83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
import os.path as osp
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import logging
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import shutil
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import time
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import argparse
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import os
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import utils
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import utils.options as option
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import utils.util as util
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from trainer.ExtensibleTrainer import ExtensibleTrainer
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from data import create_dataset, create_dataloader
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from tqdm import tqdm
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import torch
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import torchvision
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if __name__ == "__main__":
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#### options
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torch.backends.cudnn.benchmark = True
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want_metrics = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_noisy_audio_clips_classifier.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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utils.util.loaded_options = opt
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util.mkdirs(
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(path for key, path in opt['path'].items()
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if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
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util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
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screen=True, tofile=True)
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logger = logging.getLogger('base')
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logger.info(option.dict2str(opt))
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#### Create test dataset and dataloader
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test_loaders = []
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for phase, dataset_opt in sorted(opt['datasets'].items()):
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if 'test' in phase:
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test_set = create_dataset(dataset_opt)
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if hasattr(test_set, 'wrapped_dataset'):
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test_set = test_set.wrapped_dataset
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test_loader = create_dataloader(test_set, dataset_opt, opt)
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logger.info('Number of test images: {:d}'.format(len(test_set)))
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test_loaders.append((dataset_opt['name'], test_loader))
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model = ExtensibleTrainer(opt)
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# Remove all losses, since often labels will not be provided and this is not implied in test.
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for s in model.steps:
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s.losses = {}
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output_key = opt['eval']['classifier_logits_key']
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labels = opt['eval']['output_labels']
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path_key = opt['eval']['path_key']
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output_base_dir = util.opt_get(opt, ['eval', 'output_dir'], None)
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output_file = open('classify_into_folders.tsv', 'a')
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step = 0
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for test_set_name, test_loader in test_loaders:
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test_start_time = time.time()
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dataset_dir = osp.join(opt['path']['results_root'], opt['name'])
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util.mkdir(dataset_dir)
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for data in tqdm(test_loader):
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with torch.no_grad():
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model.feed_data(data, 0)
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model.test()
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lbls = torch.nn.functional.softmax(model.eval_state[output_key][0].cpu(), dim=-1)
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for k, lbl in enumerate(lbls):
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lbl = labels[torch.argmax(lbl, dim=0)]
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src_path = data[path_key][k]
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output_file.write(f'{src_path}\t{lbl}\n')
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if output_base_dir is not None:
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dest = os.path.join(output_base_dir, lbl)
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os.makedirs(dest, exist_ok=True)
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shutil.copy(str(src_path), os.path.join(dest, f'{step}_{os.path.basename(str(src_path))}'))
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step += 1
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output_file.flush()
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output_file.close()
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