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Running
on
A10G
""" | |
resnet.py - A modified ResNet structure | |
We append extra channels to the first conv by some network surgery | |
""" | |
from collections import OrderedDict | |
import math | |
import torch | |
import torch.nn as nn | |
from torch.utils import model_zoo | |
def load_weights_add_extra_dim(target, source_state, extra_dim=1): | |
new_dict = OrderedDict() | |
for k1, v1 in target.state_dict().items(): | |
if not 'num_batches_tracked' in k1: | |
if k1 in source_state: | |
tar_v = source_state[k1] | |
if v1.shape != tar_v.shape: | |
# Init the new segmentation channel with zeros | |
# print(v1.shape, tar_v.shape) | |
c, _, w, h = v1.shape | |
pads = torch.zeros((c, extra_dim, w, h), device=tar_v.device) | |
nn.init.orthogonal_(pads) | |
tar_v = torch.cat([tar_v, pads], 1) | |
new_dict[k1] = tar_v | |
target.load_state_dict(new_dict) | |
model_urls = { | |
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', | |
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', | |
} | |
def conv3x3(in_planes, out_planes, stride=1, dilation=1): | |
return nn.Conv2d(in_planes, | |
out_planes, | |
kernel_size=3, | |
stride=stride, | |
padding=dilation, | |
dilation=dilation, | |
bias=False) | |
class BasicBlock(nn.Module): | |
expansion = 1 | |
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): | |
super(BasicBlock, self).__init__() | |
self.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation) | |
self.bn1 = nn.BatchNorm2d(planes) | |
self.relu = nn.ReLU(inplace=True) | |
self.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation) | |
self.bn2 = nn.BatchNorm2d(planes) | |
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) | |
if self.downsample is not None: | |
residual = self.downsample(x) | |
out += residual | |
out = self.relu(out) | |
return out | |
class Bottleneck(nn.Module): | |
expansion = 4 | |
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): | |
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, | |
dilation=dilation, | |
padding=dilation, | |
bias=False) | |
self.bn2 = nn.BatchNorm2d(planes) | |
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
self.bn3 = nn.BatchNorm2d(planes * 4) | |
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 | |
class ResNet(nn.Module): | |
def __init__(self, block, layers=(3, 4, 23, 3), extra_dim=0): | |
self.inplanes = 64 | |
super(ResNet, self).__init__() | |
self.conv1 = nn.Conv2d(3 + extra_dim, 64, kernel_size=7, stride=2, padding=3, bias=False) | |
self.bn1 = nn.BatchNorm2d(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]) | |
self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
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, dilation=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 = [block(self.inplanes, planes, stride, downsample)] | |
self.inplanes = planes * block.expansion | |
for i in range(1, blocks): | |
layers.append(block(self.inplanes, planes, dilation=dilation)) | |
return nn.Sequential(*layers) | |
def resnet18(pretrained=True, extra_dim=0): | |
model = ResNet(BasicBlock, [2, 2, 2, 2], extra_dim) | |
if pretrained: | |
load_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet18']), extra_dim) | |
return model | |
def resnet50(pretrained=True, extra_dim=0): | |
model = ResNet(Bottleneck, [3, 4, 6, 3], extra_dim) | |
if pretrained: | |
load_weights_add_extra_dim(model, model_zoo.load_url(model_urls['resnet50']), extra_dim) | |
return model | |