def se_resnet34(pretrained, num_classes=1000): """Constructs a SE-ResNet-34 model.""" model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) if pretrained: state_dict = torch.load('/home/kg2/se_resnet34_best.pth')['state_dict'] model.load_state_dict(state_dict, strict=False) return model
def se_resnet34(num_classes=5): """Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def SE_ResNet101(num_classes=1_000): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def se_resnet18(num_classes=1_000): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def se_resnet152(num_classes=5): """Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 8, 36, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def dse_resnet50(num_classes=1000): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(DSEBottleneck, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def feasc18(num_classes=200, nparts=1): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes, nparts=nparts) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def _se_resnet(arch, block, layers, pretrained, progress, **kwargs): # adapted from the _resnet function in torch vision model = ResNet(block, layers, **kwargs) model.avgpool = nn.AdaptiveAvgPool2d(1) if pretrained: state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) model.load_state_dict(state_dict) return model
def se_resnet50(num_classes=5, pretrained=False): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) if pretrained: model.load_state_dict(load_state_dict_from_url( "https://github.com/moskomule/senet.pytorch/releases/download/archive/seresnet50-60a8950a85b2b.pkl")) return model
def se_resnet50(num_classes=1000, pretrained=False): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) if pretrained: model.load_state_dict(model_zoo.load_url("https://www.dropbox.com/s/xpq8ne7rwa4kg4c/seresnet50-60a8950a85b2b.pkl")) return model
def feasc50(num_classes=200, nparts=1, seflag=False): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ if seflag: rd = [16, 32, 64, 128] model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes, rd=rd, nparts=nparts, seflag=True) else: model = ResNet(ResBottleneck, [3, 4, 6, 3], num_classes=num_classes, nparts=nparts, seflag=False) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def se_resnet34(num_classes=1000, pretrained="/home/ibian/.torch/models/resnet34-333f7ec4.pth"): """Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) # use pretrained model if pretrained is not None: print("=> loading pretrained model '{}'".format(pretrained)) model_dict = model.state_dict() checkpoint = torch.load(pretrained) checkpoint = {k: v for k, v in checkpoint.items() if k in model_dict} model_dict.update(checkpoint) model.load_state_dict(model_dict) return model
def Net(num_classes): model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = torch.nn.AdaptiveAvgPool2d(1) return model
def srm_resnet101(num_classes=1000): model = ResNet(bottleneck_factory(layer_block=SRMWithCorrMatrix), [3, 4, 23, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def resnet101(num_classes=1000): model = ResNet(bottleneck_factory(), [3, 4, 23, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def se_resnet50(num_classes=1000): model = ResNet(bottleneck_factory(layer_block=SELayer), [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def resnet34(num_classes=1000): model = ResNet(basic_block_factory(), [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def se_resnet34(num_classes=1000): model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model def se_resnet50(num_classes=1_000, pretrained=False): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) if pretrained: model.load_state_dict( load_state_dict_from_url( "https://github.com/moskomule/senet.pytorch/releases/download/archive/seresnet50-60a8950a85b2b.pkl" )) return model def se_resnet101(num_classes=1_000): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes)
def se_resnet18(num_classes=1000): """Constructs a SE-ResNet-18 model.""" model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def se_resnet101(num_classes=1_000): model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def se_resnet18(num_classes=1_000): model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def resnet152(**kwargs): """Constructs a ResNet-152 model. """ model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def resnet18(**kwargs): """Constructs a ResNet-18 model. """ model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def se_resnet152(num_classes=1000): """Constructs a SE-ResNet-152 model.""" model = ResNet(SEBottleneck, [3, 8, 36, 3], num_classes=num_classes) model.avgpool = nn.AdaptiveAvgPool2d(1) return model
def resnet34(**kwargs): """Constructs a ResNet-34 model. """ model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) model.avgpool = nn.AdaptiveAvgPool2d(1) return model