123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109 |
- #!/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):
- 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()
- 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):
- state_dict = 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()
|