75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
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import os.path as osp
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import logging
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import time
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import argparse
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from collections import OrderedDict
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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 data.util import bgr2ycbcr
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import models.archs.SwitchedResidualGenerator_arch as srg
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from models.ExtensibleTrainer import ExtensibleTrainer
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from switched_conv.switched_conv_util import save_attention_to_image, save_attention_to_image_rgb
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from switched_conv.switched_conv import compute_attention_specificity
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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 models.networks as networks
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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/train_imgset_structural_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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dataset_opt['dataset']['includes_labels'] = False
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del dataset_opt['dataset']['labeler']
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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(test_loader)
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model = ExtensibleTrainer(opt)
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gen = model.netsG['generator']
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label_to_search_for = 4
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for test_loader in test_loaders:
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test_set_name = test_loader.dataset.opt['name']
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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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tq = tqdm(test_loader)
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step = 1
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for data in tq:
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hq = data['hq'].to('cuda')
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res = gen(hq)
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res = torch.nn.functional.interpolate(res, size=hq.shape[2:], mode="nearest")
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res_lbl = res[:, label_to_search_for, :, :].unsqueeze(1)
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res_lbl_mask = (1.0 * (res_lbl > .5))*.5 + .5
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hq = hq * res_lbl_mask
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torchvision.utils.save_image(hq, os.path.join(dataset_dir, "%i.png" % (step,)))
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step += 1
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