import argparse import sys sys.path.append("..") import datetime import json import numpy as np import os import time from pathlib import Path import torch import torch.backends.cudnn as cudnn from torch.utils.tensorboard import SummaryWriter import util.misc as misc from datasets import build_dataset from util.misc import NativeScalerWithGradNormCount as NativeScaler from models import get_model,get_criterion from engine.engine_triplane_dm import train_one_epoch,evaluate_reconstruction def get_args_parser(): parser = argparse.ArgumentParser('Latent Diffusion', add_help=False) parser.add_argument('--batch_size', default=64, type=int, help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus') parser.add_argument('--epochs', default=800, type=int) parser.add_argument('--accum_iter', default=1, type=int, help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)') parser.add_argument('--ae-pth',type=str) # Optimizer parameters parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM', help='Clip gradient norm (default: None, no clipping)') parser.add_argument('--weight_decay', type=float, default=0.05, help='weight decay (default: 0.05)') parser.add_argument('--lr', type=float, default=None, metavar='LR', help='learning rate (absolute lr)') parser.add_argument('--blr', type=float, default=1e-4, metavar='LR', # 2e-4 help='base learning rate: absolute_lr = base_lr * total_batch_size / 256') parser.add_argument('--layer_decay', type=float, default=0.75, help='layer-wise lr decay from ELECTRA/BEiT') parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR', help='lower lr bound for cyclic schedulers that hit 0') parser.add_argument('--warmup_epochs', type=int, default=40, metavar='N', help='epochs to warmup LR') # Dataset parameters parser.add_argument('--data-pth', default='../data', type=str, help='dataset path') parser.add_argument('--output_dir', default='./output/', help='path where to save, empty for no saving') parser.add_argument('--log_dir', default='./output/', help='path where to tensorboard log') parser.add_argument('--device', default='cuda', help='device to use for training / testing') parser.add_argument('--seed', default=0, type=int) parser.add_argument('--resume', default='', help='resume from checkpoint') parser.add_argument('--start_epoch', default=0, type=int, metavar='N', help='start epoch') parser.add_argument('--eval', action='store_true', help='Perform evaluation only') parser.add_argument('--dist_eval', action='store_true', default=False, help='Enabling distributed evaluation (recommended during training for faster monitor') parser.add_argument('--num_workers', default=60, type=int) parser.add_argument('--pin_mem', action='store_true', help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.') parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem') parser.set_defaults(pin_mem=True) parser.add_argument('--constant_lr', default=False, action='store_true') # distributed training parameters parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') parser.add_argument('--local_rank', default=-1, type=int) parser.add_argument('--dist_on_itp', action='store_true') parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') parser.add_argument('--load_proj_mat',default=True,type=bool) parser.add_argument('--num_objects',type=int,default=-1) parser.add_argument('--configs', type=str) parser.add_argument('--finetune', default=False, action="store_true") parser.add_argument('--finetune-pth', type=str) parser.add_argument('--use_cls_free',action="store_true",default=False) parser.add_argument('--sync_bn',action="store_true",default=False) parser.add_argument('--category',type=str) parser.add_argument('--stop',type=int,default=1000) parser.add_argument('--replica', type=int, default=5) return parser def main(args,config): misc.init_distributed_mode(args) print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) print("{}".format(args).replace(', ', ',\n')) device = torch.device(args.device) # fix the seed for reproducibility seed = args.seed + misc.get_rank() torch.manual_seed(seed) np.random.seed(seed) cudnn.benchmark = True dataset_config = config.config['dataset'] dataset_config['category']=args.category dataset_config['replica']=args.replica dataset_config['num_objects']=args.num_objects dataset_config['data_path']=args.data_pth dataset_train = build_dataset('train', dataset_config) print("training dataset len is %d"%(len(dataset_train))) dataset_val=build_dataset('val', dataset_config) #dataset_val = build_dataset('val', dataset_config) if True: # args.distributed: num_tasks = misc.get_world_size() global_rank = misc.get_rank() sampler_train = torch.utils.data.DistributedSampler( dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True ) print("Sampler_train = %s" % str(sampler_train)) if args.dist_eval: if len(dataset_val) % num_tasks != 0: print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. ' 'This will slightly alter validation results as extra duplicate entries are added to achieve ' 'equal num of samples per-process.') sampler_val = torch.utils.data.DistributedSampler( dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=True) # shuffle=True to reduce monitor bias else: sampler_val = torch.utils.data.SequentialSampler(dataset_val) else: sampler_train = torch.utils.data.RandomSampler(dataset_train) sampler_val = torch.utils.data.SequentialSampler(dataset_val) if global_rank == 0 and args.log_dir is not None and not args.eval: os.makedirs(args.log_dir, exist_ok=True) log_writer = SummaryWriter(log_dir=args.log_dir) else: log_writer = None if misc.get_rank()==0: log_dir=args.log_dir src_folder="/data1/haolin/TriplaneDiffusion" misc.log_codefiles(src_folder,log_dir+"/code_bak") #cmd="cp -r %s %s"%(src_folder,log_dir+"/code_bak") #print(cmd) #os.system(cmd) config_dict=vars(args) config_save_path=os.path.join(log_dir,"config.json") with open(config_save_path,'w') as f: json.dump(config_dict,f,indent=4) model_dict=config model_config_save_path=os.path.join(log_dir,"model.json") config.write_config(model_config_save_path) data_loader_train = torch.utils.data.DataLoader( dataset_train, sampler=sampler_train, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=True, prefetch_factor=2, ) data_loader_val = torch.utils.data.DataLoader( dataset_val, sampler=sampler_val, # batch_size=args.batch_size, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False ) ae_args=config.config['model']['ae'] ae = get_model(ae_args) ae.eval() print("Loading autoencoder %s" % args.ae_pth) ae.load_state_dict(torch.load(args.ae_pth, map_location='cpu')['model']) ae.to(device) dm_args=config.config['model']['dm'] if args.category[0] == "all": dm_args["use_cat_embedding"]=True else: dm_args["use_cat_embedding"] = False dm_model = get_model(dm_args) if args.sync_bn: dm_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(dm_model) if args.finetune: print("finetune the model, load from %s"%(args.finetune_pth)) dm_model.load_state_dict(torch.load(args.finetune_pth,map_location="cpu")['model']) dm_model.to(device) model_without_ddp = dm_model n_parameters = sum(p.numel() for p in dm_model.parameters() if p.requires_grad) print("Model = %s" % str(model_without_ddp)) print('number of params (M): %.2f' % (n_parameters / 1.e6)) eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() if args.lr is None: # only base_lr is specified args.lr = args.blr * eff_batch_size / 256 print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) print("actual lr: %.2e" % args.lr) print("accumulate grad iterations: %d" % args.accum_iter) print("effective batch size: %d" % eff_batch_size) if args.distributed: dm_model = torch.nn.parallel.DistributedDataParallel(dm_model, device_ids=[args.gpu], find_unused_parameters=False) model_without_ddp = dm_model.module # # build optimizer with layer-wise lr decay (lrd) # param_groups = lrd.param_groups_lrd(model_without_ddp, args.weight_decay, # no_weight_decay_list=model_without_ddp.no_weight_decay(), # layer_decay=args.layer_decay # ) optimizer = torch.optim.AdamW(model_without_ddp.parameters(), lr=args.lr) loss_scaler = NativeScaler() cri_args=config.config['criterion'] criterion = get_criterion(cri_args) print("criterion = %s" % str(criterion)) misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler) if args.eval: test_stats = evaluate(data_loader_val, dm_model, device) print(f"loss of the network on the {len(dataset_val)} test images: {test_stats['loss']:.3f}") exit(0) print(f"Start training for {args.epochs} epochs") start_time = time.time() min_loss = 1000.0 max_iou=0 stop_epochs=min(args.stop,args.epochs) for epoch in range(args.start_epoch, stop_epochs): if args.distributed: data_loader_train.sampler.set_epoch(epoch) #test_stats = evaluate_reconstruction(data_loader_val, dm_model, ae, criterion, device) train_stats = train_one_epoch( dm_model, ae, criterion, data_loader_train, optimizer, device, epoch, loss_scaler, args.clip_grad, log_writer=log_writer, log_dir=args.log_dir, args=args ) if args.output_dir and (epoch % 5 == 0 or epoch + 1 == args.epochs): misc.save_model( args=args, model=dm_model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch,prefix="latest") if epoch % 5 == 0 or epoch + 1 == args.epochs: test_stats = evaluate_reconstruction(data_loader_val, dm_model, ae, criterion, device) print(f"iou of the network on the {len(dataset_val)} test images: {test_stats['iou']:.3f}") # print(f"loss of the network on the {len(dataset_val)} test images: {test_stats['loss']:.3f}") if test_stats["iou"] > max_iou: max_iou = test_stats["iou"] misc.save_model( args=args, model=dm_model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch, prefix='best') else: misc.save_model( args=args, model=dm_model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch, prefix='latest') if log_writer is not None: log_writer.add_scalar('perf/test_loss', test_stats['loss'], epoch) log_writer.add_scalar('perf/test_iou', test_stats['iou'], epoch) log_writer.add_scalar('perf/test_accuracy', test_stats['accuracy'], epoch) log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, **{f'test_{k}': v for k, v in test_stats.items()}, 'epoch': epoch, 'n_parameters': n_parameters} else: log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, 'epoch': epoch, 'n_parameters': n_parameters} if args.output_dir and misc.is_main_process(): if log_writer is not None: log_writer.flush() with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: f.write(json.dumps(log_stats) + "\n") total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print('Training time {}'.format(total_time_str)) if __name__ == '__main__': args = get_args_parser() args = args.parse_args() if args.output_dir: Path(args.output_dir).mkdir(parents=True, exist_ok=True) config_path = args.configs from configs.config_utils import CONFIG config = CONFIG(config_path) main(args,config)