101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
import torch.nn as nn
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from torch import set_grad_enabled
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from torchvision import models
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import torch
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from .nets_utils import EmbeddingRecorder
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# Acknowledgement to
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# https://github.com/kuangliu/pytorch-cifar,
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# https://github.com/BIGBALLON/CIFAR-ZOO,
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class AlexNet_32x32(nn.Module):
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def __init__(self, channel, num_classes, record_embedding=False, no_grad=False):
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super().__init__()
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self.features = nn.Sequential(
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nn.Conv2d(channel, 128, kernel_size=5, stride=1, padding=4 if channel == 1 else 2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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nn.Conv2d(128, 192, kernel_size=5, padding=2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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nn.Conv2d(192, 256, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, 192, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(192, 192, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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)
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self.fc = nn.Linear(192 * 4 * 4, num_classes)
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self.embedding_recorder = EmbeddingRecorder(record_embedding)
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self.no_grad = no_grad
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def get_last_layer(self):
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return self.fc
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def forward(self, x):
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with set_grad_enabled(not self.no_grad):
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x = self.features(x)
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x = x.view(x.size(0), -1)
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x = self.embedding_recorder(x)
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x = self.fc(x)
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return x
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class AlexNet_224x224(models.AlexNet):
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def __init__(self, channel: int, num_classes: int, record_embedding: bool = False,
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no_grad: bool = False, **kwargs):
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super().__init__(num_classes, **kwargs)
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self.embedding_recorder = EmbeddingRecorder(record_embedding)
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if channel != 3:
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self.features[0] = nn.Conv2d(channel, 64, kernel_size=11, stride=4, padding=2)
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self.fc = self.classifier[-1]
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self.classifier[-1] = self.embedding_recorder
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self.classifier.add_module("fc", self.fc)
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self.no_grad = no_grad
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def get_last_layer(self):
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return self.fc
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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with set_grad_enabled(not self.no_grad):
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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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x = self.classifier(x)
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return x
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def AlexNet(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False,
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pretrained: bool = False):
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if pretrained:
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if im_size[0] != 224 or im_size[1] != 224:
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raise NotImplementedError("torchvison pretrained models only accept inputs with size of 224*224")
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net = AlexNet_224x224(channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad)
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from torch.hub import load_state_dict_from_url
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state_dict = load_state_dict_from_url('https://download.pytorch.org/models/alexnet-owt-7be5be79.pth'
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, progress=True)
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net.load_state_dict(state_dict)
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if channel != 3:
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net.features[0] = nn.Conv2d(channel, 64, kernel_size=11, stride=4, padding=2)
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if num_classes != 1000:
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net.fc = nn.Linear(4096, num_classes)
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net.classifier[-1] = net.fc
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elif im_size[0] == 224 and im_size[1] == 224:
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net = AlexNet_224x224(channel=channel, num_classes=num_classes, record_embedding=record_embedding,
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no_grad=no_grad)
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elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or (
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channel == 3 and im_size[0] == 32 and im_size[1] == 32):
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net = AlexNet_32x32(channel=channel, num_classes=num_classes, record_embedding=record_embedding,
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no_grad=no_grad)
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else:
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raise NotImplementedError("Network Architecture for current dataset has not been implemented.")
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return net
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