Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,82 @@
+---
+license: mit
+library_name: pytorch
+datasets:
+  - structlearning/isonetpp-benchmark
+tags:
+  - graphs
+  - subgraph-matching
+  - graph-retrieval
+task_categories:
+  - graph-ml
+---
+
+
+    # ISONeT++ Model: isonetpp_node on aids
+
+    Trained on the **large** split.
+
+    ## Usage
+
+    ```python
+    import torch
+    import json
+    from utils.tooling import make_read_only
+    from subgraph_matching.model_handler import get_model
+    from subgraph_matching.test import evaluate_model
+
+    
+    from huggingface_hub import hf_hub_download
+
+    model_name = "isonetpp_node"
+    dataset_name = "aids"
+    
+    REPO_ID = "structlearning/isonetpp-benchmark"  # change if you fork/rename
+
+    def _load_module_from_hub(repo_id, filename, repo_type="dataset", module_name=None):
+        path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type=repo_type)
+        name = module_name or filename.rsplit(".", 1)[0]
+        spec = importlib.util.spec_from_file_location(name, path)
+        mod = importlib.util.module_from_spec(spec)
+        sys.modules[name] = mod
+        spec.loader.exec_module(mod)
+        return mod
+
+    dataset_mod = _load_module_from_hub(REPO_ID, "subiso_dataset.py", repo_type="dataset", module_name="subiso_dataset")
+    loader = _load_module_from_hub(REPO_ID, "isonetpp_loader.py", repo_type="dataset", module_name="isonetpp_loader")
+
+
+    ds_test = loader.load_isonetpp_benchmark(
+        repo_id=REPO_ID,
+        mode="test",           # "train" | "val" | "test"
+        dataset_name="aids"
+    )
+
+    repo_id = f"structlearning/isonetpp-isonetpp_node-aids-large"
+
+    # Load config
+    config = json.load(open(hf_hub_download(repo_id, "config.json")))
+    config = make_read_only(config)
+
+    # Load weights
+    weights = hf_hub_download(repo_id, "pytorch_model.bin")
+    state = torch.load(weights,  weights_only=False)
+
+    # Load dataset
+    ds_test = loader.load_isonetpp_benchmark(dataset_name="aids", mode="test")
+
+    model = get_model(
+        model_name=config.name,
+        config=config.model_config,
+        max_node_set_size=ds_test.max_node_set_size,
+        max_edge_set_size=ds_test.max_edge_set_size,
+        device="cuda"
+    )
+    model.load_state_dict(state)
+    model.to("cuda")
+
+    _, map_val = evaluate_model(model, ds_test)
+    print(map_val)
+
+    ```
+