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import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from model.deep_lab_model.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):
import urllib.request
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
response = urllib.request.urlopen('https://download.pytorch.org/models/resnet101-5d3b4d8f.pth')
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:
# if 'conv1' in k:
# continue
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()) |