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""" Pytorch Inception-V4 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__ = ['InceptionV4'] |
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default_cfgs = { |
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'inception_v4': { |
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.pth', |
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'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8), |
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'crop_pct': 0.875, 'interpolation': 'bicubic', |
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, |
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'first_conv': 'features.0.conv', 'classifier': 'last_linear', |
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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=0.001) |
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self.relu = nn.ReLU(inplace=True) |
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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 Mixed3a(nn.Module): |
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def __init__(self): |
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super(Mixed3a, self).__init__() |
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self.maxpool = nn.MaxPool2d(3, stride=2) |
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self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2) |
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def forward(self, x): |
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x0 = self.maxpool(x) |
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x1 = self.conv(x) |
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out = torch.cat((x0, x1), 1) |
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return out |
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class Mixed4a(nn.Module): |
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def __init__(self): |
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super(Mixed4a, self).__init__() |
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self.branch0 = nn.Sequential( |
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BasicConv2d(160, 64, kernel_size=1, stride=1), |
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BasicConv2d(64, 96, kernel_size=3, stride=1) |
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) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(160, 64, kernel_size=1, stride=1), |
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BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)), |
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BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)), |
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BasicConv2d(64, 96, kernel_size=(3, 3), 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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out = torch.cat((x0, x1), 1) |
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return out |
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class Mixed5a(nn.Module): |
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def __init__(self): |
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super(Mixed5a, self).__init__() |
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self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2) |
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self.maxpool = nn.MaxPool2d(3, stride=2) |
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def forward(self, x): |
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x0 = self.conv(x) |
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x1 = self.maxpool(x) |
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out = torch.cat((x0, x1), 1) |
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return out |
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class InceptionA(nn.Module): |
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def __init__(self): |
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super(InceptionA, self).__init__() |
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self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(384, 64, kernel_size=1, stride=1), |
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BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1) |
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) |
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self.branch2 = nn.Sequential( |
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BasicConv2d(384, 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(384, 96, 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 ReductionA(nn.Module): |
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def __init__(self): |
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super(ReductionA, self).__init__() |
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self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(384, 192, kernel_size=1, stride=1), |
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BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1), |
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BasicConv2d(224, 256, 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 InceptionB(nn.Module): |
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def __init__(self): |
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super(InceptionB, self).__init__() |
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self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(1024, 192, kernel_size=1, stride=1), |
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BasicConv2d(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)), |
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BasicConv2d(224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)) |
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) |
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self.branch2 = nn.Sequential( |
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BasicConv2d(1024, 192, kernel_size=1, stride=1), |
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BasicConv2d(192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)), |
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BasicConv2d(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)), |
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BasicConv2d(224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)), |
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BasicConv2d(224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)) |
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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(1024, 128, 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 ReductionB(nn.Module): |
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def __init__(self): |
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super(ReductionB, self).__init__() |
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self.branch0 = nn.Sequential( |
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BasicConv2d(1024, 192, kernel_size=1, stride=1), |
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BasicConv2d(192, 192, kernel_size=3, stride=2) |
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) |
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self.branch1 = nn.Sequential( |
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BasicConv2d(1024, 256, kernel_size=1, stride=1), |
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BasicConv2d(256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)), |
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BasicConv2d(256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)), |
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BasicConv2d(320, 320, 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 InceptionC(nn.Module): |
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def __init__(self): |
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super(InceptionC, self).__init__() |
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self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1) |
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self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) |
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self.branch1_1a = BasicConv2d(384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)) |
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self.branch1_1b = BasicConv2d(384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)) |
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self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) |
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self.branch2_1 = BasicConv2d(384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0)) |
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self.branch2_2 = BasicConv2d(448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1)) |
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self.branch2_3a = BasicConv2d(512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)) |
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self.branch2_3b = BasicConv2d(512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)) |
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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(1536, 256, 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_0 = self.branch1_0(x) |
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x1_1a = self.branch1_1a(x1_0) |
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x1_1b = self.branch1_1b(x1_0) |
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x1 = torch.cat((x1_1a, x1_1b), 1) |
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x2_0 = self.branch2_0(x) |
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x2_1 = self.branch2_1(x2_0) |
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x2_2 = self.branch2_2(x2_1) |
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x2_3a = self.branch2_3a(x2_2) |
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x2_3b = self.branch2_3b(x2_2) |
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x2 = torch.cat((x2_3a, x2_3b), 1) |
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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 InceptionV4(nn.Module): |
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def __init__(self, num_classes=1000, in_chans=3, output_stride=32, drop_rate=0., global_pool='avg'): |
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super(InceptionV4, self).__init__() |
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assert output_stride == 32 |
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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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self.features = nn.Sequential( |
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BasicConv2d(in_chans, 32, kernel_size=3, stride=2), |
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BasicConv2d(32, 32, kernel_size=3, stride=1), |
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BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1), |
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Mixed3a(), |
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Mixed4a(), |
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Mixed5a(), |
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InceptionA(), |
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InceptionA(), |
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InceptionA(), |
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InceptionA(), |
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ReductionA(), |
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InceptionB(), |
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InceptionB(), |
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InceptionB(), |
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InceptionB(), |
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InceptionB(), |
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InceptionB(), |
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InceptionB(), |
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ReductionB(), |
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InceptionC(), |
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InceptionC(), |
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InceptionC(), |
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) |
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self.feature_info = [ |
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dict(num_chs=64, reduction=2, module='features.2'), |
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dict(num_chs=160, reduction=4, module='features.3'), |
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dict(num_chs=384, reduction=8, module='features.9'), |
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dict(num_chs=1024, reduction=16, module='features.17'), |
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dict(num_chs=1536, reduction=32, module='features.21'), |
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] |
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self.global_pool, self.last_linear = create_classifier( |
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self.num_features, self.num_classes, pool_type=global_pool) |
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def get_classifier(self): |
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return self.last_linear |
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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.last_linear = create_classifier( |
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self.num_features, self.num_classes, pool_type=global_pool) |
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def forward_features(self, x): |
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return self.features(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.last_linear(x) |
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return x |
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def _create_inception_v4(variant, pretrained=False, **kwargs): |
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return build_model_with_cfg( |
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InceptionV4, variant, pretrained, |
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default_cfg=default_cfgs[variant], |
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feature_cfg=dict(flatten_sequential=True), |
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**kwargs) |
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@register_model |
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def inception_v4(pretrained=False, **kwargs): |
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return _create_inception_v4('inception_v4', pretrained, **kwargs) |
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