Reformat
This commit is contained in:
araison
parent
fb012ad723
commit
d9628ff947
@ -3,6 +3,17 @@ import itertools
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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.explain import Explainer
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from torch_geometric.explain.config import ThresholdConfig
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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 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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@ -18,15 +29,11 @@ from explaining_framework.stats.graph.graph_stat import GraphStat
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from explaining_framework.utils.explaining.load_ckpt import (LoadModelInfo,
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_load_ckpt)
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from explaining_framework.utils.explanation.adjust import Adjust
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from scgnn.scgnn import SCGNN
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from torch_geometric.data import Batch, Data
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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.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 explaining_framework.utils.explanation.io import (
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_get_explanation, _load_explanation, _save_explanation,
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explanation_verification, get_pred)
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from explaining_framework.utils.io import (is_exists, obj_config_to_str,
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read_json, write_json, write_yaml)
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all__captum = [
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"LRP",
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@ -88,10 +95,15 @@ all_robust = [
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]
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all_sparsity = ["l0"]
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adjust_pattern = 'ranp'
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all_adjusts_filters = [''.join(filters) for i in range(len(adjust_pattern)+1)for filters in itertools.permutations(adjust_pattern,i)]
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adjust_pattern = "ranp"
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all_adjusts_filters = [
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"".join(filters)
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for i in range(len(adjust_pattern) + 1)
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for filters in itertools.permutations(adjust_pattern, i)
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]
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all_threshold_type = ["topk_hard", "hard", "topk"]
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all_threshold_type = ['topk_hard','hard','topk']
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class ExplainingOutline(object):
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def __init__(self, explaining_cfg_path: str):
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@ -131,6 +143,8 @@ class ExplainingOutline(object):
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self.load_threshold()
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self.load_graphstat()
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seed_everything(self.explaining_cfg.seed)
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def load_model_to_hardware(self):
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auto_select_device()
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device = self.cfg.accelerator
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@ -300,44 +314,50 @@ class ExplainingOutline(object):
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if self.explaining_cfg is None:
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self.load_explaining_cfg()
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name = self.explaining_cfg.metrics.fidelity.name
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if name == 'all':
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if name == "all":
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all_metrics = [
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Fidelity(name=name, model=self.model) for name in all_fidelity
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]
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elif isinstance(name,str):
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elif isinstance(name, str):
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if name in all_fidelity:
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all_metrics = [Fidelity(name=name, model=self.model)]
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else:
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raise ValueError(f'This fidelity metric {name} is nor supported yet. Supported are {all_fidelity}')
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elif isinstance(name,list):
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all_metrics = [Fidelity(name=name, model=self.model) for name_ in name if name_ in all_fidelity]
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raise ValueError(
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f"This fidelity metric {name} is nor supported yet. Supported are {all_fidelity}"
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)
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elif isinstance(name, list):
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all_metrics = [
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Fidelity(name=name, model=self.model)
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for name_ in name
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if name_ in all_fidelity
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]
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elif name is None:
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all_metrics = []
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self.fidelities = all_metrics
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def load_sparsity(self):
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if self.cfg is None:
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self.load_cfg()
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if self.explaining_cfg is None:
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self.load_explaining_cfg()
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name = self.explaining_cfg.metrics.sparsity.name
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if name == 'all':
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all_metrics = [
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Sparsity(name=name) for name in all_sparsity
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]
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elif isinstance(name,str):
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if name == "all":
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all_metrics = [Sparsity(name=name) for name in all_sparsity]
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elif isinstance(name, str):
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if name in all_sparsity:
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all_metrics = [Sparsity(name=name)]
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else:
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raise ValueError(f'This sparsity metric {name} is nor supported yet. Supported are {all_sparsity}')
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elif isinstance(name,list):
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all_metrics = [Sparsity(name=name) for name_ in name if name_ in all_sparsity]
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raise ValueError(
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f"This sparsity metric {name} is nor supported yet. Supported are {all_sparsity}"
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)
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elif isinstance(name, list):
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all_metrics = [
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Sparsity(name=name) for name_ in name if name_ in all_sparsity
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]
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elif name is None:
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all_metrics = []
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self.sparsities = all_metrics
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def load_accuracy(self):
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if self.cfg is None:
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self.load_cfg()
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@ -346,24 +366,26 @@ class ExplainingOutline(object):
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if self.explaining_cfg.dataset.name == "BASHAPES":
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name = self.explaining_cfg.metrics.accuracy.name
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if name == 'all':
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all_metrics = [
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Accuracy(name=name) for name in all_accuracy
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]
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elif isinstance(name,str):
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if name == "all":
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all_metrics = [Accuracy(name=name) for name in all_accuracy]
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elif isinstance(name, str):
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if name in all_accuracy:
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all_metrics = [Accuracy(name=name)]
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else:
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raise ValueError(f'This accuracy metric {name} is nor supported yet. Supported are {all_accuracy}')
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elif isinstance(name,list):
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all_metrics = [Accuracy(name=name) for name_ in name if name_ in all_accuracy]
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raise ValueError(
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f"This accuracy metric {name} is nor supported yet. Supported are {all_accuracy}"
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)
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elif isinstance(name, list):
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all_metrics = [
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Accuracy(name=name) for name_ in name if name_ in all_accuracy
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]
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elif name is None:
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all_metrics = []
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self.accuraties = all_metrics
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else:
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raise ValueError(f'Provided dataset needs explanation groundtruths for using Accuracies metric, e.g BASHAPES dataset')
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raise ValueError(
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f"Provided dataset needs explanation groundtruths for using Accuracies metric, e.g BASHAPES dataset"
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)
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def load_metric(self):
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if self.cfg is None:
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@ -376,27 +398,30 @@ class ExplainingOutline(object):
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self.load_sparsity()
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if self.fidelities is None:
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self.load_fidelity()
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self.metrics = self.fidelities+self.accuraties+self.sparsities
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self.metrics = self.fidelities + self.accuraties + self.sparsities
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def load_attack(self):
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if self.cfg is None:
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self.load_cfg()
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if self.explaining_cfg is None:
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self.load_explaining_cfg()
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name = self.explaining_cfg.attack.name
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if name == 'all':
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all_metrics = [
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Attack(name=name,model=self.model) for name in all_robust
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]
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elif isinstance(name,str):
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if name == "all":
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all_metrics = [Attack(name=name, model=self.model) for name in all_robust]
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elif isinstance(name, str):
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if name in all_robust:
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all_metrics = [Attack(name=name,model=self.model)]
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all_metrics = [Attack(name=name, model=self.model)]
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else:
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raise ValueError(f'This Attack metric {name} is not supported yet. Supported are {all_robust}')
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elif isinstance(name,list):
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all_metrics = [Attack(name=name,model=self.model) for name_ in name if name_ in all_robust]
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raise ValueError(
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f"This Attack metric {name} is not supported yet. Supported are {all_robust}"
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)
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elif isinstance(name, list):
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all_metrics = [
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Attack(name=name, model=self.model)
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for name_ in name
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if name_ in all_robust
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]
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elif name is None:
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all_metrics = []
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self.attacks = all_metrics
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@ -407,13 +432,17 @@ class ExplainingOutline(object):
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strategy = self.explaining_cfg.adjust.strategy
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if strategy == "all":
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self.adjusts = [Adjust(strategy=strat) for strat in all_adjusts_filters]
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elif isinstance(name,str):
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elif isinstance(name, str):
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if name in all_adjusts_filters:
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all_metrics = [Adjust(strategy=name)]
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else:
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raise ValueError(f'This Adjust metric {name} is not supported yet. Supported are {all_adjusts_filters}')
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elif isinstance(name,list):
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all_metrics = [Adjust(strategy=name_) for name_ in name if name_ in all_robust]
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raise ValueError(
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f"This Adjust metric {name} is not supported yet. Supported are {all_adjusts_filters}"
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)
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elif isinstance(name, list):
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all_metrics = [
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Adjust(strategy=name_) for name_ in name if name_ in all_robust
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]
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elif name is None:
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all_metrics = []
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self.adjusts = all_metrics
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@ -421,70 +450,90 @@ class ExplainingOutline(object):
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def load_threshold(self):
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if self.explaining_cfg is None:
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self.load_explaining_cfg()
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threshold_type =self.explaining_cfg.threshold_config.type
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if threshold_type == 'all':
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th_hard = [{"threshold_type": 'hard',"value": th_value} for th_value in self.explaining_cfg.threshold.value.hard]
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th_topk = [{"threshold_type": th_type,"value": th_value} for th_value in self.explaining_cfg.threshold.value.topk f or th_type in all_threshold_type if 'topk' in th_type]
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threshold_type = self.explaining_cfg.threshold_config.type
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if threshold_type == "all":
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th_hard = [
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{"threshold_type": "hard", "value": th_value}
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for th_value in self.explaining_cfg.threshold.value.hard
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]
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th_topk = [
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{"threshold_type": th_type, "value": th_value}
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for th_value in self.explaining_cfg.threshold.value.topk
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for th_type in all_threshold_type
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if "topk" in th_type
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]
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all_threshold = th_hard + th_topk
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elif isinstance(threshold_type,str):
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elif isinstance(threshold_type, str):
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if threshold_type in all_threshold_type:
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if 'topk' in threshold_type:
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all_threshold = [{
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"threshold_type": threshold_type,
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"value": threshold_value,
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} for threshold_value in self.explaining_cfg.threshold.value.topk]
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elif threshold_type == 'hard':
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all_threshold = [{
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"threshold_type": threshold_type,
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"value": threshold_value,
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} for threshold_value in self.explaining_cfg.threshold.value.hard]
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elif isinstance(threshold_type,list):
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if "topk" in threshold_type:
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all_threshold = [
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{
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"threshold_type": threshold_type,
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"value": threshold_value,
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}
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for threshold_value in self.explaining_cfg.threshold.value.topk
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]
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elif threshold_type == "hard":
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all_threshold = [
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{
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"threshold_type": threshold_type,
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"value": threshold_value,
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}
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for threshold_value in self.explaining_cfg.threshold.value.hard
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]
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elif isinstance(threshold_type, list):
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all_threshold = []
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for tf_type in threshold_type:
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if 'topk' in th_type:
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all_threshold.expend([{
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if "topk" in th_type:
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all_threshold.expend(
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[
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{
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"threshold_type": threshold_type,
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"value": threshold_value,
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} for threshold_value in self.explaining_cfg.threshold.value.topk])
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elif th_type == 'hard':
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all_threshold.expend([{
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}
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for threshold_value in self.explaining_cfg.threshold.value.topk
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]
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)
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elif th_type == "hard":
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all_threshold.expend(
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[
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{
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"threshold_type": threshold_type,
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"value": threshold_value,
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} for threshold_value in self.explaining_cfg.threshold.value.hard])
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}
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for threshold_value in self.explaining_cfg.threshold.value.hard
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]
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)
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elif threshold_type is None:
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all_threshold = []
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self.thresholds_configs = all_threshold
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def set_explainer_threshold_config(self,threshold_config):
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def set_explainer_threshold_config(self, threshold_config):
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self.explainer.threshold_config = ThresholdConfig.cast(threshold_config)
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def load_graphstat(self):
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self.graphstat = GraphStat()
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def get_explanation_(self,item:Data,path:str):
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def get_explanation(self, item: Data, path: str):
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if is_exists(path):
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if self.explaining_cfg.explainer.force:
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explanation = get_explanation(self.explainer, item)
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explanation = _get_explanation(self.explainer, item)
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else:
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explanation = load_explanation(path)
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explanation = _load_explanation(path)
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else:
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explanation = get_explanation(explainer, item)
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save_explanation(explanation,path)
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explanation = _get_explanation(self.explainer, item)
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_save_explanation(explanation, path)
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explanation = explanation.to(cfg.accelerator)
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return explanation
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class Explaining(object):
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def __init__(self,outline:ExplainingOutline):
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self.outline = outline
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def run(self):
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pass
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def explain(self):
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item, index = self.get_item()
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not_none = item is None or index is None
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whœ
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while
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def get_stat(self, item: Data, path: str):
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if self.graphstat is None:
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self.load_graphstat()
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if is_exists(path):
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pass
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else:
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if item.num_nodes <= 500:
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stat = self.graphstat(item)
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write_json(stat, path)
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@ -7,16 +7,18 @@ from torch_geometric.data import Data
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from torch_geometric.explain.explanation import Explanation
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def get_explanation(explainer, item):
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def _get_explanation(explainer, item):
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explanation = explainer(
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x=item.x,
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edge_index=item.edge_index,
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index=int(item.y),
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target=item.y,
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)
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# TODO return None if pas bien plutot
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assert explanation_verification(explanation)
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return explanation
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if not explanation_verification(explanation):
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# WARNING + LOG
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return None
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else:
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return explanation
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def is_empty_graph(data: Data) -> bool:
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@ -55,7 +57,7 @@ def explanation_verification(exp: Explanation) -> bool:
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return is_good
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def save_explanation(exp: Explanation, path: str) -> None:
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def _save_explanation(exp: Explanation, path: str) -> None:
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data = copy.copy(exp).to_dict()
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for k, v in data.items():
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if isinstance(v, torch.Tensor):
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@ -65,7 +67,7 @@ def save_explanation(exp: Explanation, path: str) -> None:
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json.dump(data, f)
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def load_explanation(path: str) -> Explanation:
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def _load_explanation(path: str) -> Explanation:
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with open(path, "r") as f:
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data = json.load(f)
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for k, v in data.items():
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@ -77,12 +79,3 @@ def load_explanation(path: str) -> Explanation:
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return Explanation.from_dict(data)
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def normalize_explanation_masks(exp: Explanation, p: str = "inf") -> Explanation:
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exp = copy.copy(exp)
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data = exp.to_dict()
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for k, v in data.items():
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if "_mask" in k and isinstance(v, torch.FloatTensor):
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norm = torch.norm(input=data[k], p=p, dim=None).item()
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if norm.item() > 0:
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data[k] = data[k] / norm
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return exp
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82
main.py
82
main.py
@ -27,99 +27,33 @@ from explaining_framework.utils.io import (is_exists, obj_config_to_str,
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# inference, time, force,
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def get_pred(explainer, explanation):
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pred = explainer.get_prediction(x=explanation.x, edge_index=explanation.edge_index)[
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0
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]
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setattr(explanation, "pred", pred)
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data = explanation.to_dict()
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if not data.get("node_mask") is None or not data.get("edge_mask") is None:
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pred_masked = explainer.get_masked_prediction(
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x=explanation.x,
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edge_index=explanation.edge_index,
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node_mask=data.get("node_mask"),
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edge_mask=data.get("edge_mask"),
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)[0]
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setattr(explanation, "pred_exp", pred_masked)
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if __name__ == "__main__":
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args = parse_args()
|
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outline = ExplainingOutline(args.explaining_cfg_file)
|
||||
auto_select_device()
|
||||
|
||||
# Load components
|
||||
dataset = outline.dataset
|
||||
model = outline.model.to(cfg.accelerator)
|
||||
model = model.eval()
|
||||
model_info = outline.model_info
|
||||
metrics = outline.metrics
|
||||
explaining_algorithm = outline.explaining_algorithm
|
||||
attacks = outline.attacks
|
||||
explainer_cfg = outline.explainer_cfg
|
||||
model_signature = outline.model_signature
|
||||
# RAJOUTER INDEXES
|
||||
|
||||
# Set seed
|
||||
seed_everything(explaining_cfg.seed)
|
||||
|
||||
# Global path
|
||||
global_path = os.path.join(explaining_cfg.out_dir, model_signature)
|
||||
global_path = os.path.join(outline.explaining_cfg.out_dir, outline.model_signature, outline.explaining_cfg.explainer.name + "_" + obj_config_to_str(outline.explaining_algorithm))
|
||||
makedirs(global_path)
|
||||
write_yaml(cfg, os.path.join(global_path, "config.yaml"))
|
||||
write_json(model_info, os.path.join(global_path, "info.json"))
|
||||
# SET RUN DIR
|
||||
|
||||
global_path = os.path.join(
|
||||
global_path,
|
||||
explaining_cfg.explainer.name + "_" + obj_config_to_str(explaining_algorithm),
|
||||
)
|
||||
makedirs(global_path)
|
||||
write_yaml(explaining_cfg, os.path.join(global_path, explaining_cfg.cfg_dest))
|
||||
write_yaml(explainer_cfg, os.path.join(global_path, "explainer_cfg.yaml"))
|
||||
# SET EXPLAIN_DIR
|
||||
write_yaml(outline.explaining_cfg, os.path.join(global_path, explaining_cfg.cfg_dest))
|
||||
write_yaml(outline.explainer_cfg, os.path.join(global_path, "explainer_cfg.yaml"))
|
||||
|
||||
global_path = os.path.join(global_path, obj_config_to_str(explaining_algorithm))
|
||||
global_path = os.path.join(global_path, obj_config_to_str(outline.explaining_algorithm))
|
||||
makedirs(global_path)
|
||||
# SET UP EXPLAINER
|
||||
explainer = Explainer(
|
||||
model=model,
|
||||
algorithm=explaining_algorithm,
|
||||
explainer_config=dict(
|
||||
explanation_type=explaining_cfg.explanation_type,
|
||||
node_mask_type="object",
|
||||
edge_mask_type="object",
|
||||
),
|
||||
model_config=dict(
|
||||
mode="regression",
|
||||
task_level=cfg.dataset.task,
|
||||
return_type=explaining_cfg.model_config.return_type,
|
||||
),
|
||||
)
|
||||
# CHERGER SUR LE GPU DIRECT
|
||||
if not explaining_cfg.dataset.specific_items is None:
|
||||
indexes = explaining_cfg.dataset.specific_items
|
||||
else:
|
||||
indexes = range(len(dataset))
|
||||
# Save explaining configuration
|
||||
for index, item in zip(indexes, dataset):
|
||||
item = item.to(cfg.accelerator)
|
||||
save_raw_path = os.path.join(global_path, "raw")
|
||||
makedirs(save_raw_path)
|
||||
item,index = outline.get_item()
|
||||
while not(item is None or index is None):
|
||||
raw_path = os.path.join(global_path, "raw")
|
||||
makedirs(raw_path)
|
||||
explanation_path = os.path.join(save_raw_path, f"{index}.json")
|
||||
|
||||
if is_exists(explanation_path):
|
||||
if explaining_cfg.explainer.force:
|
||||
explanation = get_explanation(explainer, item)
|
||||
else:
|
||||
explanation = load_explanation(explanation_path)
|
||||
else:
|
||||
explanation = get_explanation(explainer, item)
|
||||
|
||||
explanation = explanation.to(cfg.accelerator)
|
||||
get_pred(explainer=explainer, explanation=explanation)
|
||||
|
||||
save_explanation(explanation, explanation_path)
|
||||
for apply_relu in [True, False]:
|
||||
for apply_absolute in [True, False]:
|
||||
adjust = Adjust(apply_relu=apply_relu, apply_absolute=apply_absolute)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user