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resnet_model.py
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resnet_model.py
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import torch.nn as nn
import torch
import math
from collections import OrderedDict
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
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',
}
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):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, last_layer=False):
super(BasicBlock, self).__init__()
m = OrderedDict()
m['conv1'] = conv3x3(inplanes, planes, stride)
m['bn1'] = nn.BatchNorm2d(planes)
m['relu1'] = nn.ReLU(inplace=True)
m['conv2'] = conv3x3(planes, planes)
m['bn2'] = nn.BatchNorm2d(planes)
self.group1 = nn.Sequential(m)
self.relu = nn.Sequential(nn.ReLU(inplace=True))
self.downsample = downsample
self.last_layer = last_layer
def forward(self, x):
if self.downsample is not None:
residual = self.downsample(x)
else:
residual = x
feature = self.group1(x)
out = feature + residual
out = self.relu(out)
if self.last_layer == False:
return out
else:
return out, out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, last_layer=False):
super(Bottleneck, self).__init__()
m = OrderedDict()
m['conv1'] = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
m['bn1'] = nn.BatchNorm2d(planes)
m['relu1'] = nn.ReLU(inplace=True)
m['conv2'] = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
m['bn2'] = nn.BatchNorm2d(planes)
m['relu2'] = nn.ReLU(inplace=True)
m['conv3'] = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
m['bn3'] = nn.BatchNorm2d(planes * 4)
self.group1 = nn.Sequential(m)
self.relu= nn.Sequential(nn.ReLU(inplace=True))
self.downsample = downsample
self.last_layer = last_layer
def forward(self, x):
if self.downsample is not None:
residual = self.downsample(x)
else:
residual = x
out = self.group1(x) + residual
out = self.relu(out)
if self.last_layer == False:
return out
else:
return out, out
class ResNet(nn.Module):
def __init__(self, block, layers):
self.inplanes = 64
super(ResNet, self).__init__()
m = OrderedDict()
m['conv1'] = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=True)
m['bn1'] = nn.BatchNorm2d(64)
m['relu1'] = nn.ReLU(inplace=True)
m['maxpool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.group1 = nn.Sequential(m)
self.layer1 = self._make_layer(block, 64, layers[0], isAttn=False)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, isAttn=True)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, isAttn=True)
self.layer4_0 = self._make_layer4(block, 512, stride=2, flag=True)
self.layer4_1 = self._make_layer4(block, 512, stride=1, flag=True)
self.layer4_2 = self._make_layer4(block, 512, stride=1, flag=False)
def _make_layer(self, block, planes, blocks, stride=1, isAttn=False):
downsample = None
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, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
if isAttn == True:
if i == blocks - 1:
layers.append(block(self.inplanes, planes, last_layer=True))
else:
layers.append(block(self.inplanes, planes, last_layer=False))
else:
layers.append(block(self.inplanes, planes, last_layer=False))
return nn.Sequential(*layers)
def _make_layer4(self, block, planes, stride=1, flag=False):
downsample = None
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, downsample, last_layer=flag))
self.inplanes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
x = self.group1(x)
x = self.layer1(x)
x, fea2 = self.layer2(x)
x, fea3 = self.layer3(x)
x, fea4_0 = self.layer4_0(x)
x, fea4_1 = self.layer4_1(x)
fea4_2 = self.layer4_2(x)
return fea4_0, fea4_1, fea4_2
def load_state_dict(model, model_root):
from torch import nn
import re
from collections import OrderedDict
own_state_old = model.state_dict()
own_state = OrderedDict() # remove all 'group' string
for k, v in own_state_old.items():
k = re.sub('group\d+\.', '', k)
own_state[k] = v
state_dict = torch.load(model_root)
for name, param in state_dict.items():
if name not in own_state:
if 'fc' in name:
continue
if 'layer4.0' in name or 'layer4.1' in name or 'layer4.2' in name:
name = 'layer4_' + name.split(".", 2)[1] + '.0.' + name.split(".", 2)[2]
else:
print(own_state.keys())
raise KeyError('unexpected key "{}" in state_dict'
.format(name))
if isinstance(param, nn.Parameter):
# backwards compatibility for serialized parameters
param = param.data
own_state[name].copy_(param)
def resnet18(pretrained=False, model_root=None):
model = ResNet(BasicBlock, [2, 2, 2, 2])
if pretrained:
load_state_dict(model, model_root=model_root)
return model
def resnet34(pretrained=False, model_root=None):
model = ResNet(BasicBlock, [3, 4, 6, 3])
if pretrained:
load_state_dict(model, model_root=model_root)
return model
def resnet50(pretrained=False, model_root=None):
model = ResNet(Bottleneck, [3, 4, 6, 3])
if pretrained:
load_state_dict(model, model_root=model_root)
return model
def resnet101(pretrained=False, model_root=None):
model = ResNet(Bottleneck, [3, 4, 23, 3])
if pretrained:
load_state_dict(model, model_root=model_root)
return model
def resnet152(pretrained=False, model_root=None):
model = ResNet(Bottleneck, [3, 8, 36, 3])
if pretrained:
load_state_dict(model, model_root=model_root)
return model