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| """ | |
| Code imported from https://github.com/Cadene/pretrained-models.pytorch | |
| """ | |
| from __future__ import division, absolute_import | |
| import torch | |
| import torch.nn as nn | |
| import torch.utils.model_zoo as model_zoo | |
| __all__ = ['inceptionresnetv2'] | |
| pretrained_settings = { | |
| 'inceptionresnetv2': { | |
| 'imagenet': { | |
| 'url': | |
| 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.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/inceptionresnetv2-520b38e4.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=False) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| x = self.relu(x) | |
| return x | |
| class Mixed_5b(nn.Module): | |
| def __init__(self): | |
| super(Mixed_5b, self).__init__() | |
| self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(192, 48, kernel_size=1, stride=1), | |
| BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2) | |
| ) | |
| self.branch2 = nn.Sequential( | |
| BasicConv2d(192, 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(192, 64, 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 Block35(nn.Module): | |
| def __init__(self, scale=1.0): | |
| super(Block35, self).__init__() | |
| self.scale = scale | |
| self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(320, 32, kernel_size=1, stride=1), | |
| BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) | |
| ) | |
| self.branch2 = nn.Sequential( | |
| BasicConv2d(320, 32, kernel_size=1, stride=1), | |
| BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1), | |
| BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1) | |
| ) | |
| self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1) | |
| self.relu = nn.ReLU(inplace=False) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| x2 = self.branch2(x) | |
| out = torch.cat((x0, x1, x2), 1) | |
| out = self.conv2d(out) | |
| out = out * self.scale + x | |
| out = self.relu(out) | |
| return out | |
| class Mixed_6a(nn.Module): | |
| def __init__(self): | |
| super(Mixed_6a, self).__init__() | |
| self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(320, 256, kernel_size=1, stride=1), | |
| BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1), | |
| BasicConv2d(256, 384, 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 Block17(nn.Module): | |
| def __init__(self, scale=1.0): | |
| super(Block17, self).__init__() | |
| self.scale = scale | |
| self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(1088, 128, kernel_size=1, stride=1), | |
| BasicConv2d( | |
| 128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3) | |
| ), | |
| BasicConv2d( | |
| 160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0) | |
| ) | |
| ) | |
| self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1) | |
| self.relu = nn.ReLU(inplace=False) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| out = torch.cat((x0, x1), 1) | |
| out = self.conv2d(out) | |
| out = out * self.scale + x | |
| out = self.relu(out) | |
| return out | |
| class Mixed_7a(nn.Module): | |
| def __init__(self): | |
| super(Mixed_7a, self).__init__() | |
| self.branch0 = nn.Sequential( | |
| BasicConv2d(1088, 256, kernel_size=1, stride=1), | |
| BasicConv2d(256, 384, kernel_size=3, stride=2) | |
| ) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(1088, 256, kernel_size=1, stride=1), | |
| BasicConv2d(256, 288, kernel_size=3, stride=2) | |
| ) | |
| self.branch2 = nn.Sequential( | |
| BasicConv2d(1088, 256, kernel_size=1, stride=1), | |
| BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1), | |
| BasicConv2d(288, 320, kernel_size=3, stride=2) | |
| ) | |
| self.branch3 = nn.MaxPool2d(3, stride=2) | |
| 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 Block8(nn.Module): | |
| def __init__(self, scale=1.0, noReLU=False): | |
| super(Block8, self).__init__() | |
| self.scale = scale | |
| self.noReLU = noReLU | |
| self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1) | |
| self.branch1 = nn.Sequential( | |
| BasicConv2d(2080, 192, kernel_size=1, stride=1), | |
| BasicConv2d( | |
| 192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1) | |
| ), | |
| BasicConv2d( | |
| 224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0) | |
| ) | |
| ) | |
| self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1) | |
| if not self.noReLU: | |
| self.relu = nn.ReLU(inplace=False) | |
| def forward(self, x): | |
| x0 = self.branch0(x) | |
| x1 = self.branch1(x) | |
| out = torch.cat((x0, x1), 1) | |
| out = self.conv2d(out) | |
| out = out * self.scale + x | |
| if not self.noReLU: | |
| out = self.relu(out) | |
| return out | |
| # ---------------- | |
| # Model Definition | |
| # ---------------- | |
| class InceptionResNetV2(nn.Module): | |
| """Inception-ResNet-V2. | |
| Reference: | |
| Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual | |
| Connections on Learning. AAAI 2017. | |
| Public keys: | |
| - ``inceptionresnetv2``: Inception-ResNet-V2. | |
| """ | |
| def __init__(self, num_classes, loss='softmax', **kwargs): | |
| super(InceptionResNetV2, self).__init__() | |
| self.loss = loss | |
| # Modules | |
| self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2) | |
| self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1) | |
| self.conv2d_2b = BasicConv2d( | |
| 32, 64, kernel_size=3, stride=1, padding=1 | |
| ) | |
| self.maxpool_3a = nn.MaxPool2d(3, stride=2) | |
| self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1) | |
| self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1) | |
| self.maxpool_5a = nn.MaxPool2d(3, stride=2) | |
| self.mixed_5b = Mixed_5b() | |
| self.repeat = nn.Sequential( | |
| Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), | |
| Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), | |
| Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), | |
| Block35(scale=0.17) | |
| ) | |
| self.mixed_6a = Mixed_6a() | |
| self.repeat_1 = nn.Sequential( | |
| Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), | |
| Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), | |
| Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), | |
| Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), | |
| Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), | |
| Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), | |
| Block17(scale=0.10), Block17(scale=0.10) | |
| ) | |
| self.mixed_7a = Mixed_7a() | |
| self.repeat_2 = nn.Sequential( | |
| Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), | |
| Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), | |
| Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20) | |
| ) | |
| self.block8 = Block8(noReLU=True) | |
| self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1) | |
| self.global_avgpool = nn.AdaptiveAvgPool2d(1) | |
| self.classifier = nn.Linear(1536, num_classes) | |
| def load_imagenet_weights(self): | |
| settings = pretrained_settings['inceptionresnetv2']['imagenet'] | |
| pretrain_dict = model_zoo.load_url(settings['url']) | |
| model_dict = self.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) | |
| self.load_state_dict(model_dict) | |
| def featuremaps(self, x): | |
| x = self.conv2d_1a(x) | |
| x = self.conv2d_2a(x) | |
| x = self.conv2d_2b(x) | |
| x = self.maxpool_3a(x) | |
| x = self.conv2d_3b(x) | |
| x = self.conv2d_4a(x) | |
| x = self.maxpool_5a(x) | |
| x = self.mixed_5b(x) | |
| x = self.repeat(x) | |
| x = self.mixed_6a(x) | |
| x = self.repeat_1(x) | |
| x = self.mixed_7a(x) | |
| x = self.repeat_2(x) | |
| x = self.block8(x) | |
| x = self.conv2d_7b(x) | |
| return x | |
| def forward(self, x): | |
| f = self.featuremaps(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 inceptionresnetv2(num_classes, loss='softmax', pretrained=True, **kwargs): | |
| model = InceptionResNetV2(num_classes=num_classes, loss=loss, **kwargs) | |
| if pretrained: | |
| model.load_imagenet_weights() | |
| return model | |