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Running
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A10G
| #!/usr/bin/python | |
| # -*- encoding: utf-8 -*- | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.utils.model_zoo as modelzoo | |
| # from modules.bn import InPlaceABNSync as BatchNorm2d | |
| 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, model_path): | |
| 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(model_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) # 1/8 | |
| feat16 = self.layer3(feat8) # 1/16 | |
| feat32 = self.layer4(feat16) # 1/32 | |
| return feat8, feat16, feat32 | |
| def init_weight(self, model_path): | |
| state_dict = torch.load(model_path) #modelzoo.load_url(resnet18_url) | |
| 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() | |