from tensorflow.keras.layers import Conv2D, Concatenate, Input, Dropout, ZeroPadding2D,AveragePooling2D, BatchNormalization, Activation, Add, UpSampling2D, MaxPooling2D
from tensorflow.keras.models import Model
from tensorflow.keras.initializers import glorot_uniform
num_classes=6
INPUT_SHAPE=(32,32,3)
def identity_block(x, f_kernel_size, filters, dilation, pad):
filters_1, filters_2, filters_3 = filters
x_shortcut = x
# stage 1
x = Conv2D(filters=filters_1, kernel_size=(1, 1), strides=(1, 1), padding='valid',
kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
# stage 2
x = ZeroPadding2D(padding=pad)(x)
x = Conv2D(filters=filters_2, kernel_size=f_kernel_size, strides=(1, 1),
dilation_rate=dilation, kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
# stage 3
x = Conv2D(filters=filters_3, kernel_size=(1, 1), strides=(1, 1), padding='valid',
kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
# stage 4
x = Add()([x, x_shortcut])
x = Activation(activation='relu')(x)
return x
def convolutional_block(x, f_kernel_size, filters, strides, dilation, pad):
filters_1, filters_2, filters_3 = filters
x_shortcut = x
# stage 1
x = Conv2D(filters=filters_1, kernel_size=(1, 1), strides=strides, padding='valid',
kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
# stage 2
x = ZeroPadding2D(padding=pad)(x)
x = Conv2D(filters=filters_2, kernel_size=f_kernel_size, strides=(1, 1), dilation_rate=dilation,
kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
# stage 3
x = Conv2D(filters=filters_3, kernel_size=(1, 1), strides=(1, 1),
kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
# stage 4
x_shortcut = Conv2D(filters=filters_3, kernel_size=(1, 1), strides=strides, padding='valid',
kernel_initializer=glorot_uniform(seed=0))(x_shortcut)
x_shortcut = BatchNormalization(momentum=0.95, axis=-1)(x_shortcut)
# stage 5
x = Add()([x, x_shortcut])
x = Activation(activation='relu')(x)
return x
def ResNet50(inputs):
# stage 1
#conv1_1_
x = ZeroPadding2D(padding=(1, 1))(inputs)
x = Conv2D(filters=64, kernel_size=(3, 3), strides=(2, 2), kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
#conv1_2
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
# conv1_3
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1), kernel_initializer=glorot_uniform(seed=0))(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
#pool1
x = ZeroPadding2D(padding=(1, 1))(x)
x_stage_1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)
# x_stage_1 = Dropout(0.25)(x_stage_1)
# stage 2
x = convolutional_block(x_stage_1, f_kernel_size=(3, 3), filters=[64, 64, 256], strides=1, pad=(1, 1), dilation=1)
x = identity_block(x, f_kernel_size=(3, 3), filters=[64, 64, 256], pad=(1, 1), dilation=1)
x_stage_2 = identity_block(x, f_kernel_size=(3, 3), filters=[64, 64, 256], pad=(1, 1), dilation=1)
# x_stage_2 = Dropout(0.25)(x_stage_2)
# stage 3
x = convolutional_block(x_stage_2, f_kernel_size=(3, 3), filters=[128, 128, 512], strides=2, pad=(1, 1), dilation=1)
x = identity_block(x, f_kernel_size=(3, 3), filters=[128, 128, 512], pad=(1, 1), dilation=1)
x = identity_block(x, f_kernel_size=(3, 3), filters=[128, 128, 512], pad=(1, 1), dilation=1)
x_stage_3 = identity_block(x, f_kernel_size=(3, 3), filters=[128, 128, 512], pad=(1, 1), dilation=1)
# x_stage_3 = Dropout(0.25)(x_stage_3)
# stage 4
x = convolutional_block(x_stage_3, f_kernel_size=(3, 3), filters=[256, 256, 1024], strides=1, pad=(2, 2), dilation=2)
x = identity_block(x, f_kernel_size=(3, 3), filters=[256, 256, 1024], pad=(2, 2), dilation=2)
x = identity_block(x, f_kernel_size=(3, 3), filters=[256, 256, 1024], pad=(2, 2), dilation=2)
x = identity_block(x, f_kernel_size=(3, 3), filters=[256, 256, 1024], pad=(2, 2), dilation=2)
x = identity_block(x, f_kernel_size=(3, 3), filters=[256, 256, 1024], pad=(2, 2), dilation=2)
x_stage_4 = identity_block(x, f_kernel_size=(3, 3), filters=[256, 256, 1024], pad=(2, 2), dilation=2)
# x_stage_4 = Dropout(0.25)(x_stage_4)
# stage 5
x = convolutional_block(x_stage_4, f_kernel_size=(3, 3), filters=[512, 512, 2048], strides=1, pad=(4, 4), dilation=4)
x = identity_block(x, f_kernel_size=(3, 3), filters=[256, 256, 2048], pad=(4, 4), dilation=4)
x_stage_5 = identity_block(x, f_kernel_size=(3, 3), filters=[256, 256, 2048], pad=(4, 4), dilation=4)
# x_stage_5 = Dropout(0.25)(x_stage_5)
return x_stage_5
#构建网络结构
def build_pspnet(num_classes):
#ResNet50 提取特征
inputs = Input(shape=INPUT_SHAPE)
res_features = ResNet50(inputs)
#金字塔池化
x_c1 = AveragePooling2D(pool_size=60, strides=60, name='ave_c1')(res_features)
x_c1 = Conv2D(filters=512, kernel_size=1, strides=1, padding='same', name='conv_c1')(x_c1)
x_c1 = BatchNormalization(momentum=0.95, axis=-1)(x_c1)
x_c1 = Activation(activation='relu')(x_c1)
#x_c1 = Dropout(0.2)(x_c1)
x_c1 = UpSampling2D(size=(60, 60), name='up_c1')(x_c1)
x_c2 = AveragePooling2D(pool_size=30, strides=30, name='ave_c2')(res_features)
x_c2 = Conv2D(filters=512, kernel_size=1, strides=1, padding='same', name='conv_c2')(x_c2)
x_c2 = BatchNormalization(momentum=0.95, axis=-1)(x_c2)
x_c2 = Activation(activation='relu')(x_c2)
#x_c2 = Dropout(0.2)(x_c2)
x_c2 = UpSampling2D(size=(30, 30), name='up_c2')(x_c2)
x_c3 = AveragePooling2D(pool_size=20, strides=20, name='ave_c3')(res_features)
x_c3 = Conv2D(filters=512, kernel_size=1, strides=1, padding='same', name='conv_c3')(x_c3)
x_c3 = BatchNormalization(momentum=0.95, axis=-1)(x_c3)
x_c3 = Activation(activation='relu')(x_c3)
#x_c3 = Dropout(0.2)(x_c3)
x_c3 = UpSampling2D(size=(20, 20), name='up_c3')(x_c3)
x_c4 = AveragePooling2D(pool_size=10, strides=10, name='ave_c4')(res_features)
x_c4 = Conv2D(filters=512, kernel_size=1, strides=1, padding='same', name='conv_c4')(x_c4)
x_c4 = BatchNormalization(momentum=0.95, axis=-1)(x_c4)
x_c4 = Activation(activation='relu')(x_c4)
#x_c4 = Dropout(0.2)(x_c4)
x_c4 = UpSampling2D(size=(10, 10), name='up_c4')(x_c4)
x_c5 = Conv2D(filters=512, kernel_size=1, strides=1, name='conv_c5', padding='same')(res_features)
x_c5 = BatchNormalization(momentum=0.95, axis=-1)(x_c5)
x_c5 = Activation(activation='relu')(x_c5)
#x_c5 = Dropout(0.2)(x_c5)
x = Concatenate(axis=-1, name='concat')([x_c1, x_c2, x_c3, x_c4, x_c5])
x = Conv2D(filters=512, kernel_size=3, strides=1, padding='same', name='sum_conv_1_11')(x)
x = BatchNormalization(momentum=0.95, axis=-1)(x)
x = Activation(activation='relu')(x)
x = UpSampling2D(size=(4, 4))(x)
# x = Conv2D(filters=256, kernel_size=3, strides=1, padding='same', name='sum_conv_1_21')(x)
# x = BatchNormalization(momentum=0.95, axis=-1)(x)
# x = Activation(activation='relu')(x)
outputs = Conv2D(filters=num_classes, kernel_size=1, strides=1, padding='same', name='sum_conv_2', activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
model=build_pspnet(num_classes)
