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main_t7.py
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import os
import argparse
import torch
import torch.nn as nn
import time
from tqdm import tqdm
from torch.utils.data.distributed import DistributedSampler
from model.VSLNet_t7 import VSLNet, build_optimizer_and_scheduler
from util.data_util import load_video_features, save_json, load_json
from util.data_gen import gen_or_load_dataset
from util.data_loader_t7 import get_train_loader, get_test_loader
from util.runner_utils_t7 import set_th_config, convert_length_to_mask, eval_test, filter_checkpoints, \
get_last_checkpoint
parser = argparse.ArgumentParser()
# data parameters
parser.add_argument('--save_dir', type=str, default='datasets_t7', help='path to save processed dataset')
parser.add_argument('--task', type=str, default='charades', help='target task')
parser.add_argument('--fv', type=str, default='new', help='[new | org] for visual features')
parser.add_argument('--max_pos_len', type=int, default=128, help='maximal position sequence length allowed')
# model parameters
parser.add_argument("--word_size", type=int, default=None, help="number of words")
parser.add_argument("--char_size", type=int, default=None, help="number of characters")
parser.add_argument("--word_dim", type=int, default=300, help="word embedding dimension")
parser.add_argument("--video_feature_dim", type=int, default=1024, help="video feature input dimension")
parser.add_argument("--char_dim", type=int, default=50, help="character dimension, set to 100 for activitynet")
parser.add_argument("--dim", type=int, default=128, help="hidden size")
parser.add_argument("--highlight_lambda", type=float, default=5.0, help="lambda for highlight region")
parser.add_argument("--num_heads", type=int, default=8, help="number of heads")
parser.add_argument("--drop_rate", type=float, default=0.2, help="dropout rate")
parser.add_argument('--predictor', type=str, default='rnn', help='[rnn | transformer]')
# training/evaluation parameters
parser.add_argument("--gpu_idx", type=str, default="0", help="GPU index")
parser.add_argument("--seed", type=int, default=12345, help="random seed")
parser.add_argument("--mode", type=str, default="train", help="[train | test]")
parser.add_argument("--epochs", type=int, default=100, help="number of epochs")
parser.add_argument("--batch_size", type=int, default=16, help="batch size")
parser.add_argument("--num_train_steps", type=int, default=None, help="number of training steps")
parser.add_argument("--init_lr", type=float, default=0.0001, help="initial learning rate")
parser.add_argument("--clip_norm", type=float, default=1.0, help="gradient clip norm")
parser.add_argument("--warmup_proportion", type=float, default=0.0, help="warmup proportion")
parser.add_argument("--extend", type=float, default=0.1, help="highlight region extension")
parser.add_argument("--period", type=int, default=100, help="training loss print period")
parser.add_argument('--model_dir', type=str, default='ckpt_t7', help='path to save trained model weights')
parser.add_argument('--model_name', type=str, default='vslnet', help='model name')
parser.add_argument('--suffix', type=str, default=None, help='set to the last `_xxx` in ckpt repo to eval results')
# ##############################################
# #第一步：分布式训练加速
# parser.add_argument("--local_rank", help="local device id on current node",
# type=int)
# ##############################################
configs = parser.parse_args()
# local_rank=configs.local_rank
# #第2步，初始化分布式环境
# #######################################################################################
# #word_size表示当前结点有多少GPU卡,local_rank表示当前进程运行在哪台设备上
# n_gpus = 4 #所以world_size和nproc_per_node参数需要设置的一样
# torch.distributed.init_process_group("nccl", world_size=n_gpus, rank=configs.local_rank)
# torch.cuda.set_device(configs.local_rank)
# #######################################################################################
# set tensorflow configs
set_th_config(configs.seed)
# prepare or load dataset
dataset = gen_or_load_dataset(configs)
configs.char_size = dataset['n_chars']
configs.word_size = dataset['n_words']
# get train and test loader
visual_features = load_video_features(os.path.join('data', 'features', configs.task, configs.fv), configs.max_pos_len)
train_loader = get_train_loader(dataset=dataset['train_set'], video_features=visual_features, configs=configs)
val_loader = None if dataset['val_set'] is None else get_test_loader(dataset['val_set'], visual_features, configs)
test_loader = get_test_loader(dataset=dataset['test_set'], video_features=visual_features, configs=configs)
configs.num_train_steps = len(train_loader) * configs.epochs
num_train_batches = len(train_loader)
num_val_batches = 0 if val_loader is None else len(val_loader)
num_test_batches = len(test_loader)
# Device configuration
cuda_str = 'cuda' if configs.gpu_idx is None else 'cuda:{}'.format(configs.gpu_idx)
device = torch.device(cuda_str if torch.cuda.is_available() else 'cpu')
# create model dir
home_dir = os.path.join(configs.model_dir, '_'.join([configs.model_name, configs.task, configs.fv,
str(configs.max_pos_len), configs.predictor]))
if configs.suffix is not None:
home_dir = home_dir + '_' + configs.suffix
model_dir = os.path.join(home_dir, "model")
# train and test
if configs.mode.lower() == 'train':
if not os.path.exists(model_dir):
os.makedirs(model_dir)
eval_period = num_train_batches // 2
save_json(vars(configs), os.path.join(model_dir, 'configs.json'), sort_keys=True, save_pretty=True)
# build model
model = VSLNet(configs=configs, word_vectors=dataset['word_vector']).to(device)
optimizer, scheduler = build_optimizer_and_scheduler(model, configs=configs)
# start training
best_r1i7 = -1.0
score_writer = open(os.path.join(model_dir, "eval_results.txt"), mode="w", encoding="utf-8")
print('start training...', flush=True)
global_step = 0
start_time=time.time()
for epoch in tqdm(range(configs.epochs)):
model.train()
for data in tqdm(train_loader, total=num_train_batches, desc='Epoch %3d / %3d' % (epoch + 1, configs.epochs)):
global_step += 1
_, vfeats, vfeat_lens, word_ids, char_ids, s_labels, e_labels, h_labels = data
# prepare features
vfeats, vfeat_lens = vfeats.to(device), vfeat_lens.to(device)
word_ids, char_ids = word_ids.to(device), char_ids.to(device)
s_labels, e_labels, h_labels = s_labels.to(device), e_labels.to(device), h_labels.to(device)
# generate mask
query_mask = (torch.zeros_like(word_ids) != word_ids).float().to(device)
video_mask = convert_length_to_mask(vfeat_lens).to(device)
# compute logits
h_score, start_logits, end_logits = model(word_ids, char_ids, vfeats, video_mask, query_mask)
# compute loss
highlight_loss = model.compute_highlight_loss(h_score, h_labels, video_mask)
loc_loss = model.compute_loss(start_logits, end_logits, s_labels, e_labels)
total_loss = loc_loss + configs.highlight_lambda * highlight_loss
# compute and apply gradients
optimizer.zero_grad()
total_loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), configs.clip_norm) # clip gradient
optimizer.step()
scheduler.step()
# evaluate
if global_step % eval_period == 0 or global_step % num_train_batches == 0:
model.eval()
r1i3, r1i5, r1i7, mi, score_str = eval_test(model=model, data_loader=test_loader, device=device,
mode='test', epoch=epoch + 1, global_step=global_step)
print('\nEpoch: %2d | Step: %5d | r1i3: %.2f | r1i5: %.2f | r1i7: %.2f | mIoU: %.2f' % (
epoch + 1, global_step, r1i3, r1i5, r1i7, mi), flush=True)
score_writer.write(score_str)
score_writer.flush()
if r1i7 >= best_r1i7:
best_r1i7 = r1i7
torch.save(model.state_dict(), os.path.join(model_dir, '{}_{}.t7'.format(configs.model_name,
global_step)))
# only keep the top-3 model checkpoints
filter_checkpoints(model_dir, suffix='t7', max_to_keep=3)
model.train()
score_writer.close()
#测试的分布式还没改
elif configs.mode.lower() == 'test':
if not os.path.exists(model_dir):
raise ValueError('No pre-trained weights exist')
# load previous configs
pre_configs = load_json(os.path.join(model_dir, "configs.json"))
parser.set_defaults(**pre_configs)
configs = parser.parse_args()
# build model
model = VSLNet(configs=configs, word_vectors=dataset['word_vector']).to(device)
# get last checkpoint file
filename = get_last_checkpoint(model_dir, suffix='t7')
model.load_state_dict(torch.load(filename))
model.eval()
r1i3, r1i5, r1i7, mi, _ = eval_test(model=model, data_loader=test_loader, device=device, mode='test')
print("\n" + "\x1b[1;31m" + "Rank@1, IoU=0.3:\t{:.2f}".format(r1i3) + "\x1b[0m", flush=True)
print("\x1b[1;31m" + "Rank@1, IoU=0.5:\t{:.2f}".format(r1i5) + "\x1b[0m", flush=True)
print("\x1b[1;31m" + "Rank@1, IoU=0.7:\t{:.2f}".format(r1i7) + "\x1b[0m", flush=True)
print("\x1b[1;31m" + "{}:\t{:.2f}".format("mean IoU".ljust(15), mi) + "\x1b[0m", flush=True)
end_time=time.time()
print("程序运行时间：",end_time-start_time,"s")