66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
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import torch
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import torch.nn as nn
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class ImageNetHEAD(nn.Sequential):
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def __init__(self, C, stride=2):
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super(ImageNetHEAD, self).__init__()
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self.add_module('conv1', nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False))
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self.add_module('bn1' , nn.BatchNorm2d(C // 2))
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self.add_module('relu1', nn.ReLU(inplace=True))
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self.add_module('conv2', nn.Conv2d(C // 2, C, kernel_size=3, stride=stride, padding=1, bias=False))
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self.add_module('bn2' , nn.BatchNorm2d(C))
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class CifarHEAD(nn.Sequential):
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def __init__(self, C):
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super(CifarHEAD, self).__init__()
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self.add_module('conv', nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False))
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self.add_module('bn', nn.BatchNorm2d(C))
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class AuxiliaryHeadCIFAR(nn.Module):
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def __init__(self, C, num_classes):
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"""assuming input size 8x8"""
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super(AuxiliaryHeadCIFAR, self).__init__()
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self.features = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False), # image size = 2 x 2
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nn.Conv2d(C, 128, 1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(128, 768, 2, bias=False),
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nn.BatchNorm2d(768),
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nn.ReLU(inplace=True)
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)
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self.classifier = nn.Linear(768, num_classes)
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def forward(self, x):
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x = self.features(x)
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x = self.classifier(x.view(x.size(0),-1))
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return x
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class AuxiliaryHeadImageNet(nn.Module):
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def __init__(self, C, num_classes):
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"""assuming input size 14x14"""
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super(AuxiliaryHeadImageNet, self).__init__()
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self.features = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.AvgPool2d(5, stride=2, padding=0, count_include_pad=False),
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nn.Conv2d(C, 128, 1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(128, 768, 2, bias=False),
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nn.BatchNorm2d(768),
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nn.ReLU(inplace=True)
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)
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self.classifier = nn.Linear(768, num_classes)
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def forward(self, x):
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x = self.features(x)
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x = self.classifier(x.view(x.size(0),-1))
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return x
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