def _augment_image(self,
image,
boxes,
classes,
is_crowd,
area,
xs=0.0,
ys=0.0,
cut=None):
"""Process a single image prior to the application of patching."""
if self._random_flip:
# Randomly flip the image horizontally.
image, boxes, _ = preprocess_ops.random_horizontal_flip(
image, boxes, seed=self._seed)
# Augment the image without resizing
image, infos, crop_points = preprocessing_ops.resize_and_jitter_image(
image, [self._output_size[0], self._output_size[1]],
random_pad=False,
letter_box=self._letter_box,
jitter=self._random_crop,
shiftx=xs,
shifty=ys,
cut=cut,
seed=self._seed)
# Clip and clean boxes.
boxes, inds = preprocessing_ops.transform_and_clip_boxes(
boxes,
infos,
area_thresh=self._area_thresh,
shuffle_boxes=False,
filter_and_clip_boxes=True,
seed=self._seed)
classes, is_crowd, area = self._select_ind(inds, classes, is_crowd,
area) # pylint:disable=unbalanced-tuple-unpacking
return image, boxes, classes, is_crowd, area, crop_points
def _parse_data(self, data, is_training):
image = data['image']
if self._augmenter is not None and is_training:
image = self._augmenter.distort(image)
image = preprocess_ops.normalize_image(image)
category_mask = tf.cast(
data['groundtruth_panoptic_category_mask'][:, :, 0],
dtype=tf.float32)
instance_mask = tf.cast(
data['groundtruth_panoptic_instance_mask'][:, :, 0],
dtype=tf.float32)
# Flips image randomly during training.
if self._aug_rand_hflip and is_training:
masks = tf.stack([category_mask, instance_mask], axis=0)
image, _, masks = preprocess_ops.random_horizontal_flip(
image=image, masks=masks)
category_mask = masks[0]
instance_mask = masks[1]
# Resizes and crops image.
image, image_info = preprocess_ops.resize_and_crop_image(
image,
self._output_size,
self._output_size,
aug_scale_min=self._aug_scale_min if is_training else 1.0,
aug_scale_max=self._aug_scale_max if is_training else 1.0)
category_mask = self._resize_and_crop_mask(category_mask,
image_info,
is_training=is_training)
instance_mask = self._resize_and_crop_mask(instance_mask,
image_info,
is_training=is_training)
(instance_centers_heatmap, instance_centers_offset,
semantic_weights) = self._encode_centers_and_offets(
instance_mask=instance_mask[:, :, 0])
# Cast image and labels as self._dtype
image = tf.cast(image, dtype=self._dtype)
category_mask = tf.cast(category_mask, dtype=self._dtype)
instance_mask = tf.cast(instance_mask, dtype=self._dtype)
instance_centers_heatmap = tf.cast(instance_centers_heatmap,
dtype=self._dtype)
instance_centers_offset = tf.cast(instance_centers_offset,
dtype=self._dtype)
valid_mask = tf.not_equal(category_mask, self._ignore_label)
things_mask = tf.not_equal(instance_mask, self._ignore_label)
labels = {
'category_mask': category_mask,
'instance_mask': instance_mask,
'instance_centers_heatmap': instance_centers_heatmap,
'instance_centers_offset': instance_centers_offset,
'semantic_weights': semantic_weights,
'valid_mask': valid_mask,
'things_mask': things_mask,
'image_info': image_info
}
return image, labels
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: Groundtruth masks cropped by the bounding box and
resized to a fixed size determined by mask_crop_size.
gt_segmentation_mask: Groundtruth mask for segmentation head, this is
resized to a fixed size determined by output_size.
gt_segmentation_valid_mask: Binary mask that marks the pixels that
are supposed to be used in computing the segmentation loss while
training.
"""
segmentation_mask = data['groundtruth_segmentation_mask']
# Flips image randomly during training.
if self.aug_rand_hflip:
masks = data['groundtruth_instance_masks']
image_mask = tf.concat([data['image'], segmentation_mask], axis=2)
image_mask, boxes, masks = preprocess_ops.random_horizontal_flip(
image_mask, data['groundtruth_boxes'], masks)
segmentation_mask = image_mask[:, :, -1:]
image = image_mask[:, :, :-1]
data['image'] = image
data['groundtruth_boxes'] = boxes
data['groundtruth_instance_masks'] = masks
image, labels = super(Parser, self)._parse_train_data(data)
image_info = labels['image_info']
image_scale = image_info[2, :]
offset = image_info[3, :]
segmentation_mask = tf.reshape(
segmentation_mask, shape=[1, data['height'], data['width']])
segmentation_mask = tf.cast(segmentation_mask, tf.float32)
# Pad label and make sure the padded region assigned to the ignore label.
# The label is first offset by +1 and then padded with 0.
segmentation_mask += 1
segmentation_mask = tf.expand_dims(segmentation_mask, axis=3)
segmentation_mask = preprocess_ops.resize_and_crop_masks(
segmentation_mask, image_scale, self._output_size, offset)
segmentation_mask -= 1
segmentation_mask = tf.where(
tf.equal(segmentation_mask, -1),
self._segmentation_ignore_label * tf.ones_like(segmentation_mask),
segmentation_mask)
segmentation_mask = tf.squeeze(segmentation_mask, axis=0)
segmentation_valid_mask = tf.not_equal(
segmentation_mask, self._segmentation_ignore_label)
labels.update({
'gt_segmentation_mask': segmentation_mask,
'gt_segmentation_valid_mask': segmentation_valid_mask})
return image, labels
def _parse_train_data(self, data):
"""Parses data for training and evaluation."""
classes = data['groundtruth_classes'] + self._class_offset
boxes = data['groundtruth_boxes']
is_crowd = data['groundtruth_is_crowd']
# Gets original image.
image = data['image']
# Normalizes image with mean and std pixel values.
image = preprocess_ops.normalize_image(image)
image, boxes, _ = preprocess_ops.random_horizontal_flip(image, boxes)
do_crop = tf.greater(tf.random.uniform([]), 0.5)
if do_crop:
# Rescale
boxes = box_ops.denormalize_boxes(boxes, tf.shape(image)[:2])
index = tf.random.categorical(tf.zeros([1, 3]), 1)[0]
scales = tf.gather([400.0, 500.0, 600.0], index, axis=0)
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(image, short_side)
boxes = preprocess_ops.resize_and_crop_boxes(
boxes, image_info[2, :], image_info[1, :], image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# Do croping
shape = tf.cast(image_info[1], dtype=tf.int32)
h = tf.random.uniform([],
384,
tf.math.minimum(shape[0], 600),
dtype=tf.int32)
w = tf.random.uniform([],
384,
tf.math.minimum(shape[1], 600),
dtype=tf.int32)
i = tf.random.uniform([], 0, shape[0] - h + 1, dtype=tf.int32)
j = tf.random.uniform([], 0, shape[1] - w + 1, dtype=tf.int32)
image = tf.image.crop_to_bounding_box(image, i, j, h, w)
boxes = tf.clip_by_value(
(boxes[..., :] *
tf.cast(tf.stack([shape[0], shape[1], shape[0], shape[1]]),
dtype=tf.float32) -
tf.cast(tf.stack([i, j, i, j]), dtype=tf.float32)) /
tf.cast(tf.stack([h, w, h, w]), dtype=tf.float32), 0.0, 1.0)
scales = tf.constant(self._resize_scales, dtype=tf.float32)
index = tf.random.categorical(tf.zeros([1, 11]), 1)[0]
scales = tf.gather(scales, index, axis=0)
image_shape = tf.shape(image)[:2]
boxes = box_ops.denormalize_boxes(boxes, image_shape)
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(
image, short_side, max(self._output_size))
boxes = preprocess_ops.resize_and_crop_boxes(boxes, image_info[2, :],
image_info[1, :],
image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# 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)
is_crowd = tf.gather(is_crowd, indices)
boxes = box_ops.yxyx_to_cycxhw(boxes)
image = tf.image.pad_to_bounding_box(image, 0, 0, self._output_size[0],
self._output_size[1])
labels = {
'classes':
preprocess_ops.clip_or_pad_to_fixed_size(classes,
self._max_num_boxes),
'boxes':
preprocess_ops.clip_or_pad_to_fixed_size(boxes,
self._max_num_boxes)
}
return image, labels
def preprocess(self, inputs):
"""Preprocess COCO for DETR."""
image = inputs['image']
boxes = inputs['objects']['bbox']
classes = inputs['objects']['label'] + 1
is_crowd = inputs['objects']['is_crowd']
image = preprocess_ops.normalize_image(image)
if self._params.is_training:
image, boxes, _ = preprocess_ops.random_horizontal_flip(image, boxes)
do_crop = tf.greater(tf.random.uniform([]), 0.5)
if do_crop:
# Rescale
boxes = box_ops.denormalize_boxes(boxes, tf.shape(image)[:2])
index = tf.random.categorical(tf.zeros([1, 3]), 1)[0]
scales = tf.gather([400.0, 500.0, 600.0], index, axis=0)
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(image, short_side)
boxes = preprocess_ops.resize_and_crop_boxes(boxes,
image_info[2, :],
image_info[1, :],
image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# Do croping
shape = tf.cast(image_info[1], dtype=tf.int32)
h = tf.random.uniform(
[], 384, tf.math.minimum(shape[0], 600), dtype=tf.int32)
w = tf.random.uniform(
[], 384, tf.math.minimum(shape[1], 600), dtype=tf.int32)
i = tf.random.uniform([], 0, shape[0] - h + 1, dtype=tf.int32)
j = tf.random.uniform([], 0, shape[1] - w + 1, dtype=tf.int32)
image = tf.image.crop_to_bounding_box(image, i, j, h, w)
boxes = tf.clip_by_value(
(boxes[..., :] * tf.cast(
tf.stack([shape[0], shape[1], shape[0], shape[1]]),
dtype=tf.float32) -
tf.cast(tf.stack([i, j, i, j]), dtype=tf.float32)) /
tf.cast(tf.stack([h, w, h, w]), dtype=tf.float32), 0.0, 1.0)
scales = tf.constant(
self._params.resize_scales,
dtype=tf.float32)
index = tf.random.categorical(tf.zeros([1, 11]), 1)[0]
scales = tf.gather(scales, index, axis=0)
else:
scales = tf.constant([self._params.resize_scales[-1]], tf.float32)
image_shape = tf.shape(image)[:2]
boxes = box_ops.denormalize_boxes(boxes, image_shape)
gt_boxes = boxes
short_side = scales[0]
image, image_info = preprocess_ops.resize_image(
image,
short_side,
max(self._params.output_size))
boxes = preprocess_ops.resize_and_crop_boxes(boxes,
image_info[2, :],
image_info[1, :],
image_info[3, :])
boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
# 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)
is_crowd = tf.gather(is_crowd, indices)
boxes = box_ops.yxyx_to_cycxhw(boxes)
image = tf.image.pad_to_bounding_box(
image, 0, 0, self._params.output_size[0], self._params.output_size[1])
labels = {
'classes':
preprocess_ops.clip_or_pad_to_fixed_size(
classes, self._params.max_num_boxes),
'boxes':
preprocess_ops.clip_or_pad_to_fixed_size(
boxes, self._params.max_num_boxes)
}
if not self._params.is_training:
labels.update({
'id':
inputs['image/id'],
'image_info':
image_info,
'is_crowd':
preprocess_ops.clip_or_pad_to_fixed_size(
is_crowd, self._params.max_num_boxes),
'gt_boxes':
preprocess_ops.clip_or_pad_to_fixed_size(
gt_boxes, self._params.max_num_boxes),
})
return image, labels
def _parse_train_data(self, data):
"""Generates images and labels that are usable for model training.
We use random flip, random scaling (between 0.6 to 1.3), cropping,
and color jittering as data augmentation
Args:
data: the decoded tensor dictionary from TfExampleDecoder.
Returns:
images: the image tensor.
labels: a dict of Tensors that contains labels.
"""
image = tf.cast(data['image'], dtype=tf.float32)
boxes = data['groundtruth_boxes']
classes = data['groundtruth_classes']
image_shape = tf.shape(input=image)[0:2]
if self._aug_rand_hflip:
image, boxes, _ = preprocess_ops.random_horizontal_flip(
image, boxes)
# Image augmentation
if not self._odapi_augmentation:
# Color and lighting jittering
if self._aug_rand_hue:
image = tf.image.random_hue(image=image, max_delta=.02)
if self._aug_rand_contrast:
image = tf.image.random_contrast(image=image,
lower=0.8,
upper=1.25)
if self._aug_rand_saturation:
image = tf.image.random_saturation(image=image,
lower=0.8,
upper=1.25)
if self._aug_rand_brightness:
image = tf.image.random_brightness(image=image, max_delta=.2)
image = tf.clip_by_value(image,
clip_value_min=0.0,
clip_value_max=255.0)
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_ops.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = preprocess_ops.resize_and_crop_image(
image, [self._output_height, self._output_width],
padded_size=[self._output_height, self._output_width],
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
unpad_image_shape = tf.cast(tf.shape(image), tf.float32)
# Resizes and crops boxes.
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = preprocess_ops.resize_and_crop_boxes(
boxes, image_scale, image_info[1, :], offset)
else:
# Color and lighting jittering
if self._aug_rand_hue:
image = cn_prep_ops.random_adjust_hue(image=image,
max_delta=.02)
if self._aug_rand_contrast:
image = cn_prep_ops.random_adjust_contrast(image=image,
min_delta=0.8,
max_delta=1.25)
if self._aug_rand_saturation:
image = cn_prep_ops.random_adjust_saturation(image=image,
min_delta=0.8,
max_delta=1.25)
if self._aug_rand_brightness:
image = cn_prep_ops.random_adjust_brightness(image=image,
max_delta=.2)
sc_image, sc_boxes, classes = cn_prep_ops.random_square_crop_by_scale(
image=image,
boxes=boxes,
labels=classes,
scale_min=self._aug_scale_min,
scale_max=self._aug_scale_max)
image, unpad_image_shape = cn_prep_ops.resize_to_range(
image=sc_image,
min_dimension=self._output_width,
max_dimension=self._output_width,
pad_to_max_dimension=True)
preprocessed_shape = tf.cast(tf.shape(image), tf.float32)
unpad_image_shape = tf.cast(unpad_image_shape, tf.float32)
im_box = tf.stack([
0.0, 0.0, preprocessed_shape[0] / unpad_image_shape[0],
preprocessed_shape[1] / unpad_image_shape[1]
])
realigned_bboxes = box_list_ops.change_coordinate_frame(
boxlist=box_list.BoxList(sc_boxes), window=im_box)
valid_boxes = box_list_ops.assert_or_prune_invalid_boxes(
realigned_bboxes.get())
boxes = box_list_ops.to_absolute_coordinates(
boxlist=box_list.BoxList(valid_boxes),
height=self._output_height,
width=self._output_width).get()
image_info = tf.stack([
tf.cast(image_shape, dtype=tf.float32),
tf.constant([self._output_height, self._output_width],
dtype=tf.float32),
tf.cast(tf.shape(sc_image)[0:2] / image_shape,
dtype=tf.float32),
tf.constant([0., 0.])
])
# 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)
labels = self._build_label(unpad_image_shape=unpad_image_shape,
boxes=boxes,
classes=classes,
image_info=image_info,
data=data)
if self._bgr_ordering:
red, green, blue = tf.unstack(image, num=3, axis=2)
image = tf.stack([blue, green, red], axis=2)
image = preprocess_ops.normalize_image(image=image,
offset=self._channel_means,
scale=self._channel_stds)
image = tf.cast(image, self._dtype)
return image, labels
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']
if self._augmenter is not None:
image = self._augmenter.distort(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
def _parse_train_data(self, data):
"""Parses data for training and evaluation."""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
# If not empty, `attributes` is a dict of (name, ground_truth) pairs.
# `ground_gruth` of attributes is assumed in shape [N, attribute_size].
# TODO(xianzhi): support parsing attributes weights.
attributes = data.get('groundtruth_attributes', {})
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training:
num_groundtrtuhs = tf.shape(input=classes)[0]
with tf.control_dependencies([num_groundtrtuhs, is_crowds]):
indices = tf.cond(
pred=tf.greater(tf.size(input=is_crowds), 0),
true_fn=lambda: tf.where(tf.logical_not(is_crowds))[:, 0],
false_fn=lambda: tf.cast(tf.range(num_groundtrtuhs), tf.
int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
for k, v in attributes.items():
attributes[k] = tf.gather(v, indices)
# Gets original image.
image = data['image']
# Apply autoaug or randaug.
if self._augmenter is not None:
image, boxes = self._augmenter.distort_with_boxes(image, boxes)
image_shape = tf.shape(input=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:
image, boxes, _ = preprocess_ops.random_horizontal_flip(
image, boxes)
# Converts boxes from normalized coordinates to pixel coordinates.
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.
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)
for k, v in attributes.items():
attributes[k] = tf.gather(v, indices)
# Assigns anchors.
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.AnchorLabeler(self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets, att_targets, cls_weights,
box_weights) = anchor_labeler.label_anchors(
anchor_boxes, boxes, tf.expand_dims(classes, axis=1), attributes)
# Casts input image to desired data type.
image = tf.cast(image, dtype=self._dtype)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': anchor_boxes,
'cls_weights': cls_weights,
'box_weights': box_weights,
'image_info': image_info,
}
if att_targets:
labels['attribute_targets'] = att_targets
return image, labels
def _parse_train_data(self, data):
"""Parses data for training."""
# Initialize the shape constants.
image = data['image']
boxes = data['groundtruth_boxes']
classes = data['groundtruth_classes']
if self._random_flip:
# Randomly flip the image horizontally.
image, boxes, _ = preprocess_ops.random_horizontal_flip(
image, boxes, seed=self._seed)
if not data['is_mosaic']:
image, infos, affine = self._jitter_scale(
image, [self._image_h, self._image_w], self._letter_box,
self._jitter, self._random_pad, self._aug_scale_min,
self._aug_scale_max, self._aug_rand_translate,
self._aug_rand_angle, self._aug_rand_perspective)
# Clip and clean boxes.
boxes, inds = preprocessing_ops.transform_and_clip_boxes(
boxes,
infos,
affine=affine,
shuffle_boxes=False,
area_thresh=self._area_thresh,
filter_and_clip_boxes=True,
seed=self._seed)
classes = tf.gather(classes, inds)
info = infos[-1]
else:
image = tf.image.resize(image, (self._image_h, self._image_w),
method='nearest')
output_size = tf.cast([self._image_h, self._image_w], tf.float32)
boxes_ = bbox_ops.denormalize_boxes(boxes, output_size)
inds = bbox_ops.get_non_empty_box_indices(boxes_)
boxes = tf.gather(boxes, inds)
classes = tf.gather(classes, inds)
info = self._pad_infos_object(image)
# Apply scaling to the hue saturation and brightness of an image.
image = tf.cast(image, dtype=self._dtype)
image = image / 255.0
image = preprocessing_ops.image_rand_hsv(
image,
self._aug_rand_hue,
self._aug_rand_saturation,
self._aug_rand_brightness,
seed=self._seed,
darknet=self._darknet or self._level_limits is not None)
# Cast the image to the selcted datatype.
image, labels = self._build_label(image,
boxes,
classes,
info,
inds,
data,
is_training=True)
return image, labels
def _parse_train_data(self, data):
"""Parses data for training and evaluation."""
image, label = self._prepare_image_and_label(data)
if self._crop_size:
label = tf.reshape(label,
[data['image/height'], data['image/width'], 1])
# If output_size is specified, resize image, and label to desired
# output_size.
if self._output_size:
image = tf.image.resize(image,
self._output_size,
method='bilinear')
label = tf.image.resize(label,
self._output_size,
method='nearest')
image_mask = tf.concat([image, label], axis=2)
image_mask_crop = tf.image.random_crop(image_mask,
self._crop_size + [4])
image = image_mask_crop[:, :, :-1]
label = tf.reshape(image_mask_crop[:, :, -1],
[1] + self._crop_size)
# Flips image randomly during training.
if self._aug_rand_hflip:
image, _, label = preprocess_ops.random_horizontal_flip(
image, masks=label)
train_image_size = self._crop_size if self._crop_size else self._output_size
# Resizes and crops image.
image, image_info = preprocess_ops.resize_and_crop_image(
image,
train_image_size,
train_image_size,
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
# Resizes and crops boxes.
image_scale = image_info[2, :]
offset = image_info[3, :]
# Pad label and make sure the padded region assigned to the ignore label.
# The label is first offset by +1 and then padded with 0.
label += 1
label = tf.expand_dims(label, axis=3)
label = preprocess_ops.resize_and_crop_masks(label, image_scale,
train_image_size, offset)
label -= 1
label = tf.where(tf.equal(label, -1),
self._ignore_label * tf.ones_like(label), label)
label = tf.squeeze(label, axis=0)
valid_mask = tf.not_equal(label, self._ignore_label)
labels = {
'masks': label,
'valid_masks': valid_mask,
'image_info': image_info,
}
# Cast image as self._dtype
image = tf.cast(image, dtype=self._dtype)
return image, labels