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updated CARLA to allow using actions of size 3 + automatic downloading of the CARLA imitation dataset

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itaicaspi-intel
2018-09-16 12:07:11 +03:00
parent d3c8a5d7c1
commit cf892463e2
4 changed files with 106 additions and 32 deletions
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69
rl_coach/utilities/carla_dataset_to_replay_buffer.py
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@@ -15,26 +15,47 @@
#
import argparse
import h5py
import os
import sys
import h5py
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
from rl_coach.utils import ProgressBar, start_shell_command_and_wait
from rl_coach.logger import screen
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()
def maybe_download(dataset_root):
if not dataset_root or not os.path.exists(dataset_root):
screen.log_title("Downloading the CARLA dataset. This might take a while.")
train_set_root = os.path.join(args.dataset_root, 'SeqTrain')
validation_set_root = os.path.join(args.dataset_root, 'SeqVal')
google_drive_download_id = "1hloAeyamYn-H6MfV1dRtY1gJPhkR55sY"
filename_to_save = "datasets/CARLA_dataset.tar.gz"
download_command = 'wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=' \
'$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies ' \
'--no-check-certificate \"https://docs.google.com/uc?export=download&id={}\" -O- | ' \
'sed -rn \'s/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p\')&id={}" -O {} && rm -rf /tmp/cookies.txt'\
.format(google_drive_download_id, google_drive_download_id, filename_to_save)
# start downloading and wait for it to finish
start_shell_command_and_wait(download_command)
screen.log_title("Unzipping the dataset")
unzip_command = 'tar -xzf {}'.format(filename_to_save)
if dataset_root is not None:
unzip_command += " -C {}".format(dataset_root)
start_shell_command_and_wait(unzip_command)
def create_dataset(dataset_root, output_path):
maybe_download(dataset_root)
train_set_root = os.path.join(dataset_root, 'SeqTrain')
validation_set_root = os.path.join(dataset_root, 'SeqVal')
# training set extraction
memory = ExperienceReplay(max_size=(MemoryGranularity.Transitions, sys.maxsize))
@@ -44,11 +65,13 @@ if __name__ == "__main__":
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]
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[:, :2] = actions[:, 1:3]
# actions[:, 2] = train_set['targets'][:, 0] # gas, brake, steer
# actions[:, 1] -= actions[:, 2]
# actions = actions[:, :2][:, ::-1]
high_level_commands = train_set['targets'][:, 24].astype('int') - 2 # follow lane, left, right, straight
@@ -67,5 +90,15 @@ if __name__ == "__main__":
)
memory.store(transition)
progress_bar.close()
print("Saving pickle file to {}".format(args.output_path))
memory.save(args.output_path)
print("Saving pickle file to {}".format(output_path))
memory.save(output_path)
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()
create_dataset(args.dataset_root, args.output_path)