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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1):
conv = nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
)
return conv
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):
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.out_channels = 1
self.output0 = self._make_output(64, readout=self.out_channels)
self.output1 = self._make_output(256, readout=self.out_channels)
self.output2 = self._make_output(512, readout=self.out_channels)
self.output3 = self._make_output(1024, readout=self.out_channels)
self.output4 = self._make_output(2048, readout=self.out_channels)
self.combined = self._make_output(5, sigmoid=True)
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(2.0 / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_output(self, planes, readout=1, sigmoid=False):
layers = [
nn.Conv2d(planes, readout, kernel_size=3, padding=1),
nn.BatchNorm2d(readout),
]
if sigmoid:
layers.append(nn.Sigmoid())
else:
layers.append(nn.ReLU(inplace=True))
return nn.Sequential(*layers)
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 _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x, decode=False):
h, w = x.size(2), x.size(3)
x = self.conv1(x)
x = self.bn1(x)
out0 = self.relu(x)
x = self.maxpool(out0)
out1 = self.layer1(x)
out2 = self.layer2(out1)
out3 = self.layer3(out2)
out4 = self.layer4(out3)
out0 = self.output0(out0)
r, c = out0.size(2), out0.size(3)
out1 = self.output1(out1)
out2 = self.output2(out2)
out3 = self.output3(out3)
out4 = self.output4(out4)
if decode:
return [out0, out1, out2, out3, out4]
out1 = F.interpolate(out1, (r, c))
out2 = F.interpolate(out2, (r, c))
out3 = F.interpolate(out3, (r, c))
out4 = F.interpolate(out4, (r, c))
x = torch.cat([out0, out1, out2, out3, out4], dim=1)
x = self.combined(x)
x = F.interpolate(x, (h, w))
return x
def resnet50(model_path, **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if model_path is not None:
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
model_state = model.state_dict()
loaded_model = torch.load(model_path, weights_only=True)
if "state_dict" in loaded_model:
loaded_model = loaded_model["state_dict"]
pretrained = {k[7:]: v for k, v in loaded_model.items() if k[7:] in model_state}
if len(pretrained) == 0:
pretrained = {k: v for k, v in loaded_model.items() if k in model_state}
model_state.update(pretrained)
model.load_state_dict(model_state)
return model
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