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.gitattributes

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geneformer/Geneformer_code_only/config.json

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+  "position_embedding_type": "absolute",
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+  "vocab_size": 20275
+}

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kukanja_finetuned/main_nicheformer.py

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+#!/usr/bin/env python
+import sys, os
+sys.path.append(os.path.abspath(__file__ + '/../../..'))
+
+import random, json
+import numpy as np
+import torch
+import anndata
+
+from src.utils.dataloader_kukanja import build_dataloaders_kukanja
+from src.models.fm_nlevel import NicheformerNLevel
+from src.engines.fm_trainer_kukanja import FMTrainerKukanja
+from src.utils.logger import get_logger
+from src.utils.args import get_public_config
+
+NICHEFORMER_ASSET_ROOT = (
+    "/data1/foundation_model_based_mas/tools_layer/mcp_tools/foundation_model_checkpoints/nicheformer"
+)
+
+
+def get_args():
+    parser = get_public_config()
+    parser.set_defaults(model_name="Nicheformer", dropout=0.2)
+    parser.add_argument('--npz_path', type=str, required=True)
+    parser.add_argument('--h5ad_path', type=str, required=True)
+    parser.add_argument('--checkpoint_path', type=str, default=f'{NICHEFORMER_ASSET_ROOT}/nicheformer.ckpt')
+    parser.add_argument('--vocab_path', type=str, default=f'{NICHEFORMER_ASSET_ROOT}/model.h5ad')
+    parser.add_argument('--merfish_mean_path', type=str, default=f'{NICHEFORMER_ASSET_ROOT}/merfish_mean_script.npy')
+    parser.add_argument('--gene_name_to_ens_path', type=str, default=f'{NICHEFORMER_ASSET_ROOT}/gene_name_id_dict_gc104M.pkl')
+    parser.add_argument('--freeze_backbone', action='store_true')
+    args = parser.parse_args()
+
+    if 'ms' in os.path.basename(args.npz_path).lower():
+        dataset_tag = 'Kukanja_MS'
+    elif 'eae' in os.path.basename(args.npz_path).lower():
+        dataset_tag = 'Kukanja_EAE'
+    else:
+        dataset_tag = 'Kukanja'
+
+    ft_tag = 'freeze' if args.freeze_backbone else 'full'
+    ckpt_tag = f"{ft_tag}_s{args.seed}"
+    output_root = os.environ.get('KUKANJA_OUT_ROOT', '/tmp/kukanja_runs')
+    log_dir = os.path.join(output_root, dataset_tag)
+    os.makedirs(log_dir, exist_ok=True)
+    logger = get_logger(log_dir, __name__, f'record_nicheformer_{ckpt_tag}.log')
+    logger.info(args)
+    args.save_dir = os.path.join(log_dir, 'checkpoints', f'nicheformer_{ckpt_tag}')
+    args.dataset_tag = dataset_tag
+    return args, log_dir, logger
+
+
+def set_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+        torch.backends.cudnn.deterministic = True
+
+
+if __name__ == '__main__':
+    args, log_dir, logger = get_args()
+    set_seed(args.seed)
+    device = torch.device(args.device)
+
+    train_loader, val_loader, test_loader, info = build_dataloaders_kukanja(
+        args.npz_path, batch_size=args.bs, seed=args.seed, num_workers=args.num_workers)
+
+    args.output_num = info['output_num']
+    args.label_names = info['label_names']
+    logger.info(f"Dataset: {args.dataset_tag}, Levels: {list(zip(info['label_names'], info['output_num']))}")
+
+    adata = anndata.read_h5ad(args.h5ad_path, backed='r')
+    gene_names = list(adata.var_names)
+    logger.info(f"Gene panel: {len(gene_names)} genes")
+
+    model = NicheformerNLevel(
+        checkpoint_path=args.checkpoint_path,
+        vocab_path=args.vocab_path,
+        merfish_mean_path=args.merfish_mean_path,
+        gene_name_to_ens_path=args.gene_name_to_ens_path,
+        gene_names=gene_names,
+        output_num=info['output_num'],
+        dropout=args.dropout,
+        freeze_backbone=args.freeze_backbone,
+    ).to(device)
+
+    n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    logger.info(f"Model: Nicheformer N-Level, trainable params: {n_params:,}")
+
+    optimizer = torch.optim.Adam(
+        filter(lambda p: p.requires_grad, model.parameters()),
+        lr=args.lr, weight_decay=args.weight_decay)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+        optimizer, T_max=args.max_epochs, eta_min=1e-6)
+
+    engine = FMTrainerKukanja(
+        model, optimizer, scheduler, device, logger, args,
+        label_names=info['label_names'], output_num=info['output_num'])
+
+    if args.mode == 'train':
+        engine.train(train_loader, val_loader)
+        engine.test(test_loader)
+    else:
+        engine.test(test_loader)

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+  "save_dir": "./save/cellxgene_census_brain-May19-02-00-2023",
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+}

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