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resnet.py
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# Original code and checkpoints by Hang Zhang
# https://github.com/zhanghang1989/PyTorch-Encoding
import math
import torch
import os
import sys
import zipfile
import torch.utils.model_zoo as model_zoo
import torch.nn as nn
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
model_urls = {
'resnet50': 'https://s3.us-west-1.wasabisys.com/encoding/models/resnet50s-a75c83cf.zip',
}
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 Bottleneck(nn.Module):
"""
ResNet Bottleneck
"""
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, norm_layer=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = norm_layer(planes)
self.conv2 = nn.Conv2d(
planes, planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
self.bn2 = norm_layer(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = norm_layer(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
def _sum_each(self, x, y):
assert(len(x) == len(y))
z = []
for i in range(len(x)):
z.append(x[i]+y[i])
return z
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):
"""Dilated Pre-trained ResNet Model, which preduces the stride of 8 featuremaps at conv5.
Reference:
- He, Kaiming, et al. "Deep residual learning for image recognition." CVPR. 2016.
- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
"""
def __init__(self, block, layers, deep_base=True, norm_layer=nn.BatchNorm2d):
self.inplanes = 128 if deep_base else 64
super(ResNet, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False),
norm_layer(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
norm_layer(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
)
self.bn1 = norm_layer(self.inplanes)
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], norm_layer=norm_layer)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2, norm_layer=norm_layer)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, norm_layer=norm_layer)
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, norm_layer):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=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),
norm_layer(planes * block.expansion),
)
layers = []
if dilation == 1 or dilation == 2:
layers.append(block(self.inplanes, planes, stride, dilation=1,
downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer))
elif dilation == 4:
layers.append(block(self.inplanes, planes, stride, dilation=2, downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer))
else:
raise RuntimeError("=> unknown dilation size: {}".format(dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation, previous_dilation=dilation, norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']), strict=False)
return model
def load_url(url, model_dir='./pretrained', map_location=None):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
filename = url.split('/')[-1].split('.')[0]
cached_file = os.path.join(model_dir, filename+'.pth')
if not os.path.exists(cached_file):
cached_file = os.path.join(model_dir, filename+'.zip')
sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
urlretrieve(url, cached_file)
zip_ref = zipfile.ZipFile(cached_file, 'r')
zip_ref.extractall(model_dir)
zip_ref.close()
os.remove(cached_file)
cached_file = os.path.join(model_dir, filename+'.pth')
return torch.load(cached_file, map_location=map_location)