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import os
import time
import json
import pprint
import random
import numpy as np
from tqdm import tqdm, trange
from collections import defaultdict
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
# import sys
# print(sys.path)
# sys.path.insert(os.getcwd(),0)
# print(sys.path)
from cg_detr.config import BaseOptions
from cg_detr.start_end_dataset import StartEndDataset, start_end_collate, prepare_batch_inputs
from cg_detr.inference import eval_epoch, start_inference, setup_model
from utils.basic_utils import AverageMeter, dict_to_markdown
from utils.model_utils import count_parameters
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=logging.INFO)
def set_seed(seed, use_cuda=True):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed_all(seed)
def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
logger.info(f'[Epoch {epoch_i+1}]')
model.train()
criterion.train()
# init meters
time_meters = defaultdict(AverageMeter)
loss_meters = defaultdict(AverageMeter)
num_training_examples = len(train_loader)
timer_dataloading = time.time()
for batch_idx, batch in tqdm(enumerate(train_loader),
desc="Training Iteration",
total=num_training_examples):
time_meters["dataloading_time"].update(time.time() - timer_dataloading)
timer_start = time.time()
model_inputs, targets = prepare_batch_inputs(batch[1], opt.device, non_blocking=opt.pin_memory)
time_meters["prepare_inputs_time"].update(time.time() - timer_start)
timer_start = time.time()
outputs = model(**model_inputs, targets=targets)
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
time_meters["model_forward_time"].update(time.time() - timer_start)
timer_start = time.time()
optimizer.zero_grad()
losses.backward()
if opt.grad_clip > 0:
nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
optimizer.step()
time_meters["model_backward_time"].update(time.time() - timer_start)
loss_dict["loss_overall"] = float(losses) # for logging only
for k, v in loss_dict.items():
loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
timer_dataloading = time.time()
if opt.debug and batch_idx == 3:
break
# print/add logs
tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
for k, v in loss_meters.items():
tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
to_write = opt.train_log_txt_formatter.format(
time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
epoch=epoch_i+1,
loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
with open(opt.train_log_filepath, "a") as f:
f.write(to_write)
logger.info("Epoch time stats:")
for name, meter in time_meters.items():
d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
logger.info(f"{name} ==> {d}")
def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
if opt.device.type == "cuda":
logger.info("CUDA enabled.")
model.to(opt.device)
tb_writer = SummaryWriter(opt.tensorboard_log_dir)
tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
train_loader = DataLoader(
train_dataset,
collate_fn=start_end_collate,
batch_size=opt.bsz,
num_workers=opt.num_workers,
shuffle=True,
pin_memory=opt.pin_memory
)
prev_best_score = 0.
es_cnt = 0
# start_epoch = 0
if opt.start_epoch is None:
start_epoch = -1 if opt.eval_untrained else 0
else:
start_epoch = opt.start_epoch
save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
if epoch_i > -1:
train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
lr_scheduler.step()
eval_epoch_interval = opt.eval_epoch
if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
with torch.no_grad():
metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
# log
to_write = opt.eval_log_txt_formatter.format(
time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
epoch=epoch_i,
loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
eval_metrics_str=json.dumps(metrics_no_nms))
with open(opt.eval_log_filepath, "a") as f:
f.write(to_write)
logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
if metrics_nms is not None:
logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
metrics = metrics_no_nms
for k, v in metrics["brief"].items():
tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
if opt.dset_name in ['hl']:
stop_score = metrics["brief"]["MR-full-mAP"]
else:
stop_score = (metrics["brief"]["[email protected]"] + metrics["brief"]["[email protected]"]) / 2
if stop_score > prev_best_score:
es_cnt = 0
prev_best_score = stop_score
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
for src, tgt in zip(latest_file_paths, best_file_paths):
os.renames(src, tgt)
logger.info("The checkpoint file has been updated.")
else:
es_cnt += 1
if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
with open(opt.train_log_filepath, "a") as f:
f.write(f"Early Stop at epoch {epoch_i}")
logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
break
# save ckpt
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
# save_interval = 10 if "subs_train" in opt.train_path else 50 # smaller for pretrain
# if (epoch_i + 1) % save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
# checkpoint = {
# "model": model.state_dict(),
# "optimizer": optimizer.state_dict(),
# "epoch": epoch_i,
# "opt": opt
# }
# torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
if opt.debug:
break
tb_writer.close()
def start_training():
logger.info("Setup config, data and model...")
opt = BaseOptions().parse()
set_seed(opt.seed)
if opt.debug: # keep the model run deterministically
# 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
# Enable this only when input size is fixed.
cudnn.benchmark = False
cudnn.deterministic = True
dataset_config = dict(
dset_name=opt.dset_name,
data_path=opt.train_path,
v_feat_dirs=opt.v_feat_dirs,
q_feat_dir=opt.t_feat_dir,
q_feat_type="last_hidden_state",
max_q_l=opt.max_q_l,
max_v_l=opt.max_v_l,
ctx_mode=opt.ctx_mode,
data_ratio=opt.data_ratio,
normalize_v=not opt.no_norm_vfeat,
normalize_t=not opt.no_norm_tfeat,
clip_len=opt.clip_length,
max_windows=opt.max_windows,
span_loss_type=opt.span_loss_type,
txt_drop_ratio=opt.txt_drop_ratio,
dset_domain=opt.dset_domain,
)
dataset_config["data_path"] = opt.train_path
train_dataset = StartEndDataset(**dataset_config)
# import pdb; pdb.set_trace()
# train_dataset[0]
if opt.eval_path is not None:
dataset_config["data_path"] = opt.eval_path
dataset_config["txt_drop_ratio"] = 0
dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("sub_features", "text_features") # for pretraining
# dataset_config["load_labels"] = False # uncomment to calculate eval loss
eval_dataset = StartEndDataset(**dataset_config)
else:
eval_dataset = None
model, criterion, optimizer, lr_scheduler = setup_model(opt)
logger.info(f"Model {model}")
count_parameters(model)
logger.info("Start Training...")
train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug, opt
if __name__ == '__main__':
best_ckpt_path, eval_split_name, eval_path, debug, opt = start_training()
if not debug:
input_args = ["--resume", best_ckpt_path,
"--eval_split_name", eval_split_name,
"--eval_path", eval_path]
import sys
sys.argv[1:] = input_args
logger.info("\n\n\nFINISHED TRAINING!!!")
logger.info("Evaluating model at {}".format(best_ckpt_path))
logger.info("Input args {}".format(sys.argv[1:]))
start_inference(opt)
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