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fpn_resnet.py
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fpn_resnet.py
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import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import torch
__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/resnet101-5d3mb4d8f.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):
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, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
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, num_classes=7, scale=1):
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)
self.relu1 = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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=2)
# self.avgpool = nn.AvgPool2d(7, stride=1)
# self.fc = nn.Linear(512 * block.expansion, num_classes)
# Top layer
self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0) # Reduce channels
self.toplayer_bn = nn.BatchNorm2d(256)
self.toplayer_relu = nn.ReLU(inplace=True)
# Smooth layers
self.smooth1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smooth1_bn = nn.BatchNorm2d(256)
self.smooth1_relu = nn.ReLU(inplace=True)
self.smooth2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smooth2_bn = nn.BatchNorm2d(256)
self.smooth2_relu = nn.ReLU(inplace=True)
self.smooth3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smooth3_bn = nn.BatchNorm2d(256)
self.smooth3_relu = nn.ReLU(inplace=True)
# Lateral layers
self.latlayer1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
self.latlayer1_bn = nn.BatchNorm2d(256)
self.latlayer1_relu = nn.ReLU(inplace=True)
self.latlayer2 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
self.latlayer2_bn = nn.BatchNorm2d(256)
self.latlayer2_relu = nn.ReLU(inplace=True)
self.latlayer3 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
self.latlayer3_bn = nn.BatchNorm2d(256)
self.latlayer3_relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(1024, 256, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(256)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(256, num_classes, kernel_size=1, stride=1, padding=0)
self.scale = scale
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, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
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):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _upsample(self, x, y, scale=1):
_, _, H, W = y.size()
return F.upsample(x, size=(H // scale, W // scale), mode='bilinear')
def _upsample_add(self, x, y):
_, _, H, W = y.size()
return F.upsample(x, size=(H, W), mode='bilinear') + y
def forward(self, x):
h = x
h = self.conv1(h)
h = self.bn1(h)
h = self.relu1(h)
h = self.maxpool(h)
h = self.layer1(h)
c2 = h
h = self.layer2(h)
c3 = h
h = self.layer3(h)
c4 = h
h = self.layer4(h)
c5 = h
# Top-down
p5 = self.toplayer(c5)
p5 = self.toplayer_relu(self.toplayer_bn(p5))
c4 = self.latlayer1(c4)
c4 = self.latlayer1_relu(self.latlayer1_bn(c4))
p4 = self._upsample_add(p5, c4)
p4 = self.smooth1(p4)
p4 = self.smooth1_relu(self.smooth1_bn(p4))
c3 = self.latlayer2(c3)
c3 = self.latlayer2_relu(self.latlayer2_bn(c3))
p3 = self._upsample_add(p4, c3)
p3 = self.smooth2(p3)
p3 = self.smooth2_relu(self.smooth2_bn(p3))
c2 = self.latlayer3(c2)
c2 = self.latlayer3_relu(self.latlayer3_bn(c2))
p2 = self._upsample_add(p3, c2)
p2 = self.smooth3(p2)
p2 = self.smooth3_relu(self.smooth3_bn(p2))
p3 = self._upsample(p3, p2)
p4 = self._upsample(p4, p2)
p5 = self._upsample(p5, p2)
out = torch.cat((p2, p3, p4, p5), 1)
out = self.conv2(out)
out = self.relu2(self.bn2(out))
out = self.conv3(out)
out = self._upsample(out, x, scale=self.scale)
return out
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
pretrained_model = model_zoo.load_url(model_urls['resnet50'])
state = model.state_dict()
for key in state.keys():
if key in pretrained_model.keys():
state[key] = pretrained_model[key]
model.load_state_dict(state)
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
pretrained_model = model_zoo.load_url(model_urls['resnet101'])
state = model.state_dict()
for key in state.keys():
if key in pretrained_model.keys():
state[key] = pretrained_model[key]
model.load_state_dict(state)
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
pretrained_model = model_zoo.load_url(model_urls['resnet152'])
state = model.state_dict()
for key in state.keys():
if key in pretrained_model.keys():
state[key] = pretrained_model[key]
model.load_state_dict(state)
return model