| import datasets | |
| import numpy as np | |
| from pathlib import Path | |
| import torch | |
| import torch.nn.functional as F | |
| from einops import rearrange | |
| _DATASET_VERSION = datasets.Version("1.0.0") | |
| _N_EMBED = { | |
| "1x": 1, | |
| "2x": 4, | |
| "3x": 16, | |
| "4x": 64, | |
| } | |
| _MAG_DICT = { | |
| "1x": 0, | |
| "2x": 1, | |
| "3x": 2, | |
| "4x": 3, | |
| } | |
| _FIXED_SSL_FEATURE_DIM_1 = 1024 | |
| def get_ssl_feat_shape(mag_level): | |
| first_dim = _N_EMBED[mag_level] | |
| h = int(np.sqrt(first_dim)) | |
| return (_FIXED_SSL_FEATURE_DIM_1, h, h) | |
| def preprocess_features(feat_array): | |
| if len(feat_array.shape) == 1: | |
| feat_array = feat_array[:, None] | |
| mean = feat_array.mean(axis=0, keepdims=True) | |
| std = feat_array.std(axis=0, keepdims=True) | |
| feat_array = (feat_array - mean) / (std + 1e-8) | |
| return feat_array | |
| class MagnificationConfig(datasets.BuilderConfig): | |
| def __init__(self, mag_level=None, ssl_feat_shape=None, data_dir=None, **kwargs): | |
| super(MagnificationConfig, self).__init__(**kwargs) | |
| self.mag_level = mag_level | |
| self.ssl_feat_shape = ssl_feat_shape | |
| self.data_dir = data_dir | |
| class NAIPDataset(datasets.GeneratorBasedBuilder): | |
| VERSION = _DATASET_VERSION | |
| BUILDER_CONFIGS = [] | |
| for mag_level_str in _MAG_DICT.keys(): | |
| builder_config_instance = MagnificationConfig( | |
| name=mag_level_str, | |
| version=_DATASET_VERSION, | |
| description=f"Dataset at {mag_level_str} mag", | |
| data_dir=mag_level_str, | |
| mag_level=mag_level_str, | |
| ssl_feat_shape=get_ssl_feat_shape(mag_level_str), | |
| ) | |
| BUILDER_CONFIGS.append(builder_config_instance) | |
| DEFAULT_CONFIG_NAME = "1x" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=f"Dataset with images and SSL features. Configuration: {self.config.name}", | |
| features=datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "ssl_feat": datasets.Array3D(shape=self.config.ssl_feat_shape, dtype="float32"), | |
| "mag": datasets.Value("int32"), | |
| } | |
| ), | |
| homepage="https://github.com/cvlab-stonybrook/ZoomLDM", | |
| ) | |
| def _split_generators(self, dl_manager): | |
| original_script_dir = Path(self.base_path) | |
| mag_folder_name = self.config.data_dir | |
| mag_data_abs_path = original_script_dir / "data" / mag_folder_name | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "mag_folder_abs_path": mag_data_abs_path, | |
| "mag_level": self.config.mag_level, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, mag_folder_abs_path: Path, mag_level: str): | |
| idx = 0 | |
| for i in range(16): | |
| img_filename = f"{i}.jpg" | |
| feat_filename = f"{i}_ssl_feat.npy" | |
| img_path = mag_folder_abs_path / img_filename | |
| feat_path = mag_folder_abs_path / feat_filename | |
| ssl_feat_data = np.load(feat_path) | |
| h = np.sqrt(ssl_feat_data.shape[0]).astype(int) | |
| ssl_feat_data = torch.tensor(rearrange(ssl_feat_data, "(h1 h2) dim -> dim h1 h2", h1 = h)) | |
| feat_array = preprocess_features(ssl_feat_data) | |
| yield idx, { | |
| "image": str(img_path), | |
| "ssl_feat": feat_array, | |
| "mag": _MAG_DICT[mag_level], | |
| } | |
| idx += 1 |