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import torch.nn as nn
import torch.nn.functional as F
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, stride=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, stride=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = F.relu(self.bn1(self.conv1(x)), inplace=True)
out = F.relu(self.bn2(self.conv2(out)), inplace=True)
out = self.bn3(self.conv3(out))
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = F.relu(out, inplace=True)
return out
class ResNet(nn.Module):
""" Resnet """
def __init__(self, architecture):
super(ResNet, self).__init__()
assert architecture in ["resnet50", "resnet101"]
self.inplanes = 64
self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3]
self.block = Bottleneck
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64, eps=1e-5, momentum=0.01, affine=True)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
self.layer1 = self.make_layer(self.block, 64, self.layers[0])
self.layer2 = self.make_layer(self.block, 128, self.layers[1], stride=2)
self.layer3 = self.make_layer(self.block, 256, self.layers[2], stride=2)
self.layer4 = self.make_layer(
self.block, 512, self.layers[3], stride=2)
def forward(self, x):
x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def stages(self):
return [self.layer1, self.layer2, self.layer3, self.layer4]
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)
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