103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
# Copyright (c) 2021 Binbin Zhang(binbzha@qq.com)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import argparse
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import copy
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import logging
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import os
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import torch
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import yaml
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from torch.utils.data import DataLoader
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from kws.dataset.dataset import Dataset
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from kws.model.kws_model import init_model
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from kws.utils.checkpoint import load_checkpoint
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from kws.utils.executor import Executor
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def get_args():
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parser = argparse.ArgumentParser(description='recognize with your model')
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parser.add_argument('--config', required=True, help='config file')
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parser.add_argument('--test_data', required=True, help='test data file')
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parser.add_argument('--gpu',
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type=int,
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default=-1,
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help='gpu id for this rank, -1 for cpu')
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parser.add_argument('--checkpoint', required=True, help='checkpoint model')
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parser.add_argument('--batch_size',
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default=16,
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type=int,
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help='batch size for inference')
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parser.add_argument('--num_workers',
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default=0,
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type=int,
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help='num of subprocess workers for reading')
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parser.add_argument('--pin_memory',
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action='store_true',
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default=False,
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help='Use pinned memory buffers used for reading')
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parser.add_argument('--prefetch',
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default=100,
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type=int,
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help='prefetch number')
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args = parser.parse_args()
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return args
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def main():
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args = get_args()
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s %(levelname)s %(message)s')
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os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
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with open(args.config, 'r') as fin:
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configs = yaml.load(fin, Loader=yaml.FullLoader)
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test_conf = copy.deepcopy(configs['dataset_conf'])
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test_conf['filter_conf']['max_length'] = 102400
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test_conf['filter_conf']['min_length'] = 0
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test_conf['speed_perturb'] = False
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test_conf['spec_aug'] = False
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test_conf['shuffle'] = False
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test_conf['feature_extraction_conf']['dither'] = 0.0
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test_conf['batch_conf']['batch_size'] = args.batch_size
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test_dataset = Dataset(args.test_data, test_conf)
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test_data_loader = DataLoader(test_dataset,
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batch_size=None,
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pin_memory=args.pin_memory,
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num_workers=args.num_workers)
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# Init asr model from configs
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model = init_model(configs['model'])
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load_checkpoint(model, args.checkpoint)
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use_cuda = args.gpu >= 0 and torch.cuda.is_available()
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device = torch.device('cuda' if use_cuda else 'cpu')
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model = model.to(device)
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executor = Executor()
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model.eval()
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training_config = configs['training_config']
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with torch.no_grad():
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test_loss, test_acc = executor.test(model, test_data_loader, device,
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training_config)
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logging.info('Test Loss {} Acc {}'.format(test_loss, test_acc))
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if __name__ == '__main__':
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main()
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