From 324a941ddd9fc00f51203d1b20710cd9ed6eee12 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Thu, 22 Apr 2021 18:35:04 +0200
Subject: [PATCH] Flake shit
---
bob/learn/tensorflow/layers.py | 30 +++++++++++---
bob/learn/tensorflow/models/__init__.py | 4 +-
.../tensorflow/models/embedding_validation.py | 7 ++++
.../tensorflow/models/resnet50_modified.py | 40 +++++++++----------
4 files changed, 54 insertions(+), 27 deletions(-)
diff --git a/bob/learn/tensorflow/layers.py b/bob/learn/tensorflow/layers.py
index e15d36e5..33785878 100644
--- a/bob/learn/tensorflow/layers.py
+++ b/bob/learn/tensorflow/layers.py
@@ -5,7 +5,6 @@ import tensorflow as tf
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
-from tensorflow.keras.layers import GlobalAvgPool2D
def _check_input(
@@ -260,7 +259,12 @@ class ModifiedSoftMaxLayer(tf.keras.layers.Layer):
return logits
-def add_bottleneck(model, bottleneck_size=128, dropout_rate=0.2):
+from tensorflow.keras.layers import Flatten
+
+
+def add_bottleneck(
+ model, bottleneck_size=128, dropout_rate=0.2, w_decay=5e-4, use_bias=True
+):
"""
Amend a bottleneck layer to a Keras Model
@@ -276,15 +280,31 @@ def add_bottleneck(model, bottleneck_size=128, dropout_rate=0.2):
dropout_rate: float
Dropout rate
"""
+
if not isinstance(model, tf.keras.models.Sequential):
new_model = tf.keras.models.Sequential(model, name="bottleneck")
else:
new_model = model
- new_model.add(GlobalAvgPool2D())
+ new_model.add(BatchNormalization())
new_model.add(Dropout(dropout_rate, name="Dropout"))
- new_model.add(Dense(bottleneck_size, use_bias=False, name="embeddings"))
- new_model.add(BatchNormalization(axis=-1, scale=False, name="embeddings/BatchNorm"))
+ new_model.add(Flatten())
+
+ if w_decay is None:
+ regularizer = None
+ else:
+ regularizer = tf.keras.regularizers.l2(w_decay)
+
+ new_model.add(
+ Dense(
+ bottleneck_size,
+ use_bias=use_bias,
+ kernel_regularizer=regularizer,
+ )
+ )
+
+ new_model.add(BatchNormalization(axis=-1, name="embeddings"))
+ # new_model.add(BatchNormalization())
return new_model
diff --git a/bob/learn/tensorflow/models/__init__.py b/bob/learn/tensorflow/models/__init__.py
index a69718e5..333ab1f4 100644
--- a/bob/learn/tensorflow/models/__init__.py
+++ b/bob/learn/tensorflow/models/__init__.py
@@ -7,9 +7,9 @@ from .densenet import DenseNet
from .densenet import densenet161 # noqa: F401
from .embedding_validation import EmbeddingValidation
from .mine import MineModel
+from .resnet50_modified import resnet50_modified # noqa: F401
+from .resnet50_modified import resnet101_modified # noqa: F401
-from .arcface import ArcFaceLayer, ArcFaceLayer3Penalties, ArcFaceModel
-from .resnet50_modified import resnet50_modified, resnet101_modified
# gets sphinx autodoc done right - don't remove it
def __appropriate__(*args):
diff --git a/bob/learn/tensorflow/models/embedding_validation.py b/bob/learn/tensorflow/models/embedding_validation.py
index be342d1f..beb4498b 100644
--- a/bob/learn/tensorflow/models/embedding_validation.py
+++ b/bob/learn/tensorflow/models/embedding_validation.py
@@ -12,6 +12,7 @@ class EmbeddingValidation(tf.keras.Model):
def compile(
self,
+ single_precision=False,
**kwargs,
):
"""
@@ -27,14 +28,20 @@ class EmbeddingValidation(tf.keras.Model):
"""
X, y = data
+
with tf.GradientTape() as tape:
logits, _ = self(X, training=True)
loss = self.loss(y, logits)
+ # trainable_vars = self.trainable_variables
+
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
self.compiled_metrics.update_state(y, logits, sample_weight=None)
self.train_loss(loss)
+
+ tf.summary.scalar("training_loss", data=loss, step=self._train_counter)
+
return {m.name: m.result() for m in self.metrics + [self.train_loss]}
# self.optimizer.apply_gradients(zip(gradients, trainable_vars))
diff --git a/bob/learn/tensorflow/models/resnet50_modified.py b/bob/learn/tensorflow/models/resnet50_modified.py
index c6bb4bdf..6725301c 100644
--- a/bob/learn/tensorflow/models/resnet50_modified.py
+++ b/bob/learn/tensorflow/models/resnet50_modified.py
@@ -9,10 +9,11 @@ This resnet 50 implementation provides a cleaner version
"""
import tensorflow as tf
-
-from tensorflow.keras.regularizers import l2
-from tensorflow.keras.layers import Conv2D, Activation, BatchNormalization
+from tensorflow.keras.layers import Activation
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
+from tensorflow.keras.regularizers import l2
global weight_decay
weight_decay = 1e-4
@@ -24,15 +25,15 @@ class IdentityBlock(tf.keras.layers.Layer):
):
"""Block that has no convolutianal layer as skip connection
-
+
Parameters
----------
- kernel_size:
+ kernel_size:
The kernel size of middle conv layer at main path
-
- filters:
+
+ filters:
list of integers, the filterss of 3 conv layer at main path
- stage:
+ stage:
Current stage label, used for generating layer names
block:
@@ -94,8 +95,8 @@ class IdentityBlock(tf.keras.layers.Layer):
def call(self, input_tensor, training=None):
x = input_tensor
- for l in self.layers:
- x = l(x, training=training)
+ for lay in self.layers:
+ x = lay(x, training=training)
x = tf.keras.layers.add([x, input_tensor])
x = Activation("relu")(x)
@@ -115,15 +116,15 @@ class ConvBlock(tf.keras.layers.Layer):
name=None,
**kwargs,
):
- """ Block that has a conv layer AS shortcut.
+ """Block that has a conv layer AS shortcut.
Parameters
----------
- kernel_size:
+ kernel_size:
The kernel size of middle conv layer at main path
-
- filters:
+
+ filters:
list of integers, the filterss of 3 conv layer at main path
- stage:
+ stage:
Current stage label, used for generating layer names
block:
@@ -200,12 +201,12 @@ class ConvBlock(tf.keras.layers.Layer):
def call(self, input_tensor, training=None):
x = input_tensor
- for l in self.layers:
- x = l(x, training=training)
+ for lay in self.layers:
+ x = lay(x, training=training)
x_s = input_tensor
- for l in self.shortcut:
- x_s = l(x_s, training=training)
+ for lay in self.shortcut:
+ x_s = lay(x_s, training=training)
x = tf.keras.layers.add([x, x_s])
x = Activation("relu")(x)
@@ -348,4 +349,3 @@ if __name__ == "__main__":
print(len(model.variables))
print(model.summary())
-
--
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