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import os
import os.path as osp
import argparse
import torch
import time
from torch.utils.data import DataLoader
from mmengine.utils import mkdir_or_exist
from mmengine.config import Config, DictAction
from mmengine.logging import MMLogger
from estimator.utils import RunnerInfo, setup_env, log_env, fix_random_seed
from estimator.models.builder import build_model
from estimator.datasets.builder import build_dataset
from estimator.tester import Tester
from estimator.models.patchfusion import PatchFusion
from mmengine import print_log
from transformers import PretrainedConfig
def parse_args():
parser = argparse.ArgumentParser(description='Train a segmentor')
parser.add_argument('config', help='train config file path')
parser.add_argument(
'--ckp-path',
type=str,
help='ckp_path')
parser.add_argument(
'--save-path',
type=str,
help='ckp_path')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
# When using PyTorch version >= 2.0.0, the `torch.distributed.launch`
# will pass the `--local-rank` parameter to `tools/train.py` instead
# of `--local_rank`.
parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def main():
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
cfg.ckp_path = args.ckp_path
# folder_name = os.path.dirname(args.save_path)
# print(folder_name)
# exit(100)
# build model
model = build_model(cfg.model)
print_log('Checkpoint Path: {}'.format(cfg.ckp_path), logger='current')
if hasattr(model, 'load_dict'):
print_log(model.load_dict(torch.load(cfg.ckp_path)['model_state_dict']), logger='current')
else:
print_log(model.load_state_dict(torch.load(cfg.ckp_path)['model_state_dict'], strict=True), logger='current')
model.eval()
model.save_pretrained(args.save_path)
model.config.to_json_file(os.path.join(args.save_path, "config.json"))
# model = PatchFusion.from_pretrained('Zhyever/patchfusion_depth_anything_vits14')
if __name__ == '__main__':
main() |