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import torch.nn as nn
import torchvision.transforms as transforms
__all__ = ['alexnet']
class AlexNetOWT_BN(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNetOWT_BN, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2,
bias=False),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 192, kernel_size=5, padding=2, bias=False),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.ReLU(inplace=True),
nn.BatchNorm2d(192),
nn.Conv2d(192, 384, kernel_size=3, padding=1, bias=False),
nn.ReLU(inplace=True),
nn.BatchNorm2d(384),
nn.Conv2d(384, 256, kernel_size=3, padding=1, bias=False),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256)
)
self.classifier = nn.Sequential(
nn.Linear(256 * 6 * 6, 4096, bias=False),
nn.BatchNorm1d(4096),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(4096, 4096, bias=False),
nn.BatchNorm1d(4096),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(4096, num_classes)
)
self.regime = {
0: {'optimizer': 'SGD', 'lr': 1e-2,
'weight_decay': 5e-4, 'momentum': 0.9},
10: {'lr': 5e-3},
15: {'lr': 1e-3, 'weight_decay': 0},
20: {'lr': 5e-4},
25: {'lr': 1e-4}
}
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
self.input_transform = {
'train': transforms.Compose([
transforms.Scale(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
]),
'eval': transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize
])
}
def forward(self, x):
x = self.features(x)
x = x.view(-1, 256 * 6 * 6)
x = self.classifier(x)
return x
def alexnet(**kwargs):
num_classes = kwargs.get( 'num_classes', 1000)
return AlexNetOWT_BN(num_classes)