update

densenet of tensorlayer by yjk

densenet.py

@@ -1,153 +0,0 @@
-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()
-
-
-