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import argparse
import os
from mmengine.config import Config
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument("--config", help="model config file path")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_model_ae",
type=str,
default=None,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--root_path",
type=str,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--annotation_json",
type=str,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--output_dir",
type=str,
default="results",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=8,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--gradient_checkpointing",
type=bool,
default=True,
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--use_8bit_adam",
type=bool,
default=True,
help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--use_came",
type=bool,
default=False,
help="whether to use came",
)
parser.add_argument(
"--allow_tf32",
type=bool,
default=True,
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=4000,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument("--classes", type=str, nargs="+")
parser.add_argument("--img_path", type=str)
parser.add_argument("--save_path", type=str)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
return args
def merge_args(cfg, args):
# if args.ckpt_path is not None:
# cfg.model["from_pretrained"] = args.ckpt_path
# if cfg.get("discriminator") is not None:
# cfg.discriminator["from_pretrained"] = args.ckpt_path
# args.ckpt_path = None
for k, v in vars(args).items():
if v is not None:
cfg[k] = v
return cfg
def read_config(config_path):
cfg = Config.fromfile(config_path)
return cfg
def parse_configs():
args = parse_args()
cfg = read_config(args.config)
cfg = merge_args(cfg, args)
return cfg
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
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