learnopencv/TensorFlow-Receptive-Field-With-Backprop/utils.py

from tensorflow.keras.layers import (
    Activation,
    Add,
    BatchNormalization,
    Conv2D,
)

# https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/python/keras/applications/resnet.py#L32
BASE_WEIGHTS_PATH = (
    "https://storage.googleapis.com/tensorflow/keras-applications/resnet/"
)
WEIGHTS_HASHES = {
    "resnet50": "4d473c1dd8becc155b73f8504c6f6626",
}


# https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/python/keras/applications/resnet.py#L262
def stack1(x, filters, blocks, stride1=2, name=None):
    """
        A set of stacked residual blocks.
        Arguments:
          x: input tensor.
          filters: integer, filters of the bottleneck layer in a block.
          blocks: integer, blocks in the stacked blocks.
          stride1: default 2, stride of the first layer in the first block.
          name: string, stack label.
        Returns:
          Output tensor for the stacked blocks.
    """
    x = block1(x, filters, stride=stride1, name=name + "_block1")
    for i in range(2, blocks + 1):
        x = block1(x, filters, conv_shortcut=False, name=name + "_block" + str(i))
    return x


# https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/python/keras/applications/resnet.py#L217
def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None):
    """
        A residual block.
        Arguments:
          x: input tensor.
          filters: integer, filters of the bottleneck layer.
          kernel_size: default 3, kernel size of the bottleneck layer.
          stride: default 1, stride of the first layer.
          conv_shortcut: default True, use convolution shortcut if True,
              otherwise identity shortcut.
          name: string, block label.
        Returns:
          Output tensor for the residual block.
    """
    # channels_last format
    bn_axis = 3

    if conv_shortcut:
        shortcut = Conv2D(4 * filters, 1, strides=stride, name=name + "_0_conv")(x)
        shortcut = BatchNormalization(
            axis=bn_axis, epsilon=1.001e-5, name=name + "_0_bn",
        )(shortcut)
    else:
        shortcut = x

    x = Conv2D(filters, 1, strides=stride, name=name + "_1_conv")(x)
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn")(x)
    x = Activation("relu", name=name + "_1_relu")(x)

    x = Conv2D(filters, kernel_size, padding="SAME", name=name + "_2_conv")(x)
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + "_2_bn")(x)
    x = Activation("relu", name=name + "_2_relu")(x)

    x = Conv2D(4 * filters, 1, name=name + "_3_conv")(x)
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + "_3_bn")(x)

    x = Add(name=name + "_add")([shortcut, x])
    x = Activation("relu", name=name + "_out")(x)
    return x