def _build_outputs(self, images, labels, mode):
if 'anchor_boxes' in labels:
anchor_boxes = labels['anchor_boxes']
else:
anchor_boxes = anchor.Anchor(
self._anchor_params.min_level, self._anchor_params.max_level,
self._anchor_params.num_scales,
self._anchor_params.aspect_ratios,
self._anchor_params.anchor_size,
images.get_shape().as_list()[1:3]).multilevel_boxes
backbone_features = self._backbone_fn(
images, is_training=(mode == mode_keys.TRAIN))
fpn_features = self._fpn_fn(backbone_features,
is_training=(mode == mode_keys.TRAIN))
cls_outputs, box_outputs = self._head_fn(
fpn_features, is_training=(mode == mode_keys.TRAIN))
model_outputs = {
'cls_outputs': cls_outputs,
'box_outputs': box_outputs,
}
if mode != mode_keys.TRAIN:
detection_results = self._generate_detections_fn(
box_outputs, cls_outputs, anchor_boxes,
labels['image_info'][:, 1:2, :])
model_outputs.update(detection_results)
return model_outputs
def _serving_model_graph(features, params):
"""Build the model graph for serving."""
images = features['images']
_, height, width, _ = images.get_shape().as_list()
input_anchor = anchor.Anchor(params.anchor.min_level,
params.anchor.max_level,
params.anchor.num_scales,
params.anchor.aspect_ratios,
params.anchor.anchor_size,
(height, width))
model_fn = factory.model_generator(params)
model_outputs = model_fn.build_outputs(
features['images'],
labels={'anchor_boxes': input_anchor.multilevel_boxes},
mode=mode_keys.PREDICT)
if cast_num_detections_to_float:
model_outputs['num_detections'] = tf.cast(
model_outputs['num_detections'], dtype=tf.float32)
if output_image_info:
model_outputs.update({
'image_info': features['image_info'],
})
if output_normalized_coordinates:
model_outputs['detection_boxes'] = box_utils.normalize_boxes(
model_outputs['detection_boxes'],
features['image_info'][:, 1:2, :])
return model_outputs
def _serving_model_graph(features, params):
"""Build the model graph for serving."""
images = features['images']
batch_size, height, width, _ = images.get_shape().as_list()
input_anchor = anchor.Anchor(
params.anchor.min_level, params.anchor.max_level,
params.anchor.num_scales, params.anchor.aspect_ratios,
params.anchor.anchor_size, (height, width))
multilevel_boxes = {}
for k, v in six.iteritems(input_anchor.multilevel_boxes):
multilevel_boxes[k] = tf.tile(
tf.expand_dims(v, 0), [batch_size, 1, 1])
model_fn = factory.model_generator(params)
model_outputs = model_fn.build_outputs(
features['images'],
labels={
'anchor_boxes': multilevel_boxes,
'image_info': features['image_info'],
},
mode=mode_keys.PREDICT)
if cast_num_detections_to_float:
model_outputs['num_detections'] = tf.cast(
model_outputs['num_detections'], dtype=tf.float32)
if output_image_info:
model_outputs.update({
'image_info': features['image_info'],
})
if output_normalized_coordinates:
model_outputs['detection_boxes'] = box_utils.normalize_boxes(
model_outputs['detection_boxes'],
features['image_info'][:, 1:2, :])
predictions = {
'num_detections': tf.identity(
model_outputs['num_detections'], 'NumDetections'),
'detection_boxes': tf.identity(
model_outputs['detection_boxes'], 'DetectionBoxes'),
'detection_classes': tf.identity(
model_outputs['detection_classes'], 'DetectionClasses'),
'detection_scores': tf.identity(
model_outputs['detection_scores'], 'DetectionScores'),
}
if 'detection_masks' in model_outputs:
predictions.update({
'detection_masks':
tf.identity(model_outputs['detection_masks'], 'DetectionMasks'),
})
if output_image_info:
predictions['image_info'] = tf.identity(
model_outputs['image_info'], 'ImageInfo')
return predictions
def _build_outputs(self, images, labels, mode):
batch_size = tf.shape(images)[0]
if 'anchor_boxes' in labels:
anchor_boxes = labels['anchor_boxes']
else:
anchor_boxes = anchor.Anchor(
self._params.architecture.min_level,
self._params.architecture.max_level,
self._params.anchor.num_scales,
self._params.anchor.aspect_ratios,
self._params.anchor.anchor_size,
images.get_shape().as_list()[1:3]).multilevel_boxes
for level in anchor_boxes:
anchor_boxes[level] = tf.tile(
tf.expand_dims(anchor_boxes[level], 0), [batch_size, 1, 1])
backbone_features = self._backbone_fn(
images, is_training=(mode == mode_keys.TRAIN))
fpn_features = self._fpn_fn(backbone_features,
is_training=(mode == mode_keys.TRAIN))
if self._params.architecture.output_flat_fpn_features:
flat_fpn_features_list = []
for level in range(self._params.architecture.min_level,
self._params.architecture.max_level + 1):
flat_fpn_features_list.append(
tf.reshape(fpn_features[level], [batch_size, -1]))
flat_fpn_features = tf.concat(flat_fpn_features_list, axis=1)
flat_fpn_features = tf.identity(flat_fpn_features,
'RawFpnFeatures')
cls_outputs, box_outputs = self._head_fn(
fpn_features, is_training=(mode == mode_keys.TRAIN))
model_outputs = {
'cls_outputs': cls_outputs,
'box_outputs': box_outputs,
}
tf.logging.info('Computing number of FLOPs before NMS...')
static_batch_size = images.get_shape().as_list()[0]
if static_batch_size:
_, _ = benchmark_utils.compute_model_statistics(static_batch_size)
if mode != mode_keys.TRAIN:
detection_results = self._generate_detections_fn(
box_outputs, cls_outputs, anchor_boxes,
labels['image_info'][:, 1:2, :])
model_outputs.update(detection_results)
return model_outputs
def parse_single_example(serialized_example, params):
"""Parses a singel serialized TFExample string."""
decoder = tf_example_decoder.TfExampleDecoder()
data = decoder.decode(serialized_example)
image = data['image']
source_id = data['source_id']
source_id = dataloader_utils.process_source_id(source_id)
height = data['height']
width = data['width']
boxes = data['groundtruth_boxes']
boxes = box_utils.denormalize_boxes(boxes, tf.shape(image)[:2])
classes = data['groundtruth_classes']
is_crowds = data['groundtruth_is_crowd']
areas = data['groundtruth_area']
image = input_utils.normalize_image(image)
image, image_info = input_utils.resize_and_crop_image(
image,
params.retinanet_parser.output_size,
padded_size=input_utils.compute_padded_size(
params.retinanet_parser.output_size, 2**params.anchor.max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
anchors = anchor.Anchor(params.anchor.min_level, params.anchor.max_level,
params.anchor.num_scales,
params.anchor.aspect_ratios,
params.anchor.anchor_size,
image.get_shape().as_list()[:2])
labels = {
'anchor_boxes': anchors.multilevel_boxes,
'image_info': image_info,
}
groundtruths = {
'source_id': source_id,
'height': height,
'width': width,
'num_detections': tf.shape(classes),
'boxes': boxes,
'classes': classes,
'areas': areas,
'is_crowds': tf.cast(is_crowds, tf.int32),
}
return image, labels, groundtruths
def _build_outputs(self, images, labels, mode):
if 'anchor_boxes' in labels:
anchor_boxes = labels['anchor_boxes']
else:
anchor_boxes = anchor.Anchor(
self._anchor_params.min_level, self._anchor_params.max_level,
self._anchor_params.num_scales,
self._anchor_params.aspect_ratios,
self._anchor_params.anchor_size,
images.get_shape().as_list()[1:3]).multilevel_boxes
batch_size = tf.shape(images)[0]
for level in anchor_boxes:
anchor_boxes[level] = tf.tile(
tf.expand_dims(anchor_boxes[level], 0), [batch_size, 1, 1])
backbone_features = self._backbone_fn(
images, is_training=(mode == mode_keys.TRAIN))
fpn_features = self._fpn_fn(backbone_features,
is_training=(mode == mode_keys.TRAIN))
cls_outputs, box_outputs = self._head_fn(
fpn_features, is_training=(mode == mode_keys.TRAIN))
model_outputs = {
'cls_outputs': cls_outputs,
'box_outputs': box_outputs,
}
tf.logging.info('Computing number of FLOPs before NMS...')
_, _ = benchmark_utils.compute_model_statistics(
images.get_shape().as_list()[0])
if mode != mode_keys.TRAIN:
detection_results = self._generate_detections_fn(
box_outputs, cls_outputs, anchor_boxes,
labels['image_info'][:, 1:2, :])
model_outputs.update(detection_results)
return model_outputs
def _parse_train_data_v2(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.
"""
if self._use_autoaugment:
try:
from utils import (
autoaugment_utils, ) # pylint: disable=g-import-not-at-top
except ImportError as e:
logging.exception("Autoaugment is not supported in TF 2.x.")
raise e
classes = data["groundtruth_classes"]
boxes = data["groundtruth_boxes"]
masks = None
attributes = None
if self._include_mask:
masks = data["groundtruth_instance_masks"]
if self._num_attributes:
attributes = data["groundtruth_attributes"]
is_crowds = data["groundtruth_is_crowd"]
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_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.compat.v1.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)
if self._include_mask:
masks = tf.gather(masks, indices)
if self._num_attributes:
attributes = tf.gather(attributes, indices)
# Gets original image and its size.
image = data["image"]
# NOTE: The autoaugment method works best when used alongside the standard
# horizontal flipping of images along with size jittering and normalization.
if self._use_autoaugment and not self._apply_autoaugment_after_resizing:
(
image,
boxes,
masks,
) = autoaugment_utils.distort_image_and_masks_with_autoaugment(
image, boxes, masks, self._autoaugment_policy_name)
# Flips image randomly during training.
if self._aug_rand_hflip:
if self._include_mask:
image, boxes, masks = input_utils.random_horizontal_flip(
image, boxes, masks)
else:
image, boxes = input_utils.random_horizontal_flip(image, boxes)
# Resizes and crops image.
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max,
)
# Converts boxes from normalized coordinates to pixel coordinates.
# Now the coordinates of boxes are w.r.t. the original image.
orig_image_shape = image_info[0]
boxes = box_utils.denormalize_boxes(boxes, orig_image_shape)
# Resizes and crops boxes.
# Now the coordinates of boxes are w.r.t the scaled image.
rescaled_image_shape = tf.shape(input=image)[:2]
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = input_utils.resize_and_crop_boxes(boxes, image_scale,
rescaled_image_shape, offset)
# Filters out ground truth boxes that are all zeros.
boxes, classes, masks, attributes = self._remove_empty_boxes(
boxes, classes, masks, attributes)
# apply the autoaugment after resizing
if self._use_autoaugment and self._apply_autoaugment_after_resizing:
# prepare image and boxes for the autoaugment
image = tf.image.convert_image_dtype(image, dtype=tf.uint8)
boxes = box_utils.normalize_boxes(boxes, rescaled_image_shape)
# prepare masks for the autoaugment
masks = tf.expand_dims(masks, axis=-1)
scaled_mask_size = tf.cast(
tf.round(orig_image_shape * image_scale), tf.int32)
scaled_masks = tf.image.resize(
masks, scaled_mask_size, method=tf.image.ResizeMethod.BILINEAR)
offset_int = tf.cast(offset, tf.int32)
masks = scaled_masks[:, offset_int[0]:offset_int[0] +
rescaled_image_shape[0],
offset_int[1]:offset_int[1] +
rescaled_image_shape[1], ]
masks = tf.squeeze(masks, axis=-1)
masks = tf.cast(tf.greater(masks, 0.5), tf.float32)
# apply the autoaugment
(
image,
boxes,
masks,
) = autoaugment_utils.distort_image_and_masks_with_autoaugment(
image, boxes, masks, self._autoaugment_policy_name)
# convert the image back to float32 and denormalize bboxes
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
boxes = box_utils.denormalize_boxes(boxes, rescaled_image_shape)
# filters out empty bboxes
boxes, classes, masks, attributes = self._remove_empty_boxes(
boxes, classes, masks, attributes)
if self._include_mask:
if self._use_autoaugment and self._apply_autoaugment_after_resizing:
# don't rescale boxes as masks were already resized
cropped_boxes = box_utils.normalize_boxes(
boxes, rescaled_image_shape)
else:
# transfer boxes to the original image space
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_utils.normalize_boxes(
cropped_boxes, orig_image_shape)
num_masks = tf.shape(input=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)
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# pad the image
padded_size = input_utils.compute_padded_size(self._output_size,
2**self._max_level)
image = tf.image.pad_to_bounding_box(image, 0, 0, padded_size[0],
padded_size[1])
image_height, image_width, _ = image.get_shape().as_list()
# Assigns anchor targets.
# Note that after the target assignment, box targets are absolute pixel
# offsets w.r.t. the scaled image.
input_anchor = anchor.Anchor(
self._min_level,
self._max_level,
self._num_scales,
self._aspect_ratios,
self._anchor_size,
(image_height, image_width),
)
anchor_labeler = anchor.RpnAnchorLabeler(
input_anchor,
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(
boxes, tf.cast(tf.expand_dims(classes, axis=-1), dtype=tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Packs labels for model_fn outputs.
labels = {
"anchor_boxes": input_anchor.multilevel_boxes,
"image_info": image_info,
"rpn_score_targets": rpn_score_targets,
"rpn_box_targets": rpn_box_targets,
}
labels["gt_boxes"] = input_utils.pad_to_fixed_size(
boxes, self._max_num_instances, -1)
labels["gt_classes"] = input_utils.pad_to_fixed_size(
classes, self._max_num_instances, -1)
if self._include_mask:
labels["gt_masks"] = input_utils.pad_to_fixed_size(
masks, self._max_num_instances, -1)
if self._num_attributes:
labels["gt_attributes"] = input_utils.pad_to_fixed_size(
attributes, 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']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_training:
num_groundtrtuhs = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtrtuhs, 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_groundtrtuhs), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
# Gets original image and its size.
image = data['image']
# NOTE: The autoaugment method works best when used alongside the standard
# horizontal flipping of images along with size jittering and normalization.
if self._use_autoaugment:
try:
from utils import autoaugment_utils # pylint: disable=g-import-not-at-top
except ImportError as e:
logging.exception('Autoaugment is not supported in TF 2.x.')
raise e
image, boxes = autoaugment_utils.distort_image_with_autoaugment(
image, boxes, self._autoaugment_policy_name)
image_shape = tf.shape(image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
image, boxes = input_utils.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_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.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 = input_utils.resize_and_crop_boxes(boxes, image_scale,
(image_height, image_width),
offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
# Assigns anchor targets.
# Note that after the target assignment, box targets are absolute pixel
# offsets w.r.t. the scaled image.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
anchor_labeler = anchor.AnchorLabeler(input_anchor,
self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets,
num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': input_anchor.multilevel_boxes,
'num_positives': num_positives,
'image_info': image_info,
}
return image, labels
def _parse_predict_data(self, data):
"""Parses data for prediction.
Args:
data: the decoded tensor dictionary from TfExampleDecoder.
Returns:
A dictionary of {'images': image, 'labels': labels} where
images: 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.
source_ids: Source image id. Default value -1 if the source id is
empty in the groundtruth annotation.
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.
"""
# 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 = input_utils.normalize_image(image)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(
self._output_size, 2**self._max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
image_height, image_width, _ = image.get_shape().as_list()
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Compute Anchor boxes.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
labels = {
'source_id': dataloader_utils.process_source_id(data['source_id']),
'anchor_boxes': input_anchor.multilevel_boxes,
'image_info': image_info,
}
if self._mode == ModeKeys.PREDICT_WITH_GT:
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(data['groundtruth_boxes'],
image_shape)
groundtruths = {
'source_id': data['source_id'],
'height': data['height'],
'width': data['width'],
'num_detections': tf.shape(data['groundtruth_classes']),
'boxes': boxes,
'classes': data['groundtruth_classes'],
'areas': data['groundtruth_area'],
'is_crowds': tf.cast(data['groundtruth_is_crowd'], tf.int32),
}
groundtruths['source_id'] = dataloader_utils.process_source_id(
groundtruths['source_id'])
groundtruths = dataloader_utils.pad_groundtruths_to_fixed_size(
groundtruths, self._max_num_instances)
labels['groundtruths'] = groundtruths
return {
'images': image,
'labels': 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 and self._is_training:
num_groundtrtuhs = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtrtuhs, 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_groundtrtuhs), 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 = input_utils.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
if self._include_mask:
image, boxes, masks = input_utils.random_horizontal_flip(
image, boxes, masks)
else:
image, boxes = input_utils.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_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.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 = input_utils.resize_and_crop_boxes(boxes, image_scale,
image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.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_utils.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.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
anchor_labeler = anchor.RpnAnchorLabeler(input_anchor,
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(
boxes, tf.cast(tf.expand_dims(classes, axis=-1), dtype=tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Packs labels for model_fn outputs.
labels = {
'anchor_boxes': input_anchor.multilevel_boxes,
'image_info': image_info,
'rpn_score_targets': rpn_score_targets,
'rpn_box_targets': rpn_box_targets,
}
labels['gt_boxes'] = input_utils.clip_or_pad_to_fixed_size(
boxes, self._max_num_instances, -1)
labels['gt_classes'] = input_utils.clip_or_pad_to_fixed_size(
classes, self._max_num_instances, -1)
if self._include_mask:
labels['gt_masks'] = input_utils.clip_or_pad_to_fixed_size(
masks, self._max_num_instances, -1)
return image, labels
def _build_outputs(self, images, labels, mode):
is_training = (mode == mode_keys.TRAIN)
if 'anchor_boxes' in labels:
anchor_boxes = labels['anchor_boxes']
else:
anchor_boxes = anchor.Anchor(
self._params.architecture.min_level,
self._params.architecture.max_level,
self._params.anchor.num_scales,
self._params.anchor.aspect_ratios,
self._params.anchor.anchor_size,
images.get_shape().as_list()[1:3]).multilevel_boxes
batch_size = tf.shape(images)[0]
for level in anchor_boxes:
anchor_boxes[level] = tf.tile(
tf.expand_dims(anchor_boxes[level], 0), [batch_size, 1, 1])
backbone_features = self._backbone_fn(images, is_training=is_training)
fpn_features = self._fpn_fn(backbone_features, is_training=is_training)
cls_outputs, box_outputs = self._retinanet_head_fn(
fpn_features, is_training=is_training)
# Shapemask mask prediction.
if is_training:
boxes = labels['mask_boxes']
outer_boxes = labels['mask_outer_boxes']
classes = labels['mask_classes']
else:
detection_results = self._generate_detections_fn(
box_outputs, cls_outputs, anchor_boxes,
labels['image_info'][:, 1:2, :])
boxes = detection_results['detection_boxes']
scores = detection_results['detection_scores']
classes = detection_results['detection_classes']
valid_detections = detection_results['num_detections']
# Use list as input to avoide segmentation fault on TPU.
image_size = images.get_shape().as_list()[1:3]
outer_boxes = box_utils.compute_outer_boxes(
tf.reshape(boxes, [-1, 4]),
image_size,
scale=self._outer_box_scale)
outer_boxes = tf.reshape(outer_boxes, tf.shape(boxes))
classes = tf.cast(classes, tf.int32)
instance_features, prior_masks = self._shape_prior_head_fn(
fpn_features, boxes, outer_boxes, classes, is_training)
coarse_mask_logits = self._coarse_mask_fn(instance_features,
prior_masks, classes,
is_training)
fine_mask_logits = self._fine_mask_fn(instance_features,
coarse_mask_logits, classes,
is_training)
model_outputs = {
'cls_outputs': cls_outputs,
'box_outputs': box_outputs,
'fine_mask_logits': fine_mask_logits,
'coarse_mask_logits': coarse_mask_logits,
'prior_masks': prior_masks,
'fpn_features': fpn_features,
}
if not is_training:
model_outputs.update({
'num_detections':
valid_detections,
'detection_boxes':
boxes,
'detection_outer_boxes':
outer_boxes,
'detection_masks':
tf.sigmoid(fine_mask_logits),
'detection_classes':
tf.cast(classes, dtype=tf.int32),
'detection_scores':
scores,
})
return model_outputs
def _parse_train_data(self, data):
"""Parse data for ShapeMask training."""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
masks = data['groundtruth_instance_masks']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_training:
num_groundtrtuhs = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtrtuhs, 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_groundtrtuhs), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
masks = tf.gather(masks, indices)
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(image)[0:2]
# If not using category, makes all categories with id = 0.
if not self._use_category:
classes = tf.cast(tf.greater(classes, 0), dtype=tf.float32)
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
image, boxes, masks = input_utils.random_horizontal_flip(
image, boxes, masks)
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
self._output_size,
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
image_scale = image_info[2, :]
offset = image_info[3, :]
# Resizes and crops boxes and masks.
boxes = input_utils.resize_and_crop_boxes(boxes, image_scale,
self._output_size, offset)
masks = input_utils.resize_and_crop_masks(
tf.expand_dims(masks, axis=-1), image_scale, self._output_size,
offset)
masks = tf.squeeze(masks, axis=-1)
# Filters out ground truth boxes that are all zeros.
indices = input_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
masks = tf.gather(masks, indices)
# Assigns anchors.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size, self._output_size)
anchor_labeler = anchor.AnchorLabeler(input_anchor,
self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets,
num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# Sample groundtruth masks/boxes/classes for mask branch.
num_masks = tf.shape(masks)[0]
mask_shape = tf.shape(masks)[1:3]
# Pad sampled boxes/masks/classes to a constant batch size.
padded_boxes = input_utils.pad_to_fixed_size(boxes,
self._num_sampled_masks)
padded_classes = input_utils.pad_to_fixed_size(classes,
self._num_sampled_masks)
padded_masks = input_utils.pad_to_fixed_size(masks,
self._num_sampled_masks)
# Randomly sample groundtruth masks for mask branch training. For the image
# without groundtruth masks, it will sample the dummy padded tensors.
rand_indices = tf.random.uniform([self._num_sampled_masks],
minval=0,
maxval=tf.maximum(num_masks, 1),
dtype=tf.dtypes.int32)
sampled_boxes = tf.gather(padded_boxes, rand_indices)
sampled_classes = tf.gather(padded_classes, rand_indices)
sampled_masks = tf.gather(padded_masks, rand_indices)
# Compute mask targets in feature crop. A feature crop fully contains a
# sampled box.
mask_outer_boxes = box_utils.compute_outer_boxes(
sampled_boxes, mask_shape, scale=self._outer_box_scale)
norm_mask_outer_boxes = box_utils.normalize_boxes(
mask_outer_boxes, mask_shape)
# Set sampled_masks shape to [batch_size, height, width, 1].
sampled_masks = tf.expand_dims(sampled_masks, axis=-1)
mask_targets = tf.image.crop_and_resize(
sampled_masks,
norm_mask_outer_boxes,
box_ind=tf.range(self._num_sampled_masks),
crop_size=[self._mask_crop_size, self._mask_crop_size],
method='bilinear',
extrapolation_value=0,
name='train_mask_targets')
mask_targets = tf.where(tf.greater_equal(mask_targets, 0.5),
tf.ones_like(mask_targets),
tf.zeros_like(mask_targets))
mask_targets = tf.squeeze(mask_targets, axis=-1)
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': input_anchor.multilevel_boxes,
'num_positives': num_positives,
'image_info': image_info,
# For ShapeMask.
'mask_boxes': sampled_boxes,
'mask_outer_boxes': mask_outer_boxes,
'mask_targets': mask_targets,
'mask_classes': sampled_classes,
'mask_is_valid': tf.cast(tf.not_equal(num_masks, 0), tf.int32)
}
return image, labels
def build_outputs(self, inputs, mode):
is_training = mode == mode_keys.TRAIN
model_outputs = {}
image = inputs['image']
_, image_height, image_width, _ = image.get_shape().as_list()
backbone_features = self._backbone_fn(image, is_training)
fpn_features = self._fpn_fn(backbone_features, is_training)
rpn_score_outputs, rpn_box_outputs = self._rpn_head_fn(
fpn_features, is_training)
model_outputs.update({
'rpn_score_outputs':
tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
rpn_score_outputs),
'rpn_box_outputs':
tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
rpn_box_outputs),
})
input_anchor = anchor.Anchor(self._params.anchor.min_level,
self._params.anchor.max_level,
self._params.anchor.num_scales,
self._params.anchor.aspect_ratios,
self._params.anchor.anchor_size,
(image_height, image_width))
rpn_rois, _ = self._generate_rois_fn(rpn_box_outputs, rpn_score_outputs,
input_anchor.multilevel_boxes,
inputs['image_info'][:, 1, :],
is_training)
if is_training:
rpn_rois = tf.stop_gradient(rpn_rois)
# Sample proposals.
rpn_rois, matched_gt_boxes, matched_gt_classes, matched_gt_indices = (
self._sample_rois_fn(rpn_rois, inputs['gt_boxes'],
inputs['gt_classes']))
# Create bounding box training targets.
box_targets = box_utils.encode_boxes(
matched_gt_boxes, rpn_rois, weights=[10.0, 10.0, 5.0, 5.0])
# If the target is background, the box target is set to all 0s.
box_targets = tf.where(
tf.tile(
tf.expand_dims(tf.equal(matched_gt_classes, 0), axis=-1),
[1, 1, 4]),
tf.zeros_like(box_targets),
box_targets)
model_outputs.update({
'class_targets': matched_gt_classes,
'box_targets': box_targets,
})
roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, rpn_rois, output_size=7)
class_outputs, box_outputs = self._frcnn_head_fn(roi_features, is_training)
model_outputs.update({
'class_outputs':
tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
class_outputs),
'box_outputs':
tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
box_outputs),
})
# Add this output to train to make the checkpoint loadable in predict mode.
# If we skip it in train mode, the heads will be out-of-order and checkpoint
# loading will fail.
boxes, scores, classes, valid_detections = self._generate_detections_fn(
box_outputs, class_outputs, rpn_rois, inputs['image_info'][:, 1:2, :])
model_outputs.update({
'num_detections': valid_detections,
'detection_boxes': boxes,
'detection_classes': classes,
'detection_scores': scores,
})
if not self._include_mask:
return model_outputs
if is_training:
rpn_rois, classes, mask_targets = self._sample_masks_fn(
rpn_rois, matched_gt_boxes, matched_gt_classes, matched_gt_indices,
inputs['gt_masks'])
mask_targets = tf.stop_gradient(mask_targets)
classes = tf.cast(classes, dtype=tf.int32)
model_outputs.update({
'mask_targets': mask_targets,
'sampled_class_targets': classes,
})
else:
rpn_rois = boxes
classes = tf.cast(classes, dtype=tf.int32)
mask_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, rpn_rois, output_size=14)
mask_outputs = self._mrcnn_head_fn(mask_roi_features, classes, is_training)
if is_training:
model_outputs.update({
'mask_outputs':
tf.nest.map_structure(lambda x: tf.cast(x, tf.float32),
mask_outputs),
})
else:
model_outputs.update({
'detection_masks': tf.nn.sigmoid(mask_outputs)
})
return model_outputs
def _parse_train_data(self, data):
"""Parses data for training and evaluation."""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_training:
num_groundtrtuhs = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtrtuhs, 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_groundtrtuhs), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, 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 = input_utils.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
image, boxes = input_utils.random_horizontal_flip(image, boxes)
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.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 = input_utils.resize_and_crop_boxes(boxes, image_scale,
(image_height, image_width),
offset)
# Filters out ground truth boxes that are all zeros.
indices = input_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
# Assigns anchors.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
anchor_labeler = anchor.AnchorLabeler(input_anchor,
self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets,
num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': input_anchor.multilevel_boxes,
'num_positives': num_positives,
'image_info': image_info,
}
return image, labels
def parse_train_data(self, data):
"""Parse data for ShapeMask training."""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
masks = data['groundtruth_instance_masks']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_training:
num_groundtrtuhs = tf.shape(classes)[0]
with tf.control_dependencies([num_groundtrtuhs, 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_groundtrtuhs), tf.int64))
classes = tf.gather(classes, indices)
boxes = tf.gather(boxes, indices)
masks = tf.gather(masks, indices)
# If not using category, makes all categories with id = 0.
if not self._use_category:
classes = tf.cast(tf.greater(classes, 0), dtype=tf.float32)
image = self.get_normalized_image(data)
# Flips image randomly during training.
if self._aug_rand_hflip:
image, boxes, masks = input_utils.random_horizontal_flip(
image, boxes, masks)
# Converts boxes from normalized coordinates to pixel coordinates.
image_shape = tf.shape(image)[0:2]
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
self._output_size,
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
self._train_image_scale = image_info[2, :]
self._train_offset = image_info[3, :]
# Resizes and crops boxes and masks.
boxes = input_utils.resize_and_crop_boxes(boxes,
self._train_image_scale,
image_info[1, :],
self._train_offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
masks = tf.gather(masks, indices)
# Assigns anchors.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size, self._output_size)
anchor_labeler = anchor.AnchorLabeler(input_anchor,
self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets,
num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# Sample groundtruth masks/boxes/classes for mask branch.
num_masks = tf.shape(masks)[0]
mask_shape = tf.shape(masks)[1:3]
# Pad sampled boxes/masks/classes to a constant batch size.
padded_boxes = input_utils.pad_to_fixed_size(boxes,
self._num_sampled_masks)
padded_classes = input_utils.pad_to_fixed_size(classes,
self._num_sampled_masks)
padded_masks = input_utils.pad_to_fixed_size(masks,
self._num_sampled_masks)
# Randomly sample groundtruth masks for mask branch training. For the image
# without groundtruth masks, it will sample the dummy padded tensors.
rand_indices = tf.random.shuffle(
tf.range(tf.maximum(num_masks, self._num_sampled_masks)))
rand_indices = tf.mod(rand_indices, tf.maximum(num_masks, 1))
rand_indices = rand_indices[0:self._num_sampled_masks]
rand_indices = tf.reshape(rand_indices, [self._num_sampled_masks])
sampled_boxes = tf.gather(padded_boxes, rand_indices)
sampled_classes = tf.gather(padded_classes, rand_indices)
sampled_masks = tf.gather(padded_masks, rand_indices)
# Jitter the sampled boxes to mimic the noisy detections.
sampled_boxes = box_utils.jitter_boxes(
sampled_boxes, noise_scale=self._box_jitter_scale)
sampled_boxes = box_utils.clip_boxes(sampled_boxes, self._output_size)
# Compute mask targets in feature crop. A feature crop fully contains a
# sampled box.
mask_outer_boxes = box_utils.compute_outer_boxes(
sampled_boxes, tf.shape(image)[0:2], scale=self._outer_box_scale)
mask_outer_boxes = box_utils.clip_boxes(mask_outer_boxes,
self._output_size)
# Compensate the offset of mask_outer_boxes to map it back to original image
# scale.
mask_outer_boxes_ori = mask_outer_boxes
mask_outer_boxes_ori += tf.tile(
tf.expand_dims(self._train_offset, axis=0), [1, 2])
mask_outer_boxes_ori /= tf.tile(
tf.expand_dims(self._train_image_scale, axis=0), [1, 2])
norm_mask_outer_boxes_ori = box_utils.normalize_boxes(
mask_outer_boxes_ori, mask_shape)
# Set sampled_masks shape to [batch_size, height, width, 1].
sampled_masks = tf.cast(tf.expand_dims(sampled_masks, axis=-1),
tf.float32)
mask_targets = tf.image.crop_and_resize(
sampled_masks,
norm_mask_outer_boxes_ori,
box_ind=tf.range(self._num_sampled_masks),
crop_size=[self._mask_crop_size, self._mask_crop_size],
method='bilinear',
extrapolation_value=0,
name='train_mask_targets')
mask_targets = tf.where(tf.greater_equal(mask_targets, 0.5),
tf.ones_like(mask_targets),
tf.zeros_like(mask_targets))
mask_targets = tf.squeeze(mask_targets, axis=-1)
if self._up_sample_factor > 1:
fine_mask_targets = tf.image.crop_and_resize(
sampled_masks,
norm_mask_outer_boxes_ori,
box_ind=tf.range(self._num_sampled_masks),
crop_size=[
self._mask_crop_size * self._up_sample_factor,
self._mask_crop_size * self._up_sample_factor
],
method='bilinear',
extrapolation_value=0,
name='train_mask_targets')
fine_mask_targets = tf.where(
tf.greater_equal(fine_mask_targets, 0.5),
tf.ones_like(fine_mask_targets),
tf.zeros_like(fine_mask_targets))
fine_mask_targets = tf.squeeze(fine_mask_targets, axis=-1)
else:
fine_mask_targets = mask_targets
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
valid_image = tf.cast(tf.not_equal(num_masks, 0), tf.int32)
if self._mask_train_class == 'all':
mask_is_valid = valid_image * tf.ones_like(sampled_classes,
tf.int32)
else:
# Get the intersection of sampled classes with training splits.
mask_valid_classes = tf.cast(
tf.expand_dims(
class_utils.coco_split_class_ids(self._mask_train_class),
1), sampled_classes.dtype)
match = tf.reduce_any(
tf.equal(tf.expand_dims(sampled_classes, 0),
mask_valid_classes), 0)
mask_is_valid = valid_image * tf.cast(match, tf.int32)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': input_anchor.multilevel_boxes,
'num_positives': num_positives,
'image_info': image_info,
# For ShapeMask.
'mask_boxes': sampled_boxes,
'mask_outer_boxes': mask_outer_boxes,
'mask_targets': mask_targets,
'fine_mask_targets': fine_mask_targets,
'mask_classes': sampled_classes,
'mask_is_valid': mask_is_valid,
}
return image, labels
def parse_predict_data(self, data):
"""Parse data for ShapeMask training."""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
masks = data['groundtruth_instance_masks']
# If not using category, makes all categories with id = 0.
if not self._use_category:
classes = tf.cast(tf.greater(classes, 0), dtype=tf.float32)
image = self.get_normalized_image(data)
# Converts boxes from normalized coordinates to pixel coordinates.
image_shape = tf.shape(image)[0:2]
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
self._output_size,
aug_scale_min=1.0,
aug_scale_max=1.0)
image_scale = image_info[2, :]
offset = image_info[3, :]
# Resizes and crops boxes and masks.
boxes = input_utils.resize_and_crop_boxes(boxes, image_scale,
image_info[1, :], offset)
masks = input_utils.resize_and_crop_masks(
tf.expand_dims(masks, axis=-1), image_scale, self._output_size,
offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
# Assigns anchors.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size, self._output_size)
anchor_labeler = anchor.AnchorLabeler(input_anchor,
self._match_threshold,
self._unmatched_threshold)
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
labels = {
'anchor_boxes': input_anchor.multilevel_boxes,
'image_info': image_info,
}
if self._mode == ModeKeys.PREDICT_WITH_GT:
# Converts boxes from normalized coordinates to pixel coordinates.
groundtruths = {
'source_id':
data['source_id'],
'height':
data['height'],
'width':
data['width'],
'num_detections':
tf.shape(data['groundtruth_classes']),
'boxes':
box_utils.denormalize_boxes(data['groundtruth_boxes'],
image_shape),
'classes':
data['groundtruth_classes'],
# 'masks': tf.squeeze(masks, axis=-1),
'areas':
data['groundtruth_area'],
'is_crowds':
tf.cast(data['groundtruth_is_crowd'], tf.int32),
}
groundtruths['source_id'] = dataloader_utils.process_source_id(
groundtruths['source_id'])
groundtruths = dataloader_utils.pad_groundtruths_to_fixed_size(
groundtruths, self._max_num_instances)
# Computes training labels.
(cls_targets, box_targets,
num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# Packs labels for model_fn outputs.
labels.update({
'cls_targets': cls_targets,
'box_targets': box_targets,
'num_positives': num_positives,
'groundtruths': groundtruths,
})
return {
'images': image,
'labels': labels,
}
def _build_outputs(self, images, labels, mode):
is_training = mode == mode_keys.TRAIN
model_outputs = {}
if 'anchor_boxes' in labels:
anchor_boxes = labels['anchor_boxes']
else:
anchor_boxes = anchor.Anchor(
self._params.architecture.min_level,
self._params.architecture.max_level,
self._params.anchor.num_scales,
self._params.anchor.aspect_ratios,
self._params.anchor.anchor_size,
images.get_shape().as_list()[1:3]).multilevel_boxes
batch_size = tf.shape(images)[0]
for level in anchor_boxes:
anchor_boxes[level] = tf.tile(
tf.expand_dims(anchor_boxes[level], 0), [batch_size, 1, 1])
backbone_features = self._backbone_fn(images, is_training)
fpn_features = self._fpn_fn(backbone_features, is_training)
rpn_score_outputs, rpn_box_outputs = self._rpn_head_fn(
fpn_features, is_training)
model_outputs.update({
'rpn_score_outputs': rpn_score_outputs,
'rpn_box_outputs': rpn_box_outputs,
})
rpn_rois, _ = self._generate_rois_fn(rpn_box_outputs,
rpn_score_outputs, anchor_boxes,
labels['image_info'][:, 1, :],
is_training)
if is_training:
rpn_rois = tf.stop_gradient(rpn_rois)
# Sample proposals.
rpn_rois, matched_gt_boxes, matched_gt_classes, matched_gt_indices = (
self._sample_rois_fn(rpn_rois, labels['gt_boxes'],
labels['gt_classes']))
self.add_scalar_summary(
'fg_bg_ratio_{}'.format(0),
tf.reduce_sum(
tf.cast(tf.greater(matched_gt_classes, 0), tf.float32)) /
tf.reduce_sum(
tf.cast(tf.greater_equal(matched_gt_classes, 0),
tf.float32)))
# Create bounding box training targets.
box_targets = box_utils.encode_boxes(
matched_gt_boxes, rpn_rois, weights=[10.0, 10.0, 5.0, 5.0])
# If the target is background, the box target is set to all 0s.
box_targets = tf.where(
tf.tile(
tf.expand_dims(tf.equal(matched_gt_classes, 0), axis=-1),
[1, 1, 4]), tf.zeros_like(box_targets), box_targets)
model_outputs.update({
'class_targets': matched_gt_classes,
'box_targets': box_targets,
})
_, num_rois_before_cat, _ = rpn_rois.get_shape().as_list()
if is_training and self._feat_distill:
tf.logging.info(f'rois before concat distill boxes: {rpn_rois}')
rpn_rois = tf.concat([rpn_rois, labels['roi_boxes']], axis=1)
# [batch_size, num_rois+max_distill_rois, 4]
tf.logging.info(f'rois after concat distill boxes: {rpn_rois}')
roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, rpn_rois, output_size=7)
if is_training and self._feat_distill:
tf.logging.info(f'rois before split: {rpn_rois}')
rpn_rois, _ = tf.split(
rpn_rois, [num_rois_before_cat, self._max_distill_rois],
axis=1)
tf.logging.info(f'rois after split: {rpn_rois}')
(class_outputs, box_outputs, distill_feat_outputs,
distill_class_outputs) = self._frcnn_head_fn(roi_features,
is_training)
model_outputs.update({
'class_outputs': class_outputs,
'box_outputs': box_outputs,
})
if is_training and self._feat_distill:
model_outputs.update(
{'distill_feat_outputs': distill_feat_outputs})
if not is_training:
detection_results = self._generate_detections_fn(
box_outputs,
class_outputs,
rpn_rois,
labels['image_info'][:, 1:2, :],
bbox_per_class=not self._params.frcnn_head.
class_agnostic_bbox_pred,
distill_class_outputs=distill_class_outputs,
)
model_outputs.update(detection_results)
if not self._include_mask:
return model_outputs
if is_training:
rpn_rois, classes, mask_targets = self._sample_masks_fn(
rpn_rois, matched_gt_boxes, matched_gt_classes,
matched_gt_indices, labels['gt_masks'])
mask_targets = tf.stop_gradient(mask_targets)
classes = tf.cast(classes, dtype=tf.int32)
model_outputs.update({
'mask_targets': mask_targets,
'sampled_class_targets': classes,
})
else:
rpn_rois = detection_results['detection_boxes']
classes = tf.cast(detection_results['detection_classes'],
dtype=tf.int32)
mask_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, rpn_rois, output_size=14)
mask_outputs = self._mrcnn_head_fn(mask_roi_features, classes,
is_training)
if is_training:
model_outputs.update({
'mask_outputs': mask_outputs,
})
else:
model_outputs.update(
{'detection_masks': tf.nn.sigmoid(mask_outputs)})
return model_outputs
def build_outputs(self, features, labels, mode):
is_training = mode == mode_keys.TRAIN
model_outputs = {}
if 'anchor_boxes' in labels:
anchor_boxes = labels['anchor_boxes']
else:
anchor_boxes = anchor.Anchor(
self._anchor_params.min_level, self._anchor_params.max_level,
self._anchor_params.num_scales,
self._anchor_params.aspect_ratios,
self._anchor_params.anchor_size,
features.get_shape().as_list()[1:3]).multilevel_boxes
backbone_features = self._backbone_fn(features, is_training)
fpn_features = self._fpn_fn(backbone_features, is_training)
rpn_score_outputs, rpn_box_outputs = self._rpn_head_fn(
fpn_features, is_training)
model_outputs.update({
'rpn_score_outputs': rpn_score_outputs,
'rpn_box_outputs': rpn_box_outputs,
})
rpn_rois, _ = self._generate_rois_fn(rpn_box_outputs,
rpn_score_outputs, anchor_boxes,
labels['image_info'][:, 1, :],
is_training)
if is_training:
rpn_rois = tf.stop_gradient(rpn_rois)
# Sample proposals.
rpn_rois, matched_gt_boxes, matched_gt_classes, matched_gt_indices = (
self._sample_rois_fn(rpn_rois, labels['gt_boxes'],
labels['gt_classes']))
# Create bounding box training targets.
box_targets = box_utils.encode_boxes(
matched_gt_boxes, rpn_rois, weights=[10.0, 10.0, 5.0, 5.0])
# If the target is background, the box target is set to all 0s.
box_targets = tf.where(
tf.tile(
tf.expand_dims(tf.equal(matched_gt_classes, 0), axis=-1),
[1, 1, 4]), tf.zeros_like(box_targets), box_targets)
model_outputs.update({
'class_targets': matched_gt_classes,
'box_targets': box_targets,
})
roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, rpn_rois, output_size=7)
class_outputs, box_outputs = self._frcnn_head_fn(
roi_features, is_training)
model_outputs.update({
'class_outputs': class_outputs,
'box_outputs': box_outputs,
})
if not is_training:
detection_results = self._generate_detections_fn(
box_outputs, class_outputs, rpn_rois,
labels['image_info'][:, 1:2, :])
model_outputs.update(detection_results)
if not self._include_mask:
self._log_model_statistics(features)
return model_outputs
if is_training:
rpn_rois, classes, mask_targets = self._sample_masks_fn(
rpn_rois, matched_gt_boxes, matched_gt_classes,
matched_gt_indices, labels['gt_masks'])
mask_targets = tf.stop_gradient(mask_targets)
classes = tf.cast(classes, dtype=tf.int32)
model_outputs.update({
'mask_targets': mask_targets,
'sampled_class_targets': classes,
})
else:
rpn_rois = detection_results['detection_boxes']
classes = tf.cast(detection_results['detection_classes'],
dtype=tf.int32)
mask_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, rpn_rois, output_size=14)
mask_outputs = self._mrcnn_head_fn(mask_roi_features, classes,
is_training)
if is_training:
model_outputs.update({
'mask_outputs': mask_outputs,
})
else:
model_outputs.update(
{'detection_masks': tf.nn.sigmoid(mask_outputs)})
self._log_model_statistics(features)
return model_outputs
def _build_outputs(self, images, labels, mode):
is_training = mode == mode_keys.TRAIN
model_outputs = {}
if 'anchor_boxes' in labels:
anchor_boxes = labels['anchor_boxes']
else:
anchor_boxes = anchor.Anchor(
self._params.architecture.min_level,
self._params.architecture.max_level,
self._params.anchor.num_scales,
self._params.anchor.aspect_ratios,
self._params.anchor.anchor_size,
images.get_shape().as_list()[1:3]).multilevel_boxes
batch_size = tf.shape(images)[0]
for level in anchor_boxes:
anchor_boxes[level] = tf.tile(
tf.expand_dims(anchor_boxes[level], 0), [batch_size, 1, 1])
backbone_features = self._backbone_fn(images, is_training)
fpn_features = self._fpn_fn(backbone_features, is_training)
rpn_score_outputs, rpn_box_outputs = self._rpn_head_fn(
fpn_features, is_training)
model_outputs.update({
'rpn_score_outputs': rpn_score_outputs,
'rpn_box_outputs': rpn_box_outputs,
})
# Run the RPN layer to get bbox coordinates for first frcnn layer.
current_rois, _ = self._generate_rois_fn(rpn_box_outputs,
rpn_score_outputs,
anchor_boxes,
labels['image_info'][:, 1, :],
is_training)
cascade_ious = [-1]
if self._cascade_iou_thresholds is not None:
cascade_ious = cascade_ious + self._cascade_iou_thresholds
next_rois = current_rois
# Stores the class predictions for each RCNN head.
all_class_outputs = []
for cascade_num, iou_threshold in enumerate(cascade_ious):
# In cascade RCNN we want the higher layers to have different regression
# weights as the predicted deltas become smaller and smaller.
regression_weights = self._cascade_layer_to_weights[cascade_num]
current_rois = next_rois
(class_outputs, box_outputs, model_outputs, matched_gt_boxes,
matched_gt_classes, matched_gt_indices,
current_rois) = self._run_frcnn_head(fpn_features, current_rois,
labels, is_training,
model_outputs, cascade_num,
iou_threshold,
regression_weights)
all_class_outputs.append(class_outputs)
# Generate the next rois if we are running another cascade.
# Since bboxes are predicted for every class
# (if `class_agnostic_bbox_pred` is false) this takes the best class
# bbox and converts it to the correct format to be used for roi
# operations.
if is_training:
correct_class = matched_gt_classes
else:
correct_class = tf.arg_max(class_outputs, dimension=-1)
next_rois = self._box_outputs_to_rois(
box_outputs, current_rois, correct_class,
labels['image_info'][:, 1:2, :], regression_weights)
if not is_training:
tf.logging.info('(self._class_agnostic_bbox_pred): {}'.format(
self._class_agnostic_bbox_pred))
if self._cascade_class_ensemble:
class_outputs = tf.add_n(all_class_outputs) / len(
all_class_outputs)
# Post processing/NMS is done here for final boxes. Note NMS is done
# before to generate proposals of the output of the RPN head.
# The background class is also removed here.
detection_results = self._generate_detections_fn(
box_outputs,
class_outputs,
current_rois,
labels['image_info'][:, 1:2, :],
regression_weights,
bbox_per_class=(not self._class_agnostic_bbox_pred))
model_outputs.update(detection_results)
if not self._include_mask:
return model_outputs
if is_training:
current_rois, classes, mask_targets = self._sample_masks_fn(
current_rois, matched_gt_boxes, matched_gt_classes,
matched_gt_indices, labels['gt_masks'])
mask_targets = tf.stop_gradient(mask_targets)
classes = tf.cast(classes, dtype=tf.int32)
model_outputs.update({
'mask_targets': mask_targets,
'sampled_class_targets': classes,
})
else:
current_rois = detection_results['detection_boxes']
classes = tf.cast(detection_results['detection_classes'],
dtype=tf.int32)
mask_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, current_rois, output_size=14)
mask_outputs = self._mrcnn_head_fn(mask_roi_features, classes,
is_training)
if is_training:
model_outputs.update({
'mask_outputs': mask_outputs,
})
else:
model_outputs.update(
{'detection_masks': tf.nn.sigmoid(mask_outputs)})
return model_outputs
def _parse_eval_data(self, data):
"""Parses data for evaluation.
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.
groundtruths:
source_id: Groundtruth source id.
height: Original image height.
width: Original image width.
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].
classes: Groundtruth classes annotations. The tennsor is padded
with -1 to the fixed dimension [self._max_num_instances].
areas: Box area or mask area depend on whether mask is present.
is_crowds: Whether the ground truth label is a crowd label.
num_groundtruths: Number of ground truths in the image.
"""
# 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 = input_utils.normalize_image(image)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(
self._output_size, 2**self._max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
image_height, image_width, _ = image.get_shape().as_list()
# Assigns anchor targets.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Sets up groundtruth data for evaluation.
groundtruths = {
'source_id':
data['source_id'],
'height':
data['height'],
'width':
data['width'],
'num_groundtruths':
tf.shape(data['groundtruth_classes']),
'boxes':
box_utils.denormalize_boxes(data['groundtruth_boxes'],
image_shape),
'classes':
data['groundtruth_classes'],
'areas':
data['groundtruth_area'],
'is_crowds':
tf.cast(data['groundtruth_is_crowd'], tf.int32),
}
# TODO(b/143766089): Add ground truth masks for segmentation metrics.
groundtruths['source_id'] = dataloader_utils.process_source_id(
groundtruths['source_id'])
groundtruths = dataloader_utils.pad_groundtruths_to_fixed_size(
groundtruths, self._max_num_instances)
# Packs labels for model_fn outputs.
labels = {
'anchor_boxes': input_anchor.multilevel_boxes,
'image_info': image_info,
'groundtruths': groundtruths,
}
return image, labels
def _parse_eval_data(self, data):
"""Parses data for training and evaluation."""
groundtruths = {}
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
# Gets original image and its size.
image = data['image']
image_shape = tf.shape(input=image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(
self._output_size, 2**self._max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
image_height, image_width, _ = image.get_shape().as_list()
# Resizes and crops boxes.
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = input_utils.resize_and_crop_boxes(boxes, image_scale,
image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
# Assigns anchors.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
anchor_labeler = anchor.AnchorLabeler(input_anchor,
self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets,
num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Sets up groundtruth data for evaluation.
groundtruths = {
'source_id':
data['source_id'],
'num_groundtrtuhs':
tf.shape(data['groundtruth_classes']),
'image_info':
image_info,
'boxes':
box_utils.denormalize_boxes(data['groundtruth_boxes'],
image_shape),
'classes':
data['groundtruth_classes'],
'areas':
data['groundtruth_area'],
'is_crowds':
tf.cast(data['groundtruth_is_crowd'], tf.int32),
}
groundtruths['source_id'] = process_source_id(
groundtruths['source_id'])
groundtruths = pad_groundtruths_to_fixed_size(groundtruths,
self._max_num_instances)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': input_anchor.multilevel_boxes,
'num_positives': num_positives,
'image_info': image_info,
'groundtruths': groundtruths,
}
return image, labels
def build_predictions(features,
params,
output_image_info,
output_normalized_coordinates,
cast_num_detections_to_float,
cast_detection_classes_to_float=False):
"""Builds the model graph for serving.
Args:
features: features to be passed to the serving model graph
params: hyperparameters to be passed to the serving model graph
output_image_info: bool, whether output the image_info node.
output_normalized_coordinates: bool, whether box outputs are in the
normalized coordinates.
cast_num_detections_to_float: bool, whether to cast the number of detections
to float type.
cast_detection_classes_to_float: bool, whether or not cast the detection
classes to float type.
Returns:
predictions: model outputs for serving.
model_outputs: a dict of model output tensors.
"""
images = features['images']
batch_size, height, width, _ = images.get_shape().as_list()
input_anchor = anchor.Anchor(params.architecture.min_level,
params.architecture.max_level,
params.anchor.num_scales,
params.anchor.aspect_ratios,
params.anchor.anchor_size, (height, width))
multilevel_boxes = {}
for k, v in six.iteritems(input_anchor.multilevel_boxes):
multilevel_boxes[k] = tf.tile(tf.expand_dims(v, 0), [batch_size, 1, 1])
model_fn = factory.model_generator(params)
model_outputs = model_fn.build_outputs(features['images'],
labels={
'anchor_boxes':
multilevel_boxes,
'image_info':
features['image_info'],
},
mode=mode_keys.PREDICT)
# Return flattened raw outputs.
if not params.postprocess.apply_nms:
predictions = {
'raw_boxes': tf.identity(model_outputs['raw_boxes'], 'RawBoxes'),
'raw_scores': tf.identity(model_outputs['raw_scores'],
'RawScores'),
}
return predictions, model_outputs
if cast_num_detections_to_float:
model_outputs['num_detections'] = tf.cast(
model_outputs['num_detections'], dtype=tf.float32)
if cast_detection_classes_to_float:
model_outputs['detection_classes'] = tf.cast(
model_outputs['detection_classes'], dtype=tf.float32)
if output_image_info:
model_outputs.update({
'image_info': features['image_info'],
})
if output_normalized_coordinates:
detection_boxes = (
model_outputs['detection_boxes'] /
tf.tile(features['image_info'][:, 2:3, :], [1, 1, 2]))
model_outputs['detection_boxes'] = box_utils.normalize_boxes(
detection_boxes, features['image_info'][:, 0:1, :])
predictions = {
'num_detections':
tf.identity(model_outputs['num_detections'], 'NumDetections'),
'detection_boxes':
tf.identity(model_outputs['detection_boxes'], 'DetectionBoxes'),
'detection_classes':
tf.identity(model_outputs['detection_classes'], 'DetectionClasses'),
'detection_scores':
tf.identity(model_outputs['detection_scores'], 'DetectionScores'),
}
if 'detection_masks' in model_outputs:
predictions.update({
'detection_masks':
tf.identity(model_outputs['detection_masks'], 'DetectionMasks'),
})
if 'detection_outer_boxes' in model_outputs:
predictions.update({
'detection_outer_boxes':
tf.identity(model_outputs['detection_outer_boxes'],
'DetectionOuterBoxes'),
})
if output_image_info:
predictions['image_info'] = tf.identity(model_outputs['image_info'],
'ImageInfo')
return predictions, model_outputs
def _build_outputs(self, images, labels, mode):
is_training = mode == mode_keys.TRAIN
model_outputs = {}
if "anchor_boxes" in labels:
anchor_boxes = labels["anchor_boxes"]
else:
anchor_boxes = anchor.Anchor(
self._params.architecture.min_level,
self._params.architecture.max_level,
self._params.anchor.num_scales,
self._params.anchor.aspect_ratios,
self._params.anchor.anchor_size,
images.get_shape().as_list()[1:3],
).multilevel_boxes
batch_size = tf.shape(input=images)[0]
for level in anchor_boxes:
anchor_boxes[level] = tf.tile(
tf.expand_dims(anchor_boxes[level], 0), [batch_size, 1, 1])
backbone_features = self._backbone_fn(images, is_training)
fpn_features = self._fpn_fn(backbone_features, is_training)
rpn_score_outputs, rpn_box_outputs = self._rpn_head_fn(
fpn_features, is_training)
model_outputs.update({
"rpn_score_outputs": rpn_score_outputs,
"rpn_box_outputs": rpn_box_outputs,
})
rpn_rois, _ = self._generate_rois_fn(
rpn_box_outputs,
rpn_score_outputs,
anchor_boxes,
labels["image_info"][:, 1, :],
is_training,
)
if is_training:
rpn_rois = tf.stop_gradient(rpn_rois)
# Sample proposals.
(
rpn_rois,
matched_gt_boxes,
matched_gt_classes,
matched_gt_indices,
) = self._sample_rois_fn(rpn_rois, labels["gt_boxes"],
labels["gt_classes"])
# Create bounding box training targets.
box_targets = box_utils.encode_boxes(
matched_gt_boxes, rpn_rois, weights=[10.0, 10.0, 5.0, 5.0])
# If the target is background, the box target is set to all 0s.
box_targets = tf.compat.v1.where(
tf.tile(
tf.expand_dims(tf.equal(matched_gt_classes, 0), axis=-1),
[1, 1, 4]),
tf.zeros_like(box_targets),
box_targets,
)
model_outputs.update({
"class_targets": matched_gt_classes,
"box_targets": box_targets,
})
roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, rpn_rois, output_size=7)
class_outputs, box_outputs = self._frcnn_head_fn(
roi_features, is_training)
model_outputs.update({
"class_outputs": class_outputs,
"box_outputs": box_outputs,
})
if not is_training:
detection_results = self._generate_detections_fn(
box_outputs, class_outputs, rpn_rois,
labels["image_info"][:, 1:2, :])
model_outputs.update(detection_results)
if not self._include_mask:
return model_outputs
if is_training:
(
rpn_rois,
classes,
mask_targets,
gather_nd_gt_indices,
) = self._sample_masks_fn(
rpn_rois,
matched_gt_boxes,
matched_gt_classes,
matched_gt_indices,
labels["gt_masks"],
)
mask_targets = tf.stop_gradient(mask_targets)
classes = tf.cast(classes, dtype=tf.int32)
model_outputs.update({
"mask_targets": mask_targets,
"sampled_class_targets": classes,
})
else:
rpn_rois = detection_results["detection_boxes"]
classes = tf.cast(detection_results["detection_classes"],
dtype=tf.int32)
mask_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, rpn_rois, output_size=14)
mask_outputs = self._mrcnn_head_fn(mask_roi_features, classes,
is_training)
if is_training:
model_outputs.update({
"mask_outputs": mask_outputs,
})
else:
model_outputs.update(
{"detection_masks": tf.nn.sigmoid(mask_outputs)})
if not self._include_attributes:
return model_outputs
attribute_outputs = self._attributes_head_fn(mask_roi_features,
is_training)
if is_training:
attribute_targets = tf.gather_nd(
labels["gt_attributes"],
gather_nd_gt_indices) # [batch, K, num_attributes]
model_outputs.update({
"attribute_outputs": attribute_outputs,
"attribute_targets": attribute_targets,
})
else:
model_outputs["detection_attributes"] = tf.nn.sigmoid(
attribute_outputs)
return model_outputs
def _parse_predict_data(self, data):
"""Parses data for prediction."""
# 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 = input_utils.normalize_image(image)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.compute_padded_size(
self._output_size, 2**self._max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
image_height, image_width, _ = image.get_shape().as_list()
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Compute Anchor boxes.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
labels = {
'anchor_boxes': input_anchor.multilevel_boxes,
'image_info': image_info,
}
# If mode is PREDICT_WITH_GT, returns groundtruths and training targets
# in labels.
if self._mode == ModeKeys.PREDICT_WITH_GT:
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(data['groundtruth_boxes'],
image_shape)
groundtruths = {
'source_id': data['source_id'],
'height': data['height'],
'width': data['width'],
'num_detections': tf.shape(data['groundtruth_classes']),
'boxes': boxes,
'classes': data['groundtruth_classes'],
'areas': data['groundtruth_area'],
'is_crowds': tf.cast(data['groundtruth_is_crowd'], tf.int32),
}
groundtruths['source_id'] = dataloader_utils.process_source_id(
groundtruths['source_id'])
groundtruths = dataloader_utils.pad_groundtruths_to_fixed_size(
groundtruths, self._max_num_instances)
labels['groundtruths'] = groundtruths
# Computes training objective for evaluation loss.
classes = data['groundtruth_classes']
image_scale = image_info[2, :]
offset = image_info[3, :]
boxes = input_utils.resize_and_crop_boxes(
boxes, image_scale, (image_height, image_width), offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
# Assigns anchors.
anchor_labeler = anchor.AnchorLabeler(input_anchor,
self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets,
num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
labels['cls_targets'] = cls_targets
labels['box_targets'] = box_targets
labels['num_positives'] = num_positives
return {
'images': image,
'labels': labels,
}
def _parse_train_data(self, data):
"""Parses data for training and evaluation."""
classes = data['groundtruth_classes']
boxes = data['groundtruth_boxes']
is_crowds = data['groundtruth_is_crowd']
# Skips annotations with `is_crowd` = True.
if self._skip_crowd_during_training and self._is_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)
# Gets original image and its size.
image = data['image']
# NOTE: The autoaugment method works best when used alongside the standard
# horizontal flipping of images along with size jittering and normalization.
if self._use_autoaugment:
image, boxes = autoaugment_utils.distort_image_with_autoaugment(
image, boxes, self._autoaugment_policy_name)
image_shape = tf.shape(input=image)[0:2]
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
# Flips image randomly during training.
if self._aug_rand_hflip:
image, boxes = input_utils.random_horizontal_flip(image, boxes)
# Converts boxes from normalized coordinates to pixel coordinates.
boxes = box_utils.denormalize_boxes(boxes, image_shape)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
padded_size=input_utils.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 = input_utils.resize_and_crop_boxes(boxes, image_scale,
image_info[1, :], offset)
# Filters out ground truth boxes that are all zeros.
indices = box_utils.get_non_empty_box_indices(boxes)
boxes = tf.gather(boxes, indices)
classes = tf.gather(classes, indices)
# Assigns anchors.
input_anchor = anchor.Anchor(self._min_level, self._max_level,
self._num_scales, self._aspect_ratios,
self._anchor_size,
(image_height, image_width))
anchor_labeler = anchor.AnchorLabeler(input_anchor,
self._match_threshold,
self._unmatched_threshold)
(cls_targets, box_targets,
num_positives) = anchor_labeler.label_anchors(
boxes, tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
# Packs labels for model_fn outputs.
labels = {
'cls_targets': cls_targets,
'box_targets': box_targets,
'anchor_boxes': input_anchor.multilevel_boxes,
'num_positives': num_positives,
'image_info': image_info,
}
# return image, labels
num_anchors = input_anchor.anchors_per_location
mlvl_cls_targets = tf.concat([tf.reshape(cls_targets[lv], [-1, num_anchors]) \
for lv in range(self._min_level, self._max_level+1)], axis=0)
mlvl_box_targets = tf.concat([tf.reshape(box_targets[lv], [-1, num_anchors*4]) \
for lv in range(self._min_level, self._max_level + 1)], axis=0)
num_positives_expand = tf.ones_like(
mlvl_box_targets[..., 0:1]) * num_positives
mlvl_cls_targets_wp = tf.concat(
[mlvl_cls_targets,
tf.cast(num_positives_expand, dtype=tf.int32)],
axis=-1)
mlvl_box_targets_wp = tf.concat(
[mlvl_box_targets, num_positives_expand], axis=-1)
return image, (mlvl_cls_targets_wp, mlvl_box_targets_wp)
