-
Notifications
You must be signed in to change notification settings - Fork 2
/
main_graph_image_gcl.py
166 lines (132 loc) · 6.45 KB
/
main_graph_image_gcl.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
#!/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()