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---
license: mit
library_name: pytorch
datasets:
- structlearning/isonetpp-benchmark
tags:
- graphs
- subgraph-matching
- graph-retrieval
task_categories:
- graph-ml
---
# ISONeT++ Model: simgnn on ptc_mr
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 = "simgnn"
dataset_name = "ptc_mr"
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="ptc_mr"
)
repo_id = f"structlearning/isonetpp-simgnn-ptc_mr-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="ptc_mr", 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)
```
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