From 7960d7cb95275e24b45f6088ac2664ed31ddbbef Mon Sep 17 00:00:00 2001 From: yjk15133895098 <825885377@qq.com> Date: Wed, 29 Sep 2021 18:35:15 +0800 Subject: [PATCH] ADD file via upload --- densenet.py | 153 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 153 insertions(+) create mode 100644 densenet.py diff --git a/densenet.py b/densenet.py new file mode 100644 index 0000000..caf8eac --- /dev/null +++ b/densenet.py @@ -0,0 +1,153 @@ +import os +os.environ['TL_BACKEND'] = 'tensorflow' +import time +import multiprocessing +import tensorflow as tf +from tensorlayer.models import TrainOneStep +from tensorlayer.layers import Module +import tensorlayer as tl +from torchsummary import summary +from tensorlayer import logging +from tensorlayer.files import (assign_weights, maybe_download_and_extract) +from tensorlayer.layers import (BatchNorm, Conv2d, Dense, Elementwise, AdaptiveMeanPool2d, MaxPool2d , MeanPool2d,Concat,Dropout) +from tensorlayer.layers import Module, SequentialLayer + + +class _DenseLayer(Module): + def __init__(self, in_channels, growth_rate, bn_size): + super(_DenseLayer, self).__init__() + W_init = tl.initializers.truncated_normal(stddev=5e-2) + W_init2 = tl.initializers.truncated_normal(stddev=0.04) + b_init2 = tl.initializers.constant(value=0.1) + self.layer_list = [] + self.layer_list.append(Conv2d(bn_size * growth_rate,(1,1),in_channels=in_channels,W_init=W_init)) + self.layer_list.append(BatchNorm(num_features=bn_size * growth_rate,act='relu')) + self.layer_list.append(Conv2d(growth_rate, (3, 3), in_channels=bn_size * growth_rate,W_init=W_init)) + self.layer_list.append(BatchNorm(num_features=growth_rate, act='relu')) + self.dense_layer = SequentialLayer(self.layer_list) + self.concat = Concat(1) + + # 重载forward函数 + def forward(self, x): + new_features = self.dense_layer(x) + return self.concat([x, new_features]) + + +class _DenseBlock(Module): + def __init__(self, num_layers, in_channels, bn_size, growth_rate): + super(_DenseBlock, self).__init__() + W_init = tl.initializers.truncated_normal(stddev=5e-2) + W_init2 = tl.initializers.truncated_normal(stddev=0.04) + b_init2 = tl.initializers.constant(value=0.1) + self.layer_list = [] + for i in range(num_layers): + self.layer_list.append(_DenseLayer(in_channels + growth_rate * i,growth_rate, bn_size)) + self.dense_block = SequentialLayer(self.layer_list) + + # 重载forward函数 + def forward(self, x): + return self.dense_block(x) + + +class _Transition(Module): + def __init__(self, in_channels, out_channels): + super(_Transition, self).__init__() + W_init = tl.initializers.truncated_normal(stddev=5e-2) + W_init2 = tl.initializers.truncated_normal(stddev=0.04) + b_init2 = tl.initializers.constant(value=0.1) + self.layer_list = [] + self.layer_list.append(Conv2d(out_channels,(1,1),in_channels=in_channels,W_init=W_init)) + self.layer_list.append(BatchNorm(num_features=out_channels,act='relu')) + self.layer_list.append(MeanPool2d((2,2),strides=(2,2))) + self.transition_layer = SequentialLayer(self.layer_list) + + # 重载forward函数 + def forward(self, x): + return self.transition_layer(x) + +class DenseNet_BC(Module): + def __init__(self, growth_rate=12, block_config=(6, 12, 24, 16), + bn_size=4, theta=0.5, num_classes=10): + super(DenseNet_BC, self).__init__() + W_init = tl.initializers.truncated_normal(stddev=5e-2) + W_init2 = tl.initializers.truncated_normal(stddev=0.04) + b_init2 = tl.initializers.constant(value=0.1) + # 初始的卷积为filter:2倍的growth_rate + num_init_feature = 2 * growth_rate + self.layer_list = [] + # 表示cifar-10 + if num_classes == 10: + self.layer_list.append(Conv2d(num_init_feature,(3,3),strides=(1,1),in_channels=3,W_init=W_init)) + + else: + self.layer_list.append(Conv2d(num_init_feature,(7,7),strides=(2,2),padding="valid",in_channels=3,W_init=W_init)) + self.layer_list.append(BatchNorm(num_features=num_init_feature,act='relu')) + self.layer_list.append(MaxPool2d((3, 3), strides=(2, 2))) + + + num_feature = num_init_feature + for i, num_layers in enumerate(block_config): + self.layer_list.append( _DenseBlock(num_layers, num_feature,bn_size, growth_rate)) + num_feature = num_feature + growth_rate * num_layers + if i != len(block_config) - 1: + self.layer_list.append(_Transition(num_feature,int(num_feature * theta))) + num_feature = int(num_feature * theta) + + self.layer_list.append(BatchNorm(num_features=num_feature,act='relu')) + self.layer_list.append(AdaptiveMeanPool2d((1,1))) + + self.features = SequentialLayer(self.layer_list) + self.classifier = Dense(num_feature, num_classes,W_init=W_init2,b_init=b_init2) + + + def forward(self, x): + features = self.features(x) + out = features.view(features.size(0), -1) + out = self.classifier(out) + return out + + +# DenseNet_BC for ImageNet +def DenseNet121(): + return DenseNet_BC(growth_rate=32, block_config=(6, 12, 24, 16), num_classes=1000) + + +def DenseNet169(): + return DenseNet_BC(growth_rate=32, block_config=(6, 12, 32, 32), num_classes=1000) + + +def DenseNet201(): + return DenseNet_BC(growth_rate=32, block_config=(6, 12, 48, 32), num_classes=1000) + + +def DenseNet161(): + return DenseNet_BC(growth_rate=48, block_config=(6, 12, 36, 24), num_classes=1000, ) + + +# DenseNet_BC for cifar +def densenet_BC_100(): + return DenseNet_BC(growth_rate=12, block_config=(16, 16, 16)) + +def builddensenet(name = "densenet-100"): + if name == "densenet-100": + return densenet_BC_100() + elif name == "densenet-121": + return DenseNet121() + else: + print("not found the net") + exit(0) + +def test(): + net = densenet_BC_100() + print(summary(net, input_size=(3, 32, 32))) + + #x = torch.randn(2, 3, 32, 32) + # y = net(x) + # print(y.size()) + + +if __name__ == '__main__': + test() + + +