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| | import torch
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| | import torch.nn as nn
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| | import torch.nn.functional as F
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| | import torch.utils.model_zoo as modelzoo
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| | resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
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| | def conv3x3(in_planes, out_planes, stride=1):
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| | """3x3 convolution with padding"""
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| | return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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| | padding=1, bias=False)
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| |
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| | class BasicBlock(nn.Module):
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| | def __init__(self, in_chan, out_chan, stride=1):
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| | super(BasicBlock, self).__init__()
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| | self.conv1 = conv3x3(in_chan, out_chan, stride)
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| | self.bn1 = nn.BatchNorm2d(out_chan)
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| | self.conv2 = conv3x3(out_chan, out_chan)
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| | self.bn2 = nn.BatchNorm2d(out_chan)
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| | self.relu = nn.ReLU(inplace=True)
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| | self.downsample = None
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| | if in_chan != out_chan or stride != 1:
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| | self.downsample = nn.Sequential(
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| | nn.Conv2d(in_chan, out_chan,
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| | kernel_size=1, stride=stride, bias=False),
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| | nn.BatchNorm2d(out_chan),
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| | )
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| | def forward(self, x):
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| | residual = self.conv1(x)
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| | residual = F.relu(self.bn1(residual))
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| | residual = self.conv2(residual)
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| | residual = self.bn2(residual)
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| | shortcut = x
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| | if self.downsample is not None:
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| | shortcut = self.downsample(x)
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| | out = shortcut + residual
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| | out = self.relu(out)
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| | return out
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| | def create_layer_basic(in_chan, out_chan, bnum, stride=1):
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| | layers = [BasicBlock(in_chan, out_chan, stride=stride)]
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| | for i in range(bnum-1):
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| | layers.append(BasicBlock(out_chan, out_chan, stride=1))
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| | return nn.Sequential(*layers)
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| | class Resnet18(nn.Module):
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| | def __init__(self):
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| | super(Resnet18, self).__init__()
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| | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
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| | bias=False)
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| | self.bn1 = nn.BatchNorm2d(64)
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| | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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| | self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
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| | self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
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| | self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
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| | self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
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| | self.init_weight()
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| | def forward(self, x):
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| | x = self.conv1(x)
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| | x = F.relu(self.bn1(x))
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| | x = self.maxpool(x)
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| | x = self.layer1(x)
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| | feat8 = self.layer2(x)
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| | feat16 = self.layer3(feat8)
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| | feat32 = self.layer4(feat16)
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| | return feat8, feat16, feat32
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| | def init_weight(self):
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| | state_dict = modelzoo.load_url(resnet18_url)
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| | self_state_dict = self.state_dict()
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| | for k, v in state_dict.items():
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| | if 'fc' in k: continue
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| | self_state_dict.update({k: v})
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| | self.load_state_dict(self_state_dict)
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| | def get_params(self):
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| | wd_params, nowd_params = [], []
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| | for name, module in self.named_modules():
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| | if isinstance(module, (nn.Linear, nn.Conv2d)):
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| | wd_params.append(module.weight)
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| | if not module.bias is None:
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| | nowd_params.append(module.bias)
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| | elif isinstance(module, nn.BatchNorm2d):
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| | nowd_params += list(module.parameters())
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| | return wd_params, nowd_params
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| | if __name__ == "__main__":
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| | net = Resnet18()
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| | x = torch.randn(16, 3, 224, 224)
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| | out = net(x)
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| | print(out[0].size())
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| | print(out[1].size())
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| | print(out[2].size())
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| | net.get_params()
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