| """ |
| Dataloader for global_data_v2.npz. |
| Returns all fields needed by mjm_2a/b/c trainers. |
| """ |
| import numpy as np |
| import torch |
| from torch.utils.data import Dataset, DataLoader |
|
|
|
|
| class SpatialSEAAD_V2_Dataset(Dataset): |
| def __init__(self, X, y_class, y_subclass, y_supertype, batch_donor, |
| spatial, spatial_tiled, cps, confidence, |
| depth_norm, has_depth, |
| cell_volume_norm, has_volume, |
| meta_info): |
| self.X = torch.from_numpy(X).float() |
| self.y_class = torch.from_numpy(y_class).long() |
| self.y_subclass = torch.from_numpy(y_subclass).long() |
| self.y_supertype = torch.from_numpy(y_supertype).long() |
| self.batch_donor = torch.from_numpy(batch_donor).long() |
| self.spatial = torch.from_numpy(spatial).float() |
| self.spatial_tiled = torch.from_numpy(spatial_tiled).float() |
| self.cps = torch.from_numpy(cps).float() |
| self.confidence = torch.from_numpy(confidence).float() |
| self.depth_norm = torch.from_numpy(depth_norm).float() |
| self.has_depth = torch.from_numpy(has_depth.astype(np.float32)).float() |
| self.cell_volume_norm = torch.from_numpy(cell_volume_norm).float() |
| self.has_volume = torch.from_numpy(has_volume.astype(np.float32)).float() |
|
|
| meta = meta_info.item() |
| self.num_c = meta.get('num_class') |
| self.num_sc = meta.get('num_subclass') |
| self.num_st = meta.get('num_supertype') |
|
|
| def __len__(self): |
| return self.X.shape[0] |
|
|
| def __getitem__(self, idx): |
| return { |
| 'X': self.X[idx], |
| 'spatial': self.spatial[idx], |
| 'spatial_tiled': self.spatial_tiled[idx], |
| 'batch_id': self.batch_donor[idx], |
| 'y_class': self.y_class[idx], |
| 'y_subclass': self.y_subclass[idx], |
| 'y_supertype': self.y_supertype[idx], |
| 'cps': self.cps[idx], |
| 'confidence': self.confidence[idx], |
| 'depth_norm': self.depth_norm[idx], |
| 'has_depth': self.has_depth[idx], |
| 'cell_volume_norm': self.cell_volume_norm[idx], |
| 'has_volume': self.has_volume[idx], |
| } |
|
|
|
|
| def build_dataloaders_v2(npz_path, batch_size=512, seed=42, num_workers=8): |
| data = np.load(npz_path, allow_pickle=True) |
|
|
| X_all = data['X'] |
| y_c_all = data['y_class'] |
| y_sc_all = data['y_subclass'] |
| y_st_all = data['y_supertype'] |
| batch_all = data['batch_donor'] |
| spatial_all = data['spatial'] |
| spatial_tiled_all = data['spatial_tiled'] |
| cps_all = data['cps'] |
| conf_all = data['y_supertype_confidence'] |
| depth_all = data['depth_norm'] |
| has_depth_all = data['has_depth'] |
| vol_all = data['cell_volume_norm'] |
| has_vol_all = data['has_volume'] |
| meta_info = data['meta'] |
|
|
| |
| unique_donors = np.unique(batch_all) |
| np.random.seed(seed) |
| np.random.shuffle(unique_donors) |
| test_donors = unique_donors[:6] |
| val_donors = unique_donors[6:9] |
| train_donors = unique_donors[9:] |
|
|
| def _slice(mask): |
| return SpatialSEAAD_V2_Dataset( |
| X_all[mask], y_c_all[mask], y_sc_all[mask], y_st_all[mask], |
| batch_all[mask], spatial_all[mask], spatial_tiled_all[mask], |
| cps_all[mask], conf_all[mask], |
| depth_all[mask], has_depth_all[mask], |
| vol_all[mask], has_vol_all[mask], |
| meta_info, |
| ) |
|
|
| mask_train = np.isin(batch_all, train_donors) |
| mask_val = np.isin(batch_all, val_donors) |
| mask_test = np.isin(batch_all, test_donors) |
|
|
| train_ds = _slice(mask_train) |
| val_ds = _slice(mask_val) |
| test_ds = _slice(mask_test) |
|
|
| train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, |
| drop_last=True, num_workers=num_workers) |
| val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=True, |
| num_workers=num_workers) |
| test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False, |
| num_workers=num_workers) |
| return train_loader, val_loader, test_loader |
|
|
