From feff4f405bcd0b5a91b3427f25a5da6d70eae8a0 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Thu, 22 Apr 2021 17:07:30 +0200
Subject: [PATCH] Properly implemented resnet50 and resnet101
---
bob/learn/tensorflow/models/__init__.py | 2 +
.../tensorflow/models/resnet50_modified.py | 352 ++++++++++++++++++
2 files changed, 354 insertions(+)
create mode 100644 bob/learn/tensorflow/models/resnet50_modified.py
diff --git a/bob/learn/tensorflow/models/__init__.py b/bob/learn/tensorflow/models/__init__.py
index 4265c99d..a69718e5 100644
--- a/bob/learn/tensorflow/models/__init__.py
+++ b/bob/learn/tensorflow/models/__init__.py
@@ -8,6 +8,8 @@ from .densenet import densenet161 # noqa: F401
from .embedding_validation import EmbeddingValidation
from .mine import MineModel
+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/resnet50_modified.py b/bob/learn/tensorflow/models/resnet50_modified.py
new file mode 100644
index 00000000..bd2d4bef
--- /dev/null
+++ b/bob/learn/tensorflow/models/resnet50_modified.py
@@ -0,0 +1,352 @@
+# -*- coding: utf-8 -*-
+"""
+The resnet50 from `tf.keras.applications.Resnet50` has a problem with the convolutional layers.
+It basically add bias terms to such layers followed by batch normalizations, which is not correct
+
+https://github.com/tensorflow/tensorflow/issues/37365
+
+This resnet 50 implementation provides a cleaner version
+"""
+
+import tensorflow as tf
+
+from tensorflow.keras import layers
+from tensorflow.keras.regularizers import l2
+from tensorflow.keras.layers import Input, Conv2D, Activation, BatchNormalization
+from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D, Flatten, Dense
+
+global weight_decay
+weight_decay = 1e-4
+
+
+class IdentityBlock(tf.keras.layers.Layer):
+ def __init__(
+ self, kernel_size, filters, stage, block, weight_decay=1e-4, name=None, **kwargs
+ ):
+
+ """Block that has no convolutianal layer as skip connection
+
+ Parameters
+ ----------
+ kernel_size:
+ The kernel size of middle conv layer at main path
+
+ filters:
+ list of integers, the filterss of 3 conv layer at main path
+ stage:
+ Current stage label, used for generating layer names
+
+ block:
+ 'a','b'..., current block label, used for generating layer names
+
+ """
+ super().__init__(name=name, **kwargs)
+
+ filters1, filters2, filters3 = filters
+ bn_axis = 3
+
+ conv_name_1 = "conv" + str(stage) + "_" + str(block) + "_1x1_reduce"
+ bn_name_1 = "conv" + str(stage) + "_" + str(block) + "_1x1_reduce/bn"
+ layers = [
+ Conv2D(
+ filters1,
+ (1, 1),
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ kernel_regularizer=l2(weight_decay),
+ name=conv_name_1,
+ )
+ ]
+
+ layers += [BatchNormalization(axis=bn_axis, name=bn_name_1)]
+ layers += [Activation("relu")]
+
+ conv_name_2 = "conv" + str(stage) + "_" + str(block) + "_3x3"
+ bn_name_2 = "conv" + str(stage) + "_" + str(block) + "_3x3/bn"
+ layers += [
+ Conv2D(
+ filters2,
+ kernel_size,
+ padding="same",
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ kernel_regularizer=l2(weight_decay),
+ name=conv_name_2,
+ )
+ ]
+ layers += [BatchNormalization(axis=bn_axis, name=bn_name_2)]
+ layers += [Activation("relu")]
+
+ conv_name_3 = "conv" + str(stage) + "_" + str(block) + "_1x1_increase"
+ bn_name_3 = "conv" + str(stage) + "_" + str(block) + "_1x1_increase/bn"
+ layers += [
+ Conv2D(
+ filters3,
+ (1, 1),
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ kernel_regularizer=l2(weight_decay),
+ name=conv_name_3,
+ )
+ ]
+ layers += [BatchNormalization(axis=bn_axis, name=bn_name_3)]
+ self.layers = layers
+
+ def call(self, input_tensor, training=None):
+
+ x = input_tensor
+ for l in self.layers:
+ x = l(x, training=training)
+
+ x = tf.keras.layers.add([x, input_tensor])
+ x = Activation("relu")(x)
+
+ return x
+
+
+class ConvBlock(tf.keras.layers.Layer):
+ def __init__(
+ self,
+ kernel_size,
+ filters,
+ stage,
+ block,
+ strides=(2, 2),
+ weight_decay=1e-4,
+ name=None,
+ **kwargs,
+ ):
+ """ Block that has a conv layer AS shortcut.
+ Parameters
+ ----------
+ kernel_size:
+ The kernel size of middle conv layer at main path
+
+ filters:
+ list of integers, the filterss of 3 conv layer at main path
+ stage:
+ Current stage label, used for generating layer names
+
+ block:
+ 'a','b'..., current block label, used for generating layer names
+ """
+ super().__init__(name=name, **kwargs)
+
+ filters1, filters2, filters3 = filters
+ bn_axis = 3
+
+ conv_name_1 = "conv" + str(stage) + "_" + str(block) + "_1x1_reduce"
+ bn_name_1 = "conv" + str(stage) + "_" + str(block) + "_1x1_reduce/bn"
+ layers = [
+ Conv2D(
+ filters1,
+ (1, 1),
+ strides=strides,
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ kernel_regularizer=l2(weight_decay),
+ name=conv_name_1,
+ )
+ ]
+ layers += [BatchNormalization(axis=bn_axis, name=bn_name_1)]
+ layers += [Activation("relu")]
+
+ conv_name_2 = "conv" + str(stage) + "_" + str(block) + "_3x3"
+ bn_name_2 = "conv" + str(stage) + "_" + str(block) + "_3x3/bn"
+ layers += [
+ Conv2D(
+ filters2,
+ kernel_size,
+ padding="same",
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ kernel_regularizer=l2(weight_decay),
+ name=conv_name_2,
+ )
+ ]
+ layers += [BatchNormalization(axis=bn_axis, name=bn_name_2)]
+ layers += [Activation("relu")]
+
+ conv_name_3 = "conv" + str(stage) + "_" + str(block) + "_1x1_increase"
+ bn_name_3 = "conv" + str(stage) + "_" + str(block) + "_1x1_increase/bn"
+ layers += [
+ Conv2D(
+ filters3,
+ (1, 1),
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ kernel_regularizer=l2(weight_decay),
+ name=conv_name_3,
+ )
+ ]
+ layers += [BatchNormalization(axis=bn_axis, name=bn_name_3)]
+
+ conv_name_4 = "conv" + str(stage) + "_" + str(block) + "_1x1_proj"
+ bn_name_4 = "conv" + str(stage) + "_" + str(block) + "_1x1_proj/bn"
+ shortcut = [
+ Conv2D(
+ filters3,
+ (1, 1),
+ strides=strides,
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ kernel_regularizer=l2(weight_decay),
+ name=conv_name_4,
+ )
+ ]
+ shortcut += [BatchNormalization(axis=bn_axis, name=bn_name_4)]
+
+ self.layers = layers
+ self.shortcut = shortcut
+
+ def call(self, input_tensor, training=None):
+ x = input_tensor
+ for l in self.layers:
+ x = l(x, training=training)
+
+ x_s = input_tensor
+ for l in self.shortcut:
+ x_s = l(x_s, training=training)
+
+ x = tf.keras.layers.add([x, x_s])
+ x = Activation("relu")(x)
+ return x
+
+
+def resnet50_modified(input_tensor=None, input_shape=None, **kwargs):
+ """
+ The resnet50 from `tf.keras.applications.Resnet50` has a problem with the convolutional layers.
+ It basically add bias terms to such layers followed by batch normalizations, which is not correct
+
+ https://github.com/tensorflow/tensorflow/issues/37365
+
+ This resnet 50 implementation provides a cleaner version
+
+ """
+ if input_tensor is None:
+ input_tensor = tf.keras.Input(shape=input_shape)
+ else:
+ if not K.is_keras_tensor(input_tensor):
+ input_tensor = tf.keras.Input(tensor=input_tensor, shape=input_shape)
+
+ bn_axis = 3
+ # inputs are of size 224 x 224 x 3
+ layers = [input_tensor]
+ layers += [
+ Conv2D(
+ 64,
+ (7, 7),
+ strides=(2, 2),
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ trainable=True,
+ kernel_regularizer=l2(weight_decay),
+ padding="same",
+ name="conv1/7x7_s2",
+ )
+ ]
+
+ # inputs are of size 112 x 112 x 64
+ layers += [BatchNormalization(axis=bn_axis, name="conv1/7x7_s2/bn")]
+ layers += [Activation("relu")]
+ layers += [MaxPooling2D((3, 3), strides=(2, 2))]
+
+ # inputs are of size 56 x 56
+ layers += [ConvBlock(3, [64, 64, 256], stage=2, block=1, strides=(1, 1))]
+ layers += [IdentityBlock(3, [64, 64, 256], stage=2, block=2)]
+ layers += [IdentityBlock(3, [64, 64, 256], stage=2, block=3)]
+
+ # inputs are of size 28 x 28
+ layers += [ConvBlock(3, [128, 128, 512], stage=3, block=1)]
+ layers += [IdentityBlock(3, [128, 128, 512], stage=3, block=2)]
+ layers += [IdentityBlock(3, [128, 128, 512], stage=3, block=3)]
+ layers += [IdentityBlock(3, [128, 128, 512], stage=3, block=4)]
+
+ # inputs are of size 14 x 14
+ layers += [ConvBlock(3, [256, 256, 1024], stage=4, block=1)]
+ layers += [IdentityBlock(3, [256, 256, 1024], stage=4, block=2)]
+ layers += [IdentityBlock(3, [256, 256, 1024], stage=4, block=3)]
+ layers += [IdentityBlock(3, [256, 256, 1024], stage=4, block=4)]
+ layers += [IdentityBlock(3, [256, 256, 1024], stage=4, block=5)]
+ layers += [IdentityBlock(3, [256, 256, 1024], stage=4, block=6)]
+
+ # inputs are of size 7 x 7
+ layers += [ConvBlock(3, [512, 512, 2048], stage=5, block=1)]
+ layers += [IdentityBlock(3, [512, 512, 2048], stage=5, block=2)]
+ layers += [IdentityBlock(3, [512, 512, 2048], stage=5, block=3)]
+
+ return tf.keras.Sequential(layers)
+
+
+def resnet101_modified(input_tensor=None, input_shape=None, **kwargs):
+ """
+ The resnet101 from `tf.keras.applications.Resnet101` has a problem with the convolutional layers.
+ It basically add bias terms to such layers followed by batch normalizations, which is not correct
+
+ https://github.com/tensorflow/tensorflow/issues/37365
+
+ This resnet 10 implementation provides a cleaner version
+
+ """
+
+ if input_tensor is None:
+ input_tensor = tf.keras.Input(shape=input_shape)
+ else:
+ if not tf.keras.backend.is_keras_tensor(input_tensor):
+ input_tensor = tf.keras.Input(tensor=input_tensor, shape=input_shape)
+
+ bn_axis = 3
+ # inputs are of size 224 x 224 x 3
+ layers = [input_tensor]
+ layers += [
+ Conv2D(
+ 64,
+ (7, 7),
+ strides=(2, 2),
+ kernel_initializer="orthogonal",
+ use_bias=False,
+ trainable=True,
+ kernel_regularizer=l2(weight_decay),
+ padding="same",
+ name="conv1/7x7_s2",
+ )
+ ]
+
+ # inputs are of size 112 x 112 x 64
+ layers += [BatchNormalization(axis=bn_axis, name="conv1/7x7_s2/bn")]
+ layers += [Activation("relu")]
+ layers += [MaxPooling2D((3, 3), strides=(2, 2))]
+
+ # inputs are of size 56 x 56
+ layers += [ConvBlock(3, [64, 64, 256], stage=2, block=1, strides=(1, 1))]
+ layers += [IdentityBlock(3, [64, 64, 256], stage=2, block=2)]
+ layers += [IdentityBlock(3, [64, 64, 256], stage=2, block=3)]
+
+ # inputs are of size 28 x 28
+ layers += [ConvBlock(3, [128, 128, 512], stage=3, block=1)]
+ layers += [IdentityBlock(3, [128, 128, 512], stage=3, block=2)]
+ layers += [IdentityBlock(3, [128, 128, 512], stage=3, block=3)]
+ layers += [IdentityBlock(3, [128, 128, 512], stage=3, block=4)]
+
+ # inputs are of size 14 x 14
+ # 23 blocks here. That's the only difference from
+ # resnet-101
+ layers += [ConvBlock(3, [256, 256, 1024], stage=4, block=1)]
+ for i in range(2, 24):
+ layers += [IdentityBlock(3, [256, 256, 1024], stage=4, block=i)]
+
+ # inputs are of size 7 x 7
+ layers += [ConvBlock(3, [512, 512, 2048], stage=5, block=1)]
+ layers += [IdentityBlock(3, [512, 512, 2048], stage=5, block=2)]
+ layers += [IdentityBlock(3, [512, 512, 2048], stage=5, block=3)]
+
+ return tf.keras.Sequential(layers)
+
+
+if __name__ == "__main__":
+ input_tensor = tf.keras.layers.InputLayer([112, 112, 3])
+ model = resnet_50(input_tensor)
+
+ print(len(model.variables))
+ print(model.summary())
+
--
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