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Python datasets.ImageNet方法代码示例

本文整理汇总了Python中torchvision.datasets.ImageNet方法的典型用法代码示例。如果您正苦于以下问题:Python datasets.ImageNet方法的具体用法?Python datasets.ImageNet怎么用?Python datasets.ImageNet使用的例子?那么, 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torchvision.datasets的用法示例。


在下文中一共展示了datasets.ImageNet方法的8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: get_loaders

# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import ImageNet [as 别名]
def get_loaders(dataroot, val_batch_size, train_batch_size, input_size, workers, num_nodes, local_rank):
    # TODO: pin-memory currently broken for distributed
    pin_memory = False
    # TODO: datasets.ImageNet
    val_data = datasets.ImageFolder(root=os.path.join(dataroot, 'val'), transform=get_transform(False, input_size))
    val_sampler = DistributedSampler(val_data, num_nodes, local_rank)
    val_loader = torch.utils.data.DataLoader(val_data, batch_size=val_batch_size, sampler=val_sampler,
                                             num_workers=workers, pin_memory=pin_memory)

    train_data = datasets.ImageFolder(root=os.path.join(dataroot, 'train'),
                                      transform=get_transform(input_size=input_size))
    train_sampler = DistributedSampler(train_data, num_nodes, local_rank)
    train_loader = torch.utils.data.DataLoader(train_data, batch_size=train_batch_size, sampler=train_sampler,
                                               num_workers=workers, pin_memory=pin_memory)
    return train_loader, val_loader 
开发者ID:Randl,项目名称:MobileNetV3-pytorch,代码行数:17,代码来源:data.py

示例2: load_targets

# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import ImageNet [as 别名]
def load_targets(self):
        """
        Downloads ImageNet labels and IDs and puts into self.root, then loads to self.targets

        :return: void - update self.targets with the ImageNet validation data labels, and downloads if
        the pickled validation data is not in the root location
        """
        download_url(
            url=ARCHIVE_DICT['labels']['url'],
            root=self.root,
            md5=ARCHIVE_DICT['labels']['md5'])

        with open(os.path.join(self.root, 'imagenet_val_targets.pkl'), 'rb') as handle:
            self.targets = pickle.load(handle) 
开发者ID:paperswithcode,项目名称:sotabench-eval,代码行数:16,代码来源:imagenet.py

示例3: save

# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import ImageNet [as 别名]
def save(self):
        """
        Calculate results and then put into a BenchmarkResult object

        On the sotabench.com server, this will produce a JSON file serialisation in sotabench_results.json and results
        will be recorded on the platform.

        :return: BenchmarkResult object with results and metadata
        """
        # recalculate to ensure no mistakes made during batch-by-batch metric calculation
        self.get_results()

        # If this is the first time the model is run, then we record evaluation time information

        if not self.cached_results:
            exec_speed = (time.time() - self.init_time)
            self.speed_mem_metrics['Tasks / Evaluation Time'] = len(self.outputs) / exec_speed
            self.speed_mem_metrics['Tasks'] = len(self.outputs)
            self.speed_mem_metrics['Evaluation Time'] = exec_speed
        else:
            self.speed_mem_metrics['Tasks / Evaluation Time'] = None
            self.speed_mem_metrics['Tasks'] = None
            self.speed_mem_metrics['Evaluation Time'] = None

        return BenchmarkResult(
            task=self.task,
            config={},
            dataset='ImageNet',
            results=self.results,
            speed_mem_metrics=self.speed_mem_metrics,
            model=self.model_name,
            model_description=self.model_description,
            arxiv_id=self.paper_arxiv_id,
            pwc_id=self.paper_pwc_id,
            paper_results=self.paper_results,
            run_hash=self.batch_hash,
        ) 
开发者ID:paperswithcode,项目名称:sotabench-eval,代码行数:39,代码来源:imagenet.py

示例4: __init__

# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import ImageNet [as 别名]
def __init__(self,
                 root,
                 train=True,
                 transform=None,
                 download=False):
        assert not download, "Download dataset by yourself!"
        super(ImageNet, self).__init__(root, split="train" if train else "val", transform=transform) 
开发者ID:moskomule,项目名称:homura,代码行数:9,代码来源:datasets.py

示例5: get_dataset

# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import ImageNet [as 别名]
def get_dataset(self):
        """
        Uses torchvision.datasets.ImageNet to load dataset.
        Downloads dataset if doesn't exist already.
        Returns:
             torch.utils.data.TensorDataset: trainset, valset
        """

        trainset = datasets.ImageNet('datasets/ImageNet/train/', split='train', transform=self.train_transforms,
                                     target_transform=None, download=True)
        valset = datasets.ImageNet('datasets/ImageNet/val/', split='val', transform=self.val_transforms,
                                   target_transform=None, download=True)

        return trainset, valset 
开发者ID:MrtnMndt,项目名称:Deep_Openset_Recognition_through_Uncertainty,代码行数:16,代码来源:datasets.py

示例6: get_train_val_loaders

# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import ImageNet [as 别名]
def get_train_val_loaders(
    root_path: str,
    train_transforms: Callable,
    val_transforms: Callable,
    batch_size: int = 16,
    num_workers: int = 8,
    val_batch_size: Optional[int] = None,
    limit_train_num_samples: Optional[int] = None,
    limit_val_num_samples: Optional[int] = None,
) -> Tuple[DataLoader, DataLoader, DataLoader]:

    train_ds = ImageNet(
        root_path, split="train", transform=lambda sample: train_transforms(image=sample)["image"], loader=opencv_loader
    )
    val_ds = ImageNet(
        root_path, split="val", transform=lambda sample: val_transforms(image=sample)["image"], loader=opencv_loader
    )

    if limit_train_num_samples is not None:
        np.random.seed(limit_train_num_samples)
        train_indices = np.random.permutation(len(train_ds))[:limit_train_num_samples]
        train_ds = Subset(train_ds, train_indices)

    if limit_val_num_samples is not None:
        np.random.seed(limit_val_num_samples)
        val_indices = np.random.permutation(len(val_ds))[:limit_val_num_samples]
        val_ds = Subset(val_ds, val_indices)

    # random samples for evaluation on training dataset
    if len(val_ds) < len(train_ds):
        np.random.seed(len(val_ds))
        train_eval_indices = np.random.permutation(len(train_ds))[: len(val_ds)]
        train_eval_ds = Subset(train_ds, train_eval_indices)
    else:
        train_eval_ds = train_ds

    train_loader = idist.auto_dataloader(
        train_ds, shuffle=True, batch_size=batch_size, num_workers=num_workers, drop_last=True,
    )

    val_batch_size = batch_size * 4 if val_batch_size is None else val_batch_size
    val_loader = idist.auto_dataloader(
        val_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False,
    )

    train_eval_loader = idist.auto_dataloader(
        train_eval_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False,
    )

    return train_loader, val_loader, train_eval_loader 
开发者ID:pytorch,项目名称:ignite,代码行数:52,代码来源:dataloaders.py

示例7: get_results

# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import ImageNet [as 别名]
def get_results(self):
        """
        Gets the results for the evaluator. This method only runs if predictions for all 5,000 ImageNet validation
        images are available. Otherwise raises an error and informs you of the missing or unmatched IDs.

        :return: dict with Top 1 and Top 5 Accuracy
        """

        if self.cached_results:
            return self.results

        if set(self.targets.keys()) != set(self.outputs.keys()):
            missing_ids = set(self.targets.keys()) - set(self.outputs.keys())
            unmatched_ids = set(self.outputs.keys()) - set(self.targets.keys())

            if len(unmatched_ids) > 0:
                raise ValueError('''There are {mis_no} missing and {un_no} unmatched image IDs\n\n'''
                                     '''Missing IDs are {missing}\n\n'''
                                     '''Unmatched IDs are {unmatched}'''.format(mis_no=len(missing_ids),
                                                                                un_no=len(unmatched_ids),
                                                                                missing=missing_ids,
                                                                                unmatched=unmatched_ids))
            else:
                raise ValueError('''There are {mis_no} missing image IDs\n\n'''
                                     '''Missing IDs are {missing}'''.format(mis_no=len(missing_ids),
                                                                            missing=missing_ids))

        # Do the calculation only if we have all the results...
        self.top1 = AverageMeter()
        self.top5 = AverageMeter()

        for i, dict_key in enumerate(tqdm.tqdm(self.targets.keys())):
            output = self.outputs[dict_key]
            target = self.targets[dict_key]
            prec1 = top_k_accuracy_score(y_true=target, y_pred=np.array([output]), k=1)
            prec5 = top_k_accuracy_score(y_true=target, y_pred=np.array([output]), k=5)
            self.top1.update(prec1, 1)
            self.top5.update(prec5, 1)

        self.results = {'Top 1 Accuracy': self.top1.avg, 'Top 5 Accuracy': self.top5.avg}
        self.speed_mem_metrics['Max Memory Allocated (Total)'] = get_max_memory_allocated()

        return self.results 
开发者ID:paperswithcode,项目名称:sotabench-eval,代码行数:45,代码来源:imagenet.py

示例8: pre_benchmark_atk

# 需要导入模块: from torchvision import datasets [as 别名]
# 或者: from torchvision.datasets import ImageNet [as 别名]
def pre_benchmark_atk(**kwargs):
  """
  Helper function that sets all the defaults while performing checks
  for all the options passed before benchmarking attacks.
  """

  # Set the Default options if nothing explicit provided
  def_dict = {
    'bs'       : 4,
    'trf'      : get_trf('rz256_cc224_tt_normimgnet'),
    'dset'     : 'NA',
    'root'     : './',
    'topk'     : (1, 5),
    'dfunc'    : datasets.ImageFolder,
    'download' : True,
  }

  for key, val in def_dict.items(): 
    if key not in kwargs: kwargs[key] = val
  
  if kwargs['dset'] == 'NA':
    if 'loader' not in kwargs:
      dset = kwargs['dfunc'](kwargs['root'], transform=kwargs['trf'])
      loader = DataLoader(dset, batch_size=kwargs['bs'], num_workers=2)
    else:
      loader = kwargs['loader']
  
  # Set dataset specific functions here
  else:
    if kwargs['dset'] == IMGNET12:
      dset = datasets.ImageNet(kwargs['root'], split='test',
                      download=kwargs['download'], transform=kwargs['trf'])
    elif kwargs['dset'] == MNIST:
      kwargs['trf'] = get_trf('tt_normmnist')
      kwargs['dfunc'] = datasets.MNIST
      dset = kwargs['dfunc'](kwargs['root'], train=False, 
                     download=kwargs['download'], transform=kwargs['trf'])
    else: raise

    loader = DataLoader(dset, shuffle=False, batch_size=kwargs['bs'])
  topk = kwargs['topk']
    
  for key, val in kwargs.items():
    print ('[INFO] Setting {} to {}.'.format(key, kwargs[key]))

  # Deleting keys that is used just for benchmark_atk() function is 
  # important as the same kwargs dict is passed to initialize the attack
  # So, otherwise the attack will throw an exception
  for key in def_dict:
    del kwargs[key]
  if 'loader' in kwargs: del kwargs['loader']

  return loader, topk, kwargs 
开发者ID:iArunava,项目名称:scratchai,代码行数:55,代码来源:utils.py


注:本文中的torchvision.datasets.ImageNet方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。