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| from __future__ import division, absolute_import | |
| import torch | |
| import torch.nn as nn | |
| import torch.utils.model_zoo as model_zoo | |
| __all__ = ['inceptionv4'] | |
| """ | |
| Code imported from https://github.com/Cadene/pretrained-models.pytorch | |
| """ | |
| pretrained_settings = { | |
| 'inceptionv4': { | |
| 'imagenet': { | |
| 'url': | |
| 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth', | |
| 'input_space': 'RGB', | |
| 'input_size': [3, 299, 299], | |
| 'input_range': [0, 1], | |
| 'mean': [0.5, 0.5, 0.5], | |
| 'std': [0.5, 0.5, 0.5], | |
| 'num_classes': 1000 | |
| }, | |
| 'imagenet+background': { | |
| 'url': | |
| 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth', | |
| 'input_space': 'RGB', | |
| 'input_size': [3, 299, 299], | |
| 'input_range': [0, 1], | |
| 'mean': [0.5, 0.5, 0.5], | |
| 'std': [0.5, 0.5, 0.5], | |
| 'num_classes': 1001 | |
| } | |
| } | |
| } | |
| class BasicConv2d(nn.Module): | |
| def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): | |
| super(BasicConv2d, self).__init__() | |
| self.conv = nn.Conv2d( | |
| in_planes, | |
| out_planes, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=padding, | |
| bias=False | |
| ) # verify bias false | |
| self.bn = nn.BatchNorm2d( | |
| out_planes, | |
| eps=0.001, # value found in tensorflow | |
| momentum=0.1, # default pytorch value | |
| affine=True | |
| ) | |
| self.relu = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| x = self.relu(x) | |
| return x | |
| class Mixed_3a(nn.Module): | |
| def __init__(self): | |
| super(Mixed_3a, self).__init__() | |
| self.maxpool = nn.MaxPool2d(3, stride=2) | |
| self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2) | |
| def forward(self, x): | |
| x0 = self.maxpool(x) | |
| x1 = self.conv(x) | |
| out = torch.cat((x0, x1), 1) | |
| return out | |
| class Mixed_4a(nn.Module): | |
| def __init__(self): | |
| super(Mixed_4a, self).__init__() | |
| self.branch0 = nn.Sequential( | |
| BasicConv2d(160, 64, kernel_size=1, stride=1), | |
| BasicConv2d(64, 96, kernel_size=3, stride=1) | |
| ) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(160, 64, kernel_size=1, stride=1), | |
| BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)), | |
| BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)), | |
| BasicConv2d(64, 96, kernel_size=(3, 3), stride=1) | |
| ) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| out = torch.cat((x0, x1), 1) | |
| return out | |
| class Mixed_5a(nn.Module): | |
| def __init__(self): | |
| super(Mixed_5a, self).__init__() | |
| self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2) | |
| self.maxpool = nn.MaxPool2d(3, stride=2) | |
| def forward(self, x): | |
| x0 = self.conv(x) | |
| x1 = self.maxpool(x) | |
| out = torch.cat((x0, x1), 1) | |
| return out | |
| class Inception_A(nn.Module): | |
| def __init__(self): | |
| super(Inception_A, self).__init__() | |
| self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(384, 64, kernel_size=1, stride=1), | |
| BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1) | |
| ) | |
| self.branch2 = nn.Sequential( | |
| BasicConv2d(384, 64, kernel_size=1, stride=1), | |
| BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1), | |
| BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1) | |
| ) | |
| self.branch3 = nn.Sequential( | |
| nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), | |
| BasicConv2d(384, 96, kernel_size=1, stride=1) | |
| ) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| x2 = self.branch2(x) | |
| x3 = self.branch3(x) | |
| out = torch.cat((x0, x1, x2, x3), 1) | |
| return out | |
| class Reduction_A(nn.Module): | |
| def __init__(self): | |
| super(Reduction_A, self).__init__() | |
| self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(384, 192, kernel_size=1, stride=1), | |
| BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1), | |
| BasicConv2d(224, 256, kernel_size=3, stride=2) | |
| ) | |
| self.branch2 = nn.MaxPool2d(3, stride=2) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| x2 = self.branch2(x) | |
| out = torch.cat((x0, x1, x2), 1) | |
| return out | |
| class Inception_B(nn.Module): | |
| def __init__(self): | |
| super(Inception_B, self).__init__() | |
| self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(1024, 192, kernel_size=1, stride=1), | |
| BasicConv2d( | |
| 192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3) | |
| ), | |
| BasicConv2d( | |
| 224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0) | |
| ) | |
| ) | |
| self.branch2 = nn.Sequential( | |
| BasicConv2d(1024, 192, kernel_size=1, stride=1), | |
| BasicConv2d( | |
| 192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0) | |
| ), | |
| BasicConv2d( | |
| 192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3) | |
| ), | |
| BasicConv2d( | |
| 224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0) | |
| ), | |
| BasicConv2d( | |
| 224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3) | |
| ) | |
| ) | |
| self.branch3 = nn.Sequential( | |
| nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), | |
| BasicConv2d(1024, 128, kernel_size=1, stride=1) | |
| ) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| x2 = self.branch2(x) | |
| x3 = self.branch3(x) | |
| out = torch.cat((x0, x1, x2, x3), 1) | |
| return out | |
| class Reduction_B(nn.Module): | |
| def __init__(self): | |
| super(Reduction_B, self).__init__() | |
| self.branch0 = nn.Sequential( | |
| BasicConv2d(1024, 192, kernel_size=1, stride=1), | |
| BasicConv2d(192, 192, kernel_size=3, stride=2) | |
| ) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(1024, 256, kernel_size=1, stride=1), | |
| BasicConv2d( | |
| 256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3) | |
| ), | |
| BasicConv2d( | |
| 256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0) | |
| ), BasicConv2d(320, 320, kernel_size=3, stride=2) | |
| ) | |
| self.branch2 = nn.MaxPool2d(3, stride=2) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| x2 = self.branch2(x) | |
| out = torch.cat((x0, x1, x2), 1) | |
| return out | |
| class Inception_C(nn.Module): | |
| def __init__(self): | |
| super(Inception_C, self).__init__() | |
| self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1) | |
| self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) | |
| self.branch1_1a = BasicConv2d( | |
| 384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1) | |
| ) | |
| self.branch1_1b = BasicConv2d( | |
| 384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0) | |
| ) | |
| self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) | |
| self.branch2_1 = BasicConv2d( | |
| 384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0) | |
| ) | |
| self.branch2_2 = BasicConv2d( | |
| 448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1) | |
| ) | |
| self.branch2_3a = BasicConv2d( | |
| 512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1) | |
| ) | |
| self.branch2_3b = BasicConv2d( | |
| 512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0) | |
| ) | |
| self.branch3 = nn.Sequential( | |
| nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), | |
| BasicConv2d(1536, 256, kernel_size=1, stride=1) | |
| ) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1_0 = self.branch1_0(x) | |
| x1_1a = self.branch1_1a(x1_0) | |
| x1_1b = self.branch1_1b(x1_0) | |
| x1 = torch.cat((x1_1a, x1_1b), 1) | |
| x2_0 = self.branch2_0(x) | |
| x2_1 = self.branch2_1(x2_0) | |
| x2_2 = self.branch2_2(x2_1) | |
| x2_3a = self.branch2_3a(x2_2) | |
| x2_3b = self.branch2_3b(x2_2) | |
| x2 = torch.cat((x2_3a, x2_3b), 1) | |
| x3 = self.branch3(x) | |
| out = torch.cat((x0, x1, x2, x3), 1) | |
| return out | |
| class InceptionV4(nn.Module): | |
| """Inception-v4. | |
| Reference: | |
| Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual | |
| Connections on Learning. AAAI 2017. | |
| Public keys: | |
| - ``inceptionv4``: InceptionV4. | |
| """ | |
| def __init__(self, num_classes, loss, **kwargs): | |
| super(InceptionV4, self).__init__() | |
| self.loss = loss | |
| self.features = nn.Sequential( | |
| BasicConv2d(3, 32, kernel_size=3, stride=2), | |
| BasicConv2d(32, 32, kernel_size=3, stride=1), | |
| BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1), | |
| Mixed_3a(), | |
| Mixed_4a(), | |
| Mixed_5a(), | |
| Inception_A(), | |
| Inception_A(), | |
| Inception_A(), | |
| Inception_A(), | |
| Reduction_A(), # Mixed_6a | |
| Inception_B(), | |
| Inception_B(), | |
| Inception_B(), | |
| Inception_B(), | |
| Inception_B(), | |
| Inception_B(), | |
| Inception_B(), | |
| Reduction_B(), # Mixed_7a | |
| Inception_C(), | |
| Inception_C(), | |
| Inception_C() | |
| ) | |
| self.global_avgpool = nn.AdaptiveAvgPool2d(1) | |
| self.classifier = nn.Linear(1536, num_classes) | |
| def forward(self, x): | |
| f = self.features(x) | |
| v = self.global_avgpool(f) | |
| v = v.view(v.size(0), -1) | |
| 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 inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs): | |
| model = InceptionV4(num_classes, loss, **kwargs) | |
| if pretrained: | |
| model_url = pretrained_settings['inceptionv4']['imagenet']['url'] | |
| init_pretrained_weights(model, model_url) | |
| return model | |