def test_kitti_eval(self):
dataset = kitti.make_dataset(
path=dataset_locations['kitti15-train-pairs'],
mode='eval')
dataset = dataset.prefetch(1)
dataset = dataset.batch(1)
results = kitti.evaluate(mock_inference_fn, dataset, height=320,
width=320)
expected_keys = kitti.list_eval_keys()
self.assertEqual(set(expected_keys), set(results.keys()))
def test_kitti(self):
for dataset in ['kitti15-train-pairs', 'kitti15-test-pairs']:
size = DATASETS_AND_SIZE[dataset]
path = dataset_locations[dataset]
ds = kitti.make_dataset(path, mode='eval')
self._check_size(ds, size)
def make_train_iterator(
train_on,
height,
width,
shuffle_buffer_size,
batch_size,
seq_len,
crop_instead_of_resize=False,
apply_augmentation=True,
include_ground_truth=False,
resize_gt_flow=True,
include_occlusions=False,
seed=41,
mode='train',
):
"""Build joint training iterator for all data in train_on.
Args:
train_on: string of the format 'format0:path0;format1:path1', e.g.
'kitti:/usr/local/home/...'.
height: int, height to which the images will be resized or cropped.
width: int, width to which the images will be resized or cropped.
shuffle_buffer_size: int, size that will be used for the shuffle buffer.
batch_size: int, batch size for the iterator.
seq_len: int, number of frames per sequences (at the moment this should
always be 2)
crop_instead_of_resize: bool, indicates if cropping should be used instead
of resizing
apply_augmentation: bool, indicates if geometric and photometric data
augmentation shall be activated (paramaters are gin configurable)
include_ground_truth: bool, if True, return ground truth optical flow with
the training images. This only exists for some datasets (Kitti, Sintel).
resize_gt_flow: bool, indicates if ground truth flow should be resized (only
important if resizing and supervised training is used)
include_occlusions: bool, indicates if ground truth occlusions should be
loaded (currently not supported in combination with augmentation)
seed: A seed for a random number generator, controls shuffling of data.
mode: str, will be passed on to the data iterator class. Can be used to
specify different settings within the data iterator.
Returns:
A tf.data.Iterator that produces batches of images of shape [batch
size, sequence length=3, height, width, channels=3]
"""
train_datasets = []
# Split strings according to pattern "format0:path0;format1:path1".
for format_and_path in train_on.split(';'):
data_format, path = format_and_path.split(':')
if include_occlusions:
mode += '-include-occlusions'
if include_ground_truth:
mode += '-supervised'
if include_occlusions and 'sintel' not in data_format:
raise ValueError(
'The parameter include_occlusions is only supported for'
'sintel data.')
if include_ground_truth and ('chairs' not in data_format
and 'sintel' not in data_format
and 'kitti' not in data_format):
raise NotImplementedError(
'The parameter include_ground_truth is only'
'supported for flying_chairs, sintel, kitti and'
'wod data at the moment.')
# Add a dataset based on format and path.
if 'kitti' in data_format:
dataset = kitti.make_dataset(
path,
mode=mode,
seq_len=seq_len,
shuffle_buffer_size=shuffle_buffer_size,
height=None if crop_instead_of_resize else height,
width=None if crop_instead_of_resize else width,
resize_gt_flow=resize_gt_flow,
seed=seed,
)
elif 'chairs' in data_format:
dataset = flow_dataset.make_dataset(
path,
mode=mode,
seq_len=seq_len,
shuffle_buffer_size=shuffle_buffer_size,
height=None if crop_instead_of_resize else height,
width=None if crop_instead_of_resize else width,
resize_gt_flow=resize_gt_flow,
gt_flow_shape=[384, 512, 2],
seed=seed,
)
elif 'sintel' in data_format:
dataset = sintel.make_dataset(
path,
mode=mode,
seq_len=seq_len,
shuffle_buffer_size=shuffle_buffer_size,
height=None if crop_instead_of_resize else height,
width=None if crop_instead_of_resize else width,
resize_gt_flow=resize_gt_flow,
seed=seed,
)
else:
print('Unknown data format "{}"'.format(data_format))
continue
train_datasets.append(dataset)
# prepare augmentation function
# in case no crop is desired set it to the size images have been resized to
# This will fail if none or both are specified.
augmentation_fn = partial(uflow_augmentation.apply_augmentation,
crop_height=height,
crop_width=width)
# returns a function to apply ensure_shape on all the available data
def _ensure_shapes():
# shape of the data
imgs_shape = (batch_size, seq_len, height, width, 3)
if resize_gt_flow:
flow_shape = (batch_size, height, width, 2)
valid_shape = (batch_size, height, width, 1)
else:
flow_shape = (batch_size, None, None, 2)
valid_shape = (batch_size, None, None, 1)
occ_shape = (batch_size, height, width, 1)
# different cases of data combinations
if include_ground_truth and apply_augmentation:
return lambda imgs, imgs_na, flow, valid: (tf.ensure_shape(
imgs, imgs_shape), {
'images_without_photo_aug':
tf.ensure_shape(imgs_na, imgs_shape),
'flow_uv':
tf.ensure_shape(flow, flow_shape),
'flow_valid':
tf.ensure_shape(valid, valid_shape)
})
elif include_ground_truth and include_occlusions:
return lambda imgs, flow, valid, occ: (
tf.ensure_shape(imgs, imgs_shape), {
'flow_uv': tf.ensure_shape(flow, flow_shape),
'flow_valid': tf.ensure_shape(valid, valid_shape),
'occlusions': tf.ensure_shape(occ, occ_shape)
})
elif include_ground_truth:
return lambda imgs, flow, valid: (
tf.ensure_shape(imgs, imgs_shape), {
'flow_uv': tf.ensure_shape(flow, flow_shape),
'flow_valid': tf.ensure_shape(valid, valid_shape)
})
elif include_occlusions:
return lambda imgs, occ: (tf.ensure_shape(imgs, imgs_shape), {
'occlusions':
tf.ensure_shape(occ, occ_shape)
})
elif apply_augmentation:
return lambda imgs, imgs_na: (tf.ensure_shape(imgs, imgs_shape), {
'images_without_photo_aug':
tf.ensure_shape(imgs_na, imgs_shape)
})
else:
return lambda imgs: (tf.ensure_shape(imgs, imgs_shape), {})
train_ds = train_datasets[0]
# Perform data augmentation
# This cannot handle occlusions at the moment.
if apply_augmentation:
train_ds = train_ds.map(augmentation_fn)
train_ds = train_ds.batch(batch_size)
train_ds = train_ds.prefetch(1)
train_ds = train_ds.map(_ensure_shapes())
train_it = tf.compat.v1.data.make_one_shot_iterator(train_ds)
return train_it
def make_eval_function(eval_on, height, width, progress_bar, plot_dir,
num_plots):
"""Build an evaluation function for uflow.
Args:
eval_on: string of the format 'format0:path0;format1:path1', e.g.
'kitti:/usr/local/home/...'.
height: int, the height to which the images should be resized for inference.
width: int, the width to which the images should be resized for inference.
progress_bar: boolean, flag to indicate whether the function should print a
progress_bar during evaluaton.
plot_dir: string, optional path to a directory in which plots are saved (if
num_plots > 0).
num_plots: int, maximum number of qualitative results to plot for the
evaluation.
Returns:
A pair consisting of an evaluation function and a list of strings
that holds the keys of the evaluation result.
"""
eval_functions_and_datasets = []
eval_keys = []
# Split strings according to pattern "format0:path0;format1:path1".
for format_and_path in eval_on.split(';'):
data_format, path = format_and_path.split(':')
# Add a dataset based on format and path.
if 'kitti' in data_format:
if 'benchmark' in data_format:
dataset = kitti.make_dataset(path, mode='test')
eval_fn = kitti.benchmark
else:
dataset = kitti.make_dataset(path, mode='eval')
eval_fn = partial(kitti.evaluate, prefix=data_format)
eval_keys += kitti.list_eval_keys(prefix=data_format)
elif 'chairs' in data_format or 'custom' in data_format:
dataset = flow_dataset.make_dataset(path, mode='eval')
eval_fn = partial(
flow_dataset.evaluate,
prefix=data_format,
max_num_evals=
1000, # We do this to avoid evaluating on 22k samples.
has_occlusion=False)
eval_keys += flow_dataset.list_eval_keys(prefix=data_format)
elif 'sintel' in data_format:
if 'benchmark' in data_format:
# pylint:disable=g-long-lambda
# pylint:disable=cell-var-from-loop
eval_fn = lambda uflow: sintel.benchmark(inference_fn=uflow.
infer,
height=height,
width=width,
sintel_path=path,
plot_dir=plot_dir,
num_plots=num_plots)
if len(eval_on.split(';')) != 1:
raise ValueError(
'Sintel benchmark should be done in isolation.')
return eval_fn, []
dataset = sintel.make_dataset(path, mode='eval-occlusion')
eval_fn = partial(sintel.evaluate, prefix=data_format)
eval_keys += sintel.list_eval_keys(prefix=data_format)
else:
print('Unknown data format "{}"'.format(data_format))
continue
dataset = dataset.prefetch(1)
eval_functions_and_datasets.append((eval_fn, dataset))
# Make an eval function that aggregates all evaluations.
def eval_function(uflow):
result = dict()
for eval_fn, ds in eval_functions_and_datasets:
results = eval_fn(uflow.infer, ds, height, width, progress_bar,
plot_dir, num_plots)
for k, v in results.items():
result[k] = v
return result
return eval_function, eval_keys
