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pt_models.py
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pt_models.py
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import torch.utils.model_zoo as model_zoo
from torch import nn as nn
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class MyAlexNet(nn.Module):
def __init__(self, num_classes=1000, dropout=True):
super(MyAlexNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
self.classifier = nn.Sequential(
nn.Dropout(p=0.5 if dropout else 0.0),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5 if dropout else 0.0),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.classifier(x)
return x
def get_inhw(self, x):
res = []
for module in self.features._modules.values():
if isinstance(module, nn.Conv2d):
res.append((x.size(2), x.size(3)))
x = module(x)
for module in self.classifier._modules.values():
if isinstance(module, nn.Linear):
res.append((1, 1))
return res
def myalexnet(pretrained=False, model_root=None, **kwargs):
model = MyAlexNet(**kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['alexnet'], model_root), strict=False)
return model