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Python imagenet.__dict__方法代码示例

本文整理汇总了Python中models.imagenet.__dict__方法的典型用法代码示例。如果您正苦于以下问题:Python imagenet.__dict__方法的具体用法?Python imagenet.__dict__怎么用?Python imagenet.__dict__使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在models.imagenet的用法示例。


在下文中一共展示了imagenet.__dict__方法的1个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: create_model

# 需要导入模块: from models import imagenet [as 别名]
# 或者: from models.imagenet import __dict__ [as 别名]
def create_model(pretrained, dataset, arch, parallel=True, device_ids=None):
    """Create a pytorch model based on the model architecture and dataset

    Args:
        pretrained: True is you wish to load a pretrained model.  Only torchvision models
          have a pretrained model.
        dataset:
        arch:
        parallel:
    """
    msglogger.info('==> using %s dataset' % dataset)

    model = None
    if dataset == 'imagenet':
        str_pretrained = 'pretrained ' if pretrained else ''
        msglogger.info("=> using %s%s model for ImageNet" % (str_pretrained, arch))
        assert arch in torch_models.__dict__ or arch in imagenet_extra_models.__dict__, \
            "Model %s is not supported for dataset %s" % (arch, 'ImageNet')
        if arch in torch_models.__dict__:
            model = torch_models.__dict__[arch](pretrained=pretrained)
        else:
            assert not pretrained, "Model %s (ImageNet) does not have a pretrained model" % arch
            model = imagenet_extra_models.__dict__[arch]()
    elif dataset == 'cifar10':
        msglogger.info("=> creating %s model for CIFAR10" % arch)
        assert arch in cifar10_models.__dict__, "Model %s is not supported for dataset CIFAR10" % arch
        assert not pretrained, "Model %s (CIFAR10) does not have a pretrained model" % arch
        model = cifar10_models.__dict__[arch]()
    else:
        print("FATAL ERROR: create_model does not support models for dataset %s" % dataset)
        exit()

    if (arch.startswith('alexnet') or arch.startswith('vgg')) and parallel:
        model.features = torch.nn.DataParallel(model.features, device_ids=device_ids)
    elif parallel:
        model = torch.nn.DataParallel(model, device_ids=device_ids)

    model.cuda()
    return model 
开发者ID:cornell-zhang,项目名称:dnn-quant-ocs,代码行数:41,代码来源:__init__.py


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