def post_init(self): # add default options
if self.data_directories is None:
self.data_directories = []
assert self.subsample >= 1
if self.input_size is None:
self.input_size = []
if self.hdf5_files is None:
self.hdf5_files = []
if self.data_directories is not None and len(
self.data_directories) != 0:
self.data_directories = [
os.path.join(get_data_dir(os.environ['HOME']), d)
if not d.startswith('/') else d for d in self.data_directories
]
# check if raw_data is a subdirectory of d and fill in raw_data runs if this is the case.
data_directories = []
for d in self.data_directories:
if os.path.isdir(os.path.join(d, 'raw_data')):
data_directories.extend(glob(f'{d}/raw_data/*'))
else:
data_directories.append(d)
self.data_directories = data_directories
if self.hdf5_files is not None and len(self.hdf5_files) != 0:
self.hdf5_files = [
os.path.join(get_data_dir(os.environ['HOME']), hdf5_f)
if not hdf5_f.startswith('/') else hdf5_f
for hdf5_f in self.hdf5_files
]
def test_get_data_dir(self):
# with datadir environment variable
if "DATADIR" not in os.environ.keys():
os.environ["DATADIR"] = '/my/wonderful/data/dir'
self.assertTrue("DATADIR" in os.environ.keys())
result = get_data_dir(os.environ['HOME'])
self.assertTrue(result, os.environ["DATADIR"])
del os.environ['DATADIR']
self.assertFalse("DATADIR" in os.environ.keys())
result = get_data_dir(os.environ['HOME'])
self.assertTrue(result, os.environ["HOME"])
def test_load_imagenet_pretrained_checkpoint(self):
base_config['architecture'] = 'auto_encoder_deeply_supervised'
base_config['batch_normalisation'] = True
network = eval(base_config['architecture']).Net(
config=ArchitectureConfig().create(config_dict=base_config), )
checkpoint = torch.load(
os.path.join(get_data_dir(os.environ['PWD']), 'pretrained_models',
'auto_encoder_deeply_supervised', 'torch_checkpoints',
'checkpoint_latest.ckpt'))
network.load_checkpoint(checkpoint['net_ckpt'])
def adjust_hdf5_files(hdf5_files: List[str]) -> List[str]:
hdf5_files = [
os.path.join(get_data_dir(os.environ['HOME']),
original_hdf5_file) if
not original_hdf5_file.startswith('/') else original_hdf5_file
for original_hdf5_file in hdf5_files
]
new_hdf5_files = [
os.path.join(
self.local_home,
f"{len(original_to_new_location_tuples) + index}.hdf5")
for index in range(len(hdf5_files))
]
original_to_new_location_tuples.extend(
zip(hdf5_files, new_hdf5_files))
return new_hdf5_files
def augment_background_textured(dataset: Dataset, texture_directory: str,
p: float = 0.0, p_empty: float = 0.0,
binary_images: List[np.ndarray] = None) -> Dataset:
"""
parse background and fore ground. Give fore ground random color and background a crop of a textured image.
:param binary_images:
:param p_empty: in case of augmentation with texture, potentially augment without any foreground and empty action map (hacky!)
:param p: probability for each image to be augmented with background
:param dataset: augmented dataset in which observations are adjusted with new back- and foregrounds
:param texture_directory: directory with sub-directories for each texture
:return: augmented dataset
"""
# 1. load texture image paths and keep in RAM
if not texture_directory.startswith('/'):
texture_directory = os.path.join(get_data_dir(os.environ['HOME']), texture_directory)
texture_paths = [os.path.join(texture_directory, sub_directory, image)
for sub_directory in os.listdir(texture_directory)
if os.path.isdir(os.path.join(texture_directory, sub_directory))
for image in os.listdir(os.path.join(texture_directory, sub_directory))
if image.endswith('.jpg')]
assert len(texture_paths) != 0
# 2. parse binary images to extract fore- and background
if binary_images is None:
binary_images = parse_binary_maps(copy.deepcopy(dataset.observations), invert=True)
# 3. combine foreground and background in augmented dataset
augmented_dataset = copy.deepcopy(dataset)
augmented_dataset.observations = []
num_channels = dataset.observations[0].shape[0]
for index, image in tqdm(enumerate(binary_images[:-1])):
if np.random.binomial(1, p):
bg_image = np.random.choice(texture_paths)
bg = load_and_preprocess_file(bg_image,
size=(num_channels, image.shape[0], image.shape[1])).permute(1, 2, 0).numpy()
if not np.random.binomial(1, p_empty):
# blurred_mask = motion_blur_mask(binary_images[index], binary_images[index + 1], num_channels)
blurred_mask = gaussian_blur_mask(binary_images[index], num_channels)
fg = create_random_gradient_image(size=bg.shape, mean=bg.mean())
new_img = torch.as_tensor(((1 - blurred_mask) * bg + blurred_mask * fg)).permute(2, 0, 1)
augmented_dataset.observations.append(new_img)
else: # for ratio p_empty put only background
augmented_dataset.observations.append(torch.as_tensor(bg).permute(2, 0, 1))
augmented_dataset.actions[index] = torch.zeros_like(augmented_dataset.actions[index])
else:
augmented_dataset.observations.append(dataset.observations[index])
return augmented_dataset
def post_init(self): # add default options
if not self.output_path.startswith('/'):
self.output_path = os.path.join(get_data_dir(self.codebase_dir),
self.output_path)
if self.use_singularity: # set singularity directory to gluster or opal
if not self.singularity_dir.startswith('/'):
if os.path.isdir(
'/gluster/visics/kkelchte/singularity_images'):
self.singularity_dir = os.path.join(
'/gluster/visics/kkelchte/singularity_images',
self.singularity_dir)
else:
self.singularity_dir = os.path.join(
'/esat/opal/kkelchte/singularity_images',
self.singularity_dir)
if self.singularity_file == '':
self.singularity_file = sorted(
glob.glob(f'{self.singularity_dir}/*'))[-1]
elif not self.singularity_file.startswith('/'):
self.singularity_file = os.path.join(self.singularity_dir,
self.singularity_file)
assert os.path.isfile(self.singularity_file)
def test_load_dronet_checkpoint(self):
base_config['architecture'] = 'dronet'
network = eval(base_config['architecture']).Net(
config=ArchitectureConfig().create(config_dict=base_config), )
checkpoint = torch.load(
os.path.join(get_data_dir(os.environ['PWD']), 'pretrained_models',
'dronet', 'torch_checkpoints',
'checkpoint_latest.ckpt'))
network.load_checkpoint(checkpoint['net_ckpt'])
self.assertLess(network.conv2d_1.weight.sum().item() - conv2d, 0.001)
self.assertLess(
network.batch_normalization_1.weight.sum().item() -
batch_normalization, 0.001)
self.assertLess(network.conv2d_2.weight.sum().item() - conv2d_1, 0.001)
self.assertLess(
network.batch_normalization_2.weight.sum().item() -
batch_normalization_1, 0.001)
self.assertLess(network.conv2d_4.weight.sum().item() - conv2d_3, 0.001)
self.assertLess(network.conv2d_3.weight.sum().item() - conv2d_2, 0.001)
self.assertLess(
network.batch_normalization_3.weight.sum().item() -
batch_normalization_2, 0.001)
self.assertLess(network.conv2d_5.weight.sum().item() - conv2d_4, 0.001)
self.assertLess(
network.batch_normalization_4.weight.sum().item() -
batch_normalization_3, 0.001)
self.assertLess(network.conv2d_7.weight.sum().item() - conv2d_6, 0.001)
self.assertLess(network.conv2d_6.weight.sum().item() - conv2d_5, 0.001)
self.assertLess(
network.batch_normalization_5.weight.sum().item() -
batch_normalization_4, 0.001)
self.assertLess(network.conv2d_8.weight.sum().item() - conv2d_7, 0.001)
self.assertLess(
network.batch_normalization_6.weight.sum().item() -
batch_normalization_5, 0.001)
self.assertLess(network.conv2d_10.weight.sum().item() - conv2d_9,
0.001)
self.assertLess(network.conv2d_9.weight.sum().item() - conv2d_8, 0.001)
cprint(f'Terminated successfully? {bool(result)}', self._logger,
msg_type=MessageType.info if result else MessageType.warning)
if self._data_saver is not None:
self._data_saver.remove()
if self._trainer is not None:
self._trainer.remove()
if self._evaluator is not None:
self._evaluator.remove()
if self._net is not None:
self._net.remove()
if self._episode_runner is not None:
self._episode_runner.remove()
[h.close() for h in self._logger.handlers]
if __name__ == "__main__":
arguments = Parser().parse_args()
config_file = arguments.config
if arguments.rm:
with open(config_file, 'r') as f:
configuration = yaml.load(f, Loader=yaml.FullLoader)
if not configuration['output_path'].startswith('/'):
configuration['output_path'] = os.path.join(get_data_dir(os.environ['HOME']), configuration['output_path'])
shutil.rmtree(configuration['output_path'], ignore_errors=True)
experiment_config = ExperimentConfig().create(config_file=config_file,
seed=arguments.seed)
experiment = Experiment(experiment_config)
experiment.run()
experiment.shutdown()