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adapt.py
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from utils.data_utils import get_supervised_data, get_supervised_data_v2
from model.gcope import get_prompt_model, get_answer_model
from utils.adapt_utils import prog, finetune
from torch_geometric.loader import DataLoader
import torch
import numpy as np
from model.trigger import TriggerGenerator
from model.generator import TriggerGenerator_v1
import matplotlib.pyplot as plt
# def calculate_parameter_changes(backbone_model, initial_parameters):
# max_change = 0
# min_change = 1e10
# parameter_changes = {}
# for name, initial_param in initial_parameters:
# if name == 'conv_feat.bias': continue
# current_param = backbone_model.state_dict()[name]
# change = torch.abs(current_param - initial_param)
# parameter_changes[name] = torch.mean(change).item()
# if torch.max(change).item() > max_change:
# max_change = torch.max(change).item()
# if torch.min(change).item() < min_change and torch.min(change) > 1e-04:
# min_change = torch.min(change).item()
# print(f'max: {max_change}')
# print(f'min: {min_change}')
# return parameter_changes
def calculate_parameter_changes(backbone_model, initial_parameters):
parameter_changes = []
for initial_param, current_param in zip(initial_parameters, backbone_model.parameters()):
change = torch.abs(current_param.cpu() - initial_param.cpu())
parameter_changes.extend(change.flatten().tolist())
return parameter_changes
from preprocess import mdgpt_data_preprocess
from utils.adapt_utils import mdgpt_prompt, samgpt_prompt
def adapt(args, model):
# Data
if args.gfm_model == "GCOPE":
datasets, num_classes, data = get_supervised_data(args.name, args.unify_dim, args.ratios, args.data_path_origin, args)
loaders = {k: DataLoader(v, batch_size=args.batch_size, shuffle=True) for k, v in datasets.items() }
elif args.gfm_model == "MDGPT" or args.gfm_model == "SAMGPT":
feature, adj, labels, idx_train, idx_test = mdgpt_data_preprocess(args, args.name, pretrain=False)
else:
raise NotImplementedError
# Finetune
if args.gfm_model == "GCOPE":
answer_model = get_answer_model(args.hid_dim, num_classes, args.mlp_num_layers)
saliency_model = torch.nn.Identity()
if args.adapt_method == "prompt":
total_graph = sum([len(v) for k, v in datasets.items()]) # the number of graphs
train_node_num = sum([g.num_nodes for g in datasets['train']]) # the number of nodes in train graph
val_node_num = sum([g.num_nodes for g in datasets['val']]) # the number of nodes in val graph
test_node_num = sum([g.num_nodes for g in datasets['test']]) # the number of nodes in test graph
prompt_node_num = int((train_node_num + val_node_num + test_node_num) / total_graph)
prompt_model = get_prompt_model(datasets['train'][0].x.size(-1), prompt_node_num, args.cross_prune, args.inner_prune, args.edge_attr_dim)
results = prog(loaders=loaders,
backbone_model=model,
prompt_model=prompt_model,
answer_model=answer_model,
saliency_model=saliency_model,
epoch=args.adapt_epoch,
reconstruct=args.adapt_reconstruct,
backbone_tuning=bool(args.backbone_tuning),
saliency_tuning=bool(args.saliency_tuning),
prompt_lr=args.prompt_lr,
prompt_weight_decay=args.prompt_weight_decay,
ans_lr=args.ans_lr,
ans_weight_decay=args.ans_weight_decay)
elif args.adapt_method == "finetune":
# initial_backbone_params = [(name, param.detach().cpu().clone()) for name, param in model.named_parameters()]
initial_backbone_params = [param.detach().cpu().clone() for param in model.parameters()]
results, backbone_model, answer_model, saliency_model = finetune(args=args,
loaders=loaders,
backbone_model=model,
saliency_model=saliency_model,
answer_model=answer_model,
reconstruct=args.adapt_reconstruct,
backbone_tuning=bool(args.backbone_tuning),
saliency_tuning=bool(args.saliency_tuning),
learning_rate=args.finetune_lr,
weight_decay=args.finetune_weight_decay,
epoch=args.tuning_epoch)
parameter_changes = calculate_parameter_changes(backbone_model, initial_backbone_params)
torch.save(parameter_changes, f'./fig/parameter_changes_{args.name}.pt'); exit()
# for name, change in parameter_changes.items():
# print(f"Parameter {name} change magnitude: {change}")
plt.figure(figsize=(5, 3))
plt.hist(parameter_changes, bins=50, edgecolor='black', alpha=0.7)
# plt.title('Distribution of Parameter Changes after Fine-tuning')
plt.xlabel('Parameter Change Magnitude')
plt.ylabel('Frequency')
plt.grid(True)
plt.savefig('./fig/temp.pdf')
exit()
if args.backdoor:
from utils.backdoor_utils import gcope_backdoor_evaluate_v1, gcope_backdoor_evaluate_v0
# gcope_backdoor_evaluate_v1(
# args=args,
# data=data,
# num_trigger=args.num_trigger_node,
# backbone_model=backbone_model,
# saliency_model=saliency_model,
# answer_model=answer_model,
# device=args.device)
gcope_backdoor_evaluate_v0(
args=args,
data_loader=loaders,
num_trigger=args.num_trigger_node,
backbone_model=backbone_model,
saliency_model=saliency_model,
answer_model=answer_model,
device=args.device)
elif args.gfm_model == "MDGPT":
answer_model = mdgpt_prompt(args=args,
model=model,
feature=feature,
adj=adj,
labels=labels,
idx_train=idx_train,
idx_test=idx_test)
if args.backdoor:
from utils.backdoor_utils import mdgpt_backdoor_evaluate
mdgpt_backdoor_evaluate(args=args,
num_trigger=args.num_trigger_node,
model=model,
idx_test=idx_test,
answer_model=answer_model)
elif args.gfm_model == "SAMGPT":
answer_model = samgpt_prompt(args=args,
model=model,
feature=feature,
adj=adj,
labels=labels,
idx_train=idx_train,
idx_test=idx_test)
if args.backdoor:
from utils.backdoor_utils import samgpt_backdoor_evaluate
samgpt_backdoor_evaluate(args=args,
num_trigger=args.num_trigger_node,
model=model,
idx_test=idx_test,
answer_model=answer_model)
else:
raise NotImplementedError
return