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)