Fix pre-commit complains

.pre-commit-config.yaml

@@ -2,16 +2,16 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/timothycrosley/isort
-    rev: 4.3.21-2
+    rev: 5.6.4
     hooks:
     - id: isort
-      args: [-sl]
+      args: [--sl, --line-length, "88"]
   - repo: https://github.com/psf/black
-    rev: stable
+    rev: 20.8b1
     hooks:
       - id: black
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.0.0
+    rev: v3.3.0
     hooks:
       - id: check-ast
       - id: check-case-conflict
@@ -19,22 +19,8 @@ repos:
       - id: end-of-file-fixer
       - id: debug-statements
       - id: check-added-large-files
-      - id: flake8
-  - repo: local
+  - repo: https://gitlab.com/pycqa/flake8
+    rev: 3.8.4
     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
+      - id: flake8
+        args: [--ignore, "E203,W503,E501,E302,E111,E114,E121,E402"]

bob/learn/tensorflow/callbacks.py

@@ -2,7 +2,6 @@ import json
 import os
 
 import tensorflow as tf
-from tensorflow.keras import callbacks
 
 
 class CustomBackupAndRestore(tf.keras.callbacks.experimental.BackupAndRestore):

bob/learn/tensorflow/data/__init__.py

-from .generator import Generator, dataset_using_generator
-from .tfrecords import dataset_to_tfrecord, dataset_from_tfrecord, TFRECORDS_EXT
+from .generator import Generator
+from .generator import dataset_using_generator  # noqa: F401
+from .tfrecords import TFRECORDS_EXT  # noqa: F401
+from .tfrecords import dataset_from_tfrecord  # noqa: F401
+from .tfrecords import dataset_to_tfrecord  # noqa: F401
+
 
 # gets sphinx autodoc done right - don't remove it
 def __appropriate__(*args):

bob/learn/tensorflow/data/tfrecords.py

@@ -8,7 +8,6 @@ import json
 
 import tensorflow as tf
 
-
 TFRECORDS_EXT = ".tfrecords"
 
 
@@ -102,7 +101,7 @@ def dataset_from_tfrecord(tfrecord, num_parallel_reads=None):
         A dataset that contains the data from the TFRecord file.
     """
     # these imports are needed so that eval can work
-    from tensorflow import TensorShape
+    from tensorflow import TensorShape  # noqa: F401
 
     if isinstance(tfrecord, str):
         tfrecord = [tfrecord]

bob/learn/tensorflow/losses/__init__.py

-from .center_loss import CenterLoss, CenterLossLayer
+from .center_loss import CenterLoss
+from .center_loss import CenterLossLayer
+
 
 # gets sphinx autodoc done right - don't remove it
 def __appropriate__(*args):
@@ -16,8 +18,5 @@ def __appropriate__(*args):
         obj.__module__ = __name__
 
 
-__appropriate__(
-    CenterLoss,
-    CenterLossLayer
-)
+__appropriate__(CenterLoss, CenterLossLayer)
 __all__ = [_ for _ in dir() if not _.startswith("_")]

bob/learn/tensorflow/metrics/__init__.py

-from .embedding_accuracy import EmbeddingAccuracy, predict_using_tensors
+from .embedding_accuracy import EmbeddingAccuracy
+from .embedding_accuracy import predict_using_tensors  # noqa: F401
+
 
 # gets sphinx autodoc done right - don't remove it
 def __appropriate__(*args):

bob/learn/tensorflow/models/__init__.py

@@ -2,6 +2,7 @@ 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.
@@ -18,9 +19,5 @@ def __appropriate__(*args):
         obj.__module__ = __name__
 
 
-__appropriate__(
-    AlexNet_simplified,
-    DenseNet,
-    MineModel
-)
+__appropriate__(AlexNet_simplified, DenseNet, MineModel)
 __all__ = [_ for _ in dir() if not _.startswith("_")]

bob/learn/tensorflow/models/alexnet.py

@@ -61,7 +61,7 @@ def AlexNet_simplified(name="AlexNet", **kwargs):
 
 
 if __name__ == "__main__":
-    import pkg_resources
+    import pkg_resources  # noqa: F401
 
     from bob.learn.tensorflow.utils import model_summary
 

bob/learn/tensorflow/models/autoencoder_face.py

@@ -113,7 +113,7 @@ def autoencoder_face(z_dim=256, weight_decay=1e-10, decoder_last_act="tanh"):
 
 
 if __name__ == "__main__":
-    import pkg_resources
+    import pkg_resources  # noqa: F401
     from tabulate import tabulate
 
     from bob.learn.tensorflow.utils import model_summary

bob/learn/tensorflow/models/densenet.py

@@ -446,7 +446,7 @@ class DeepPixBiS(tf.keras.Model):
 
 
 if __name__ == "__main__":
-    import pkg_resources
+    import pkg_resources  # noqa: F401
     from tabulate import tabulate
 
     from bob.learn.tensorflow.utils import model_summary

bob/learn/tensorflow/models/inception.py

@@ -142,7 +142,7 @@ def GoogLeNet(*, num_classes=1000, name="GoogLeNet", **kwargs):
 
 
 if __name__ == "__main__":
-    import pkg_resources
+    import pkg_resources  # noqa: F401
     from tabulate import tabulate
 
     from bob.learn.tensorflow.utils import model_summary

bob/learn/tensorflow/models/inception_resnet_v1.py

@@ -6,7 +6,6 @@ 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
@@ -14,9 +13,7 @@ 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 bob.learn.tensorflow.utils import SequentialLayer
@@ -240,7 +237,7 @@ class ReductionA(tf.keras.layers.Layer):
         self,
         padding,
         k=256,
-        l=256,
+        l=256,  # noqa: E741
         m=384,
         n=384,
         use_atrous=False,
@@ -250,7 +247,7 @@ class ReductionA(tf.keras.layers.Layer):
         super().__init__(name=name, **kwargs)
         self.padding = padding
         self.k = k
-        self.l = l
+        self.l = l  # noqa: E741
         self.m = m
         self.n = n
         self.use_atrous = use_atrous
@@ -448,7 +445,6 @@ def InceptionResNetV1(
         Conv2D_BN(80, 1, padding="valid", name="Conv2d_3b_1x1"),
         Conv2D_BN(192, 3, padding="valid", name="Conv2d_4a_3x3"),
         Conv2D_BN(256, 3, strides=2, padding="valid", name="Conv2d_4b_3x3"),
-        
     ]
 
     # 5x block35 (Inception-ResNet-A block): 35 x 35 x 320
@@ -503,7 +499,6 @@ def InceptionResNetV1(
         )
     )
 
-    
     # 5x block8 (Inception-ResNet-C block): 8 x 8 x 2080
     for block_idx in range(1, 5):
         layers.append(
@@ -515,7 +510,7 @@ def InceptionResNetV1(
                 name=f"block8_{block_idx}",
             )
         )
-    
+
     layers.append(
         InceptionResnetBlock(
             n_channels=1792,
@@ -523,10 +518,10 @@ def InceptionResNetV1(
             activation=None,
             block_type="block8",
             block_idx=5,
-            name=f"block8_5",
+            name="block8_5",
         )
     )
-    
+
     if (include_top and pooling is None) or (bottleneck):
         pooling = "avg"
 
@@ -545,7 +540,7 @@ def InceptionResNetV1(
     # Classification block
     if include_top:
         layers.append(Dense(classes, name="logits"))
-    
+
     # Create model and call it on input to create its variables.
     model = Sequential(layers, name=name, **kwargs)
     model(img_input)
@@ -554,10 +549,11 @@ def InceptionResNetV1(
 
 
 if __name__ == "__main__":
-    import pkg_resources
-    from bob.learn.tensorflow.utils import model_summary
+    import pkg_resources  # noqa: F401
     from tabulate import tabulate
 
+    from bob.learn.tensorflow.utils import model_summary
+
     def print_model(inputs, outputs, name=None):
         print("")
         print("===============")
@@ -568,7 +564,9 @@ if __name__ == "__main__":
         del rows[-2]
         print(tabulate(rows, headers="firstrow", tablefmt="latex"))
 
-    model = InceptionResNetV1(input_shape=(160, 160, 3), bottleneck=True, include_top=False)
+    model = InceptionResNetV1(
+        input_shape=(160, 160, 3), bottleneck=True, include_top=False
+    )
     inputs = tf.keras.Input((160, 160, 3))
     outputs = model.call(inputs)
-    model.summary()
\ No newline at end of file
+    model.summary()

bob/learn/tensorflow/models/inception_resnet_v2.py

@@ -744,10 +744,11 @@ def MultiScaleInceptionResNetV2(
 
 
 if __name__ == "__main__":
-    import pkg_resources
-    from bob.learn.tensorflow.utils import model_summary
+    import pkg_resources  # noqa: F401
     from tabulate import tabulate
 
+    from bob.learn.tensorflow.utils import model_summary
+
     def print_model(inputs, outputs, name=None):
         print("")
         print("===============")

bob/learn/tensorflow/models/lenet5.py

@@ -31,7 +31,7 @@ def LeNet5_simplified(name="LeNet5", **kwargs):
 
 
 if __name__ == "__main__":
-    import pkg_resources
+    import pkg_resources  # noqa: F401
 
     from bob.learn.tensorflow.utils import model_summary
 

bob/learn/tensorflow/models/mine.py

@@ -7,6 +7,7 @@ Mutual Information Neural Estimation (https://arxiv.org/pdf/1801.04062.pdf)
 
 import tensorflow as tf
 
+
 class MineModel(tf.keras.Model):
     """
 
@@ -21,7 +22,7 @@ class MineModel(tf.keras.Model):
         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)
@@ -32,19 +33,21 @@ class MineModel(tf.keras.Model):
             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)))
+            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)
+            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))
+                    - tf.reduce_mean(tf.math.exp(t_x_z - 1))
                 )
             else:
                 lb = -(
@@ -60,9 +63,7 @@ class MineModel(tf.keras.Model):
 
         return compute_lower_bound(x, z)
 
-
     def get_config(self):
         config = super().get_config()
         config.update({"units": self.units})
         return config
-

bob/learn/tensorflow/models/msu_patch.py

@@ -65,7 +65,7 @@ def MSUPatch(name="MSUPatch", **kwargs):
 
 
 if __name__ == "__main__":
-    import pkg_resources
+    import pkg_resources  # noqa: F401
     from tabulate import tabulate
 
     from bob.learn.tensorflow.utils import model_summary

bob/learn/tensorflow/models/simple_cnn.py

@@ -41,7 +41,7 @@ def SimpleCNN(input_shape=(28, 28, 3), inputs=None, name="SimpleCNN", **kwargs):
 
 
 if __name__ == "__main__":
-    import pkg_resources
+    import pkg_resources  # noqa: F401
     from tabulate import tabulate
 
     from bob.learn.tensorflow.utils import model_summary

bob/learn/tensorflow/scripts/datasets_to_tfrecords.py

@@ -12,7 +12,6 @@ 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__)
 
 
@@ -45,8 +44,9 @@ def datasets_to_tfrecords(dataset, output, force, **kwargs):
     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.extension.scripts.click_helper import log_parameters
     from bob.learn.tensorflow.data.tfrecords import dataset_to_tfrecord
     from bob.learn.tensorflow.data.tfrecords import tfrecord_name_and_json_name
 

bob/learn/tensorflow/tests/data/db_to_tfrecords_config.py

 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()
+x_train, y_train = x_train[:10], y_train[:10]
 samples = zip(tf.keras.backend.arange(len(x_train)), x_train, y_train)
 
 

bob/learn/tensorflow/tests/test_datasets_to_tfrecords.py

 import pkg_resources
 import tensorflow as tf
+from click.testing import CliRunner
+
 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
 

bob/learn/tensorflow/tests/test_mine.py

 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])
+    EPOCHS = 10
 
+    X = np.sign(np.random.normal(0.0, 1.0, [N, d]))
+    Z = X + np.random.normal(0.0, 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
-    )
+    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)
+    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
+    run_mine(True)

bob/learn/tensorflow/utils/__init__.py

-from .keras import *
-from .math import *
-from .image import *
+from .image import *  # noqa: F401,F403
+from .keras import *  # noqa: F401,F403
+from .math import *  # noqa: F401,F403

doc/conf.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
 
-import glob
 import os
-import sys
 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:
@@ -234,9 +229,9 @@ rst_epilog = """
 autoclass_content = "class"
 autodoc_member_order = "bysource"
 autodoc_default_options = {
-  "members": True,
-  "undoc-members": True,
-  "show-inheritance": True,
+    "members": True,
+    "undoc-members": True,
+    "show-inheritance": True,
 }
 
 
@@ -247,5 +242,3 @@ if os.path.exists(sphinx_requirements):
     )
 else:
     intersphinx_mapping = link_documentation()
-
-

version.txt

-1.2.2b0
\ No newline at end of file
+1.2.2b0