132 lines
4.6 KiB
Python
132 lines
4.6 KiB
Python
import argparse
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import os
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import warnings
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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.core import wrap_fp16_model
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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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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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parser.add_argument(
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'--metric', type=str, default='accuracy', help='evaluation metric')
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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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'--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.')
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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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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.options is not None:
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cfg.merge_from_dict(args.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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# 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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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=False)
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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 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, 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.metric != '':
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results = dataset.evaluate(outputs, args.metric)
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for topk, acc in results.items():
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print(f'\n{topk} accuracy: {acc:.2f}')
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else:
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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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if 'CLASSES' in checkpoint['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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pred_class = [CLASSES[lb] for lb in pred_label]
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results = {
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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 not args.out:
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print('\nthe predicted result for the first element is '
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f'pred_score = {pred_score[0]:.2f}, '
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f'pred_label = {pred_label[0]} '
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f'and pred_class = {pred_class[0]}. '
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'Specify --out to save all results to files.')
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if args.out and rank == 0:
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print(f'\nwriting 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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