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import time
import wandb
import logging
import numpy as np
import os.path as osp
from collections import OrderedDict
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from .logger import CustomLogger
from utils.utils import AverageMeterGroups
from metrics.psnr_ssim import calculate_psnr
from utils.build_utils import build_from_cfg
class Trainer:
def __init__(self, config):
super().__init__()
self.config = config
self.rank = self.config['local_rank']
init_log = self._init_logger()
self._init_dataset()
self._init_loss()
self.model_name = config['exp_name']
self.model = build_from_cfg(config.network).to(self.config.device)
if config['distributed']:
self.model = DDP(self.model,
device_ids=[self.rank],
output_device=self.rank,
broadcast_buffers=True,
find_unused_parameters=False)
init_log += str(self.model)
self.optimizer = AdamW(self.model.parameters(),
lr=config.lr, weight_decay=config.weight_decay)
if self.rank == 0:
print(init_log)
self.logger(init_log)
self.resume_training()
def resume_training(self):
ckpt_path = self.config.get('resume_state')
if ckpt_path is not None:
ckpt = torch.load(self.config['resume_state'])
if self.config['distributed']:
self.model.module.load_state_dict(ckpt['state_dict'])
else:
self.model.load_state_dict(ckpt['state_dict'])
self.optimizer.load_state_dict(ckpt['optim'])
self.resume_epoch = ckpt.get('epoch')
self.logger(
f'load model from {ckpt_path} and training resumes from epoch {self.resume_epoch}')
else:
self.resume_epoch = 0
def _init_logger(self):
init_log = ''
console_cfg = dict(
level=logging.INFO,
format="%(asctime)s %(filename)s[line:%(lineno)d]"
"%(levelname)s %(message)s",
datefmt="%a, %d %b %Y %H:%M:%S",
filename=f"{self.config['save_dir']}/log",
filemode='w')
tb_cfg = dict(log_dir=osp.join(self.config['save_dir'], 'tb_logger'))
wandb_cfg = None
use_wandb = self.config['logger'].get('use_wandb', False)
if use_wandb:
resume_id = self.config['logger'].get('resume_id', None)
if resume_id:
wandb_id = resume_id
resume = 'allow'
init_log += f'Resume wandb logger with id={wandb_id}.'
else:
wandb_id = wandb.util.generate_id()
resume = 'never'
wandb_cfg = dict(id=wandb_id,
resume=resume,
name=osp.basename(self.config['save_dir']),
config=self.config,
project="YOUR PROJECT",
entity="YOUR ENTITY",
sync_tensorboard=True)
init_log += f'Use wandb logger with id={wandb_id}; project=[YOUR PROJECT].'
self.logger = CustomLogger(console_cfg, tb_cfg, wandb_cfg, self.rank)
return init_log
def _init_dataset(self):
dataset_train = build_from_cfg(self.config.data.train)
dataset_val = build_from_cfg(self.config.data.val)
self.sampler = DistributedSampler(
dataset_train, num_replicas=self.config['world_size'], rank=self.config['local_rank'])
self.config.data.train_loader.batch_size //= self.config['world_size']
self.loader_train = DataLoader(dataset_train,
**self.config.data.train_loader,
pin_memory=True, drop_last=True, sampler=self.sampler)
self.loader_val = DataLoader(dataset_val, **self.config.data.val_loader,
pin_memory=True, shuffle=False, drop_last=False)
def _init_loss(self):
self.loss_dict = dict()
for loss_cfg in self.config.losses:
loss = build_from_cfg(loss_cfg)
self.loss_dict[loss_cfg['nickname']] = loss
def set_lr(self, optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def get_lr(self, iters):
ratio = 0.5 * (1.0 + np.cos(iters /
(self.config['epochs'] * self.loader_train.__len__()) * np.pi))
lr = (self.config['lr'] - self.config['lr_min']
) * ratio + self.config['lr_min']
return lr
def train(self):
local_rank = self.config['local_rank']
best_psnr = 0.0
loss_group = AverageMeterGroups()
time_group = AverageMeterGroups()
iters_per_epoch = self.loader_train.__len__()
iters = self.resume_epoch * iters_per_epoch
total_iters = self.config['epochs'] * iters_per_epoch
start_t = time.time()
total_t = 0
for epoch in range(self.resume_epoch, self.config['epochs']):
self.sampler.set_epoch(epoch)
for data in self.loader_train:
for k, v in data.items():
data[k] = v.to(self.config['device'])
data_t = time.time() - start_t
lr = self.get_lr(iters)
self.set_lr(self.optimizer, lr)
self.optimizer.zero_grad()
results = self.model(**data)
total_loss = torch.tensor(0., device=self.config['device'])
for name, loss in self.loss_dict.items():
l = loss(**results, **data)
loss_group.update({name: l.cpu().data})
total_loss += l
total_loss.backward()
self.optimizer.step()
iters += 1
iter_t = time.time() - start_t
total_t += iter_t
time_group.update({'data_t': data_t, 'iter_t': iter_t})
if (iters+1) % 100 == 0 and local_rank == 0:
tpi = total_t / (iters - self.resume_epoch * iters_per_epoch)
eta = total_iters * tpi
remainder = (total_iters - iters) * tpi
eta = self.eta_format(eta)
remainder = self.eta_format(remainder)
log_str = f"[{self.model_name}]epoch:{epoch +1}/{self.config['epochs']} "
log_str += f"iter:{iters + 1}/{self.config['epochs'] * iters_per_epoch} "
log_str += f"time:{time_group.avg('iter_t'):.3f}({time_group.avg('data_t'):.3f}) "
log_str += f"lr:{lr:.3e} eta:{remainder}({eta})\n"
for name in self.loss_dict.keys():
avg_l = loss_group.avg(name)
log_str += f"{name}:{avg_l:.3e} "
self.logger(tb_msg=[f'loss/{name}', avg_l, iters])
log_str += f'best:{best_psnr:.2f}dB\n\n'
self.logger(log_str)
loss_group.reset()
time_group.reset()
start_t = time.time()
if (epoch+1) % self.config['eval_interval'] == 0 and local_rank == 0:
psnr, eval_t = self.evaluate(epoch)
total_t += eval_t
self.logger(tb_msg=['eval/psnr', psnr, epoch])
if psnr > best_psnr:
best_psnr = psnr
self.save('psnr_best.pth', epoch)
if self.logger.enable_wandb:
wandb.run.summary["best_psnr"] = best_psnr
if (epoch+1) % 50 == 0:
self.save(f'epoch_{epoch+1}.pth', epoch)
self.save('latest.pth', epoch)
self.logger.close()
def evaluate(self, epoch):
psnr_list = []
time_stamp = time.time()
for i, data in enumerate(self.loader_val):
for k, v in data.items():
data[k] = v.to(self.config['device'])
with torch.no_grad():
results = self.model(**data, eval=True)
imgt_pred = results['imgt_pred']
for j in range(data['img0'].shape[0]):
psnr = calculate_psnr(imgt_pred[j].detach().unsqueeze(
0), data['imgt'][j].unsqueeze(0)).cpu().data
psnr_list.append(psnr)
eval_time = time.time() - time_stamp
self.logger('eval epoch:{}/{} time:{:.2f} psnr:{:.3f}'.format(
epoch+1, self.config["epochs"], eval_time, np.array(psnr_list).mean()))
return np.array(psnr_list).mean(), eval_time
def save(self, name, epoch):
save_path = '{}/{}/{}'.format(self.config['save_dir'], 'ckpts', name)
ckpt = OrderedDict(epoch=epoch)
if self.config['distributed']:
ckpt['state_dict'] = self.model.module.state_dict()
else:
ckpt['state_dict'] = self.model.state_dict()
ckpt['optim'] = self.optimizer.state_dict()
torch.save(ckpt, save_path)
def eta_format(self, eta):
time_str = ''
if eta >= 3600:
hours = int(eta // 3600)
eta -= hours * 3600
time_str = f'{hours}'
if eta >= 60:
mins = int(eta // 60)
eta -= mins * 60
time_str = f'{time_str}:{mins:02}'
eta = int(eta)
time_str = f'{time_str}:{eta:02}'
return time_str
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