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import argparse
from pathlib import Path
import yaml
def get_args_parser():
parser = argparse.ArgumentParser('MAR training with Diffusion Loss', add_help=False)
parser.add_argument('--batch_size', default=16, type=int,
help='Batch size per GPU (effective batch size is batch_size * # gpus')
parser.add_argument('--epochs', default=2000, type=int)
# Model parameters
parser.add_argument('--model', default='mar_base', type=str, metavar='MODEL',
help='Name of model to train')
parser.add_argument('--ckpt_path', default="pretrained_models/mar/city768.16.pth", type=str,
help='model checkpoint path')
# VAE parameters
parser.add_argument('--img_size', default=768, type=int,
help='images input size')
parser.add_argument('--vae_path', default="pretrained_models/vae/modelf16.ckpt", type=str,
help='images input size')
parser.add_argument('--vae_embed_dim', default=16, type=int,
help='vae output embedding dimension')
parser.add_argument('--vae_stride', default=16, type=int,
help='tokenizer stride, default use KL16')
parser.add_argument('--patch_size', default=1, type=int,
help='number of tokens to group as a patch.')
parser.add_argument('--config', default="ldm/config.yaml", type=str,
help='vae model configuration file')
# Generation parameters
parser.add_argument('--num_iter', default=64, type=int,
help='number of autoregressive iterations to generate an image')
parser.add_argument('--num_images', default=3000, type=int,
help='number of images to generate')
parser.add_argument('--cfg', default=1.0, type=float, help="classifier-free guidance")
parser.add_argument('--cfg_schedule', default="linear", type=str)
parser.add_argument('--label_drop_prob', default=0.1, type=float)
parser.add_argument('--eval_freq', type=int, default=40, help='evaluation frequency')
parser.add_argument('--save_last_freq', type=int, default=5, help='save last frequency')
parser.add_argument('--online_eval', action='store_true')
parser.add_argument('--evaluate', action='store_true')
parser.add_argument('--eval_bsz', type=int, default=64, help='generation batch size')
# Optimizer parameters
parser.add_argument('--weight_decay', type=float, default=0.02,
help='weight decay (default: 0.02)')
parser.add_argument('--grad_checkpointing', action='store_true')
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',
help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
parser.add_argument('--min_lr', type=float, default=0., metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0')
parser.add_argument('--lr_schedule', type=str, default='constant',
help='learning rate schedule')
parser.add_argument('--warmup_epochs', type=int, default=100, metavar='N',
help='epochs to warmup LR')
parser.add_argument('--ema_rate', default=0.9999, type=float)
# MAR params
parser.add_argument('--mask_ratio_min', type=float, default=0.7,
help='Minimum mask ratio')
parser.add_argument('--grad_clip', type=float, default=3.0,
help='Gradient clip')
parser.add_argument('--attn_dropout', type=float, default=0.1,
help='attention dropout')
parser.add_argument('--proj_dropout', type=float, default=0.1,
help='projection dropout')
parser.add_argument('--buffer_size', type=int, default=64)
# Diffusion Loss params
parser.add_argument('--diffloss_d', type=int, default=6)
parser.add_argument('--diffloss_w', type=int, default=1024)
parser.add_argument('--num_sampling_steps', type=str, default="100")
parser.add_argument('--diffusion_batch_mul', type=int, default=4)
parser.add_argument('--temperature', default=1.0, type=float, help='diffusion loss sampling temperature')
# Dataset parameters
parser.add_argument('--output_dir', default='./output_dir',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default='./output_dir',
help='path where to tensorboard log')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=1, type=int)
parser.add_argument('--resume', default=None,#'pretrained_models/mar/mar_base',
help='resume from checkpoint')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--num_workers', default=10, 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)
# 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')
# caching latents
parser.add_argument('--use_cached', action='store_true', dest='use_cached',
help='Use cached latents')
parser.set_defaults(use_cached=False)
parser.add_argument('--cached_path', default='', help='path to cached latents')
return parser
args = get_args_parser()
args = args.parse_args()
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
args.log_dir = args.output_dir
with open(args.config, "r") as f:
config = yaml.safe_load(f)
args.ddconfig = config["ddconfig"] |