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train_process.py
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"""
Name: train_process
Date: 2022/4/11 上午10:26
Version: 1.0
"""
import torch
# from transformers import AdamW
from torch.optim import Adam, AdamW, SGD
from tqdm import tqdm, trange
from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score
from util.write_file import WriteFile
import dev_process
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from model import ModelParam
# import tensorflow as tf
def train_process(opt, train_loader, dev_loader, test_loader, cl_model, critertion, log_summary_writer:SummaryWriter=None, tokenizer=None, image_id_list=None):
optimizer = None
pre_train_model_param = [name for name, param in cl_model.named_parameters() if 'text_model' in name]
optimizer_grouped_parameters = [
{
"params": [p for n, p in cl_model.named_parameters() if n in pre_train_model_param],
"lr": 0,
},
{
"params": [p for n, p in cl_model.named_parameters() if n not in pre_train_model_param],
"lr": opt.fuse_lr,
},
]
if opt.optim == 'adam':
optimizer = Adam(optimizer_grouped_parameters, betas=(opt.optim_b1, opt.optim_b2))
elif opt.optim == 'adamw':
optimizer = AdamW(optimizer_grouped_parameters, betas=(opt.optim_b1, opt.optim_b2))
elif opt.optim == 'sgd':
optimizer = SGD(optimizer_grouped_parameters, momentum=opt.momentum)
orgin_param = ModelParam()
augment_param = ModelParam()
last_F1 = 0
last_Accuracy = 0
for epoch in trange(opt.epoch, desc='Epoch:'):
y_true = []
y_pre = []
run_loss = 0
total_labels = 0
cl_model.train()
cl_model.zero_grad()
if epoch >= opt.train_fuse_model_epoch:
optimizer.param_groups[0]['lr'] = opt.lr
optimizer.param_groups[1]['lr'] = opt.lr
train_loader_tqdm = tqdm(train_loader, desc='Train Iteration:')
epoch_step_num = epoch * train_loader_tqdm.total
step_num = 0
for index, data in enumerate(train_loader_tqdm):
texts_origin, bert_attention_mask, image_origin, text_image_mask, labels,\
texts_augment, bert_attention_mask_augment, image_augment, text_image_mask_augment, target_labels = data
if opt.cuda is True:
texts_origin = texts_origin.cuda()
bert_attention_mask = bert_attention_mask.cuda()
image_origin = image_origin.cuda()
text_image_mask = text_image_mask.cuda()
labels = labels.cuda()
texts_augment = texts_augment.cuda()
bert_attention_mask_augment = bert_attention_mask_augment.cuda()
image_augment = image_augment.cuda()
text_image_mask_augment = text_image_mask_augment.cuda()
for i in range(len(target_labels)):
target_labels[i] = target_labels[i].cuda()
orgin_param.set_data_param(texts=texts_origin, bert_attention_mask=bert_attention_mask, images=image_origin, text_image_mask=text_image_mask)
augment_param.set_data_param(texts=texts_augment, bert_attention_mask=bert_attention_mask_augment, images=image_augment, text_image_mask=text_image_mask_augment)
origin_res, l_pos_neg, cl_lables, cl_self_loss = cl_model(orgin_param, augment_param, labels, target_labels)
classify_loss = critertion(origin_res, labels)
cl_loss = critertion(l_pos_neg, cl_lables)
loss = (classify_loss + cl_loss * opt.cl_loss_alpha + cl_self_loss * opt.cl_self_loss_alpha) / opt.acc_batch_size
loss.backward()
train_loader_tqdm.set_description("Train Iteration, loss: %.6f, lr: %e" %
(loss, optimizer.param_groups[0]['lr']))
if (index + 1) % opt.acc_grad == 0:
if log_summary_writer:
log_summary_writer.add_scalar('train_info/loss', loss.item(), global_step=step_num + epoch_step_num)
log_summary_writer.add_scalar('train_info/classify_loss', classify_loss.item(), global_step=step_num + epoch_step_num)
log_summary_writer.add_scalar('train_info/cl_loss', cl_loss.item(), global_step=step_num + epoch_step_num)
log_summary_writer.add_scalar('train_info/cl_self_loss', cl_self_loss.item(), global_step=step_num + epoch_step_num)
log_summary_writer.add_scalar('train_info/lr', optimizer.param_groups[0]['lr'], global_step=step_num + epoch_step_num)
log_summary_writer.add_scalar('train_info/fuse_lr', optimizer.param_groups[1]['lr'], global_step=step_num + epoch_step_num)
optimizer.step()
optimizer.zero_grad()
step_num += 1
_, predicted = torch.max(origin_res, 1)
y_true.extend(labels.cpu())
y_pre.extend(predicted.cpu())
run_loss += loss.item()
total_labels += labels.size(0)
run_loss /= total_labels
y_true = np.array(y_true)
y_pre = np.array(y_pre)
train_accuracy = accuracy_score(y_true, y_pre)
train_F1_weighted = f1_score(y_true, y_pre, average='weighted')
train_R_weighted = recall_score(y_true, y_pre, average='weighted')
train_precision_weighted = precision_score(y_true, y_pre, average='weighted')
train_F1 = f1_score(y_true, y_pre, average='macro')
train_R = recall_score(y_true, y_pre, average='macro')
train_precision = precision_score(y_true, y_pre, average='macro')
save_content = 'Epoch: %d:\nTrain: Accuracy: %.6f, F1(weighted): %.6f, Precision(weighted): %.6f, R(weighted): %.6f, F1(macro): %.6f, Precision: %.6f, R: %.6f, loss: %.6f' % \
(epoch, train_accuracy, train_F1_weighted, train_precision_weighted, train_R_weighted, train_F1, train_precision, train_R, run_loss)
WriteFile(opt.save_model_path, 'train_correct_log.txt', save_content + '\n', 'a+')
print(save_content, ' ' * 200)
if log_summary_writer:
log_summary_writer.add_scalar('train_info/loss_epoch', run_loss, global_step=epoch)
log_summary_writer.add_scalar('train_info/acc', train_accuracy, global_step=epoch)
log_summary_writer.add_scalar('train_info/f1_w', train_F1_weighted, global_step=epoch)
log_summary_writer.add_scalar('train_info/r_w', train_R_weighted, global_step=epoch)
log_summary_writer.add_scalar('train_info/p_w', train_precision_weighted, global_step=epoch)
log_summary_writer.add_scalar('train_info/f1_ma', train_F1, global_step=epoch)
log_summary_writer.flush()
train_log = {
"epoch": epoch,
"train_accuracy": train_accuracy,
"train_F1": train_F1,
"train_R": train_R,
"train_precision": train_precision,
"train_F1_weighted": train_F1_weighted,
"train_precision_weighted": train_precision_weighted,
"train_R_weighted": train_R_weighted,
"run_loss": run_loss
}
# debug：正常运行不要把下面的代码注释掉
last_F1, last_Accuracy = dev_process.dev_process(opt, critertion, cl_model, dev_loader, test_loader, last_F1, last_Accuracy, train_log, log_summary_writer)