_datasets(dataset1, dataset2, sess)
diff --git a/bob/learn/tensorflow/test/test_estimator_onegraph.py b/bob/learn/tensorflow/test/test_estimator_onegraph.py
deleted file mode 100644
index 3dea478fa1ff3cce39c4c2dd8d3cecceb79c4c46..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_estimator_onegraph.py
+++ /dev/null
@@ -1,336 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-from nose.plugins.attrib import attr
-import tensorflow as tf
-
-from bob.learn.tensorflow.network import dummy
-from bob.learn.tensorflow.estimators import Logits, LogitsCenterLoss
-
-from bob.learn.tensorflow.dataset.tfrecords import shuffle_data_and_labels, batch_data_and_labels, \
- shuffle_data_and_labels_image_augmentation
-
-from bob.learn.tensorflow.utils import load_mnist, create_mnist_tfrecord
-from bob.learn.tensorflow.utils.hooks import LoggerHookEstimator
-from bob.learn.tensorflow.loss import mean_cross_entropy_loss
-from bob.learn.tensorflow.utils import reproducible
-
-import numpy
-
-import shutil
-import os
-
-# Fixing problem with MAC https://github.com/dmlc/xgboost/issues/1715
-os.environ['KMP_DUPLICATE_LIB_OK']='True'
-
-tfrecord_train = "./train_mnist.tfrecord"
-tfrecord_validation = "./validation_mnist.tfrecord"
-model_dir = "./temp"
-
-learning_rate = 0.1
-data_shape = (28, 28, 1) # size of atnt images
-data_type = tf.float32
-batch_size = 32
-validation_batch_size = 250
-epochs = 6
-steps = 5000
-reproducible.set_seed()
-
-# @attr('slow')
-# def test_logitstrainer():
-# # Trainer logits
-# try:
-# embedding_validation = False
-# _, run_config, _, _, _ = reproducible.set_seed()
-# run_config = run_config.replace(
-# keep_checkpoint_max=10, save_checkpoints_steps=100, save_checkpoints_secs=None)
-# trainer = Logits(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# loss_op=mean_cross_entropy_loss,
-# embedding_validation=embedding_validation,
-# validation_batch_size=validation_batch_size,
-# config=run_config)
-# run_logitstrainer_mnist(trainer, augmentation=True)
-# finally:
-# try:
-# os.unlink(tfrecord_train)
-# os.unlink(tfrecord_validation)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# except Exception:
-# pass
-
-# @attr('slow')
-# def test_logitstrainer_embedding():
-# try:
-# embedding_validation = True
-# _, run_config, _, _, _ = reproducible.set_seed()
-# trainer = Logits(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# loss_op=mean_cross_entropy_loss,
-# embedding_validation=embedding_validation,
-# validation_batch_size=validation_batch_size,
-# config=run_config)
-
-# run_logitstrainer_mnist(trainer)
-# finally:
-# try:
-# os.unlink(tfrecord_train)
-# os.unlink(tfrecord_validation)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# except Exception:
-# pass
-
-# @attr('slow')
-# def test_logitstrainer_centerloss():
-# try:
-# embedding_validation = False
-# _, run_config, _, _, _ = reproducible.set_seed()
-# run_config = run_config.replace(save_checkpoints_steps=1000)
-# trainer = LogitsCenterLoss(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# embedding_validation=embedding_validation,
-# validation_batch_size=validation_batch_size,
-# factor=0.01,
-# config=run_config)
-
-# run_logitstrainer_mnist(trainer)
-
-# # Checking if the centers were updated
-# sess = tf.Session()
-# checkpoint_path = tf.train.get_checkpoint_state(
-# model_dir).model_checkpoint_path
-# saver = tf.train.import_meta_graph(
-# checkpoint_path + ".meta", clear_devices=True)
-# saver.restore(sess, tf.train.latest_checkpoint(model_dir))
-# centers = tf.get_collection(
-# tf.GraphKeys.GLOBAL_VARIABLES, scope="center_loss/centers:0")[0]
-# assert numpy.sum(numpy.abs(centers.eval(sess))) > 0.0
-
-# finally:
-# try:
-# os.unlink(tfrecord_train)
-# os.unlink(tfrecord_validation)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# except Exception:
-# pass
-
-# @attr('slow')
-# def test_logitstrainer_centerloss_embedding():
-# try:
-# embedding_validation = True
-# _, run_config, _, _, _ = reproducible.set_seed()
-# trainer = LogitsCenterLoss(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# embedding_validation=embedding_validation,
-# validation_batch_size=validation_batch_size,
-# factor=0.01,
-# config=run_config)
-# run_logitstrainer_mnist(trainer)
-
-# # Checking if the centers were updated
-# sess = tf.Session()
-# checkpoint_path = tf.train.get_checkpoint_state(
-# model_dir).model_checkpoint_path
-# saver = tf.train.import_meta_graph(
-# checkpoint_path + ".meta", clear_devices=True)
-# saver.restore(sess, tf.train.latest_checkpoint(model_dir))
-# centers = tf.get_collection(
-# tf.GraphKeys.GLOBAL_VARIABLES, scope="center_loss/centers:0")[0]
-# assert numpy.sum(numpy.abs(centers.eval(sess))) > 0.0
-# finally:
-# try:
-# os.unlink(tfrecord_train)
-# os.unlink(tfrecord_validation)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# except Exception:
-# pass
-
-
-def run_logitstrainer_mnist(trainer, augmentation=False):
- # Cleaning up
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
- # Creating tf records for mnist
- train_data, train_labels, validation_data, validation_labels = load_mnist()
- create_mnist_tfrecord(
- tfrecord_train, train_data, train_labels, n_samples=6000)
- create_mnist_tfrecord(
- tfrecord_validation,
- validation_data,
- validation_labels,
- n_samples=validation_batch_size)
-
- def input_fn():
- if augmentation:
- return shuffle_data_and_labels_image_augmentation(
- tfrecord_train,
- data_shape,
- data_type,
- batch_size,
- random_flip=True,
- random_rotate=False,
- epochs=epochs)
- else:
- return shuffle_data_and_labels(
- tfrecord_train,
- data_shape,
- data_type,
- batch_size,
- epochs=epochs)
-
- def input_fn_validation():
- return batch_data_and_labels(
- tfrecord_validation,
- data_shape,
- data_type,
- validation_batch_size,
- epochs=1000)
-
- hooks = [
- LoggerHookEstimator(trainer, 16, 300),
- tf.train.SummarySaverHook(
- save_steps=1000,
- output_dir=model_dir,
- scaffold=tf.train.Scaffold(),
- summary_writer=tf.summary.FileWriter(model_dir))
- ]
- trainer.train(input_fn, steps=steps, hooks=hooks)
- if not trainer.embedding_validation:
- acc = trainer.evaluate(input_fn_validation)
- assert acc['accuracy'] > 0.10
- else:
- acc = trainer.evaluate(input_fn_validation)
- assert acc['accuracy'] > 0.10
-
- # Cleaning up
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
-# @attr('slow')
-# def test_moving_average_trainer():
-# # define a fixed input data
-# # train the same network with the same initialization
-# # evaluate it
-# # train and evaluate it again with moving average
-# # Accuracy should be lower when moving average is on
-
-# try:
-# # Creating tf records for mnist
-# train_data, train_labels, validation_data, validation_labels = load_mnist()
-# create_mnist_tfrecord(
-# tfrecord_train, train_data, train_labels, n_samples=6000)
-# create_mnist_tfrecord(
-# tfrecord_validation,
-# validation_data,
-# validation_labels,
-# n_samples=validation_batch_size)
-
-# def input_fn():
-# return batch_data_and_labels(
-# tfrecord_train,
-# data_shape,
-# data_type,
-# batch_size,
-# epochs=1)
-
-# def input_fn_validation():
-# return batch_data_and_labels(
-# tfrecord_validation,
-# data_shape,
-# data_type,
-# validation_batch_size,
-# epochs=1)
-
-# from bob.learn.tensorflow.network.Dummy import dummy as architecture
-
-# run_config = reproducible.set_seed(183, 183)[1]
-# run_config = run_config.replace(save_checkpoints_steps=2000)
-
-# def _estimator(apply_moving_averages):
-# return Logits(
-# architecture,
-# tf.train.GradientDescentOptimizer(1e-1),
-# tf.losses.sparse_softmax_cross_entropy,
-# 10,
-# model_dir=model_dir,
-# config=run_config,
-# apply_moving_averages=apply_moving_averages,
-# )
-
-# def _evaluate(estimator, delete=True):
-# try:
-# estimator.train(input_fn)
-# evaluations = estimator.evaluate(input_fn_validation)
-# finally:
-# if delete:
-# shutil.rmtree(estimator.model_dir, ignore_errors=True)
-# return evaluations
-
-# estimator = _estimator(False)
-# evaluations = _evaluate(estimator, delete=True)
-# no_moving_average_acc = evaluations['accuracy']
-
-# # same as above with moving average
-# estimator = _estimator(True)
-# evaluations = _evaluate(estimator, delete=False)
-# with_moving_average_acc = evaluations['accuracy']
-
-# assert no_moving_average_acc > with_moving_average_acc, \
-# (no_moving_average_acc, with_moving_average_acc)
-
-# # Can it resume training?
-# del estimator
-# tf.reset_default_graph()
-# estimator = _estimator(True)
-# _evaluate(estimator, delete=True)
-
-# finally:
-# try:
-# os.unlink(tfrecord_train)
-# os.unlink(tfrecord_validation)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# except Exception:
-# pass
-
-# @attr('slow')
-# def test_saver_with_moving_average():
-# try:
-# _, run_config, _, _, _ = reproducible.set_seed()
-# run_config = run_config.replace(
-# keep_checkpoint_max=10, save_checkpoints_steps=100,
-# save_checkpoints_secs=None)
-# estimator = Logits(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# loss_op=mean_cross_entropy_loss,
-# embedding_validation=False,
-# validation_batch_size=validation_batch_size,
-# config=run_config)
-# run_logitstrainer_mnist(estimator, augmentation=True)
-# ckpt = tf.train.get_checkpoint_state(estimator.model_dir)
-# assert ckpt, "Failed to get any checkpoint!"
-# assert len(
-# ckpt.all_model_checkpoint_paths) == 10, ckpt.all_model_checkpoint_paths
-# finally:
-# try:
-# os.unlink(tfrecord_train)
-# os.unlink(tfrecord_validation)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# except Exception:
-# pass
diff --git a/bob/learn/tensorflow/test/test_estimator_scripts.py b/bob/learn/tensorflow/test/test_estimator_scripts.py
deleted file mode 100644
index b8caeca6f46ba16f4957f321d9c1cc6c59c485b5..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_estimator_scripts.py
+++ /dev/null
@@ -1,227 +0,0 @@
-from __future__ import print_function
-import os
-import shutil
-from glob import glob
-from tempfile import mkdtemp
-from click.testing import CliRunner
-from bob.extension.scripts.click_helper import assert_click_runner_result
-from bob.io.base.test_utils import datafile
-
-from bob.learn.tensorflow.script.db_to_tfrecords import db_to_tfrecords
-from bob.learn.tensorflow.script.train import train
-from bob.learn.tensorflow.script.eval import eval as eval_script
-from bob.learn.tensorflow.script.train_and_evaluate import train_and_evaluate
-from bob.learn.tensorflow.script.predict_bio import predict_bio
-from nose.plugins.attrib import attr
-
-
-db_to_tfrecords_config = datafile('db_to_tfrecords_config.py', __name__)
-input_predict_bio_config = datafile('input_predict_bio_config.py', __name__)
-input_biogenerator_config = datafile('input_biogenerator_config.py', __name__)
-
-
-def input_tfrecords_config(tfrecord_path):
- with open(datafile('input_tfrecords_config.py', __name__)) as f:
- doc = '\n' + f.read() + '\n'
- return doc % {'tfrecord_filenames': tfrecord_path}
-
-
-def estimator_atnt_faces_config(model_dir):
- with open(datafile('estimator_atnt_faces_config.py', __name__)) as f:
- doc = '\n' + f.read() + '\n'
- return doc % {'model_dir': model_dir}
-
-
-def _create_tfrecord(test_dir):
- output = os.path.join(test_dir, 'dev.tfrecords')
- runner = CliRunner()
- result = runner.invoke(
- db_to_tfrecords, args=[db_to_tfrecords_config, '--output', output])
- assert_click_runner_result(result)
- return output
-
-
-def _create_checkpoint(tmpdir, model_dir, tfrecord_path):
- config = input_tfrecords_config(
- tfrecord_path) + estimator_atnt_faces_config(model_dir)
- config_path = os.path.join(tmpdir, 'train_config.py')
- with open(config_path, 'w') as f:
- f.write(config)
- runner = CliRunner()
- result = runner.invoke(train, args=[config_path])
- assert_click_runner_result(result)
-
-
-def _eval(tmpdir, model_dir, tfrecord_path, extra_args=['--run-once']):
- config = input_tfrecords_config(
- tfrecord_path) + estimator_atnt_faces_config(model_dir)
- config_path = os.path.join(tmpdir, 'eval_config.py')
- with open(config_path, 'w') as f:
- f.write(config)
- runner = CliRunner()
- result = runner.invoke(eval_script, args=[config_path] + extra_args)
- assert_click_runner_result(result)
-
-
-def _train_and_evaluate(tmpdir, model_dir, tfrecord_path):
- config = input_tfrecords_config(
- tfrecord_path) + estimator_atnt_faces_config(model_dir)
- config_path = os.path.join(tmpdir, 'train_config.py')
- with open(config_path, 'w') as f:
- f.write(config)
- runner = CliRunner()
- runner.invoke(train_and_evaluate, args=[config_path])
-
-
-def _predict_bio(tmpdir, model_dir, tfrecord_path, extra_options=tuple()):
- config = input_tfrecords_config(
- tfrecord_path) + estimator_atnt_faces_config(model_dir)
- config_path = os.path.join(tmpdir, 'train_config.py')
- with open(config_path, 'w') as f:
- f.write(config)
- runner = CliRunner()
- return runner.invoke(
- predict_bio,
- args=[config_path, input_predict_bio_config] + list(extra_options))
-
-@attr('slow')
-def test_eval():
- tmpdir = mkdtemp(prefix='bob_')
- try:
- model_dir = os.path.join(tmpdir, 'model_dir')
- eval_dir = os.path.join(model_dir, 'eval')
-
- print('\nCreating a dummy tfrecord')
- tfrecord_path = _create_tfrecord(tmpdir)
-
- print('Training a dummy network')
- _create_checkpoint(tmpdir, model_dir, tfrecord_path)
-
- print('Evaluating a dummy network')
- _eval(tmpdir, model_dir, tfrecord_path)
-
- evaluated_path = os.path.join(eval_dir, 'evaluated')
- assert os.path.exists(evaluated_path), evaluated_path
- with open(evaluated_path) as f:
- doc = f.read()
-
- assert '0' in doc, doc
- assert '200' in doc, doc
-
- print('Train and evaluate a dummy network')
- _train_and_evaluate(tmpdir, model_dir, tfrecord_path)
-
- finally:
- try:
- shutil.rmtree(tmpdir)
- except Exception:
- pass
-
-@attr('slow')
-def test_eval_keep_n_model():
- tmpdir = mkdtemp(prefix='bob_')
- try:
- model_dir = os.path.join(tmpdir, 'model_dir')
- eval_dir = os.path.join(model_dir, 'eval')
-
- print('\nCreating a dummy tfrecord')
- tfrecord_path = _create_tfrecord(tmpdir)
-
- print('Training a dummy network')
- _create_checkpoint(tmpdir, model_dir, tfrecord_path)
-
- print('Evaluating a dummy network')
- _eval(tmpdir, model_dir, tfrecord_path, ['-K', '1', '--run-once'])
-
- evaluated_path = os.path.join(eval_dir, 'evaluated')
- assert os.path.exists(evaluated_path), evaluated_path
- with open(evaluated_path) as f:
- doc = f.read()
- assert '0 ' in doc, doc
- assert '200 ' in doc, doc
- assert len(glob('{}/model.ckpt-*'.format(eval_dir))) == 3, \
- os.listdir(eval_dir)
-
- finally:
- try:
- shutil.rmtree(tmpdir)
- except Exception:
- pass
-
-@attr('slow')
-def test_predict_bio():
- tmpdir = mkdtemp(prefix='bob_')
- try:
- model_dir = os.path.join(tmpdir, 'model_dir')
-
- tfrecord_path = _create_tfrecord(tmpdir)
- _create_checkpoint(tmpdir, model_dir, tfrecord_path)
-
- # Run predict_bio
- result = _predict_bio(
- tmpdir, model_dir, tfrecord_path, ['-o', tmpdir, '-vvv'])
- assert_click_runner_result(result)
-
- finally:
- try:
- shutil.rmtree(tmpdir)
- except Exception:
- pass
-
-@attr('slow')
-def test_predict_bio_empty_eval():
- tmpdir = mkdtemp(prefix='bob_')
- try:
- model_dir = os.path.join(tmpdir, 'model_dir')
- eval_dir = os.path.join(model_dir, 'eval')
-
- tfrecord_path = _create_tfrecord(tmpdir)
- _create_checkpoint(tmpdir, model_dir, tfrecord_path)
-
- # Make an empty eval folder
- os.makedirs(eval_dir)
- open(os.path.join(eval_dir, 'checkpoint'), 'w')
-
- # Run predict_bio
- result = _predict_bio(
- tmpdir, model_dir, tfrecord_path,
- ['-o', tmpdir, '-c', eval_dir, '-vvv'])
- # the command should fail when the checkpoint path is empty
- assert_click_runner_result(result, 1)
-
- finally:
- try:
- shutil.rmtree(tmpdir)
- except Exception:
- pass
-
-
-# uncomment to run this test locally
-# def test_eval_too_many_open_files_with_biogenerator():
-# tmpdir = mkdtemp(prefix='bob_')
-# try:
-# # create estimator config file
-# model_dir = os.path.join(tmpdir, 'model_dir')
-# estimator_config = os.path.join(tmpdir, 'estimator_config.py')
-# with open(estimator_config, 'w') as f:
-# f.write(estimator_atnt_faces_config(model_dir))
-
-# runner = CliRunner()
-
-# # train and eval with biogenerators
-# result = runner.invoke(
-# train, args=[estimator_config, input_biogenerator_config])
-# assert_click_runner_result(result)
-
-# print("This test will not stop running. You should kill the process!")
-# result = runner.invoke(
-# eval_script, args=[estimator_config,
-# input_biogenerator_config,
-# '--force-re-run'])
-# assert_click_runner_result(result)
-
-# finally:
-# try:
-# shutil.rmtree(tmpdir)
-# except Exception:
-# pass
diff --git a/bob/learn/tensorflow/test/test_estimator_siamese.py b/bob/learn/tensorflow/test/test_estimator_siamese.py
deleted file mode 100644
index ee324520e4be30eb0a6135ed2cae8d290a263334..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_estimator_siamese.py
+++ /dev/null
@@ -1,264 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-from nose.plugins.attrib import attr
-import tensorflow as tf
-
-from bob.learn.tensorflow.network import dummy
-from bob.learn.tensorflow.estimators import Siamese, Logits
-
-from bob.learn.tensorflow.dataset.siamese_image import shuffle_data_and_labels_image_augmentation as siamese_batch
-from bob.learn.tensorflow.dataset.image import shuffle_data_and_labels_image_augmentation as single_batch
-
-from bob.learn.tensorflow.loss import contrastive_loss, mean_cross_entropy_loss
-from bob.learn.tensorflow.utils.hooks import LoggerHookEstimator
-from .test_estimator_transfer import dummy_adapted
-from bob.learn.tensorflow.utils import reproducible
-
-import pkg_resources
-import shutil
-
-
-# Fixing problem with MAC https://github.com/dmlc/xgboost/issues/1715
-import os
-os.environ['KMP_DUPLICATE_LIB_OK']='True'
-
-tfrecord_train = "./train_mnist.tfrecord"
-tfrecord_validation = "./validation_mnist.tfrecord"
-model_dir = "./temp"
-model_dir_adapted = "./temp2"
-
-learning_rate = 0.0001
-data_shape = (250, 250, 3) # size of atnt images
-output_shape = (50, 50)
-data_type = tf.float32
-batch_size = 4
-validation_batch_size = 2
-epochs = 1
-steps = 5000
-
-# Data
-filenames = [
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
-]
-labels = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-
-# @attr('slow')
-# def test_siamesetrainer():
-# # Trainer logits
-# try:
-
-# # Setting seed
-# session_config, run_config, _, _, _ = reproducible.set_seed()
-# run_config = run_config.replace(save_checkpoints_steps=500)
-
-# trainer = Siamese(
-# model_dir=model_dir,
-# architecture=dummy,
-# config=run_config,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# loss_op=contrastive_loss,
-# validation_batch_size=validation_batch_size)
-# run_siamesetrainer(trainer)
-# finally:
-# try:
-# shutil.rmtree(model_dir, ignore_errors=True)
-# # pass
-# except Exception:
-# pass
-
-# @attr('slow')
-# def test_siamesetrainer_transfer():
-# def logits_input_fn():
-# return single_batch(
-# filenames,
-# labels,
-# data_shape,
-# data_type,
-# batch_size,
-# epochs=epochs,
-# output_shape=output_shape)
-
-# # Trainer logits first than siamese
-# try:
-# # Setting seed
-# session_config, run_config, _, _, _ = reproducible.set_seed()
-# run_config = run_config.replace(save_checkpoints_steps=500)
-
-# extra_checkpoint = {
-# "checkpoint_path": model_dir,
-# "scopes": dict({
-# "Dummy/": "Dummy/"
-# }),
-# "trainable_variables": []
-# }
-
-# # LOGISTS
-# logits_trainer = Logits(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=2,
-# config=run_config,
-# loss_op=mean_cross_entropy_loss,
-# embedding_validation=False,
-# validation_batch_size=validation_batch_size)
-# logits_trainer.train(logits_input_fn, steps=steps)
-
-# # NOW THE FUCKING SIAMESE
-# trainer = Siamese(
-# model_dir=model_dir_adapted,
-# architecture=dummy_adapted,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# config=run_config,
-# loss_op=contrastive_loss,
-# validation_batch_size=validation_batch_size,
-# extra_checkpoint=extra_checkpoint)
-# run_siamesetrainer(trainer)
-# finally:
-# try:
-# shutil.rmtree(model_dir, ignore_errors=True)
-# shutil.rmtree(model_dir_adapted, ignore_errors=True)
-# except Exception:
-# pass
-
-# @attr('slow')
-# def test_siamesetrainer_transfer_extraparams():
-# def logits_input_fn():
-# return single_batch(
-# filenames,
-# labels,
-# data_shape,
-# data_type,
-# batch_size,
-# epochs=epochs,
-# output_shape=output_shape)
-
-# # Trainer logits first than siamese
-# try:
-
-# extra_checkpoint = {
-# "checkpoint_path": model_dir,
-# "scopes": dict({
-# "Dummy/": "Dummy/"
-# }),
-# "trainable_variables": ["Dummy"]
-# }
-
-# # Setting seed
-# session_config, run_config, _, _, _ = reproducible.set_seed()
-# run_config = run_config.replace(save_checkpoints_steps=500)
-
-# # LOGISTS
-# logits_trainer = Logits(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=2,
-# config=run_config,
-# loss_op=mean_cross_entropy_loss,
-# embedding_validation=False,
-# validation_batch_size=validation_batch_size)
-
-# logits_trainer.train(logits_input_fn, steps=steps)
-
-# # NOW THE FUCKING SIAMESE
-# trainer = Siamese(
-# model_dir=model_dir_adapted,
-# architecture=dummy_adapted,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# loss_op=contrastive_loss,
-# config=run_config,
-# validation_batch_size=validation_batch_size,
-# extra_checkpoint=extra_checkpoint)
-# run_siamesetrainer(trainer)
-# finally:
-# try:
-# shutil.rmtree(model_dir, ignore_errors=True)
-# shutil.rmtree(model_dir_adapted, ignore_errors=True)
-# except Exception:
-# pass
-
-
-def run_siamesetrainer(trainer):
- # Cleaning up
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
- def input_fn():
- return siamese_batch(
- filenames,
- labels,
- data_shape,
- data_type,
- batch_size,
- epochs=epochs,
- output_shape=output_shape,
- random_flip=True,
- random_brightness=True,
- random_contrast=True,
- random_saturation=True)
-
- def input_validation_fn():
- return single_batch(
- filenames,
- labels,
- data_shape,
- data_type,
- validation_batch_size,
- epochs=10,
- output_shape=output_shape)
-
- hooks = [
- LoggerHookEstimator(trainer, batch_size, 300),
- tf.train.SummarySaverHook(
- save_steps=1000,
- output_dir=model_dir,
- scaffold=tf.train.Scaffold(),
- summary_writer=tf.summary.FileWriter(model_dir))
- ]
-
- trainer.train(input_fn, steps=1, hooks=hooks)
-
- acc = trainer.evaluate(input_validation_fn)
- assert acc['accuracy'] > 0.3
-
- # Cleaning up
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
diff --git a/bob/learn/tensorflow/test/test_estimator_transfer.py b/bob/learn/tensorflow/test/test_estimator_transfer.py
deleted file mode 100644
index 97e692f85adad13b3a887071c0b410341ad4ac11..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_estimator_transfer.py
+++ /dev/null
@@ -1,181 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-from nose.plugins.attrib import attr
-import tensorflow as tf
-
-from bob.learn.tensorflow.network import dummy
-from bob.learn.tensorflow.estimators import Logits, LogitsCenterLoss
-from bob.learn.tensorflow.utils import reproducible
-from bob.learn.tensorflow.loss import mean_cross_entropy_loss
-from .test_estimator_onegraph import run_logitstrainer_mnist
-
-import shutil
-import os
-
-# Fixing problem with MAC https://github.com/dmlc/xgboost/issues/1715
-os.environ['KMP_DUPLICATE_LIB_OK']='True'
-
-tfrecord_train = "./train_mnist.tfrecord"
-tfrecord_validation = "./validation_mnist.tfrecord"
-model_dir = "./temp"
-model_dir_adapted = "./temp2"
-
-learning_rate = 0.1
-data_shape = (28, 28, 1) # size of atnt images
-data_type = tf.float32
-batch_size = 32
-validation_batch_size = 250
-epochs = 6
-steps = 5000
-
-
-def dummy_adapted(inputs,
- reuse=False,
- mode=tf.estimator.ModeKeys.TRAIN,
- trainable_variables=None,
- **kwargs):
- """
- Create all the necessary variables for this CNN
-
- Parameters
- ----------
- inputs:
-
- reuse:
-
- mode:
-
- trainable_variables:
- """
-
- slim = tf.contrib.slim
- graph, end_points = dummy(
- inputs,
- reuse=reuse,
- mode=mode,
- trainable_variables=trainable_variables)
-
- initializer = tf.contrib.layers.xavier_initializer()
- with tf.variable_scope('Adapted', reuse=reuse):
- name = 'fc2'
- graph = slim.fully_connected(
- graph,
- 50,
- weights_initializer=initializer,
- activation_fn=tf.nn.relu,
- scope=name,
- trainable=True)
- end_points[name] = graph
-
- name = 'fc3'
- graph = slim.fully_connected(
- graph,
- 25,
- weights_initializer=initializer,
- activation_fn=None,
- scope=name,
- trainable=True)
- end_points[name] = graph
-
- return graph, end_points
-
-# @attr('slow')
-# def test_logitstrainer():
-# # Trainer logits
-# try:
-# _, run_config, _, _, _ = reproducible.set_seed()
-# embedding_validation = False
-# trainer = Logits(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# loss_op=mean_cross_entropy_loss,
-# embedding_validation=embedding_validation,
-# validation_batch_size=validation_batch_size,
-# config=run_config)
-# run_logitstrainer_mnist(trainer, augmentation=True)
-# del trainer
-
-# ## Again
-# extra_checkpoint = {
-# "checkpoint_path": "./temp",
-# "scopes": dict({
-# "Dummy/": "Dummy/"
-# }),
-# "trainable_variables": []
-# }
-
-# trainer = Logits(
-# model_dir=model_dir_adapted,
-# architecture=dummy_adapted,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# loss_op=mean_cross_entropy_loss,
-# embedding_validation=embedding_validation,
-# validation_batch_size=validation_batch_size,
-# extra_checkpoint=extra_checkpoint,
-# config=run_config)
-
-# run_logitstrainer_mnist(trainer, augmentation=True)
-
-# finally:
-# try:
-# os.unlink(tfrecord_train)
-# os.unlink(tfrecord_validation)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# shutil.rmtree(model_dir_adapted, ignore_errors=True)
-# pass
-# except Exception:
-# pass
-
-# @attr('slow')
-# def test_logitstrainer_center_loss():
-# # Trainer logits
-# try:
-# embedding_validation = False
-# _, run_config, _, _, _ = reproducible.set_seed()
-# trainer = LogitsCenterLoss(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# embedding_validation=embedding_validation,
-# validation_batch_size=validation_batch_size,
-# apply_moving_averages=False,
-# config=run_config)
-# run_logitstrainer_mnist(trainer, augmentation=True)
-# del trainer
-
-# ## Again
-# extra_checkpoint = {
-# "checkpoint_path": "./temp",
-# "scopes": dict({
-# "Dummy/": "Dummy/"
-# }),
-# "trainable_variables": ["Dummy"]
-# }
-
-# trainer = LogitsCenterLoss(
-# model_dir=model_dir_adapted,
-# architecture=dummy_adapted,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# embedding_validation=embedding_validation,
-# validation_batch_size=validation_batch_size,
-# extra_checkpoint=extra_checkpoint,
-# apply_moving_averages=False,
-# config=run_config)
-
-# run_logitstrainer_mnist(trainer, augmentation=True)
-
-# finally:
-# try:
-# os.unlink(tfrecord_train)
-# os.unlink(tfrecord_validation)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# shutil.rmtree(model_dir_adapted, ignore_errors=True)
-# except Exception:
-# pass
diff --git a/bob/learn/tensorflow/test/test_estimator_triplet.py b/bob/learn/tensorflow/test/test_estimator_triplet.py
deleted file mode 100644
index a376c4c4c2a630dff32c66be2c64c13c06f47692..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_estimator_triplet.py
+++ /dev/null
@@ -1,194 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-from nose.plugins.attrib import attr
-import tensorflow as tf
-
-from bob.learn.tensorflow.network import dummy
-from bob.learn.tensorflow.estimators import Triplet, Logits
-from bob.learn.tensorflow.dataset.triplet_image import shuffle_data_and_labels_image_augmentation as triplet_batch
-from bob.learn.tensorflow.dataset.image import shuffle_data_and_labels_image_augmentation as single_batch
-
-from bob.learn.tensorflow.loss import triplet_loss, mean_cross_entropy_loss
-from bob.learn.tensorflow.utils.hooks import LoggerHookEstimator
-from bob.learn.tensorflow.utils import reproducible
-import pkg_resources
-from .test_estimator_transfer import dummy_adapted
-
-import shutil
-
-# Fixing problem with MAC https://github.com/dmlc/xgboost/issues/1715
-import os
-os.environ['KMP_DUPLICATE_LIB_OK']='True'
-
-tfrecord_train = "./train_mnist.tfrecord"
-tfrecord_validation = "./validation_mnist.tfrecord"
-model_dir = "./temp"
-model_dir_adapted = "./temp2"
-
-learning_rate = 0.001
-data_shape = (250, 250, 3) # size of atnt images
-output_shape = (50, 50)
-data_type = tf.float32
-batch_size = 4
-validation_batch_size = 2
-epochs = 1
-steps = 5000
-
-# Data
-filenames = [
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
-]
-labels = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-
-# @attr('slow')
-# def test_triplet_estimator():
-# # Trainer logits
-# try:
-# trainer = Triplet(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# loss_op=triplet_loss,
-# validation_batch_size=validation_batch_size)
-# run_triplet_estimator(trainer)
-# finally:
-# try:
-# shutil.rmtree(model_dir, ignore_errors=True)
-# # pass
-# except Exception:
-# pass
-
-# @attr('slow')
-# def test_triplettrainer_transfer():
-# def logits_input_fn():
-# return single_batch(
-# filenames,
-# labels,
-# data_shape,
-# data_type,
-# batch_size,
-# epochs=epochs,
-# output_shape=output_shape)
-
-# # Trainer logits first than siamese
-# try:
-
-# extra_checkpoint = {
-# "checkpoint_path": model_dir,
-# "scopes": dict({
-# "Dummy/": "Dummy/"
-# }),
-# "trainable_variables": []
-# }
-
-# # LOGISTS
-# logits_trainer = Logits(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=2,
-# loss_op=mean_cross_entropy_loss,
-# embedding_validation=False,
-# validation_batch_size=validation_batch_size)
-# logits_trainer.train(logits_input_fn, steps=steps)
-
-# # NOW THE FUCKING SIAMESE
-# trainer = Triplet(
-# model_dir=model_dir_adapted,
-# architecture=dummy_adapted,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# loss_op=triplet_loss,
-# validation_batch_size=validation_batch_size,
-# extra_checkpoint=extra_checkpoint)
-# run_triplet_estimator(trainer)
-# finally:
-# try:
-# shutil.rmtree(model_dir, ignore_errors=True)
-# shutil.rmtree(model_dir_adapted, ignore_errors=True)
-# except Exception:
-# pass
-
-
-def run_triplet_estimator(trainer):
- # Cleaning up
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
- def input_fn():
- return triplet_batch(
- filenames,
- labels,
- data_shape,
- data_type,
- batch_size,
- epochs=epochs,
- output_shape=output_shape,
- random_flip=True,
- random_brightness=True,
- random_contrast=True,
- random_saturation=True)
-
- def input_validation_fn():
- return single_batch(
- filenames,
- labels,
- data_shape,
- data_type,
- validation_batch_size,
- epochs=10,
- output_shape=output_shape)
-
- hooks = [
- LoggerHookEstimator(trainer, batch_size, 300),
- tf.train.SummarySaverHook(
- save_steps=1000,
- output_dir=model_dir,
- scaffold=tf.train.Scaffold(),
- summary_writer=tf.summary.FileWriter(model_dir))
- ]
-
- trainer.train(input_fn, steps=steps, hooks=hooks)
-
- acc = trainer.evaluate(input_validation_fn)
- assert acc['accuracy'] > 0.3
-
- # Cleaning up
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
diff --git a/bob/learn/tensorflow/test/test_hooks.py b/bob/learn/tensorflow/test/test_hooks.py
deleted file mode 100644
index 62013907744764b930f508a20727fb6bc2faca75..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_hooks.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from bob.extension.config import load as read_config_files
-from bob.io.base.test_utils import datafile
-from bob.learn.tensorflow.estimators import Logits
-from bob.learn.tensorflow.loss.BaseLoss import mean_cross_entropy_loss
-from bob.learn.tensorflow.utils.hooks import EarlyStopping, EarlyStopException
-import nose
-import tensorflow as tf
-import shutil
-from nose.plugins.attrib import attr
-
-
-# @nose.tools.raises(EarlyStopException)
-# @attr('slow')
-# def test_early_stopping_linear_classifier():
-# config = read_config_files([
-# datafile('mnist_input_fn.py', __name__),
-# datafile('mnist_estimator.py', __name__),
-# ])
-# estimator = config.estimator
-# train_input_fn = config.train_input_fn
-# eval_input_fn = config.eval_input_fn
-
-# hooks = [
-# EarlyStopping(
-# 'linear/metrics/accuracy/total', min_delta=0.001, patience=1),
-# ]
-
-# train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn)
-# eval_spec = tf.estimator.EvalSpec(
-# input_fn=eval_input_fn, hooks=hooks, throttle_secs=2, steps=10)
-
-# try:
-# tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
-# finally:
-# shutil.rmtree(estimator.model_dir)
-
-
-# @nose.tools.raises(EarlyStopException)
-# @attr('slow')
-# def test_early_stopping_logit_trainer():
-# config = read_config_files([
-# datafile('mnist_input_fn.py', __name__),
-# ])
-# train_input_fn = config.train_input_fn
-# eval_input_fn = config.eval_input_fn
-
-# hooks = [
-# EarlyStopping('accuracy/value', min_delta=0.001, patience=1),
-# ]
-
-# train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn)
-# eval_spec = tf.estimator.EvalSpec(
-# input_fn=eval_input_fn, hooks=hooks, throttle_secs=2, steps=10)
-
-# def architecture(data, mode, **kwargs):
-# return data, dict()
-
-# optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-1)
-# loss_op = mean_cross_entropy_loss
-
-# estimator = Logits(
-# architecture, optimizer, loss_op, n_classes=10, model_dir=None)
-
-# try:
-# tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
-# finally:
-# shutil.rmtree(estimator.model_dir)
diff --git a/bob/learn/tensorflow/test/test_image_dataset.py b/bob/learn/tensorflow/test/test_image_dataset.py
deleted file mode 100644
index 959c219dbb5282db842ac48e80d05aa8fd591662..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_image_dataset.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-
-from bob.learn.tensorflow.network import dummy
-from bob.learn.tensorflow.estimators import Logits, LogitsCenterLoss
-
-from bob.learn.tensorflow.dataset.image import shuffle_data_and_labels_image_augmentation
-import pkg_resources
-
-from bob.learn.tensorflow.utils.hooks import LoggerHookEstimator
-from bob.learn.tensorflow.loss import mean_cross_entropy_loss
-from nose.plugins.attrib import attr
-
-import shutil
-import os
-
-model_dir = "./temp"
-
-learning_rate = 0.1
-data_shape = (250, 250, 3) # size of atnt images
-data_type = tf.float32
-batch_size = 16
-validation_batch_size = 250
-epochs = 1
-steps = 5000
-
-@attr('slow')
-def test_logitstrainer_images():
- # Trainer logits
- try:
- embedding_validation = False
- trainer = Logits(
- model_dir=model_dir,
- architecture=dummy,
- optimizer=tf.train.GradientDescentOptimizer(learning_rate),
- n_classes=10,
- loss_op=mean_cross_entropy_loss,
- embedding_validation=embedding_validation,
- validation_batch_size=validation_batch_size,
- apply_moving_averages=False)
- run_logitstrainer_images(trainer)
- finally:
- try:
- shutil.rmtree(model_dir, ignore_errors=True)
- except Exception:
- pass
-
-
-def run_logitstrainer_images(trainer):
- # Cleaning up
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
- filenames = [
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
- pkg_resources.resource_filename(
- __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png')
- ]
- labels = [0, 0, 1, 1]
-
- def input_fn():
-
- return shuffle_data_and_labels_image_augmentation(
- filenames,
- labels,
- data_shape,
- data_type,
- batch_size,
- epochs=epochs)
-
- def input_fn_validation():
- return shuffle_data_and_labels_image_augmentation(
- filenames,
- labels,
- data_shape,
- data_type,
- validation_batch_size,
- epochs=1000)
-
- hooks = [
- LoggerHookEstimator(trainer, 16, 300),
- tf.train.SummarySaverHook(
- save_steps=1000,
- output_dir=model_dir,
- scaffold=tf.train.Scaffold(),
- summary_writer=tf.summary.FileWriter(model_dir))
- ]
-
- trainer.train(input_fn, steps=steps, hooks=hooks)
-
- acc = trainer.evaluate(input_fn_validation)
- assert acc['accuracy'] > 0.30, acc['accuracy']
-
- # Cleaning up
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
diff --git a/bob/learn/tensorflow/test/test_layers.py b/bob/learn/tensorflow/test/test_layers.py
deleted file mode 100644
index 43064bde7f07b9424cb91d7c9bae99087bb58456..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_layers.py
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# @date: Thu 13 Oct 2016 13:35 CEST
-
-import tensorflow as tf
-import numpy as np
-from bob.learn.tensorflow.layers import maxout
-from nose.tools import assert_raises_regexp
-
-slim = tf.contrib.slim
-
-
-def test_fully_connected():
- tf.reset_default_graph()
- x = np.zeros([64, 50])
- graph = slim.fully_connected(x, 50, activation_fn=None)
- graph = maxout(graph, num_units=10)
- assert graph.get_shape().as_list() == [64, 10]
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
-
-def test_nchw():
- tf.reset_default_graph()
- x = np.random.uniform(size=(10, 100, 100, 3)).astype(np.float32)
- graph = slim.conv2d(x, 10, [3, 3])
- graph = maxout(graph, num_units=1)
- assert graph.get_shape().as_list() == [10, 100, 100, 1]
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
-
-
-def test_invalid_shape():
- tf.reset_default_graph()
- x = np.random.uniform(size=(10, 100, 100, 3)).astype(np.float32)
- graph = slim.conv2d(x, 3, [3, 3])
- with assert_raises_regexp(ValueError, 'number of features'):
- graph = maxout(graph, num_units=2)
- tf.reset_default_graph()
- assert len(tf.global_variables()) == 0
diff --git a/bob/learn/tensorflow/test/test_loss.py b/bob/learn/tensorflow/test/test_loss.py
deleted file mode 100644
index 92d94c8f1812512b23167be086807a0ffe65a0ce..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_loss.py
+++ /dev/null
@@ -1,76 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-import numpy
-from bob.learn.tensorflow.loss import balanced_softmax_cross_entropy_loss_weights,\
- balanced_sigmoid_cross_entropy_loss_weights
-
-
-def test_balanced_softmax_cross_entropy_loss_weights():
- labels = numpy.array([[1, 0, 0],
- [1, 0, 0],
- [0, 0, 1],
- [0, 1, 0],
- [0, 0, 1],
- [1, 0, 0],
- [1, 0, 0],
- [0, 0, 1],
- [1, 0, 0],
- [1, 0, 0],
- [1, 0, 0],
- [1, 0, 0],
- [1, 0, 0],
- [1, 0, 0],
- [0, 1, 0],
- [1, 0, 0],
- [0, 1, 0],
- [1, 0, 0],
- [0, 0, 1],
- [0, 0, 1],
- [1, 0, 0],
- [0, 0, 1],
- [1, 0, 0],
- [1, 0, 0],
- [0, 1, 0],
- [1, 0, 0],
- [1, 0, 0],
- [1, 0, 0],
- [0, 1, 0],
- [1, 0, 0],
- [0, 0, 1],
- [1, 0, 0]], dtype="int32")
-
- with tf.Session() as session:
- weights = session.run(balanced_softmax_cross_entropy_loss_weights(labels))
-
- expected_weights = numpy.array([0.53333336, 0.53333336, 1.5238096 , 2.1333334,\
- 1.5238096 , 0.53333336, 0.53333336, 1.5238096,\
- 0.53333336, 0.53333336, 0.53333336, 0.53333336,\
- 0.53333336, 0.53333336, 2.1333334 , 0.53333336,\
- 2.1333334 , 0.53333336, 1.5238096 , 1.5238096 ,\
- 0.53333336, 1.5238096 , 0.53333336, 0.53333336,\
- 2.1333334 , 0.53333336, 0.53333336, 0.53333336,\
- 2.1333334 , 0.53333336, 1.5238096 , 0.53333336],\
- dtype="float32")
-
- assert numpy.allclose(weights, expected_weights)
-
-
-def test_balanced_sigmoid_cross_entropy_loss_weights():
- labels = numpy.array([1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
- 1, 1, 0, 1, 1, 1, 0, 1, 0, 1], dtype="int32")
-
- with tf.Session() as session:
- weights = session.run(balanced_sigmoid_cross_entropy_loss_weights(labels, dtype='float32'))
-
- expected_weights = numpy.array([0.8, 0.8, 1.3333334, 1.3333334, 1.3333334, 0.8,
- 0.8, 1.3333334, 0.8, 0.8, 0.8, 0.8,
- 0.8, 0.8, 1.3333334, 0.8, 1.3333334, 0.8,
- 1.3333334, 1.3333334, 0.8, 1.3333334, 0.8, 0.8,
- 1.3333334, 0.8, 0.8, 0.8, 1.3333334, 0.8,
- 1.3333334, 0.8], dtype="float32")
-
- assert numpy.allclose(weights, expected_weights)
-
diff --git a/bob/learn/tensorflow/test/test_regression.py b/bob/learn/tensorflow/test/test_regression.py
deleted file mode 100644
index 6d8e05ff03e3385d20745ae0b1e625e46c8bde82..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_regression.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from nose.plugins.attrib import attr
-from bob.learn.tensorflow.estimators import Regressor
-from tensorflow import keras
-import tensorflow as tf
-import tensorflow.contrib.slim as slim
-
-# @attr('slow')
-# def test_regressor():
-
-# boston_housing = keras.datasets.boston_housing
-# (train_data, train_labels), (test_data,
-# test_labels) = boston_housing.load_data()
-
-# mean = train_data.mean(axis=0)
-# std = train_data.std(axis=0)
-# train_data = (train_data - mean) / std
-# test_data = (test_data - mean) / std
-
-# def input_fn(mode):
-# if mode == tf.estimator.ModeKeys.TRAIN:
-# features, labels = train_data, train_labels
-# else:
-# features, labels, = test_data, test_labels
-# dataset = tf.data.Dataset.from_tensor_slices(
-# (features, labels, [str(x) for x in labels]))
-# dataset = dataset.batch(1)
-# if mode == tf.estimator.ModeKeys.TRAIN:
-# dataset = dataset.apply(
-# tf.contrib.data.shuffle_and_repeat(len(labels), 2))
-# data, label, key = dataset.make_one_shot_iterator().get_next()
-# return {'data': data, 'key': key}, label
-
-# def train_input_fn():
-# return input_fn(tf.estimator.ModeKeys.TRAIN)
-
-# def eval_input_fn():
-# return input_fn(tf.estimator.ModeKeys.EVAL)
-
-# def architecture(data, mode, **kwargs):
-# endpoints = {}
-
-# with tf.variable_scope('DNN'):
-
-# name = 'fc1'
-# net = slim.fully_connected(data, 64, scope=name)
-# endpoints[name] = net
-
-# name = 'fc2'
-# net = slim.fully_connected(net, 64, scope=name)
-# endpoints[name] = net
-
-# return net, endpoints
-
-# estimator = Regressor(architecture)
-
-# estimator.train(train_input_fn)
-
-# list(estimator.predict(eval_input_fn))
-
-# evaluations = estimator.evaluate(eval_input_fn)
-
-# assert 'rmse' in evaluations
-# assert 'loss' in evaluations
diff --git a/bob/learn/tensorflow/test/test_style_transfer.py b/bob/learn/tensorflow/test/test_style_transfer.py
deleted file mode 100644
index 0a91f74b0d143a99811b09914be497de8887d5d5..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_style_transfer.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from __future__ import print_function
-import os
-import shutil
-from glob import glob
-from tempfile import mkdtemp
-from click.testing import CliRunner
-from bob.io.base.test_utils import datafile
-import pkg_resources
-
-import tensorflow as tf
-
-from bob.learn.tensorflow.utils import load_mnist, create_mnist_tfrecord
-from bob.learn.tensorflow.utils.hooks import LoggerHookEstimator
-from bob.learn.tensorflow.loss import mean_cross_entropy_loss
-from bob.learn.tensorflow.utils import reproducible
-from .test_estimator_onegraph import run_logitstrainer_mnist
-
-from bob.learn.tensorflow.estimators import Logits
-from bob.learn.tensorflow.network import dummy
-from bob.learn.tensorflow.script.style_transfer import style_transfer
-from nose.plugins.attrib import attr
-
-dummy_config = datafile('style_transfer.py', __name__)
-CONFIG = '''
-from bob.learn.tensorflow.network import dummy
-architecture = dummy
-import pkg_resources
-
-checkpoint_dir = "./temp/"
-
-style_end_points = ["conv1"]
-content_end_points = ["fc1"]
-
-scopes = {"Dummy/":"Dummy/"}
-
-'''
-
-
-#tfrecord_train = "./train_mnist.tfrecord"
-model_dir = "./temp"
-output_style_image = 'output_style.png'
-
-learning_rate = 0.1
-data_shape = (28, 28, 1) # size of atnt images
-data_type = tf.float32
-batch_size = 32
-epochs = 1
-steps = 100
-
-# @attr('slow')
-# def test_style_transfer():
-# with open(dummy_config, 'w') as f:
-# f.write(CONFIG)
-
-# # Trainer logits
-
-# # CREATING FAKE MODEL USING MNIST
-# _, run_config,_,_,_ = reproducible.set_seed()
-# trainer = Logits(
-# model_dir=model_dir,
-# architecture=dummy,
-# optimizer=tf.train.GradientDescentOptimizer(learning_rate),
-# n_classes=10,
-# loss_op=mean_cross_entropy_loss,
-# config=run_config)
-# run_logitstrainer_mnist(trainer)
-
-# # Style transfer using this fake model
-# runner = CliRunner()
-# result = runner.invoke(style_transfer,
-# args=[pkg_resources.resource_filename( __name__, 'data/dummy_image_database/m301_01_p01_i0_0_GRAY.png'),
-# output_style_image, dummy_config])
-
-# try:
-# os.unlink(dummy_config)
-# shutil.rmtree(model_dir, ignore_errors=True)
-# except Exception:
-# pass
-
-
diff --git a/bob/learn/tensorflow/test/test_utils.py b/bob/learn/tensorflow/test/test_utils.py
deleted file mode 100644
index e08b5311581c25f46dccc47b78a8a73549ab5142..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/test/test_utils.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import numpy
-from bob.learn.tensorflow.utils import compute_embedding_accuracy, \
- compute_embedding_accuracy_tensors
-
-import tensorflow as tf
-"""
-Some unit tests for the datashuffler
-"""
-
-
-def test_embedding_accuracy():
-
- numpy.random.seed(10)
- samples_per_class = 5
-
- class_a = numpy.random.normal(
- loc=0, scale=0.1, size=(samples_per_class, 2))
- labels_a = numpy.zeros(samples_per_class)
-
- class_b = numpy.random.normal(
- loc=10, scale=0.1, size=(samples_per_class, 2))
- labels_b = numpy.ones(samples_per_class)
-
- data = numpy.vstack((class_a, class_b))
- labels = numpy.concatenate((labels_a, labels_b))
-
- assert compute_embedding_accuracy(data, labels) == 1.
-
- # Adding noise
- noise = numpy.random.normal(loc=0, scale=0.1, size=(samples_per_class, 2))
- noise_labels = numpy.ones(samples_per_class)
-
- data = numpy.vstack((data, noise))
- labels = numpy.concatenate((labels, noise_labels))
-
- assert compute_embedding_accuracy(data, labels) == 10 / 15.
-
-
-def test_embedding_accuracy_tensors():
-
- numpy.random.seed(10)
- samples_per_class = 5
-
- class_a = numpy.random.normal(
- loc=0, scale=0.1, size=(samples_per_class, 2))
- labels_a = numpy.zeros(samples_per_class)
-
- class_b = numpy.random.normal(
- loc=10, scale=0.1, size=(samples_per_class, 2))
- labels_b = numpy.ones(samples_per_class)
-
- data = numpy.vstack((class_a, class_b))
- labels = numpy.concatenate((labels_a, labels_b))
-
- data = tf.convert_to_tensor(data.astype("float32"))
- labels = tf.convert_to_tensor(labels.astype("int64"))
-
- sess = tf.Session()
- accuracy = sess.run(compute_embedding_accuracy_tensors(data, labels))
- assert accuracy == 1.
diff --git a/bob/learn/tensorflow/test/__init__.py b/bob/learn/tensorflow/tests/__init__.py
similarity index 100%
rename from bob/learn/tensorflow/test/__init__.py
rename to bob/learn/tensorflow/tests/__init__.py
diff --git a/bob/learn/tensorflow/tests/data/db_to_tfrecords_config.py b/bob/learn/tensorflow/tests/data/db_to_tfrecords_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..52799ddd8bf9cd2c1cbf2f52bab0419c90678d62
--- /dev/null
+++ b/bob/learn/tensorflow/tests/data/db_to_tfrecords_config.py
@@ -0,0 +1,17 @@
+import tensorflow as tf
+from bob.learn.tensorflow.data import dataset_using_generator
+
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train), (_, _) = mnist.load_data()
+samples = (tf.keras.backend.arange(len(x_train)), x_train, y_train)
+
+
+def reader(sample):
+ data = sample[1]
+ label = sample[2]
+ key = str(sample[0]).encode("utf-8")
+ return ({"data": data, "key": key}, label)
+
+
+dataset = dataset_using_generator(samples, reader)
diff --git a/bob/learn/tensorflow/tests/test_dataset.py b/bob/learn/tensorflow/tests/test_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b5d46fa91ac00ad7ac064da962bb729f106eb94
--- /dev/null
+++ b/bob/learn/tensorflow/tests/test_dataset.py
@@ -0,0 +1,18 @@
+import numpy as np
+
+from bob.learn.tensorflow.data import dataset_using_generator
+
+
+def test_dataset_using_generator():
+ def reader(f):
+ key = 0
+ label = 0
+ yield {"data": f, "key": key}, label
+
+ shape = (2, 2, 1)
+ samples = [np.ones(shape, dtype="float32") * i for i in range(10)]
+
+ dataset = dataset_using_generator(samples, reader, multiple_samples=True)
+ for i, sample in enumerate(dataset):
+ assert sample[0]["data"].shape == shape
+ assert np.allclose(sample[0]["data"], samples[i])
diff --git a/bob/learn/tensorflow/tests/test_datasets_to_tfrecords.py b/bob/learn/tensorflow/tests/test_datasets_to_tfrecords.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b6548abab4d14e5b308cb73a96fe7ddacc89d25
--- /dev/null
+++ b/bob/learn/tensorflow/tests/test_datasets_to_tfrecords.py
@@ -0,0 +1,38 @@
+import pkg_resources
+import tensorflow as tf
+from bob.extension.config import load
+from bob.extension.scripts.click_helper import assert_click_runner_result
+from bob.learn.tensorflow.data.tfrecords import dataset_from_tfrecord
+from bob.learn.tensorflow.scripts.datasets_to_tfrecords import datasets_to_tfrecords
+from click.testing import CliRunner
+
+regenerate_reference = False
+
+dummy_config = pkg_resources.resource_filename(
+ "bob.learn.tensorflow", "tests/data/db_to_tfrecords_config.py"
+)
+
+
+def compare_datasets(ds1, ds2):
+ for values1, values2 in zip(ds1, ds2):
+ values1 = tf.nest.flatten(values1)
+ values2 = tf.nest.flatten(values2)
+ for v1, v2 in zip(values1, values2):
+ if not tf.reduce_all(input_tensor=tf.math.equal(v1, v2)):
+ return False
+ return True
+
+
+def test_datasets_to_tfrecords():
+ runner = CliRunner()
+ with runner.isolated_filesystem():
+ output_path = "./test"
+ args = (dummy_config, "--output", output_path)
+ result = runner.invoke(datasets_to_tfrecords, args=args, standalone_mode=False)
+ assert_click_runner_result(result)
+ # read back the tfrecod
+ dataset2 = dataset_from_tfrecord(output_path)
+ dataset1 = load(
+ [dummy_config], attribute_name="dataset", entry_point_group="bob"
+ )
+ assert compare_datasets(dataset1, dataset2)
diff --git a/bob/learn/tensorflow/tests/test_mine.py b/bob/learn/tensorflow/tests/test_mine.py
new file mode 100644
index 0000000000000000000000000000000000000000..89cf943b858bd87e7c61707a9616c7894254c1fd
--- /dev/null
+++ b/bob/learn/tensorflow/tests/test_mine.py
@@ -0,0 +1,33 @@
+import numpy as np
+import tensorflow as tf
+from bob.learn.tensorflow.models import MineModel
+
+def run_mine(is_mine_f):
+ np.random.seed(10)
+ N = 20000
+ d = 1
+ EPOCHS = 100
+
+ X = np.sign(np.random.normal(0.,1.,[N, d]))
+ Z = X + np.random.normal(0.,np.sqrt(0.2),[N, d])
+
+
+ from sklearn.feature_selection import mutual_info_regression
+ mi_numerical = mutual_info_regression(X.reshape(-1, 1), Z.ravel())[0]
+
+ model = MineModel(is_mine_f=is_mine_f)
+ model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01))
+
+ callback = model.fit(
+ x=[X, Z], epochs=EPOCHS, verbose=1, batch_size=100
+ )
+ mine = -np.array(callback.history["loss"])[-1]
+
+ np.allclose(mine,mi_numerical, atol=0.01)
+
+
+def test_mine():
+ run_mine(False)
+
+def test_mine_f():
+ run_mine(True)
\ No newline at end of file
diff --git a/bob/learn/tensorflow/tests/test_utils.py b/bob/learn/tensorflow/tests/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6ae3aee142ff30fd3bb4ccde5d9aadce8542c76
--- /dev/null
+++ b/bob/learn/tensorflow/tests/test_utils.py
@@ -0,0 +1,26 @@
+import numpy
+import tensorflow as tf
+
+from bob.learn.tensorflow.metrics import EmbeddingAccuracy
+
+
+def test_embedding_accuracy_tensors():
+
+ numpy.random.seed(10)
+ samples_per_class = 5
+ m = EmbeddingAccuracy()
+
+ class_a = numpy.random.normal(loc=0, scale=0.1, size=(samples_per_class, 2))
+ labels_a = numpy.zeros(samples_per_class)
+
+ class_b = numpy.random.normal(loc=10, scale=0.1, size=(samples_per_class, 2))
+ labels_b = numpy.ones(samples_per_class)
+
+ data = numpy.vstack((class_a, class_b))
+ labels = numpy.concatenate((labels_a, labels_b))
+
+ data = tf.convert_to_tensor(value=data.astype("float32"))
+ labels = tf.convert_to_tensor(value=labels.astype("int64"))
+ m(labels, data)
+
+ assert m.result() == 1.0
diff --git a/bob/learn/tensorflow/utils/__init__.py b/bob/learn/tensorflow/utils/__init__.py
index aab52096a4dfd437279f16c55e808b5f9b06e8f1..444a481681f2f06bb9bb06377db7c492e3f201fc 100644
--- a/bob/learn/tensorflow/utils/__init__.py
+++ b/bob/learn/tensorflow/utils/__init__.py
@@ -1,11 +1,3 @@
-from .util import *
-from .singleton import Singleton
-from .session import Session
-from .hooks import *
-from .eval import *
from .keras import *
-from .train import *
-from .graph import *
-from .network import *
from .math import *
-from .reproducible import *
+from .image import *
diff --git a/bob/learn/tensorflow/utils/eval.py b/bob/learn/tensorflow/utils/eval.py
deleted file mode 100644
index cf836f6e0d1f6742b10fee3d5fa9390d255f1dab..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/eval.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import tensorflow as tf
-
-
-def get_global_step(path):
- """Returns the global number associated with the model checkpoint path. The
- checkpoint must have been saved with the
- :any:`tf.train.MonitoredTrainingSession`.
-
- Parameters
- ----------
- path : str
- The path to model checkpoint, usually ckpt.model_checkpoint_path
-
- Returns
- -------
- global_step : int
- The global step number.
- """
- checkpoint_reader = tf.train.NewCheckpointReader(path)
- return checkpoint_reader.get_tensor(tf.GraphKeys.GLOBAL_STEP)
diff --git a/bob/learn/tensorflow/utils/graph.py b/bob/learn/tensorflow/utils/graph.py
deleted file mode 100644
index e434289d12a4fad5cfa5cfab41d4555fa39fc485..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/graph.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import tensorflow as tf
-
-
-def call_on_frozen_graph(
- graph_def_path,
- input,
- return_elements,
- input_name,
- name=None,
- **kwargs
-):
- """Loads a frozen graph def file (.pb) and replaces its input with the given input
- and return the requested output tensors.
-
- Parameters
- ----------
- graph_def_path : str
- Path to the graph definition file
- input : object
- Input tensor
- return_elements : [str]
- A list of strings which corresponds to operations in the graph.
- input_name : str, optional
- The name of input in the graph that will be replaced by input.
- name : str, optional
- The scope of the imported operations. Defaults to "import".
- **kwargs
- Extra arguments to be passed to tf.import_graph_def
-
- Returns
- -------
- list
- List of requested operations. Normally you would use
- ``returned_operations[0].outputs[0]``
- """
- with tf.gfile.GFile(graph_def_path, "rb") as f:
- graph_def = tf.GraphDef()
- graph_def.ParseFromString(f.read())
- input_map = {input_name: input}
-
- return tf.import_graph_def(
- graph_def,
- input_map=input_map,
- return_elements=return_elements,
- name=name,
- **kwargs
- )
diff --git a/bob/learn/tensorflow/utils/hooks.py b/bob/learn/tensorflow/utils/hooks.py
deleted file mode 100644
index 37adae26163d5b8ef0cef188c9408f14b22d7923..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/hooks.py
+++ /dev/null
@@ -1,199 +0,0 @@
-from datetime import datetime
-from tensorflow.python.training.basic_session_run_hooks import _as_graph_element
-import logging
-import numpy as np
-import tensorflow as tf
-import time
-
-logger = logging.getLogger(__name__)
-
-
-class TensorSummary(tf.estimator.SessionRunHook):
- """Adds the given (scalar) tensors to tensorboard summaries"""
-
- def __init__(self, tensors, tensor_names=None, **kwargs):
- super().__init__(**kwargs)
- self.tensors = list(tensors)
- if tensor_names is None:
- tensor_names = [t.name for t in self.tensors]
- self.tensor_names = list(tensor_names)
-
- def begin(self):
- for name, tensor in zip(self.tensor_names, self.tensors):
- tf.summary.scalar(name, tensor)
-
-
-class LoggerHook(tf.estimator.SessionRunHook):
- """Logs loss and runtime."""
-
- def __init__(self, loss, batch_size, log_frequency):
- self.loss = loss
- self.batch_size = batch_size
- self.log_frequency = log_frequency
-
- def begin(self):
- self._step = -1
- self._start_time = time.time()
-
- def before_run(self, run_context):
- self._step += 1
- return tf.train.SessionRunArgs(self.loss) # Asks for loss value.
-
- def after_run(self, run_context, run_values):
- if self._step % self.log_frequency == 0:
- current_time = time.time()
- duration = current_time - self._start_time
- self._start_time = current_time
-
- loss_value = run_values.results
- examples_per_sec = self.log_frequency * self.batch_size / duration
- sec_per_batch = float(duration / self.log_frequency)
-
- format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
- 'sec/batch)')
- print(format_str % (datetime.now(), self._step, loss_value,
- examples_per_sec, sec_per_batch))
-
-
-class LoggerHookEstimator(tf.estimator.SessionRunHook):
- """Logs loss and runtime."""
-
- def __init__(self, estimator, batch_size, log_frequency):
- self.estimator = estimator
- self.batch_size = batch_size
- self.log_frequency = log_frequency
-
- def begin(self):
- self._step = -1
- self._start_time = time.time()
-
- def before_run(self, run_context):
- self._step += 1
- # Asks for loss value.
- return tf.train.SessionRunArgs(self.estimator.loss)
-
- def after_run(self, run_context, run_values):
- if self._step % self.log_frequency == 0:
- current_time = time.time()
- duration = current_time - self._start_time
- self._start_time = current_time
-
- loss_value = run_values.results
- examples_per_sec = self.log_frequency * self.batch_size / duration
- sec_per_batch = float(duration / self.log_frequency)
-
- format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
- 'sec/batch)')
- print(format_str % (datetime.now(), self._step, loss_value,
- examples_per_sec, sec_per_batch))
-
-
-class EarlyStopException(Exception):
- pass
-
-
-class EarlyStopping(tf.estimator.SessionRunHook):
- """Stop training when a monitored quantity has stopped improving.
- Based on Keras's EarlyStopping callback:
- https://keras.io/callbacks/#earlystopping
- The original implementation worked for epochs. Currently there is no way
- to know the epoch count in estimator training. Hence, the criteria is done
- using steps instead of epochs.
-
- Parameters
- ----------
- monitor
- quantity to be monitored.
- min_delta
- minimum change in the monitored quantity to qualify as an improvement,
- i.e. an absolute change of less than min_delta, will count as no
- improvement.
- patience
- number of steps with no improvement after which training will be
- stopped. Please use large patience values since this hook is
- implemented using steps instead of epochs compared to the equivalent
- one in Keras.
- mode
- one of {auto, min, max}. In `min` mode, training will stop when the
- quantity monitored has stopped decreasing; in `max` mode it will stop
- when the quantity monitored has stopped increasing; in `auto` mode, the
- direction is automatically inferred from the name of the monitored
- quantity.
- """
-
- def __init__(self,
- monitor='accuracy/value',
- min_delta=0,
- patience=0,
- mode='auto'):
- super(EarlyStopping, self).__init__()
-
- self.monitor = monitor
- self.patience = patience
- self.min_delta = min_delta
- self.wait = 0
-
- if mode not in ['auto', 'min', 'max']:
- logger.warn('EarlyStopping mode %s is unknown, '
- 'fallback to auto mode.' % mode)
- mode = 'auto'
-
- if mode == 'min':
- self.monitor_op = np.less
- elif mode == 'max':
- self.monitor_op = np.greater
- else:
- if 'acc' in self.monitor:
- self.monitor_op = np.greater
- else:
- self.monitor_op = np.less
-
- if self.monitor_op == np.greater:
- self.min_delta *= 1
- else:
- self.min_delta *= -1
- # Allow instances to be re-used
- self.wait = 0
- self.best = np.Inf if self.monitor_op == np.less else -np.Inf
- self.global_step_of_best = 0
-
- def begin(self):
- self.values = []
- if isinstance(self.monitor, str):
- self.monitor = _as_graph_element(self.monitor)
- else:
- self.monitor = _as_graph_element(self.monitor.name)
- self.global_step_tensor = tf.train.get_global_step()
-
- def before_run(self, run_context):
- return tf.train.SessionRunArgs([self.monitor, self.global_step_tensor])
-
- def after_run(self, run_context, run_values):
- monitor, global_step = run_values.results
- self.values.append(monitor)
- # global step does not change during evaluation so keeping one of them
- # is enough.
- self.global_step_value = global_step
-
- def _should_stop(self):
- current = np.mean(self.values)
- logger.info(
- '%s is currently at %f (at step of %d) and the best value was %f '
- '(at step of %d)', self.monitor.name, current,
- self.global_step_value, self.best, self.global_step_of_best)
- if self.monitor_op(current - self.min_delta, self.best):
- self.best = current
- self.wait = 0
- self.global_step_of_best = self.global_step_value
- else:
- if self.wait >= self.patience:
- message = 'Early stopping happened with {} at best of ' \
- '{} (at step {}) and current of {} (at step {})'.format(
- self.monitor.name, self.best, self.global_step_of_best,
- current, self.global_step_value)
- logger.info(message)
- raise EarlyStopException(message)
- self.wait += 1
-
- def end(self, session):
- self._should_stop()
diff --git a/bob/learn/tensorflow/utils/image.py b/bob/learn/tensorflow/utils/image.py
new file mode 100644
index 0000000000000000000000000000000000000000..b05b2509e52319d09b3aa1529377fed58c8ac6d8
--- /dev/null
+++ b/bob/learn/tensorflow/utils/image.py
@@ -0,0 +1,63 @@
+import tensorflow as tf
+
+
+def to_channels_last(image):
+ """Converts the image to channel_last format. This is the same format as in
+ matplotlib, skimage, and etc.
+
+ Parameters
+ ----------
+ image : `tf.Tensor`
+ At least a 3 dimensional image. If the dimension is more than 3, the
+ last 3 dimensions are assumed to be [C, H, W].
+
+ Returns
+ -------
+ image : `tf.Tensor`
+ The image in [..., H, W, C] format.
+
+ Raises
+ ------
+ ValueError
+ If dim of image is less than 3.
+ """
+ ndim = image.ndim
+ if ndim < 3:
+ raise ValueError(
+ "The image needs to be at least 3 dimensional but it " "was {}".format(ndim)
+ )
+ axis_order = [1, 2, 0]
+ shift = ndim - 3
+ axis_order = list(range(ndim - 3)) + [n + shift for n in axis_order]
+ return tf.transpose(a=image, perm=axis_order)
+
+
+def to_channels_first(image):
+ """Converts the image to channel_first format. This is the same format as
+ in bob.io.image and bob.io.video.
+
+ Parameters
+ ----------
+ image : `tf.Tensor`
+ At least a 3 dimensional image. If the dimension is more than 3, the
+ last 3 dimensions are assumed to be [H, W, C].
+
+ Returns
+ -------
+ image : `tf.Tensor`
+ The image in [..., C, H, W] format.
+
+ Raises
+ ------
+ ValueError
+ If dim of image is less than 3.
+ """
+ ndim = image.ndim
+ if ndim < 3:
+ raise ValueError(
+ "The image needs to be at least 3 dimensional but it " "was {}".format(ndim)
+ )
+ axis_order = [2, 0, 1]
+ shift = ndim - 3
+ axis_order = list(range(ndim - 3)) + [n + shift for n in axis_order]
+ return tf.transpose(a=image, perm=axis_order)
diff --git a/bob/learn/tensorflow/utils/keras.py b/bob/learn/tensorflow/utils/keras.py
index c74021620069100b909d82a2c77998b976e4643a..6fceb5a4192266b663fe2e1957040c2571bf45c4 100644
--- a/bob/learn/tensorflow/utils/keras.py
+++ b/bob/learn/tensorflow/utils/keras.py
@@ -1,10 +1,82 @@
-import tensorflow.keras.backend as K
-from .network import is_trainable
-import tensorflow as tf
+import copy
import logging
+import tensorflow as tf
+import tensorflow.keras.backend as K
+from tensorflow.python.keras import layers as layer_module
+from tensorflow.python.keras.utils import generic_utils
+from tensorflow.python.util import nest
+from tensorflow.python.util import tf_inspect
+
logger = logging.getLogger(__name__)
+SINGLE_LAYER_OUTPUT_ERROR_MSG = (
+ "All layers in a Sequential model should have "
+ "a single output tensor. For multi-output "
+ "layers, use the functional API."
+)
+
+
+class SequentialLayer(tf.keras.layers.Layer):
+ """A Layer that does the same thing as tf.keras.Sequential but
+ its variables can be scoped.
+
+ Parameters
+ ----------
+ layers : list
+ List of layers. All layers must be provided at initialization time
+ """
+
+ def __init__(self, layers, **kwargs):
+ super().__init__(**kwargs)
+ self.sequential_layers = list(layers)
+
+ def call(self, inputs, training=None, mask=None):
+ outputs = inputs
+ for layer in self.sequential_layers:
+ # During each iteration, `inputs` are the inputs to `layer`, and `outputs`
+ # are the outputs of `layer` applied to `inputs`. At the end of each
+ # iteration `inputs` is set to `outputs` to prepare for the next layer.
+ kwargs = {}
+ argspec = tf_inspect.getfullargspec(layer.call).args
+ if "mask" in argspec:
+ kwargs["mask"] = mask
+ if "training" in argspec:
+ kwargs["training"] = training
+
+ outputs = layer(outputs, **kwargs)
+
+ if len(nest.flatten(outputs)) != 1:
+ raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
+
+ mask = getattr(outputs, "_keras_mask", None)
+
+ return outputs
+
+ def get_config(self):
+ layer_configs = []
+ for layer in self.sequential_layers:
+ layer_configs.append(generic_utils.serialize_keras_object(layer))
+ config = {"name": self.name, "layers": copy.deepcopy(layer_configs)}
+ return config
+
+ @classmethod
+ def from_config(cls, config, custom_objects=None):
+ if "name" in config:
+ name = config["name"]
+ layer_configs = config["layers"]
+ else:
+ name = None
+ layer_configs = config
+ layers = []
+ for layer_config in layer_configs:
+ layer = layer_module.deserialize(
+ layer_config, custom_objects=custom_objects
+ )
+ layers.append(layer)
+ model = cls(layers, name=name)
+ return model
+
def keras_channels_index():
return -3 if K.image_data_format() == "channels_first" else -1
@@ -21,7 +93,7 @@ def keras_model_weights_as_initializers_for_variables(model):
model : object
A Keras model.
"""
- sess = K.get_session()
+ sess = tf.compat.v1.keras.backend.get_session()
n = len(model.variables)
logger.debug("Initializing %d variables with their current weights", n)
for variable in model.variables:
@@ -31,25 +103,6 @@ def keras_model_weights_as_initializers_for_variables(model):
variable._initial_value = initial_value
-def apply_trainable_variables_on_keras_model(model, trainable_variables, mode):
- """Changes the trainable status of layers in a keras model.
- It can only turn off the trainable status of layer.
-
- Parameters
- ----------
- model : object
- A Keras model
- trainable_variables : list or None
- See bob.learn.tensorflow.estimators.Logits
- mode : str
- One of tf.estimator.ModeKeys
- """
- for layer in model.layers:
- trainable = is_trainable(layer.name, trainable_variables, mode=mode)
- if layer.trainable:
- layer.trainable = trainable
-
-
def _create_var_map(variables, normalizer=None):
if normalizer is None:
@@ -65,10 +118,10 @@ def restore_model_variables_from_checkpoint(
model, checkpoint, session=None, normalizer=None
):
if session is None:
- session = tf.keras.backend.get_session()
+ session = tf.compat.v1.keras.backend.get_session()
var_list = _create_var_map(model.variables, normalizer=normalizer)
- saver = tf.train.Saver(var_list=var_list)
+ saver = tf.compat.v1.train.Saver(var_list=var_list)
ckpt_state = tf.train.get_checkpoint_state(checkpoint)
logger.info("Loading checkpoint %s", ckpt_state.model_checkpoint_path)
saver.restore(session, ckpt_state.model_checkpoint_path)
@@ -76,15 +129,11 @@ def restore_model_variables_from_checkpoint(
def initialize_model_from_checkpoint(model, checkpoint, normalizer=None):
assignment_map = _create_var_map(model.variables, normalizer=normalizer)
- tf.train.init_from_checkpoint(checkpoint, assignment_map=assignment_map)
+ tf.compat.v1.train.init_from_checkpoint(checkpoint, assignment_map=assignment_map)
def model_summary(model, do_print=False):
- try:
- from tensorflow.python.keras.utils.layer_utils import count_params
- except ImportError:
- from tensorflow_core.python.keras.utils.layer_utils import count_params
- nest = tf.nest
+ from tensorflow.keras.backend import count_params
if model.__class__.__name__ == "Sequential":
sequential_like = True
diff --git a/bob/learn/tensorflow/utils/math.py b/bob/learn/tensorflow/utils/math.py
index 64ed7349d7dce745bf3c3fe5ad84bd347a9d493c..304d3d71fef9d361b4c2080344568745263a6884 100644
--- a/bob/learn/tensorflow/utils/math.py
+++ b/bob/learn/tensorflow/utils/math.py
@@ -28,7 +28,7 @@ def gram_matrix(input_tensor):
[0., 0., 0., ..., 0., 0., 0.]],
"""
result = tf.linalg.einsum("bijc,bijd->bcd", input_tensor, input_tensor)
- input_shape = tf.shape(input_tensor)
+ input_shape = tf.shape(input=input_tensor)
num_locations = tf.cast(input_shape[1] * input_shape[2], tf.float32)
return result / (num_locations)
@@ -61,17 +61,79 @@ def upper_triangle_and_diagonal(A):
"""
ones = tf.ones_like(A)
# Upper triangular matrix of 0s and 1s (including diagonal)
- mask = tf.matrix_band_part(ones, 0, -1)
- upper_triangular_flat = tf.boolean_mask(A, mask)
+ mask = tf.linalg.band_part(ones, 0, -1)
+ upper_triangular_flat = tf.boolean_mask(tensor=A, mask=mask)
return upper_triangular_flat
def upper_triangle(A):
ones = tf.ones_like(A)
# Upper triangular matrix of 0s and 1s (including diagonal)
- mask_a = tf.matrix_band_part(ones, 0, -1)
+ mask_a = tf.linalg.band_part(ones, 0, -1)
# Diagonal
- mask_b = tf.matrix_band_part(ones, 0, 0)
+ mask_b = tf.linalg.band_part(ones, 0, 0)
mask = tf.cast(mask_a - mask_b, dtype=tf.bool)
- upper_triangular_flat = tf.boolean_mask(A, mask)
+ upper_triangular_flat = tf.boolean_mask(tensor=A, mask=mask)
return upper_triangular_flat
+
+
+def pdist(A, metric="sqeuclidean"):
+ if metric != "sqeuclidean":
+ raise NotImplementedError()
+ r = tf.reduce_sum(input_tensor=A * A, axis=1)
+ r = tf.reshape(r, [-1, 1])
+ D = r - 2 * tf.matmul(A, A, transpose_b=True) + tf.transpose(a=r)
+ return D
+
+
+def cdist(A, B, metric="sqeuclidean"):
+ if metric != "sqeuclidean":
+ raise NotImplementedError()
+ M1, M2 = tf.shape(input=A)[0], tf.shape(input=B)[0]
+ # code from https://stackoverflow.com/a/43839605/1286165
+ p1 = tf.matmul(
+ tf.expand_dims(tf.reduce_sum(input_tensor=tf.square(A), axis=1), 1),
+ tf.ones(shape=(1, M2)),
+ )
+ p2 = tf.transpose(
+ a=tf.matmul(
+ tf.reshape(tf.reduce_sum(input_tensor=tf.square(B), axis=1), shape=[-1, 1]),
+ tf.ones(shape=(M1, 1)),
+ transpose_b=True,
+ )
+ )
+
+ D = tf.add(p1, p2) - 2 * tf.matmul(A, B, transpose_b=True)
+ return D
+
+
+def random_choice_no_replacement(one_dim_input, num_indices_to_drop=3, sort=False):
+ """Similar to np.random.choice with no replacement.
+ Code from https://stackoverflow.com/a/54755281/1286165
+ """
+ input_length = tf.shape(input=one_dim_input)[0]
+
+ # create uniform distribution over the sequence
+ uniform_distribution = tf.random.uniform(
+ shape=[input_length],
+ minval=0,
+ maxval=None,
+ dtype=tf.float32,
+ seed=None,
+ name=None,
+ )
+
+ # grab the indices of the greatest num_words_to_drop values from the distibution
+ _, indices_to_keep = tf.nn.top_k(
+ uniform_distribution, input_length - num_indices_to_drop
+ )
+
+ # sort the indices
+ if sort:
+ sorted_indices_to_keep = tf.sort(indices_to_keep)
+ else:
+ sorted_indices_to_keep = indices_to_keep
+
+ # gather indices from the input array using the filtered actual array
+ result = tf.gather(one_dim_input, sorted_indices_to_keep)
+ return result
diff --git a/bob/learn/tensorflow/utils/network.py b/bob/learn/tensorflow/utils/network.py
deleted file mode 100644
index fa56276ed98e2f2b6db309ac0989931d618b6eab..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/network.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import tensorflow as tf
-import tensorflow.contrib.slim as slim
-
-
-def append_logits(
- graph,
- n_classes,
- reuse=False,
- l2_regularizer=5e-05,
- weights_std=0.1,
- trainable_variables=None,
- name="Logits",
-):
- trainable = is_trainable(name, trainable_variables)
- return slim.fully_connected(
- graph,
- n_classes,
- activation_fn=None,
- weights_initializer=tf.truncated_normal_initializer(stddev=weights_std),
- weights_regularizer=slim.l2_regularizer(l2_regularizer),
- scope=name,
- reuse=reuse,
- trainable=trainable,
- )
-
-
-def is_trainable(name, trainable_variables, mode=tf.estimator.ModeKeys.TRAIN):
- """
- Check if a variable is trainable or not
-
- Parameters
- ----------
-
- name: str
- Layer name
-
- trainable_variables: list
- List containing the variables or scopes to be trained.
- If None, the variable/scope is trained
- """
-
- # if mode is not training, so we shutdown
- if mode != tf.estimator.ModeKeys.TRAIN:
- return False
-
- # If None, we train by default
- if trainable_variables is None:
- return True
-
- # Here is my choice to shutdown the whole scope
- return name in trainable_variables
diff --git a/bob/learn/tensorflow/utils/reproducible.py b/bob/learn/tensorflow/utils/reproducible.py
deleted file mode 100644
index 677994b9beae33667c43dcd2a0cf5069a512ce77..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/reproducible.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""Helps training reproducible networks.
-"""
-import os
-import random as rn
-import numpy as np
-import tensorflow as tf
-from tensorflow.core.protobuf import rewriter_config_pb2
-
-
-def set_seed(
- seed=0,
- python_hash_seed=0,
- log_device_placement=False,
- allow_soft_placement=False,
- arithmetic_optimization=None,
- allow_growth=None,
- memory_optimization=None,
-):
- """Sets the seeds in python, numpy, and tensorflow in order to help
- training reproducible networks.
-
- Parameters
- ----------
- seed : :obj:`int`, optional
- The seed to set.
- python_hash_seed : :obj:`int`, optional
- https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSEED
- log_device_placement : :obj:`bool`, optional
- Optionally, log device placement of tensorflow variables.
-
- Returns
- -------
- :any:`tf.ConfigProto`
- Session config.
- :any:`tf.estimator.RunConfig`
- A run config to help training estimators.
-
- Notes
- -----
- This functions return a list and its length might change. Please use
- indices to select one of returned values. For example
- ``sess_config, run_config = set_seed()[:2]``.
- """
- # reproducible networks
- # The below is necessary in Python 3.2.3 onwards to
- # have reproducible behavior for certain hash-based operations.
- # See these references for further details:
- # https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSEED
- # https://github.com/fchollet/keras/issues/2280#issuecomment-306959926
- os.environ["PYTHONHASHSEED"] = "{}".format(python_hash_seed)
-
- # The below is necessary for starting Numpy generated random numbers
- # in a well-defined initial state.
- np.random.seed(seed)
-
- # The below is necessary for starting core Python generated random numbers
- # in a well-defined state.
- rn.seed(seed)
-
- # Force TensorFlow to use single thread.
- # Multiple threads are a potential source of
- # non-reproducible results.
- # For further details, see:
- # https://stackoverflow.com/questions/42022950/which-seeds-have-to-be-set-where-to-realize-100-reproducibility-of-training-res
- session_config = tf.ConfigProto(
- intra_op_parallelism_threads=1,
- inter_op_parallelism_threads=1,
- log_device_placement=log_device_placement,
- allow_soft_placement=allow_soft_placement,
- )
-
- off = rewriter_config_pb2.RewriterConfig.OFF
- if arithmetic_optimization == "off":
- session_config.graph_options.rewrite_options.arithmetic_optimization = off
-
- if memory_optimization == "off":
- session_config.graph_options.rewrite_options.memory_optimization = off
-
- if allow_growth is not None:
- session_config.gpu_options.allow_growth = allow_growth
- session_config.gpu_options.per_process_gpu_memory_fraction = 0.8
-
- # The below tf.set_random_seed() will make random number generation
- # in the TensorFlow backend have a well-defined initial state.
- # For further details, see:
- # https://www.tensorflow.org/api_docs/python/tf/set_random_seed
- tf.set_random_seed(seed)
- # sess = tf.Session(graph=tf.get_default_graph(), config=session_config)
- # keras.backend.set_session(sess)
-
- run_config = tf.estimator.RunConfig()
- run_config = run_config.replace(session_config=session_config)
- run_config = run_config.replace(tf_random_seed=seed)
-
- return [session_config, run_config, None, None, None]
diff --git a/bob/learn/tensorflow/utils/sequences.py b/bob/learn/tensorflow/utils/sequences.py
deleted file mode 100644
index c2de433a50c65e13d3cb7353bd1f036a8055ddc6..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/sequences.py
+++ /dev/null
@@ -1,144 +0,0 @@
-from __future__ import division
-import numpy
-from keras.utils import Sequence
-# documentation imports
-from bob.dap.base.database import PadDatabase, PadFile
-from bob.bio.base.preprocessor import Preprocessor
-
-
-class PadSequence(Sequence):
- """A data shuffler for bob.dap.base database interfaces.
-
- Attributes
- ----------
- batch_size : int
- The number of samples to return in every batch.
- files : list of :any:`PadFile`
- List of file objects for a particular group and protocol.
- labels : list of bool
- List of labels for the files. ``True`` if bona-fide, ``False`` if
- attack.
- preprocessor : :any:`Preprocessor`
- The preprocessor to be used to load and process the data.
- """
-
- def __init__(self, files, labels, batch_size, preprocessor,
- original_directory, original_extension):
- super(PadSequence, self).__init__()
- self.files = files
- self.labels = labels
- self.batch_size = int(batch_size)
- self.preprocessor = preprocessor
- self.original_directory = original_directory
- self.original_extension = original_extension
-
- def __len__(self):
- """Number of batch in the Sequence.
-
- Returns
- -------
- int
- The number of batches in the Sequence.
- """
- return int(numpy.ceil(len(self.files) / self.batch_size))
-
- def __getitem__(self, idx):
- files = self.files[idx * self.batch_size:(idx + 1) * self.batch_size]
- labels = self.labels[idx * self.batch_size:(idx + 1) * self.batch_size]
- return self.load_batch(files, labels)
-
- def load_batch(self, files, labels):
- """Loads a batch of files and processes them.
-
- Parameters
- ----------
- files : list of :any:`PadFile`
- List of files to load.
- labels : list of bool
- List of labels corresponding to the files.
-
- Returns
- -------
- tuple of :any:`numpy.array`
- A tuple of (x, y): the data and their targets.
- """
- data, targets = [], []
- for file_object, target in zip(files, labels):
- loaded_data = self.preprocessor.read_original_data(
- file_object, self.original_directory, self.original_extension)
- preprocessed_data = self.preprocessor(loaded_data)
- data.append(preprocessed_data)
- targets.append(target)
- return numpy.array(data), numpy.array(targets)
-
- def on_epoch_end(self):
- pass
-
-
-def shuffle_data(files, labels):
- indexes = numpy.arange(len(files))
- numpy.random.shuffle(indexes)
- return [files[i] for i in indexes], [labels[i] for i in indexes]
-
-
-def get_pad_files_labels(database, groups):
- """Returns the pad files and their labels.
-
- Parameters
- ----------
- database : :any:`PadDatabase`
- The database to be used. The database should have a proper
- ``database.protocol`` attribute.
- groups : str
- The group to be used to return the data. One of ('world', 'dev',
- 'eval'). 'world' means training data and 'dev' means validation data.
-
- Returns
- -------
- tuple
- A tuple of (files, labels) for that particular group and protocol.
- """
- files = database.samples(groups=groups, protocol=database.protocol)
- labels = ((f.attack_type is None) for f in files)
- labels = numpy.fromiter(labels, bool, len(files))
- return files, labels
-
-
-def get_pad_sequences(database,
- preprocessor,
- batch_size,
- groups=('world', 'dev', 'eval'),
- shuffle=False,
- limit=None):
- """Returns a list of :any:`Sequence` objects for the database.
-
- Parameters
- ----------
- database : :any:`PadDatabase`
- The database to be used. The database should have a proper
- ``database.protocol`` attribute.
- preprocessor : :any:`Preprocessor`
- The preprocessor to be used to load and process the data.
- batch_size : int
- The number of samples to return in every batch.
- groups : str
- The group to be used to return the data. One of ('world', 'dev',
- 'eval'). 'world' means training data and 'dev' means validation data.
-
- Returns
- -------
- list of :any:`Sequence`
- The requested sequences to be used.
- """
- seqs = []
- for grp in groups:
- files, labels = get_pad_files_labels(database, grp)
- if shuffle:
- files, labels = shuffle_data(files, labels)
- if limit is not None:
- files, labels = files[:limit], labels[:limit]
- seqs.append(
- PadSequence(files, labels, batch_size, preprocessor,
- database.original_directory,
- database.original_extension))
- return seqs
diff --git a/bob/learn/tensorflow/utils/session.py b/bob/learn/tensorflow/utils/session.py
deleted file mode 100644
index 3976f9f0446f14a74f0d5b1e237dd28a7937fbb9..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/session.py
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# @date: Wed 11 May 2016 09:39:36 CEST
-
-import tensorflow as tf
-from .singleton import Singleton
-from tensorflow.python import debug as tf_debug
-
-
-@Singleton
-class Session(object):
- """
- Encapsulates a tf.session
- """
-
- def __init__(self, debug=False):
- config = tf.ConfigProto(
- log_device_placement=False,
- allow_soft_placement=True,
- gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.5))
- config.gpu_options.allow_growth = True
- self.session = tf.Session()
- if debug:
- self.session = tf_debug.LocalCLIDebugWrapperSession(self.session)
diff --git a/bob/learn/tensorflow/utils/singleton.py b/bob/learn/tensorflow/utils/singleton.py
deleted file mode 100644
index d91f3c3ab5cfc339936066e3642c4f728edd20da..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/singleton.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# A singleton class decorator, based on http://stackoverflow.com/a/7346105/3301902
-
-
-class Singleton(object):
- """
- A non-thread-safe helper class to ease implementing singletons.
- This should be used as a **decorator** -- not a metaclass -- to the class that should be a singleton.
-
- The decorated class can define one `__init__` function that takes an arbitrary list of parameters.
-
- To get the singleton instance, use the :py:meth:`instance` method. Trying to use `__call__` will result in a `TypeError` being raised.
-
- Limitations:
-
- * The decorated class cannot be inherited from.
- * The documentation of the decorated class is replaced with the documentation of this class.
- """
-
- def __init__(self, decorated):
- self._decorated = decorated
- # see: functools.WRAPPER_ASSIGNMENTS:
- self.__doc__ = decorated.__doc__
- self.__name__ = decorated.__name__
- self.__module__ = decorated.__module__
- self.__mro__ = decorated.__mro__
- self.__bases__ = []
-
- self._instance = None
-
- def create(self, *args, **kwargs):
- """Creates the singleton instance, by passing the given parameters to the class' constructor."""
- # TODO: I still having problems in killing all the elements of the current session
-
- if self._instance is not None:
- self._instance.session.close()
- del self._instance
- self._instance = self._decorated(*args, **kwargs)
-
- def instance(self, new=False):
- """Returns the singleton instance.
- The function :py:meth:`create` must have been called before."""
- if self._instance is None or new:
-
- self.create()
- return self._instance
-
- def __call__(self):
- raise TypeError(
- 'Singletons must be accessed through the `instance()` method.')
-
- def __instancecheck__(self, inst):
- return isinstance(inst, self._decorated)
diff --git a/bob/learn/tensorflow/utils/train.py b/bob/learn/tensorflow/utils/train.py
deleted file mode 100644
index 999891047c46c7101f38eb3f7bc39c4add37ea7d..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/train.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import tensorflow as tf
-
-
-def check_features(features):
- if "data" not in features or "key" not in features:
- raise ValueError(
- "The input function needs to contain a dictionary with the keys `data` and `key` "
- )
- return True
-
-
-def get_trainable_variables(extra_checkpoint, mode=tf.estimator.ModeKeys.TRAIN):
- """
- Given the extra_checkpoint dictionary provided to the estimator,
- extract the content of "trainable_variables".
-
- If trainable_variables is not provided, all end points are trainable by
- default.
- If trainable_variables==[], all end points are NOT trainable.
- If trainable_variables contains some end_points, ONLY these endpoints will
- be trainable.
-
- Attributes
- ----------
-
- extra_checkpoint: dict
- The extra_checkpoint dictionary provided to the estimator
-
- mode:
- The estimator mode. TRAIN, EVAL, and PREDICT. If not TRAIN, None is
- returned.
-
- Returns
- -------
-
- Returns `None` if **trainable_variables** is not in extra_checkpoint;
- otherwise returns the content of extra_checkpoint .
- """
- if mode != tf.estimator.ModeKeys.TRAIN:
- return None
-
- # If you don't set anything, everything is trainable
- if extra_checkpoint is None or "trainable_variables" not in extra_checkpoint:
- return None
-
- return extra_checkpoint["trainable_variables"]
diff --git a/bob/learn/tensorflow/utils/util.py b/bob/learn/tensorflow/utils/util.py
deleted file mode 100644
index 263a9ec00970b03786e6e98db35b65617fa323eb..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/utils/util.py
+++ /dev/null
@@ -1,467 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# @date: Wed 11 May 2016 09:39:36 CEST
-
-import numpy
-import tensorflow as tf
-from tensorflow.python.client import device_lib
-from tensorflow.python.framework import function
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-@function.Defun(tf.float32, tf.float32)
-def norm_grad(x, dy):
- return tf.expand_dims(dy, -1) * (
- x / (tf.expand_dims(tf.norm(x, ord=2, axis=-1), -1) + 1.0e-19)
- )
-
-
-@function.Defun(tf.float32, grad_func=norm_grad)
-def norm(x):
- return tf.norm(x, ord=2, axis=-1)
-
-
-def compute_euclidean_distance(x, y):
- """
- Computes the euclidean distance between two tensorflow variables
- """
-
- with tf.name_scope("euclidean_distance"):
- # d = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(x, y)), 1))
- d = norm(tf.subtract(x, y))
- return d
-
-
-def pdist_safe(A, metric="sqeuclidean"):
- if metric != "sqeuclidean":
- raise NotImplementedError()
- r = tf.reduce_sum(A * A, 1)
- r = tf.reshape(r, [-1, 1])
- D = r - 2 * tf.matmul(A, A, transpose_b=True) + tf.transpose(r)
- return D
-
-
-def cdist(A, B, metric="sqeuclidean"):
- if metric != "sqeuclidean":
- raise NotImplementedError()
- M1, M2 = tf.shape(A)[0], tf.shape(B)[0]
- # code from https://stackoverflow.com/a/43839605/1286165
- p1 = tf.matmul(
- tf.expand_dims(tf.reduce_sum(tf.square(A), 1), 1), tf.ones(shape=(1, M2))
- )
- p2 = tf.transpose(
- tf.matmul(
- tf.reshape(tf.reduce_sum(tf.square(B), 1), shape=[-1, 1]),
- tf.ones(shape=(M1, 1)),
- transpose_b=True,
- )
- )
-
- D = tf.add(p1, p2) - 2 * tf.matmul(A, B, transpose_b=True)
- return D
-
-
-def load_mnist(perc_train=0.9):
- numpy.random.seed(0)
- import bob.db.mnist
-
- db = bob.db.mnist.Database()
- raw_data = db.data()
-
- # data = raw_data[0].astype(numpy.float64)
- data = raw_data[0]
- labels = raw_data[1]
-
- # Shuffling
- total_samples = data.shape[0]
- indexes = numpy.array(range(total_samples))
- numpy.random.shuffle(indexes)
-
- # Spliting train and validation
- n_train = int(perc_train * indexes.shape[0])
- n_validation = total_samples - n_train
-
- train_data = data[0:n_train, :].astype("float32") * 0.00390625
- train_labels = labels[0:n_train]
-
- validation_data = (
- data[n_train : n_train + n_validation, :].astype("float32") * 0.00390625
- )
- validation_labels = labels[n_train : n_train + n_validation]
-
- return train_data, train_labels, validation_data, validation_labels
-
-
-def create_mnist_tfrecord(tfrecords_filename, data, labels, n_samples=6000):
- def _bytes_feature(value):
- return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
-
- def _int64_feature(value):
- return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
-
- writer = tf.python_io.TFRecordWriter(tfrecords_filename)
-
- for i in range(n_samples):
- img = data[i]
- img_raw = img.tostring()
- feature = {
- "data": _bytes_feature(img_raw),
- "label": _int64_feature(labels[i]),
- "key": _bytes_feature(b"-"),
- }
-
- example = tf.train.Example(features=tf.train.Features(feature=feature))
- writer.write(example.SerializeToString())
- writer.close()
-
-
-def compute_eer(
- data_train, labels_train, data_validation, labels_validation, n_classes
-):
- import bob.measure
- from scipy.spatial.distance import cosine
-
- # Creating client models
- models = []
- for i in range(n_classes):
- indexes = labels_train == i
- models.append(numpy.mean(data_train[indexes, :], axis=0))
-
- # Probing
- positive_scores = numpy.zeros(shape=0)
- negative_scores = numpy.zeros(shape=0)
-
- for i in range(n_classes):
- # Positive scoring
- indexes = labels_validation == i
- positive_data = data_validation[indexes, :]
- p = [cosine(models[i], positive_data[j]) for j in range(positive_data.shape[0])]
- positive_scores = numpy.hstack((positive_scores, p))
-
- # negative scoring
- indexes = labels_validation != i
- negative_data = data_validation[indexes, :]
- n = [cosine(models[i], negative_data[j]) for j in range(negative_data.shape[0])]
- negative_scores = numpy.hstack((negative_scores, n))
-
- # Computing performance based on EER
- negative_scores = (-1) * negative_scores
- positive_scores = (-1) * positive_scores
-
- threshold = bob.measure.eer_threshold(negative_scores, positive_scores)
- far, frr = bob.measure.farfrr(negative_scores, positive_scores, threshold)
- eer = (far + frr) / 2.0
-
- return eer
-
-
-def compute_accuracy(
- data_train, labels_train, data_validation, labels_validation, n_classes
-):
- from scipy.spatial.distance import cosine
-
- # Creating client models
- models = []
- for i in range(n_classes):
- indexes = labels_train == i
- models.append(numpy.mean(data_train[indexes, :], axis=0))
-
- # Probing
- tp = 0
- for i in range(data_validation.shape[0]):
-
- d = data_validation[i, :]
- l = labels_validation[i]
-
- scores = [cosine(m, d) for m in models]
- predict = numpy.argmax(scores)
-
- if predict == l:
- tp += 1
-
- return (float(tp) / data_validation.shape[0]) * 100
-
-
-def debug_embbeding(image, architecture, embbeding_dim=2, feature_layer="fc3"):
- """
- """
- import tensorflow as tf
- from bob.learn.tensorflow.utils.session import Session
-
- session = Session.instance(new=False).session
- inference_graph = architecture.compute_graph(
- architecture.inference_placeholder, feature_layer=feature_layer, training=False
- )
-
- embeddings = numpy.zeros(shape=(image.shape[0], embbeding_dim))
- for i in range(image.shape[0]):
- feed_dict = {architecture.inference_placeholder: image[i : i + 1, :, :, :]}
- embedding = session.run(
- [tf.nn.l2_normalize(inference_graph, 1, 1e-10)], feed_dict=feed_dict
- )[0]
- embedding = numpy.reshape(embedding, numpy.prod(embedding.shape[1:]))
- embeddings[i] = embedding
-
- return embeddings
-
-
-def pdist(A):
- """
- Compute a pairwise euclidean distance in the same fashion
- as in scipy.spation.distance.pdist
- """
- with tf.name_scope("Pairwisedistance"):
- ones_1 = tf.reshape(tf.cast(tf.ones_like(A), tf.float32)[:, 0], [1, -1])
- p1 = tf.matmul(tf.expand_dims(tf.reduce_sum(tf.square(A), 1), 1), ones_1)
-
- ones_2 = tf.reshape(tf.cast(tf.ones_like(A), tf.float32)[:, 0], [-1, 1])
- p2 = tf.transpose(
- tf.matmul(
- tf.reshape(tf.reduce_sum(tf.square(A), 1), shape=[-1, 1]),
- ones_2,
- transpose_b=True,
- )
- )
-
- return tf.sqrt(tf.add(p1, p2) - 2 * tf.matmul(A, A, transpose_b=True))
-
-
-def predict_using_tensors(embedding, labels, num=None):
- """
- Compute the predictions through exhaustive comparisons between
- embeddings using tensors
- """
-
- # Fitting the main diagonal with infs (removing comparisons with the same
- # sample)
- inf = tf.cast(tf.ones_like(labels), tf.float32) * numpy.inf
-
- distances = pdist(embedding)
- distances = tf.matrix_set_diag(distances, inf)
- indexes = tf.argmin(distances, axis=1)
- return [labels[i] for i in tf.unstack(indexes, num=num)]
-
-
-def compute_embedding_accuracy_tensors(embedding, labels, num=None):
- """
- Compute the accuracy in a closed-set
-
- **Parameters**
-
- embeddings: `tf.Tensor`
- Set of embeddings
-
- labels: `tf.Tensor`
- Correspondent labels
- """
-
- # Fitting the main diagonal with infs (removing comparisons with the same
- # sample)
- predictions = predict_using_tensors(embedding, labels, num=num)
- matching = [
- tf.equal(p, l)
- for p, l in zip(tf.unstack(predictions, num=num), tf.unstack(labels, num=num))
- ]
-
- return tf.reduce_sum(tf.cast(matching, tf.uint8)) / len(predictions)
-
-
-def compute_embedding_accuracy(embedding, labels):
- """
- Compute the accuracy in a closed-set
-
- **Parameters**
-
- embeddings: :any:`numpy.array`
- Set of embeddings
-
- labels: :any:`numpy.array`
- Correspondent labels
- """
-
- from scipy.spatial.distance import pdist, squareform
-
- distances = squareform(pdist(embedding))
-
- n_samples = embedding.shape[0]
-
- # Fitting the main diagonal with infs (removing comparisons with the same
- # sample)
- numpy.fill_diagonal(distances, numpy.inf)
-
- indexes = distances.argmin(axis=1)
-
- # Computing the argmin excluding comparisons with the same samples
- # Basically, we are excluding the main diagonal
-
- # valid_indexes = distances[distances>0].reshape(n_samples, n_samples-1).argmin(axis=1)
-
- # Getting the original positions of the indexes in the 1-axis
- # corrected_indexes = [ i if i<j else i+1 for i, j in zip(valid_indexes, range(n_samples))]
-
- matching = [labels[i] == labels[j] for i, j in zip(range(n_samples), indexes)]
- accuracy = sum(matching) / float(n_samples)
-
- return accuracy
-
-
-def get_available_gpus():
- """Returns the number of GPU devices that are available.
-
- Returns
- -------
- [str]
- The names of available GPU devices.
- """
- local_device_protos = device_lib.list_local_devices()
- return [x.name for x in local_device_protos if x.device_type == "GPU"]
-
-
-def to_channels_last(image):
- """Converts the image to channel_last format. This is the same format as in
- matplotlib, skimage, and etc.
-
- Parameters
- ----------
- image : `tf.Tensor`
- At least a 3 dimensional image. If the dimension is more than 3, the
- last 3 dimensions are assumed to be [C, H, W].
-
- Returns
- -------
- image : `tf.Tensor`
- The image in [..., H, W, C] format.
-
- Raises
- ------
- ValueError
- If dim of image is less than 3.
- """
- ndim = len(image.shape)
- if ndim < 3:
- raise ValueError(
- "The image needs to be at least 3 dimensional but it " "was {}".format(ndim)
- )
- axis_order = [1, 2, 0]
- shift = ndim - 3
- axis_order = list(range(ndim - 3)) + [n + shift for n in axis_order]
- return tf.transpose(image, axis_order)
-
-
-def to_channels_first(image):
- """Converts the image to channel_first format. This is the same format as
- in bob.io.image and bob.io.video.
-
- Parameters
- ----------
- image : `tf.Tensor`
- At least a 3 dimensional image. If the dimension is more than 3, the
- last 3 dimensions are assumed to be [H, W, C].
-
- Returns
- -------
- image : `tf.Tensor`
- The image in [..., C, H, W] format.
-
- Raises
- ------
- ValueError
- If dim of image is less than 3.
- """
- ndim = len(image.shape)
- if ndim < 3:
- raise ValueError(
- "The image needs to be at least 3 dimensional but it " "was {}".format(ndim)
- )
- axis_order = [2, 0, 1]
- shift = ndim - 3
- axis_order = list(range(ndim - 3)) + [n + shift for n in axis_order]
- return tf.transpose(image, axis_order)
-
-
-to_skimage = to_matplotlib = to_channels_last
-to_bob = to_channels_first
-
-
-def bytes2human(n, format="%(value).1f %(symbol)s", symbols="customary"):
- """Convert n bytes into a human readable string based on format.
- From: https://code.activestate.com/recipes/578019-bytes-to-human-human-to-
- bytes-converter/
- Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>
- License: MIT
- symbols can be either "customary", "customary_ext", "iec" or "iec_ext",
- see: http://goo.gl/kTQMs
- """
- SYMBOLS = {
- "customary": ("B", "K", "M", "G", "T", "P", "E", "Z", "Y"),
- "customary_ext": (
- "byte",
- "kilo",
- "mega",
- "giga",
- "tera",
- "peta",
- "exa",
- "zetta",
- "iotta",
- ),
- "iec": ("Bi", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi", "Yi"),
- "iec_ext": (
- "byte",
- "kibi",
- "mebi",
- "gibi",
- "tebi",
- "pebi",
- "exbi",
- "zebi",
- "yobi",
- ),
- }
- n = int(n)
- if n < 0:
- raise ValueError("n < 0")
- symbols = SYMBOLS[symbols]
- prefix = {}
- for i, s in enumerate(symbols[1:]):
- prefix[s] = 1 << (i + 1) * 10
- for symbol in reversed(symbols[1:]):
- if n >= prefix[symbol]:
- value = float(n) / prefix[symbol]
- return format % locals()
- return format % dict(symbol=symbols[0], value=n)
-
-
-def random_choice_no_replacement(one_dim_input, num_indices_to_drop=3, sort=False):
- """Similar to np.random.choice with no replacement.
- Code from https://stackoverflow.com/a/54755281/1286165
- """
- input_length = tf.shape(one_dim_input)[0]
-
- # create uniform distribution over the sequence
- uniform_distribution = tf.random.uniform(
- shape=[input_length],
- minval=0,
- maxval=None,
- dtype=tf.float32,
- seed=None,
- name=None,
- )
-
- # grab the indices of the greatest num_words_to_drop values from the distibution
- _, indices_to_keep = tf.nn.top_k(
- uniform_distribution, input_length - num_indices_to_drop
- )
-
- # sort the indices
- if sort:
- sorted_indices_to_keep = tf.sort(indices_to_keep)
- else:
- sorted_indices_to_keep = indices_to_keep
-
- # gather indices from the input array using the filtered actual array
- result = tf.gather(one_dim_input, sorted_indices_to_keep)
- return result
diff --git a/buildout.cfg b/buildout.cfg
index 26bc4af9ef91ddaab87393f1405958f8d9f88f49..3ba4b6f695b3cd05a01208af83d2f293cef002a7 100644
--- a/buildout.cfg
+++ b/buildout.cfg
@@ -11,4 +11,4 @@ verbose = true
[scripts]
recipe = bob.buildout:scripts
-dependent-scripts = true
\ No newline at end of file
+dependent-scripts = true
diff --git a/conda/meta.yaml b/conda/meta.yaml
index 0f06eafa7eb4e937c6e192af3ec1b367a428f0ab..87eacc443e98438c71d6ce9d2ed316569d7a71a0 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -20,23 +20,10 @@ requirements:
host:
- python {{ python }}
- setuptools {{ setuptools }}
- - bob.blitz
- - bob.core
- - bob.db.base
- bob.extension
- bob.io.base
- bob.io.image
- - bob.learn.activation
- - bob.learn.em
- - bob.learn.linear
- - bob.ip.base
- - bob.math
- bob.measure
- - bob.sp
- - bob.db.mnist
- - bob.db.atnt
- - bob.bio.base
- - bob.ip.color
- click {{ click }}
- click-plugins {{ click_plugins }}
run:
diff --git a/doc/conf.py b/doc/conf.py
index efb56b77423f80b23671cedadca42b3c3e2ef65d..4c1a316d41c76ea2bc587a80d9c62a35d3094e7a 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -1,33 +1,42 @@
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
+import glob
import os
import sys
-import glob
+import time
+
import pkg_resources
+# The theme to use for HTML and HTML Help pages. See the documentation for
+# a list of builtin themes.
+import sphinx_rtd_theme
+
+# For inter-documentation mapping:
+from bob.extension.utils import link_documentation
+from bob.extension.utils import load_requirements
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
-needs_sphinx = '1.3'
+needs_sphinx = "1.3"
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = [
- 'sphinx.ext.todo',
- 'sphinx.ext.coverage',
- 'sphinx.ext.ifconfig',
- 'sphinx.ext.autodoc',
- 'sphinx.ext.autosummary',
- 'sphinx.ext.doctest',
- 'sphinx.ext.graphviz',
- 'sphinx.ext.intersphinx',
- 'sphinx.ext.napoleon',
- 'sphinx.ext.viewcode',
- 'sphinx.ext.mathjax',
- 'matplotlib.sphinxext.plot_directive'
- ]
+ "sphinx.ext.todo",
+ "sphinx.ext.coverage",
+ "sphinx.ext.ifconfig",
+ "sphinx.ext.autodoc",
+ "sphinx.ext.autosummary",
+ "sphinx.ext.doctest",
+ "sphinx.ext.graphviz",
+ "sphinx.ext.intersphinx",
+ "sphinx.ext.napoleon",
+ "sphinx.ext.viewcode",
+ "sphinx.ext.mathjax",
+ "matplotlib.sphinxext.plot_directive",
+]
# Be picky about warnings
nitpicky = True
@@ -36,13 +45,13 @@ nitpicky = True
nitpick_ignore = []
# Allows the user to override warnings from a separate file
-if os.path.exists('nitpick-exceptions.txt'):
- for line in open('nitpick-exceptions.txt'):
+if os.path.exists("nitpick-exceptions.txt"):
+ for line in open("nitpick-exceptions.txt"):
if line.strip() == "" or line.startswith("#"):
continue
dtype, target = line.split(None, 1)
target = target.strip()
- try: # python 2.x
+ try: # python 2.x
target = unicode(target)
except NameError:
pass
@@ -58,25 +67,26 @@ autosummary_generate = True
numfig = True
# If we are on OSX, the 'dvipng' path maybe different
-dvipng_osx = '/opt/local/libexec/texlive/binaries/dvipng'
-if os.path.exists(dvipng_osx): pngmath_dvipng = dvipng_osx
+dvipng_osx = "/opt/local/libexec/texlive/binaries/dvipng"
+if os.path.exists(dvipng_osx):
+ pngmath_dvipng = dvipng_osx
# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
# The suffix of source filenames.
-source_suffix = '.rst'
+source_suffix = ".rst"
# The encoding of source files.
-#source_encoding = 'utf-8-sig'
+# source_encoding = 'utf-8-sig'
# The master toctree document.
-master_doc = 'index'
+master_doc = "index"
# General information about the project.
-project = u'bob.learn.tensorflow'
-import time
-copyright = u'%s, Idiap Research Institute' % time.strftime('%Y')
+project = u"bob.learn.tensorflow"
+
+copyright = u"%s, Idiap Research Institute" % time.strftime("%Y")
# Grab the setup entry
distribution = pkg_resources.require(project)[0]
@@ -92,123 +102,121 @@ release = distribution.version
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
-#language = None
+# language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
-#today = ''
+# today = ''
# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
+# today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
-exclude_patterns = ['links.rst']
+exclude_patterns = ["links.rst"]
# The reST default role (used for this markup: `text`) to use for all documents.
-#default_role = None
+# default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
+# add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
-#add_module_names = True
+# add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
-#show_authors = False
+# show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
+pygments_style = "sphinx"
# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
+# modindex_common_prefix = []
# Some variables which are useful for generated material
-project_variable = project.replace('.', '_')
-short_description = u'bob.learn.tensorflow API'
-owner = [u'Idiap Research Institute']
+project_variable = project.replace(".", "_")
+short_description = u"bob.learn.tensorflow API"
+owner = [u"Idiap Research Institute"]
# -- Options for HTML output ---------------------------------------------------
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-import sphinx_rtd_theme
-html_theme = 'sphinx_rtd_theme'
+
+html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
-#html_theme_options = {}
+# html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
-#html_title = None
+# html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
-#html_short_title = project_variable
+# html_short_title = project_variable
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
-html_logo = 'img/logo.png'
+html_logo = "img/logo.png"
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
-html_favicon = 'img/favicon.ico'
+html_favicon = "img/favicon.ico"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-#html_static_path = ['_static']
+# html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
-#html_last_updated_fmt = '%b %d, %Y'
+# html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
-#html_use_smartypants = True
+# html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
-#html_sidebars = {}
+# html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
-#html_additional_pages = {}
+# html_additional_pages = {}
# If false, no module index is generated.
-#html_domain_indices = True
+# html_domain_indices = True
# If false, no index is generated.
-#html_use_index = True
+# html_use_index = True
# If true, the index is split into individual pages for each letter.
-#html_split_index = False
+# html_split_index = False
# If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
+# html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#html_show_sphinx = True
+# html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#html_show_copyright = True
+# html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
-#html_use_opensearch = ''
+# html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
-#html_file_suffix = None
+# html_file_suffix = None
# Output file base name for HTML help builder.
-htmlhelp_basename = project_variable + u'_doc'
+htmlhelp_basename = project_variable + u"_doc"
# -- Post configuration --------------------------------------------------------
@@ -218,46 +226,47 @@ rst_epilog = """
.. |project| replace:: Bob
.. |version| replace:: %s
.. |current-year| date:: %%Y
-""" % (version,)
+""" % (
+ version,
+)
# Default processing flags for sphinx
-autoclass_content = 'class'
-autodoc_member_order = 'bysource'
+autoclass_content = "class"
+autodoc_member_order = "bysource"
autodoc_default_flags = [
- 'members',
- 'undoc-members',
- 'show-inheritance',
- ]
+ "members",
+ "undoc-members",
+ "show-inheritance",
+]
+
-# For inter-documentation mapping:
-from bob.extension.utils import link_documentation, load_requirements
sphinx_requirements = "extra-intersphinx.txt"
if os.path.exists(sphinx_requirements):
- intersphinx_mapping = link_documentation(
- additional_packages=['python','numpy'] + \
- load_requirements(sphinx_requirements)
- )
+ intersphinx_mapping = link_documentation(
+ additional_packages=["python", "numpy"] + load_requirements(sphinx_requirements)
+ )
else:
- intersphinx_mapping = link_documentation()
+ intersphinx_mapping = link_documentation()
# We want to remove all private (i.e. _. or __.__) members
# that are not in the list of accepted functions
-accepted_private_functions = ['__array__']
+accepted_private_functions = ["__array__"]
+
def member_function_test(app, what, name, obj, skip, options):
- # test if we have a private function
- if len(name) > 1 and name[0] == '_':
- # test if this private function should be allowed
- if name not in accepted_private_functions:
- # omit privat functions that are not in the list of accepted private functions
- return skip
- else:
- # test if the method is documented
- if not hasattr(obj, '__doc__') or not obj.__doc__:
- return skip
- return False
+ # test if we have a private function
+ if len(name) > 1 and name[0] == "_":
+ # test if this private function should be allowed
+ if name not in accepted_private_functions:
+ # omit privat functions that are not in the list of accepted private functions
+ return skip
+ else:
+ # test if the method is documented
+ if not hasattr(obj, "__doc__") or not obj.__doc__:
+ return skip
+ return False
-def setup(app):
- app.connect('autodoc-skip-member', member_function_test)
+def setup(app):
+ app.connect("autodoc-skip-member", member_function_test)
diff --git a/doc/index.rst b/doc/index.rst
index b383b757967302c56f7d487089f4b71f0fb609c3..f0bae7a089df9d3d15ed09f2e79a3fc98449681f 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -3,11 +3,12 @@
.. _bob.learn.tensorflow:
=======================
- Tensorflow Bob Bridge
+ Tensorflow Biometrics
=======================
-`bob.learn.tensorflow` is a high-level library, written in Python that runs on top of TensorFlow.
-The goal here is to be able to do fast experimentation with neural networks.
+This package extends the high-level API of Tensorflow to allow biometrics
+experiments. Especially, it provides the tools to train biometrics recognition
+and presentation attack detection deep models.
Users Guide
===========
@@ -16,9 +17,6 @@ Users Guide
:maxdepth: 2
user_guide
- regression
- style_transfer
- transfer_learning
Reference Manual
================
@@ -34,4 +32,3 @@ Indices and tables
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
-
diff --git a/doc/py_api.rst b/doc/py_api.rst
index 4c885e20f69d93208561cedc94e0680858a2415f..2fec97b9c55bcd617d16a2cef044b327c3007560 100644
--- a/doc/py_api.rst
+++ b/doc/py_api.rst
@@ -24,19 +24,6 @@ Estimators
Architectures
=============
-.. autosummary::
- bob.learn.tensorflow.network.chopra
- bob.learn.tensorflow.network.light_cnn9
- bob.learn.tensorflow.network.dummy
- bob.learn.tensorflow.network.mlp
- bob.learn.tensorflow.network.mlp_with_batchnorm_and_dropout
- bob.learn.tensorflow.network.inception_resnet_v2
- bob.learn.tensorflow.network.inception_resnet_v1
- bob.learn.tensorflow.network.inception_resnet_v2_batch_norm
- bob.learn.tensorflow.network.inception_resnet_v1_batch_norm
- bob.learn.tensorflow.network.SimpleCNN.slim_architecture
- bob.learn.tensorflow.network.vgg_19
- bob.learn.tensorflow.network.vgg_16
Data
diff --git a/doc/regression.rst b/doc/regression.rst
deleted file mode 100644
index 54cd1ad51fd20705880329c439d824d8b8b996b6..0000000000000000000000000000000000000000
--- a/doc/regression.rst
+++ /dev/null
@@ -1,125 +0,0 @@
-.. vim: set fileencoding=utf-8 :
-
-
-===========
- Regression
-===========
-
-A flexible estimator for regression problems is implemented in
-:py:class:`bob.learn.tensorflow.estimators.Regressor`. You can use this
-estimator for various regression problems. The guide below (taken from
-https://www.tensorflow.org/tutorials/keras/basic_regression) outlines a basic
-regression example using the API of this package.
-
-The Boston Housing Prices dataset
-=================================
-
-.. testsetup::
-
- import tempfile
- model_dir = tempfile.mkdtemp()
-
-
-1. Let's do some imports:
-*************************
-
-.. doctest::
-
- >>> import tensorflow as tf
- >>> from tensorflow import keras
- >>> import tensorflow.contrib.slim as slim
- >>> from bob.learn.tensorflow.estimators import Regressor
-
-2. Download the dataset:
-************************
-
-.. doctest::
-
- >>> boston_housing = keras.datasets.boston_housing
- >>> print("doctest s**t"); (train_data, train_labels), (test_data, test_labels) = boston_housing.load_data() # doctest: +ELLIPSIS
- doc...
- >>> print("Training set: {}".format(train_data.shape))
- Training set: (404, 13)
- >>> print("Testing set: {}".format(test_data.shape))
- Testing set: (102, 13)
-
-3. Normalize features
-*********************
-
-.. doctest::
-
- >>> # Test data is *not* used when calculating the mean and std.
- >>>
- >>> mean = train_data.mean(axis=0)
- >>> std = train_data.std(axis=0)
- >>> train_data = (train_data - mean) / std
- >>> test_data = (test_data - mean) / std
-
-4. Define the input functions
-*****************************
-
-.. doctest::
-
- >>> EPOCH = 2
- >>> def input_fn(mode):
- ... if mode == tf.estimator.ModeKeys.TRAIN:
- ... features, labels = train_data, train_labels
- ... else:
- ... features, labels, = test_data, test_labels
- ... dataset = tf.data.Dataset.from_tensor_slices((features, labels, [str(x) for x in labels]))
- ... dataset = dataset.batch(1)
- ... if mode == tf.estimator.ModeKeys.TRAIN:
- ... dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(len(labels), EPOCH))
- ... data, label, key = dataset.make_one_shot_iterator().get_next()
- ... # key is a unique string identifier of each sample.
- ... # Here we just use the string version of labels.
- ... return {'data': data, 'key': key}, label
- ...
- >>> def train_input_fn():
- ... return input_fn(tf.estimator.ModeKeys.TRAIN)
- ...
- >>> def eval_input_fn():
- ... return input_fn(tf.estimator.ModeKeys.EVAL)
-
-
-5. Create the estimator
-***********************
-
-.. doctest::
-
- >>> def architecture(data, mode, **kwargs):
- ... endpoints = {}
- ...
- ... with tf.variable_scope('DNN'):
- ...
- ... name = 'fc1'
- ... net = slim.fully_connected(data, 64, scope=name)
- ... endpoints[name] = net
- ...
- ... name = 'fc2'
- ... net = slim.fully_connected(net, 64, scope=name)
- ... endpoints[name] = net
- ...
- ... return net, endpoints
- ...
- >>> estimator = Regressor(architecture, model_dir=model_dir)
-
-
-5. Train and evaluate the model
-*******************************
-
-.. doctest::
-
- >>> estimator.train(train_input_fn) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE +SKIP
- <bob.learn.tensorflow.estimators.Regressor ...
-
- >>> 'rmse' in estimator.evaluate(eval_input_fn) # doctest: +SKIP
- True
-
- >>> list(estimator.predict(eval_input_fn)) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE +SKIP
- [...
-
-.. testcleanup::
-
- import shutil
- shutil.rmtree(model_dir, True)
diff --git a/doc/style_transfer.rst b/doc/style_transfer.rst
deleted file mode 100644
index 2babdeeb1e9c4219f19e663a16f4dcafee62b68b..0000000000000000000000000000000000000000
--- a/doc/style_transfer.rst
+++ /dev/null
@@ -1,81 +0,0 @@
-.. vim: set fileencoding=utf-8 :
-
-
-===============
- Style Transfer
-===============
-
-
-We have implemented the style transfer strategy from::
-
- Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015).
-
-
-Check as the usage possibilities with the command::
-
- $ bob tf style_transfer --help
-
-
-Here we have an example on how to do a style transfer using VGG 19 trained with the image net
-
-.. doctest::
-
- >>> from bob.learn.tensorflow.network import vgg_19
- >>> # --architecture
- >>> architecture = vgg_19
-
- >>> import numpy
-
- >>> # YOU CAN DOWNLOAD THE CHECKPOINTS FROM HERE
- >>> # https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models
- >>> checkpoint_dir = "[DOWNLOAD_YOUR_MODEL]"
-
- >>> # --style-end-points and -- content-end-points
- >>> content_end_points = ['vgg_19/conv4/conv4_2', 'vgg_19/conv5/conv5_2']
- >>> style_end_points = ['vgg_19/conv1/conv1_2',
- ... 'vgg_19/conv2/conv2_1',
- ... 'vgg_19/conv3/conv3_1',
- ... 'vgg_19/conv4/conv4_1',
- ... 'vgg_19/conv5/conv5_1'
- ... ]
-
- >>> # Transfering variables
- >>> scopes = {"vgg_19/":"vgg_19/"}
-
- >>> # Set if images using
- >>> style_image_paths = ["vincent_van_gogh.jpg"]
-
- >>> # Functions used to preprocess the input signal and
- >>> # --preprocess-fn and --un-preprocess-fn
- >>> # Taken from VGG19
- >>> def mean_norm(tensor):
- ... return tensor - numpy.array([ 123.68 , 116.779, 103.939])
-
- >>> def un_mean_norm(tensor):
- ... return tensor + numpy.array([ 123.68 , 116.779, 103.939])
-
- >>> preprocess_fn = mean_norm
-
- >>> un_preprocess_fn = un_mean_norm
-
-
-Here we use an image from Angelina Jolie using Van Gogh style as an example::
-
- $ bob tf style_transfer angelina.jpg angelina_output.jpg vgg19_example.py -i 1000.
-
-.. figure:: img/angelina.jpg
- :width: 35%
-
- Source (content) image
-
-.. figure:: img/vincent_van_gogh.jpg
- :width: 27%
-
- Style image
-
-.. figure:: img/angelina_output.jpg
- :width: 35%
-
- Generated image
-
-
diff --git a/doc/transfer_learning.rst b/doc/transfer_learning.rst
deleted file mode 100644
index 3558619b00555b524da426ac372fa093332f504e..0000000000000000000000000000000000000000
--- a/doc/transfer_learning.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-.. vim: set fileencoding=utf-8 :
-
-
-==================
- Transfer Learning
-==================
-
-
-To be done...
diff --git a/doc/user_guide.rst b/doc/user_guide.rst
index e58b57cf77a9669208f9e4bc811469a239e5fc82..52b530db3726d5594aa7435a1cad72a1343c5a17 100644
--- a/doc/user_guide.rst
+++ b/doc/user_guide.rst
@@ -1,325 +1,126 @@
.. vim: set fileencoding=utf-8 :
-
===========
User guide
===========
-This package builds on top of tensorflow_. You are expected to have some
-familiarity with it before continuing. We recommend reading at least the
-following pages:
+This package builds on top of tensorflow_ (at least 2.3 is needed). You are
+expected to have some familiarity with it before continuing. The best way to use
+tensorflow_ is to use its ``tf.keras`` and ``tf.data`` API. We recommend reading
+at least the following pages:
-* https://www.tensorflow.org/get_started
-* https://www.tensorflow.org/guide/
-* https://www.tensorflow.org/guide/estimators
-* https://www.tensorflow.org/guide/datasets
+* https://www.tensorflow.org/tutorials/quickstart/beginner
+* https://www.tensorflow.org/tutorials/quickstart/advanced
+* https://keras.io/getting_started/intro_to_keras_for_engineers/
+* https://keras.io/getting_started/intro_to_keras_for_researchers/
+* https://www.tensorflow.org/tutorials/load_data/images
+* https://www.tensorflow.org/guide/data
-The best way to use tensorflow_ is to use its ``tf.estimator`` and ``tf.data``
-API. The estimators are an abstraction API for machine learning models and the
-data API is here to help you build complex and efficient input pipelines to
-your model. Using the estimators and dataset API of tensorflow will make your
-code more complex but instead you will enjoy more efficiency and avoid code
-redundancy.
+If you were used to Tensorflow 1 API, then reading these pages are also
+recommended:
+* https://www.tensorflow.org/guide/effective_tf2
+* https://www.tensorflow.org/guide/migrate
+* https://www.tensorflow.org/guide/upgrade
+* https://github.com/tensorflow/community/blob/master/sigs/testing/faq.md
-Face recognition example using bob.db databases
-===============================================
+In the rest of this guide, you will learn a few tips and examples on how to:
+* Port v1 checkpoints to tf v2 format.
+* Create datasets and save TFRecords.
+* Create models with custom training and evaluation logic.
+* Mixed-precision training
+* Multi-GPU and multi-worker training
-Let's take a look at a complete example of using a convolutional neural network
-(CNN) for recognizing faces from the ATNT database. At the end, we will explain
-the data pipeline in more detail.
+After reading this page, you may look at a complete example in:
+https://gitlab.idiap.ch/bob/bob.learn.tensorflow/-/blob/master/examples/MSCeleba_centerloss_mixed_precision_multi_worker.py
-1. Let's do some imports:
-*************************
-.. testsetup::
+Porting V1 Tensorflow checkpoints to V2
+=======================================
- import tempfile
- temp_dir = model_dir = tempfile.mkdtemp()
+Take a look at the notebook located at:
+https://gitlab.idiap.ch/bob/bob.learn.tensorflow/-/blob/master/examples/convert_v1_checkpoints_to_v2.ipynb
+for an example.
-.. doctest::
- >>> from bob.learn.tensorflow.dataset.bio import BioGenerator
- >>> from bob.learn.tensorflow.utils import to_channels_last
- >>> from bob.learn.tensorflow.estimators import Logits
- >>> import bob.db.atnt
- >>> import tensorflow as tf
- >>> import tensorflow.contrib.slim as slim
-
-2. Define the inputs:
-*********************
-
-.. _input_fn:
-
-.. doctest::
+Creating datasets from data
+===========================
- >>> def input_fn(mode):
- ... db = bob.db.atnt.Database()
- ...
- ... if mode == tf.estimator.ModeKeys.TRAIN:
- ... groups = 'world'
- ... elif mode == tf.estimator.ModeKeys.EVAL:
- ... groups = 'dev'
- ...
- ... files = db.objects(groups=groups)
- ...
- ... # construct integer labels for each identity in the database
- ... CLIENT_IDS = (str(f.client_id) for f in files)
- ... CLIENT_IDS = list(set(CLIENT_IDS))
- ... CLIENT_IDS = dict(zip(CLIENT_IDS, range(len(CLIENT_IDS))))
- ...
- ... def biofile_to_label(f):
- ... return CLIENT_IDS[str(f.client_id)]
- ...
- ... def load_data(database, f):
- ... img = f.load(database.original_directory, database.original_extension)
- ... # make a channels_first image (bob format) with 1 channel
- ... img = img.reshape(1, 112, 92)
- ... return img
- ...
- ... generator = BioGenerator(db, files, load_data, biofile_to_label)
- ...
- ... dataset = tf.data.Dataset.from_generator(
- ... generator, generator.output_types, generator.output_shapes)
- ...
- ... def transform(image, label, key):
- ... # convert to channels last
- ... image = to_channels_last(image)
- ...
- ... # per_image_standardization
- ... image = tf.image.per_image_standardization(image)
- ... return (image, label, key)
- ...
- ... dataset = dataset.map(transform)
- ... dataset = dataset.cache(temp_dir)
- ... if mode == tf.estimator.ModeKeys.TRAIN:
- ... dataset = dataset.repeat(1)
- ... dataset = dataset.batch(8)
- ...
- ... data, label, key = dataset.make_one_shot_iterator().get_next()
- ... return {'data': data, 'key': key}, label
- ...
- ...
- >>> def train_input_fn():
- ... return input_fn(tf.estimator.ModeKeys.TRAIN)
- ...
- ...
- >>> def eval_input_fn():
- ... return input_fn(tf.estimator.ModeKeys.EVAL)
- ...
- ...
- >>> # supply this hook for debugging
- >>> # from tensorflow.python import debug as tf_debug
- >>> # hooks = [tf_debug.LocalCLIDebugHook()]
- >>> hooks = None
- ...
- >>> train_spec = tf.estimator.TrainSpec(
- ... input_fn=train_input_fn, max_steps=50, hooks=hooks)
- >>> eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn)
-
-3. Define the architecture:
-***************************
+If you are working with Bob databases, below is an example of converting them to
+``tf.data.Dataset``'s using :any:`bob.learn.tensorflow.data.dataset_using_generator`:
-.. doctest::
+.. testsetup::
- >>> def architecture(data, mode, **kwargs):
- ... endpoints = {}
- ... training = mode == tf.estimator.ModeKeys.TRAIN
- ...
- ... with tf.variable_scope('CNN'):
- ...
- ... name = 'conv'
- ... net = slim.conv2d(data, 32, kernel_size=(
- ... 5, 5), stride=2, padding='SAME', activation_fn=tf.nn.relu, scope=name)
- ... endpoints[name] = net
- ...
- ... name = 'pool'
- ... net = slim.max_pool2d(net, (2, 2),
- ... stride=1, padding='SAME', scope=name)
- ... endpoints[name] = net
- ...
- ... name = 'pool-flat'
- ... net = slim.flatten(net, scope=name)
- ... endpoints[name] = net
- ...
- ... name = 'dense'
- ... net = slim.fully_connected(net, 128, scope=name)
- ... endpoints[name] = net
- ...
- ... name = 'dropout'
- ... net = slim.dropout(
- ... inputs=net, keep_prob=0.4, is_training=training)
- ... endpoints[name] = net
- ...
- ... return net, endpoints
-
-
-.. important ::
-
- Practical advice: use ``tf.contrib.slim`` to craft your CNNs. Although
- Tensorflow's documentation recommend the usage of ``tf.layers`` and
- ``tf.keras``, in our experience ``slim`` has better defaults and is more
- integrated with tensorflow's framework (compared to ``tf.keras``),
- probably because it is used more often internally at Google.
-
-
-4. Estimator:
-************************
-
-Explicitly triggering the estimator
-...................................
+ import tempfile
+ temp_dir = model_dir = tempfile.mkdtemp()
.. doctest::
- >>> estimator = Logits(
- ... architecture,
- ... optimizer=tf.train.GradientDescentOptimizer(1e-4),
- ... loss_op=tf.losses.sparse_softmax_cross_entropy,
- ... n_classes=20, # the number of identities in the world set of ATNT database
- ... embedding_validation=True,
- ... validation_batch_size=8,
- ... model_dir=model_dir,
- ... )
- >>> tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) # doctest: +SKIP
- ({'accuracy':...
-
-
-Triggering the estimator via command line
-..........................................
-
-In the example above we explicitly triggered the training and validation via
-`tf.estimator.train`. We provide command line scripts that does that for you.
-
-Check the command bellow fro training::
-
- $ bob tf train --help
-
-and to evaluate::
-
- $ bob tf eval --help
-
-
-Data pipeline
-=============
-
-There are several ways to provide data to Tensorflow graphs. In this section we
-provide some examples on how to make the bridge between `bob.db` databases and
-tensorflow `input_fn`.
-
-The BioGenerator input pipeline
-*******************************
-
-The :any:`bob.learn.tensorflow.dataset.bio.BioGenerator` class can be used to
-convert any database of bob (not just bob.bio.base's databases) to a
-``tf.data.Dataset`` instance.
-
-While building the input pipeline, you can manipulate your data in two
-sections:
-
-* In the ``load_data`` function where everything is a numpy array.
-* In the ``transform`` function where the data are tensorflow tensors.
-
-For example, you can annotate, crop to bounding box, and scale your images in
-the ``load_data`` function and apply transformations on images (e.g. random
-crop, mean normalization, random flip, ...) in the ``transform`` function.
-
-Once these transformations are applied on your data, you can easily cache them
-to disk (using ``tf.data.Dataset.cache``) for faster reading of data in your
-training.
-
-
-Input pipeline with TFRecords
-*****************************
-
-An optimized way to provide data to Tensorflow graphs is using tfrecords. In
-this `link <http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/>`_
-you have a very nice guide on how TFRecord works.
-
-In `bob.learn.tensorflow` we provide a command line interface
-``bob tf db_to_tfrecords`` that converts ``bob.db`` databases to TFRecords.
-Type the snippet bellow for help::
+ >>> import bob.db.atnt
+ >>> from bob.learn.tensorflow.data import dataset_using_generator
+ >>> import tensorflow as tf
- $ bob tf db_to_tfrecords --help
+ >>> db = bob.db.atnt.Database()
+ >>> samples = db.objects(groups="world")
+ >>> # construct integer labels for each identity in the database
+ >>> CLIENT_IDS = (str(f.client_id) for f in samples)
+ >>> CLIENT_IDS = list(set(CLIENT_IDS))
+ >>> CLIENT_IDS = dict(zip(CLIENT_IDS, range(len(CLIENT_IDS))))
-To generate a tfrecord for our
-`Face recognition example using bob.db databases`_ example use the following
-snippet.
+ >>> def reader(sample):
+ ... img = sample.load(db.original_directory, db.original_extension)
+ ... label = CLIENT_IDS[str(sample.client_id)]
+ ... return img, label
-.. doctest::
+ >>> dataset = dataset_using_generator(samples, reader)
+ >>> dataset
- >>> from bob.bio.base.utils import read_original_data
- >>> from bob.bio.base.test.dummy.database import database # this is based on bob.db.atnt
+Create TFRecords from tf.data.Datasets
+======================================
- >>> groups = 'dev'
+Use :any:`bob.learn.tensorflow.data.dataset_to_tfrecord` and
+:any:`bob.learn.tensorflow.data.dataset_from_tfrecord` to painlessly convert
+**any** ``tf.data.Dataset`` to TFRecords and create datasets back from those
+TFRecords:
- >>> samples = database.all_files(groups=groups)
+ >>> from bob.learn.tensorflow.data import dataset_to_tfrecord
+ >>> from bob.learn.tensorflow.data import dataset_from_tfrecord
+ >>> path = f"{temp_dir}/my_dataset"
+ >>> dataset_to_tfrecord(dataset, path)
+ >>> dataset = dataset_from_tfrecord(path)
+ >>> dataset
- >>> CLIENT_IDS = (str(f.client_id) for f in database.objects(groups=groups))
- >>> CLIENT_IDS = set(CLIENT_IDS)
- >>> CLIENT_IDS = dict(zip(CLIENT_IDS, range(len(CLIENT_IDS))))
+There is also a script called ``bob tf dataset-to-tfrecord`` that wraps the
+:any:`bob.learn.tensorflow.data.dataset_to_tfrecord` for easy Grid job
+submission.
- >>> def file_to_label(f):
- ... return CLIENT_IDS[str(f.client_id)]
+Create models with custom training and evaluation logic
+=======================================================
- >>> def reader(biofile):
- ... data = read_original_data(biofile, database.original_directory, database.original_extension)
- ... label = file_to_label(biofile)
- ... key = biofile.path
- ... return (data, label, key)
+Training models for biometrics recognition (and metric learning in general) is
+different from the typical classification problems since the labels during
+training and testing are different. We found that overriding the ``compile``,
+``train_step``, and ``test_step`` methods as explained in
+https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit is the
+best trade-off between the control of what happens during training and
+evaluation and writing boilerplate code.
-After saving this snippet in a python file (let's say `tfrec.py`) run the
-following command ::
+Mixed-precision training
+========================
+When doing mixed precision training: https://www.tensorflow.org/guide/mixed_precision
+it is important to scale the loss before computing the gradients.
- $ bob tf db_to_tfrecords tfrec.py -o atnt.tfrecord
-Once this is done you can replace the `input_fn`_ defined above by the snippet
-bellow.
+Multi-GPU and multi-worker training
+===================================
-.. doctest::
+It is important that custom metrics and losses do not average their results by the batch
+size as the values should be averaged by the global batch size:
+https://www.tensorflow.org/tutorials/distribute/custom_training Take a look at custom
+metrics and losses in this package for examples of correct implementations.
- >>>
- >>> from bob.learn.tensorflow.dataset.tfrecords import shuffle_data_and_labels_image_augmentation
- >>>
- >>> tfrecords_filename = ['/path/to/atnt.tfrecord']
- >>> data_shape = (112, 92 , 3)
- >>> data_type = tf.uint8
- >>> batch_size = 16
- >>> epochs = 1
- >>>
- >>> def train_input_fn():
- ... return shuffle_data_and_labels_image_augmentation(
- ... tfrecords_filename,
- ... data_shape,
- ... data_type,
- ... batch_size,
- ... epochs=epochs)
-
-.. testcleanup::
-
- import shutil
- shutil.rmtree(model_dir, True)
-
-The Estimator
-=============
-
-In this package we have crafted 4 types of estimators.
-
- - Logits: `Cross entropy loss
- <https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits>`_
- in the hot-encoded layer
- :py:class:`bob.learn.tensorflow.estimators.Logits`
- - LogitsCenterLoss: `Cross entropy loss
- <https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits>`_
- PLUS the `center loss <https://ydwen.github.io/papers/WenECCV16.pdf>`_ in
- the hot-encoded layer
- :py:class:`bob.learn.tensorflow.estimators.LogitsCenterLoss`
- - Siamese: Siamese network estimator
- :py:class:`bob.learn.tensorflow.estimators.Siamese`
- - Triplet: Triplet network estimator
- :py:class:`bob.learn.tensorflow.estimators.Triplet`
.. _tensorflow: https://www.tensorflow.org/
-
diff --git a/examples/MSCeleba_centerloss_mixed_precision_multi_worker.py b/examples/MSCeleba_centerloss_mixed_precision_multi_worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd4b7e53b2fd2e6c71361784be926b542a2633c1
--- /dev/null
+++ b/examples/MSCeleba_centerloss_mixed_precision_multi_worker.py
@@ -0,0 +1,322 @@
+#!/usr/bin/env python
+
+import os
+import pickle
+from functools import partial
+from multiprocessing import cpu_count
+
+import pkg_resources
+import tensorflow as tf
+from bob.extension import rc
+from bob.learn.tensorflow.callbacks import add_backup_callback
+from bob.learn.tensorflow.losses import CenterLoss
+from bob.learn.tensorflow.losses import CenterLossLayer
+from bob.learn.tensorflow.models.inception_resnet_v2 import InceptionResNetV2
+from bob.learn.tensorflow.utils import predict_using_tensors
+from tensorflow.keras import layers
+from tensorflow.keras.mixed_precision import experimental as mixed_precision
+
+policy = mixed_precision.Policy("mixed_float16")
+mixed_precision.set_policy(policy)
+
+
+TRAIN_TF_RECORD_PATHS = (
+ f"{rc['htface']}/databases/tfrecords/msceleba/"
+ "tfrecord_182x_hand_prunned_44/*.tfrecord"
+)
+VALIDATION_TF_RECORD_PATHS = (
+ f"{rc['htface']}/databases/tfrecords/lfw/182x/RGB/*.tfrecord"
+)
+# there are 2812 samples in the validation set
+VALIDATION_SAMPLES = 2812
+
+CHECKPOINT = f"{rc['temp']}/models/inception_v2_batchnorm_rgb_msceleba_mixed_precision"
+
+AUTOTUNE = tf.data.experimental.AUTOTUNE
+TFRECORD_PARALLEL_READ = cpu_count()
+N_CLASSES = 87662
+DATA_SHAPE = (182, 182, 3) # size of faces
+DATA_TYPE = tf.uint8
+OUTPUT_SHAPE = (160, 160)
+
+SHUFFLE_BUFFER = int(2e4)
+
+LEARNING_RATE = 0.1
+BATCH_SIZE = 90 * 2 # should be a multiple of 8
+# we want to run 35 epochs of tfrecords. There are 959083 samples in train tfrecords,
+# depending on batch size, steps per epoch, and keras epoch multiplier should change
+EPOCHS = 35
+# number of training steps to do before validating a model. This also defines an epoch
+# for keras which is not really true. We want to evaluate every 180000 (90 * 2000)
+# samples
+STEPS_PER_EPOCH = 180000 // BATCH_SIZE
+# np.ceil(959083/180000=5.33)
+KERAS_EPOCH_MULTIPLIER = 6
+
+VALIDATION_BATCH_SIZE = 38 # should be a multiple of 8
+
+
+FEATURES = {
+ "data": tf.io.FixedLenFeature([], tf.string),
+ "label": tf.io.FixedLenFeature([], tf.int64),
+ "key": tf.io.FixedLenFeature([], tf.string),
+}
+LOSS_WEIGHTS = {"cross_entropy": 1.0, "center_loss": 0.01}
+
+
+def decode_tfrecords(x):
+ features = tf.io.parse_single_example(x, FEATURES)
+ image = tf.io.decode_raw(features["data"], DATA_TYPE)
+ image = tf.reshape(image, DATA_SHAPE)
+ features["data"] = image
+ return features
+
+
+def get_preprocessor():
+ preprocessor = tf.keras.Sequential(
+ [
+ # rotate before cropping
+ # 5 random degree rotation
+ layers.experimental.preprocessing.RandomRotation(5 / 360),
+ layers.experimental.preprocessing.RandomCrop(
+ height=OUTPUT_SHAPE[0], width=OUTPUT_SHAPE[1]
+ ),
+ layers.experimental.preprocessing.RandomFlip("horizontal"),
+ # FIXED_STANDARDIZATION from https://github.com/davidsandberg/facenet
+ # [-0.99609375, 0.99609375]
+ layers.experimental.preprocessing.Rescaling(
+ scale=1 / 128, offset=-127.5 / 128
+ ),
+ ]
+ )
+ return preprocessor
+
+
+def preprocess(preprocessor, features, augment=False):
+ image = features["data"]
+ label = features["label"]
+ image = preprocessor(image, training=augment)
+ return image, label
+
+
+def prepare_dataset(tf_record_paths, batch_size, shuffle=False, augment=False):
+ ds = tf.data.Dataset.list_files(tf_record_paths, shuffle=shuffle)
+ ds = tf.data.TFRecordDataset(ds, num_parallel_reads=TFRECORD_PARALLEL_READ)
+ if shuffle:
+ # ignore order and read files as soon as they come in
+ ignore_order = tf.data.Options()
+ ignore_order.experimental_deterministic = False
+ ds = ds.with_options(ignore_order)
+ ds = ds.map(decode_tfrecords).prefetch(buffer_size=AUTOTUNE)
+ if shuffle:
+ ds = ds.shuffle(SHUFFLE_BUFFER).repeat(EPOCHS)
+ preprocessor = get_preprocessor()
+ ds = ds.batch(batch_size).map(
+ partial(preprocess, preprocessor, augment=augment),
+ num_parallel_calls=AUTOTUNE,
+ )
+
+ # Use buffered prefecting on all datasets
+ return ds.prefetch(buffer_size=AUTOTUNE)
+ # return ds.apply(tf.data.experimental.prefetch_to_device(
+ # device, buffer_size=AUTOTUNE))
+
+
+def accuracy_from_embeddings(labels, prelogits):
+ labels = tf.reshape(labels, (-1,))
+ embeddings = tf.nn.l2_normalize(prelogits, 1)
+ predictions = predict_using_tensors(embeddings, labels)
+ return tf.math.equal(labels, predictions)
+
+
+class CustomModel(tf.keras.Model):
+ def compile(
+ self,
+ cross_entropy,
+ center_loss,
+ loss_weights,
+ train_loss,
+ train_cross_entropy,
+ train_center_loss,
+ test_acc,
+ global_batch_size,
+ **kwargs,
+ ):
+ super().compile(**kwargs)
+ self.cross_entropy = cross_entropy
+ self.center_loss = center_loss
+ self.loss_weights = loss_weights
+ self.train_loss = train_loss
+ self.train_cross_entropy = train_cross_entropy
+ self.train_center_loss = train_center_loss
+ self.test_acc = test_acc
+ self.global_batch_size = global_batch_size
+
+ def train_step(self, data):
+ images, labels = data
+ with tf.GradientTape() as tape:
+ logits, prelogits = self(images, training=True)
+ loss_cross = self.cross_entropy(labels, logits)
+ loss_center = self.center_loss(labels, prelogits)
+ loss = (
+ loss_cross * self.loss_weights[self.cross_entropy.name]
+ + loss_center * self.loss_weights[self.center_loss.name]
+ )
+ unscaled_loss = tf.nn.compute_average_loss(
+ loss, global_batch_size=self.global_batch_size
+ )
+ loss = self.optimizer.get_scaled_loss(unscaled_loss)
+
+ trainable_vars = self.trainable_variables
+ gradients = tape.gradient(loss, trainable_vars)
+ gradients = self.optimizer.get_unscaled_gradients(gradients)
+ self.optimizer.apply_gradients(zip(gradients, trainable_vars))
+
+ self.train_loss(unscaled_loss)
+ self.train_cross_entropy(loss_cross)
+ self.train_center_loss(loss_center)
+ return {
+ m.name: m.result()
+ for m in [self.train_loss, self.train_cross_entropy, self.train_center_loss]
+ }
+
+ def test_step(self, data):
+ images, labels = data
+ logits, prelogits = self(images, training=False)
+ self.test_acc(accuracy_from_embeddings(labels, prelogits))
+ return {m.name: m.result() for m in [self.test_acc]}
+
+
+def create_model():
+
+ model = InceptionResNetV2(
+ include_top=True,
+ classes=N_CLASSES,
+ bottleneck=True,
+ input_shape=OUTPUT_SHAPE + (3,),
+ )
+ float32_layer = layers.Activation("linear", dtype="float32")
+
+ prelogits = model.get_layer("Bottleneck/BatchNorm").output
+ prelogits = CenterLossLayer(
+ n_classes=N_CLASSES, n_features=prelogits.shape[-1], name="centers"
+ )(prelogits)
+ prelogits = float32_layer(prelogits)
+ logits = float32_layer(model.get_layer("logits").output)
+ model = CustomModel(
+ inputs=model.input, outputs=[logits, prelogits], name=model.name
+ )
+ return model
+
+
+def build_and_compile_model(global_batch_size):
+ model = create_model()
+
+ cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(
+ from_logits=True, name="cross_entropy", reduction=tf.keras.losses.Reduction.NONE
+ )
+ center_loss = CenterLoss(
+ centers_layer=model.get_layer("centers"),
+ alpha=0.9,
+ name="center_loss",
+ reduction=tf.keras.losses.Reduction.NONE,
+ )
+
+ optimizer = tf.keras.optimizers.RMSprop(
+ learning_rate=LEARNING_RATE, rho=0.9, momentum=0.9, epsilon=1.0
+ )
+ optimizer = mixed_precision.LossScaleOptimizer(optimizer, loss_scale="dynamic")
+
+ train_loss = tf.keras.metrics.Mean(name="loss")
+ train_cross_entropy = tf.keras.metrics.Mean(name="cross_entropy")
+ train_center_loss = tf.keras.metrics.Mean(name="center_loss")
+
+ test_acc = tf.keras.metrics.Mean(name="accuracy")
+
+ model.compile(
+ optimizer=optimizer,
+ cross_entropy=cross_entropy,
+ center_loss=center_loss,
+ loss_weights=LOSS_WEIGHTS,
+ train_loss=train_loss,
+ train_cross_entropy=train_cross_entropy,
+ train_center_loss=train_center_loss,
+ test_acc=test_acc,
+ global_batch_size=global_batch_size,
+ )
+ return model
+
+
+def train_and_evaluate(tf_config):
+ os.environ["TF_CONFIG"] = json.dumps(tf_config)
+
+ per_worker_batch_size = BATCH_SIZE
+ num_workers = len(tf_config["cluster"]["worker"])
+
+ strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
+
+ global_batch_size = per_worker_batch_size * num_workers
+ val_global_batch_size = VALIDATION_BATCH_SIZE * num_workers
+
+ train_ds = prepare_dataset(
+ TRAIN_TF_RECORD_PATHS, batch_size=global_batch_size, shuffle=True, augment=True
+ )
+
+ val_ds = prepare_dataset(
+ VALIDATION_TF_RECORD_PATHS,
+ batch_size=val_global_batch_size,
+ shuffle=False,
+ augment=False,
+ )
+
+ with strategy.scope():
+ model = build_and_compile_model(global_batch_size=global_batch_size)
+
+ val_metric_name = "val_accuracy"
+
+ def scheduler(epoch, lr):
+ # 20 epochs at 0.1, 10 at 0.01 and 5 0.001
+ # The epoch number here is Keras's which is different from actual epoch number
+ epoch = epoch // KERAS_EPOCH_MULTIPLIER
+ if epoch in range(20):
+ return 0.1
+ elif epoch in range(20, 30):
+ return 0.01
+ else:
+ return 0.001
+
+ callbacks = {
+ "latest": tf.keras.callbacks.ModelCheckpoint(f"{CHECKPOINT}/latest", verbose=1),
+ "best": tf.keras.callbacks.ModelCheckpoint(
+ f"{CHECKPOINT}/best",
+ monitor=val_metric_name,
+ save_best_only=True,
+ mode="max",
+ verbose=1,
+ ),
+ "tensorboard": tf.keras.callbacks.TensorBoard(
+ log_dir=f"{CHECKPOINT}/logs", update_freq=15, profile_batch="10,50"
+ ),
+ "lr": tf.keras.callbacks.LearningRateScheduler(scheduler, verbose=1),
+ # "lr": tf.keras.callbacks.ReduceLROnPlateau(
+ # monitor=val_metric_name, factor=0.2, patience=5, min_lr=0.001
+ # ),
+ "nan": tf.keras.callbacks.TerminateOnNaN(),
+ }
+ callbacks = add_backup_callback(
+ callbacks=callbacks, backup_dir=f"{CHECKPOINT}/backup"
+ )
+
+ model.fit(
+ train_ds,
+ validation_data=val_ds,
+ epochs=EPOCHS * KERAS_EPOCH_MULTIPLIER,
+ steps_per_epoch=STEPS_PER_EPOCH,
+ validation_steps=VALIDATION_SAMPLES // VALIDATION_BATCH_SIZE,
+ callbacks=callbacks,
+ verbose=2 if os.environ.get("SGE_TASK_ID") else 1,
+ )
+
+
+if __name__ == "__main__":
+ train_and_evaluate({})
diff --git a/examples/convert_v1_checkpoints_to_v2.ipynb b/examples/convert_v1_checkpoints_to_v2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d76f5c491e059e2bf00de9650bb702ed79e6919f
--- /dev/null
+++ b/examples/convert_v1_checkpoints_to_v2.ipynb
@@ -0,0 +1,4063 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import tarfile\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "from bob.learn.tensorflow.models.inception_resnet_v2 import InceptionResNetV2\n",
+ "from bob.learn.tensorflow.utils import restore_model_variables_from_checkpoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'2.3.1'"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# make sure you are using tensorflow 2\n",
+ "tf.__version__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "checkpoint = \"/tmp/msceleb/inception-v2_batchnorm_rgb\"\n",
+ "path = f\"{checkpoint}.tar.gz\"\n",
+ "os.makedirs(checkpoint, exist_ok=True)\n",
+ "tf.keras.utils.get_file(\n",
+ " path,\n",
+ " \"https://www.idiap.ch/software/bob/data/bob/bob.bio.face_ongoing/master/msceleb/inception-v2_batchnorm_rgb.tar.gz\",\n",
+ ")\n",
+ "with tarfile.open(path) as f:\n",
+ " f.extractall(os.path.dirname(checkpoint))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "# strip the folder names from saved checkpoint\n",
+ "sed -i \"s|/.*/||\" $checkpoint/checkpoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/beta', [192]),\n",
+ " ('InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean', [192]),\n",
+ " ('InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/weights', [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta', [192]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta', [224]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/weights', [1, 3, 192, 224]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta', [256]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/weights', [3, 1, 224, 256]),\n",
+ " ('InceptionResnetV2/Block8/Conv2d_1x1/biases', [2080]),\n",
+ " ('InceptionResnetV2/Block8/Conv2d_1x1/weights', [1, 1, 448, 2080]),\n",
+ " ('InceptionResnetV2/Bottleneck/BatchNorm/beta', [128]),\n",
+ " ('InceptionResnetV2/Bottleneck/BatchNorm/moving_mean', [128]),\n",
+ " ('InceptionResnetV2/Bottleneck/BatchNorm/moving_variance', [128]),\n",
+ " ('InceptionResnetV2/Bottleneck/weights', [1536, 128]),\n",
+ " ('InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/beta', [32]),\n",
+ " ('InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_mean', [32]),\n",
+ " ('InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_variance', [32]),\n",
+ " ('InceptionResnetV2/Conv2d_1a_3x3/weights', [3, 3, 3, 32]),\n",
+ " ('InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/beta', [32]),\n",
+ " ('InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_mean', [32]),\n",
+ " ('InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_variance', [32]),\n",
+ " ('InceptionResnetV2/Conv2d_2a_3x3/weights', [3, 3, 32, 32]),\n",
+ " ('InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/beta', [64]),\n",
+ " ('InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_mean', [64]),\n",
+ " ('InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_variance', [64]),\n",
+ " ('InceptionResnetV2/Conv2d_2b_3x3/weights', [3, 3, 32, 64]),\n",
+ " ('InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/beta', [80]),\n",
+ " ('InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_mean', [80]),\n",
+ " ('InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_variance', [80]),\n",
+ " ('InceptionResnetV2/Conv2d_3b_1x1/weights', [1, 1, 64, 80]),\n",
+ " ('InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/beta', [192]),\n",
+ " ('InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_mean', [192]),\n",
+ " ('InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_variance', [192]),\n",
+ " ('InceptionResnetV2/Conv2d_4a_3x3/weights', [3, 3, 80, 192]),\n",
+ " ('InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta', [1536]),\n",
+ " ('InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_mean', [1536]),\n",
+ " ('InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance', [1536]),\n",
+ " ('InceptionResnetV2/Conv2d_7b_1x1/weights', [1, 1, 2080, 1536]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/beta', [96]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',\n",
+ " [96]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',\n",
+ " [96]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/weights', [1, 1, 192, 96]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/beta', [48]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',\n",
+ " [48]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',\n",
+ " [48]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/weights',\n",
+ " [1, 1, 192, 48]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/beta', [64]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_mean',\n",
+ " [64]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_variance',\n",
+ " [64]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/weights', [5, 5, 48, 64]),\n",
+ " ('InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/beta', [64]),\n",
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+ " ('InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/weights',\n",
+ " [3, 1, 224, 256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_4/Conv2d_1x1/biases', [2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_4/Conv2d_1x1/weights', [1, 1, 448, 2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/weights',\n",
+ " [1, 3, 192, 224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/weights',\n",
+ " [3, 1, 224, 256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Conv2d_1x1/biases', [2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_5/Conv2d_1x1/weights', [1, 1, 448, 2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/weights',\n",
+ " [1, 3, 192, 224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/weights',\n",
+ " [3, 1, 224, 256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Conv2d_1x1/biases', [2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_6/Conv2d_1x1/weights', [1, 1, 448, 2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/weights',\n",
+ " [1, 3, 192, 224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/weights',\n",
+ " [3, 1, 224, 256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Conv2d_1x1/biases', [2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_7/Conv2d_1x1/weights', [1, 1, 448, 2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/weights',\n",
+ " [1, 3, 192, 224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/weights',\n",
+ " [3, 1, 224, 256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Conv2d_1x1/biases', [2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_8/Conv2d_1x1/weights', [1, 1, 448, 2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',\n",
+ " [192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/weights',\n",
+ " [1, 1, 2080, 192]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',\n",
+ " [224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/weights',\n",
+ " [1, 3, 192, 224]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',\n",
+ " [256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/weights',\n",
+ " [3, 1, 224, 256]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/biases', [2080]),\n",
+ " ('InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/weights', [1, 1, 448, 2080])]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# investigate the variable names from the checkpoint\n",
+ "# This model was saved using the following architecture code:\n",
+ "# https://gitlab.idiap.ch/bob/bob.learn.tensorflow/-/blob/3a6a2531c7978edfba09c601667694ab2ae2359e/bob/learn/tensorflow/network/InceptionResnetV2.py\n",
+ "tf.train.list_variables(tf.train.latest_checkpoint(checkpoint))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"InceptionResnetV2\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Conv2d_1a_3x3 (SequentialLay (None, 79, 79, 32) 960 \n",
+ "_________________________________________________________________\n",
+ "Conv2d_2a_3x3 (SequentialLay (None, 77, 77, 32) 9312 \n",
+ "_________________________________________________________________\n",
+ "Conv2d_2b_3x3 (SequentialLay (None, 77, 77, 64) 18624 \n",
+ "_________________________________________________________________\n",
+ "MaxPool_3a_3x3 (MaxPooling2D (None, 38, 38, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "Conv2d_3b_1x1 (SequentialLay (None, 38, 38, 80) 5360 \n",
+ "_________________________________________________________________\n",
+ "Conv2d_4a_3x3 (SequentialLay (None, 36, 36, 192) 138816 \n",
+ "_________________________________________________________________\n",
+ "MaxPool_5a_3x3 (MaxPooling2D (None, 17, 17, 192) 0 \n",
+ "_________________________________________________________________\n",
+ "Mixed_5b (InceptionA) (None, 17, 17, 320) 268848 \n",
+ "_________________________________________________________________\n",
+ "block35_1 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_2 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_3 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_4 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_5 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_6 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_7 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_8 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_9 (InceptionResnetBl (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "block35_10 (InceptionResnetB (None, 17, 17, 320) 123408 \n",
+ "_________________________________________________________________\n",
+ "Mixed_6a (ReductionA) (None, 8, 8, 1088) 2666240 \n",
+ "_________________________________________________________________\n",
+ "block17_1 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_2 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_3 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_4 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_5 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_6 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_7 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_8 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_9 (InceptionResnetBl (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_10 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_11 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_12 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_13 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_14 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_15 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_16 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_17 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_18 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_19 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "block17_20 (InceptionResnetB (None, 8, 8, 1088) 1127456 \n",
+ "_________________________________________________________________\n",
+ "Mixed_7a (ReductionB) (None, 3, 3, 2080) 3883008 \n",
+ "_________________________________________________________________\n",
+ "block8_1 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_2 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_3 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_4 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_5 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_6 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_7 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_8 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_9 (InceptionResnetBlo (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "block8_10 (InceptionResnetBl (None, 3, 3, 2080) 2036288 \n",
+ "_________________________________________________________________\n",
+ "Conv2d_7b_1x1 (SequentialLay (None, 3, 3, 1536) 3199488 \n",
+ "_________________________________________________________________\n",
+ "global_average_pooling2d (Gl (None, 1536) 0 \n",
+ "_________________________________________________________________\n",
+ "Dropout (Dropout) (None, 1536) 0 \n",
+ "_________________________________________________________________\n",
+ "Bottleneck (Dense) (None, 128) 196608 \n",
+ "_________________________________________________________________\n",
+ "Bottleneck/BatchNorm (BatchN (None, 128) 384 \n",
+ "=================================================================\n",
+ "Total params: 54,533,728\n",
+ "Trainable params: 54,472,928\n",
+ "Non-trainable params: 60,800\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create the model and look at its structure\n",
+ "# This is the model's code in tf2 format:\n",
+ "# https://gitlab.idiap.ch/bob/bob.learn.tensorflow/-/blob/ee013d0d47dbd43a447abcb7b665a7cd75398cce/bob/learn/tensorflow/models/inception_resnet_v2.py\n",
+ "model = InceptionResNetV2(input_shape=(160, 160, 3), include_top=False, bottleneck=True)\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['InceptionResnetV2/Conv2d_1a_3x3/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/Conv2d_2a_3x3/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/beta:0',\n",
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+ " 'InceptionResnetV2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_4/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_4/Conv2d_1x1/Conv2D/bias:0',\n",
+ " 'InceptionResnetV2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_0/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0a_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0b_1x3/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0c_3x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_5/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_5/Conv2d_1x1/Conv2D/bias:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_0/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0a_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0b_1x3/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0c_3x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_6/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_6/Conv2d_1x1/Conv2D/bias:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_0/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0a_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0b_1x3/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0c_3x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_7/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_7/Conv2d_1x1/Conv2D/bias:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_0/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0a_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0b_1x3/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0c_3x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_8/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_8/Conv2d_1x1/Conv2D/bias:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_0/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0a_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0b_1x3/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0c_3x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_9/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_9/Conv2d_1x1/Conv2D/bias:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_0/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_0/Conv2d_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0a_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0a_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0b_1x3/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0b_1x3/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0c_3x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0c_3x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/block8_10/Conv2d_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/block8_10/Conv2d_1x1/Conv2D/bias:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_0/Conv2d_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_0/Conv2d_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/block8_10/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/Conv2d_7b_1x1/Conv2D/kernel:0',\n",
+ " 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance:0',\n",
+ " 'InceptionResnetV2/Bottleneck/kernel:0',\n",
+ " 'InceptionResnetV2/Bottleneck/BatchNorm/beta:0',\n",
+ " 'InceptionResnetV2/Bottleneck/BatchNorm/moving_mean:0',\n",
+ " 'InceptionResnetV2/Bottleneck/BatchNorm/moving_variance:0']"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# investigate the variable names in the model\n",
+ "[var.name for var in model.variables]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /idiap/home/amohammadi/git/bob.bio.face_ongoing/src/bob.learn.tensorflow/bob/learn/tensorflow/utils/keras.py:121: The name tf.keras.backend.get_session is deprecated. Please use tf.compat.v1.keras.backend.get_session instead.\n",
+ "\n",
+ "InceptionResnetV2/Conv2d_1a_3x3/Conv2D/kernel -> InceptionResnetV2/Conv2d_1a_3x3/weights\n",
+ "InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/beta -> InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Conv2d_2a_3x3/Conv2D/kernel -> InceptionResnetV2/Conv2d_2a_3x3/weights\n",
+ "InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/beta -> InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Conv2d_2b_3x3/Conv2D/kernel -> InceptionResnetV2/Conv2d_2b_3x3/weights\n",
+ "InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/beta -> InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Conv2d_3b_1x1/Conv2D/kernel -> InceptionResnetV2/Conv2d_3b_1x1/weights\n",
+ "InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/beta -> InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Conv2d_4a_3x3/Conv2D/kernel -> InceptionResnetV2/Conv2d_4a_3x3/weights\n",
+ "InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/beta -> InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/Conv2D/kernel -> InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/weights\n",
+ "InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/beta -> InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/Conv2D/kernel -> InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/weights\n",
+ "InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/beta -> InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_1/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_1/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_1/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_1/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_1/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_1/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_1/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_1/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_2/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_2/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_2/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_2/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_2/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_2/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_2/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_2/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_3/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_3/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_3/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_3/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_3/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_3/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_3/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_3/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_3/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_3/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_4/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_4/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_4/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_4/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_4/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_4/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_4/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_4/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_4/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_4/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_5/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_5/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_5/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_5/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_5/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_5/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_5/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_5/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_5/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_5/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_6/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_6/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_6/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_6/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_6/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_6/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_6/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_6/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_6/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_6/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_7/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_7/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_7/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_7/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_7/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_7/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_7/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_7/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_7/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_7/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_8/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_8/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_8/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_8/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_8/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_8/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_8/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_8/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_8/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_8/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_9/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_9/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_9/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_9/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_9/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_9/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_9/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_9/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_10/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_10/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_10/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_10/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_10/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_10/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0c_3x3/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0c_3x3/weights\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/beta -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block35_10/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat/block35_10/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block35_10/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat/block35_10/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block35_10/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_10/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_10/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_10/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/weights\n",
+ "InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/weights\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_1/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_1/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_1/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_1/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_1/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_1/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_1/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_1/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_2/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_2/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_2/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_2/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_2/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_2/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_2/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_2/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_3/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_3/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_3/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_3/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_3/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_3/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_3/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_3/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_4/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_4/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_4/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_4/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_4/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_4/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_4/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_4/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_5/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_5/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_5/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_5/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_5/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_5/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_5/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_5/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_6/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_6/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_6/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_6/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_6/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_6/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_6/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_6/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_7/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_7/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_7/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_7/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_7/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_7/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_7/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_7/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_8/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_8/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_8/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_8/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_8/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_8/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_8/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_8/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_9/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_9/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_9/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_9/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_9/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_9/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_9/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_9/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_10/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_10/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_10/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_10/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_10/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_10/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_10/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_10/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_10/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_10/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_10/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_10/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_11/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_11/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_11/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_11/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_11/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_11/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_11/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_11/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_11/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_11/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_11/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_11/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_12/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_12/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_12/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_12/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_12/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_12/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_12/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_12/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_12/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_12/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_12/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_12/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_13/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_13/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_13/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_13/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_13/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_13/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_13/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_13/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_13/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_13/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_13/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_13/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_14/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_14/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_14/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_14/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_14/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_14/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_14/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_14/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_14/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_14/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_14/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_14/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_15/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_15/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_15/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_15/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_15/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_15/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_15/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_15/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_15/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_15/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_15/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_15/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_16/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_16/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_16/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_16/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_16/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_16/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_16/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_16/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_16/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_16/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_16/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_16/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_17/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_17/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_17/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_17/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_17/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_17/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_17/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_17/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_17/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_17/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_17/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_17/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_18/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_18/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_18/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_18/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_18/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_18/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_18/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_18/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_18/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_18/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_18/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_18/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_19/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_19/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_19/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_19/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_19/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_19/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_19/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_19/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_19/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_19/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_19/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_19/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_20/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_20/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_20/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_20/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0b_1x7/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0b_1x7/weights\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0c_7x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0c_7x1/weights\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/beta -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block17_20/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_1/block17_20/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block17_20/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_1/block17_20/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block17_20/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_20/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_20/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_20/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/weights\n",
+ "InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/weights\n",
+ "InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/weights\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/Conv2D/kernel -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/weights\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/beta -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/beta\n",
+ "InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/moving_mean -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/moving_variance -> InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_1/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_1/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_1/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_1/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_1/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_1/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_2/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_2/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_2/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_2/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_2/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_2/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_3/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_3/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_3/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_3/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_3/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_3/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_4/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_4/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_4/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_4/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_4/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_4/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_5/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_5/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_5/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_5/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_5/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_6/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_6/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_6/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_6/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_6/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_7/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_7/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_7/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_7/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_7/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_8/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_8/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_8/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_8/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_8/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_9/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_9/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_9/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_10/Branch_0/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_10/Branch_0/Conv2d_1x1/BatchNorm/beta -> InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0a_1x1/Conv2D/kernel -> InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/weights\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0a_1x1/BatchNorm/beta -> InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0b_1x3/Conv2D/kernel -> InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/weights\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0b_1x3/BatchNorm/beta -> InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0c_3x1/Conv2D/kernel -> InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/weights\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0c_3x1/BatchNorm/beta -> InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta\n",
+ "InceptionResnetV2/block8_10/Conv2d_1x1/Conv2D/kernel -> InceptionResnetV2/Block8/Conv2d_1x1/weights\n",
+ "InceptionResnetV2/block8_10/Conv2d_1x1/Conv2D/bias -> InceptionResnetV2/Block8/Conv2d_1x1/biases\n",
+ "InceptionResnetV2/block8_10/Branch_0/Conv2d_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_10/Branch_0/Conv2d_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean -> InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance -> InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean -> InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/block8_10/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance -> InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Conv2d_7b_1x1/Conv2D/kernel -> InceptionResnetV2/Conv2d_7b_1x1/weights\n",
+ "InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta -> InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta\n",
+ "InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_mean -> InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance -> InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance\n",
+ "InceptionResnetV2/Bottleneck/kernel -> InceptionResnetV2/Bottleneck/weights\n",
+ "InceptionResnetV2/Bottleneck/BatchNorm/beta -> InceptionResnetV2/Bottleneck/BatchNorm/beta\n",
+ "InceptionResnetV2/Bottleneck/BatchNorm/moving_mean -> InceptionResnetV2/Bottleneck/BatchNorm/moving_mean\n",
+ "InceptionResnetV2/Bottleneck/BatchNorm/moving_variance -> InceptionResnetV2/Bottleneck/BatchNorm/moving_variance\n",
+ "WARNING:tensorflow:Saver is deprecated, please switch to tf.train.Checkpoint or tf.keras.Model.save_weights for training checkpoints. When executing eagerly variables do not necessarily have unique names, and so the variable.name-based lookups Saver performs are error-prone.\n",
+ "INFO:tensorflow:Restoring parameters from /idiap/home/amohammadi/scratch/tmp/msceleb/inception-v2_batchnorm_rgb/model.ckpt-444274\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a function that converts variable names of the model to checkpoint names\n",
+ "# If the mapped variable names are not found in the checkpoint, an error is raised\n",
+ "# To make this job easier, it's best to change the variables in the Keras model to\n",
+ "# match the names in the checkpoint and then do minor modifications here.\n",
+ "def normalizer(name):\n",
+ " old_name = name = name.split(\":\")[0]\n",
+ "\n",
+ " # name differences between slim layer names and keras layer names\n",
+ " name = name.replace(\"/kernel\", \"/weights\")\n",
+ " name = name.replace(\"/bias\", \"/biases\")\n",
+ "\n",
+ " # for Conv2D layers\n",
+ " name = name.replace(\"Conv2D/weights\", \"weights\")\n",
+ " name = name.replace(\"Conv2D/biases\", \"biases\")\n",
+ "\n",
+ " # name differences between our architecture and the checkpoint\n",
+ " name = name.replace(\"block35\", \"Repeat/block35\")\n",
+ " name = name.replace(\"block17\", \"Repeat_1/block17\")\n",
+ " name = name.replace(\"block8\", \"Repeat_2/block8\")\n",
+ " name = name.replace(\"Repeat_2/block8_10\", \"Block8\")\n",
+ "\n",
+ " print(f\"{old_name} -> {name}\")\n",
+ " return name\n",
+ "\n",
+ "\n",
+ "restore_model_variables_from_checkpoint(model, checkpoint, normalizer=normalizer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /idiap/temp/amohammadi/conda/envs/tf2/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
+ "WARNING:tensorflow:From /idiap/temp/amohammadi/conda/envs/tf2/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
+ "INFO:tensorflow:Assets written to: /idiap/home/amohammadi/scratch/tmp/msceleb/inception-v2_batchnorm_rgb_v2_format/assets\n"
+ ]
+ }
+ ],
+ "source": [
+ "# now the save the model in tensorflow 2 format\n",
+ "model.save(f\"{checkpoint}_v2_format\", save_format=\"tf\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load the old model with a conda installation of old tensorflow\n",
+ "# create a random input, give it to the network, and save both input and output\n",
+ "# the code below is an exmple, but this cell must run in another notebook with an\n",
+ "# old bob (like bob 7) installation\n",
+ "assert (\n",
+ " False\n",
+ "), \"run the code in this cell in another notebook with a Bob 7 conda environment, do `conda create -n bob7 -c <bob-conda-channel> bob=7 bob.learn.tensorflow`\"\n",
+ "\n",
+ "import numpy as np\n",
+ "import tensorflow as tf\n",
+ "from bob.learn.tensorflow.network import inception_resnet_v2_batch_norm\n",
+ "\n",
+ "input_shape = (1, 160, 160, 3)\n",
+ "input_tensor = tf.placeholder(tf.float32, shape=input_shape)\n",
+ "\n",
+ "np.random.seed(10)\n",
+ "data = np.random.rand(1, 160, 160, 3).astype(\"float32\")\n",
+ "np.save(\"input.npy\", data)\n",
+ "\n",
+ "for mode, name in [\n",
+ " (tf.estimator.ModeKeys.TRAIN, \"train_mode\"),\n",
+ " (tf.estimator.ModeKeys.PREDICT, \"predict_mode\"),\n",
+ "]:\n",
+ " prelogits = inception_resnet_v2_batch_norm(input_tensor, mode=mode)[0]\n",
+ " checkpoint = \"/tmp/msceleb/inception-v2_batchnorm_rgb\"\n",
+ " session = tf.compat.v1.Session()\n",
+ " session.run(tf.compat.v1.global_variables_initializer())\n",
+ " saver = tf.compat.v1.train.Saver()\n",
+ " saver.restore(session, tf.train.latest_checkpoint(checkpoint))\n",
+ " output = session.run(prelogits, feed_dict={input_tensor: data})\n",
+ " np.save(f\"output_{name}.npy\", output)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((1, 160, 160, 3), dtype('float32'))"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# now test the input on our new model and see if it matches the old output\n",
+ "import numpy as np\n",
+ "\n",
+ "data = np.load(\"input.npy\")\n",
+ "ref_predict = np.load(\"output_predict_mode.npy\")\n",
+ "ref_train = np.load(\"output_train_mode.npy\")\n",
+ "data.shape, data.dtype"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.0397079 , 0.9887184 , -1.0198569 , -0.715245 , 1.2795925 ,\n",
+ " -0.87935054, 0.78027606, -0.72593015, -2.5908837 , 0.12451646,\n",
+ " -0.902159 , -0.47172827, -0.18754381, 1.522578 , -2.1243768 ,\n",
+ " -0.01743102, -0.7484883 , 0.99135643, -0.20708205, -0.21483701,\n",
+ " 0.02057505, 1.129887 , 1.3154736 , -0.6671123 , 0.49072063,\n",
+ " -1.5672749 , -0.89510536, 0.7542453 , 0.9450621 , -1.4345936 ,\n",
+ " 0.7585461 , -0.07696939, -2.1004128 , -0.49109945, -1.1812558 ,\n",
+ " -1.1648568 , -0.87621725, 0.8581683 , 0.7901715 , -1.0273908 ,\n",
+ " 0.7262468 , 0.9869791 , -2.0192435 , 1.1500988 , 1.0887905 ,\n",
+ " 2.6515627 , 0.00478584, -1.6734318 , -0.01522779, -0.7636968 ,\n",
+ " 2.0314407 , -0.7928898 , 0.45450714, -2.1267045 , -0.4153372 ,\n",
+ " 0.62672305, 0.10927075, 0.41156793, -1.0595654 , 0.3506987 ,\n",
+ " 0.20369251, 0.39730582, 1.7632729 , 0.57014084, 0.08623758,\n",
+ " 0.5078342 , -0.28096065, 1.9077072 , -1.2491908 , 0.7497569 ,\n",
+ " -0.8506024 , 0.88291407, -0.69780695, 0.9216435 , -0.16078427,\n",
+ " 2.8368008 , 1.3023876 , 2.0996416 , 0.37888718, 0.0656991 ,\n",
+ " -0.9813288 , -0.3329994 , 0.7650786 , -1.2162722 , 2.029986 ,\n",
+ " 1.4763666 , 1.0834064 , -0.5205603 , -0.7379463 , -2.6376038 ,\n",
+ " 0.8725066 , -0.28173503, 0.98598987, -1.1632819 , 0.01457607,\n",
+ " 0.02376672, -0.19675732, 0.83125085, 1.4034328 , -0.32809442,\n",
+ " 2.0181274 , -1.9440424 , 0.30915022, -0.3909355 , 2.0706754 ,\n",
+ " -1.5847499 , 1.0432297 , -1.2549043 , 0.00503191, -0.4830594 ,\n",
+ " -1.728585 , -0.97627854, -0.05380236, 0.25582924, -0.49854404,\n",
+ " -0.41763973, 1.4456007 , -0.29970488, -0.21606404, -0.14868468,\n",
+ " -0.19190133, -0.8587687 , -1.212713 , 0.9776963 , 0.6765988 ,\n",
+ " -1.5350266 , 2.462872 , 0.01092067]], dtype=float32)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "output_predict = model(data, training=False).numpy()\n",
+ "output_predict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AssertionError",
+ "evalue": "\nNot equal to tolerance rtol=1e-05, atol=0\n\nMismatched elements: 12 / 128 (9.38%)\nMax absolute difference: 1.66893e-06\nMax relative difference: 9.847395e-05\n x: array([[ 1.039708, 0.988718, -1.019857, -0.715245, 1.279593, -0.879351,\n 0.780276, -0.72593 , -2.590884, 0.124516, -0.902159, -0.471728,\n -0.187544, 1.522578, -2.124377, -0.017431, -0.748488, 0.991356,...\n y: array([[ 1.039708, 0.988719, -1.019857, -0.715245, 1.279593, -0.879351,\n 0.780276, -0.725931, -2.590884, 0.124517, -0.902159, -0.471728,\n -0.187544, 1.522577, -2.124377, -0.017431, -0.748489, 0.991358,...",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-16-a3b015d911f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_allclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_predict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref_predict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ " \u001b[0;31m[... skipping hidden 2 frame]\u001b[0m\n",
+ "\u001b[0;31mAssertionError\u001b[0m: \nNot equal to tolerance rtol=1e-05, atol=0\n\nMismatched elements: 12 / 128 (9.38%)\nMax absolute difference: 1.66893e-06\nMax relative difference: 9.847395e-05\n x: array([[ 1.039708, 0.988718, -1.019857, -0.715245, 1.279593, -0.879351,\n 0.780276, -0.72593 , -2.590884, 0.124516, -0.902159, -0.471728,\n -0.187544, 1.522578, -2.124377, -0.017431, -0.748488, 0.991356,...\n y: array([[ 1.039708, 0.988719, -1.019857, -0.715245, 1.279593, -0.879351,\n 0.780276, -0.725931, -2.590884, 0.124517, -0.902159, -0.471728,\n -0.187544, 1.522577, -2.124377, -0.017431, -0.748489, 0.991358,..."
+ ]
+ }
+ ],
+ "source": [
+ "# they are close enough\n",
+ "np.testing.assert_allclose(output_predict, ref_predict, rtol=1e-05)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# they are close enough\n",
+ "np.testing.assert_allclose(output_predict, ref_predict, rtol=1e-04)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.2059948 , 0.90457535, -1.6966419 , -0.6469312 , 1.8390388 ,\n",
+ " -0.82886887, 0.20409298, -1.2975912 , -2.2515354 , 0.16945124,\n",
+ " -0.6430161 , -0.7341013 , -0.37252808, 1.208333 , -2.1846251 ,\n",
+ " 0.5799656 , -0.9761491 , 0.5671108 , -0.46018916, -1.0497742 ,\n",
+ " 0.36604214, 1.0983484 , 1.6091515 , -0.4587916 , 0.49420023,\n",
+ " -1.3356848 , -0.7289448 , 0.98772526, 1.3689709 , -1.3146491 ,\n",
+ " 0.69885445, -0.82313013, -1.8048697 , -0.0885191 , -0.95188236,\n",
+ " -1.2162575 , -0.20026398, 0.84856796, 0.2631035 , -1.0577492 ,\n",
+ " 0.42345095, 1.2333517 , -1.5610876 , -0.29854918, 0.58075297,\n",
+ " 0.9959574 , 0.18556404, -1.828722 , -0.43610954, -0.6100631 ,\n",
+ " 1.5355668 , -0.88698196, -0.24892426, -1.5401478 , -0.20694387,\n",
+ " -0.43096346, 0.34370852, 0.42531586, -1.1192467 , -0.26605558,\n",
+ " 0.32238102, -0.09467888, 1.9468937 , 0.1408875 , 0.33964324,\n",
+ " 0.47477245, -0.40066433, 1.738759 , -1.5919895 , 0.4351349 ,\n",
+ " -0.6090865 , 0.8377094 , 0.55933 , 0.1080389 , -0.04150879,\n",
+ " 2.6030731 , -0.07762146, 2.0182095 , 0.5724497 , 0.10365391,\n",
+ " -0.935689 , -0.66622305, 0.71880484, -1.1720471 , 2.35266 ,\n",
+ " 1.5547676 , 1.0567551 , -0.5162802 , -0.3426876 , -2.376378 ,\n",
+ " 1.2193823 , -0.03678417, 1.1837311 , -0.3259096 , -0.33225632,\n",
+ " -0.13122219, -0.68957293, 0.6326618 , 1.4436685 , -1.070931 ,\n",
+ " 1.0629826 , -1.3427727 , -0.4593153 , -0.82589483, 1.7713051 ,\n",
+ " -1.6192143 , 1.014153 , -0.51861286, -0.08098346, -0.05481911,\n",
+ " -1.7958102 , -0.27956295, 0.4140396 , 0.53047895, -0.76364136,\n",
+ " 0.07553291, 1.597281 , -0.04399967, -0.49591756, 0.4441948 ,\n",
+ " 0.20369053, 0.01039219, -0.74036837, 0.8180666 , 0.835022 ,\n",
+ " -1.2091255 , 1.5800235 , -0.41675472]], dtype=float32)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# test in train mode\n",
+ "output_train = model(data, training=True).numpy()\n",
+ "output_train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AssertionError",
+ "evalue": "\nNot equal to tolerance rtol=1e-05, atol=0\n\nMismatched elements: 1 / 128 (0.781%)\nMax absolute difference: 7.748604e-07\nMax relative difference: 4.5886292e-05\n x: array([[ 1.205995, 0.904575, -1.696642, -0.646931, 1.839039, -0.828869,\n 0.204093, -1.297591, -2.251535, 0.169451, -0.643016, -0.734101,\n -0.372528, 1.208333, -2.184625, 0.579966, -0.976149, 0.567111,...\n y: array([[ 1.205995, 0.904575, -1.696642, -0.646931, 1.839038, -0.828869,\n 0.204093, -1.297591, -2.251535, 0.169451, -0.643016, -0.734101,\n -0.372528, 1.208333, -2.184625, 0.579965, -0.976149, 0.567111,...",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-21-e2ac78cb0d24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# they are close enough\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_allclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ " \u001b[0;31m[... skipping hidden 2 frame]\u001b[0m\n",
+ "\u001b[0;31mAssertionError\u001b[0m: \nNot equal to tolerance rtol=1e-05, atol=0\n\nMismatched elements: 1 / 128 (0.781%)\nMax absolute difference: 7.748604e-07\nMax relative difference: 4.5886292e-05\n x: array([[ 1.205995, 0.904575, -1.696642, -0.646931, 1.839039, -0.828869,\n 0.204093, -1.297591, -2.251535, 0.169451, -0.643016, -0.734101,\n -0.372528, 1.208333, -2.184625, 0.579966, -0.976149, 0.567111,...\n y: array([[ 1.205995, 0.904575, -1.696642, -0.646931, 1.839038, -0.828869,\n 0.204093, -1.297591, -2.251535, 0.169451, -0.643016, -0.734101,\n -0.372528, 1.208333, -2.184625, 0.579965, -0.976149, 0.567111,..."
+ ]
+ }
+ ],
+ "source": [
+ "# they are close enough\n",
+ "np.testing.assert_allclose(output_train, ref_train, rtol=1e-05)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# they are close enough\n",
+ "np.testing.assert_allclose(output_train, ref_train, rtol=1e-04)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:tf2]",
+ "language": "python",
+ "name": "conda-env-tf2-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/requirements.txt b/requirements.txt
index 08102dedde30eea810e0e99d7f175f81392c4f05..d4073f98022ae3c0d249fe10c5072dca5c20739d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,22 +1,8 @@
setuptools
-docopt
-bob.blitz
-bob.core
-bob.db.base
bob.extension
bob.io.base
bob.io.image
-bob.learn.activation
-bob.learn.em
-bob.learn.linear
-bob.ip.base
-bob.math
bob.measure
-bob.sp
-bob.db.mnist
-bob.db.atnt
-gridtk
-bob.bio.base
numpy
-scipy
click >= 7
+scipy
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 2b1997fbdcb0bf7d8bbe19fa0280a3f20d01f0e1..a2ce901099acc2d2c5f608b0f317b5bf22a6d17f 100644
--- a/setup.py
+++ b/setup.py
@@ -3,36 +3,35 @@
# Andre Anjos <andre.anjos@idiap.ch>
# Mon 16 Apr 08:18:08 2012 CEST
+from setuptools import dist
from setuptools import setup
-from setuptools import setup, dist
-dist.Distribution(dict(setup_requires=['bob.extension']))
+from bob.extension.utils import find_packages
+from bob.extension.utils import load_requirements
+
+dist.Distribution(dict(setup_requires=["bob.extension"]))
+
-from bob.extension.utils import load_requirements, find_packages
install_requires = load_requirements()
# The only thing we do in this file is to call the setup() function with all
# parameters that define our package.
setup(
-
# This is the basic information about your project. Modify all this
# information before releasing code publicly.
- name='bob.learn.tensorflow',
+ name="bob.learn.tensorflow",
version=open("version.txt").read().rstrip(),
- description='Bob bindings for tensorflow',
- url='',
- license='BSD',
- author='Tiago de Freitas Pereira',
- author_email='tiago.pereira@idiap.ch',
- keywords='tensorflow',
-
+ description="Bob bindings for tensorflow",
+ url="",
+ license="BSD",
+ author="Tiago de Freitas Pereira",
+ author_email="tiago.pereira@idiap.ch",
+ keywords="tensorflow",
# If you have a better, long description of your package, place it on the
# 'doc' directory and then hook it here
- long_description=open('README.rst').read(),
-
+ long_description=open("README.rst").read(),
# This line is required for any distutils based packaging.
include_package_data=True,
-
# This line defines which packages should be installed when you "install"
# this package. All packages that are mentioned here, but are not installed
# on the current system will be installed locally and only visible to the
@@ -42,46 +41,43 @@ setup(
packages=find_packages(),
zip_safe=False,
entry_points={
-
# main entry for bob tf cli
- 'bob.cli': [
- 'tf = bob.learn.tensorflow.script.tf:tf',
- 'keras = bob.learn.tensorflow.script.keras:keras',
+ "bob.cli": [
+ "tf = bob.learn.tensorflow.script.tf:tf",
+ "keras = bob.learn.tensorflow.script.keras:keras",
],
-
# bob tf scripts
- 'bob.learn.tensorflow.cli': [
- 'cache-dataset = bob.learn.tensorflow.script.cache_dataset:cache_dataset',
- 'compute-statistics = bob.learn.tensorflow.script.compute_statistics:compute_statistics',
- 'dataset-to-hdf5 = bob.learn.tensorflow.script.db_to_tfrecords:dataset_to_hdf5',
- 'datasets-to-tfrecords = bob.learn.tensorflow.script.db_to_tfrecords:datasets_to_tfrecords',
- 'db-to-tfrecords = bob.learn.tensorflow.script.db_to_tfrecords:db_to_tfrecords',
- 'describe-tfrecord = bob.learn.tensorflow.script.db_to_tfrecords:describe_tfrecord',
- 'distance-matrix = bob.learn.tensorflow.script.cgm:distance_matrix',
- 'eval = bob.learn.tensorflow.script.eval:eval',
- 'predict = bob.learn.tensorflow.script.predict_bio:predict',
- 'predict-bio = bob.learn.tensorflow.script.predict_bio:predict_bio',
- 'style-transfer = bob.learn.tensorflow.script.style_transfer:style_transfer',
- 'train = bob.learn.tensorflow.script.train:train',
- 'train-and-evaluate = bob.learn.tensorflow.script.train_and_evaluate:train_and_evaluate',
- 'trim = bob.learn.tensorflow.script.trim:trim',
+ "bob.learn.tensorflow.cli": [
+ "cache-dataset = bob.learn.tensorflow.script.cache_dataset:cache_dataset",
+ "compute-statistics = bob.learn.tensorflow.script.compute_statistics:compute_statistics",
+ "dataset-to-hdf5 = bob.learn.tensorflow.script.db_to_tfrecords:dataset_to_hdf5",
+ "datasets-to-tfrecords = bob.learn.tensorflow.script.db_to_tfrecords:datasets_to_tfrecords",
+ "db-to-tfrecords = bob.learn.tensorflow.script.db_to_tfrecords:db_to_tfrecords",
+ "describe-tfrecord = bob.learn.tensorflow.script.db_to_tfrecords:describe_tfrecord",
+ "distance-matrix = bob.learn.tensorflow.script.cgm:distance_matrix",
+ "eval = bob.learn.tensorflow.script.eval:eval",
+ "predict = bob.learn.tensorflow.script.predict_bio:predict",
+ "predict-bio = bob.learn.tensorflow.script.predict_bio:predict_bio",
+ "style-transfer = bob.learn.tensorflow.script.style_transfer:style_transfer",
+ "train = bob.learn.tensorflow.script.train:train",
+ "train-and-evaluate = bob.learn.tensorflow.script.train_and_evaluate:train_and_evaluate",
+ "trim = bob.learn.tensorflow.script.trim:trim",
],
# bob keras scripts
- 'bob.learn.tensorflow.keras_cli': [
- 'fit = bob.learn.tensorflow.script.fit:fit',
+ "bob.learn.tensorflow.keras_cli": [
+ "fit = bob.learn.tensorflow.script.fit:fit",
],
},
-
# Classifiers are important if you plan to distribute this package through
# PyPI. You can find the complete list of classifiers that are valid and
# useful here (http://pypi.python.org/pypi?%3Aaction=list_classifiers).
classifiers=[
- 'Framework :: Bob',
- 'Development Status :: 3 - Alpha',
- 'Intended Audience :: Developers',
- 'License :: OSI Approved :: BSD License',
- 'Natural Language :: English',
- 'Programming Language :: Python',
- 'Topic :: Scientific/Engineering :: Artificial Intelligence',
+ "Framework :: Bob",
+ "Development Status :: 3 - Alpha",
+ "Intended Audience :: Developers",
+ "License :: OSI Approved :: BSD License",
+ "Natural Language :: English",
+ "Programming Language :: Python",
+ "Topic :: Scientific/Engineering :: Artificial Intelligence",
],
)