Properly implemented resnet50 and resnet101

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):

bob/learn/tensorflow/models/resnet50_modified.py

+# -*- 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())
+