Fixing bugs and adding new features
This commit is contained in:
araison
parent
e1b2a64bd6
commit
7c4f5ca196
@ -66,17 +66,18 @@ set_eixgnn_cfg(eixgnn_cfg)
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# eixgnn_cfg.dump(stream=f)
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#
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#
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# def load_eixgnn_cfg(eixgnn_cfg, args):
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# r"""
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# Load configurations from file system and command line
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# Args:
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# eixgnn_cfg (CfgNode): Configuration node
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# args (ArgumentParser): Command argument parser
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# """
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# eixgnn_cfg.merge_from_file(args.eixgnn_cfg_file)
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# eixgnn_cfg.merge_from_list(args.opts)
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# assert_eixgnn_cfg(eixgnn_cfg)
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#
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def load_eixgnn_cfg(eixgnn_cfg, args):
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r"""
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Load configurations from file system and command line
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Args:
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eixgnn_cfg (CfgNode): Configuration node
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args (ArgumentParser): Command argument parser
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"""
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eixgnn_cfg.merge_from_file(args.eixgnn_cfg_file)
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eixgnn_cfg.merge_from_list(args.opts)
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assert_eixgnn_cfg(eixgnn_cfg)
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#
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# def makedirs_rm_exist(dir):
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# if os.path.isdir(dir):
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@ -36,27 +36,25 @@ def set_scgnn_cfg(scgnn_cfg):
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if scgnn_cfg is None:
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return scgnn_cfg
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scgnn_cfg.depth = 'all'
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scgnn_cfg.depth = "all"
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scgnn_cfg.interest_map_norm = True
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scgnn_cfg.score_map_norm = True
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def assert_scgnn_cfg(scgnn_cfg):
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def assert_cfg(scgnn_cfg):
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r"""Checks config values, do necessary post processing to the configs
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TODO
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TODO
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"""
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if scgnn_cfg. not in ["node", "edge", "graph", "link_pred"]:
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raise ValueError(
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"Task {} not supported, must be one of node, "
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"edge, graph, link_pred".format(scgnn_cfg.dataset.task)
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)
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scgnn_cfg.run_dir = scgnn_cfg.out_dir
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"""
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# if scgnn_cfg. not in ["node", "edge", "graph", "link_pred"]:
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# raise ValueError(
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# "Task {} not supported, must be one of node, "
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# "edge, graph, link_pred".format(scgnn_cfg.dataset.task)
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# )
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# scgnn_cfg.run_dir = scgnn_cfg.out_dir
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def dump_scgnn_cfg(scgnn_cfg,path):
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def dump_cfg(scgnn_cfg, path):
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r"""
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TODO
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Dumps the config to the output directory specified in
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@ -65,14 +63,12 @@ def dump_scgnn_cfg(scgnn_cfg,path):
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scgnn_cfg (CfgNode): Configuration node
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"""
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makedirs(scgnn_cfg.out_dir)
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scgnn_cfg_file = os.path.join(
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scgnn_cfg.out_dir, scgnn_cfg.scgnn_cfg_dest
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)
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scgnn_cfg_file = os.path.join(scgnn_cfg.out_dir, scgnn_cfg.scgnn_cfg_dest)
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with open(scgnn_cfg_file, "w") as f:
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scgnn_cfg.dump(stream=f)
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def load_scgnn_cfg(scgnn_cfg, args):
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def load_cfg(scgnn_cfg, args):
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r"""
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Load configurations from file system and command line
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Args:
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@ -116,8 +112,6 @@ def set_out_dir(out_dir, fname):
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# Make output directory
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if scgnn_cfg.train.auto_resume:
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os.makedirs(scgnn_cfg.out_dir, exist_ok=True)
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else:
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makedirs_rm_exist(scgnn_cfg.out_dir)
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def set_run_dir(out_dir):
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@ -131,8 +125,6 @@ def set_run_dir(out_dir):
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# Make output directory
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if scgnn_cfg.train.auto_resume:
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os.makedirs(scgnn_cfg.run_dir, exist_ok=True)
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else:
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makedirs_rm_exist(scgnn_cfg.run_dir)
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set_scgnn_cfg(scgnn_cfg)
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@ -122,7 +122,8 @@ def set_cfg(explaining_cfg):
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explaining_cfg.accelerator = "auto"
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# which objectives metrics to computes, either all or one in particular if implemented
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explaining_cfg.metrics = "all"
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explaining_cfg.metrics = CN()
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explaining_cfg.metrics.type = "all"
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# Whether or not recomputing metrics if they already exist
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explaining_cfg.metrics.force = False
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@ -86,7 +86,7 @@ class GraphXAIWrapper(ExplainerAlgorithm):
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if criterion == "mse":
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loss = MSELoss()
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return loss
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elif criterion == "cross-entropy":
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elif criterion == "cross_entropy":
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loss = CrossEntropyLoss()
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return loss
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else:
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@ -5,13 +5,6 @@ def parse_args() -> argparse.Namespace:
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r"""Parses the command line arguments."""
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parser = argparse.ArgumentParser(description="ExplainingFramework")
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parser.add_argument(
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"--cfg",
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dest="cfg_file",
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type=str,
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required=True,
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help="The configuration file path.",
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)
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parser.add_argument(
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"--explaining_cfg",
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dest="explaining_cfg_file",
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@ -1,14 +0,0 @@
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class ExplainingOutline(object):
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def __init__(self, explaining_cfg: CN, explainer_cfg: CN = None):
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self.explaining_cfg = explaining_cfg
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self.explainer_cfg = explainer_cfg
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def load_cfg(self):
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if self
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174
explaining_framework/utils/explaining/explaining_outline.py
Normal file
174
explaining_framework/utils/explaining/explaining_outline.py
Normal file
@ -0,0 +1,174 @@
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import copy
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from eixgnn.eixgnn import EiXGNN
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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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from explaining_framework.config.explaining_config import explaining_cfg
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from explaining_framework.explainers.wrappers.from_captum import CaptumWrapper
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from explaining_framework.explainers.wrappers.from_graphxai import \
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GraphXAIWrapper
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from explaining_framework.metric.accuracy import Accuracy
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from explaining_framework.metric.fidelity import Fidelity
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from explaining_framework.metric.robust import Attack
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from explaining_framework.metric.sparsity import Sparsity
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from explaining_framework.utils.explaining.load_ckpt import (LoadModelInfo,
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_load_ckpt)
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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.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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all__captum = [
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"LRP",
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"DeepLift",
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"DeepLiftShap",
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"FeatureAblation",
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"FeaturePermutation",
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"GradientShap",
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"GuidedBackprop",
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"GuidedGradCam",
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"InputXGradient",
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"IntegratedGradients",
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"Lime",
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"Occlusion",
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"Saliency",
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]
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all__graphxai = [
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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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]
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all__own = ["EIXGNN", "SCGNN"]
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class ExplainingOutline(object):
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def __init__(self, explaining_cfg_path: str):
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self.explaining_cfg_path = explaining_cfg_path
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self.explaining_cfg = None
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self.explainer_cfg_path = None
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self.explainer_cfg = None
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self.explaining_algorithm = None
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self.cfg = None
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self.model = None
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self.dataset = None
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self.model_info = None
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self.load_explaining_cfg()
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self.load_model_info()
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self.load_cfg()
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self.load_dataset()
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self.load_model()
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self.load_explainer_cfg()
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self.load_explainer()
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def load_model_info(self):
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info = LoadModelInfo(
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dataset_name=self.explaining_cfg.dataset.name,
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model_dir=self.explaining_cfg.model.path,
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which=self.explaining_cfg.model.ckpt,
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)
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self.model_info = info.set_info()
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def load_cfg(self):
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cfg.set_new_allowed(True)
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cfg.merge_from_file(self.model_info["cfg_path"])
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self.cfg = cfg
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def load_explaining_cfg(self):
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explaining_cfg.set_new_allowed(True)
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explaining_cfg.merge_from_file(self.explaining_cfg_path)
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self.explaining_cfg = explaining_cfg
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def load_explainer_cfg(self):
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if self.explaining_cfg is None:
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self.explaining_cfg()
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else:
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if self.explaining_cfg.explainer.cfg == "default":
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if self.explaining_cfg.explainer.name == "EIXGNN":
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self.explainer_cfg = copy.copy(eixgnn_cfg)
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elif self.explaining_cfg.explainer.name == "SCGNN":
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self.explainer_cfg = copy.copy(scgnn_cfg)
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else:
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self.explainer_cfg = None
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else:
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if self.explaining_cfg.explainer.name == "EIXGNN":
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eixgnn_cfg.set_new_allowed(True)
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eixgnn_cfg.merge_from_file(self.explaining_cfg.explainer.cfg)
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self.explainer_cfg = eixgnn_cfg
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elif self.explaining_cfg.explainer.name == "SCGNN":
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scgnn_cfg.set_new_allowed(True)
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scgnn_cfg.merge_from_file(self.explaining_cfg.explainer.cfg)
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self.explainer_cfg = scgnn_cfg
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def load_model(self):
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if self.cfg is None:
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self.load_cfg()
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auto_select_device()
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self.model = create_model()
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self.model = _load_ckpt(self.model, self.model_info["ckpt_path"])
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if self.model is None:
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raise ValueError("Model ckpt has not been loaded, ckpt file not found")
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def load_dataset(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.explaining_cfg()
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if self.explaining_cfg.dataset.name != self.cfg.dataset.name:
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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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def load_explainer(self):
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self.load_explainer_cfg()
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if self.model is None:
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self.load_model()
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if self.dataset is None:
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self.load_dataset()
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name = self.explaining_cfg.explainer.name
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if name in all__captum:
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explaining_algorithm = CaptumWrapper(name)
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elif name in all__graphxai:
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explaining_algorithm = GraphXAIWrapper(
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name,
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in_channels=self.dataset.num_classes,
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criterion=self.cfg.model.loss_fun,
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)
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elif name in all__own:
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if name == "EIXGNN":
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explaining_algorithm = EiXGNN(
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L=self.explainer_cfg.L,
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p=self.explainer_cfg.p,
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importance_sampling_strategy=self.explainer_cfg.importance_sampling_strategy,
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domain_similarity=self.explainer_cfg.domain_similarity,
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signal_similarity=self.explainer_cfg.signal_similarity,
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shap_val_approx=self.explainer_cfg.shapley_value_approx,
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)
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elif name == "SCGNN":
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explaining_algorithm = SCGNN(
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depth=self.explainer_cfg.depth,
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interest_map_norm=self.explainer_cfg.interest_map_norm,
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score_map_norm=self.explainer_cfg.score_map_norm,
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)
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self.explaining_algorithm = explaining_algorithm
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print(self.explaining_algorithm.__dict__)
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PATH = "config_exp.yaml"
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test = ExplainingOutline(explaining_cfg_path=PATH)
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@ -23,78 +23,104 @@ def _load_ckpt(
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) -> torch.nn.Module:
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r"""Loads the model at given checkpoint."""
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if not osp.exists(path):
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if not os.path.exists(ckpt_path):
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return None
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ckpt = torch.load(ckpt_path)
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model.load_state_dict(ckpt[MODEL_STATE])
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model.load_state_dict(ckpt["state_dict"])
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return model
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def xp_stats(path_to_xp: str, wrt_metric: str = "val") -> str:
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acc = []
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for path in glob.glob(os.path.join(path_to_xp, "[0-9]", wrt_metric, "stats.json")):
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stats = json_to_dict_list(path)
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for stat in stats:
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acc.append(
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{"path": path, "epoch": stat["epoch"], "accuracy": stat["accuracy"]}
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class LoadModelInfo(object):
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def __init__(
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self, dataset_name: str, model_dir: str, which: str, wrt_metric: str = "val"
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):
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self.dataset_name = dataset_name
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self.model_dir = model_dir
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self.which = which
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self.wrt_metric = wrt_metric
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self.info = None
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def list_stats(self, path) -> list:
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info = []
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for path in glob.glob(
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os.path.join(path, "[0-9]", self.wrt_metric, "stats.json")
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):
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stats = json_to_dict_list(path)
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for stat in stats:
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xp_dir_path = os.path.dirname(os.path.dirname(os.path.dirname(path)))
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ckpt_dir_path = os.path.join(
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os.path.dirname(os.path.dirname(path)), "ckpt"
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)
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cfg_path = os.path.join(xp_dir_path, "config.yaml")
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epoch = stat["epoch"]
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accuracy = stat["accuracy"]
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loss = stat["loss"]
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lr = stat["lr"]
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params = stat["params"]
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time_iter = stat["time_iter"]
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ckpt_path = self.get_ckpt_path(epoch=epoch, ckpt_dir_path=ckpt_dir_path)
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info.append(
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{
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"xp_dir_path": xp_dir_path,
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"ckpt_path": self.get_ckpt_path(
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epoch=epoch, ckpt_dir_path=ckpt_dir_path
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),
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"cfg_path": cfg_path,
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"epoch": epoch,
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"accuracy": accuracy,
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"loss": loss,
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"lr": lr,
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"params": params,
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"time_iter": time_iter,
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}
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)
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return info
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def list_xp(self):
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paths = []
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for path in glob.glob(os.path.join(self.model_dir, "**", "config.yaml")):
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file = self.load_cfg(path)
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dataset_name_ = file["dataset"]["name"]
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if self.dataset_name == dataset_name_:
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paths.append(os.path.dirname(path))
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return paths
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def load_cfg(self, config_path):
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return read_yaml(config_path)
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def set_info(self):
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if self.which in ["best", "worst"] or isinstance(self.which, int):
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paths = self.list_xp()
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infos = []
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for path in paths:
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info = self.list_stats(path)
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infos.extend(info)
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infos = sorted(infos, key=lambda item: item["accuracy"])
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if self.which == "best":
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self.info = infos[-1]
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elif self.which == "worst":
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self.info = infos[0]
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elif isinstance(self.which, int):
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self.info = infos[self.which]
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else:
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specific_xp_dir_path = os.path.dirname(
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os.path.dirname(os.path.dirname(self.which))
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)
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return acc
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stats = self.list_stats(specific_xp_dir_path)
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self.info = [item for item in stats if item["ckpt_path"] == self.which][0]
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return self.info
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def xp_parser_dataset(dataset_name: str, models_dir_path) -> str:
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paths = []
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for path in glob.glob(os.path.join(models_dir_path, "**", "config.yaml")):
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file = read_yaml(path)
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dataset_name_ = file["dataset"]["name"]
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if dataset_name == dataset_name_:
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paths.append(os.path.dirname(path))
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return paths
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def best_xp_ckpt(paths, which: str = "best"):
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acc = []
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for path in paths:
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accuracies = xp_stats(path)
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acc.extend(accuracies)
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acc = sorted(acc, key=lambda item: item["accuracy"])
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if which == "best":
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return acc[-1]
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elif which == "worst":
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return acc[0]
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|
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def stats_to_ckpt(parse):
|
||||
paths = os.path.join(
|
||||
os.path.dirname(os.path.dirname(parse["path"])), "ckpt", "*.ckpt"
|
||||
)
|
||||
ckpts = []
|
||||
for path in glob.glob(paths):
|
||||
feat = os.path.basename(path)
|
||||
feat = feat.split("-")
|
||||
for fe in feat:
|
||||
if "epoch" in fe:
|
||||
fe_ep = int(fe.split("=")[1])
|
||||
ckpts.append({"path": path, "div": abs(fe_ep - parse["epoch"])})
|
||||
return sorted(ckpts, key=lambda item: item["div"])[0]
|
||||
|
||||
|
||||
def stats_to_cfg(parse):
|
||||
path = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(parse["path"])))
|
||||
)
|
||||
path = os.path.join(path, "config.yaml")
|
||||
if os.path.exists(path):
|
||||
return path
|
||||
else:
|
||||
raise FileNotFoundError(f"{path} does not exists")
|
||||
|
||||
|
||||
PATH = "/home/SIC/araison/test_ggym/pytorch_geometric/graphgym/results/"
|
||||
best = best_xp_ckpt(
|
||||
paths=xp_parser_dataset(dataset_name="CIFAR10", models_dir_path=PATH), which="worst"
|
||||
)
|
||||
print(best)
|
||||
print(stats_to_ckpt(best))
|
||||
print(stats_to_cfg(best))
|
||||
def get_ckpt_path(self, epoch: int, ckpt_dir_path: str):
|
||||
paths = os.path.join(ckpt_dir_path, "*.ckpt")
|
||||
ckpts = []
|
||||
for path in glob.glob(paths):
|
||||
feat = os.path.basename(path)
|
||||
feat = feat.split("-")
|
||||
for fe in feat:
|
||||
if "epoch" in fe:
|
||||
fe_ep = int(fe.split("=")[1])
|
||||
ckpts.append({"path": path, "div": abs(fe_ep - epoch)})
|
||||
ckpt = sorted(ckpts, key=lambda item: item["div"])[0]
|
||||
return ckpt["path"]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user