def _override_keypoint_masks(keypoint_masks, keypoint_profile, part_names,
overriding_func):
"""Overrides keypoint masks by part.
Args:
keypoint_masks: A tensor for input keypoint masks.
keypoint_profile: A KeypointProfile object for keypoints.
part_names: A list of standard names of parts of which the masks are
overridden. See `KeypointProfile.get_standard_part_index` for standard
part names.
overriding_func: A function that returns overriding tensors.
Returns:
keypoint_masks: A tensor for output keypoint masks.
"""
part_indices = []
for name in part_names:
part_indices.extend(keypoint_profile.get_standard_part_index(name))
part_indices = list(set(part_indices))
keypoint_masks = data_utils.update_sub_tensor(keypoint_masks,
indices=part_indices,
axis=-1,
update_func=overriding_func)
return keypoint_masks
def test_update_sub_tensor(self):
# Shape = [3, 5, 2].
x = tf.constant([
[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0], [6.0, 7.0], [8.0, 9.0]],
[[10.0, 11.0], [12.0, 13.0], [14.0, 15.0], [16.0, 17.0],
[18.0, 19.0]],
[[20.0, 21.0], [22.0, 23.0], [24.0, 25.0], [26.0, 27.0],
[28.0, 29.0]],
])
def update_func(sub_tensor):
# Shape = [3, 3, 2].
delta = tf.constant([[[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]],
[[0.7, 0.8], [0.9, 1.0], [1.1, 1.2]],
[[1.3, 1.4], [1.5, 1.6], [1.7, 1.8]]])
return sub_tensor + delta
updated_x = data_utils.update_sub_tensor(x,
indices=[0, 2, 4],
axis=-2,
update_func=update_func)
self.assertAllClose(updated_x, [
[[0.1, 1.2], [2.0, 3.0], [4.3, 5.4], [6.0, 7.0], [8.5, 9.6]],
[[10.7, 11.8], [12.0, 13.0], [14.9, 16.0], [16.0, 17.0],
[19.1, 20.2]],
[[21.3, 22.4], [22.0, 23.0], [25.5, 26.6], [26.0, 27.0],
[29.7, 30.8]],
])
def override_points(points, from_indices_list, to_indices):
"""Overrides points with other points.
Points at `to_indices` will be overridden with centers of points from
`from_indices_list`.
For example:
from_indices_list = [[0, 1], [2]]
to_indices = [3, 4]
updated_points = override_points(from_indices_list, to_indices)
Will result in:
updated_points[..., 3, :] ==
((points[..., 0, :] + points[..., 1, :]) / 2 + points[..., 2, :]) / 2
updated_points[..., 4, :] ==
((points[..., 0, :] + points[..., 1, :]) / 2 + points[..., 2, :]) / 2
Args:
points: A tensor for points to override. Shape = [..., num_points,
point_dim].
from_indices_list: A list of integer lists for point indices to compute
overriding points.
to_indices: A list of integers for point indices to be overridden.
Returns:
A tensor for updated points.
"""
overriding_points = [
get_points(points, from_indices) for from_indices in from_indices_list
]
overriding_points = tf.concat(overriding_points, axis=-2)
overriding_points = tf.math.reduce_mean(overriding_points,
axis=-2,
keepdims=True)
overriding_points = data_utils.tile_last_dims(
overriding_points, last_dim_multiples=[len(to_indices), 1])
return data_utils.update_sub_tensor(
points,
indices=to_indices,
axis=-2,
update_func=lambda _: overriding_points)
def create_model_input(keypoints_2d,
keypoint_masks_2d,
keypoints_3d,
model_input_keypoint_type,
model_input_keypoint_mask_type=(
common.MODEL_INPUT_KEYPOINT_MASK_TYPE_NO_USE),
normalize_keypoints_2d=True,
keypoint_profile_2d=None,
uniform_keypoint_jittering_max_offset_2d=0.0,
gaussian_keypoint_jittering_offset_stddev_2d=0.0,
keypoint_dropout_probs=(0.0, 0.0),
structured_keypoint_mask_processor=None,
set_on_mask_for_non_anchors=False,
mix_mask_sub_batches=False,
rescale_features=False,
forced_mask_on_part_names=None,
forced_mask_off_part_names=None,
keypoint_profile_3d=None,
azimuth_range=(-math.pi, math.pi),
elevation_range=(-math.pi / 6.0, math.pi / 6.0),
roll_range=(-math.pi / 6.0, math.pi / 6.0),
normalized_camera_depth_range=(),
sequential_inputs=False,
seed=None):
"""Creates model input features from input data.
Args:
keypoints_2d: A tensor for input 2D keyopints. Shape = [..., num_keypoints,
2]. Use None if irrelevant.
keypoint_masks_2d: A tensor for input 2D keypoint masks. Shape = [...,
num_keypoints]. Use None if irrelevant.
keypoints_3d: A tensor for input 3D keyopints. Shape = [..., num_keypoints,
3]. Use None if irrelevant.
model_input_keypoint_type: An enum string for model input type. See
`MODEL_INPUT_TYPE_*` for supported values.
model_input_keypoint_mask_type: An enum string for model input keypoint mask
type. See `MODEL_INPUT_KEYPOINT_MASK_TYPE_*` for supported values.
normalize_keypoints_2d: A boolean for whether to normalize 2D keypoints at
the end.
keypoint_profile_2d: A KeypointProfile2D object for input 2D keypoints.
Required for normalizing 2D keypoints, 3D-to-2D projection, or forcing
masks on/off.
uniform_keypoint_jittering_max_offset_2d: A float for maximum 2D keypoint
jittering offset. Random jittering offset within
[-uniform_keypoint_jittering_max_offset_2d,
uniform_keypoint_jittering_max_offset_2d] is to be added to each keypoint
2D. Note that the jittering happens after the 2D normalization. Ignored if
non-positive.
gaussian_keypoint_jittering_offset_stddev_2d: A float for standard deviation
of Gaussian 2D keypoint jittering offset. Random jittering offset sampled
from N(0, gaussian_keypoint_jittering_offset_stddev_2d) is to be added to
each keypoint. Note that the jittering happens after the 2D normalization.
Ignored if non-positive.
keypoint_dropout_probs: A tuple of floats for the keypoint random dropout
probabilities in the format (probability_to_apply, probability_to_drop).
We perform stratified dropout as first select instances with
`probability_to_apply` and then drop their keypoints with
`probability_to_drop`. When sequential_input is True, there might be a
third element indicating the probability of using sequence-level dropout.
Only used when keypoint scores are relevant.
structured_keypoint_mask_processor: A Python function for generating
keypoint masks with structured dropout. Ignored if None.
set_on_mask_for_non_anchors: A boolean for whether to always use on (1)
masks for non-anchor samples. We assume the second from the left tensor
dimension is for anchor/non-anchor, and the non-anchor samples start at
the second element along that dimension.
mix_mask_sub_batches: A boolean for whether to apply sub-batch mixing to
processed masks and all-one masks.
rescale_features: A boolean for whether to rescale features by the ratio
between total number of mask elements and kept mask elements.
forced_mask_on_part_names: A list of standard names of parts of which the
masks are forced on (by setting value to 1.0). See
`KeypointProfile.get_standard_part_index` for standard part names.
forced_mask_off_part_names: A list of standard names of parts of which the
masks are forced off (by setting value to 0.0). See
`KeypointProfile.get_standard_part_index` for standard part names.
keypoint_profile_3d: A KeypointProfile3D object for input 3D keypoints. Only
used when 3D-to-2D projection is involved.
azimuth_range: A tuple for minimum and maximum azimuth angles to randomly
rotate 3D keypoints with. For non-sequential inputs, a 2-tuple for
(minimum angle, maximum angle) is expected. For sequence inputs, uses
2-tuple to independently sample starting and ending camera angles, or uses
4-tuple for (minimum starting angle, maximum starting angle, minimum angle
increment, maximum angle increment) to first sample starting angles and
add random delta angles to them as ending angles.
elevation_range: A tuple for minimum and maximum elevation angles to
randomly rotate 3D keypoints with. For non-sequential inputs, a 2-tuple
for (minimum angle, maximum angle) is expected. For sequence inputs, uses
2-tuple to independently sample starting and ending camera angles, or uses
4-tuple for (minimum starting angle, maximum starting angle, minimum angle
increment, maximum angle increment) to first sample starting angles and
add random delta angles to them as ending angles.
roll_range: A tuple for minimum and maximum roll angles to randomly rotate
3D keypoints with. For non-sequential inputs, a 2-tuple for (minimum
angle, maximum angle) is expected. For sequence inputs, uses 2-tuple to
independently sample starting and ending camera angles, or uses 4-tuple
for (minimum starting angle, maximum starting angle, minimum angle
increment, maximum angle increment) to first sample starting angles and
add random delta angles to them as ending angles.
normalized_camera_depth_range: A tuple for minimum and maximum normalized
camera depth for random camera augmentation. If empty, uses constant depth
as 1 over the 2D pose normalization scale unit.
sequential_inputs: A boolean flag indicating whether the inputs are
sequential. If True, the input keypoints are supposed to be in shape [...,
sequence_length, num_keypoints, keypoint_dim].
seed: An integer for random seed.
Returns:
features: A tensor for input features. Shape = [..., feature_dim].
side_outputs: A dictionary for side outputs, which includes
`offset_points_2d` (shape = [..., 1, 2]) and `scale_distances_2d` (shape =
[..., 1, 1]) if `normalize_keypoints_2d` is True.
Raises:
ValueError: If `model_input_keypoint_type` is not supported.
ValueError: If `keypoint_dropout_probs` is not of length 2 or 3.
ValueError: If `keypoint_profile_2d` is not specified when normalizing 2D
keypoints.
ValueError: If keypoint profile name is not 'LEGACY_2DCOCO13', '2DSTD13',
or 'INTERNAL_2DSTD13' when applying structured keypoint dropout.
ValueError: If number of instances is not 1 or 2.
ValueError: If `keypoint_profile_2d` is not specified when forcing keypoint
masks on.
"""
keypoints_2d, keypoint_masks_2d = preprocess_keypoints_2d(
keypoints_2d,
keypoint_masks_2d,
keypoints_3d,
model_input_keypoint_type,
keypoint_profile_2d=keypoint_profile_2d,
keypoint_profile_3d=keypoint_profile_3d,
azimuth_range=azimuth_range,
elevation_range=elevation_range,
roll_range=roll_range,
normalized_camera_depth_range=normalized_camera_depth_range,
sequential_inputs=sequential_inputs,
seed=seed)
side_outputs = {}
if len(keypoint_dropout_probs) not in [2, 3]:
raise ValueError('Invalid keypoint dropout probability tuple: `%s`.' %
str(keypoint_dropout_probs))
if keypoint_dropout_probs[0] > 0.0 and keypoint_dropout_probs[1] > 0.0:
instance_keypoint_masks_2d = apply_stratified_instance_keypoint_dropout(
keypoint_masks_2d,
probability_to_apply=keypoint_dropout_probs[0],
probability_to_drop=keypoint_dropout_probs[1],
seed=seed)
if (sequential_inputs and len(keypoint_dropout_probs) == 3
and keypoint_dropout_probs[2] > 0.0):
sequence_keypoint_masks_2d = apply_stratified_sequence_keypoint_dropout(
keypoint_masks_2d,
probability_to_apply=keypoint_dropout_probs[0],
probability_to_drop=keypoint_dropout_probs[1],
seed=seed)
sequence_axis = sequence_keypoint_masks_2d.shape.ndims - 1
keypoint_masks_2d = data_utils.mix_batch(
[sequence_keypoint_masks_2d], [instance_keypoint_masks_2d],
axis=sequence_axis,
keep_lhs_prob=keypoint_dropout_probs[2],
seed=seed)[0]
else:
keypoint_masks_2d = instance_keypoint_masks_2d
if structured_keypoint_mask_processor is not None:
keypoint_masks_2d = structured_keypoint_mask_processor(
keypoint_masks=keypoint_masks_2d,
keypoint_profile=keypoint_profile_2d,
seed=seed)
if normalize_keypoints_2d:
if keypoint_profile_2d is None:
raise ValueError(
'Failed to normalize 2D keypoints due to unspecified '
'keypoint profile.')
keypoints_2d, offset_points, scale_distances = (
keypoint_profile_2d.normalize(keypoints_2d, keypoint_masks_2d))
side_outputs.update({
common.KEY_OFFSET_POINTS_2D: offset_points,
common.KEY_SCALE_DISTANCES_2D: scale_distances
})
if uniform_keypoint_jittering_max_offset_2d > 0.0:
keypoints_2d = _add_uniform_keypoint_jittering(
keypoints_2d,
max_jittering_offset=uniform_keypoint_jittering_max_offset_2d,
seed=seed)
if gaussian_keypoint_jittering_offset_stddev_2d > 0.0:
keypoints_2d = _add_gaussian_keypoint_jittering(
keypoints_2d,
jittering_offset_stddev=
gaussian_keypoint_jittering_offset_stddev_2d,
seed=seed)
if set_on_mask_for_non_anchors:
non_anchor_indices = list(
range(1,
keypoint_masks_2d.shape.as_list()[1]))
if non_anchor_indices:
keypoint_masks_2d = data_utils.update_sub_tensor(
keypoint_masks_2d,
indices=non_anchor_indices,
axis=1,
update_func=tf.ones_like)
if mix_mask_sub_batches:
keypoint_masks_2d = data_utils.mix_batch(
[tf.ones_like(keypoint_masks_2d)], [keypoint_masks_2d], axis=1)[0]
if forced_mask_on_part_names:
keypoint_masks_2d = _override_keypoint_masks(
keypoint_masks_2d,
keypoint_profile=keypoint_profile_2d,
part_names=forced_mask_on_part_names,
overriding_func=tf.ones_like)
if forced_mask_off_part_names:
keypoint_masks_2d = _override_keypoint_masks(
keypoint_masks_2d,
keypoint_profile=keypoint_profile_2d,
part_names=forced_mask_off_part_names,
overriding_func=tf.zeros_like)
if model_input_keypoint_mask_type in [
common.MODEL_INPUT_KEYPOINT_MASK_TYPE_MASK_KEYPOINTS,
common.MODEL_INPUT_KEYPOINT_MASK_TYPE_MASK_KEYPOINTS_AND_AS_INPUT
]:
# Mask out invalid keypoints.
keypoints_2d = tf.where(
data_utils.tile_last_dims(
tf.expand_dims(tf.math.equal(keypoint_masks_2d, 1.0), axis=-1),
last_dim_multiples=[tf.shape(keypoints_2d)[-1]]), keypoints_2d,
tf.zeros_like(keypoints_2d))
side_outputs.update({
common.KEY_PREPROCESSED_KEYPOINTS_2D:
keypoints_2d,
common.KEY_PREPROCESSED_KEYPOINT_MASKS_2D:
keypoint_masks_2d,
})
features = keypoints_2d
if model_input_keypoint_mask_type in [
common.MODEL_INPUT_KEYPOINT_MASK_TYPE_AS_INPUT,
common.MODEL_INPUT_KEYPOINT_MASK_TYPE_MASK_KEYPOINTS_AND_AS_INPUT
]:
features = tf.concat(
[keypoints_2d,
tf.expand_dims(keypoint_masks_2d, axis=-1)],
axis=-1)
if rescale_features:
# Scale up features to compensate for any keypoint masking.
feature_rescales = keypoint_masks_2d.shape.as_list()[-1] / (
tf.math.maximum(
1e-12,
tf.math.reduce_sum(keypoint_masks_2d, axis=-1, keepdims=True)))
features *= tf.expand_dims(feature_rescales, axis=-1)
features = data_utils.flatten_last_dims(features, num_last_dims=2)
return features, side_outputs