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resnet.py
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resnet.py
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import torch.nn as nn
import math
import torch
import numpy as np
import torch.nn.functional as F
affine_par = True
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):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes, affine = affine_par)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes, affine = affine_par)
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, inplanes, planes, stride=1, dilation_ = 1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
self.bn1 = nn.BatchNorm2d(planes,affine = affine_par)
for i in self.bn1.parameters():
i.requires_grad = False
padding = 1
if dilation_ == 2:
padding = 2
elif dilation_ == 4:
padding = 4
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
padding=padding, bias=False, dilation = dilation_)
self.bn2 = nn.BatchNorm2d(planes,affine = affine_par)
for i in self.bn2.parameters():
i.requires_grad = False
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4, affine = affine_par)
for i in self.bn3.parameters():
i.requires_grad = False
self.relu = nn.ReLU(inplace=True)
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)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64,affine = affine_par)
for i in self.bn1.parameters():
i.requires_grad = False
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__ = 2)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, 0.01)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1,dilation__ = 1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion,affine = affine_par),
)
for i in downsample._modules['1'].parameters():
i.requires_grad = False
layers = []
layers.append(block(self.inplanes, planes, stride,dilation_=dilation__, downsample = downsample ))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,dilation_=dilation__))
return nn.Sequential(*layers)
def forward(self, x):
tmp_x = []
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
tmp_x.append(x)
x = self.maxpool(x)
x = self.layer1(x)
tmp_x.append(x)
x = self.layer2(x)
tmp_x.append(x)
x = self.layer3(x)
tmp_x.append(x)
x = self.layer4(x)
tmp_x.append(x)
return tmp_x
class ResNet_locate(nn.Module):
def __init__(self, block, layers):
super(ResNet_locate,self).__init__()
self.resnet = ResNet(block, layers)
self.in_planes = 512
self.out_planes = [512, 256, 256, 128]
self.ppms_pre = nn.Conv2d(2048, self.in_planes, 1, 1, bias=False)
ppms, infos = [], []
for ii in [1, 3, 5]:
ppms.append(nn.Sequential(nn.AdaptiveAvgPool2d(ii), nn.Conv2d(self.in_planes, self.in_planes, 1, 1, bias=False), nn.ReLU(inplace=True)))
self.ppms = nn.ModuleList(ppms)
self.ppm_cat = nn.Sequential(nn.Conv2d(self.in_planes * 4, self.in_planes, 3, 1, 1, bias=False), nn.ReLU(inplace=True))
for ii in self.out_planes:
infos.append(nn.Sequential(nn.Conv2d(self.in_planes, ii, 3, 1, 1, bias=False), nn.ReLU(inplace=True)))
self.infos = nn.ModuleList(infos)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, 0.01)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def load_pretrained_model(self, model):
self.resnet.load_state_dict(model)
def forward(self, x):
x_size = x.size()[2:]
xs = self.resnet(x)
xs_1 = self.ppms_pre(xs[-1])
xls = [xs_1]
for k in range(len(self.ppms)):
xls.append(F.interpolate(self.ppms[k](xs_1), xs_1.size()[2:], mode='bilinear', align_corners=True))
xls = self.ppm_cat(torch.cat(xls, dim=1))
top_score = None
infos = []
for k in range(len(self.infos)):
infos.append(self.infos[k](F.interpolate(xls, xs[len(self.infos) - 1 - k].size()[2:], mode='bilinear', align_corners=True)))
return xs, top_score, infos
class BottleneckEZ(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation_ = 1, downsample=None):
super(BottleneckEZ, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
padding = 1
if dilation_ == 2:
padding = 2
elif dilation_ == 4:
padding = 4
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
padding=padding, bias=False, dilation = dilation_)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.relu(out)
out = self.conv2(out)
out = self.relu(out)
out = self.conv3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
def resnet50(pretrained=False):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(Bottleneck, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
return model
def resnet101(pretrained=False):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet_locate(Bottleneck, [3, 4, 23, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model