ResNet.py

from tensorflow import keras
from keras.models import Model
from keras.layers import *
from keras.utils import conv_utils
from keras.engine.topology import get_source_inputs
from SE_Block import squeeze_excite_block


def resnet_block(input, filters, k=1, strides=(1, 1)):
    init = input
    channel_axis = -1 

    x = BatchNormalization(axis=channel_axis)(input)
    x = Activation('relu')(x)

    if strides != (1, 1) or init._keras_shape[channel_axis] != filters * k:
        init = Conv2D(filters * k, (1, 1), padding='same', kernel_initializer='he_normal',
                      use_bias=False, strides=strides)(x)

    x = Conv2D(filters * k, (3, 3), padding='same', kernel_initializer='he_normal',
               use_bias=False, strides=strides)(x)
    x = BatchNormalization(axis=channel_axis)(x)
    x = Activation('relu')(x)

    x = Conv2D(filters * k, (3, 3), padding='same', kernel_initializer='he_normal',
               use_bias=False)(x)

    # squeeze and excite block
    x = squeeze_excite_block(x)

    m = add([x, init])
    return m