def testAnchorRpnSample(self, num_anchors, num_positives, num_negatives,
expected_positives, expected_negatives,
image_size):
match_results_np = np.empty([num_anchors])
match_results_np.fill(-2)
match_results_np[:num_positives] = 0
match_results_np[num_positives:num_positives + num_negatives] = -1
match_results = tf.convert_to_tensor(value=match_results_np,
dtype=tf.int32)
anchor_labeler = anchor.RpnAnchorLabeler(match_threshold=0.7,
unmatched_threshold=0.3,
rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5)
rpn_sample_op = anchor_labeler._get_rpn_samples(match_results)
labels = [v.numpy() for v in rpn_sample_op]
self.assertLen(labels[0], num_anchors)
positives = np.sum(np.array(labels[0]) == 1)
negatives = np.sum(np.array(labels[0]) == 0)
self.assertEqual(positives, expected_positives)
self.assertEqual(negatives, expected_negatives)
def _parse_train_data(self, data):
"""Parses data for training.
Args:
data: the decoded tensor dictionary from TfExampleDecoder.
Returns:
image: image tensor that is preproessed to have normalized value and
dimension [output_size[0], output_size[1], 3]
labels: a dictionary of tensors used for training. The following describes
{key: value} pairs in the dictionary.
image_info: a 2D `Tensor` that encodes the information of the image and
the applied preprocessing. It is in the format of
[[original_height, original_width], [scaled_height, scaled_width],
anchor_boxes: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, 4] representing anchor boxes at each level.
rpn_score_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location]. The height_l and
width_l represent the dimension of class logits at l-th level.
rpn_box_targets: ordered dictionary with keys
[min_level, min_level+1, ..., max_level]. The values are tensor with
shape [height_l, width_l, anchors_per_location * 4]. The height_l and
width_l represent the dimension of bounding box regression output at
l-th level.
gt_boxes: Groundtruth bounding box annotations. The box is represented
in [y1, x1, y2, x2] format. The coordinates are w.r.t the scaled
image that is fed to the network. The tennsor is padded with -1 to
the fixed dimension [self._max_num_instances, 4].
gt_classes: Groundtruth classes annotations. The tennsor is padded
with -1 to the fixed dimension [self._max_num_instances].
gt_masks: groundtrugh masks cropped by the bounding box and
resized to a fixed size determined by mask_crop_size.
"""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
if self._include_mask:
masks = data['groundtruth_instance_masks']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training:
num_groundtruths = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtruths, is_crowds]):
indices = tf.cond(
tf.greater(tf.size(is_crowds), 0),
lambda: tf.where(tf.logical_not(is_crowds))[:, 0],
lambda: tf.cast(tf.range(num_groundtruths), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
if self._include_mask:
masks = tf.gather(masks, indices)
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(image)[0:2]
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
if self._include_mask:
image, boxes, masks = preprocess_ops.random_horizontal_flip(
image, boxes, masks)
else:
image, boxes, _ = preprocess_ops.random_horizontal_flip(
image, boxes)
# Converts boxes from normalized coordinates to pixel coordinates.
# Now the coordinates of boxes are w.r.t. the original image.
boxes = box_ops.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = preprocess_ops.resize_and_crop_image(
image,
self._output_size,
padded_size=preprocess_ops.compute_padded_size(
self._output_size, 2**self._max_level),
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
image_height, image_width, _ = image.get_shape().as_list()
# Resizes and crops boxes.
# Now the coordinates of boxes are w.r.t the scaled image.
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = preprocess_ops.resize_and_crop_boxes(boxes, image_scale,
image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_ops.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
if self._include_mask:
masks = tf.gather(masks, indices)
# Transfer boxes to the original image space and do normalization.
cropped_boxes = boxes + tf.tile(tf.expand_dims(offset, axis=0),
[1, 2])
cropped_boxes /= tf.tile(tf.expand_dims(image_scale, axis=0),
[1, 2])
cropped_boxes = box_ops.normalize_boxes(cropped_boxes, image_shape)
num_masks = tf.shape(masks)[0]
masks = tf.image.crop_and_resize(
tf.expand_dims(masks, axis=-1),
cropped_boxes,
box_indices=tf.range(num_masks, dtype=tf.int32),
crop_size=[self._mask_crop_size, self._mask_crop_size],
method='bilinear')
masks = tf.squeeze(masks, axis=-1)
# Assigns anchor targets.
# Note that after the target assignment, box targets are absolute pixel
# offsets w.r.t. the scaled image.
input_anchor = anchor.build_anchor_generator(
min_level=self._min_level,
max_level=self._max_level,
num_scales=self._num_scales,
aspect_ratios=self._aspect_ratios,
anchor_size=self._anchor_size)
anchor_boxes = input_anchor(image_size=(image_height, image_width))
anchor_labeler = anchor.RpnAnchorLabeler(self._rpn_match_threshold,
self._rpn_unmatched_threshold,
self._rpn_batch_size_per_im,
self._rpn_fg_fraction)
rpn_score_targets, rpn_box_targets = anchor_labeler.label_anchors(
anchor_boxes, boxes,
tf.cast(tf.expand_dims(classes, axis=-1), dtype=tf.float32))
# Casts input image to self._dtype
image = tf.cast(image, dtype=self._dtype)
# Packs labels for model_fn outputs.
labels = {
'anchor_boxes':
anchor_boxes,
'image_info':
image_info,
'rpn_score_targets':
rpn_score_targets,
'rpn_box_targets':
rpn_box_targets,
'gt_boxes':
preprocess_ops.clip_or_pad_to_fixed_size(boxes,
self._max_num_instances,
-1),
'gt_classes':
preprocess_ops.clip_or_pad_to_fixed_size(classes,
self._max_num_instances,
-1),
}
if self._include_mask:
labels['gt_masks'] = preprocess_ops.clip_or_pad_to_fixed_size(
masks, self._max_num_instances, -1)
return image, labels