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jblitzar
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github-python-corpus

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app.run(main)
# <FILESEP>
# Copyright 2023 Huawei Technologies Co., Ltd
#
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import json
import os
import random
import sys
import time
from pathlib import Path
import mindspore
import numpy as np
from mindspore import context
import util.misc as utils
from models.focus_detr.coco_eval import CocoEvaluator, post_process
from models.focus_detr.dataset import build_dataset
from models.focus_detr.focus_detr import build_focus_detr
from util.logger import setup_logger
from util.slconfig import DictAction, SLConfig
def get_args_parser():
parser = argparse.ArgumentParser("Set transformer detector", add_help=False)
parser.add_argument("--config_file", "-c", type=str, required=True)
parser.add_argument(
"--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.",
)
#
# dataset parameters
parser.add_argument("--dataset_file", default="coco")
parser.add_argument("--coco_path", type=str, default="/comp_robot/cv_public_dataset/COCO2017/")
parser.add_argument("--coco_panoptic_path", type=str)
parser.add_argument("--remove_difficult", action="store_true")
parser.add_argument("--fix_size", action="store_true")
# training parameters
parser.add_argument("--output_dir", default="", help="path where to save, empty for no saving")
parser.add_argument("--note", default="", help="add some notes to the experiment")
parser.add_argument("--device", default="cuda", help="device to use for training / testing")
parser.add_argument("--seed", default=42, type=int)
parser.add_argument("--resume", default="", help="resume from checkpoint")
parser.add_argument("--pretrain_model_path", help="load from other checkpoint")
parser.add_argument("--finetune_ignore", type=str, nargs="+")
parser.add_argument("--start_epoch", default=0, type=int, metavar="N", help="start epoch")
parser.add_argument("--eval", action="store_true")
parser.add_argument("--num_workers", default=2, type=int)
parser.add_argument("--test", action="store_true")
parser.add_argument("--debug", action="store_true")
parser.add_argument("--find_unused_params", action="store_true")
parser.add_argument("--save_results", action="store_true")
parser.add_argument("--save_log", action="store_true")
# distributed training parameters
parser.add_argument("--world_size", default=1, type=int, help="number of distributed processes")
parser.add_argument("--dist_url", default="env://", help="url used to set up distributed training")
parser.add_argument("--rank", default=0, type=int, help="number of distributed processes")
parser.add_argument("--local_rank", type=int, help="local rank for DistributedDataParallel")
parser.add_argument("--amp", action="store_true", help="Train with mixed precision")
return parser
class dataset_param(object):
def __init__(self):
# self.image_set="val"
self.eval = True
self.max_img_size = 1333
self.num_queries = 900
self.img_scales = [480, 512, 640, 800]
self.device_num = 1
self.num_classes = 91
self.rank = 0
self.num_workers = 1
self.batch_size = 1
self.coco_path = "coco2017/"
def main(args):
utils.init_distributed_mode(args)
print("Loading config file from {}".format(args.config_file))
time.sleep(args.rank * 0.02)
cfg = SLConfig.fromfile(args.config_file)
if args.options is not None:
cfg.merge_from_dict(args.options)
if args.rank == 0:
save_cfg_path = os.path.join(args.output_dir, "config_cfg.py")
cfg.dump(save_cfg_path)
save_json_path = os.path.join(args.output_dir, "config_args_raw.json")
with open(save_json_path, "w") as f:
json.dump(vars(args), f, indent=2)