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models.py
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models.py
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import math
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.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=1000):
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.relu = 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.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.fc.weight.data.normal_(0, 0.01)
self.fc.bias.data.fill_(0.0)
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 forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
def output_num(self):
return resnet50(True).fc.in_features
class ResNet50(nn.Module):
def __init__(self):
super(ResNet50, self).__init__()
self.encoder = resnet50(True)
def forward(self, x):
x = self.encoder(x)
return x
class Head(nn.Module):
def __init__(self):
super(Head, self).__init__()
self.head = nn.Sequential(
nn.Linear(2048, 256),
nn.ReLU()
)
init_weights(self)
def forward(self, x):
x = self.head(x)
return x
class EmpLearner(nn.Module):
def __init__(self):
super(EmpLearner, self).__init__()
self.emp_layer = nn.Sequential(
nn.Conv2d(2048 * 2, 2048 * 2, kernel_size=3, stride=1),
nn.BatchNorm2d(2048 * 2),
nn.ReLU(),
nn.Conv2d(2048 * 2, 2048, kernel_size=3, stride=1),
nn.BatchNorm2d(2048),
nn.ReLU(),
nn.Conv2d(2048, 2048, kernel_size=3, stride=1),
nn.BatchNorm2d(2048),
nn.ReLU(),
nn.Conv2d(in_channels=2048, out_channels=11, kernel_size=1)
)
init_weights(self)
def forward(self, x):
x = self.emp_layer(x)
x = x.view(x.size(0), -1)
return x
def init_weights(obj):
from torch.nn import init
for m in obj.modules():
if isinstance(m, nn.Conv2d):
# init.xavier_normal_(m.weight)
m.weight.data.normal_(0, 0.01).clamp_(min=-0.02, max=0.02)
try:
m.bias.data.zero_()
except AttributeError:
# no bias
pass
if isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01).clamp_(min=-0.02, max=0.02)
try:
m.bias.data.zero_()
except AttributeError:
# no bias
pass
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
m.reset_parameters()
elif isinstance(m, nn.Embedding):
init.normal_(m.weight, 0, 0.01)
def resnet50(pretrained=False, **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model