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explaining_framework
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Reformating, fixing

This commit is contained in:
araison 2023-01-04 12:56:37 +01:00
parent 7e32d6fd3a
commit 10baa1d443
7 changed files with 49 additions and 45 deletions
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2
explaining_framework/config/explaining_config.py
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@ -98,7 +98,7 @@ def set_cfg(explaining_cfg):
explaining_cfg.model_config = CN()
# Do not modify it, will be handled by dataset , assuming one dataset = one learning task
explaining_cfg.model_config.mode = None
explaining_cfg.model_config.mode = "regression"
# Do not modify it, will be handled by dataset , assuming one dataset = one learning task
explaining_cfg.model_config.task_level = None

10
explaining_framework/metric/robust.py
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@ -39,7 +39,7 @@ class FGSM(Metric):
self.zero_thresh = 10**-6
def forward(self, input, target, epsilon: float) -> Explanation:
input_ = copy.copy(input)
input_ = input.clone()
grad = compute_gradient(
model=self.model, inp=input_, target=target, loss=self.loss
)
@ -168,24 +168,24 @@ class Attack(Metric):
def _gaussian_noise(self, exp) -> Explanation:
x = torch.clone(exp.x)
x = x + torch.randn(*x.shape)
exp_ = copy.copy(exp)
exp_ = exp.clone()
exp_.x = x
return exp_
def _add_edge(self, exp, p: float) -> Explanation:
exp_ = copy.copy(exp)
exp_ = exp.clone()
exp_.edge_index, _ = add_random_edge(
exp_.edge_index, p=p, num_nodes=exp_.x.shape[0]
)
return exp_
def _remove_edge(self, exp, p: float) -> Explanation:
exp_ = copy.copy(exp)
exp_ = exp.clone()
exp_.edge_index, _ = dropout_edge(exp_.edge_index, p=p)
return exp_
def _remove_node(self, exp, p: float) -> Explanation:
exp_ = copy.copy(exp)
exp_ = exp.clone()
exp_.edge_index, _, _ = dropout_node(
exp_.edge_index, p=p, num_nodes=exp_.x.shape[0]
)

4
explaining_framework/stats/graph/graph_stat.py
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@ -153,7 +153,7 @@ class GraphStat(object):
return maps
def __call__(self, data):
data_ = copy.copy(data)
data_ = data.clone()
datahash = hash(data.__repr__)
stats = {}
for k, v in self.maps.items():
@ -161,7 +161,7 @@ class GraphStat(object):
_data_ = to_networkx(data)
_data_ = _data_.to_undirected()
elif k == "torch_geometric":
_data_ = copy.copy(data)
_data_ = data.clone()
for name, func in v.items():
try:
val = func(_data_)

60
explaining_framework/utils/explaining/outline.py
View File

@ -3,6 +3,18 @@ import itertools
from typing import Any
from eixgnn.eixgnn import EiXGNN
from scgnn.scgnn import SCGNN
from torch_geometric import seed_everything
from torch_geometric.data import Batch, Data
from torch_geometric.explain import Explainer
from torch_geometric.explain.config import ThresholdConfig
from torch_geometric.explain.explanation import Explanation
from torch_geometric.graphgym.config import cfg
from torch_geometric.graphgym.loader import create_dataset
from torch_geometric.graphgym.model_builder import cfg, create_model
from torch_geometric.graphgym.utils.device import auto_select_device
from torch_geometric.loader.dataloader import DataLoader
from explaining_framework.config.explainer_config.eixgnn_config import \
eixgnn_cfg
from explaining_framework.config.explainer_config.scgnn_config import scgnn_cfg
@ -24,17 +36,6 @@ from explaining_framework.utils.explanation.io import (
explanation_verification, get_pred)
from explaining_framework.utils.io import (is_exists, obj_config_to_str,
read_json, write_json, write_yaml)
from scgnn.scgnn import SCGNN
from torch_geometric import seed_everything
from torch_geometric.data import Batch, Data
from torch_geometric.explain import Explainer
from torch_geometric.explain.config import ThresholdConfig
from torch_geometric.explain.explanation import Explanation
from torch_geometric.graphgym.config import cfg
from torch_geometric.graphgym.loader import create_dataset
from torch_geometric.graphgym.model_builder import cfg, create_model
from torch_geometric.graphgym.utils.device import auto_select_device
from torch_geometric.loader.dataloader import DataLoader
all__captum = [
"LRP",
@ -133,6 +134,7 @@ class ExplainingOutline(object):
self.load_explaining_cfg()
self.load_model_info()
self.load_cfg()
self.load_cfg_to_explaining_cfg()
self.load_dataset()
self.load_model()
self.load_model_to_hardware()
@ -165,8 +167,10 @@ class ExplainingOutline(object):
return None
def load_indexes(self):
if not self.explaining_cfg.dataset.specific_items is None:
indexes = explaining_cfg.dataset.specific_items
items = self.explaining_cfg.dataset.items
if isinstance(items, (list, int)):
indexes = items
else:
indexes = list(range(len(self.dataset)))
self.indexes = iter(indexes)
@ -195,15 +199,20 @@ class ExplainingOutline(object):
self.model_signature = info.get_model_signature()
def load_cfg(self):
cfg.set_new_allowed(True)
cfg.merge_from_file(self.model_info["cfg_path"])
self.cfg = cfg
def load_explaining_cfg(self):
explaining_cfg.set_new_allowed(True)
explaining_cfg.merge_from_file(self.explaining_cfg_path)
self.explaining_cfg = explaining_cfg
def load_cfg_to_explaining_cfg(self):
if self.cfg is None:
self.load_cfg()
if self.explaining_cfg is None:
self.load_explaining_cfg()
self.explaining_cfg.model_config.task_level = self.cfg.dataset.task
def load_explainer_cfg(self):
if self.explaining_cfg is None:
self.load_explaining_cfg()
@ -217,11 +226,9 @@ class ExplainingOutline(object):
self.explainer_cfg = None
else:
if self.explaining_cfg.explainer.name == "EIXGNN":
eixgnn_cfg.set_new_allowed(True)
eixgnn_cfg.merge_from_file(self.explaining_cfg.explainer.cfg)
self.explainer_cfg = eixgnn_cfg
elif self.explaining_cfg.explainer.name == "SCGNN":
scgnn_cfg.set_new_allowed(True)
scgnn_cfg.merge_from_file(self.explaining_cfg.explainer.cfg)
self.explainer_cfg = scgnn_cfg
@ -245,12 +252,13 @@ class ExplainingOutline(object):
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."
)
self.dataset = create_dataset()
if isinstance(self.explaining_cfg.dataset.specific_items, int):
ind = self.explaining_cfg.dataset.specific_items
self.dataset = self.dataset[ind : ind + 1]
elif isinstance(self.explaining_cfg.dataset.specific_items, list):
ind = self.explaining_cfg.dataset.specific_items
self.dataset = self.dataset[ind]
items = self.explaining_cfg.dataset.items
print(items)
print(type(items))
if isinstance(items, int):
self.dataset = self.dataset[items : items + 1]
elif isinstance(items, list):
self.dataset = self.dataset[items]
def load_dataset_to_dataloader(self, to_iter=True):
self.dataset = DataLoader(dataset=self.dataset, shuffle=False, batch_size=1)
@ -308,7 +316,7 @@ class ExplainingOutline(object):
),
model_config=dict(
mode="regression",
task_level=self.cfg.dataset.task,
task_level=self.explaining_cfg.model_config.task_level,
return_type=self.explaining_cfg.model_config.return_type,
),
)
@ -535,8 +543,6 @@ class ExplainingOutline(object):
explanation = _get_explanation(self.explainer, item)
get_pred(self.explainer, explanation)
_save_explanation(explanation, path)
explanation = explanation.to(cfg.accelerator)
return explanation
def get_adjust(self, adjust: Adjust, item: Explanation, path: str):
@ -549,7 +555,6 @@ class ExplainingOutline(object):
exp_adjust = adjust.forward(item)
get_pred(self.explainer, exp_adjust)
_save_explanation(exp_adjust, path)
exp_adjust = exp_adjust.to(cfg.accelerator)
return exp_adjust
def get_threshold(self, item: Explanation, path: str):
@ -562,7 +567,6 @@ class ExplainingOutline(object):
exp_threshold = self.explainer._post_process(item)
get_pred(self.explainer, exp_threshold)
_save_explanation(exp_threshold, path)
exp_threshold = exp_threshold.to(cfg.accelerator)
return exp_threshold
def get_metric(self, metric: Metric, item: Explanation, path: str):

4
explaining_framework/utils/explanation/adjust.py
View File

@ -14,7 +14,7 @@ class Adjust(object):
self.strategy = strategy
def forward(self, exp: Explanation) -> Explanation:
exp_ = copy.copy(exp)
exp_ = exp.clone()
_store = exp_.to_dict()
for k, v in _store.items():
if "mask" in k:
@ -41,7 +41,7 @@ class Adjust(object):
return mask_
def normalize(self, mask: FloatTensor) -> FloatTensor:
norm = torch.norm(mask, p="inf")
norm = torch.norm(mask, p=float("inf"))
if norm.item() > 0:
mask_ = mask / norm.item()
return mask_

9
explaining_framework/utils/explanation/io.py
View File

@ -5,6 +5,7 @@ import os
import torch
from torch_geometric.data import Data
from torch_geometric.explain.explanation import Explanation
from torch_geometric.graphgym.config import cfg
def _get_explanation(explainer, item):
@ -18,6 +19,7 @@ def _get_explanation(explainer, item):
# WARNING + LOG
return None
else:
explanation = explanation.to(cfg.accelerator)
return explanation
@ -47,9 +49,8 @@ def explanation_verification(exp: Explanation) -> bool:
for mask in masks:
is_nan = mask.isnan().any().item()
is_inf = mask.isinf().any().item()
is_const = mask.max() == mask.min()
is_ok = exp.validate()
if is_nan or is_inf or not is_ok or is_const:
if is_nan or is_inf or not is_ok:
is_good = False
return is_good
else:
@ -58,7 +59,7 @@ def explanation_verification(exp: Explanation) -> bool:
def _save_explanation(exp: Explanation, path: str) -> None:
data = copy.copy(exp).to_dict()
data = exp.clone().to_dict()
for k, v in data.items():
if isinstance(v, torch.Tensor):
data[k] = v.detach().cpu().tolist()
@ -77,5 +78,3 @@ def _load_explanation(path: str) -> Explanation:
else:
data[k] = torch.FloatTensor(v)
return Explanation.from_dict(data)

5
main.py
View File

@ -27,6 +27,7 @@ from explaining_framework.utils.io import (is_exists, obj_config_to_str,
if __name__ == "__main__":
args = parse_args()
outline = ExplainingOutline(args.explaining_cfg_file)
print(outline.explaining_cfg)
out_dir = os.path.join(outline.explaining_cfg.out_dir, outline.model_signature)
makedirs(out_dir)
@ -64,14 +65,14 @@ if __name__ == "__main__":
for adjust in outline.adjusts:
adjust_path = os.path.join(raw_path, f"adjust-{obj_config_to_str(adjust)}")
makedirs(adjust_path)
exp_adjust_path = os.path.join(exp_adjust_path, f"{index}.json")
exp_adjust_path = os.path.join(adjust_path, f"{index}.json")
exp_adjust = outline.get_adjust(
adjust=adjust, item=raw_exp, path=exp_adjust_path
)
for threshold_conf in outline.thresholds_configs:
outline.set_explainer_threshold_config(threshold_conf)
masking_path = os.path.join(
save_raw_path_,
adjust_path,
"-".join([f"{k}={v}" for k, v in threshold_conf.items()]),
)
makedirs(masking_path)