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import math
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
import torch.utils.checkpoint as cp
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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, with_cp=False):
super(BasicBlock, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.with_cp = with_cp
def forward(self, x):
def _inner_forward(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
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, output_channels=512):
super(ResNet, self).__init__()
channels = [output_channels//(2**i) for i in reversed(range(5))]
self.inplanes = channels[0]
self.conv1 = nn.Conv2d(3, channels[0], kernel_size=3, stride=1, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(channels[0])
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, channels[0], layers[0], stride=2)
self.layer2 = self._make_layer(block, channels[1], layers[1], stride=1)
self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2)
self.layer4 = self._make_layer(block, channels[3], layers[3], stride=1)
self.layer5 = self._make_layer(block, channels[4], layers[4], stride=1)
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, extra_feats=None):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
if extra_feats is not None:
if extra_feats[0].shape[1]>0:
x = x+F.interpolate(extra_feats[0], x.shape[2:], mode='nearest')
x = self.layer1(x)
if extra_feats is not None:
if extra_feats[1].shape[1]>0:
x = x+F.interpolate(extra_feats[1], x.shape[2:], mode='nearest')
x = self.layer2(x)
if extra_feats is not None:
if extra_feats[2].shape[1]>0:
x = x+F.interpolate(extra_feats[2], x.shape[2:], mode='nearest')
x = self.layer3(x)
if extra_feats is not None:
if extra_feats[3].shape[1]>0:
x = x+F.interpolate(extra_feats[3], x.shape[2:], mode='nearest')
x = self.layer4(x)
if extra_feats is not None:
if extra_feats[4].shape[1]>0:
x = x+F.interpolate(extra_feats[4], x.shape[2:], mode='nearest')
x = self.layer5(x)
if extra_feats is not None:
if extra_feats[5].shape[1]>0:
x = x+F.interpolate(extra_feats[5], x.shape[2:], mode='nearest')
return x
def resnet45(alpha_d, output_channels=512):
layers = [int(round(x*alpha_d)) for x in [3, 4, 6, 6, 3]]
return ResNet(BasicBlock, layers, output_channels=output_channels)
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