Machine-Learning-Collection/ML/TensorFlow/CNN_architectures/ResNet/block.py

# 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