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| """ | |
| Credit to https://github.com/XingangPan/IBN-Net. | |
| """ | |
| from __future__ import division, absolute_import | |
| import math | |
| import torch.nn as nn | |
| import torch.utils.model_zoo as model_zoo | |
| __all__ = ['resnet50_ibn_b'] | |
| model_urls = { | |
| '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): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = nn.BatchNorm2d(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): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, IN=False): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.conv2 = nn.Conv2d( | |
| planes, | |
| planes, | |
| kernel_size=3, | |
| stride=stride, | |
| padding=1, | |
| bias=False | |
| ) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.conv3 = nn.Conv2d( | |
| planes, planes * self.expansion, kernel_size=1, bias=False | |
| ) | |
| self.bn3 = nn.BatchNorm2d(planes * self.expansion) | |
| self.IN = None | |
| if IN: | |
| self.IN = nn.InstanceNorm2d(planes * 4, affine=True) | |
| self.relu = nn.ReLU(inplace=True) | |
| 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) | |
| out = self.relu(out) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| if self.IN is not None: | |
| out = self.IN(out) | |
| out = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| """Residual network + IBN layer. | |
| Reference: | |
| - He et al. Deep Residual Learning for Image Recognition. CVPR 2016. | |
| - Pan et al. Two at Once: Enhancing Learning and Generalization | |
| Capacities via IBN-Net. ECCV 2018. | |
| """ | |
| def __init__( | |
| self, | |
| block, | |
| layers, | |
| num_classes=1000, | |
| loss='softmax', | |
| fc_dims=None, | |
| dropout_p=None, | |
| **kwargs | |
| ): | |
| scale = 64 | |
| self.inplanes = scale | |
| super(ResNet, self).__init__() | |
| self.loss = loss | |
| self.feature_dim = scale * 8 * block.expansion | |
| self.conv1 = nn.Conv2d( | |
| 3, scale, kernel_size=7, stride=2, padding=3, bias=False | |
| ) | |
| self.bn1 = nn.InstanceNorm2d(scale, affine=True) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer( | |
| block, scale, layers[0], stride=1, IN=True | |
| ) | |
| self.layer2 = self._make_layer( | |
| block, scale * 2, layers[1], stride=2, IN=True | |
| ) | |
| self.layer3 = self._make_layer(block, scale * 4, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, scale * 8, layers[3], stride=2) | |
| self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | |
| self.fc = self._construct_fc_layer( | |
| fc_dims, scale * 8 * block.expansion, dropout_p | |
| ) | |
| self.classifier = nn.Linear(self.feature_dim, 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, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.InstanceNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, block, planes, blocks, stride=1, IN=False): | |
| 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 | |
| ), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks - 1): | |
| layers.append(block(self.inplanes, planes)) | |
| layers.append(block(self.inplanes, planes, IN=IN)) | |
| 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 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.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) | |
| def resnet50_ibn_b(num_classes, loss='softmax', pretrained=False, **kwargs): | |
| model = ResNet( | |
| Bottleneck, [3, 4, 6, 3], num_classes=num_classes, loss=loss, **kwargs | |
| ) | |
| if pretrained: | |
| init_pretrained_weights(model, model_urls['resnet50']) | |
| return model | |