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