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traineval_trans.py
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traineval_trans.py
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import argparse
import os
import random
import numpy as np
import torch
import torch.nn.parallel
import torch.optim
from utils.utils import Monitor, get_dataset, get_network, get_network_Trans, print_args, save_args, load_checkpoint, save_checkpoint
from utils.epoch import Eval_epoch_Trans, Train_epoch, Eval_epoch, Train_epoch_Trans, Val_epoch_Trans
from utils.options import add_opts
from utils.renderer import Renderer
from utils._mano import MANO
from src.utils.comm import synchronize, is_main_process, get_rank, get_world_size, all_gather
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2"
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
def main(args):
# Initialize randoms seeds
torch.cuda.manual_seed_all(args.manual_seed)
torch.manual_seed(args.manual_seed)
np.random.seed(args.manual_seed)
random.seed(args.manual_seed)
args.local_rank = 0
# create exp result dir
os.makedirs(args.host_folder, exist_ok=True)
#TODO: initialize renderer for the visualization
mano_model = MANO()
renderer = Renderer(faces=mano_model.face)
args.mano_model = mano_model
#print("Init distributed training on local rank {}".format(args.local_rank))
#torch.cuda.set_device(args.local_rank)
#torch.distributed.init_process_group(
# backend='nccl', init_method='env://', world_size=1, rank=args.local_rank)
#synchronize()
# Initialize model
model = get_network_Trans(args)
if args.use_cuda and torch.cuda.is_available():
print("Using {} GPUs !".format(torch.cuda.device_count()))
model.cuda()
start_epoch = 0
if not args.evaluate:
model_params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(model_params, lr=args.lr, weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [50, 100, 150, 200], gamma=args.lr_decay_gamma)
# TODO: train split
train_dat = get_dataset(args, mode="train")
print("training dataset size: {}".format(len(train_dat)))
train_loader = torch.utils.data.DataLoader(train_dat, batch_size=args.train_batch, shuffle=True,
num_workers=int(args.workers), pin_memory=False, drop_last=False)
# TODO: validation split
val_dat = get_dataset(args, mode="validation")
print(f"validation dataset size:{len(val_dat)}")
val_loader = torch.utils.data.DataLoader(val_dat, batch_size=args.test_batch, shuffle=False, num_workers=int(args.workers), pin_memory=False, drop_last=False)
monitor = Monitor(hosting_folder=args.host_folder)
device = torch.device('cuda')if torch.cuda.is_available() and args.use_cuda else torch.device('cpu')
if args.resume is not None:
start_epoch = load_checkpoint(model, resume_path=args.resume, strict=False, device=device)
else:
assert args.resume is not None, "need trained model for evaluation"
device = torch.device('cuda')if torch.cuda.is_available() and args.use_cuda else torch.device('cpu')
start_epoch = load_checkpoint(model, resume_path=args.resume, strict=False, device=device)
args.epochs = start_epoch + 1
# Initialize testing dataset
test_dat = get_dataset(args, mode="evaluation")
print("evaluation dataset size: {}".format(len(test_dat)))
test_loader = torch.utils.data.DataLoader(test_dat, batch_size=args.test_batch,
shuffle=False, num_workers=int(args.workers),
pin_memory=False, drop_last=False)
#TODO: change 'single_epoch' function to 'Epoch'-'Train_epoch'-'Val_epoch' class, first initialize the epoch object
if not args.evaluate:
train_epoch = Train_epoch_Trans(dataloader=train_loader, model=model, optimizer=optimizer,save_path=args.host_folder, mode="train", save_results=False, use_cuda=args.use_cuda, args=args, renderer=renderer)
# TODO: Initialize validation epoch
val_epoch = Val_epoch_Trans(dataloader=val_loader, model=model, optimizer=optimizer,save_path=args.host_folder, mode="validation", save_results=False, use_cuda=args.use_cuda, args=args, renderer=renderer)
test_epoch = Eval_epoch_Trans(dataloader=test_loader, model=model,
optimizer=None, save_path=args.host_folder,
mode="evaluation", save_results=args.save_results, use_cuda=args.use_cuda,
indices_order=test_dat.jointsMapSimpleToMano if hasattr(test_dat, "jointsMapSimpleToMano") else None, args=args,renderer=renderer)
xyz_dict = {"auc":[],"mean":[], "al_auc":[], "al_mean":[]}
handmesh_dict = {"auc":[], "mean":[], "al_auc":[], "al_mean":[]}
val_epoch_list = []
for epoch in range(start_epoch, args.epochs):
train_dict = {}
# Evaluate on validation set
#print(f"epoch:{epoch+1}; test_freq:{args.test_freq}; save_result:{args.save_results}.")
if not args.evaluate:
print("Using lr {}".format(optimizer.param_groups[0]["lr"]))
# train_avg_meters = train_epoch.update(epoch=epoch)
# train_dict = {meter_name: meter.avg
# for (meter_name, meter) in train_avg_meters.average_meters.items()}
# monitor.log_train(epoch + 1, train_dict)
if (epoch+1) % args.snapshot == 0:
print(f"save epoch {epoch+1} checkpoint to {args.host_folder}")
save_checkpoint(
{
"epoch": epoch,
"network": args.network,
"state_dict": model.state_dict(),
},
checkpoint=args.host_folder, filename=f"checkpoint_{epoch+1}.pth.tar")
if args.lr_decay_gamma:
if args.lr_decay_step is None:
scheduler.step(train_dict["joints3d_loss"])
else:
scheduler.step()
#continue
if (epoch) % args.val_freq == 0:
val_epoch_list.append(epoch+1)
with torch.no_grad():
val_avg_meters, xyz_dict, handmesh_dict = val_epoch.update(epoch, xyz_dict, handmesh_dict, val_epoch_list)
val_dict = {meter_name: meter.avg for (meter_name, meter) in val_avg_meters.average_meters.items()}
monitor.log_val(epoch + 1, val_dict)
if args.evaluate or (epoch+1) % args.test_freq == 0:
with torch.no_grad():
test_epoch.update(epoch=epoch if not args.evaluate else None)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Hand-Object training")
add_opts(parser)
CUDA_LAUNCH_BLOCKING=1
args = parser.parse_args()
#args.test_freq = 10
args.save_results = True
#args.snapshot = 10
print_args(args)
save_args(args, save_folder=args.host_folder, opt_prefix="option")
main(args)
print("All done !")