tensorlayer3/densenet.py

import tensorflow as tf
import tensorlayer as tl
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)

class BottleNeck(tl.layers.Module):
    def __init__(self, growth_rate, drop_rate):
        super(BottleNeck, self).__init__()
        self.bn1 = tl.layers.BatchNorm()
        self.conv1 = tl.layers.Conv2d(n_filter=4 * growth_rate,
                                            filter_size=(1, 1),
                                            strides=(1,1),
                                            padding="SAME")
        self.bn2 = tl.layers.BatchNorm()
        self.conv2 = tl.layers.Conv2d(n_filter=growth_rate,
                                            filter_size=(3, 3),
                                            strides=(1,1),
                                            padding="SAME")
        self.dropout = tl.layers.Dropout(keep=drop_rate)

        self.listLayers = [self.bn1,
                           tl.layers.PRelu(channel_shared=True),
                           self.conv1,
                           self.bn2,
                           tl.layers.PRelu(channel_shared=True),
                           self.conv2,
                           self.dropout]

    def forward(self, x):
        y = x
        for layer in self.listLayers:
            y = layer(y)
        y = tf.keras.layers.concatenate([x, y], axis=-1)
        return y

# 构建密集块
class DenseBlock(tl.layers.Module):
    def __init__(self, num_layers, growth_rate, drop_rate=0.5):
        super(DenseBlock, self).__init__()
        self.num_layers = num_layers
        self.growth_rate = growth_rate
        self.drop_rate = drop_rate
        self.listLayers = []
        for _ in range(num_layers):
            self.listLayers.append(BottleNeck(growth_rate=self.growth_rate, drop_rate=self.drop_rate))

    def forward(self, x):
        for layer in self.listLayers:
            x = layer(x)
        return x

# 构建过渡层
class TransitionLayer(tl.layers.Module):
    def __init__(self, out_channels):
        super(TransitionLayer, self).__init__()
        self.bn = tl.layers.BatchNorm()
        self.conv = tl.layers.Conv2d(n_filter=out_channels,
                                           filter_size=(1, 1),
                                           strides=(1,1),
                                           padding="same")
        self.pool = tl.layers.MaxPool2d(filter_size=(2, 2),
                                              strides=(2,2),
                                              padding="SAME")

    def forward(self, inputs):
        x = self.bn(inputs)
        x = tl.relu(x)
        x = self.conv(x)
        x = self.pool(x)
        return x

# DenseNet-121,169,201,264模型
class DenseNet(tl.layers.Module):
    def __init__(self, num_init_features, growth_rate, block_layers, compression_rate, drop_rate):
        super(DenseNet, self).__init__()
        self.conv = tl.layers.Conv2d(n_filter=num_init_features,
                                           filter_size=(7, 7),
                                           strides=(2,2),
                                           padding="SAME")
        self.bn = tl.layers.BatchNorm()
        self.pool = tl.layers.MaxPool2d(filter_size=(3, 3),
                                              strides=(2,2),
                                              padding="SAME")
        self.num_channels = num_init_features
        self.dense_block_1 = DenseBlock(num_layers=block_layers[0], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[0]
        self.num_channels = compression_rate * self.num_channels
        self.transition_1 = TransitionLayer(out_channels=int(self.num_channels))
        self.dense_block_2 = DenseBlock(num_layers=block_layers[1], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[1]
        self.num_channels = compression_rate * self.num_channels
        self.transition_2 = TransitionLayer(out_channels=int(self.num_channels))
        self.dense_block_3 = DenseBlock(num_layers=block_layers[2], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[2]
        self.num_channels = compression_rate * self.num_channels
        self.transition_3 = TransitionLayer(out_channels=int(self.num_channels))
        self.dense_block_4 = DenseBlock(num_layers=block_layers[3], growth_rate=growth_rate, drop_rate=drop_rate)

        self.avgpool = tl.layers.GlobalMeanPool2d()
        self.fc = tl.layers.Dense(n_units=10,act=tl.softmax(logits=()))
        
    def forward(self, inputs):
        x = self.conv(inputs)
        x = self.bn(x)
        x = tl.relu(x)
        x = self.pool(x)

        x = self.dense_block_1(x)
        x = self.transition_1(x)
        x = self.dense_block_2(x)
        x = self.transition_2(x)
        x = self.dense_block_3(x)
        x = self.transition_3(x,)
        x = self.dense_block_4(x)

        x = self.avgpool(x)
        x = self.fc(x)

        return x

# DenseNet-100模型
class DenseNet_100(tl.layers.Module):
    def __init__(self, num_init_features, growth_rate, block_layers, compression_rate, drop_rate):
        super(DenseNet_100, self).__init__()
        self.conv = tl.layers.Conv2d(n_filter=num_init_features,
                                           filter_size=(7, 7),
                                           strides=(2,2),
                                           padding="SAME")
        self.bn = tl.layers.BatchNorm()
        self.pool = tl.layers.MaxPool2d(filter_size=(3, 3),
                                              strides=(2,2),
                                              padding="SAME")
        self.num_channels = num_init_features
        self.dense_block_1 = DenseBlock(num_layers=block_layers[0], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[0]
        self.num_channels = compression_rate * self.num_channels
        self.transition_1 = TransitionLayer(out_channels=int(self.num_channels))
        self.dense_block_2 = DenseBlock(num_layers=block_layers[1], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[1]
        self.num_channels = compression_rate * self.num_channels
        self.transition_2 = TransitionLayer(out_channels=int(self.num_channels))
        self.dense_block_3 = DenseBlock(num_layers=block_layers[2], growth_rate=growth_rate, drop_rate=drop_rate)
        self.num_channels += growth_rate * block_layers[2]
        self.num_channels = compression_rate * self.num_channels
        self.transition_3 = TransitionLayer(out_channels=int(self.num_channels))


        self.avgpool = tl.layers.GlobalMeanPool2d()
        self.fc = tl.layers.Dense(n_units=10,act=tl.softmax(logits=()))
        
    def forward(self, inputs):
        x = self.conv(inputs)
        x = self.bn(x)
        x = tl.relu(x)
        x = self.pool(x)

        x = self.dense_block_1(x)
        x = self.transition_1(x)
        x = self.dense_block_2(x)
        x = self.transition_2(x)
        x = self.dense_block_3(x)
        x = self.transition_3(x,)

        x = self.avgpool(x)
        # x = tl.layers.Dense(n_units=10,act=tl.softmax(logits=x))
        x = self.fc(x)

        return x


def densenet(x):
    if x == 'densenet-121':
        return DenseNet(num_init_features=64, growth_rate=32, block_layers=[6, 12, 24, 16], compression_rate=0.5,
                            drop_rate=0.5)
    elif x == 'densenet-169':
        return DenseNet(num_init_features=64, growth_rate=32, block_layers=[6 , 12, 32, 32], compression_rate=0.5,
                            drop_rate=0.5)
    elif x == 'densenet-201':
        return DenseNet(num_init_features=64, growth_rate=32, block_layers=[6, 12, 48, 32], compression_rate=0.5,
                            drop_rate=0.5)
    elif x == 'densenet-264':
        return DenseNet(num_init_features=64, growth_rate=32, block_layers=[6, 12, 64, 48], compression_rate=0.5,
                            drop_rate=0.5)
    elif x=='densenet-100':
        return DenseNet_100(num_init_features=64, growth_rate=12, block_layers=[16, 16, 16], compression_rate=0.5,
                            drop_rate=0.5)