327 lines
13 KiB
Python
327 lines
13 KiB
Python
# All modification made by Kneron Corp.: Copyright (c) 2022 Kneron Corp.
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import os.path as osp
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import time
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import warnings
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import mmcv
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import torch
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from mmcv import Config, DictAction
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from mmcv.cnn import fuse_conv_bn
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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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wrap_fp16_model)
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from mmdet.apis import multi_gpu_test, single_gpu_test
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from mmdet.datasets import (build_dataloader, build_dataset,
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replace_ImageToTensor)
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from mmdet.models import build_detector
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from mmdet.utils import setup_multi_processes
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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='weight file ( .pth or .onnx ) ')
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parser.add_argument(
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'--work-dir',
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help='the directory to save the file containing evaluation metrics')
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parser.add_argument('--out', help='output result file in pickle format')
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parser.add_argument(
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'--out-kneron',
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help='output result file for kneron public field json file'
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)
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parser.add_argument(
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'--fuse-conv-bn',
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action='store_true',
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help='Whether to fuse conv and bn, this will slightly increase'
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'the inference speed')
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parser.add_argument(
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'--gpu-ids',
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type=int,
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nargs='+',
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help='(Deprecated, please use --gpu-id) ids of gpus to use '
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'(only applicable to non-distributed training)')
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parser.add_argument(
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'--gpu-id',
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type=int,
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default=0,
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help='id of gpu to use '
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'(only applicable to non-distributed testing)')
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parser.add_argument(
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'--format-only',
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action='store_true',
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help='Format the output results without perform evaluation. It is'
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'useful when you want to format the result to a specific format and '
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'submit it to the test server')
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parser.add_argument(
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'--eval',
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type=str,
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nargs='+',
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help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
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' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
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parser.add_argument('--show', action='store_true', help='show results')
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parser.add_argument(
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'--show-dir', help='directory where painted images will be saved')
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parser.add_argument(
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'--show-score-thr',
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type=float,
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default=0.3,
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help='score threshold (default: 0.3)')
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parser.add_argument(
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'--gpu-collect',
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action='store_true',
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help='whether to use gpu to collect results.')
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parser.add_argument(
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'--tmpdir',
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help='tmp directory used for collecting results from multiple '
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'workers, available when gpu-collect is not specified')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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parser.add_argument(
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'--options',
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nargs='+',
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action=DictAction,
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help='custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be kwargs for dataset.evaluate() function (deprecate), '
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'change to --eval-options instead.')
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parser.add_argument(
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'--eval-options',
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nargs='+',
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action=DictAction,
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help='custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be kwargs for dataset.evaluate() function')
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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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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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if args.options and args.eval_options:
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raise ValueError(
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'--options and --eval-options cannot be both '
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'specified, --options is deprecated in favor of --eval-options')
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if args.options:
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warnings.warn('--options is deprecated in favor of --eval-options')
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args.eval_options = args.options
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return args
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def main():
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args = parse_args()
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assert args.out or args.eval or args.format_only or args.show \
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or args.show_dir, \
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('Please specify at least one operation (save/eval/format/show the '
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'results / save the results) with the argument "--out", "--eval"'
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', "--format-only", "--show" or "--show-dir"')
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if args.eval and args.format_only:
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raise ValueError('--eval and --format_only cannot be both specified')
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if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
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raise ValueError('The output file must be a pkl file.')
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cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# set multi-process settings
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setup_multi_processes(cfg)
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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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cfg.model.pretrained = None
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if cfg.model.get('neck'):
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if isinstance(cfg.model.neck, list):
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for neck_cfg in cfg.model.neck:
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if neck_cfg.get('rfp_backbone'):
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if neck_cfg.rfp_backbone.get('pretrained'):
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neck_cfg.rfp_backbone.pretrained = None
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elif cfg.model.neck.get('rfp_backbone'):
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if cfg.model.neck.rfp_backbone.get('pretrained'):
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cfg.model.neck.rfp_backbone.pretrained = None
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# in case the test dataset is concatenated
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samples_per_gpu = 1
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if isinstance(cfg.data.test, dict):
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cfg.data.test.test_mode = True
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samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
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if samples_per_gpu > 1:
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# Replace 'ImageToTensor' to 'DefaultFormatBundle'
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cfg.data.test.pipeline = replace_ImageToTensor(
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cfg.data.test.pipeline)
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elif isinstance(cfg.data.test, list):
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for ds_cfg in cfg.data.test:
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ds_cfg.test_mode = True
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samples_per_gpu = max(
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[ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
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if samples_per_gpu > 1:
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for ds_cfg in cfg.data.test:
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ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
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if args.gpu_ids is not None:
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cfg.gpu_ids = args.gpu_ids[0:1]
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warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
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'Because we only support single GPU mode in '
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'non-distributed testing. Use the first GPU '
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'in `gpu_ids` now.')
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else:
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cfg.gpu_ids = [args.gpu_id]
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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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init_dist(args.launcher, **cfg.dist_params)
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rank, _ = get_dist_info()
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# allows not to create
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if args.work_dir is not None and rank == 0:
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mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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json_file = osp.join(args.work_dir, f'eval_{timestamp}.json')
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# build the dataloader
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dataset = build_dataset(cfg.data.test)
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=samples_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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cfg.model.train_cfg = None
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model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
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fp16_cfg = cfg.get('fp16', None)
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if os.path.splitext(args.checkpoint)[-1] == '.pth':
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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checkpoint = load_checkpoint(
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model, args.checkpoint, map_location='cpu'
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)
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if args.fuse_conv_bn:
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model = fuse_conv_bn(model)
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# old versions did not save class info in checkpoints,
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# this workaround is for backward compatibility
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if 'CLASSES' in checkpoint.get('meta', {}):
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model.CLASSES = checkpoint['meta']['CLASSES']
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else:
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model.CLASSES = dataset.CLASSES
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elif os.path.splitext(args.checkpoint)[-1] == '.onnx':
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import onnxruntime
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onnx_sess = onnxruntime.InferenceSession(args.checkpoint)
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setattr(model, '__Kn_ONNX_Sess__', onnx_sess)
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model.forward = model.forward_kneron
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elif os.path.splitext(args.checkpoint)[-1] == '.nef':
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try:
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import kp
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except Exception:
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warnings.warn(
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'Kneron PLUS software failed to import; please check document '
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'http://doc.kneron.com/docs/#plus_python '
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'for installation guide')
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# Use first scaned kneron usb dongle
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device_group = kp.core.connect_devices(usb_port_ids=[0])
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# Load model
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model_nef_descriptor = kp.core.load_model_from_file(
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device_group=device_group, file_path=args.checkpoint
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)
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# Generate preprocess setting for PLUS
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generic_raw_image_header = kp.GenericRawImageHeader(
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model_id=model_nef_descriptor.models[0].id,
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resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
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padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
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normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON,
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inference_number=0
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)
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kp_params = {
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'device_group': device_group,
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'model_nef_descriptor': model_nef_descriptor,
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'generic_raw_image_header': generic_raw_image_header
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}
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setattr(model, '__Kn_PLUS_Params__', kp_params)
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model.forward = model.forward_kneron
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if not distributed:
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model = MMDataParallel(model, device_ids=cfg.gpu_ids)
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outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
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args.show_score_thr)
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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, args.tmpdir,
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args.gpu_collect)
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rank, _ = get_dist_info()
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if rank == 0:
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if args.out:
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print(f'\nwriting results to {args.out}')
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mmcv.dump(outputs, args.out)
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if args.out_kneron:
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print(f'\nwriting results to {args.out_kneron}')
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import json
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import numpy as np
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formate_data = []
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for dets, data in zip(outputs, data_loader):
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for label, det in enumerate(dets):
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cls = np.full((len(det), 1), label + 1)
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det = np.hstack((det, cls))
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det[:, 2] = det[:, 2] - det[:, 0]
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det[:, 3] = det[:, 3] - det[:, 1]
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if label == 0:
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tmp = det
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else:
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tmp = np.vstack((tmp, det))
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formate_data.append({
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'img_path': data['img_metas'][0].data[0][0]['filename'],
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'bbox': tmp.tolist()
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})
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with open(args.out_kneron, 'w') as f:
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json.dump(formate_data, f, ensure_ascii=True, indent=4)
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kwargs = {} if args.eval_options is None else args.eval_options
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if args.format_only:
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dataset.format_results(outputs, **kwargs)
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if args.eval:
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eval_kwargs = cfg.get('evaluation', {}).copy()
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# hard-code way to remove EvalHook args
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for key in [
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'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
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'rule', 'dynamic_intervals'
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]:
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eval_kwargs.pop(key, None)
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eval_kwargs.update(dict(metric=args.eval, **kwargs))
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metric = dataset.evaluate(outputs, **eval_kwargs)
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print(metric)
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metric_dict = dict(config=args.config, metric=metric)
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if args.work_dir is not None and rank == 0:
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mmcv.dump(metric_dict, json_file)
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if __name__ == '__main__':
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main()
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