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pipeline.py
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pipeline.py
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import os
import glob
import time
import torch
from tqdm import tqdm
from models import GnnNets, GnnNets_NC
from utils import PlotUtils
from pgexplainer import PGExplainer
from torch_geometric.data import Data
from torch_geometric.utils import to_networkx
from metrics import top_k_fidelity, top_k_sparsity
from load_dataset import get_dataset, get_dataloader
from Configures import data_args, model_args, train_args
def pipeline_GC(top_k):
dataset = get_dataset(data_args)
if data_args.dataset_name == 'mutag':
data_indices = list(range(len(dataset)))
pgexplainer_trainset = dataset
else:
loader = get_dataloader(dataset,
batch_size=train_args.batch_size,
random_split_flag=data_args.random_split,
data_split_ratio=data_args.data_split_ratio,
seed=data_args.seed)
data_indices = loader['test'].dataset.indices
pgexplainer_trainset = loader['train'].dataset
input_dim = dataset.num_node_features
output_dim = dataset.num_classes
gnnNets = GnnNets(input_dim, output_dim, model_args)
checkpoint = torch.load(model_args.model_path)
gnnNets.update_state_dict(checkpoint['net'])
gnnNets.to_device()
gnnNets.eval()
save_dir = os.path.join('./results', f"{data_args.dataset_name}_"
f"{model_args.model_name}_"
f"pgexplainer")
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
pgexplainer = PGExplainer(gnnNets)
if torch.cuda.is_available():
torch.cuda.synchronize()
tic = time.perf_counter()
pgexplainer.get_explanation_network(pgexplainer_trainset)
if torch.cuda.is_available():
torch.cuda.synchronize()
toc = time.perf_counter()
training_duration = toc - tic
print(f"training time is {training_duration: .4}s ")
explain_duration = 0.0
plotutils = PlotUtils(dataset_name=data_args.dataset_name)
fidelity_score_list = []
sparsity_score_list = []
for data_idx in tqdm(data_indices):
data = dataset[data_idx]
if torch.cuda.is_available():
torch.cuda.synchronize()
tic = time.perf_counter()
prob = pgexplainer.eval_probs(data.x, data.edge_index)
pred_label = prob.argmax(-1).item()
if glob.glob(os.path.join(save_dir, f"example_{data_idx}.pt")):
file = glob.glob(os.path.join(save_dir, f"example_{data_idx}.pt"))[0]
edge_mask = torch.from_numpy(torch.load(file))
else:
edge_mask = pgexplainer.explain_edge_mask(data.x, data.edge_index)
save_path = os.path.join(save_dir, f"example_{data_idx}.pt")
edge_mask = edge_mask.cpu()
torch.save(edge_mask.detach().numpy(), save_path)
if torch.cuda.is_available():
torch.cuda.synchronize()
toc = time.perf_counter()
explain_duration += (toc - tic)
graph = to_networkx(data)
fidelity_score = top_k_fidelity(data, edge_mask, top_k, gnnNets, pred_label)
sparsity_score = top_k_sparsity(data, edge_mask, top_k)
fidelity_score_list.append(fidelity_score)
sparsity_score_list.append(sparsity_score)
# visualization
if hasattr(dataset, 'supplement'):
words = dataset.supplement['sentence_tokens'][str(data_idx)]
plotutils.plot_soft_edge_mask(graph, edge_mask, top_k,
x=data.x,
words=words,
un_directed=True,
figname=os.path.join(save_dir, f"example_{data_idx}.png"))
else:
plotutils.plot_soft_edge_mask(graph, edge_mask, top_k,
x=data.x,
un_directed=True,
figname=os.path.join(save_dir, f"example_{data_idx}.png"))
fidelity_scores = torch.tensor(fidelity_score_list)
sparsity_scores = torch.tensor(sparsity_score_list)
return fidelity_scores, sparsity_scores
def pipeline_NC(top_k):
dataset = get_dataset(data_args)
input_dim = dataset.num_node_features
output_dim = dataset.num_classes
data = dataset[0]
node_indices = torch.where(data.test_mask * data.y != 0)[0].tolist()
gnnNets = GnnNets_NC(input_dim, output_dim, model_args)
checkpoint = torch.load(model_args.model_path)
gnnNets.update_state_dict(checkpoint['net'])
gnnNets.to_device()
gnnNets.eval()
save_dir = os.path.join('./results', f"{data_args.dataset_name}_"
f"{model_args.model_name}_"
f"pgexplainer")
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
pgexplainer = PGExplainer(gnnNets)
if torch.cuda.is_available():
torch.cuda.synchronize()
tic = time.perf_counter()
pgexplainer.get_explanation_network(dataset, is_graph_classification=False)
if torch.cuda.is_available():
torch.cuda.synchronize()
toc = time.perf_counter()
training_duration = toc - tic
print(f"training time is {training_duration}s ")
duration = 0.0
data = dataset[0]
fidelity_score_list = []
sparsity_score_list = []
plotutils = PlotUtils(dataset_name=data_args.dataset_name)
for ori_node_idx in tqdm(node_indices):
tic = time.perf_counter()
if glob.glob(os.path.join(save_dir, f"node_{ori_node_idx}.pt")):
file = glob.glob(os.path.join(save_dir, f"node_{ori_node_idx}.pt"))[0]
edge_mask, x, edge_index, y, subset = torch.load(file)
edge_mask = torch.from_numpy(edge_mask)
node_idx = int(torch.where(subset == ori_node_idx)[0])
pred_label = pgexplainer.get_node_prediction(node_idx, x, edge_index)
else:
x, edge_index, y, subset, kwargs = \
pgexplainer.get_subgraph(node_idx=ori_node_idx, x=data.x, edge_index=data.edge_index, y=data.y)
node_idx = int(torch.where(subset == ori_node_idx)[0])
edge_mask = pgexplainer.explain_edge_mask(x, edge_index)
pred_label = pgexplainer.get_node_prediction(node_idx, x, edge_index)
save_path = os.path.join(save_dir, f"node_{ori_node_idx}.pt")
edge_mask = edge_mask.cpu()
cache_list = [edge_mask.numpy(), x, edge_index, y, subset]
torch.save(cache_list, save_path)
duration += time.perf_counter() - tic
sub_data = Data(x=x, edge_index=edge_index, y=y)
graph = to_networkx(sub_data)
fidelity_score = top_k_fidelity(sub_data, edge_mask, top_k, gnnNets, pred_label,
node_idx=node_idx, undirected=True)
sparsity_score = top_k_sparsity(sub_data, edge_mask, top_k, undirected=True)
fidelity_score_list.append(fidelity_score)
sparsity_score_list.append(sparsity_score)
# visualization
plotutils.plot_soft_edge_mask(graph, edge_mask, top_k,
y=sub_data.y,
node_idx=node_idx,
un_directed=True,
figname=os.path.join(save_dir, f"example_{ori_node_idx}.png"))
fidelity_scores = torch.tensor(fidelity_score_list)
sparsity_scores = torch.tensor(sparsity_score_list)
return fidelity_scores, sparsity_scores
def pipeline(top_k):
if data_args.dataset_name.lower() == 'BA_shapes'.lower():
rets = pipeline_NC(top_k)
else:
rets = pipeline_GC(top_k)
return rets
if __name__ == '__main__':
top_k = 6
fidelity_scores, sparsity_scores = pipeline(top_k)
print(f"fidelity score: {fidelity_scores.mean().item():.4f}, "
f"sparsity score: {sparsity_scores.mean().item():.4f}")