220 lines
8.1 KiB
Python
220 lines
8.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import warnings
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from numbers import Number
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import mmcv
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import numpy as np
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import torch
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from mmcv import DictAction
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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 mmcls.apis import multi_gpu_test, single_gpu_test
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from mmcls.datasets import build_dataloader, build_dataset
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from mmcls.models import build_classifier
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# TODO import `wrap_fp16_model` from mmcv and delete them from mmcls
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try:
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from mmcv.runner import wrap_fp16_model
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except ImportError:
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warnings.warn('wrap_fp16_model from mmcls will be deprecated.'
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'Please install mmcv>=1.1.4.')
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from mmcls.core import wrap_fp16_model
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def parse_args():
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parser = argparse.ArgumentParser(description='mmcls test 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('--out', help='output result file')
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out_options = ['class_scores', 'pred_score', 'pred_label', 'pred_class']
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parser.add_argument(
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'--out-items',
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nargs='+',
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default=['all'],
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choices=out_options + ['none', 'all'],
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help='Besides metrics, what items will be included in the output '
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f'result file. You can choose some of ({", ".join(out_options)}), '
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'or use "all" to include all above, or use "none" to disable all of '
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'above. Defaults to output all.',
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metavar='')
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parser.add_argument(
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'--metrics',
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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., '
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'"accuracy", "precision", "recall", "f1_score", "support" for single '
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'label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for '
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'multi-label dataset')
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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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'--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('--tmpdir', help='tmp dir for writing some results')
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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='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 (deprecate), '
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'change to --cfg-options instead.')
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parser.add_argument(
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'--metric-options',
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nargs='+',
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action=DictAction,
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default={},
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help='custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be parsed as a dict metric_options for dataset.evaluate()'
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' function.')
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parser.add_argument(
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'--show-options',
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nargs='+',
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action=DictAction,
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help='custom options for show_result. key-value pair in xxx=yyy.'
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'Check available options in `model.show_result`.')
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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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'--device',
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choices=['cpu', 'cuda'],
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default='cuda',
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help='device used for testing')
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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.cfg_options:
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raise ValueError(
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'--options and --cfg-options cannot be both '
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'specified, --options is deprecated in favor of --cfg-options')
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if args.options:
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warnings.warn('--options is deprecated in favor of --cfg-options')
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args.cfg_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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cfg = mmcv.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 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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cfg.data.test.test_mode = True
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assert args.metrics or args.out, \
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'Please specify at least one of output path and evaluation metrics.'
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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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# build the dataloader
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dataset = build_dataset(cfg.data.test)
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# the extra round_up data will be removed during gpu/cpu collect
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=cfg.data.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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round_up=True)
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# build the model and load checkpoint
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model = build_classifier(cfg.model)
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fp16_cfg = cfg.get('fp16', None)
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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(model, args.checkpoint, map_location='cpu')
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if 'CLASSES' in checkpoint.get('meta', {}):
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CLASSES = checkpoint['meta']['CLASSES']
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else:
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from mmcls.datasets import ImageNet
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warnings.simplefilter('once')
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warnings.warn('Class names are not saved in the checkpoint\'s '
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'meta data, use imagenet by default.')
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CLASSES = ImageNet.CLASSES
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if not distributed:
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if args.device == 'cpu':
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model = model.cpu()
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else:
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model = MMDataParallel(model, device_ids=[0])
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model.CLASSES = CLASSES
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show_kwargs = {} if args.show_options is None else args.show_options
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outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
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**show_kwargs)
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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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results = {}
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if args.metrics:
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eval_results = dataset.evaluate(outputs, args.metrics,
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args.metric_options)
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results.update(eval_results)
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for k, v in eval_results.items():
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if isinstance(v, np.ndarray):
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v = [round(out, 2) for out in v.tolist()]
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elif isinstance(v, Number):
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v = round(v, 2)
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else:
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raise ValueError(f'Unsupport metric type: {type(v)}')
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print(f'\n{k} : {v}')
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if args.out:
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if 'none' not in args.out_items:
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scores = np.vstack(outputs)
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pred_score = np.max(scores, axis=1)
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pred_label = np.argmax(scores, axis=1)
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pred_class = [CLASSES[lb] for lb in pred_label]
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res_items = {
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'class_scores': scores,
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'pred_score': pred_score,
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'pred_label': pred_label,
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'pred_class': pred_class
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}
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if 'all' in args.out_items:
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results.update(res_items)
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else:
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for key in args.out_items:
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results[key] = res_items[key]
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print(f'\ndumping results to {args.out}')
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mmcv.dump(results, args.out)
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if __name__ == '__main__':
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main()
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