import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url
__all__ = ['get_resnet', 'BasicBlock']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.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, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
# if dilation > 1:
# raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, dilation=dilation)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None, out_keys=None, in_channels=3, **kwargs):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.out_keys = out_keys
self.num_classes = num_classes
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(in_channels, self.inplanes, kernel_size=7, stride=2, padding=3,
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])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])
if 'block5' in self.out_keys:
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
if self.num_classes is not None:
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, self.num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
endpoints = dict()
endpoints['block0'] = x
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
endpoints['block1'] = x
x = self.maxpool(x)
x = self.layer1(x)
endpoints['block2'] = x
x = self.layer2(x)
endpoints['block3'] = x
x = self.layer3(x)
endpoints['block4'] = x
if 'block5' in self.out_keys:
x = self.layer4(x)
endpoints['block5'] = x
if self.num_classes is not None:
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
if self.out_keys is not None:
endpoints = {key: endpoints[key] for key in self.out_keys}
return x, endpoints
def _resnet(arch, block, layers, pretrained, progress, num_classes=1000, in_channels=3, out_keys=None, **kwargs):
model = ResNet(block, layers, num_classes, out_keys=out_keys, in_channels=in_channels, **kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch],
progress=progress)
if in_channels != 3:
keys = state_dict.keys()
keys = [x for x in keys if 'conv1.weight' in x]
for key in keys:
del state_dict[key]
if num_classes !=1000:
keys = state_dict.keys()
keys = [x for x in keys if 'fc' in x]
for key in keys:
del state_dict[key]
if 'block5' not in out_keys:
keys = state_dict.keys()
keys = [x for x in keys if 'layer4' in x]
for key in keys:
del state_dict[key]
model.load_state_dict(state_dict)
print('load resnet model...')
return model
def _resnet18(name='resnet18', pretrained=True, progress=True, num_classes=1000, out_keys=None, **kwargs):
r"""ResNet-18 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet(name, BasicBlock, [2, 2, 2, 2], pretrained, progress,
num_classes=num_classes, out_keys=out_keys, **kwargs)
def _resnet50(name='resnet50',pretrained=False, progress=True,num_classes=1000,out_keys=None, **kwargs):
r"""ResNet-50 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet(name, Bottleneck, [3, 4, 6, 3], pretrained, progress,
num_classes=num_classes,out_keys=out_keys,
**kwargs)
def _resnet101(name='resnet101',pretrained=False, progress=True, num_classes=1000,out_keys=None,**kwargs):
r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet(name, Bottleneck, [3, 4, 23, 3], pretrained, progress,
num_classes=num_classes, out_keys=out_keys,
**kwargs)
def _resnet152(name='resnet152',pretrained=False, progress=True,num_classes=1000,out_keys=None,**kwargs):
r"""ResNet-152 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet(name, Bottleneck, [3, 8, 36, 3], pretrained, progress,
num_classes=num_classes, out_keys=out_keys,
**kwargs)
def get_resnet(model_name='resnet50', pretrained=True, progress=True, num_classes=1000, out_keys=None, in_channels=3, **kwargs):
'''
Get resnet model with name.
:param name: resnet model name, optional values:[resnet18, reset50, resnet101, resnet152]
:param pretrained: If True, returns a model pre-trained on ImageNet
'''
if pretrained and num_classes != 1000:
print('warning: num_class is not equal to 1000, which will cause some parameters to fail to load!')
if pretrained and in_channels != 3:
print('warning: in_channels is not equal to 3, which will cause some parameters to fail to load!')
if model_name == 'resnet18':
return _resnet18(name=model_name, pretrained=pretrained, progress=progress,
num_classes=num_classes, out_keys=out_keys, in_channels=in_channels, **kwargs)
elif model_name == 'resnet50':
return _resnet50(name=model_name, pretrained=pretrained, progress=progress,
num_classes=num_classes, out_keys=out_keys, in_channels=in_channels, **kwargs)
elif model_name == 'resnet101':
return _resnet101(name=model_name, pretrained=pretrained, progress=progress,
num_classes=num_classes, out_keys=out_keys, in_channels=in_channels, **kwargs)
elif model_name == 'resnet152':
return _resnet152(name=model_name, pretrained=pretrained, progress=progress,
num_classes=num_classes, out_keys=out_keys, in_channels=in_channels, **kwargs)
else:
raise NotImplementedError(r'''{0} is not an available values. \
Please choose one of the available values in
[resnet18, reset50, resnet101, resnet152]'''.format(name))
if __name__ == '__main__':
model = get_resnet('resnet18', pretrained=True, num_classes=None, in_channels=3, out_keys=['block4'])
x = torch.rand([2, 3, 256, 256])
torch.save(model.state_dict(), 'res18nofc.pth')