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resnet.py
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"""Dilated ResNet"""
import math
import torch
import torch.utils.model_zoo as model_zoo
import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'BasicBlock', 'Bottleneck']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
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):
"""ResNet BasicBlock
"""
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, previous_dilation=1,
norm_layer=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
padding=previous_dilation, dilation=previous_dilation, bias=False)
self.bn2 = norm_layer(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):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1,
downsample=None, previous_dilation=1, 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.
Parameters
----------
block : Block
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
classes : int, default 1000
Number of classification classes.
dilated : bool, default False
Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
typically used in Semantic Segmentation.
norm_layer : object
Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
for Synchronized Cross-GPU BachNormalization).
Reference:
- He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
"""
# pylint: disable=unused-variable
def __init__(self, block, layers, num_classes=1000, dilated=True, norm_layer=nn.BatchNorm2d, multi_grid=False, multi_dilation=None):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = norm_layer(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], norm_layer=norm_layer)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
if dilated:
if multi_grid:
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,
multi_grid=multi_grid, multi_dilation=multi_dilation)
else:
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)
else:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
norm_layer=norm_layer)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
norm_layer=norm_layer)
self.avgpool = nn.AvgPool2d(7)
self.fc = nn.Linear(512 * block.expansion, num_classes)
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, multi_grid=False, multi_dilation=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 multi_grid == False:
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))
else:
layers.append(block(self.inplanes, planes, stride, dilation=multi_dilation[0],
downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer))
self.inplanes = planes * block.expansion
if multi_grid:
div = len(multi_dilation)
for i in range(1,blocks):
layers.append(block(self.inplanes, planes, dilation=multi_dilation[i%div], previous_dilation=dilation,
norm_layer=norm_layer))
else:
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)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False, root='./pretrain_models', **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:
# from ..models.model_store import get_model_file
# model.load_state_dict(torch.load(
# get_model_file('resnet50', root=root)), strict=False)
return model
def resnet101(pretrained=False, root='./pretrain_models', **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
#Remove the following lines of comments
#if u want to train from a pretrained model
if pretrained:
from ..models.model_store import get_model_file
model.load_state_dict(torch.load(
get_model_file('resnet101', root=root)), strict=False)
return model
def resnet152(pretrained=False, root='~/.encoding/models', **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
# model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
model.load_state_dict(torch.load(
'./pretrain_models/resnet152-b121ed2d.pth'), strict=False)
return model