Fixings somes bugs, and adding new features
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araison
parent
db04fbfaeb
commit
3372f81576
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@ -57,7 +57,7 @@ def set_cfg(explaining_cfg):
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explaining_cfg.dataset.name = "Cora"
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explaining_cfg.dataset.item = None
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explaining_cfg.dataset.item = []
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# ----------------------------------------------------------------------- #
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# Model options
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@ -1,150 +0,0 @@
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import glob
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import os
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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 create_dataset
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from torch_geometric.graphgym.utils.io import string_to_python
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if "__main__" == __name__:
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config_folder = os.path.abspath(
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os.path.join(os.path.dirname(__name__), "../../", "configs")
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)
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makedirs(config_folder)
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explaining_folder = os.path.join(config_folder, "explaining")
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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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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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]
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EXPLAINER = [
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"CAM",
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"GradCAM",
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"GNN_LRP",
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"GradExplainer",
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"GuidedBackPropagation",
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"IntegratedGradients",
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# "PGExplainer",
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"PGMExplainer",
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"RandomExplainer",
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# "SubgraphX",
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# "GraphMASK",
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"GNNExplainer",
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"EIXGNN",
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"SCGNN",
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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["cfg_dest"] = string_to_python(
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f"dataset={dataset_name}-model={model_kind}-explainer={explainer_name}.yaml"
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)
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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"] = string_to_python(dataset_name)
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explaining_cfg["dataset"]["item"] = [3, 45, 78, 23]
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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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# "/media/data/SIC/araison/exps/pyg_fork/graphgym/results/graph_classif_base_grid_graph_classif_grid"
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"/home/SIC/araison/exps/pytorch_geometric/graphgym/results/"
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# "/media/data/SIC/araison/exps/pyg_fork/graphgym/results/graph_classif_base_grid_graph_classif_grid"
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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"],
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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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# for index, config_chunk in enumerate(
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# chunkizing_list(configs, int(len(configs) / 5))
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# ):
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# PATH_ = os.path.join(explaining_folder, f"gpu={index}")
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# makedirs(PATH_)
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# for path in config_chunk:
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# filename = os.path.basename(path)
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# shutil.copy2(path, os.path.join(PATH_, filename))
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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, create_dataset2
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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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@ -155,10 +156,9 @@ class ExplainingOutline(object):
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self.load_explainer_cfg()
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self.load_explaining_algorithm()
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self.load_explainer()
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# self.load_dataset_to_dataloader()
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self.load_metric()
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self.load_attack()
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self.load_dataset_to_dataloader()
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self.load_indexes()
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self.load_adjust()
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self.load_threshold()
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self.load_graphstat()
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@ -171,38 +171,16 @@ class ExplainingOutline(object):
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device = self.cfg.accelerator
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self.model = self.model.to(device)
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def get_data(self):
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if self.dataset is None:
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self.load_dataset()
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try:
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item = next(self.dataset)
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device = self.cfg.accelerator
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item = item.to(device)
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return item
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except StopIteration:
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return None
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def load_indexes(self):
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item = self.explaining_cfg.dataset.item
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if isinstance(item, (list, int)):
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indexes = item
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else:
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indexes = list(range(len(self.dataset)))
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self.indexes = iter(indexes)
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def get_index(self):
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if self.indexes is None:
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self.load_indexes()
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try:
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item = next(self.indexes)
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return item
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except StopIteration:
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return None
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def get_item(self):
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item = self.get_data()
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index = self.get_index()
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return item, index
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# def get_data(self):
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# if self.dataset is None:
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# self.load_dataset()
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# try:
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# item = next(self.dataset)
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# device = self.cfg.accelerator
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# item = item.to(device)
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# return item
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# except StopIteration:
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# return None
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def load_model_info(self):
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info = LoadModelInfo(
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@ -270,26 +248,19 @@ class ExplainingOutline(object):
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raise ValueError(
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f"Expecting that the dataset to perform explanation on is the same as the model has trained on. Get {self.explaining_cfg.dataset.name} for explanation part, and {self.cfg.dataset.name} for the model."
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)
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self.dataset = create_dataset()
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self.dataset = create_dataset2()
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item = self.explaining_cfg.dataset.item
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if isinstance(item, int):
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self.dataset = self.dataset[item : item + 1]
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elif isinstance(item, list):
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self.dataset = self.dataset[item]
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if isinstance(item, (list)):
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if len(item) == 0:
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self.indexes = list(range(len(self.dataset)))
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else:
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self.indexes = item
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def load_dataset_to_dataloader(self, to_iter=True):
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self.dataset = self.dataset[self.indexes]
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def load_dataset_to_dataloader(self, to_iter=False):
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self.dataset = DataLoader(dataset=self.dataset, shuffle=False, batch_size=1)
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if to_iter:
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self.dataset = iter(self.dataset)
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def reload_dataset(self):
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self.load_dataset()
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self.load_indexes()
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def reload_dataloader(self):
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self.load_dataset()
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self.load_dataset_to_dataloader()
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self.load_indexes()
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def load_explaining_algorithm(self):
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self.load_explainer_cfg()
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21
main.py
21
main.py
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@ -26,12 +26,11 @@ from explaining_framework.utils.io import (dump_cfg, is_exists,
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if __name__ == "__main__":
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args = parse_args()
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outline = ExplainingOutline(args.explaining_cfg_file, args.gpu_id)
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pbar = tqdm(total=len(outline.dataset) * len(outline.attacks))
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for item, index in zip(outline.dataset, outline.indexes):
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item = item.to(outline.cfg.accelerator)
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for attack in outline.attacks:
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for item, index in tqdm(
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zip(outline.dataset, outline.indexes), total=len(outline.dataset)
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):
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item = item.to(outline.cfg.accelerator)
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attack_path = os.path.join(
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outline.out_dir, attack.__class__.__name__, obj_config_to_str(attack)
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)
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@ -40,13 +39,12 @@ if __name__ == "__main__":
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data_attack = outline.get_attack(
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attack=attack, item=item, path=data_attack_path
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)
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if data_attack is None:
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continue
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outline.reload_dataloader()
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for item, index in zip(outline.dataset, outline.indexes):
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item = item.to(outline.cfg.accelerator)
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for attack in outline.attacks:
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for item, index in tqdm(
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zip(outline.dataset, outline.indexes), total=len(outline.dataset)
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):
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item = item.to(outline.cfg.accelerator)
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attack_path_ = os.path.join(
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outline.explainer_path,
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attack.__class__.__name__,
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@ -60,7 +58,6 @@ if __name__ == "__main__":
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if attack_data is None:
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continue
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exp = outline.get_explanation(item=attack_data, path=data_attack_path_)
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pbar.update(1)
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if exp is None:
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continue
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else:
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@ -103,5 +100,3 @@ if __name__ == "__main__":
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)
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with open(os.path.join(outline.out_dir, "done"), "w") as f:
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f.write("")
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pbar.close()
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