in research/object_detection/core/post_processing.py [0:0]
def batch_multiclass_non_max_suppression(boxes,
scores,
score_thresh,
iou_thresh,
max_size_per_class,
max_total_size=0,
clip_window=None,
change_coordinate_frame=False,
num_valid_boxes=None,
masks=None,
additional_fields=None,
soft_nms_sigma=0.0,
scope=None,
use_static_shapes=False,
use_partitioned_nms=False,
parallel_iterations=32,
use_class_agnostic_nms=False,
max_classes_per_detection=1,
use_dynamic_map_fn=False,
use_combined_nms=False,
use_hard_nms=False,
use_cpu_nms=False):
"""Multi-class version of non maximum suppression that operates on a batch.
This op is similar to `multiclass_non_max_suppression` but operates on a batch
of boxes and scores. See documentation for `multiclass_non_max_suppression`
for details.
Args:
boxes: A [batch_size, num_anchors, q, 4] float32 tensor containing
detections. If `q` is 1 then same boxes are used for all classes
otherwise, if `q` is equal to number of classes, class-specific boxes are
used.
scores: A [batch_size, num_anchors, num_classes] float32 tensor containing
the scores for each of the `num_anchors` detections. The scores have to be
non-negative when use_static_shapes is set True.
score_thresh: scalar threshold for score (low scoring boxes are removed).
iou_thresh: scalar threshold for IOU (new boxes that have high IOU overlap
with previously selected boxes are removed).
max_size_per_class: maximum number of retained boxes per class.
max_total_size: maximum number of boxes retained over all classes. By
default returns all boxes retained after capping boxes per class.
clip_window: A float32 tensor of shape [batch_size, 4] where each entry is
of the form [y_min, x_min, y_max, x_max] representing the window to clip
boxes to before performing non-max suppression. This argument can also be
a tensor of shape [4] in which case, the same clip window is applied to
all images in the batch. If clip_widow is None, all boxes are used to
perform non-max suppression.
change_coordinate_frame: Whether to normalize coordinates after clipping
relative to clip_window (this can only be set to True if a clip_window is
provided)
num_valid_boxes: (optional) a Tensor of type `int32`. A 1-D tensor of shape
[batch_size] representing the number of valid boxes to be considered for
each image in the batch. This parameter allows for ignoring zero
paddings.
masks: (optional) a [batch_size, num_anchors, q, mask_height, mask_width]
float32 tensor containing box masks. `q` can be either number of classes
or 1 depending on whether a separate mask is predicted per class.
additional_fields: (optional) If not None, a dictionary that maps keys to
tensors whose dimensions are [batch_size, num_anchors, ...].
soft_nms_sigma: A scalar float representing the Soft NMS sigma parameter;
See Bodla et al, https://arxiv.org/abs/1704.04503). When
`soft_nms_sigma=0.0` (which is default), we fall back to standard (hard)
NMS. Soft NMS is currently only supported when pad_to_max_output_size is
False.
scope: tf scope name.
use_static_shapes: If true, the output nmsed boxes are padded to be of
length `max_size_per_class` and it doesn't clip boxes to max_total_size.
Defaults to false.
use_partitioned_nms: If true, use partitioned version of
non_max_suppression.
parallel_iterations: (optional) number of batch items to process in
parallel.
use_class_agnostic_nms: If true, this uses class-agnostic non max
suppression
max_classes_per_detection: Maximum number of retained classes per detection
box in class-agnostic NMS.
use_dynamic_map_fn: If true, images in the batch will be processed within a
dynamic loop. Otherwise, a static loop will be used if possible.
use_combined_nms: If true, it uses tf.image.combined_non_max_suppression (
multi-class version of NMS that operates on a batch).
It greedily selects a subset of detection bounding boxes, pruning away
boxes that have high IOU (intersection over union) overlap (> thresh) with
already selected boxes. It operates independently for each batch.
Within each batch, it operates independently for each class for which
scores are provided (via the scores field of the input box_list),
pruning boxes with score less than a provided threshold prior to applying
NMS. This operation is performed on *all* batches and *all* classes
in the batch, therefore any background classes should be removed prior to
calling this function.
Masks and additional fields are not supported.
See argument checks in the code below for unsupported arguments.
use_hard_nms: Enforce the usage of hard NMS.
use_cpu_nms: Enforce NMS to run on CPU.
Returns:
'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor
containing the non-max suppressed boxes.
'nmsed_scores': A [batch_size, max_detections] float32 tensor containing
the scores for the boxes.
'nmsed_classes': A [batch_size, max_detections] float32 tensor
containing the class for boxes.
'nmsed_masks': (optional) a
[batch_size, max_detections, mask_height, mask_width] float32 tensor
containing masks for each selected box. This is set to None if input
`masks` is None.
'nmsed_additional_fields': (optional) a dictionary of
[batch_size, max_detections, ...] float32 tensors corresponding to the
tensors specified in the input `additional_fields`. This is not returned
if input `additional_fields` is None.
'num_detections': A [batch_size] int32 tensor indicating the number of
valid detections per batch item. Only the top num_detections[i] entries in
nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the
entries are zero paddings.
Raises:
ValueError: if `q` in boxes.shape is not 1 or not equal to number of
classes as inferred from scores.shape.
"""
if use_combined_nms:
if change_coordinate_frame:
raise ValueError(
'change_coordinate_frame (normalizing coordinates'
' relative to clip_window) is not supported by combined_nms.')
if num_valid_boxes is not None:
raise ValueError('num_valid_boxes is not supported by combined_nms.')
if masks is not None:
raise ValueError('masks is not supported by combined_nms.')
if soft_nms_sigma != 0.0:
raise ValueError('Soft NMS is not supported by combined_nms.')
if use_class_agnostic_nms:
raise ValueError('class-agnostic NMS is not supported by combined_nms.')
if clip_window is None:
tf.logging.warning(
'A default clip window of [0. 0. 1. 1.] will be applied for the '
'boxes.')
if additional_fields is not None:
tf.logging.warning('additional_fields is not supported by combined_nms.')
if parallel_iterations != 32:
tf.logging.warning('Number of batch items to be processed in parallel is'
' not configurable by combined_nms.')
if max_classes_per_detection > 1:
tf.logging.warning(
'max_classes_per_detection is not configurable by combined_nms.')
with tf.name_scope(scope, 'CombinedNonMaxSuppression'):
(batch_nmsed_boxes, batch_nmsed_scores, batch_nmsed_classes,
batch_num_detections) = tf.image.combined_non_max_suppression(
boxes=boxes,
scores=scores,
max_output_size_per_class=max_size_per_class,
max_total_size=max_total_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh,
clip_boxes=(True if clip_window is None else False),
pad_per_class=use_static_shapes)
if clip_window is not None:
if clip_window.shape.ndims == 1:
boxes_shape = boxes.shape
batch_size = shape_utils.get_dim_as_int(boxes_shape[0])
clip_window = tf.tile(clip_window[tf.newaxis, :], [batch_size, 1])
batch_nmsed_boxes = _clip_boxes(batch_nmsed_boxes, clip_window)
# Not supported by combined_non_max_suppression.
batch_nmsed_masks = None
# Not supported by combined_non_max_suppression.
batch_nmsed_additional_fields = None
return (batch_nmsed_boxes, batch_nmsed_scores, batch_nmsed_classes,
batch_nmsed_masks, batch_nmsed_additional_fields,
batch_num_detections)
q = shape_utils.get_dim_as_int(boxes.shape[2])
num_classes = shape_utils.get_dim_as_int(scores.shape[2])
if q != 1 and q != num_classes:
raise ValueError('third dimension of boxes must be either 1 or equal '
'to the third dimension of scores.')
if change_coordinate_frame and clip_window is None:
raise ValueError('if change_coordinate_frame is True, then a clip_window'
'must be specified.')
original_masks = masks
# Create ordered dictionary using the sorted keys from
# additional fields to ensure getting the same key value assignment
# in _single_image_nms_fn(). The dictionary is thus a sorted version of
# additional_fields.
if additional_fields is None:
ordered_additional_fields = collections.OrderedDict()
else:
ordered_additional_fields = collections.OrderedDict(
sorted(additional_fields.items(), key=lambda item: item[0]))
with tf.name_scope(scope, 'BatchMultiClassNonMaxSuppression'):
boxes_shape = boxes.shape
batch_size = shape_utils.get_dim_as_int(boxes_shape[0])
num_anchors = shape_utils.get_dim_as_int(boxes_shape[1])
if batch_size is None:
batch_size = tf.shape(boxes)[0]
if num_anchors is None:
num_anchors = tf.shape(boxes)[1]
# If num valid boxes aren't provided, create one and mark all boxes as
# valid.
if num_valid_boxes is None:
num_valid_boxes = tf.ones([batch_size], dtype=tf.int32) * num_anchors
# If masks aren't provided, create dummy masks so we can only have one copy
# of _single_image_nms_fn and discard the dummy masks after map_fn.
if masks is None:
masks_shape = tf.stack([batch_size, num_anchors, q, 1, 1])
masks = tf.zeros(masks_shape)
if clip_window is None:
clip_window = tf.stack([
tf.reduce_min(boxes[:, :, :, 0]),
tf.reduce_min(boxes[:, :, :, 1]),
tf.reduce_max(boxes[:, :, :, 2]),
tf.reduce_max(boxes[:, :, :, 3])
])
if clip_window.shape.ndims == 1:
clip_window = tf.tile(tf.expand_dims(clip_window, 0), [batch_size, 1])
def _single_image_nms_fn(args):
"""Runs NMS on a single image and returns padded output.
Args:
args: A list of tensors consisting of the following:
per_image_boxes - A [num_anchors, q, 4] float32 tensor containing
detections. If `q` is 1 then same boxes are used for all classes
otherwise, if `q` is equal to number of classes, class-specific
boxes are used.
per_image_scores - A [num_anchors, num_classes] float32 tensor
containing the scores for each of the `num_anchors` detections.
per_image_masks - A [num_anchors, q, mask_height, mask_width] float32
tensor containing box masks. `q` can be either number of classes
or 1 depending on whether a separate mask is predicted per class.
per_image_clip_window - A 1D float32 tensor of the form
[ymin, xmin, ymax, xmax] representing the window to clip the boxes
to.
per_image_additional_fields - (optional) A variable number of float32
tensors each with size [num_anchors, ...].
per_image_num_valid_boxes - A tensor of type `int32`. A 1-D tensor of
shape [batch_size] representing the number of valid boxes to be
considered for each image in the batch. This parameter allows for
ignoring zero paddings.
Returns:
'nmsed_boxes': A [max_detections, 4] float32 tensor containing the
non-max suppressed boxes.
'nmsed_scores': A [max_detections] float32 tensor containing the scores
for the boxes.
'nmsed_classes': A [max_detections] float32 tensor containing the class
for boxes.
'nmsed_masks': (optional) a [max_detections, mask_height, mask_width]
float32 tensor containing masks for each selected box. This is set to
None if input `masks` is None.
'nmsed_additional_fields': (optional) A variable number of float32
tensors each with size [max_detections, ...] corresponding to the
input `per_image_additional_fields`.
'num_detections': A [batch_size] int32 tensor indicating the number of
valid detections per batch item. Only the top num_detections[i]
entries in nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The
rest of the entries are zero paddings.
"""
per_image_boxes = args[0]
per_image_scores = args[1]
per_image_masks = args[2]
per_image_clip_window = args[3]
# Make sure that the order of elements passed in args is aligned with
# the iteration order of ordered_additional_fields
per_image_additional_fields = {
key: value
for key, value in zip(ordered_additional_fields, args[4:-1])
}
per_image_num_valid_boxes = args[-1]
if use_static_shapes:
total_proposals = tf.shape(per_image_scores)
per_image_scores = tf.where(
tf.less(tf.range(total_proposals[0]), per_image_num_valid_boxes),
per_image_scores,
tf.fill(total_proposals, np.finfo('float32').min))
else:
per_image_boxes = tf.reshape(
tf.slice(per_image_boxes, 3 * [0],
tf.stack([per_image_num_valid_boxes, -1, -1])), [-1, q, 4])
per_image_scores = tf.reshape(
tf.slice(per_image_scores, [0, 0],
tf.stack([per_image_num_valid_boxes, -1])),
[-1, num_classes])
per_image_masks = tf.reshape(
tf.slice(per_image_masks, 4 * [0],
tf.stack([per_image_num_valid_boxes, -1, -1, -1])),
[-1, q, shape_utils.get_dim_as_int(per_image_masks.shape[2]),
shape_utils.get_dim_as_int(per_image_masks.shape[3])])
if per_image_additional_fields is not None:
for key, tensor in per_image_additional_fields.items():
additional_field_shape = tensor.get_shape()
additional_field_dim = len(additional_field_shape)
per_image_additional_fields[key] = tf.reshape(
tf.slice(
per_image_additional_fields[key],
additional_field_dim * [0],
tf.stack([per_image_num_valid_boxes] +
(additional_field_dim - 1) * [-1])), [-1] + [
shape_utils.get_dim_as_int(dim)
for dim in additional_field_shape[1:]
])
if use_class_agnostic_nms:
nmsed_boxlist, num_valid_nms_boxes = class_agnostic_non_max_suppression(
per_image_boxes,
per_image_scores,
score_thresh,
iou_thresh,
max_classes_per_detection,
max_total_size,
clip_window=per_image_clip_window,
change_coordinate_frame=change_coordinate_frame,
masks=per_image_masks,
pad_to_max_output_size=use_static_shapes,
use_partitioned_nms=use_partitioned_nms,
additional_fields=per_image_additional_fields,
soft_nms_sigma=soft_nms_sigma)
else:
nmsed_boxlist, num_valid_nms_boxes = multiclass_non_max_suppression(
per_image_boxes,
per_image_scores,
score_thresh,
iou_thresh,
max_size_per_class,
max_total_size,
clip_window=per_image_clip_window,
change_coordinate_frame=change_coordinate_frame,
masks=per_image_masks,
pad_to_max_output_size=use_static_shapes,
use_partitioned_nms=use_partitioned_nms,
additional_fields=per_image_additional_fields,
soft_nms_sigma=soft_nms_sigma,
use_hard_nms=use_hard_nms,
use_cpu_nms=use_cpu_nms)
if not use_static_shapes:
nmsed_boxlist = box_list_ops.pad_or_clip_box_list(
nmsed_boxlist, max_total_size)
num_detections = num_valid_nms_boxes
nmsed_boxes = nmsed_boxlist.get()
nmsed_scores = nmsed_boxlist.get_field(fields.BoxListFields.scores)
nmsed_classes = nmsed_boxlist.get_field(fields.BoxListFields.classes)
nmsed_masks = nmsed_boxlist.get_field(fields.BoxListFields.masks)
nmsed_additional_fields = []
# Sorting is needed here to ensure that the values stored in
# nmsed_additional_fields are always kept in the same order
# across different execution runs.
for key in sorted(per_image_additional_fields.keys()):
nmsed_additional_fields.append(nmsed_boxlist.get_field(key))
return ([nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks] +
nmsed_additional_fields + [num_detections])
num_additional_fields = 0
if ordered_additional_fields:
num_additional_fields = len(ordered_additional_fields)
num_nmsed_outputs = 4 + num_additional_fields
if use_dynamic_map_fn:
map_fn = tf.map_fn
else:
map_fn = shape_utils.static_or_dynamic_map_fn
batch_outputs = map_fn(
_single_image_nms_fn,
elems=([boxes, scores, masks, clip_window] +
list(ordered_additional_fields.values()) + [num_valid_boxes]),
dtype=(num_nmsed_outputs * [tf.float32] + [tf.int32]),
parallel_iterations=parallel_iterations)
batch_nmsed_boxes = batch_outputs[0]
batch_nmsed_scores = batch_outputs[1]
batch_nmsed_classes = batch_outputs[2]
batch_nmsed_masks = batch_outputs[3]
batch_nmsed_values = batch_outputs[4:-1]
batch_nmsed_additional_fields = {}
if num_additional_fields > 0:
# Sort the keys to ensure arranging elements in same order as
# in _single_image_nms_fn.
batch_nmsed_keys = list(ordered_additional_fields.keys())
for i in range(len(batch_nmsed_keys)):
batch_nmsed_additional_fields[
batch_nmsed_keys[i]] = batch_nmsed_values[i]
batch_num_detections = batch_outputs[-1]
if original_masks is None:
batch_nmsed_masks = None
if not ordered_additional_fields:
batch_nmsed_additional_fields = None
return (batch_nmsed_boxes, batch_nmsed_scores, batch_nmsed_classes,
batch_nmsed_masks, batch_nmsed_additional_fields,
batch_num_detections)