"""
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
__all__ = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512'
]
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',
'resnext50_32x4d':
'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
'resnext101_32x8d':
'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.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)
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
[docs]class ResNet(nn.Module):
"""Residual network.
Reference:
- He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
- Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
Public keys:
- ``resnet18``: ResNet18.
- ``resnet34``: ResNet34.
- ``resnet50``: ResNet50.
- ``resnet101``: ResNet101.
- ``resnet152``: ResNet152.
- ``resnext50_32x4d``: ResNeXt50.
- ``resnext101_32x8d``: ResNeXt101.
- ``resnet50_fc512``: ResNet50 + FC.
"""
def __init__(
self,
num_classes,
loss,
block,
layers,
zero_init_residual=False,
groups=1,
width_per_group=64,
replace_stride_with_dilation=None,
norm_layer=None,
last_stride=2,
fc_dims=None,
dropout_p=None,
**kwargs
):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.loss = loss
self.feature_dim = 512 * block.expansion
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(
3, 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]
)
self.layer4 = self._make_layer(
block,
512,
layers[3],
stride=last_stride,
dilate=replace_stride_with_dilation[2]
)
self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = self._construct_fc_layer(
fc_dims, 512 * block.expansion, dropout_p
)
self.classifier = nn.Linear(self.feature_dim, num_classes)
self._init_params()
# 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 _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
"""Constructs fully connected layer
Args:
fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
input_dim (int): input dimension
dropout_p (float): dropout probability, if None, dropout is unused
"""
if fc_dims is None:
self.feature_dim = input_dim
return None
assert isinstance(
fc_dims, (list, tuple)
), 'fc_dims must be either list or tuple, but got {}'.format(
type(fc_dims)
)
layers = []
for dim in fc_dims:
layers.append(nn.Linear(input_dim, dim))
layers.append(nn.BatchNorm1d(dim))
layers.append(nn.ReLU(inplace=True))
if dropout_p is not None:
layers.append(nn.Dropout(p=dropout_p))
input_dim = dim
self.feature_dim = fc_dims[-1]
return nn.Sequential(*layers)
def _init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu'
)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def featuremaps(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 forward(self, x):
f = self.featuremaps(x)
v = self.global_avgpool(f)
v = v.view(v.size(0), -1)
if self.fc is not None:
v = self.fc(v)
if not self.training:
return v
y = self.classifier(v)
if self.loss == 'softmax':
return y
elif self.loss == 'triplet':
return y, v
else:
raise KeyError("Unsupported loss: {}".format(self.loss))
def init_pretrained_weights(model, model_url):
"""Initializes model with pretrained weights.
Layers that don't match with pretrained layers in name or size are kept unchanged.
"""
pretrain_dict = model_zoo.load_url(model_url)
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
"""ResNet"""
def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
loss=loss,
block=BasicBlock,
layers=[2, 2, 2, 2],
last_stride=2,
fc_dims=None,
dropout_p=None,
**kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['resnet18'])
return model
def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
loss=loss,
block=BasicBlock,
layers=[3, 4, 6, 3],
last_stride=2,
fc_dims=None,
dropout_p=None,
**kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['resnet34'])
return model
def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
loss=loss,
block=Bottleneck,
layers=[3, 4, 6, 3],
last_stride=2,
fc_dims=None,
dropout_p=None,
**kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['resnet50'])
return model
def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
loss=loss,
block=Bottleneck,
layers=[3, 4, 23, 3],
last_stride=2,
fc_dims=None,
dropout_p=None,
**kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['resnet101'])
return model
def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
loss=loss,
block=Bottleneck,
layers=[3, 8, 36, 3],
last_stride=2,
fc_dims=None,
dropout_p=None,
**kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['resnet152'])
return model
"""ResNeXt"""
def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
loss=loss,
block=Bottleneck,
layers=[3, 4, 6, 3],
last_stride=2,
fc_dims=None,
dropout_p=None,
groups=32,
width_per_group=4,
**kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['resnext50_32x4d'])
return model
def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
loss=loss,
block=Bottleneck,
layers=[3, 4, 23, 3],
last_stride=2,
fc_dims=None,
dropout_p=None,
groups=32,
width_per_group=8,
**kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['resnext101_32x8d'])
return model
"""
ResNet + FC
"""
def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
loss=loss,
block=Bottleneck,
layers=[3, 4, 6, 3],
last_stride=1,
fc_dims=[512],
dropout_p=None,
**kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['resnet50'])
return model