mirror of https://github.com/JDAI-CV/fast-reid.git
64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
# encoding: utf-8
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"""
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@author: l1aoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import argparse
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import os
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import sys
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from os import mkdir
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import torch
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from torch.backends import cudnn
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sys.path.append('.')
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from config import cfg
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from data import get_data_bunch
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from engine.inference import inference
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from utils.logger import setup_logger
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from modeling import build_model
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def main():
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parser = argparse.ArgumentParser(description="ReID Baseline Inference")
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parser.add_argument('-cfg',
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"--config_file", default="", help="path to config file", type=str
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)
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parser.add_argument("opts", help="Modify config options using the command-line", default=None,
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nargs=argparse.REMAINDER)
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args = parser.parse_args()
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num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
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if args.config_file != "":
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cfg.merge_from_file(args.config_file)
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cfg.merge_from_list(args.opts)
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cfg.freeze()
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if not os.path.exists(cfg.OUTPUT_DIR): os.makedirs(cfg.OUTPUT_DIR)
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logger = setup_logger("reid_baseline", cfg.OUTPUT_DIR, 0)
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logger.info("Using {} GPUS".format(num_gpus))
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logger.info(args)
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if args.config_file != "":
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logger.info("Loaded configuration file {}".format(args.config_file))
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logger.info("Running with config:\n{}".format(cfg))
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cudnn.benchmark = True
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data_bunch, test_labels, num_query = get_data_bunch(cfg)
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# model = build_model(cfg, data_bunch.c)
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# state_dict = torch.load(cfg.TEST.WEIGHT)
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# model.load_state_dict(state_dict['model'])
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# model.cuda()
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model = torch.jit.load("/export/home/lxy/reid_baseline/pcb_model_v0.2.pt")
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inference(cfg, model, data_bunch, test_labels, num_query)
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if __name__ == '__main__':
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main()
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