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kubric_train_joint.py
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kubric_train_joint.py
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import os
import pprint
import random
import numpy as np
import torch
import torch.nn.parallel
import torch.optim
import itertools
import torch.utils.data
import torch.utils.data.distributed
import torch.distributed as dist
import argparse
from config.config import config, update_config
from utils import exp_utils, train_utils
from models.model import FORGE as ReconModel
from models.perceptual_loss import VGGPerceptualLoss as PerceptualLoss
from dataset.kubric import Kubric
from scripts.kubric_trainer import train_epoch
from scripts.kubric_compute_loss import compute_pose_loss, compute_all_loss_nvs
from scripts.kubric_validation import validate
def set_model_train(model, config):
'''
The model (with both 3D-based and 2D-based pose estimator) has three training steps
1. mode 'pose_head': train pose head fusing 2D and 3D pose estimator features
2. mode 'pose': train the two pose estimator which uses 3D features as inputs
only includes pose estimator parameters
3. mode 'joint': jointly tune the model (encoder backbone and pose estimator 2D is fixed)
'''
model.eval()
if config.train.parameter == 'pose_head':
model.module.pose_head.train()
elif config.train.parameter == 'pose':
model.module.pose_head.train()
model.module.encoder_traj.train()
model.module.encoder_traj_2d.train()
elif config.train.parameter == 'joint':
model.module.pose_head.train()
model.module.encoder_traj.train()
model.module.encoder_3d.fusion_feature.train()
model.module.encoder_3d.density_head.train()
model.module.render.train()
def parse_args():
parser = argparse.ArgumentParser(description='Train FORGE with only 3D pose estimator')
parser.add_argument(
'--cfg', help='experiment configure file name', required=True, type=str)
parser.add_argument(
'--local_rank', default=-1, type=int, help='node rank for distributed training')
args, rest = parser.parse_known_args()
update_config(args.cfg)
return args
def main():
# Get args and config
args = parse_args()
logger, output_dir, _ = exp_utils.create_logger(config, args.cfg, phase='train')
logger.info(pprint.pformat(args))
logger.info(pprint.pformat(config))
# set random seeds
torch.cuda.manual_seed_all(config.seed)
torch.manual_seed(config.seed)
np.random.seed(config.seed)
random.seed(config.seed)
# set device
gpus = range(torch.cuda.device_count())
device = torch.device('cuda') if len(gpus) > 0 else torch.device('cpu')
if device == torch.device("cuda"):
dist.init_process_group(backend='nccl', init_method='env://')
torch.cuda.set_device(args.local_rank)
# get model
model = ReconModel(config).to(device)
perceptual_loss = PerceptualLoss().to(device)
# get loss function and parameters, load model weights
assert config.train.parameter in ['pose_head', 'pose', 'joint']
if config.train.parameter == 'pose_head':
loss_func = compute_pose_loss
param = model.pose_head.parameters()
# model = exp_utils.load_model_finetune(model,
# resume_root='./output/kubric/general_seq_camera_predPose2/pred_pose_quat_pretrain_sup_transformer_64_128img_batch16',
# cpt_name='cpt_best_rot.pth.tar')
model = exp_utils.load_pose2d(model,
resume_root='./output/kubric/pred_pose_2d/pred_pose_2d',
cpt_name='cpt_best_rot_11.268242299860368.pth.tar')
model = exp_utils.load_pose3d(model,
resume_root='./output/kubric/pred_pose_3d/pred_pose_3d',
cpt_name='cpt_best_rot_10.288583489094188.pth.tar')
model = exp_utils.load_encoder_pretrained(model,
resume_root='./output/kubric/gt_pose/gt_pose',
cpt_name='cpt_best_psnr_31.842686198427398.pth.tar', strict=True)
if config.train.parameter == 'pose':
loss_func = compute_pose_loss
param = list(model.pose_head.parameters()) + \
list(model.encoder_traj_2d.parameters()) + \
list(model.encoder_traj.parameters())
# model = exp_utils.load_model_full(model,
# resume_root='output/kubric/general_seq_camera_predPos_2d3d/pred_pose_quat_2d_3d_2_dropout0.5_avg_sequential_regu',
# cpt_name='cpt_best_rot.pth.tar')
model = exp_utils.load_model_full(model,
resume_root='output/kubric/pretrain_pose_2d3d/pred_pose_2d3d_pretrain',
cpt_name='cpt_best_rot_8.886850933429821.pth.tar')
elif config.train.parameter == 'joint':
# jointly train the whole model (except backbone)
loss_func = compute_all_loss_nvs
param = list(model.encoder_traj.parameters()) + \
list(model.pose_head.parameters()) + \
list(model.encoder_3d.fusion_feature.parameters()) + \
list(model.encoder_3d.density_head.parameters()) + \
list(model.render.parameters())
model = exp_utils.load_model_without_fusion(model,
resume_root='./output/kubric/pred_pose_2d3d/pred_pose_2d3d',
cpt_name='cpt_last_114k.pth.tar')
model = exp_utils.load_encoder_pretrained(model,
resume_root='./output/kubric/gt_pose/gt_pose',
cpt_name='cpt_best_psnr_31.842686198427398.pth.tar', strict=True)
# get optimizer
optimizer = torch.optim.Adam(param,
lr=config.train.lr * config.train.accumulation_step,
weight_decay=config.train.weight_decay)
# resume training
best_psnr, best_rot, ep_resume = 0, float('inf'), None
if config.train.resume:
model, optimizer, ep_resume, best_psnr, best_rot = exp_utils.resume_training(model, optimizer, output_dir,
cpt_name='cpt_last.pth.tar')
# distributed training
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
if device == torch.device("cuda"):
torch.backends.cudnn.benchmark = True
device_ids = range(torch.cuda.device_count())
print("using {} cuda".format(len(device_ids)))
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True)
perceptual_loss = torch.nn.parallel.DistributedDataParallel(perceptual_loss, device_ids=[args.local_rank], find_unused_parameters=True)
device_num = len(device_ids)
# get dataset and dataloader
train_data = Kubric(config, split='train')
train_sampler = torch.utils.data.distributed.DistributedSampler(train_data)
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=config.train.batch_size,
shuffle=False,
num_workers=int(config.workers),
pin_memory=True,
drop_last=True,
sampler=train_sampler)
val_data = Kubric(config, split='test')
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=config.test.batch_size,
shuffle=False,
num_workers=int(config.workers),
pin_memory=True,
drop_last=False)
start_ep = ep_resume if ep_resume is not None else 0
end_ep = int(config.train.total_iteration / len(train_loader)) + 1
# train
for epoch in range(start_ep, end_ep):
train_sampler.set_epoch(epoch)
train_epoch(config,
loader=train_loader,
dataset=train_data,
model=model,
optimizer=optimizer,
epoch=epoch,
output_dir=output_dir,
device=device,
rank=args.local_rank,
perceptual_loss=perceptual_loss,
loss_func=loss_func,
set_model_train=set_model_train)
if args.local_rank == 0:
train_utils.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
},
checkpoint=output_dir, filename="cpt_last.pth.tar")
# validation
if epoch % (config.train.batch_size) == 0:
print('Doing validation...')
cur_psnr, cur_rot, return_dict = validate(config,
loader=val_loader,
dataset=val_data,
model=model,
epoch=epoch,
output_dir=output_dir,
device=device,
rank=args.local_rank)
torch.cuda.empty_cache()
if config.train.parameter == 'joint' and (cur_psnr > best_psnr):
best_psnr = cur_psnr
if args.local_rank == 0:
train_utils.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_psnr': best_psnr,
'eval_dict': return_dict,
},
checkpoint=output_dir, filename="cpt_best_psnr_{}_rot_{}.pth.tar".format(best_psnr, cur_rot))
if cur_rot < best_rot:
best_rot = cur_rot
if args.local_rank == 0:
train_utils.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_rot': best_rot,
'eval_dict': return_dict,
},
checkpoint=output_dir, filename="cpt_best_rot_{}_psnr_{}.pth.tar".format(best_rot, cur_psnr))
if args.local_rank == 0:
logger.info('Best PSNR: {} (current {}), best rot: {} (current {})'.format(best_psnr, cur_psnr, best_rot, cur_rot))
if __name__ == '__main__':
main()