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