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main_graph_image_gcl.py
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#!/usr/bin/env python
# encoding: utf-8
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
from torch import nn
from utils.getdata import NodeImageDataset, get_transforms,load_data_for_pretrain
from scripts.CMCL import NodeImageCLModel
from utils.util import AvgMeter, get_lr
import torch.nn.utils.prune as prune
import finetune
import time
from utils.params import args
import warnings
warnings.filterwarnings("ignore")
CUDA_LAUNCH_BLOCKING=1
def make_train_valid_dfs():
dataframe = pd.read_csv(args.node_entity_matching_path,sep=' ')
max_id = dataframe.shape[0] if not args.debug else args.number_samples
image_ids = np.arange(0, max_id)
np.random.seed(42)
valid_ids = np.random.choice(
image_ids, size=int(0.2 * len(image_ids)), replace=False
)
train_ids = [id_ for id_ in image_ids if id_ not in valid_ids]
dataframe['id'] = list(dataframe.index)
train_dataframe = dataframe[dataframe["id"].isin(train_ids)].reset_index(drop=True)
valid_dataframe = dataframe[dataframe["id"].isin(valid_ids)].reset_index(drop=True)
return train_dataframe, valid_dataframe
def build_loaders(dataframe, mode):
transforms = get_transforms(mode=mode)
dataset = NodeImageDataset(
dataframe["entity_id"].values,
dataframe["image_path"].values,
dataframe['label'].values,
transforms=transforms,
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=True if mode == "train" else False,
)
return dataloader
def train_epoch(model, feature,adj,train_loader, optimizer, lr_scheduler, step,pos):
loss_meter = AvgMeter()
tqdm_object = tqdm(train_loader, total=len(train_loader))
for batch in tqdm_object:
try:
batch = {k: v.to(args.device) for k, v in batch.items() if k != "caption"}
except:
batch = {k: v.to(args.device) for k, v in batch.items() if k != "caption"}
loss, node_embed_prune,node_embeds = model(batch, feature, adj, pos)
optimizer.zero_grad()
if args.prune:
for name, module in model.named_modules():
if isinstance(module, torch.nn.Conv2d) or isinstance(module,nn.Linear):
module.weight = module.weight_orig.clone()
elif 'node_encoder_prune.' in name:
module.weight = module.weight_orig.clone()
loss.backward()
optimizer.step()
if step == "batch":
lr_scheduler.step()
count = batch["image"].size(0)
loss_meter.update(loss.item(), count)
tqdm_object.set_postfix(train_loss=loss_meter.avg, lr=get_lr(optimizer))
return loss_meter,node_embed_prune,node_embeds
def valid_epoch(model, feature,adj,valid_loader, pos):
loss_meter = AvgMeter()
tqdm_object = tqdm(valid_loader, total=len(valid_loader))
for batch in tqdm_object:
batch = {k: v.to(args.device) for k, v in batch.items() if k != "caption"}
loss, node_embed_prune,node_embeds = model(batch, feature, adj, pos)
count = batch["image"].size(0)
loss_meter.update(loss.item(), count)
tqdm_object.set_postfix(valid_loss=loss_meter.avg)
return loss_meter,node_embed_prune,node_embeds
def main():
my_time = time.strftime('%Y%m%d%H%M', time.gmtime(time.time()))
save_model_path = "./pretrain/{}_node_image_{}.pt".format(args.dataset, my_time)
train_df, valid_df = make_train_valid_dfs() # make train, validation datasets
train_loader = build_loaders(train_df, mode="train")
valid_loader = build_loaders(valid_df, mode="valid")
# adj, features, labels, idx_train, idx_val, idx_test, pos = load_finetune_data_for_imbalanced()
adj, features, labels, pos = load_data_for_pretrain()
transforms = get_transforms(mode='train')
model = NodeImageCLModel(features,transform=transforms).to(args.device)
if args.prune:
for name, module in model.named_modules():
if isinstance(module,torch.nn.Conv2d) or isinstance(module,nn.Linear):
prune.l1_unstructured(module, name='weight', amount=int(module.weight.shape[0]*module.weight.shape[1]*args.prune_ratio))
elif 'node_encoder_prune.' in name:
prune.l1_unstructured(module, name='weight', amount=int(module.weight.shape[0]*module.weight.shape[1]*args.prune_ratio))
optimizer = torch.optim.AdamW(
model.parameters(), lr=args.lr, weight_decay=args.weight_decay
)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode="min", patience=args.patience, factor=args.factor
)
step = "epoch"
best_loss = float('inf')
for epoch in range(args.pretrain_epochs):
print(f"Epoch: {epoch + 1}")
model.train()
train_loss,node_embed_prune_train,node_embeds_train = train_epoch(model,features,adj, train_loader, optimizer, lr_scheduler, step, pos)
if epoch % 5 == 0:
# model.eval()
with torch.no_grad():
valid_loss,node_embed_prune_val,node_embeds_val = valid_epoch(model,features,adj, valid_loader, pos)
if valid_loss.avg < best_loss:
best_loss = valid_loss.avg
torch.save(model.state_dict(), save_model_path)
print("Saved Best Model!")
with torch.no_grad():
df = pd.read_csv(args.id_content_path,sep='\t')
finetune_feature_path = r'./finetune/{}_data/finetune_feature_{}.txt'.format(args.dataset,my_time)
fo = open(finetune_feature_path, 'w', encoding='utf8')
for i in range(df.shape[0]):
embed_ = model.image_encoder.get_finetune_embed(df.loc[i, 'image_path'])
np.savetxt(fo, np.array(embed_.detach().cpu()).reshape([1, 768]))
if i % 1000 == 0:
print("{} fine-tuned features have been generated!".format(i))
save_embed_path = r'./finetune/{}_data/node_embed_{}.txt'.format(args.dataset,my_time)
np.savetxt(save_embed_path, node_embeds_train.cpu().data.numpy())
print("The fine-tuned features are save in {}!".format(finetune_feature_path))
print("The pre-trained encoders are save in {}!".format(save_model_path))
if args.finetune:
finetune.fine_tune(save_model_path,finetune_feature_path)
if __name__ == "__main__":
main()