from torchvision import models
from .network import ConvUnit
import torch.nn as nn
# borrowed from https://github.com/gjy3035/C-3-Framework
[docs]class Res50(nn.Module):
"""Refer from `"C-3-Framework..." <https://github.com/gjy3035/C-3-Framework>`_ paper
Args:
pretrain (bool): if True, this model will be pre-trianed on ImageNet
"""
def __init__(self, pretrained=True):
super(Res50, self).__init__()
self.de_pred = nn.Sequential(ConvUnit(1024, 128, 1),
ConvUnit(128, 1, 1))
self._initialize_weights()
res = models.resnet50(pretrained=pretrained)
self.frontend = nn.Sequential(
res.conv1, res.bn1, res.relu, res.maxpool, res.layer1, res.layer2
)
self.own_reslayer_3 = make_res_layer(Bottleneck, 256, 6, stride=1)
self.own_reslayer_3.load_state_dict(res.layer3.state_dict())
def forward(self, x):
x = self.frontend(x)
x = self.own_reslayer_3(x)
x = self.de_pred(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0.0, std=0.01)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, nn.BatchNorm2d):
m.weight.fill_(1)
m.bias.data.fill_(0)
def make_res_layer(block, planes, blocks, stride=1):
downsample = None
inplanes = 512
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(inplanes, planes, stride, downsample))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(inplanes, planes))
return nn.Sequential(*layers)
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 * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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