|
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 |
|
|
|
|
|
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._init_weight() |
|
|
|
|
|
|
|
|
|
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: |
|
|
|
|
|
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()) |
