research/object_detection/core/post_processing.py
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Post-processing operations on detected boxes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
from six.moves import range
from six.moves import zip
import tensorflow.compat.v1 as tf
from object_detection.core import box_list
from object_detection.core import box_list_ops
from object_detection.core import keypoint_ops
from object_detection.core import standard_fields as fields
from object_detection.utils import shape_utils
_NMS_TILE_SIZE = 512
def batch_iou(boxes1, boxes2):
"""Calculates the overlap between proposal and ground truth boxes.
Some `boxes2` may have been padded. The returned `iou` tensor for these
boxes will be -1.
Args:
boxes1: a tensor with a shape of [batch_size, N, 4]. N is the number of
proposals before groundtruth assignment. The last dimension is the pixel
coordinates in [ymin, xmin, ymax, xmax] form.
boxes2: a tensor with a shape of [batch_size, MAX_NUM_INSTANCES, 4]. This
tensor might have paddings with a negative value.
Returns:
iou: a tensor with as a shape of [batch_size, N, MAX_NUM_INSTANCES].
"""
with tf.name_scope('BatchIOU'):
y1_min, x1_min, y1_max, x1_max = tf.split(
value=boxes1, num_or_size_splits=4, axis=2)
y2_min, x2_min, y2_max, x2_max = tf.split(
value=boxes2, num_or_size_splits=4, axis=2)
# Calculates the intersection area.
intersection_xmin = tf.maximum(x1_min, tf.transpose(x2_min, [0, 2, 1]))
intersection_xmax = tf.minimum(x1_max, tf.transpose(x2_max, [0, 2, 1]))
intersection_ymin = tf.maximum(y1_min, tf.transpose(y2_min, [0, 2, 1]))
intersection_ymax = tf.minimum(y1_max, tf.transpose(y2_max, [0, 2, 1]))
intersection_area = tf.maximum(
(intersection_xmax - intersection_xmin), 0) * tf.maximum(
(intersection_ymax - intersection_ymin), 0)
# Calculates the union area.
area1 = (y1_max - y1_min) * (x1_max - x1_min)
area2 = (y2_max - y2_min) * (x2_max - x2_min)
# Adds a small epsilon to avoid divide-by-zero.
union_area = area1 + tf.transpose(area2,
[0, 2, 1]) - intersection_area + 1e-8
# Calculates IoU.
iou = intersection_area / union_area
# Fills -1 for padded ground truth boxes.
padding_mask = tf.logical_and(
tf.less(intersection_xmax, 0), tf.less(intersection_ymax, 0))
iou = tf.where(padding_mask, -tf.ones_like(iou), iou)
return iou
def _self_suppression(iou, iou_threshold, loop_condition, iou_sum):
"""Bounding-boxes self-suppression loop body.
Args:
iou: A float Tensor with shape [1, num_boxes, max_num_instance]: IOUs.
iou_threshold: A scalar, representing IOU threshold.
loop_condition: The loop condition returned from last iteration.
iou_sum: iou_sum_new returned from last iteration.
Returns:
iou_suppressed: A float Tensor with shape [1, num_boxes, max_num_instance],
IOU after suppression.
iou_threshold: A scalar, representing IOU threshold.
loop_condition: Bool Tensor of shape [], the loop condition.
iou_sum_new: The new IOU sum.
"""
del loop_condition
can_suppress_others = tf.cast(
tf.reshape(tf.reduce_max(iou, 1) <= iou_threshold, [1, -1, 1]), iou.dtype)
iou_suppressed = tf.reshape(
tf.cast(
tf.reduce_max(can_suppress_others * iou, 1) <= iou_threshold,
iou.dtype), [1, -1, 1]) * iou
iou_sum_new = tf.reduce_sum(iou_suppressed, [1, 2])
return [
iou_suppressed, iou_threshold,
tf.reduce_any(iou_sum - iou_sum_new > iou_threshold), iou_sum_new
]
def _cross_suppression(boxes, box_slice, iou_threshold, inner_idx):
"""Bounding-boxes cross-suppression loop body.
Args:
boxes: A float Tensor of shape [1, anchors, 4], representing boxes.
box_slice: A float Tensor of shape [1, _NMS_TILE_SIZE, 4], the box tile
returned from last iteration
iou_threshold: A scalar, representing IOU threshold.
inner_idx: A scalar, representing inner index.
Returns:
boxes: A float Tensor of shape [1, anchors, 4], representing boxes.
ret_slice: A float Tensor of shape [1, _NMS_TILE_SIZE, 4], the box tile
after suppression
iou_threshold: A scalar, representing IOU threshold.
inner_idx: A scalar, inner index incremented.
"""
new_slice = tf.slice(boxes, [0, inner_idx * _NMS_TILE_SIZE, 0],
[1, _NMS_TILE_SIZE, 4])
iou = batch_iou(new_slice, box_slice)
ret_slice = tf.expand_dims(
tf.cast(tf.reduce_all(iou < iou_threshold, [1]), box_slice.dtype),
2) * box_slice
return boxes, ret_slice, iou_threshold, inner_idx + 1
def _suppression_loop_body(boxes, iou_threshold, output_size, idx):
"""Process boxes in the range [idx*_NMS_TILE_SIZE, (idx+1)*_NMS_TILE_SIZE).
Args:
boxes: a tensor with a shape of [1, anchors, 4].
iou_threshold: a float representing the threshold for deciding whether boxes
overlap too much with respect to IOU.
output_size: an int32 tensor of size [1]. Representing the number of
selected boxes.
idx: an integer scalar representing induction variable.
Returns:
boxes: updated boxes.
iou_threshold: pass down iou_threshold to the next iteration.
output_size: the updated output_size.
idx: the updated induction variable.
"""
num_tiles = tf.shape(boxes)[1] // _NMS_TILE_SIZE
# Iterates over tiles that can possibly suppress the current tile.
box_slice = tf.slice(boxes, [0, idx * _NMS_TILE_SIZE, 0],
[1, _NMS_TILE_SIZE, 4])
_, box_slice, _, _ = tf.while_loop(
lambda _boxes, _box_slice, _threshold, inner_idx: inner_idx < idx,
_cross_suppression, [boxes, box_slice, iou_threshold,
tf.constant(0)])
# Iterates over the current tile to compute self-suppression.
iou = batch_iou(box_slice, box_slice)
mask = tf.expand_dims(
tf.reshape(tf.range(_NMS_TILE_SIZE), [1, -1]) > tf.reshape(
tf.range(_NMS_TILE_SIZE), [-1, 1]), 0)
iou *= tf.cast(tf.logical_and(mask, iou >= iou_threshold), iou.dtype)
suppressed_iou, _, _, _ = tf.while_loop(
lambda _iou, _threshold, loop_condition, _iou_sum: loop_condition,
_self_suppression,
[iou, iou_threshold,
tf.constant(True),
tf.reduce_sum(iou, [1, 2])])
suppressed_box = tf.reduce_sum(suppressed_iou, 1) > 0
box_slice *= tf.expand_dims(1.0 - tf.cast(suppressed_box, box_slice.dtype), 2)
# Uses box_slice to update the input boxes.
mask = tf.reshape(
tf.cast(tf.equal(tf.range(num_tiles), idx), boxes.dtype), [1, -1, 1, 1])
boxes = tf.tile(tf.expand_dims(box_slice, [1]),
[1, num_tiles, 1, 1]) * mask + tf.reshape(
boxes, [1, num_tiles, _NMS_TILE_SIZE, 4]) * (1 - mask)
boxes = tf.reshape(boxes, [1, -1, 4])
# Updates output_size.
output_size += tf.reduce_sum(
tf.cast(tf.reduce_any(box_slice > 0, [2]), tf.int32), [1])
return boxes, iou_threshold, output_size, idx + 1
def partitioned_non_max_suppression_padded(boxes,
scores,
max_output_size,
iou_threshold=0.5,
score_threshold=float('-inf')):
"""A tiled version of [`tf.image.non_max_suppression_padded`](https://www.tensorflow.org/api_docs/python/tf/image/non_max_suppression_padded).
The overall design of the algorithm is to handle boxes tile-by-tile:
boxes = boxes.pad_to_multiple_of(tile_size)
num_tiles = len(boxes) // tile_size
output_boxes = []
for i in range(num_tiles):
box_tile = boxes[i*tile_size : (i+1)*tile_size]
for j in range(i - 1):
suppressing_tile = boxes[j*tile_size : (j+1)*tile_size]
iou = batch_iou(box_tile, suppressing_tile)
# if the box is suppressed in iou, clear it to a dot
box_tile *= _update_boxes(iou)
# Iteratively handle the diagonal tile.
iou = _box_overlap(box_tile, box_tile)
iou_changed = True
while iou_changed:
# boxes that are not suppressed by anything else
suppressing_boxes = _get_suppressing_boxes(iou)
# boxes that are suppressed by suppressing_boxes
suppressed_boxes = _get_suppressed_boxes(iou, suppressing_boxes)
# clear iou to 0 for boxes that are suppressed, as they cannot be used
# to suppress other boxes any more
new_iou = _clear_iou(iou, suppressed_boxes)
iou_changed = (new_iou != iou)
iou = new_iou
# remaining boxes that can still suppress others, are selected boxes.
output_boxes.append(_get_suppressing_boxes(iou))
if len(output_boxes) >= max_output_size:
break
Args:
boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
score corresponding to each box (each row of boxes).
max_output_size: a scalar integer `Tensor` representing the maximum number
of boxes to be selected by non max suppression.
iou_threshold: a float representing the threshold for deciding whether boxes
overlap too much with respect to IOU.
score_threshold: A float representing the threshold for deciding when to
remove boxes based on score.
Returns:
selected_indices: a tensor of shape [anchors].
num_valid_boxes: a scalar int tensor.
nms_proposals: a tensor with a shape of [anchors, 4]. It has
same dtype as input boxes.
nms_scores: a tensor with a shape of [anchors]. It has same
dtype as input scores.
argsort_ids: a tensor of shape [anchors], mapping from input order of boxes
to output order of boxes.
"""
num_boxes = tf.shape(boxes)[0]
pad = tf.cast(
tf.ceil(tf.cast(num_boxes, tf.float32) / _NMS_TILE_SIZE),
tf.int32) * _NMS_TILE_SIZE - num_boxes
scores, argsort_ids = tf.nn.top_k(scores, k=num_boxes, sorted=True)
boxes = tf.gather(boxes, argsort_ids)
num_boxes = tf.shape(boxes)[0]
num_boxes += pad
boxes = tf.pad(
tf.cast(boxes, tf.float32), [[0, pad], [0, 0]], constant_values=-1)
scores = tf.pad(tf.cast(scores, tf.float32), [[0, pad]])
# mask boxes to -1 by score threshold
scores_mask = tf.expand_dims(
tf.cast(scores > score_threshold, boxes.dtype), axis=1)
boxes = ((boxes + 1.) * scores_mask) - 1.
boxes = tf.expand_dims(boxes, axis=0)
scores = tf.expand_dims(scores, axis=0)
def _loop_cond(unused_boxes, unused_threshold, output_size, idx):
return tf.logical_and(
tf.reduce_min(output_size) < max_output_size,
idx < num_boxes // _NMS_TILE_SIZE)
selected_boxes, _, output_size, _ = tf.while_loop(
_loop_cond, _suppression_loop_body,
[boxes, iou_threshold,
tf.zeros([1], tf.int32),
tf.constant(0)])
idx = num_boxes - tf.cast(
tf.nn.top_k(
tf.cast(tf.reduce_any(selected_boxes > 0, [2]), tf.int32) *
tf.expand_dims(tf.range(num_boxes, 0, -1), 0), max_output_size)[0],
tf.int32)
idx = tf.minimum(idx, num_boxes - 1 - pad)
idx = tf.reshape(idx + tf.reshape(tf.range(1) * num_boxes, [-1, 1]), [-1])
num_valid_boxes = tf.reduce_sum(output_size)
return (idx, num_valid_boxes, tf.reshape(boxes, [-1, 4]),
tf.reshape(scores, [-1]), argsort_ids)
def _validate_boxes_scores_iou_thresh(boxes, scores, iou_thresh,
change_coordinate_frame, clip_window):
"""Validates boxes, scores and iou_thresh.
This function validates the boxes, scores, iou_thresh
and if change_coordinate_frame is True, clip_window must be specified.
Args:
boxes: A [k, q, 4] float32 tensor containing k detections. `q` can be either
number of classes or 1 depending on whether a separate box is predicted
per class.
scores: A [k, num_classes] float32 tensor containing the scores for each of
the k detections. The scores have to be non-negative when
pad_to_max_output_size is True.
iou_thresh: scalar threshold for IOU (new boxes that have high IOU overlap
with previously selected boxes are removed).
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)
clip_window: A float32 tensor of the form [y_min, x_min, y_max, x_max]
representing the window to clip and normalize boxes to before performing
non-max suppression.
Raises:
ValueError: if iou_thresh is not in [0, 1] or if input boxlist does not
have a valid scores field.
"""
if not 0 <= iou_thresh <= 1.0:
raise ValueError('iou_thresh must be between 0 and 1')
if scores.shape.ndims != 2:
raise ValueError('scores field must be of rank 2')
if shape_utils.get_dim_as_int(scores.shape[1]) is None:
raise ValueError('scores must have statically defined second ' 'dimension')
if boxes.shape.ndims != 3:
raise ValueError('boxes must be of rank 3.')
if not (shape_utils.get_dim_as_int(
boxes.shape[1]) == shape_utils.get_dim_as_int(scores.shape[1]) or
shape_utils.get_dim_as_int(boxes.shape[1]) == 1):
raise ValueError('second dimension of boxes must be either 1 or equal '
'to the second dimension of scores')
if shape_utils.get_dim_as_int(boxes.shape[2]) != 4:
raise ValueError('last dimension of boxes must be of size 4.')
if change_coordinate_frame and clip_window is None:
raise ValueError('if change_coordinate_frame is True, then a clip_window'
'must be specified.')
def _clip_window_prune_boxes(sorted_boxes, clip_window, pad_to_max_output_size,
change_coordinate_frame):
"""Prune boxes with zero area.
Args:
sorted_boxes: A BoxList containing k detections.
clip_window: A float32 tensor of the form [y_min, x_min, y_max, x_max]
representing the window to clip and normalize boxes to before performing
non-max suppression.
pad_to_max_output_size: flag indicating whether to pad to max output size or
not.
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).
Returns:
sorted_boxes: A BoxList containing k detections after pruning.
num_valid_nms_boxes_cumulative: Number of valid NMS boxes
"""
sorted_boxes = box_list_ops.clip_to_window(
sorted_boxes,
clip_window,
filter_nonoverlapping=not pad_to_max_output_size)
# Set the scores of boxes with zero area to -1 to keep the default
# behaviour of pruning out zero area boxes.
sorted_boxes_size = tf.shape(sorted_boxes.get())[0]
non_zero_box_area = tf.cast(box_list_ops.area(sorted_boxes), tf.bool)
sorted_boxes_scores = tf.where(
non_zero_box_area, sorted_boxes.get_field(fields.BoxListFields.scores),
-1 * tf.ones(sorted_boxes_size))
sorted_boxes.add_field(fields.BoxListFields.scores, sorted_boxes_scores)
num_valid_nms_boxes_cumulative = tf.reduce_sum(
tf.cast(tf.greater_equal(sorted_boxes_scores, 0), tf.int32))
sorted_boxes = box_list_ops.sort_by_field(sorted_boxes,
fields.BoxListFields.scores)
if change_coordinate_frame:
sorted_boxes = box_list_ops.change_coordinate_frame(sorted_boxes,
clip_window)
if sorted_boxes.has_field(fields.BoxListFields.keypoints):
sorted_keypoints = sorted_boxes.get_field(fields.BoxListFields.keypoints)
sorted_keypoints = keypoint_ops.change_coordinate_frame(sorted_keypoints,
clip_window)
sorted_boxes.set_field(fields.BoxListFields.keypoints, sorted_keypoints)
return sorted_boxes, num_valid_nms_boxes_cumulative
def _clip_boxes(boxes, clip_window):
"""Clips boxes to the given window.
Args:
boxes: A [batch, num_boxes, 4] float32 tensor containing box coordinates in
[ymin, xmin, ymax, xmax] form.
clip_window: A [batch, 4] float32 tensor with left top and right bottom
coordinate of the window in [ymin, xmin, ymax, xmax] form.
Returns:
A [batch, num_boxes, 4] float32 tensor containing boxes clipped to the given
window.
"""
ymin, xmin, ymax, xmax = tf.unstack(boxes, axis=-1)
clipped_ymin = tf.maximum(ymin, clip_window[:, 0, tf.newaxis])
clipped_xmin = tf.maximum(xmin, clip_window[:, 1, tf.newaxis])
clipped_ymax = tf.minimum(ymax, clip_window[:, 2, tf.newaxis])
clipped_xmax = tf.minimum(xmax, clip_window[:, 3, tf.newaxis])
return tf.stack([clipped_ymin, clipped_xmin, clipped_ymax, clipped_xmax],
axis=-1)
class NullContextmanager(object):
def __enter__(self):
pass
def __exit__(self, type_arg, value_arg, traceback_arg):
return False
def 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,
masks=None,
boundaries=None,
pad_to_max_output_size=False,
use_partitioned_nms=False,
additional_fields=None,
soft_nms_sigma=0.0,
use_hard_nms=False,
use_cpu_nms=False,
scope=None):
"""Multi-class version of non maximum suppression.
This op 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 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.
Please note that this operation is performed on *all* classes, therefore any
background classes should be removed prior to calling this function.
Selected boxes are guaranteed to be sorted in decreasing order by score (but
the sort is not guaranteed to be stable).
Args:
boxes: A [k, q, 4] float32 tensor containing k detections. `q` can be either
number of classes or 1 depending on whether a separate box is predicted
per class.
scores: A [k, num_classes] float32 tensor containing the scores for each of
the k detections. The scores have to be non-negative when
pad_to_max_output_size is 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 the form [y_min, x_min, y_max, x_max]
representing the window to clip and normalize boxes to before performing
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)
masks: (optional) a [k, 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.
boundaries: (optional) a [k, q, boundary_height, boundary_width] float32
tensor containing box boundaries. `q` can be either number of classes or 1
depending on whether a separate boundary is predicted per class.
pad_to_max_output_size: If true, the output nmsed boxes are padded to be of
length `max_size_per_class`. Defaults to false.
use_partitioned_nms: If true, use partitioned version of
non_max_suppression.
additional_fields: (optional) If not None, a dictionary that maps keys to
tensors whose first dimensions are all of size `k`. After non-maximum
suppression, all tensors corresponding to the selected boxes will be
added to resulting BoxList.
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.
use_hard_nms: Enforce the usage of hard NMS.
use_cpu_nms: Enforce NMS to run on CPU.
scope: name scope.
Returns:
A tuple of sorted_boxes and num_valid_nms_boxes. The sorted_boxes is a
BoxList holds M boxes with a rank-1 scores field representing
corresponding scores for each box with scores sorted in decreasing order
and a rank-1 classes field representing a class label for each box. The
num_valid_nms_boxes is a 0-D integer tensor representing the number of
valid elements in `BoxList`, with the valid elements appearing first.
Raises:
ValueError: if iou_thresh is not in [0, 1] or if input boxlist does not have
a valid scores field.
ValueError: if Soft NMS (tf.image.non_max_suppression_with_scores) is not
supported in the current TF version and `soft_nms_sigma` is nonzero.
"""
_validate_boxes_scores_iou_thresh(boxes, scores, iou_thresh,
change_coordinate_frame, clip_window)
if pad_to_max_output_size and soft_nms_sigma != 0.0:
raise ValueError('Soft NMS (soft_nms_sigma != 0.0) is currently not '
'supported when pad_to_max_output_size is True.')
with tf.name_scope(scope, 'MultiClassNonMaxSuppression'), tf.device(
'cpu:0') if use_cpu_nms else NullContextmanager():
num_scores = tf.shape(scores)[0]
num_classes = shape_utils.get_dim_as_int(scores.get_shape()[1])
selected_boxes_list = []
num_valid_nms_boxes_cumulative = tf.constant(0)
per_class_boxes_list = tf.unstack(boxes, axis=1)
if masks is not None:
per_class_masks_list = tf.unstack(masks, axis=1)
if boundaries is not None:
per_class_boundaries_list = tf.unstack(boundaries, axis=1)
boxes_ids = (range(num_classes) if len(per_class_boxes_list) > 1
else [0] * num_classes)
for class_idx, boxes_idx in zip(range(num_classes), boxes_ids):
per_class_boxes = per_class_boxes_list[boxes_idx]
boxlist_and_class_scores = box_list.BoxList(per_class_boxes)
class_scores = tf.reshape(
tf.slice(scores, [0, class_idx], tf.stack([num_scores, 1])), [-1])
boxlist_and_class_scores.add_field(fields.BoxListFields.scores,
class_scores)
if masks is not None:
per_class_masks = per_class_masks_list[boxes_idx]
boxlist_and_class_scores.add_field(fields.BoxListFields.masks,
per_class_masks)
if boundaries is not None:
per_class_boundaries = per_class_boundaries_list[boxes_idx]
boxlist_and_class_scores.add_field(fields.BoxListFields.boundaries,
per_class_boundaries)
if additional_fields is not None:
for key, tensor in additional_fields.items():
boxlist_and_class_scores.add_field(key, tensor)
nms_result = None
selected_scores = None
if pad_to_max_output_size:
max_selection_size = max_size_per_class
if use_partitioned_nms:
(selected_indices, num_valid_nms_boxes,
boxlist_and_class_scores.data['boxes'],
boxlist_and_class_scores.data['scores'],
_) = partitioned_non_max_suppression_padded(
boxlist_and_class_scores.get(),
boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
max_selection_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh)
else:
selected_indices, num_valid_nms_boxes = (
tf.image.non_max_suppression_padded(
boxlist_and_class_scores.get(),
boxlist_and_class_scores.get_field(
fields.BoxListFields.scores),
max_selection_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh,
pad_to_max_output_size=True))
nms_result = box_list_ops.gather(boxlist_and_class_scores,
selected_indices)
selected_scores = nms_result.get_field(fields.BoxListFields.scores)
else:
max_selection_size = tf.minimum(max_size_per_class,
boxlist_and_class_scores.num_boxes())
if (hasattr(tf.image, 'non_max_suppression_with_scores') and
tf.compat.forward_compatible(2019, 6, 6) and not use_hard_nms):
(selected_indices, selected_scores
) = tf.image.non_max_suppression_with_scores(
boxlist_and_class_scores.get(),
boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
max_selection_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh,
soft_nms_sigma=soft_nms_sigma)
num_valid_nms_boxes = tf.shape(selected_indices)[0]
selected_indices = tf.concat(
[selected_indices,
tf.zeros(max_selection_size-num_valid_nms_boxes, tf.int32)], 0)
selected_scores = tf.concat(
[selected_scores,
tf.zeros(max_selection_size-num_valid_nms_boxes,
tf.float32)], -1)
nms_result = box_list_ops.gather(boxlist_and_class_scores,
selected_indices)
else:
if soft_nms_sigma != 0:
raise ValueError('Soft NMS not supported in current TF version!')
selected_indices = tf.image.non_max_suppression(
boxlist_and_class_scores.get(),
boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
max_selection_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh)
num_valid_nms_boxes = tf.shape(selected_indices)[0]
selected_indices = tf.concat(
[selected_indices,
tf.zeros(max_selection_size-num_valid_nms_boxes, tf.int32)], 0)
nms_result = box_list_ops.gather(boxlist_and_class_scores,
selected_indices)
selected_scores = nms_result.get_field(fields.BoxListFields.scores)
# Make the scores -1 for invalid boxes.
valid_nms_boxes_indices = tf.less(
tf.range(max_selection_size), num_valid_nms_boxes)
nms_result.add_field(
fields.BoxListFields.scores,
tf.where(valid_nms_boxes_indices,
selected_scores, -1*tf.ones(max_selection_size)))
num_valid_nms_boxes_cumulative += num_valid_nms_boxes
nms_result.add_field(
fields.BoxListFields.classes, (tf.zeros_like(
nms_result.get_field(fields.BoxListFields.scores)) + class_idx))
selected_boxes_list.append(nms_result)
selected_boxes = box_list_ops.concatenate(selected_boxes_list)
sorted_boxes = box_list_ops.sort_by_field(selected_boxes,
fields.BoxListFields.scores)
if clip_window is not None:
# When pad_to_max_output_size is False, it prunes the boxes with zero
# area.
sorted_boxes, num_valid_nms_boxes_cumulative = _clip_window_prune_boxes(
sorted_boxes, clip_window, pad_to_max_output_size,
change_coordinate_frame)
if max_total_size:
max_total_size = tf.minimum(max_total_size, sorted_boxes.num_boxes())
sorted_boxes = box_list_ops.gather(sorted_boxes, tf.range(max_total_size))
num_valid_nms_boxes_cumulative = tf.where(
max_total_size > num_valid_nms_boxes_cumulative,
num_valid_nms_boxes_cumulative, max_total_size)
# Select only the valid boxes if pad_to_max_output_size is False.
if not pad_to_max_output_size:
sorted_boxes = box_list_ops.gather(
sorted_boxes, tf.range(num_valid_nms_boxes_cumulative))
return sorted_boxes, num_valid_nms_boxes_cumulative
def class_agnostic_non_max_suppression(boxes,
scores,
score_thresh,
iou_thresh,
max_classes_per_detection=1,
max_total_size=0,
clip_window=None,
change_coordinate_frame=False,
masks=None,
boundaries=None,
pad_to_max_output_size=False,
use_partitioned_nms=False,
additional_fields=None,
soft_nms_sigma=0.0,
scope=None):
"""Class-agnostic version of non maximum suppression.
This op 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 on all the boxes using
max scores across all classes 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.
Please note that this operation is performed in a class-agnostic way,
therefore any background classes should be removed prior to calling this
function.
Selected boxes are guaranteed to be sorted in decreasing order by score (but
the sort is not guaranteed to be stable).
Args:
boxes: A [k, q, 4] float32 tensor containing k detections. `q` can be either
number of classes or 1 depending on whether a separate box is predicted
per class.
scores: A [k, num_classes] float32 tensor containing the scores for each of
the k detections. The scores have to be non-negative when
pad_to_max_output_size is 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_classes_per_detection: maximum number of retained classes per detection
box in class-agnostic NMS.
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 the form [y_min, x_min, y_max, x_max]
representing the window to clip and normalize boxes to before performing
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)
masks: (optional) a [k, 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.
boundaries: (optional) a [k, q, boundary_height, boundary_width] float32
tensor containing box boundaries. `q` can be either number of classes or 1
depending on whether a separate boundary is predicted per class.
pad_to_max_output_size: If true, the output nmsed boxes are padded to be of
length `max_size_per_class`. Defaults to false.
use_partitioned_nms: If true, use partitioned version of
non_max_suppression.
additional_fields: (optional) If not None, a dictionary that maps keys to
tensors whose first dimensions are all of size `k`. After non-maximum
suppression, all tensors corresponding to the selected boxes will be added
to resulting BoxList.
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: name scope.
Returns:
A tuple of sorted_boxes and num_valid_nms_boxes. The sorted_boxes is a
BoxList holds M boxes with a rank-1 scores field representing
corresponding scores for each box with scores sorted in decreasing order
and a rank-1 classes field representing a class label for each box. The
num_valid_nms_boxes is a 0-D integer tensor representing the number of
valid elements in `BoxList`, with the valid elements appearing first.
Raises:
ValueError: if iou_thresh is not in [0, 1] or if input boxlist does not have
a valid scores field or if non-zero soft_nms_sigma is provided when
pad_to_max_output_size is True.
"""
_validate_boxes_scores_iou_thresh(boxes, scores, iou_thresh,
change_coordinate_frame, clip_window)
if pad_to_max_output_size and soft_nms_sigma != 0.0:
raise ValueError('Soft NMS (soft_nms_sigma != 0.0) is currently not '
'supported when pad_to_max_output_size is True.')
if max_classes_per_detection > 1:
raise ValueError('Max classes per detection box >1 not supported.')
q = shape_utils.get_dim_as_int(boxes.shape[1])
if q > 1:
class_ids = tf.expand_dims(
tf.argmax(scores, axis=1, output_type=tf.int32), axis=1)
boxes = tf.batch_gather(boxes, class_ids)
if masks is not None:
masks = tf.batch_gather(masks, class_ids)
if boundaries is not None:
boundaries = tf.batch_gather(boundaries, class_ids)
boxes = tf.squeeze(boxes, axis=[1])
if masks is not None:
masks = tf.squeeze(masks, axis=[1])
if boundaries is not None:
boundaries = tf.squeeze(boundaries, axis=[1])
with tf.name_scope(scope, 'ClassAgnosticNonMaxSuppression'):
boxlist_and_class_scores = box_list.BoxList(boxes)
max_scores = tf.reduce_max(scores, axis=-1)
classes_with_max_scores = tf.argmax(scores, axis=-1)
boxlist_and_class_scores.add_field(fields.BoxListFields.scores, max_scores)
if masks is not None:
boxlist_and_class_scores.add_field(fields.BoxListFields.masks, masks)
if boundaries is not None:
boxlist_and_class_scores.add_field(fields.BoxListFields.boundaries,
boundaries)
if additional_fields is not None:
for key, tensor in additional_fields.items():
boxlist_and_class_scores.add_field(key, tensor)
nms_result = None
selected_scores = None
if pad_to_max_output_size:
max_selection_size = max_total_size
if use_partitioned_nms:
(selected_indices, num_valid_nms_boxes,
boxlist_and_class_scores.data['boxes'],
boxlist_and_class_scores.data['scores'],
argsort_ids) = partitioned_non_max_suppression_padded(
boxlist_and_class_scores.get(),
boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
max_selection_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh)
classes_with_max_scores = tf.gather(classes_with_max_scores,
argsort_ids)
else:
selected_indices, num_valid_nms_boxes = (
tf.image.non_max_suppression_padded(
boxlist_and_class_scores.get(),
boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
max_selection_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh,
pad_to_max_output_size=True))
nms_result = box_list_ops.gather(boxlist_and_class_scores,
selected_indices)
selected_scores = nms_result.get_field(fields.BoxListFields.scores)
else:
max_selection_size = tf.minimum(max_total_size,
boxlist_and_class_scores.num_boxes())
if (hasattr(tf.image, 'non_max_suppression_with_scores') and
tf.compat.forward_compatible(2019, 6, 6)):
(selected_indices, selected_scores
) = tf.image.non_max_suppression_with_scores(
boxlist_and_class_scores.get(),
boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
max_selection_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh,
soft_nms_sigma=soft_nms_sigma)
num_valid_nms_boxes = tf.shape(selected_indices)[0]
selected_indices = tf.concat([
selected_indices,
tf.zeros(max_selection_size - num_valid_nms_boxes, tf.int32)
], 0)
selected_scores = tf.concat(
[selected_scores,
tf.zeros(max_selection_size-num_valid_nms_boxes, tf.float32)], -1)
nms_result = box_list_ops.gather(boxlist_and_class_scores,
selected_indices)
else:
if soft_nms_sigma != 0:
raise ValueError('Soft NMS not supported in current TF version!')
selected_indices = tf.image.non_max_suppression(
boxlist_and_class_scores.get(),
boxlist_and_class_scores.get_field(fields.BoxListFields.scores),
max_selection_size,
iou_threshold=iou_thresh,
score_threshold=score_thresh)
num_valid_nms_boxes = tf.shape(selected_indices)[0]
selected_indices = tf.concat(
[selected_indices,
tf.zeros(max_selection_size-num_valid_nms_boxes, tf.int32)], 0)
nms_result = box_list_ops.gather(boxlist_and_class_scores,
selected_indices)
selected_scores = nms_result.get_field(fields.BoxListFields.scores)
valid_nms_boxes_indices = tf.less(
tf.range(max_selection_size), num_valid_nms_boxes)
nms_result.add_field(
fields.BoxListFields.scores,
tf.where(valid_nms_boxes_indices,
selected_scores, -1*tf.ones(max_selection_size)))
selected_classes = tf.gather(classes_with_max_scores, selected_indices)
selected_classes = tf.cast(selected_classes, tf.float32)
nms_result.add_field(fields.BoxListFields.classes, selected_classes)
selected_boxes = nms_result
sorted_boxes = box_list_ops.sort_by_field(selected_boxes,
fields.BoxListFields.scores)
if clip_window is not None:
# When pad_to_max_output_size is False, it prunes the boxes with zero
# area.
sorted_boxes, num_valid_nms_boxes = _clip_window_prune_boxes(
sorted_boxes, clip_window, pad_to_max_output_size,
change_coordinate_frame)
if max_total_size:
max_total_size = tf.minimum(max_total_size, sorted_boxes.num_boxes())
sorted_boxes = box_list_ops.gather(sorted_boxes, tf.range(max_total_size))
num_valid_nms_boxes = tf.where(max_total_size > num_valid_nms_boxes,
num_valid_nms_boxes, max_total_size)
# Select only the valid boxes if pad_to_max_output_size is False.
if not pad_to_max_output_size:
sorted_boxes = box_list_ops.gather(sorted_boxes,
tf.range(num_valid_nms_boxes))
return sorted_boxes, num_valid_nms_boxes
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)