Merge code into open source
This commit is contained in:
Eric_lai
parent
d2fb51b8a9
commit
c032d28a5c
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@ -1,153 +1,153 @@
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import os
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os.environ['TL_BACKEND'] = 'tensorflow'
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import time
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import multiprocessing
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import tensorflow as tf
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from tensorlayer.models import TrainOneStep
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from tensorlayer.layers import Module
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import tensorlayer as tl
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from torchsummary import summary
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from tensorlayer import logging
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from tensorlayer.files import (assign_weights, maybe_download_and_extract)
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from tensorlayer.layers import (BatchNorm, Conv2d, Dense, Elementwise, AdaptiveMeanPool2d, MaxPool2d , MeanPool2d,Concat,Dropout)
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from tensorlayer.layers import Module, SequentialLayer
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class _DenseLayer(Module):
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def __init__(self, in_channels, growth_rate, bn_size):
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super(_DenseLayer, self).__init__()
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W_init = tl.initializers.truncated_normal(stddev=5e-2)
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W_init2 = tl.initializers.truncated_normal(stddev=0.04)
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b_init2 = tl.initializers.constant(value=0.1)
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self.layer_list = []
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self.layer_list.append(Conv2d(bn_size * growth_rate,(1,1),in_channels=in_channels,W_init=W_init))
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self.layer_list.append(BatchNorm(num_features=bn_size * growth_rate,act='relu'))
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self.layer_list.append(Conv2d(growth_rate, (3, 3), in_channels=bn_size * growth_rate,W_init=W_init))
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self.layer_list.append(BatchNorm(num_features=growth_rate, act='relu'))
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self.dense_layer = SequentialLayer(self.layer_list)
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self.concat = Concat(1)
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# 重载forward函数
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def forward(self, x):
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new_features = self.dense_layer(x)
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return self.concat([x, new_features])
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class _DenseBlock(Module):
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def __init__(self, num_layers, in_channels, bn_size, growth_rate):
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super(_DenseBlock, self).__init__()
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W_init = tl.initializers.truncated_normal(stddev=5e-2)
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W_init2 = tl.initializers.truncated_normal(stddev=0.04)
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b_init2 = tl.initializers.constant(value=0.1)
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self.layer_list = []
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for i in range(num_layers):
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self.layer_list.append(_DenseLayer(in_channels + growth_rate * i,growth_rate, bn_size))
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self.dense_block = SequentialLayer(self.layer_list)
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# 重载forward函数
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def forward(self, x):
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return self.dense_block(x)
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class _Transition(Module):
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def __init__(self, in_channels, out_channels):
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super(_Transition, self).__init__()
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W_init = tl.initializers.truncated_normal(stddev=5e-2)
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W_init2 = tl.initializers.truncated_normal(stddev=0.04)
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b_init2 = tl.initializers.constant(value=0.1)
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self.layer_list = []
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self.layer_list.append(Conv2d(out_channels,(1,1),in_channels=in_channels,W_init=W_init))
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self.layer_list.append(BatchNorm(num_features=out_channels,act='relu'))
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self.layer_list.append(MeanPool2d((2,2),strides=(2,2)))
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self.transition_layer = SequentialLayer(self.layer_list)
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# 重载forward函数
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def forward(self, x):
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return self.transition_layer(x)
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class DenseNet_BC(Module):
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def __init__(self, growth_rate=12, block_config=(6, 12, 24, 16),
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bn_size=4, theta=0.5, num_classes=10):
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super(DenseNet_BC, self).__init__()
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W_init = tl.initializers.truncated_normal(stddev=5e-2)
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W_init2 = tl.initializers.truncated_normal(stddev=0.04)
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b_init2 = tl.initializers.constant(value=0.1)
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# 初始的卷积为filter:2倍的growth_rate
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num_init_feature = 2 * growth_rate
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self.layer_list = []
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# 表示cifar-10
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if num_classes == 10:
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self.layer_list.append(Conv2d(num_init_feature,(3,3),strides=(1,1),in_channels=3,W_init=W_init))
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else:
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self.layer_list.append(Conv2d(num_init_feature,(7,7),strides=(2,2),padding="valid",in_channels=3,W_init=W_init))
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self.layer_list.append(BatchNorm(num_features=num_init_feature,act='relu'))
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self.layer_list.append(MaxPool2d((3, 3), strides=(2, 2)))
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num_feature = num_init_feature
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for i, num_layers in enumerate(block_config):
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self.layer_list.append( _DenseBlock(num_layers, num_feature,bn_size, growth_rate))
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num_feature = num_feature + growth_rate * num_layers
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if i != len(block_config) - 1:
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self.layer_list.append(_Transition(num_feature,int(num_feature * theta)))
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num_feature = int(num_feature * theta)
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self.layer_list.append(BatchNorm(num_features=num_feature,act='relu'))
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self.layer_list.append(AdaptiveMeanPool2d((1,1)))
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self.features = SequentialLayer(self.layer_list)
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self.classifier = Dense(num_feature, num_classes,W_init=W_init2,b_init=b_init2)
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def forward(self, x):
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features = self.features(x)
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out = features.view(features.size(0), -1)
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out = self.classifier(out)
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return out
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# DenseNet_BC for ImageNet
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def DenseNet121():
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return DenseNet_BC(growth_rate=32, block_config=(6, 12, 24, 16), num_classes=1000)
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def DenseNet169():
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return DenseNet_BC(growth_rate=32, block_config=(6, 12, 32, 32), num_classes=1000)
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def DenseNet201():
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return DenseNet_BC(growth_rate=32, block_config=(6, 12, 48, 32), num_classes=1000)
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def DenseNet161():
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return DenseNet_BC(growth_rate=48, block_config=(6, 12, 36, 24), num_classes=1000, )
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# DenseNet_BC for cifar
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def densenet_BC_100():
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return DenseNet_BC(growth_rate=12, block_config=(16, 16, 16))
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def builddensenet(name = "densenet-100"):
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if name == "densenet-100":
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return densenet_BC_100()
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elif name == "densenet-121":
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return DenseNet121()
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else:
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print("not found the net")
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exit(0)
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def test():
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net = densenet_BC_100()
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print(summary(net, input_size=(3, 32, 32)))
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#x = torch.randn(2, 3, 32, 32)
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# y = net(x)
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# print(y.size())
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if __name__ == '__main__':
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test()
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import os
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os.environ['TL_BACKEND'] = 'tensorflow'
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import time
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import multiprocessing
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import tensorflow as tf
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from tensorlayer.models import TrainOneStep
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from tensorlayer.layers import Module
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import tensorlayer as tl
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from torchsummary import summary
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from tensorlayer import logging
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from tensorlayer.files import (assign_weights, maybe_download_and_extract)
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from tensorlayer.layers import (BatchNorm, Conv2d, Dense, Elementwise, AdaptiveMeanPool2d, MaxPool2d , MeanPool2d,Concat,Dropout)
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from tensorlayer.layers import Module, SequentialLayer
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class _DenseLayer(Module):
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def __init__(self, in_channels, growth_rate, bn_size):
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super(_DenseLayer, self).__init__()
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W_init = tl.initializers.truncated_normal(stddev=5e-2)
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W_init2 = tl.initializers.truncated_normal(stddev=0.04)
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b_init2 = tl.initializers.constant(value=0.1)
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self.layer_list = []
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self.layer_list.append(Conv2d(bn_size * growth_rate,(1,1),in_channels=in_channels,W_init=W_init))
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self.layer_list.append(BatchNorm(num_features=bn_size * growth_rate,act='relu'))
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self.layer_list.append(Conv2d(growth_rate, (3, 3), in_channels=bn_size * growth_rate,W_init=W_init))
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self.layer_list.append(BatchNorm(num_features=growth_rate, act='relu'))
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self.dense_layer = SequentialLayer(self.layer_list)
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self.concat = Concat(1)
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# 重载forward函数
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def forward(self, x):
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new_features = self.dense_layer(x)
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return self.concat([x, new_features])
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class _DenseBlock(Module):
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def __init__(self, num_layers, in_channels, bn_size, growth_rate):
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super(_DenseBlock, self).__init__()
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W_init = tl.initializers.truncated_normal(stddev=5e-2)
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W_init2 = tl.initializers.truncated_normal(stddev=0.04)
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b_init2 = tl.initializers.constant(value=0.1)
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self.layer_list = []
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for i in range(num_layers):
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self.layer_list.append(_DenseLayer(in_channels + growth_rate * i,growth_rate, bn_size))
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self.dense_block = SequentialLayer(self.layer_list)
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# 重载forward函数
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def forward(self, x):
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return self.dense_block(x)
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class _Transition(Module):
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def __init__(self, in_channels, out_channels):
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super(_Transition, self).__init__()
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W_init = tl.initializers.truncated_normal(stddev=5e-2)
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W_init2 = tl.initializers.truncated_normal(stddev=0.04)
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b_init2 = tl.initializers.constant(value=0.1)
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self.layer_list = []
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self.layer_list.append(Conv2d(out_channels,(1,1),in_channels=in_channels,W_init=W_init))
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self.layer_list.append(BatchNorm(num_features=out_channels,act='relu'))
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self.layer_list.append(MeanPool2d((2,2),strides=(2,2)))
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self.transition_layer = SequentialLayer(self.layer_list)
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# 重载forward函数
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def forward(self, x):
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return self.transition_layer(x)
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class DenseNet_BC(Module):
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def __init__(self, growth_rate=12, block_config=(6, 12, 24, 16),
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bn_size=4, theta=0.5, num_classes=10):
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super(DenseNet_BC, self).__init__()
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W_init = tl.initializers.truncated_normal(stddev=5e-2)
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W_init2 = tl.initializers.truncated_normal(stddev=0.04)
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b_init2 = tl.initializers.constant(value=0.1)
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# 初始的卷积为filter:2倍的growth_rate
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num_init_feature = 2 * growth_rate
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self.layer_list = []
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# 表示cifar-10
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if num_classes == 10:
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self.layer_list.append(Conv2d(num_init_feature,(3,3),strides=(1,1),in_channels=3,W_init=W_init))
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else:
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self.layer_list.append(Conv2d(num_init_feature,(7,7),strides=(2,2),padding="valid",in_channels=3,W_init=W_init))
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self.layer_list.append(BatchNorm(num_features=num_init_feature,act='relu'))
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self.layer_list.append(MaxPool2d((3, 3), strides=(2, 2)))
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num_feature = num_init_feature
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for i, num_layers in enumerate(block_config):
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self.layer_list.append( _DenseBlock(num_layers, num_feature,bn_size, growth_rate))
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num_feature = num_feature + growth_rate * num_layers
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if i != len(block_config) - 1:
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self.layer_list.append(_Transition(num_feature,int(num_feature * theta)))
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num_feature = int(num_feature * theta)
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self.layer_list.append(BatchNorm(num_features=num_feature,act='relu'))
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self.layer_list.append(AdaptiveMeanPool2d((1,1)))
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self.features = SequentialLayer(self.layer_list)
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self.classifier = Dense(num_feature, num_classes,W_init=W_init2,b_init=b_init2)
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def forward(self, x):
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features = self.features(x)
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out = features.view(features.size(0), -1)
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out = self.classifier(out)
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return out
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# DenseNet_BC for ImageNet
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def DenseNet121():
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return DenseNet_BC(growth_rate=32, block_config=(6, 12, 24, 16), num_classes=1000)
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def DenseNet169():
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return DenseNet_BC(growth_rate=32, block_config=(6, 12, 32, 32), num_classes=1000)
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def DenseNet201():
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return DenseNet_BC(growth_rate=32, block_config=(6, 12, 48, 32), num_classes=1000)
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def DenseNet161():
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return DenseNet_BC(growth_rate=48, block_config=(6, 12, 36, 24), num_classes=1000, )
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# DenseNet_BC for cifar
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def densenet_BC_100():
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return DenseNet_BC(growth_rate=12, block_config=(16, 16, 16))
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def builddensenet(name = "densenet-100"):
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if name == "densenet-100":
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return densenet_BC_100()
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elif name == "densenet-121":
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return DenseNet121()
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else:
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print("not found the net")
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exit(0)
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def test():
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net = densenet_BC_100()
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print(summary(net, input_size=(3, 32, 32)))
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#x = torch.randn(2, 3, 32, 32)
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# y = net(x)
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# print(y.size())
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if __name__ == '__main__':
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test()
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