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