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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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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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