65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
import torch
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import torch.nn as nn
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import torch.utils.model_zoo as model_zoo
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from .build import BACKBONE_REGISTRY
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from .backbone import Backbone
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model_urls = {
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"alexnet": "https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth",
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}
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class AlexNet(Backbone):
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def __init__(self):
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super().__init__()
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self.features = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2),
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nn.Conv2d(64, 192, kernel_size=5, padding=2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2),
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nn.Conv2d(192, 384, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(384, 256, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, 256, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2),
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)
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self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
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# Note that self.classifier outputs features rather than logits
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self.classifier = nn.Sequential(
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nn.Dropout(),
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nn.Linear(256 * 6 * 6, 4096),
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nn.ReLU(inplace=True),
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nn.Dropout(),
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nn.Linear(4096, 4096),
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nn.ReLU(inplace=True),
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)
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self._out_features = 4096
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def forward(self, x):
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x = self.features(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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return self.classifier(x)
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def init_pretrained_weights(model, model_url):
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pretrain_dict = model_zoo.load_url(model_url)
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model.load_state_dict(pretrain_dict, strict=False)
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@BACKBONE_REGISTRY.register()
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def alexnet(pretrained=True, **kwargs):
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model = AlexNet()
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if pretrained:
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init_pretrained_weights(model, model_urls["alexnet"])
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return model
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