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resnet.py
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resnet.py
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import torch
import torch.nn as nn
import math
from wsi.model.layers import CRF
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
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=1, num_nodes=1,
use_crf=True):
"""Constructs a ResNet model.
Args:
num_classes: int, since we are doing binary classification
(tumor vs normal), num_classes is set to 1 and sigmoid instead
of softmax is used later
num_nodes: int, number of nodes/patches within the fully CRF
use_crf: bool, use the CRF component or not
"""
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.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.crf = CRF(num_nodes) if use_crf else None
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):
"""
Args:
x: 5D tensor with shape of
[batch_size, grid_size, 3, crop_size, crop_size],
where grid_size is the number of patches within a grid (e.g. 9 for
a 3x3 grid); crop_size is 224 by default for ResNet input;
Returns:
logits, 2D tensor with shape of [batch_size, grid_size], the logit
of each patch within the grid being tumor
"""
batch_size, grid_size, _, crop_size = x.shape[0:4]
# flatten grid_size dimension and combine it into batch dimension
x = x.view(-1, 3, crop_size, crop_size)
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)
# feats means features, i.e. patch embeddings from ResNet
feats = x.view(x.size(0), -1)
logits = self.fc(feats)
# restore grid_size dimension for CRF
feats = feats.view((batch_size, grid_size, -1))
logits = logits.view((batch_size, grid_size, -1))
if self.crf:
logits = self.crf(feats, logits)
logits = torch.squeeze(logits)
return logits
def resnet18(**kwargs):
"""Constructs a ResNet-18 model.
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
return model
def resnet34(**kwargs):
"""Constructs a ResNet-34 model.
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
return model
def resnet50(**kwargs):
"""Constructs a ResNet-50 model.
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
return model
def resnet101(**kwargs):
"""Constructs a ResNet-101 model.
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
return model
def resnet152(**kwargs):
"""Constructs a ResNet-152 model.
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
return model