| import os | |
| import numpy as np | |
| import datasets | |
| class MedMNIST(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description="OrganCMNIST dataset from MedMNIST collection", | |
| features=datasets.Features( | |
| { | |
| "image": datasets.Array3D(dtype="uint8", shape=(28, 28, 3)), | |
| "label": datasets.ClassLabel(num_classes=11), | |
| } | |
| ), | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.manual_dir | |
| npz_path = os.path.join(data_dir, "organcmnist.npz") | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"npz_path": npz_path, "split": "train"}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"npz_path": npz_path, "split": "val"}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"npz_path": npz_path, "split": "test"}), | |
| ] | |
| def _generate_examples(self, npz_path, split): | |
| data = np.load(npz_path) | |
| images = data[f"{split}_images.npy"] | |
| labels = data[f"{split}_labels.npy"].squeeze() | |
| for idx in range(len(images)): | |
| yield idx, {"image": images[idx], "label": int(labels[idx])} | |