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import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import os
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
'resnet18stem': 'https://download.openmmlab.com/pretrain/third_party/resnet18_v1c-b5776b93.pth',
'resnet50stem': 'https://download.openmmlab.com/pretrain/third_party/resnet50_v1c-2cccc1ad.pth',
'resnet101stem': 'https://download.openmmlab.com/pretrain/third_party/resnet101_v1c-e67eebb6.pth',
}
def conv3x3(in_planes, outplanes, stride=1):
# 带padding的3*3卷积
return nn.Conv2d(in_planes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
"""
Basic Block for Resnet
"""
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1, dilation=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, 1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, 3, stride=stride, padding=dilation,
dilation=dilation, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes*self.expansion, 1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.relu = nn.ReLU(inplace=False)
self.relu_inplace = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
def forward(self, x):
residual = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu_inplace(out)
return out
class Resnet(nn.Module):
def __init__(self, block, layers, out_stride=8, use_stem=False, stem_channels=64, in_channels=3):
self.inplanes = 64
super(Resnet, self).__init__()
outstride_to_strides_and_dilations = {
8: ((1, 2, 1, 1), (1, 1, 2, 4)),
16: ((1, 2, 2, 1), (1, 1, 1, 2)),
32: ((1, 2, 2, 2), (1, 1, 1, 1)),
}
stride_list, dilation_list = outstride_to_strides_and_dilations[out_stride]
self.use_stem = use_stem
if use_stem:
self.stem = nn.Sequential(
conv3x3(in_channels, stem_channels//2, stride=2),
nn.BatchNorm2d(stem_channels//2),
nn.ReLU(inplace=False),
conv3x3(stem_channels//2, stem_channels//2),
nn.BatchNorm2d(stem_channels//2),
nn.ReLU(inplace=False),
conv3x3(stem_channels//2, stem_channels),
nn.BatchNorm2d(stem_channels),
nn.ReLU(inplace=False)
)
else:
self.conv1 = nn.Conv2d(in_channels, stem_channels, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(stem_channels)
self.relu = nn.ReLU(inplace=False)
# self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, blocks=layers[0], stride=stride_list[0], dilation=dilation_list[0])
self.layer2 = self._make_layer(block, 128, blocks=layers[1], stride=stride_list[1], dilation=dilation_list[1])
self.layer3 = self._make_layer(block, 256, blocks=layers[2], stride=stride_list[2], dilation=dilation_list[2])
self.layer4 = self._make_layer(block, 512, blocks=layers[3], stride=stride_list[3], dilation=dilation_list[3])
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, contract_dilation=True):
downsample = None
dilations = [dilation] * blocks
if contract_dilation and dilation > 1: dilations[0] = dilation // 2
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes*block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes*block.expansion)
)
layers = []
layers.append(block(self.inplanes, planes, stride, dilation=dilations[0], downsample=downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilations[i]))
return nn.Sequential(*layers)
def forward(self, x):
if self.use_stem:
x = self.stem(x)
else:
x = self.relu(self.bn1(self.conv1(x)))
x = self.maxpool(x)
x1 = self.layer1(x)
x2 = self.layer2(x1)
x3 = self.layer3(x2)
x4 = self.layer4(x3)
outs = [x1, x2, x3, x4]
return tuple(outs)
def get_resnet18(pretrained=True):
model = Resnet(BasicBlock, [2, 2, 2, 2], out_stride=32)
if pretrained:
checkpoint = model_zoo.load_url(model_urls['resnet18'])
if 'state_dict' in checkpoint: state_dict = checkpoint['state_dict']
else: state_dict = checkpoint
model.load_state_dict(state_dict, strict=False)
return model
def get_resnet50_OS8(pretrained=True):
model = Resnet(Bottleneck, [3, 4, 6, 3], out_stride=8, use_stem=True)
if pretrained:
checkpoint = model_zoo.load_url(model_urls['resnet50stem'])
if 'state_dict' in checkpoint: state_dict = checkpoint['state_dict']
else: state_dict = checkpoint
model.load_state_dict(state_dict, strict=False)
return model
def get_resnet50_OS32(pretrained=True):
model = Resnet(Bottleneck, [3, 4, 6, 3], out_stride=32, use_stem=False)
if pretrained:
checkpoint = model_zoo.load_url(model_urls['resnet50'])
if 'state_dict' in checkpoint: state_dict = checkpoint['state_dict']
else: state_dict = checkpoint
model.load_state_dict(state_dict, strict=False)
return model
if __name__ == "__main__":
model = get_resnet50_OS32()
x = torch.randn(4, 3, 256, 256)
x = model(x)[-1]
print(x.shape) |