Add data augmentation in LeRobotDataset (#234)

Co-authored-by: Simon Alibert <alibert.sim@gmail.com>
Co-authored-by: Remi Cadene <re.cadene@gmail.com>

tests/scripts/save_image_transforms_to_safetensors.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from pathlib import Path
+
+import torch
+from safetensors.torch import save_file
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.transforms import get_image_transforms
+from lerobot.common.utils.utils import init_hydra_config, seeded_context
+from tests.test_image_transforms import ARTIFACT_DIR, DATASET_REPO_ID
+from tests.utils import DEFAULT_CONFIG_PATH
+
+
+def save_default_config_transform(original_frame: torch.Tensor, output_dir: Path):
+    cfg = init_hydra_config(DEFAULT_CONFIG_PATH)
+    cfg_tf = cfg.training.image_transforms
+    default_tf = get_image_transforms(
+        brightness_weight=cfg_tf.brightness.weight,
+        brightness_min_max=cfg_tf.brightness.min_max,
+        contrast_weight=cfg_tf.contrast.weight,
+        contrast_min_max=cfg_tf.contrast.min_max,
+        saturation_weight=cfg_tf.saturation.weight,
+        saturation_min_max=cfg_tf.saturation.min_max,
+        hue_weight=cfg_tf.hue.weight,
+        hue_min_max=cfg_tf.hue.min_max,
+        sharpness_weight=cfg_tf.sharpness.weight,
+        sharpness_min_max=cfg_tf.sharpness.min_max,
+        max_num_transforms=cfg_tf.max_num_transforms,
+        random_order=cfg_tf.random_order,
+    )
+
+    with seeded_context(1337):
+        img_tf = default_tf(original_frame)
+
+    save_file({"default": img_tf}, output_dir / "default_transforms.safetensors")
+
+
+def save_single_transforms(original_frame: torch.Tensor, output_dir: Path):
+    transforms = {
+        "brightness": [(0.5, 0.5), (2.0, 2.0)],
+        "contrast": [(0.5, 0.5), (2.0, 2.0)],
+        "saturation": [(0.5, 0.5), (2.0, 2.0)],
+        "hue": [(-0.25, -0.25), (0.25, 0.25)],
+        "sharpness": [(0.5, 0.5), (2.0, 2.0)],
+    }
+
+    frames = {"original_frame": original_frame}
+    for transform, values in transforms.items():
+        for min_max in values:
+            kwargs = {
+                f"{transform}_weight": 1.0,
+                f"{transform}_min_max": min_max,
+            }
+            tf = get_image_transforms(**kwargs)
+            key = f"{transform}_{min_max[0]}_{min_max[1]}"
+            frames[key] = tf(original_frame)
+
+    save_file(frames, output_dir / "single_transforms.safetensors")
+
+
+def main():
+    dataset = LeRobotDataset(DATASET_REPO_ID, image_transforms=None)
+    output_dir = Path(ARTIFACT_DIR)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    original_frame = dataset[0][dataset.camera_keys[0]]
+
+    save_single_transforms(original_frame, output_dir)
+    save_default_config_transform(original_frame, output_dir)
+
+
+if __name__ == "__main__":
+    main()