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