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Train_Codec.py
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Train_Codec.py
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import torch
import os
import sys
import time
import datetime
from modules.torch_utils import fix_seed, seed_worker
from tqdm import tqdm
import yaml
from torch.utils.tensorboard import SummaryWriter
from importlib import import_module
import argparse
from TempDataset.Temp_Dataset import TempDataset
from Eval import eval_image
from contextlib import nullcontext
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data.distributed import DistributedSampler
from collections import defaultdict
from typing import Optional
def main(model_name: str, exp_name: str, train_config_name: str, data_path: str, save_path: str) -> None:
"""
Main function for training an image compression model.
Args:
model_name (str): The name of the compression model, corresponding to the model config file in './config/model'.
exp_name (str): The postfix for saving the experiment.
train_config_name (str): The name of the training configuration, corresponding to the files in './config/train'.
data_path (str): The path to the data.
save_path (str): The directory where training results will be saved.
Returns:
None
"""
USE_CUDA = torch.cuda.is_available()
# Check the number of GPUs for training
num_gpus = len(os.environ.get('CUDA_VISIBLE_DEVICES', '').split(','))
use_ddp = True if num_gpus > 1 else False
rank = 0 if not use_ddp else None
if use_ddp:
dist.init_process_group("nccl", timeout=datetime.timedelta(seconds=9000))
rank = dist.get_rank()
torch.cuda.set_device(rank)
world_size = dist.get_world_size()
print(f'World size: {world_size}') if rank == 0 else None
device = torch.cuda.current_device() if USE_CUDA else 'cpu'
print(f'Device: {device} is used\n')
model_exp_name = f'{model_name}_{exp_name}' if exp_name != "" else model_name
''' Set logging dir '''
_base_log_dir = os.path.join(save_path, 'Eval_log', '{}', model_exp_name)
tensorboard_dir = os.path.join(save_path, 'Train_record', model_exp_name, "tensorboard")
''' Get train configure '''
train_conf_file = f'./config/train/{train_config_name}.yaml'
with open(train_conf_file) as f:
config = yaml.load(f, Loader=yaml.FullLoader)
args = argparse.Namespace(**config)
args.optim = config['optimizer']
if rank == 0:
print(vars(args))
''' Fix random seed'''
fix_seed(args.seed)
''' Tensorboard '''
writer = SummaryWriter(tensorboard_dir)
print(f"\nSave dir: {os.path.join(save_path, 'Train_record', model_exp_name)}\n") if rank == 0 else None
''' Get model '''
model_conf_file = f'./config/model/{model_name}.yaml'
with open(model_conf_file) as f:
config = yaml.load(f, Loader=yaml.FullLoader)
module_name = config['model']['module_name']
class_name = config['model']['args']['class']
model = getattr(import_module(f'modules.{module_name}.models'), class_name)(model_conf_file, device)
if rank == 0:
print(f"Model '{class_name}' with configure file '{model_name}' is loaded")
print(f"Loaded model details: {config}\n")
training_consumed_sec = 0
''' Get dataloader '''
train_dataset = TempDataset(data_path, 'temp_train', is_train=True, input_resolution=args.input_resolution)
''' Create DistributedSampler '''
sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=rank, shuffle=True) if use_ddp else None
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=sampler,
num_workers=args.num_workers, pin_memory=False, drop_last=True,
worker_init_fn=seed_worker, shuffle=(sampler is None))
# Get Test Dataloader (Temp)
test_dataset = TempDataset(data_path, 'temp_test', is_train=False)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=1,
pin_memory=False, drop_last=False)
''' Optimizer '''
optimizer = getattr(import_module(f"modules.{module_name}.optimizers"), 'CustomOptimizer')(model, train_conf_file)
''' Make distributed data parallel module '''
model = DistributedDataParallel(model, device_ids=[device], output_device=device) if use_ddp else model
module = model.module if isinstance(model, DistributedDataParallel) else model
# postfix = ""
# model_dir = os.path.join(save_path, 'Train_record', model_exp_name, f"Param{postfix}.pth")
# module.load(model_dir)
''' Train Loop '''
for epoch in range(args.epoch):
module.train(True)
postfix = f'_{epoch}' if epoch is not None else ""
_base_log_dir = os.path.join(save_path, 'Eval_log', '{}', model_exp_name)
tensorboard_dir = os.path.join(save_path, 'Train_record', model_exp_name, "tensorboard")
total_loss_per_epoch = 0.0
loss_add_count = 0.0
loss_per_epoch_dict = defaultdict(float)
if rank == 0:
train_start_time_per_epoch = time.time()
pbar = tqdm(train_dataloader, desc=f"Train Epoch {epoch}...", disable=(rank != 0))
sampler.set_epoch(epoch) if use_ddp else None
for step, data in enumerate(pbar):
images, ids = data['images'].to(module.device), data['ids']
out = module(images)
loss, aux_loss = out['loss'], out['aux_loss']
if rank == 0 and (torch.isnan(loss + aux_loss) or torch.isinf(loss + aux_loss)):
# Stop if loss is nan
print('************Training stopped due to inf/nan loss.************')
sys.exit(-1)
for loss_name, loss_value in out['loss_dict'].items():
loss_per_epoch_dict[loss_name] += loss_value.item()
total_loss_per_epoch += loss.item()
loss_add_count += 1.0
optimizer.zero_grad()
optimizer.step(loss, aux_loss)
module.entropy_bottleneck.update()
avr_loss = total_loss_per_epoch / loss_add_count # Aux loss is not included
if rank == 0:
pbar.set_description(f"Training Epoch {epoch}, Loss = {round(avr_loss, 5)}")
dist.barrier() if use_ddp else None
if rank == 0:
loss_per_epoch_dict = dict(
(loss_name, loss / loss_add_count) for loss_name, loss in loss_per_epoch_dict.items())
training_consumed_sec += (time.time() - train_start_time_per_epoch)
loss_keys = list(loss_per_epoch_dict.keys())
main_loss_per_epoch_dict = {k: loss_per_epoch_dict[k] for k in loss_keys if k != 'aux_loss'}
aux_loss_per_epoch_dict = {'aux_loss': loss_per_epoch_dict['aux_loss']} if 'aux_loss' in loss_keys else None
writer.add_scalars('train/overall', {'loss': total_loss_per_epoch / loss_add_count}, epoch)
writer.add_scalars('train/main_loss', main_loss_per_epoch_dict, epoch)
writer.add_scalars('train/aux_loss', aux_loss_per_epoch_dict, epoch) if 'aux_loss' in loss_keys else None
for i, param in enumerate(optimizer.main_optimizer.param_groups):
writer.add_scalars('train/lr', {f'param{i}': param['lr']}, epoch)
''' Evaluate '''
module.train(False)
with torch.no_grad():
eval_dir = _base_log_dir.format('temp')
for quality_level in range(1, 9):
# save_flag = True if quality_level == 8 else False
save_flag = False
eval_image(module, test_dataloader, eval_dir, epoch, quality_level, tensorboard_dir, save_flag)
save_dir = os.path.join(save_path, 'Train_record', model_exp_name, f"Param{postfix}"+".pth")
torch.save(module.state_dict(), save_dir)
module.train(True)
writer.close()
if rank == 0:
result_list = str(datetime.timedelta(seconds=training_consumed_sec)).split(".")
print("Training time :", result_list[0])
dist.destroy_process_group() if use_ddp else None
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--local-rank", type=int, default=0)
parser.add_argument('--model_name', type=str, default='SCR', help='Use model config file name')
parser.add_argument('--exp_name', type=str, default='', help='postfix for save experiment')
parser.add_argument('--train_config', type=str, default='Custom_v1', help='Use train config file name')
parser.add_argument('--data_path', type=str, default='', help='Dataset directory')
parser.add_argument('--save_path', type=str, default='', help='Checkpoints directory')
args = parser.parse_args()
# Run example
main(args.model_name, args.exp_name, args.train_config, args.data_path, args.save_path)