def graph_fn():
keypoint_visibilities = tf.constant([[True, True, False],
[False, True, False]])
per_keypoint_weights = [1.0, 2.0, 3.0]
keypoint_weights = keypoint_ops.keypoint_weights_from_visibilities(
keypoint_visibilities, per_keypoint_weights)
return keypoint_weights
def test_keypoint_weights_from_visibilities_no_per_kpt_weights(self):
keypoint_visibilities = tf.constant([[True, True, False],
[False, True, False]])
keypoint_weights = keypoint_ops.keypoint_weights_from_visibilities(
keypoint_visibilities)
expected_keypoint_weights = [[1.0, 1.0, 0.0], [0.0, 1.0, 0.0]]
with self.test_session() as sess:
output = sess.run(keypoint_weights)
self.assertAllClose(expected_keypoint_weights, output)
def graph_fn():
keypoint_visibilities = tf.constant([[True, True, False],
[False, True, False]])
keypoint_weights = keypoint_ops.keypoint_weights_from_visibilities(
keypoint_visibilities)
return keypoint_weights
def transform_input_data(tensor_dict,
model_preprocess_fn,
image_resizer_fn,
num_classes,
data_augmentation_fn=None,
merge_multiple_boxes=False,
retain_original_image=False,
use_multiclass_scores=False,
use_bfloat16=False,
retain_original_image_additional_channels=False,
keypoint_type_weight=None):
"""A single function that is responsible for all input data transformations.
Data transformation functions are applied in the following order.
1. If key fields.InputDataFields.image_additional_channels is present in
tensor_dict, the additional channels will be merged into
fields.InputDataFields.image.
2. data_augmentation_fn (optional): applied on tensor_dict.
3. model_preprocess_fn: applied only on image tensor in tensor_dict.
4. keypoint_type_weight (optional): If groundtruth keypoints are in
the tensor dictionary, per-keypoint weights are produced. These weights are
initialized by `keypoint_type_weight` (or ones if left None).
Then, for all keypoints that are not visible, the weights are set to 0 (to
avoid penalizing the model in a loss function).
5. image_resizer_fn: applied on original image and instance mask tensor in
tensor_dict.
6. one_hot_encoding: applied to classes tensor in tensor_dict.
7. merge_multiple_boxes (optional): when groundtruth boxes are exactly the
same they can be merged into a single box with an associated k-hot class
label.
Args:
tensor_dict: dictionary containing input tensors keyed by
fields.InputDataFields.
model_preprocess_fn: model's preprocess function to apply on image tensor.
This function must take in a 4-D float tensor and return a 4-D preprocess
float tensor and a tensor containing the true image shape.
image_resizer_fn: image resizer function to apply on groundtruth instance
`masks. This function must take a 3-D float tensor of an image and a 3-D
tensor of instance masks and return a resized version of these along with
the true shapes.
num_classes: number of max classes to one-hot (or k-hot) encode the class
labels.
data_augmentation_fn: (optional) data augmentation function to apply on
input `tensor_dict`.
merge_multiple_boxes: (optional) whether to merge multiple groundtruth boxes
and classes for a given image if the boxes are exactly the same.
retain_original_image: (optional) whether to retain original image in the
output dictionary.
use_multiclass_scores: whether to use multiclass scores as class targets
instead of one-hot encoding of `groundtruth_classes`. When
this is True and multiclass_scores is empty, one-hot encoding of
`groundtruth_classes` is used as a fallback.
use_bfloat16: (optional) a bool, whether to use bfloat16 in training.
retain_original_image_additional_channels: (optional) Whether to retain
original image additional channels in the output dictionary.
keypoint_type_weight: A list (of length num_keypoints) containing
groundtruth loss weights to use for each keypoint. If None, will use a
weight of 1.
Returns:
A dictionary keyed by fields.InputDataFields containing the tensors obtained
after applying all the transformations.
"""
out_tensor_dict = tensor_dict.copy()
if fields.InputDataFields.multiclass_scores in out_tensor_dict:
out_tensor_dict[
fields.InputDataFields.
multiclass_scores] = _multiclass_scores_or_one_hot_labels(
out_tensor_dict[fields.InputDataFields.multiclass_scores],
out_tensor_dict[fields.InputDataFields.groundtruth_boxes],
out_tensor_dict[fields.InputDataFields.groundtruth_classes],
num_classes)
if fields.InputDataFields.groundtruth_boxes in out_tensor_dict:
out_tensor_dict = util_ops.filter_groundtruth_with_nan_box_coordinates(
out_tensor_dict)
out_tensor_dict = util_ops.filter_unrecognized_classes(out_tensor_dict)
if retain_original_image:
out_tensor_dict[fields.InputDataFields.original_image] = tf.cast(
image_resizer_fn(out_tensor_dict[fields.InputDataFields.image],
None)[0], tf.uint8)
if fields.InputDataFields.image_additional_channels in out_tensor_dict:
channels = out_tensor_dict[
fields.InputDataFields.image_additional_channels]
out_tensor_dict[fields.InputDataFields.image] = tf.concat(
[out_tensor_dict[fields.InputDataFields.image], channels], axis=2)
if retain_original_image_additional_channels:
out_tensor_dict[
fields.InputDataFields.image_additional_channels] = tf.cast(
image_resizer_fn(channels, None)[0], tf.uint8)
# Apply data augmentation ops.
if data_augmentation_fn is not None:
out_tensor_dict = data_augmentation_fn(out_tensor_dict)
# Apply model preprocessing ops and resize instance masks.
image = out_tensor_dict[fields.InputDataFields.image]
preprocessed_resized_image, true_image_shape = model_preprocess_fn(
tf.expand_dims(tf.cast(image, dtype=tf.float32), axis=0))
preprocessed_shape = tf.shape(preprocessed_resized_image)
new_height, new_width = preprocessed_shape[1], preprocessed_shape[2]
im_box = tf.stack([
0.0, 0.0,
tf.to_float(new_height) / tf.to_float(true_image_shape[0, 0]),
tf.to_float(new_width) / tf.to_float(true_image_shape[0, 1])
])
if fields.InputDataFields.groundtruth_boxes in tensor_dict:
bboxes = out_tensor_dict[fields.InputDataFields.groundtruth_boxes]
boxlist = box_list.BoxList(bboxes)
realigned_bboxes = box_list_ops.change_coordinate_frame(
boxlist, im_box)
out_tensor_dict[
fields.InputDataFields.groundtruth_boxes] = realigned_bboxes.get()
if fields.InputDataFields.groundtruth_keypoints in tensor_dict:
keypoints = out_tensor_dict[
fields.InputDataFields.groundtruth_keypoints]
realigned_keypoints = keypoint_ops.change_coordinate_frame(
keypoints, im_box)
out_tensor_dict[
fields.InputDataFields.groundtruth_keypoints] = realigned_keypoints
flds_gt_kpt = fields.InputDataFields.groundtruth_keypoints
flds_gt_kpt_vis = fields.InputDataFields.groundtruth_keypoint_visibilities
flds_gt_kpt_weights = fields.InputDataFields.groundtruth_keypoint_weights
if flds_gt_kpt_vis not in out_tensor_dict:
out_tensor_dict[flds_gt_kpt_vis] = tf.ones_like(
out_tensor_dict[flds_gt_kpt][:, :, 0], dtype=tf.bool)
out_tensor_dict[flds_gt_kpt_weights] = (
keypoint_ops.keypoint_weights_from_visibilities(
out_tensor_dict[flds_gt_kpt_vis], keypoint_type_weight))
if use_bfloat16:
preprocessed_resized_image = tf.cast(preprocessed_resized_image,
tf.bfloat16)
out_tensor_dict[fields.InputDataFields.image] = tf.squeeze(
preprocessed_resized_image, axis=0)
out_tensor_dict[fields.InputDataFields.true_image_shape] = tf.squeeze(
true_image_shape, axis=0)
if fields.InputDataFields.groundtruth_instance_masks in out_tensor_dict:
masks = out_tensor_dict[
fields.InputDataFields.groundtruth_instance_masks]
_, resized_masks, _ = image_resizer_fn(image, masks)
if use_bfloat16:
resized_masks = tf.cast(resized_masks, tf.bfloat16)
out_tensor_dict[
fields.InputDataFields.groundtruth_instance_masks] = resized_masks
label_offset = 1
zero_indexed_groundtruth_classes = out_tensor_dict[
fields.InputDataFields.groundtruth_classes] - label_offset
if use_multiclass_scores:
out_tensor_dict[
fields.InputDataFields.groundtruth_classes] = out_tensor_dict[
fields.InputDataFields.multiclass_scores]
else:
out_tensor_dict[
fields.InputDataFields.groundtruth_classes] = tf.one_hot(
zero_indexed_groundtruth_classes, num_classes)
out_tensor_dict.pop(fields.InputDataFields.multiclass_scores, None)
if fields.InputDataFields.groundtruth_confidences in out_tensor_dict:
groundtruth_confidences = out_tensor_dict[
fields.InputDataFields.groundtruth_confidences]
# Map the confidences to the one-hot encoding of classes
out_tensor_dict[fields.InputDataFields.groundtruth_confidences] = (
tf.reshape(groundtruth_confidences, [-1, 1]) *
out_tensor_dict[fields.InputDataFields.groundtruth_classes])
else:
groundtruth_confidences = tf.ones_like(
zero_indexed_groundtruth_classes, dtype=tf.float32)
out_tensor_dict[fields.InputDataFields.groundtruth_confidences] = (
out_tensor_dict[fields.InputDataFields.groundtruth_classes])
if merge_multiple_boxes:
merged_boxes, merged_classes, merged_confidences, _ = (
util_ops.merge_boxes_with_multiple_labels(
out_tensor_dict[fields.InputDataFields.groundtruth_boxes],
zero_indexed_groundtruth_classes, groundtruth_confidences,
num_classes))
merged_classes = tf.cast(merged_classes, tf.float32)
out_tensor_dict[
fields.InputDataFields.groundtruth_boxes] = merged_boxes
out_tensor_dict[
fields.InputDataFields.groundtruth_classes] = merged_classes
out_tensor_dict[fields.InputDataFields.groundtruth_confidences] = (
merged_confidences)
if fields.InputDataFields.groundtruth_boxes in out_tensor_dict:
out_tensor_dict[
fields.InputDataFields.num_groundtruth_boxes] = tf.shape(
out_tensor_dict[fields.InputDataFields.groundtruth_boxes])[0]
return out_tensor_dict