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import torch
import torch.backends.cudnn as cudnn
from config import cfg
import os.path as osp
# ddp
import torch.distributed as dist
from common.utils.distribute_utils import (
init_distributed_mode, is_main_process, set_seed
)
import torch.distributed as dist
from mmcv.runner import get_dist_info
import math
def parse_args():
parser = argparse.ArgumentParser()
# parser.add_argument('--gpu', type=str, dest='gpu_ids')
parser.add_argument('--num_gpus', type=int, dest='num_gpus')
parser.add_argument('--master_port', type=int, dest='master_port')
parser.add_argument('--exp_name', type=str, default='output/test')
parser.add_argument('--config', type=str, default='./config/config_base.py')
args = parser.parse_args()
return args
# def main():
# args = parse_args()
# config_path = osp.join('./config', args.config)
# cfg.get_config_fromfile(config_path)
# cfg.update_config(args.num_gpus, args.exp_name)
# cudnn.benchmark = True
# set_seed(2023)
# # ddp by default in this branch
# distributed, gpu_idx = init_distributed_mode(args.master_port)
# from base import Trainer
# trainer = Trainer(distributed, gpu_idx)
# # ddp
# if distributed:
# trainer.logger_info('### Set DDP ###')
# trainer.logger.info(f'Distributed: {distributed}, init done {gpu_idx}')
# else:
# raise Exception("DDP not setup properly")
# trainer.logger_info(f"Using {cfg.num_gpus} GPUs, batch size {cfg.train_batch_size} per GPU.")
# trainer._make_batch_generator()
# trainer._make_model()
# trainer.logger_info('### Set some hyper parameters ###')
# for k in cfg.__dict__:
# trainer.logger_info(f'set {k} to {cfg.__dict__[k]}')
# trainer.logger_info(f'train with train_3d={cfg.trainset_3d}')
# trainer.logger_info(f'train with train_2d={cfg.trainset_2d}')
# trainer.logger_info(f'train with trainset_humandata={cfg.trainset_humandata}')
# trainer.logger_info('### Start training ###')
# for epoch in range(trainer.start_epoch, cfg.end_epoch):
# trainer.tot_timer.tic()
# trainer.read_timer.tic()
# # ddp, align random seed between devices
# trainer.batch_generator.sampler.set_epoch(epoch)
# for itr, (inputs, targets, meta_info) in enumerate(trainer.batch_generator):
# trainer.read_timer.toc()
# trainer.gpu_timer.tic()
# # forward
# trainer.optimizer.zero_grad()
# loss= trainer.model(inputs, targets, meta_info, 'train')
# loss_mean = {k: loss[k].mean() for k in loss}
# loss_sum = sum(loss_mean[k] for k in loss_mean)
# # backward
# loss_sum.backward()
# trainer.optimizer.step()
# trainer.scheduler.step()
# trainer.gpu_timer.toc()
# if (itr + 1) % cfg.print_iters == 0:
# # loss of all ranks
# rank, world_size = get_dist_info()
# loss_print = loss_mean.copy()
# for k in loss_print:
# dist.all_reduce(loss_print[k])
# total_loss = 0
# for k in loss_print:
# loss_print[k] = loss_print[k] / world_size
# total_loss += loss_print[k]
# loss_print['total'] = total_loss
# screen = [
# 'Epoch %d/%d itr %d/%d:' % (epoch, cfg.end_epoch, itr, trainer.itr_per_epoch),
# 'lr: %g' % (trainer.get_lr()),
# 'speed: %.2f(%.2fs r%.2f)s/itr' % (
# trainer.tot_timer.average_time, trainer.gpu_timer.average_time,
# trainer.read_timer.average_time),
# '%.2fh/epoch' % (trainer.tot_timer.average_time / 3600. * trainer.itr_per_epoch),
# ]
# screen += ['%s: %.4f' % ('loss_' + k, v.detach()) for k, v in loss_print.items()]
# trainer.logger_info(' '.join(screen))
# trainer.tot_timer.toc()
# trainer.tot_timer.tic()
# trainer.read_timer.tic()
# # save model ddp, save model.module on rank 0 only
# save_epoch = getattr(cfg, 'save_epoch', 10)
# if is_main_process() and (epoch % save_epoch == 0 or epoch == cfg.end_epoch - 1):
# trainer.save_model({
# 'epoch': epoch,
# 'network': trainer.model.state_dict(),
# 'optimizer': trainer.optimizer.state_dict(),
# }, epoch)
# dist.barrier()
def main():
args = parse_args()
config_path = osp.join('./config', args.config)
cfg.get_config_fromfile(config_path)
cfg.update_config(args.num_gpus, args.exp_name)
cudnn.benchmark = True
set_seed(2023)
# check for DDP usage
distributed = False
gpu_idx = 0
use_lora = cfg.use_lora
if args.num_gpus > 1:
distributed, gpu_idx = init_distributed_mode(args.master_port)
else:
print("Running in single GPU mode.")
from base import Trainer
trainer = Trainer(distributed, gpu_idx, use_lora)
trainer.logger_info(f"Using {cfg.num_gpus} GPU(s), batch size {cfg.train_batch_size} per GPU.")
trainer._make_batch_generator()
trainer._make_model()
trainer.logger_info('### Set some hyper parameters ###')
for k in cfg.__dict__:
trainer.logger_info(f'set {k} to {cfg.__dict__[k]}')
trainer.logger_info(f'train with train_3d={cfg.trainset_3d}')
trainer.logger_info(f'train with train_2d={cfg.trainset_2d}')
trainer.logger_info(f'train with trainset_humandata={cfg.trainset_humandata}')
trainer.logger_info('### Start training ###')
for epoch in range(trainer.start_epoch, cfg.end_epoch):
trainer.tot_timer.tic()
trainer.read_timer.tic()
# set_epoch only when using distributed sampler
if distributed:
trainer.batch_generator.sampler.set_epoch(epoch)
for itr, (inputs, targets, meta_info) in enumerate(trainer.batch_generator):
trainer.read_timer.toc()
trainer.gpu_timer.tic()
trainer.optimizer.zero_grad()
# loss = trainer.model(inputs, targets, meta_info, 'train')
# loss_mean = {k: loss[k].mean() for k in loss}
# Dposerx cosine weight
loss = trainer.model(inputs, targets, meta_info, 'train')
loss_mean = {k: loss[k].mean() for k in loss}
w0 = getattr(cfg, 'dposer_x_weight', 1.0)
w_min = getattr(cfg, 'dposer_x_weight_min', 0.001)
warmup = getattr(cfg, 'dposer_x_weight_warmup', 2)
if epoch < warmup:
w_dposerx = w0
else:
frac = (itr + 1) / max(1, trainer.itr_per_epoch)
t = (epoch + frac - warmup) / max(1, cfg.end_epoch - warmup)
t = min(max(t, 0.0), 1.0)
w_dposerx = w_min + 0.5 * (w0 - w_min) * (1.0 + math.cos(math.pi * t))
if 'dposerx' in loss_mean:
loss_mean['dposerx'] = loss_mean['dposerx'] * w_dposerx
loss_sum = sum(loss_mean[k] for k in loss_mean)
loss_sum.backward()
trainer.optimizer.step()
trainer.scheduler.step()
trainer.gpu_timer.toc()
if (itr + 1) % cfg.print_iters == 0:
if distributed:
rank, world_size = get_dist_info()
loss_print = loss_mean.copy()
for k in loss_print:
dist.all_reduce(loss_print[k])
total_loss = 0
for k in loss_print:
loss_print[k] = loss_print[k] / world_size
total_loss += loss_print[k]
loss_print['total'] = total_loss
else:
loss_print = loss_mean
loss_print['total'] = loss_sum
screen = [
'Epoch %d/%d itr %d/%d:' % (epoch, cfg.end_epoch, itr, trainer.itr_per_epoch),
'lr: %g' % (trainer.get_lr()),
'speed: %.2f(%.2fs r%.2f)s/itr' % (
trainer.tot_timer.average_time, trainer.gpu_timer.average_time,
trainer.read_timer.average_time),
'%.2fh/epoch' % (trainer.tot_timer.average_time / 3600. * trainer.itr_per_epoch),
]
screen += ['%s: %.4f' % ('loss_' + k, v.detach()) for k, v in loss_print.items()]
screen += [f'dposerx_w: {float(w_dposerx):.4f}']
trainer.logger_info(' '.join(screen))
trainer.tot_timer.toc()
trainer.tot_timer.tic()
trainer.read_timer.tic()
# save model
if not distributed or is_main_process():
save_epoch = getattr(cfg, 'save_epoch', 10)
if epoch % save_epoch == 0 or epoch == cfg.end_epoch - 1:
trainer.save_model({
'epoch': epoch,
'network': trainer.model.state_dict(),
'optimizer': trainer.optimizer.state_dict(),
}, epoch)
if distributed:
dist.barrier()
if __name__ == "__main__":
main() |