'Places': '/home/zhmiao/datasets/Places365'} parser = argparse.ArgumentParser() parser.add_argument('--config', default='./config/Imagenet_LT/Stage_1.py', type=str) parser.add_argument('--test', default=False, action='store_true') parser.add_argument('--test_open', default=False, action='store_true') parser.add_argument('--output_logits', default=False) args = parser.parse_args() test_mode = args.test test_open = args.test_open if test_open: test_mode = True output_logits = args.output_logits config = source_import(args.config).config training_opt = config['training_opt'] # change relatin_opt = config['memory'] dataset = training_opt['dataset'] if not os.path.isdir(training_opt['log_dir']): os.makedirs(training_opt['log_dir']) print('Loading dataset from: %s' % data_root[dataset.rstrip('_LT')]) pprint.pprint(config) if not test_mode: sampler_defs = training_opt['sampler'] if sampler_defs:
parser.add_argument('--config', default='./config/ImageNet_LT/stage_1.py', type=str) parser.add_argument('--test', default=False, action='store_true') parser.add_argument('--test_open', default=False, action='store_true') parser.add_argument('--output_logits', default=False) parser.add_argument('--col', type=str) args = parser.parse_args() test_mode = args.test test_open = args.test_open if test_open: test_mode = True output_logits = args.output_logits config = source_import(args.config).config training_opt = config['training_opt'] # change relatin_opt = config['memory'] dataset = training_opt['dataset'] data_loader = dataloader.load_data(data_root=data_root[dataset.rstrip('_LT')], dataset=dataset, phase='val', batch_size=1, num_workers=training_opt['num_workers']) baseline_model = model(config, data_loader, test=False) baseline_model.load_model() for model in baseline_model.networks.values(): model.eval()
else: data_root = data_root_dict[dataset.rstrip('_LT')] print('Loading dataset from: %s' % data_root) pprint.pprint(config) # ============================================================================ # TRAINING if not test_mode: # during training, different sampler may be applied sampler_defs = training_opt['sampler'] if sampler_defs: if sampler_defs['type'] == 'ClassAwareSampler': sampler_dic = { 'sampler': source_import(sampler_defs['def_file']).get_sampler(), 'params': { 'num_samples_cls': sampler_defs['num_samples_cls'] } } elif sampler_defs['type'] in [ 'MixedPrioritizedSampler', 'ClassPrioritySampler' ]: sampler_dic = { 'sampler': source_import(sampler_defs['def_file']).get_sampler(), 'params': {k: v for k, v in sampler_defs.items() \ if k not in ['type', 'def_file']} } else: sampler_dic = None