#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# @Time : 2020/10/26 13:38
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# @Author : Scheaven
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# @File : defaults.py
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# @description:
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"""
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This file contains components with some default boilerplate logic user may need
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in training / testing. They will not work for everyone, but many users may find them useful.
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The behavior of functions/classes in this file is subject to change,
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since they are meant to represent the "common default behavior" people need in their projects.
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"""
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import argparse
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import logging
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import os
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import sys
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from utils import comm
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from utils.env import seed_all_rng
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from utils.file_io import PathManager
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from utils.logger import setup_logger
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from utils.collect_env import collect_env_info
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from collections import OrderedDict
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import torch
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# import torch.nn.functional as F
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# from torch.nn.parallel import DistributedDataParallel
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def default_argument_parser():
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"""
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Create a parser with some common arguments used by fastreid users.
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Returns:
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argparse.ArgumentParser:
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"""
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parser = argparse.ArgumentParser(description="fastreid Training")
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parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
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parser.add_argument(
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"--finetune",
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action="store_true",
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help="whether to attempt to finetune from the trained model",
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)
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parser.add_argument(
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"--resume",
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action="store_true",
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help="whether to attempt to resume from the checkpoint directory",
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)
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parser.add_argument("--eval-only", action="store_true", help="perform evaluation only")
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parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*")
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parser.add_argument("--num-machines", type=int, default=1, help="total number of machines")
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parser.add_argument("--img_a1", default="1.jpg", help="input image")
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parser.add_argument("--img_a2", default="2.jpg", help="input image2")
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parser.add_argument("--img_b1", default="1.jpg", help="input image")
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parser.add_argument("--img_b2", default="2.jpg", help="input image2")
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parser.add_argument(
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"--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)"
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)
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# PyTorch still may leave orphan processes in multi-gpu training.
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# Therefore we use a deterministic way to obtain port,
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# so that users are aware of orphan processes by seeing the port occupied.
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port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14
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parser.add_argument("--dist-url", default="tcp://127.0.0.1:{}".format(port))
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parser.add_argument(
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"opts",
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help="Modify config options using the command-line",
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default=None,
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nargs=argparse.REMAINDER,
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)
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return parser
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def default_setup(cfg, args):
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"""
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Perform some basic common setups at the beginning of a job, including:
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1. Set up the detectron2 logger
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2. Log basic information about environment, cmdline arguments, and config
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3. Backup the config to the output directory
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Args:
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cfg (CfgNode): the full config to be used
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args (argparse.NameSpace): the command line arguments to be logged
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"""
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output_dir = cfg.OUTPUT_DIR
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if comm.is_main_process() and output_dir:
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PathManager.mkdirs(output_dir)
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rank = comm.get_rank()
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setup_logger(output_dir, distributed_rank=rank, name="fvcore")
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logger = setup_logger(output_dir, distributed_rank=rank)
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logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size()))
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logger.info("Environment info:\n" + collect_env_info())
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logger.info("Command line arguments: " + str(args))
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if hasattr(args, "config_file") and args.config_file != "":
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logger.info(
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"Contents of args.config_file={}:\n{}".format(
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args.config_file, PathManager.open(args.config_file, "r").read()
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)
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)
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logger.info("Running with full config:\n{}".format(cfg))
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if comm.is_main_process() and output_dir:
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# Note: some of our scripts may expect the existence of
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# config.yaml in output directory
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path = os.path.join(output_dir, "config.yaml")
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with PathManager.open(path, "w") as f:
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f.write(cfg.dump())
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logger.info("Full config saved to {}".format(os.path.abspath(path)))
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# make sure each worker has a different, yet deterministic seed if specified
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seed_all_rng()
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# cudnn benchmark has large overhead. It shouldn't be used considering the small size of
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# typical validation set.
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if not (hasattr(args, "eval_only") and args.eval_only):
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torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
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