Initial commit

ML/TensorFlow/CNN_architectures/ResNet/block.py

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+# Tensorflow v.2.3.1
+
+"""
+Programmed by the-robot <https://github.com/the-robot>
+"""
+
+from tensorflow.keras.layers import (
+    Activation,
+    Add,
+    BatchNormalization,
+    Conv2D,
+)
+import tensorflow as tf
+import typing
+
+@tf.function
+def block(
+    X: tf.Tensor,
+    kernel_size: int,
+    filters: typing.List[int],
+    stage_no: int,
+    block_name: str,
+    is_conv_layer: bool = False,
+    stride: int = 2
+) -> tf.Tensor:
+    """
+    Block for residual network.
+
+    Arguments:
+    X             -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
+    kernel_size   -- integer, specifying the shape of the middle CONV's window for the main path
+    filters       -- python list of integers, defining the number of filters in the CONV layers of the main path
+    stage_no      -- integer, used to name the layers, depending on their position in the network
+    block_name    -- string/character, used to name the layers, depending on their position in the network
+    is_conv_layer -- to identiy if identity downsample is needed
+    stride        -- integer specifying the stride to be used
+    
+    Returns:
+    X             -- output of the identity block, tensor of shape (n_H, n_W, n_C)
+    """
+
+    # names
+    conv_name_base = "res" + str(stage_no) + block_name + "_branch"
+    bn_name_base = "bn" + str(stage_no) + block_name + "_branch"
+
+    # filters
+    F1, F2, F3 = filters
+
+    # save the input value for shortcut.
+    X_shortcut = X
+
+    #  First component
+    # NOTE: if conv_layer, you need to do downsampling
+    X = Conv2D(
+        filters = F1,
+        kernel_size = (1, 1),
+        strides = (stride, stride) if is_conv_layer else (1, 1),
+        padding = "valid",
+        name = conv_name_base + "2a",
+        kernel_initializer = "glorot_uniform",
+    )(X)
+    X = BatchNormalization(axis = 3, name = bn_name_base + "2a")(X)
+    X = Activation("relu")(X)
+
+    # Second component
+    X = Conv2D(
+        filters = F2,
+        kernel_size = (kernel_size, kernel_size),
+        strides = (1, 1),
+        padding = "same",
+        name = conv_name_base + "2b",
+        kernel_initializer = "glorot_uniform",
+    )(X)
+    X = BatchNormalization(axis = 3, name = bn_name_base + "2b")(X)
+    X = Activation("relu")(X)
+
+    # Third component
+    X = Conv2D(
+        filters = F3,
+        kernel_size = (1, 1),
+        strides = (1, 1),
+        padding = "valid",
+        name = conv_name_base + "2c",
+        kernel_initializer = "glorot_uniform",
+    )(X)
+    X = BatchNormalization(axis = 3, name = bn_name_base + "2c")(X)
+
+    # NOTE: if is_conv_layer, you need to do downsampling the X_shortcut to match the output (X) channel
+    #       so it can be added together
+    if is_conv_layer:
+        X_shortcut = Conv2D(
+            filters = F3,
+            kernel_size = (1, 1),
+            strides = (stride, stride),
+            padding = "valid",
+            name = conv_name_base + "1",
+            kernel_initializer = "glorot_uniform",
+        )(X_shortcut)
+        X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + "1")(X_shortcut)
+
+    # Shortcut value
+    X = Add()([X, X_shortcut])
+    X = Activation("relu")(X)
+
+    return X