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""" | |
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 | |
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 | |