Upload predict.py

predict.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+
+# Example command:
+# ./bin/predict.py \
+#       model.path=<path to checkpoint, prepared by make_checkpoint.py> \
+#       indir=<path to input data> \
+#       outdir=<where to store predicts>
+
+import logging
+import os
+import sys
+import traceback
+
+from saicinpainting.evaluation.utils import move_to_device
+
+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:
+        register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log
+
+        device = torch.device(predict_config.device)
+
+        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
+
+        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()
+        model.to(device)
+
+        if not predict_config.indir.endswith('/'):
+            predict_config.indir += '/'
+
+        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
+        with torch.no_grad():
+            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 = move_to_device(default_collate([dataset[img_i]]), 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()
+
+                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()