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| """ Pytorch Inception-V4 implementation | |
| Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is | |
| based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License) | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD | |
| from .helpers import build_model_with_cfg | |
| from .layers import create_classifier | |
| from .registry import register_model | |
| __all__ = ['InceptionV4'] | |
| default_cfgs = { | |
| 'inception_v4': { | |
| 'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.pth', | |
| 'num_classes': 1001, 'input_size': (3, 299, 299), 'pool_size': (8, 8), | |
| 'crop_pct': 0.875, 'interpolation': 'bicubic', | |
| 'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, | |
| 'first_conv': 'features.0.conv', 'classifier': 'last_linear', | |
| } | |
| } | |
| 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) | |
| self.bn = nn.BatchNorm2d(out_planes, eps=0.001) | |
| 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 Mixed3a(nn.Module): | |
| def __init__(self): | |
| super(Mixed3a, 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 Mixed4a(nn.Module): | |
| def __init__(self): | |
| super(Mixed4a, 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 Mixed5a(nn.Module): | |
| def __init__(self): | |
| super(Mixed5a, 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 InceptionA(nn.Module): | |
| def __init__(self): | |
| super(InceptionA, 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 ReductionA(nn.Module): | |
| def __init__(self): | |
| super(ReductionA, 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 InceptionB(nn.Module): | |
| def __init__(self): | |
| super(InceptionB, 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 ReductionB(nn.Module): | |
| def __init__(self): | |
| super(ReductionB, 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 InceptionC(nn.Module): | |
| def __init__(self): | |
| super(InceptionC, 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): | |
| def __init__(self, num_classes=1001, in_chans=3, output_stride=32, drop_rate=0., global_pool='avg'): | |
| super(InceptionV4, self).__init__() | |
| assert output_stride == 32 | |
| self.drop_rate = drop_rate | |
| self.num_classes = num_classes | |
| self.num_features = 1536 | |
| self.features = nn.Sequential( | |
| BasicConv2d(in_chans, 32, kernel_size=3, stride=2), | |
| BasicConv2d(32, 32, kernel_size=3, stride=1), | |
| BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1), | |
| Mixed3a(), | |
| Mixed4a(), | |
| Mixed5a(), | |
| InceptionA(), | |
| InceptionA(), | |
| InceptionA(), | |
| InceptionA(), | |
| ReductionA(), # Mixed6a | |
| InceptionB(), | |
| InceptionB(), | |
| InceptionB(), | |
| InceptionB(), | |
| InceptionB(), | |
| InceptionB(), | |
| InceptionB(), | |
| ReductionB(), # Mixed7a | |
| InceptionC(), | |
| InceptionC(), | |
| InceptionC(), | |
| ) | |
| self.feature_info = [ | |
| dict(num_chs=64, reduction=2, module='features.2'), | |
| dict(num_chs=160, reduction=4, module='features.3'), | |
| dict(num_chs=384, reduction=8, module='features.9'), | |
| dict(num_chs=1024, reduction=16, module='features.17'), | |
| dict(num_chs=1536, reduction=32, module='features.21'), | |
| ] | |
| self.global_pool, self.last_linear = create_classifier( | |
| self.num_features, self.num_classes, pool_type=global_pool) | |
| def get_classifier(self): | |
| return self.last_linear | |
| def reset_classifier(self, num_classes, global_pool='avg'): | |
| self.num_classes = num_classes | |
| self.global_pool, self.last_linear = create_classifier( | |
| self.num_features, self.num_classes, pool_type=global_pool) | |
| def forward_features(self, x): | |
| return self.features(x) | |
| def forward(self, x): | |
| x = self.forward_features(x) | |
| x = self.global_pool(x) | |
| if self.drop_rate > 0: | |
| x = F.dropout(x, p=self.drop_rate, training=self.training) | |
| x = self.last_linear(x) | |
| return x | |
| def _create_inception_v4(variant, pretrained=False, **kwargs): | |
| return build_model_with_cfg( | |
| InceptionV4, variant, pretrained, default_cfg=default_cfgs[variant], | |
| feature_cfg=dict(flatten_sequential=True), **kwargs) | |
| def inception_v4(pretrained=False, **kwargs): | |
| return _create_inception_v4('inception_v4', pretrained, **kwargs) | |