|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| import logging
|
| import os
|
| import sys
|
| import traceback
|
|
|
| from saicinpainting.evaluation.utils import move_to_device
|
| from saicinpainting.evaluation.refinement import refine_predict
|
| os.environ['OMP_NUM_THREADS'] = '1'
|
| os.environ['OPENBLAS_NUM_THREADS'] = '1'
|
| os.environ['MKL_NUM_THREADS'] = '1'
|
| os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
|
| os.environ['NUMEXPR_NUM_THREADS'] = '1'
|
|
|
| import cv2
|
| import hydra
|
| import numpy as np
|
| import torch
|
| import tqdm
|
| import yaml
|
| from omegaconf import OmegaConf
|
| from torch.utils.data._utils.collate import default_collate
|
|
|
| from saicinpainting.training.data.datasets import make_default_val_dataset
|
| from saicinpainting.training.trainers import load_checkpoint
|
| from saicinpainting.utils import register_debug_signal_handlers
|
|
|
| LOGGER = logging.getLogger(__name__)
|
|
|
|
|
| @hydra.main(config_path='../configs/prediction', config_name='default.yaml')
|
| def main(predict_config: OmegaConf):
|
| try:
|
| if sys.platform != 'win32':
|
| register_debug_signal_handlers()
|
|
|
| device = torch.device("cpu")
|
|
|
| train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
|
| with open(train_config_path, 'r') as f:
|
| train_config = OmegaConf.create(yaml.safe_load(f))
|
|
|
| train_config.training_model.predict_only = True
|
| train_config.visualizer.kind = 'noop'
|
|
|
| out_ext = predict_config.get('out_ext', '.png')
|
|
|
| checkpoint_path = os.path.join(predict_config.model.path,
|
| 'models',
|
| predict_config.model.checkpoint)
|
| model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')
|
| model.freeze()
|
| if not predict_config.get('refine', False):
|
| model.to(device)
|
|
|
| if not predict_config.indir.endswith('/'):
|
| predict_config.indir += '/'
|
|
|
| dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
|
| for img_i in tqdm.trange(len(dataset)):
|
| mask_fname = dataset.mask_filenames[img_i]
|
| cur_out_fname = os.path.join(
|
| predict_config.outdir,
|
| os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext
|
| )
|
| os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
|
| batch = default_collate([dataset[img_i]])
|
| if predict_config.get('refine', False):
|
| assert 'unpad_to_size' in batch, "Unpadded size is required for the refinement"
|
|
|
|
|
| cur_res = refine_predict(batch, model, **predict_config.refiner)
|
| cur_res = cur_res[0].permute(1,2,0).detach().cpu().numpy()
|
| else:
|
| with torch.no_grad():
|
| batch = move_to_device(batch, device)
|
| batch['mask'] = (batch['mask'] > 0) * 1
|
| batch = model(batch)
|
| cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
|
| unpad_to_size = batch.get('unpad_to_size', None)
|
| if unpad_to_size is not None:
|
| orig_height, orig_width = unpad_to_size
|
| cur_res = cur_res[:orig_height, :orig_width]
|
|
|
| cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
|
| cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
|
| cv2.imwrite(cur_out_fname, cur_res)
|
|
|
| except KeyboardInterrupt:
|
| LOGGER.warning('Interrupted by user')
|
| except Exception as ex:
|
| LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
|
| sys.exit(1)
|
|
|
|
|
| if __name__ == '__main__':
|
| main()
|
|
|
