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import datasets |
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import numpy as np |
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from pathlib import Path |
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import torch |
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import torch.nn.functional as F |
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_DATASET_VERSION = datasets.Version("1.0.0") |
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_N_EMBED = { |
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"20x": 1, |
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"10x": 4, |
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"5x": 16, |
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"2_5x": 64, |
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"1_25x": 256, |
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} |
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_MAG_DICT = { |
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"20x": 0, |
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"10x": 1, |
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"5x": 2, |
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"2_5x": 3, |
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"1_25x": 4, |
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} |
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_FIXED_SSL_FEATURE_DIM_1 = 1024 |
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def get_ssl_feat_shape(mag_level, pool=False): |
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first_dim = _N_EMBED[mag_level] |
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h = int(np.sqrt(first_dim)) |
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if pool: |
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h = min(h, 8) |
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return (_FIXED_SSL_FEATURE_DIM_1, h, h) |
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def preprocess_features(feat_array): |
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if len(feat_array.shape) == 1: |
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feat_array = feat_array[:, None] |
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mean = feat_array.mean(axis=0, keepdims=True) |
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std = feat_array.std(axis=0, keepdims=True) |
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feat_array = (feat_array - mean) / (std + 1e-8) |
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return feat_array |
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class MagnificationConfig(datasets.BuilderConfig): |
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def __init__(self, mag_level=None, ssl_feat_shape_pooled=None, ssl_feat_shape_unpooled=None, data_dir=None, **kwargs): |
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super(MagnificationConfig, self).__init__(**kwargs) |
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self.mag_level = mag_level |
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self.ssl_feat_shape_pooled = ssl_feat_shape_pooled |
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self.ssl_feat_shape_unpooled = ssl_feat_shape_unpooled |
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self.data_dir = data_dir |
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class TCGADataset(datasets.GeneratorBasedBuilder): |
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VERSION = _DATASET_VERSION |
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BUILDER_CONFIGS = [] |
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for mag_level_str in _MAG_DICT.keys(): |
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builder_config_instance = MagnificationConfig( |
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name=mag_level_str, |
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version=_DATASET_VERSION, |
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description=f"Dataset at {mag_level_str} mag", |
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data_dir=mag_level_str, |
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mag_level=mag_level_str, |
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ssl_feat_shape_pooled=get_ssl_feat_shape(mag_level_str, pool=True), |
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ssl_feat_shape_unpooled=get_ssl_feat_shape(mag_level_str, pool=False) |
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) |
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BUILDER_CONFIGS.append(builder_config_instance) |
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DEFAULT_CONFIG_NAME = "20x" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=f"Dataset with images and SSL features. Configuration: {self.config.name}", |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"ssl_feat": datasets.Array3D(shape=self.config.ssl_feat_shape_pooled, dtype="float32"), |
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"ssl_feat_unpooled": datasets.Array3D(shape=self.config.ssl_feat_shape_unpooled, dtype="float32"), |
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"mag": datasets.Value("int32"), |
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} |
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), |
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homepage="https://github.com/cvlab-stonybrook/ZoomLDM", |
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) |
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def _split_generators(self, dl_manager): |
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original_script_dir = Path(self.base_path) |
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mag_folder_name = self.config.data_dir |
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mag_data_abs_path = original_script_dir / "data" / mag_folder_name |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"mag_folder_abs_path": mag_data_abs_path, |
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"mag_level": self.config.mag_level, |
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}, |
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), |
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] |
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def _generate_examples(self, mag_folder_abs_path: Path, mag_level: str): |
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idx = 0 |
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for i in range(16): |
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img_filename = f"{i}.jpg" |
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feat_filename = f"{i}_ssl_feat.npy" |
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img_path = mag_folder_abs_path / img_filename |
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feat_path = mag_folder_abs_path / feat_filename |
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ssl_feat_data = np.load(feat_path) |
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ssl_feat_data = np.float32(ssl_feat_data) |
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processed_feature = preprocess_features(ssl_feat_data) |
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h = np.sqrt(processed_feature.shape[1]).astype(int) |
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feat_array_unpooled = torch.tensor(processed_feature.reshape((-1, h, h))) |
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if h > 8: |
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shape = (8, 8) |
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feat_array_pooled = F.adaptive_avg_pool2d(feat_array_unpooled, shape) |
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else: |
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feat_array_pooled = feat_array_unpooled |
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yield idx, { |
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"image": str(img_path), |
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"ssl_feat": feat_array_pooled, |
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"ssl_feat_unpooled": feat_array_unpooled, |
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"mag": _MAG_DICT[mag_level], |
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} |
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idx += 1 |
