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resnet.py
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# 2019.07.24-Changed output of forward function
# Huawei Technologies Co., Ltd. <[email protected]>
# taken from https://github.com/huawei-noah/Data-Efficient-Model-Compression/blob/master/DAFL/resnet.py
# for comparison with DAFL
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1,p=0.0):
super(BasicBlock, self).__init__()
self.p = p
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
self.relu2 = nn.ReLU()
def forward(self, x):
out = self.bn1(self.conv1(x))
out = self.relu1(out)
out = self.bn2(self.conv2(out))
if self.shortcut is not None:
out += self.shortcut(x)
out = self.relu2(out)
output_do = F.dropout(out,p=self.p,training=True)
return output_do
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1,p=0.0):
super(Bottleneck, self).__init__()
self.p = p
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion*planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
#output_do = F.dropout(out,p=self.p,trainig=True)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10,p=0.0):
super(ResNet, self).__init__()
self.in_planes = 64
self.p=p
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.linear = nn.Linear(512*block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x, out_feature=False):
x = self.conv1(x)
x = self.bn1(x)
out0 = F.relu(x)
#out = F.dropout(out,p=self.p,training=True)
out = self.layer1(out0)
out1 = self.layer2(out)
out2 = self.layer3(out1)
out3 = self.layer4(out2)
#out = F.avg_pool2d(out, 4)
out4 = self.avgpool(out3)
#out = F.dropout(out,p=self.p,training=True)
'''
print("layer 1 ",out.shape)
print("layer 2 ",out1.shape)
print("layer 3 : ",out2.shape)
print("layer 4 : ",out3.shape)
print("avgpool : ",out4.shape)
'''
feature = out4.view(out4.size(0), -1)
img = self.linear(feature)
if out_feature == False:
return img, out3, out2, out1, out, out0
else:
return img, feature, out3, out2, out1, out, out0
def ResNet18(num_classes=10):
return ResNet(BasicBlock, [2,2,2,2], num_classes)
def ResNet34(num_classes=10,p=0.0):
return ResNet(BasicBlock, [3,4,6,3], num_classes,p)
def ResNet50(num_classes=10):
return ResNet(Bottleneck, [3,4,6,3], num_classes)
def ResNet101(num_classes=10):
return ResNet(Bottleneck, [3,4,23,3], num_classes)
def ResNet152(num_classes=10):
return ResNet(Bottleneck, [3,8,36,3], num_classes)