init

backend/inpaint/lama/bin/predict_inner_features.py

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+#!/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, DefaultInpaintingTrainingModule
+from saicinpainting.utils import register_debug_signal_handlers, get_shape
+
+LOGGER = logging.getLogger(__name__)
+
+
+@hydra.main(config_path='../configs/prediction', config_name='default_inner_features.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))
+
+        checkpoint_path = os.path.join(predict_config.model.path, 'models', predict_config.model.checkpoint)
+        model = load_checkpoint(train_config, checkpoint_path, strict=False)
+        model.freeze()
+        model.to(device)
+
+        assert isinstance(model, DefaultInpaintingTrainingModule), 'Only DefaultInpaintingTrainingModule is supported'
+        assert isinstance(getattr(model.generator, 'model', None), torch.nn.Sequential)
+
+        if not predict_config.indir.endswith('/'):
+            predict_config.indir += '/'
+
+        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
+
+        max_level = max(predict_config.levels)
+
+        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])
+                os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
+
+                batch = move_to_device(default_collate([dataset[img_i]]), device)
+
+                img = batch['image']
+                mask = batch['mask']
+                mask[:] = 0
+                mask_h, mask_w = mask.shape[-2:]
+                mask[:, :,
+                    mask_h // 2 - predict_config.hole_radius : mask_h // 2 + predict_config.hole_radius,
+                    mask_w // 2 - predict_config.hole_radius : mask_w // 2 + predict_config.hole_radius] = 1
+
+                masked_img = torch.cat([img * (1 - mask), mask], dim=1)
+
+                feats = masked_img
+                for level_i, level in enumerate(model.generator.model):
+                    feats = level(feats)
+                    if level_i in predict_config.levels:
+                        cur_feats = torch.cat([f for f in feats if torch.is_tensor(f)], dim=1) \
+                            if isinstance(feats, tuple) else feats
+
+                        if predict_config.slice_channels:
+                            cur_feats = cur_feats[:, slice(*predict_config.slice_channels)]
+
+                        cur_feat = cur_feats.pow(2).mean(1).pow(0.5).clone()
+                        cur_feat -= cur_feat.min()
+                        cur_feat /= cur_feat.std()
+                        cur_feat = cur_feat.clamp(0, 1) / 1
+                        cur_feat = cur_feat.cpu().numpy()[0]
+                        cur_feat *= 255
+                        cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')
+                        cv2.imwrite(cur_out_fname + f'_lev{level_i:02d}_norm.png', cur_feat)
+
+                        # for channel_i in predict_config.channels:
+                        #
+                        #     cur_feat = cur_feats[0, channel_i].clone().detach().cpu().numpy()
+                        #     cur_feat -= cur_feat.min()
+                        #     cur_feat /= cur_feat.max()
+                        #     cur_feat *= 255
+                        #     cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')
+                        #     cv2.imwrite(cur_out_fname + f'_lev{level_i}_ch{channel_i}.png', cur_feat)
+                    elif level_i >= max_level:
+                        break
+    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()