163 lines
6.1 KiB
Python
163 lines
6.1 KiB
Python
import math
|
|
import torch.nn as nn
|
|
import torch.utils.model_zoo as model_zoo
|
|
from models.invertibility.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
|
|
|
|
|
|
class Bottleneck(nn.Module):
|
|
expansion = 4
|
|
|
|
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None):
|
|
super(Bottleneck, self).__init__()
|
|
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
|
|
self.bn1 = BatchNorm(planes)
|
|
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
|
|
dilation=dilation, padding=dilation, bias=False)
|
|
self.bn2 = BatchNorm(planes)
|
|
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
|
|
self.bn3 = BatchNorm(planes * 4)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
self.dilation = dilation
|
|
|
|
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, output_stride, BatchNorm, pretrained=True):
|
|
self.inplanes = 64
|
|
super(ResNet, self).__init__()
|
|
blocks = [1, 2, 4]
|
|
if output_stride == 16:
|
|
strides = [1, 2, 2, 1]
|
|
dilations = [1, 1, 1, 2]
|
|
elif output_stride == 8:
|
|
strides = [1, 2, 1, 1]
|
|
dilations = [1, 1, 2, 4]
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
# Modules
|
|
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
|
|
bias=False)
|
|
self.bn1 = BatchNorm(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], stride=strides[0], dilation=dilations[0], BatchNorm=BatchNorm)
|
|
self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1], BatchNorm=BatchNorm)
|
|
self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2], BatchNorm=BatchNorm)
|
|
self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
|
|
# self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
|
|
self._init_weight()
|
|
|
|
if pretrained:
|
|
self._load_pretrained_model()
|
|
|
|
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
|
|
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),
|
|
BatchNorm(planes * block.expansion),
|
|
)
|
|
|
|
layers = []
|
|
layers.append(block(self.inplanes, planes, stride, dilation, downsample, BatchNorm))
|
|
self.inplanes = planes * block.expansion
|
|
for i in range(1, blocks):
|
|
layers.append(block(self.inplanes, planes, dilation=dilation, BatchNorm=BatchNorm))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
|
|
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),
|
|
BatchNorm(planes * block.expansion),
|
|
)
|
|
|
|
layers = []
|
|
layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,
|
|
downsample=downsample, BatchNorm=BatchNorm))
|
|
self.inplanes = planes * block.expansion
|
|
for i in range(1, len(blocks)):
|
|
layers.append(block(self.inplanes, planes, stride=1,
|
|
dilation=blocks[i]*dilation, BatchNorm=BatchNorm))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def forward(self, input):
|
|
x = self.conv1(input)
|
|
x = self.bn1(x)
|
|
x = self.relu(x)
|
|
x = self.maxpool(x)
|
|
|
|
x = self.layer1(x)
|
|
low_level_feat = x
|
|
x = self.layer2(x)
|
|
x = self.layer3(x)
|
|
x = self.layer4(x)
|
|
return x, low_level_feat
|
|
|
|
def _init_weight(self):
|
|
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, SynchronizedBatchNorm2d):
|
|
m.weight.data.fill_(1)
|
|
m.bias.data.zero_()
|
|
elif isinstance(m, nn.BatchNorm2d):
|
|
m.weight.data.fill_(1)
|
|
m.bias.data.zero_()
|
|
|
|
def _load_pretrained_model(self):
|
|
pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/resnet101-5d3b4d8f.pth')
|
|
model_dict = {}
|
|
state_dict = self.state_dict()
|
|
for k, v in pretrain_dict.items():
|
|
if k in state_dict:
|
|
model_dict[k] = v
|
|
state_dict.update(model_dict)
|
|
self.load_state_dict(state_dict)
|
|
|
|
def ResNet101(output_stride, BatchNorm, pretrained=True):
|
|
"""Constructs a ResNet-101 model.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
"""
|
|
model = ResNet(Bottleneck, [3, 4, 23, 3], output_stride, BatchNorm, pretrained=pretrained)
|
|
return model
|
|
|
|
if __name__ == "__main__":
|
|
import torch
|
|
model = ResNet101(BatchNorm=nn.BatchNorm2d, pretrained=True, output_stride=8)
|
|
input = torch.rand(1, 3, 512, 512)
|
|
output, low_level_feat = model(input)
|
|
print(output.size())
|
|
print(low_level_feat.size()) |