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110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
#!/usr/bin/python
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# -*- encoding: utf-8 -*-
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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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# from modules.bn import InPlaceABNSync as BatchNorm2d
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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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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) # 1/8
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feat16 = self.layer3(feat8) # 1/16
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feat32 = self.layer4(feat16) # 1/32
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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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