initial CIL implementation (WIP)

rl_coach/utilities/carla_dataset_to_replay_buffer.py

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+#
+# Copyright (c) 2017 Intel Corporation
+#
+# 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.
+#
+
+import argparse
+
+import h5py
+import os
+import sys
+import numpy as np
+from rl_coach.utils import ProgressBar
+from rl_coach.core_types import Transition
+from rl_coach.memories.memory import MemoryGranularity
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplay
+
+
+if __name__ == "__main__":
+    argparser = argparse.ArgumentParser(description=__doc__)
+    argparser.add_argument('-d', '--dataset_root', help='The path to the CARLA dataset root folder')
+    argparser.add_argument('-o', '--output_path', help='The path to save the resulting replay buffer',
+                           default='carla_train_set_replay_buffer.p')
+    args = argparser.parse_args()
+
+    train_set_root = os.path.join(args.dataset_root, 'SeqTrain')
+    validation_set_root = os.path.join(args.dataset_root, 'SeqVal')
+
+    # training set extraction
+    memory = ExperienceReplay(max_size=(MemoryGranularity.Transitions, sys.maxsize))
+    train_set_files = sorted(os.listdir(train_set_root))
+    print("found {} files".format(len(train_set_files)))
+    progress_bar = ProgressBar(len(train_set_files))
+    for file_idx, file in enumerate(train_set_files[:3000]):
+        progress_bar.update(file_idx, "extracting file {}".format(file))
+        train_set = h5py.File(os.path.join(train_set_root, file), 'r')
+        observations = train_set['rgb'][:]                                   # forward camera
+        measurements = np.expand_dims(train_set['targets'][:, 10], -1)       # forward speed
+        actions = train_set['targets'][:, :3]                                # steer, gas, brake
+        actions[:, 1] -= actions[:, 2]
+        actions = actions[:, :2][:, ::-1]
+
+        high_level_commands = train_set['targets'][:, 24].astype('int') - 2  # follow lane, left, right, straight
+
+        file_length = train_set['rgb'].len()
+        assert train_set['rgb'].len() == train_set['targets'].len()
+
+        for transition_idx in range(file_length):
+            transition = Transition(
+                state={
+                    'forward_camera': observations[transition_idx],
+                    'measurements': measurements[transition_idx],
+                    'high_level_command': high_level_commands[transition_idx]
+                },
+                action=actions[transition_idx],
+                reward=0
+            )
+            memory.store(transition)
+    progress_bar.close()
+    print("Saving pickle file to {}".format(args.output_path))
+    memory.save(args.output_path)