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""" Pytorch Inception-Resnet-V2 implementation |
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Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is |
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based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License) |
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""" |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD |
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from .helpers import build_model_with_cfg |
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from .layers import create_classifier |
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from .registry import register_model |
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__all__ = ['InceptionResnetV2'] |
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default_cfgs = { |
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'inception_resnet_v2': { |
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/inception_resnet_v2-940b1cd6.pth', |
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'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8), |
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'crop_pct': 0.8975, 'interpolation': 'bicubic', |
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, |
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'first_conv': 'conv2d_1a.conv', 'classifier': 'classif', |
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'label_offset': 1, |
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}, |
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'ens_adv_inception_resnet_v2': { |
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ens_adv_inception_resnet_v2-2592a550.pth', |
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'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8), |
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'crop_pct': 0.8975, 'interpolation': 'bicubic', |
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, |
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'first_conv': 'conv2d_1a.conv', 'classifier': 'classif', |
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'label_offset': 1, |
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} |
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} |
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class BasicConv2d(nn.Module): |
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def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): |
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super(BasicConv2d, self).__init__() |
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self.conv = nn.Conv2d( |
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in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False) |
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self.bn = nn.BatchNorm2d(out_planes, eps=.001) |
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self.relu = nn.ReLU(inplace=False) |
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def forward(self, x): |
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x = self.conv(x) |
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x = self.bn(x) |
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x = self.relu(x) |
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return x |
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class Mixed_5b(nn.Module): |
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def __init__(self): |
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super(Mixed_5b, self).__init__() |
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self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(192, 48, kernel_size=1, stride=1), |
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BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2) |
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) |
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self.branch2 = nn.Sequential( |
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BasicConv2d(192, 64, kernel_size=1, stride=1), |
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BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1), |
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BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1) |
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) |
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self.branch3 = nn.Sequential( |
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nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), |
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BasicConv2d(192, 64, kernel_size=1, stride=1) |
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) |
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def forward(self, x): |
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x0 = self.branch0(x) |
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x1 = self.branch1(x) |
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x2 = self.branch2(x) |
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x3 = self.branch3(x) |
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out = torch.cat((x0, x1, x2, x3), 1) |
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return out |
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class Block35(nn.Module): |
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def __init__(self, scale=1.0): |
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super(Block35, self).__init__() |
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self.scale = scale |
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self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(320, 32, kernel_size=1, stride=1), |
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BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) |
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) |
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self.branch2 = nn.Sequential( |
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BasicConv2d(320, 32, kernel_size=1, stride=1), |
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BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1), |
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BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1) |
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) |
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self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1) |
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self.relu = nn.ReLU(inplace=False) |
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def forward(self, x): |
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x0 = self.branch0(x) |
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x1 = self.branch1(x) |
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x2 = self.branch2(x) |
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out = torch.cat((x0, x1, x2), 1) |
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out = self.conv2d(out) |
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out = out * self.scale + x |
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out = self.relu(out) |
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return out |
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class Mixed_6a(nn.Module): |
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def __init__(self): |
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super(Mixed_6a, self).__init__() |
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self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(320, 256, kernel_size=1, stride=1), |
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BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1), |
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BasicConv2d(256, 384, kernel_size=3, stride=2) |
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) |
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self.branch2 = nn.MaxPool2d(3, stride=2) |
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def forward(self, x): |
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x0 = self.branch0(x) |
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x1 = self.branch1(x) |
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x2 = self.branch2(x) |
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out = torch.cat((x0, x1, x2), 1) |
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return out |
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class Block17(nn.Module): |
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def __init__(self, scale=1.0): |
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super(Block17, self).__init__() |
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self.scale = scale |
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self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(1088, 128, kernel_size=1, stride=1), |
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BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)), |
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BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)) |
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) |
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self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1) |
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self.relu = nn.ReLU(inplace=False) |
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def forward(self, x): |
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x0 = self.branch0(x) |
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x1 = self.branch1(x) |
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out = torch.cat((x0, x1), 1) |
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out = self.conv2d(out) |
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out = out * self.scale + x |
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out = self.relu(out) |
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return out |
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class Mixed_7a(nn.Module): |
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def __init__(self): |
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super(Mixed_7a, self).__init__() |
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self.branch0 = nn.Sequential( |
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BasicConv2d(1088, 256, kernel_size=1, stride=1), |
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BasicConv2d(256, 384, kernel_size=3, stride=2) |
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) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(1088, 256, kernel_size=1, stride=1), |
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BasicConv2d(256, 288, kernel_size=3, stride=2) |
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) |
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self.branch2 = nn.Sequential( |
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BasicConv2d(1088, 256, kernel_size=1, stride=1), |
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BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1), |
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BasicConv2d(288, 320, kernel_size=3, stride=2) |
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) |
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self.branch3 = nn.MaxPool2d(3, stride=2) |
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def forward(self, x): |
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x0 = self.branch0(x) |
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x1 = self.branch1(x) |
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x2 = self.branch2(x) |
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x3 = self.branch3(x) |
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out = torch.cat((x0, x1, x2, x3), 1) |
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return out |
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class Block8(nn.Module): |
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def __init__(self, scale=1.0, no_relu=False): |
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super(Block8, self).__init__() |
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self.scale = scale |
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self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(2080, 192, kernel_size=1, stride=1), |
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BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)), |
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BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)) |
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) |
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self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1) |
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self.relu = None if no_relu else nn.ReLU(inplace=False) |
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def forward(self, x): |
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x0 = self.branch0(x) |
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x1 = self.branch1(x) |
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out = torch.cat((x0, x1), 1) |
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out = self.conv2d(out) |
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out = out * self.scale + x |
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if self.relu is not None: |
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out = self.relu(out) |
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return out |
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class InceptionResnetV2(nn.Module): |
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def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., output_stride=32, global_pool='avg'): |
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super(InceptionResnetV2, self).__init__() |
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self.drop_rate = drop_rate |
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self.num_classes = num_classes |
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self.num_features = 1536 |
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assert output_stride == 32 |
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self.conv2d_1a = BasicConv2d(in_chans, 32, kernel_size=3, stride=2) |
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self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1) |
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self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1) |
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self.feature_info = [dict(num_chs=64, reduction=2, module='conv2d_2b')] |
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self.maxpool_3a = nn.MaxPool2d(3, stride=2) |
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self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1) |
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self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1) |
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self.feature_info += [dict(num_chs=192, reduction=4, module='conv2d_4a')] |
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self.maxpool_5a = nn.MaxPool2d(3, stride=2) |
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self.mixed_5b = Mixed_5b() |
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self.repeat = nn.Sequential( |
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Block35(scale=0.17), |
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Block35(scale=0.17), |
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Block35(scale=0.17), |
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Block35(scale=0.17), |
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Block35(scale=0.17), |
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Block35(scale=0.17), |
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Block35(scale=0.17), |
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Block35(scale=0.17), |
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Block35(scale=0.17), |
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Block35(scale=0.17) |
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) |
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self.feature_info += [dict(num_chs=320, reduction=8, module='repeat')] |
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self.mixed_6a = Mixed_6a() |
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self.repeat_1 = nn.Sequential( |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10), |
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Block17(scale=0.10) |
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) |
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self.feature_info += [dict(num_chs=1088, reduction=16, module='repeat_1')] |
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self.mixed_7a = Mixed_7a() |
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self.repeat_2 = nn.Sequential( |
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Block8(scale=0.20), |
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Block8(scale=0.20), |
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Block8(scale=0.20), |
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Block8(scale=0.20), |
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Block8(scale=0.20), |
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Block8(scale=0.20), |
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Block8(scale=0.20), |
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Block8(scale=0.20), |
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Block8(scale=0.20) |
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) |
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self.block8 = Block8(no_relu=True) |
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self.conv2d_7b = BasicConv2d(2080, self.num_features, kernel_size=1, stride=1) |
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self.feature_info += [dict(num_chs=self.num_features, reduction=32, module='conv2d_7b')] |
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self.global_pool, self.classif = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) |
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def get_classifier(self): |
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return self.classif |
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def reset_classifier(self, num_classes, global_pool='avg'): |
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self.num_classes = num_classes |
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self.global_pool, self.classif = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) |
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def forward_features(self, x): |
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x = self.conv2d_1a(x) |
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x = self.conv2d_2a(x) |
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x = self.conv2d_2b(x) |
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x = self.maxpool_3a(x) |
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x = self.conv2d_3b(x) |
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x = self.conv2d_4a(x) |
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x = self.maxpool_5a(x) |
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x = self.mixed_5b(x) |
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x = self.repeat(x) |
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x = self.mixed_6a(x) |
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x = self.repeat_1(x) |
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x = self.mixed_7a(x) |
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x = self.repeat_2(x) |
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x = self.block8(x) |
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x = self.conv2d_7b(x) |
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return x |
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def forward(self, x): |
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x = self.forward_features(x) |
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x = self.global_pool(x) |
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if self.drop_rate > 0: |
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x = F.dropout(x, p=self.drop_rate, training=self.training) |
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x = self.classif(x) |
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return x |
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def _create_inception_resnet_v2(variant, pretrained=False, **kwargs): |
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return build_model_with_cfg( |
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InceptionResnetV2, variant, pretrained, |
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default_cfg=default_cfgs[variant], |
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**kwargs) |
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@register_model |
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def inception_resnet_v2(pretrained=False, **kwargs): |
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r"""InceptionResnetV2 model architecture from the |
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`"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>` paper. |
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""" |
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return _create_inception_resnet_v2('inception_resnet_v2', pretrained=pretrained, **kwargs) |
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@register_model |
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def ens_adv_inception_resnet_v2(pretrained=False, **kwargs): |
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r""" Ensemble Adversarially trained InceptionResnetV2 model architecture |
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As per https://arxiv.org/abs/1705.07204 and |
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https://github.com/tensorflow/models/tree/master/research/adv_imagenet_models. |
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""" |
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return _create_inception_resnet_v2('ens_adv_inception_resnet_v2', pretrained=pretrained, **kwargs) |
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