FER2013/ResNet.py at main · MeB4You/FER2013 · GitHub

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from keras import layers
from keras.layers import Dense
from keras.layers import Conv2D
from keras.layers import AveragePooling2D
from keras.layers import Flatten
from keras.layers import MaxPool2D
from keras.layers import Input
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.models import Model
from keras.regularizers import l2


def conv2d_bn(x, filters, kernel_size, weight_decay=.0, strides=(1, 1)):
    layer = Conv2D(filters=filters,
                   kernel_size=kernel_size,
                   strides=strides,
                   padding='same',
                   use_bias=False,
                   kernel_regularizer=l2(weight_decay)
                   )(x)
    layer = BatchNormalization()(layer)
    return layer


def conv2d_bn_relu(x, filters, kernel_size, weight_decay=.0, strides=(1, 1)):
    layer = conv2d_bn(x, filters, kernel_size, weight_decay, strides)
    layer = Activation('relu')(layer)
    return layer


def ResidualBlock(x, filters, kernel_size, weight_decay, downsample=True):
    if downsample:
        # residual_x = conv2d_bn_relu(x, filters, kernel_size=1, strides=2)
        residual_x = conv2d_bn(x, filters, kernel_size=1, strides=2)
        stride = 2
    else:
        residual_x = x
        stride = 1
    residual = conv2d_bn_relu(x,
                              filters=filters,
                              kernel_size=kernel_size,
                              weight_decay=weight_decay,
                              strides=stride,
                              )
    residual = conv2d_bn(residual,
                         filters=filters,
                         kernel_size=kernel_size,
                         weight_decay=weight_decay,
                         strides=1,
                         )
    out = layers.add([residual_x, residual])
    out = Activation('relu')(out)
    return out


def ResNet18(classes, input_shape, weight_decay=1e-4):
    input = Input(shape=input_shape)
    x = input
    x = conv2d_bn_relu(x, filters=24, kernel_size=(3, 3), weight_decay=weight_decay, strides=(2, 2))
    x = MaxPool2D(pool_size=(3, 3), strides=(2, 2),  padding='same')(x)

    # # conv 2
    x = ResidualBlock(x, filters=24, kernel_size=(3, 3), weight_decay=weight_decay, downsample=False)
    x = ResidualBlock(x, filters=24, kernel_size=(3, 3), weight_decay=weight_decay, downsample=False)
    # # conv 3
    x = ResidualBlock(x, filters=48, kernel_size=(3, 3), weight_decay=weight_decay, downsample=True)
    x = ResidualBlock(x, filters=48, kernel_size=(3, 3), weight_decay=weight_decay, downsample=False)
    # # conv 4
    x = ResidualBlock(x, filters=96, kernel_size=(3, 3), weight_decay=weight_decay, downsample=True)
    x = ResidualBlock(x, filters=96, kernel_size=(3, 3), weight_decay=weight_decay, downsample=False)
    # # conv 5
    x = ResidualBlock(x, filters=192, kernel_size=(3, 3), weight_decay=weight_decay, downsample=True)
    x = ResidualBlock(x, filters=192, kernel_size=(3, 3), weight_decay=weight_decay, downsample=False)
    x = AveragePooling2D(pool_size=(2, 2), padding='valid')(x)
    x = Flatten()(x)
    x = Dense(classes, activation='softmax')(x)
    model = Model(input, x, name='ResNet18')
    return model