mirror of https://github.com/alibaba/EasyCV.git
146 lines
4.6 KiB
Python
146 lines
4.6 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import argparse
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import importlib
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import os
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import os.path as osp
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import sys
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import time
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import mmcv
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import torch
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import get_dist_info, init_dist, load_checkpoint
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from easycv.core.evaluation.builder import build_evaluator
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from easycv.datasets import build_dataloader, build_dataset
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from easycv.models import build_model
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from easycv.utils.collect import dist_forward_collect, nondist_forward_collect
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# from mmcv import Config
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from easycv.utils.config_tools import mmcv_config_fromfile, traverse_replace
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from easycv.utils.logger import get_root_logger
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sys.path.append(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
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sys.path.append(
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os.path.abspath(
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osp.join(os.path.dirname(os.path.dirname(__file__)), '../')))
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def single_gpu_test(model, data_loader):
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model.eval()
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func = lambda **x: model(mode='test', **x)
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results = nondist_forward_collect(func, data_loader,
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len(data_loader.dataset))
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return results
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def multi_gpu_test(model, data_loader):
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model.eval()
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func = lambda **x: model(mode='test', **x)
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rank, world_size = get_dist_info()
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results = dist_forward_collect(func, data_loader, rank,
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len(data_loader.dataset))
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return results
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMDet test (and eval) a model')
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parser.add_argument('config', help='test config file path')
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parser.add_argument('checkpoint', help='checkpoint file')
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parser.add_argument(
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'--work_dir',
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type=str,
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default=None,
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help='the dir to save logs and models')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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parser.add_argument(
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'--port',
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type=int,
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default=29500,
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help='port only works when launcher=="slurm"')
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parser.add_argument(
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'--model_type',
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choices=['classification', 'pose'],
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default='classification',
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help='model type')
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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return args
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def main():
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args = parse_args()
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cfg = mmcv_config_fromfile(args.config)
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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# update configs according to CLI args
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if args.work_dir is not None:
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cfg.work_dir = args.work_dir
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cfg.model.pretrained = None # ensure to use checkpoint rather than pretraining
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# check memcached package exists
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if importlib.util.find_spec('mc') is None:
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traverse_replace(cfg, 'memcached', False)
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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if args.launcher == 'slurm':
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cfg.dist_params['port'] = args.port
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init_dist(args.launcher, **cfg.dist_params)
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# logger
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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log_file = osp.join(cfg.work_dir, 'test_{}.log'.format(timestamp))
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logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
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# build the dataloader
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dataset = build_dataset(cfg.data.val)
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data_loader = build_dataloader(
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dataset,
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imgs_per_gpu=cfg.data.imgs_per_gpu,
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workers_per_gpu=cfg.data.workers_per_gpu,
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dist=distributed,
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shuffle=False)
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# build the model and load checkpoint
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model = build_model(cfg.model)
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load_checkpoint(model, args.checkpoint, map_location='cpu')
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if not distributed:
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model = MMDataParallel(model, device_ids=[0])
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outputs = single_gpu_test(model, data_loader)
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else:
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model = MMDistributedDataParallel(
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model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False)
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outputs = multi_gpu_test(model, data_loader) # dict{key: np.ndarray}
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rank, _ = get_dist_info()
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if rank == 0:
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if args.model_type == 'pose':
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evaluators = build_evaluator(
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cfg.eval_pipelines[0]['evaluators'][0])
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dataset.evaluate(outputs, evaluators)
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else:
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for name, val in outputs.items():
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dataset.evaluate(
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torch.from_numpy(val), name, logger, topk=(1, 5))
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if __name__ == '__main__':
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main()
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