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| """ | |
| Code source: https://github.com/pytorch/vision | |
| """ | |
| from __future__ import division, absolute_import | |
| import torch | |
| import torch.nn as nn | |
| import torch.utils.model_zoo as model_zoo | |
| __all__ = ['squeezenet1_0', 'squeezenet1_1', 'squeezenet1_0_fc512'] | |
| model_urls = { | |
| 'squeezenet1_0': | |
| 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth', | |
| 'squeezenet1_1': | |
| 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth', | |
| } | |
| class Fire(nn.Module): | |
| def __init__( | |
| self, inplanes, squeeze_planes, expand1x1_planes, expand3x3_planes | |
| ): | |
| super(Fire, self).__init__() | |
| self.inplanes = inplanes | |
| self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1) | |
| self.squeeze_activation = nn.ReLU(inplace=True) | |
| self.expand1x1 = nn.Conv2d( | |
| squeeze_planes, expand1x1_planes, kernel_size=1 | |
| ) | |
| self.expand1x1_activation = nn.ReLU(inplace=True) | |
| self.expand3x3 = nn.Conv2d( | |
| squeeze_planes, expand3x3_planes, kernel_size=3, padding=1 | |
| ) | |
| self.expand3x3_activation = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| x = self.squeeze_activation(self.squeeze(x)) | |
| return torch.cat( | |
| [ | |
| self.expand1x1_activation(self.expand1x1(x)), | |
| self.expand3x3_activation(self.expand3x3(x)) | |
| ], 1 | |
| ) | |
| class SqueezeNet(nn.Module): | |
| """SqueezeNet. | |
| Reference: | |
| Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters | |
| and< 0.5 MB model size. arXiv:1602.07360. | |
| Public keys: | |
| - ``squeezenet1_0``: SqueezeNet (version=1.0). | |
| - ``squeezenet1_1``: SqueezeNet (version=1.1). | |
| - ``squeezenet1_0_fc512``: SqueezeNet (version=1.0) + FC. | |
| """ | |
| def __init__( | |
| self, | |
| num_classes, | |
| loss, | |
| version=1.0, | |
| fc_dims=None, | |
| dropout_p=None, | |
| **kwargs | |
| ): | |
| super(SqueezeNet, self).__init__() | |
| self.loss = loss | |
| self.feature_dim = 512 | |
| if version not in [1.0, 1.1]: | |
| raise ValueError( | |
| 'Unsupported SqueezeNet version {version}:' | |
| '1.0 or 1.1 expected'.format(version=version) | |
| ) | |
| if version == 1.0: | |
| self.features = nn.Sequential( | |
| nn.Conv2d(3, 96, kernel_size=7, stride=2), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), | |
| Fire(96, 16, 64, 64), | |
| Fire(128, 16, 64, 64), | |
| Fire(128, 32, 128, 128), | |
| nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), | |
| Fire(256, 32, 128, 128), | |
| Fire(256, 48, 192, 192), | |
| Fire(384, 48, 192, 192), | |
| Fire(384, 64, 256, 256), | |
| nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), | |
| Fire(512, 64, 256, 256), | |
| ) | |
| else: | |
| self.features = nn.Sequential( | |
| nn.Conv2d(3, 64, kernel_size=3, stride=2), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), | |
| Fire(64, 16, 64, 64), | |
| Fire(128, 16, 64, 64), | |
| nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), | |
| Fire(128, 32, 128, 128), | |
| Fire(256, 32, 128, 128), | |
| nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), | |
| Fire(256, 48, 192, 192), | |
| Fire(384, 48, 192, 192), | |
| Fire(384, 64, 256, 256), | |
| Fire(512, 64, 256, 256), | |
| ) | |
| self.global_avgpool = nn.AdaptiveAvgPool2d(1) | |
| self.fc = self._construct_fc_layer(fc_dims, 512, dropout_p) | |
| self.classifier = nn.Linear(self.feature_dim, num_classes) | |
| self._init_params() | |
| 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 forward(self, x): | |
| f = self.features(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, map_location=None) | |
| 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 squeezenet1_0(num_classes, loss='softmax', pretrained=True, **kwargs): | |
| model = SqueezeNet( | |
| num_classes, loss, version=1.0, fc_dims=None, dropout_p=None, **kwargs | |
| ) | |
| if pretrained: | |
| init_pretrained_weights(model, model_urls['squeezenet1_0']) | |
| return model | |
| def squeezenet1_0_fc512( | |
| num_classes, loss='softmax', pretrained=True, **kwargs | |
| ): | |
| model = SqueezeNet( | |
| num_classes, | |
| loss, | |
| version=1.0, | |
| fc_dims=[512], | |
| dropout_p=None, | |
| **kwargs | |
| ) | |
| if pretrained: | |
| init_pretrained_weights(model, model_urls['squeezenet1_0']) | |
| return model | |
| def squeezenet1_1(num_classes, loss='softmax', pretrained=True, **kwargs): | |
| model = SqueezeNet( | |
| num_classes, loss, version=1.1, fc_dims=None, dropout_p=None, **kwargs | |
| ) | |
| if pretrained: | |
| init_pretrained_weights(model, model_urls['squeezenet1_1']) | |
| return model | |