import torch.nn as nn import torchvision.transforms as transforms from .binarized_modules import BinarizeLinear,BinarizeConv2d __all__ = ['alexnet_binary'] class AlexNetOWT_BN(nn.Module): def __init__(self, num_classes=1000): super(AlexNetOWT_BN, self).__init__() self.ratioInfl=3 self.features = nn.Sequential( BinarizeConv2d(3, int(64*self.ratioInfl), kernel_size=11, stride=4, padding=2), nn.MaxPool2d(kernel_size=3, stride=2), nn.BatchNorm2d(int(64*self.ratioInfl)), nn.Hardtanh(inplace=True), BinarizeConv2d(int(64*self.ratioInfl), int(192*self.ratioInfl), kernel_size=5, padding=2), nn.MaxPool2d(kernel_size=3, stride=2), nn.BatchNorm2d(int(192*self.ratioInfl)), nn.Hardtanh(inplace=True), BinarizeConv2d(int(192*self.ratioInfl), int(384*self.ratioInfl), kernel_size=3, padding=1), nn.BatchNorm2d(int(384*self.ratioInfl)), nn.Hardtanh(inplace=True), BinarizeConv2d(int(384*self.ratioInfl), int(256*self.ratioInfl), kernel_size=3, padding=1), nn.BatchNorm2d(int(256*self.ratioInfl)), nn.Hardtanh(inplace=True), BinarizeConv2d(int(256*self.ratioInfl), 256, kernel_size=3, padding=1), nn.MaxPool2d(kernel_size=3, stride=2), nn.BatchNorm2d(256), nn.Hardtanh(inplace=True) ) self.classifier = nn.Sequential( BinarizeLinear(256 * 6 * 6, 4096), nn.BatchNorm1d(4096), nn.Hardtanh(inplace=True), #nn.Dropout(0.5), BinarizeLinear(4096, 4096), nn.BatchNorm1d(4096), nn.Hardtanh(inplace=True), #nn.Dropout(0.5), BinarizeLinear(4096, num_classes), nn.BatchNorm1d(1000), nn.LogSoftmax() ) #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} #} self.regime = { 0: {'optimizer': 'Adam', 'lr': 5e-3}, 20: {'lr': 1e-3}, 30: {'lr': 5e-4}, 35: {'lr': 1e-4}, 40: {'lr': 1e-5} } 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_binary(**kwargs): num_classes = kwargs.get( 'num_classes', 1000) return AlexNetOWT_BN(num_classes)