Fixing
This commit is contained in:
araison
parent
e871f045a4
commit
f7bb1e7858
@ -4,14 +4,12 @@ import shutil
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from explaining_framework.utils.io import read_yaml, write_yaml
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from torch_geometric.data.makedirs import makedirs
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# from torch_geometric.graphgym.loader import load_pyg_dataset
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from torch_geometric.graphgym.loader import create_dataset
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from torch_geometric.graphgym.utils.io import string_to_python
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def chunkizing_list(l, n):
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for i in range(0, len(l), n):
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yield l[i : i + n]
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return [l[i : i + n] for i in range(0, len(l), n)]
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if "__main__" == __name__:
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@ -23,60 +21,61 @@ if "__main__" == __name__:
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makedirs(explaining_folder)
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explainer_folder = os.path.join(config_folder, "explaining")
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makedirs(explainer_folder)
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chunk = list(range(154, 160, 2))
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DATASET = [
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"CIFAR10",
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"TRIANGLES",
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"COLORS-3",
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"REDDIT-BINARY",
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"REDDIT-MULTI-5K",
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"REDDIT-MULTI-12K",
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"COLLAB",
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"DBLP_v1",
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"COIL-DEL",
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"COIL-RAG",
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"Fingerprint",
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"Letter-high",
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"Letter-low",
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"Letter-med",
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"MSRC_9",
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"MSRC_21",
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"MSRC_21C",
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"DD",
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"ENZYMES",
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"PROTEINS",
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"QM9",
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"MUTAG",
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"Mutagenicity",
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"AIDS",
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"PATTERN",
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"CLUSTER",
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"MNIST",
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"CIFAR10",
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"TSP",
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"CSL",
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"KarateClub",
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"CS",
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"Physics",
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"BBBP",
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"Tox21",
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"HIV",
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"PCBA",
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"MUV",
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"BACE",
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"SIDER",
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"ClinTox",
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"AIFB",
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"AM",
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"MUTAG",
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"BGS",
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"FAUST",
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"DynamicFAUST",
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"ShapeNet",
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"ModelNet10",
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"ModelNet40",
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"PascalVOC-SP",
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"COCO-SP",
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# "TRIANGLES",
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# "COLORS-3",
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# "REDDIT-BINARY",
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# "REDDIT-MULTI-5K",
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# "REDDIT-MULTI-12K",
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# "COLLAB",
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# "DBLP_v1",
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# "COIL-DEL",
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# "COIL-RAG",
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# "Fingerprint",
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# "Letter-high",
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# "Letter-low",
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# "Letter-med",
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# "MSRC_9",
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# "MSRC_21",
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# "MSRC_21C",
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# "DD",
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# "ENZYMES",
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# "PROTEINS",
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# "QM9",
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# "MUTAG",
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# "Mutagenicity",
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# "AIDS",
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# "PATTERN",
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# "CLUSTER",
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# "MNIST",
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# "CIFAR10",
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# "TSP",
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# "CSL",
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# "KarateClub",
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# "CS",
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# "Physics",
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# "BBBP",
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# "Tox21",
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# "HIV",
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# "PCBA",
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# "MUV",
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# "BACE",
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# "SIDER",
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# "ClinTox",
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# "AIFB",
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# "AM",
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# "MUTAG",
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# "BGS",
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# "FAUST",
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# "DynamicFAUST",
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# "ShapeNet",
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# "ModelNet10",
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# "ModelNet40",
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# "PascalVOC-SP",
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# "COCO-SP",
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]
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EXPLAINER = [
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"CAM",
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@ -95,57 +94,56 @@ if "__main__" == __name__:
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"SCGNN",
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]
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# for dataset_name in DATASET:
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# try:
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# dataset = load_pyg_dataset(name=dataset_name, dataset_dir="/tmp/")
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# except Exception as e:
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# print(e)
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# continue
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# for chunk in chunkizing_list(list(range(len(dataset))), 1000):
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# for model_kind in ["best", "worst"]:
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# for explainer_name in EXPLAINER:
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# explaining_cfg = {}
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# # explaining_cfg['adjust']['strategy']= 'rpns'
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# # explaining_cfg['attack']['name']= 'all'
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# explaining_cfg[
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# "cfg_dest"
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# ] = f"dataset={dataset_name}-model={model_kind}=explainer={explainer_name}-chunk=[{chunk[0]},{chunk[-1]}]"
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# explaining_cfg["dataset"] = {}
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# explaining_cfg["dataset"]["name"] = dataset_name
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# explaining_cfg["dataset"]["item"] = chunk
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# # explaining_cfg['explainer']['cfg']= 'default'
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# explaining_cfg["explainer"] = {}
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# explaining_cfg["explainer"]["name"] = explainer_name
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# explaining_cfg["explanation_type"] = "phenomenon"
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# # explaining_cfg['metrics']['accuracy']['name']='all'
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# # explaining_cfg['metrics']['fidelity']['name']='all'
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# # explaining_cfg['metrics']['sparsity']['name']='all'
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# explaining_cfg["model"] = {}
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# explaining_cfg["model"]["ckpt"] = model_kind
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# explaining_cfg["model"][
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# "path"
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# ] = "/home/SIC/araison/test_ggym/pytorch_geometric/graphgym/results"
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# # explaining_cfg['out_dir']='./explanation'
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# # explaining_cfg['print']='both'
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# # explaining_cfg['threshold']['config']['type']='all'
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# # explaining_cfg['threshold']['value']['hard']=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
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# # explaining_cfg['threshold']['value']['topk']=[2, 3, 5, 10, 20, 30, 50]
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# PATH = os.path.join(
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# explaining_folder + "/" + explaining_cfg["cfg_dest"] + ".yaml",
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# )
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for dataset_name in DATASET:
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for model_kind in ["best", "worst"]:
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for explainer_name in EXPLAINER:
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explaining_cfg = {}
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# explaining_cfg['adjust']['strategy']= 'rpns'
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# explaining_cfg['attack']['name']= 'all'
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explaining_cfg[
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"cfg_dest"
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] = f"dataset={dataset_name}-model={model_kind}=explainer={explainer_name}-chunk=[{chunk[0]},{chunk[-1]}]"
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# = string_to_python(
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# f"dataset={dataset_name}-model={model_kind}=explainer={explainer_name}"
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explaining_cfg["dataset"] = {}
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explaining_cfg["dataset"]["name"] = string_to_python(dataset_name)
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explaining_cfg["dataset"]["item"] = chunk
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# explaining_cfg['explainer']['cfg']= 'default'
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explaining_cfg["explainer"] = {}
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explaining_cfg["explainer"]["name"] = string_to_python(explainer_name)
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explaining_cfg["explainer"]["force"] = True
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explaining_cfg["explanation_type"] = string_to_python("phenomenon")
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# explaining_cfg['metrics']['accuracy']['name']='all'
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# explaining_cfg['metrics']['fidelity']['name']='all'
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# explaining_cfg['metrics']['sparsity']['name']='all'
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explaining_cfg["model"] = {}
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explaining_cfg["model"]["ckpt"] = string_to_python(model_kind)
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explaining_cfg["model"]["path"] = string_to_python(
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"/home/SIC/araison/test_ggym/pytorch_geometric/graphgym/results"
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)
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# explaining_cfg['out_dir']='./explanation'
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# explaining_cfg['print']='both'
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# explaining_cfg['threshold']['config']['type']='all'
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# explaining_cfg['threshold']['value']['hard']=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
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# explaining_cfg['threshold']['value']['topk']=[2, 3, 5, 10, 20, 30, 50]
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PATH = os.path.join(
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explaining_folder + "/" + explaining_cfg["cfg_dest"] + ".yaml",
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)
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write_yaml(explaining_cfg, PATH)
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# if os.path.exists(PATH):
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# continue
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# else:
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# write_yaml(explaining_cfg, PATH)
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configs = [
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path for path in glob.glob(os.path.join(explaining_folder, "**", "*.yaml"))
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]
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for path in configs:
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data = read_yaml(path)
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data["model"][
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"path"
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] = "/media/data/SIC/araison/exps/pyg_fork/graphgym/results/graph_classif_base_grid_graph_classif_grid"
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write_yaml(data, path)
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# configs = [
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# path for path in glob.glob(os.path.join(explaining_folder, "**", "*.yaml"))
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# ]
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# for path in configs:
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# data = read_yaml(path)
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# data["model"][
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# "path"
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# ] = "/media/data/SIC/araison/exps/pyg_fork/graphgym/results/graph_classif_base_grid_graph_classif_grid"
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# write_yaml(data, path)
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# for index, config_chunk in enumerate(
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# chunkizing_list(configs, int(len(configs) / 5))
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@ -6,6 +6,20 @@ import os
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from typing import Any
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from eixgnn.eixgnn import EiXGNN
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from scgnn.scgnn import SCGNN
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from torch_geometric import seed_everything
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from torch_geometric.data import Batch, Data
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from torch_geometric.data.makedirs import makedirs
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from torch_geometric.explain import Explainer
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from torch_geometric.explain.config import ThresholdConfig
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from torch_geometric.explain.explanation import Explanation
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from torch_geometric.graphgym.config import cfg
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from torch_geometric.graphgym.loader import create_dataset
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from torch_geometric.graphgym.model_builder import cfg, create_model
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from torch_geometric.graphgym.utils.device import auto_select_device
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from torch_geometric.loader.dataloader import DataLoader
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from yacs.config import CfgNode as CN
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from explaining_framework.config.explainer_config.eixgnn_config import \
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eixgnn_cfg
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from explaining_framework.config.explainer_config.scgnn_config import scgnn_cfg
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@ -31,19 +45,6 @@ from explaining_framework.utils.io import (dump_cfg, is_exists,
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obj_config_to_str, read_json,
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set_printing, write_json,
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write_yaml)
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from scgnn.scgnn import SCGNN
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from torch_geometric import seed_everything
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from torch_geometric.data import Batch, Data
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from torch_geometric.data.makedirs import makedirs
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from torch_geometric.explain import Explainer
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from torch_geometric.explain.config import ThresholdConfig
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from torch_geometric.explain.explanation import Explanation
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from torch_geometric.graphgym.config import cfg
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from torch_geometric.graphgym.loader import create_dataset
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from torch_geometric.graphgym.model_builder import cfg, create_model
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from torch_geometric.graphgym.utils.device import auto_select_device
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from torch_geometric.loader.dataloader import DataLoader
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from yacs.config import CfgNode as CN
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all__captum = [
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"LRP",
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@ -26,7 +26,7 @@ def read_yaml(path: str) -> dict:
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def write_yaml(data: dict, path: str) -> None:
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with open(path, "w") as f:
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data = yaml.dump(data, f)
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data = yaml.safe_dump(data, f, default_flow_style=False)
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def dump_cfg(cfg, path):
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