DL-Art-School/codes/test_image_patch_classifier.py

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import os.path as osp
import logging
import time
import argparse
import os
import utils
import utils.options as option
import utils.util as util
2020-12-18 16:18:34 +00:00
from trainer.ExtensibleTrainer import ExtensibleTrainer
from data import create_dataset, create_dataloader
from tqdm import tqdm
import torch
import torchvision
if __name__ == "__main__":
#### options
torch.backends.cudnn.benchmark = True
want_metrics = False
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/train_imgset_structural_classifier.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)
utils.util.loaded_options = opt
util.mkdirs(
(path for key, path in opt['path'].items()
if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt))
#### Create test dataset and dataloader
test_loaders = []
for phase, dataset_opt in sorted(opt['datasets'].items()):
dataset_opt['dataset']['includes_labels'] = False
del dataset_opt['dataset']['labeler']
test_set = create_dataset(dataset_opt)
if hasattr(test_set, 'wrapped_dataset'):
test_set = test_set.wrapped_dataset
test_loader = create_dataloader(test_set, dataset_opt, opt)
logger.info('Number of test images: {:d}'.format(len(test_set)))
test_loaders.append(test_loader)
model = ExtensibleTrainer(opt)
gen = model.netsG['generator']
label_to_search_for = 4
for test_loader in test_loaders:
test_set_name = test_loader.dataset.opt['name']
test_start_time = time.time()
dataset_dir = osp.join(opt['path']['results_root'], opt['name'])
util.mkdir(dataset_dir)
tq = tqdm(test_loader)
step = 1
for data in tq:
hq = data['hq'].to('cuda')
res = gen(hq)
res = torch.nn.functional.interpolate(res, size=hq.shape[2:], mode="nearest")
res_lbl = res[:, label_to_search_for, :, :].unsqueeze(1)
res_lbl_mask = (1.0 * (res_lbl > .5))*.5 + .5
hq = hq * res_lbl_mask
torchvision.utils.save_image(hq, os.path.join(dataset_dir, "%i.png" % (step,)))
step += 1