mindformers.dataset.ZeroShotImageClassificationDataset

class mindformers.dataset.ZeroShotImageClassificationDataset(dataset_config: Optional[dict] = None)

Zero Shot Image Classification Dataset API. output image, text, and label columns

Args:

dataset_config (dict): Config for dataset.

Returns:

A dataset for ZeroShotImageClassificationTrainer.

Examples:

>>> import os
>>> from mindformers import MindFormerBook, MindFormerConfig, build_dataset
>>> project_path = MindFormerBook.get_project_path()
>>> config_path = os.path.join(project_path, "configs", "clip",
>>>                     "run_clip_vit_b_32_zero_shot_image_classification_cifar100.yaml")
>>> config = MindFormerConfig(config_path)
    Note:
        Put cifar100 dataset to ./
        The detailed data setting could refer to ./configs/clip/clip.md
>>> config.eval_dataset_task.dataset_config.batch_size = 1
>>> dataset = build_dataset(config.eval_dataset_task)
>>> for item in dataset:
>>>     print(item)
>>>     break
[Tensor(shape=[1, 3, 224, 224], dtype=Float32, value=
[[[[1.11282456e+000, 1.11282456e+000, ... 1.47778523e+000, 1.47778523e+000],
[1.11282456e+000, 1.11282456e+000, ... 1.47778523e+000, 1.47778523e+000],
[1.11282456e+000, 1.11282456e+000, ... 1.47778523e+000, 1.47778523e+000],
...
[1.97748125e-001, 1.97748125e-001, ... 1.12205243e+000, 1.12205243e+000],
[1.97748125e-001, 1.97748125e-001, ... 1.12205243e+000, 1.12205243e+000],
[1.97748125e-001, 1.97748125e-001, ... 1.12205243e+000, 1.12205243e+000]]]]),
Tensor(shape=[1, 100, 77], dtype=Int32, value=
[[[49406,   320,  1674 ...     0,     0,     0],
[49406,   320,  1674 ...     0,     0,     0],
[49406,   320,  1674 ...     0,     0,     0],
...
[49406,   320,  1674 ...     0,     0,     0],
[49406,   320,  1674 ...     0,     0,     0],
[49406,   320,  1674 ...     0,     0,     0]]]),
Tensor(shape=[1], dtype=Int32, value= [49])]