Faster RCNN Generate Proposal Labels (#12616)

* Add generate_proposal_labels for Faster-RCNN.

paddle/fluid/API.spec

@@ -303,6 +303,7 @@ paddle.fluid.layers.ssd_loss ArgSpec(args=['location', 'confidence', 'gt_box', '
 paddle.fluid.layers.detection_map ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral'))
 paddle.fluid.layers.rpn_target_assign ArgSpec(args=['loc', 'scores', 'anchor_box', 'gt_box', 'rpn_batch_size_per_im', 'fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap'], varargs=None, keywords=None, defaults=(256, 0.25, 0.7, 0.3))
 paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None))
+paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'gt_boxes', 'im_scales', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None))
 paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
 paddle.fluid.layers.iou_similarity ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.box_coder ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)

paddle/fluid/operators/detection/CMakeLists.txt

@@ -29,6 +29,7 @@ target_assign_op.cu)
 detection_library(polygon_box_transform_op SRCS polygon_box_transform_op.cc
 polygon_box_transform_op.cu)
 detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
+detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
 detection_library(generate_proposals_op SRCS generate_proposals_op.cc)
 #Export local libraries to parent
 set(DETECTION_LIBRARY ${LOCAL_DETECTION_LIBS} PARENT_SCOPE)

paddle/fluid/operators/detection/rpn_target_assign_op.cc

@@ -86,7 +86,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
                          std::minstd_rand engine,
                          std::vector<int>* inds) const {
     std::uniform_real_distribution<float> uniform(0, 1);
-    const int64_t size = static_cast<int64_t>(inds->size());
+    const int64_t size = static_cast<int64_t>(inds->size() - offset);
     if (size > num) {
       for (int64_t i = num; i < size; ++i) {
         int rng_ind = std::floor(uniform(engine) * i);
@@ -126,7 +126,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
                 neg_threshold, target_label_data, fg_inds, bg_inds);
     // Reservoir Sampling
     ReservoirSampling(fg_num, fg_offset, engine, fg_inds);
-    int bg_num = rpn_batch_size - fg_inds->size();
+    int bg_num = rpn_batch_size - (fg_inds->size() - fg_offset);
     ReservoirSampling(bg_num, bg_offset, engine, bg_inds);
   }
 

paddle/fluid/operators/gather_op.cc

@@ -101,5 +101,8 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(gather, ops::GatherOp, ops::GatherOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(gather_grad, ops::GatherGradOp);
-REGISTER_OP_CPU_KERNEL(gather, ops::GatherOpKernel<float>);
-REGISTER_OP_CPU_KERNEL(gather_grad, ops::GatherGradientOpKernel<float>);
+REGISTER_OP_CPU_KERNEL(gather, ops::GatherOpKernel<float>,
+                       ops::GatherOpKernel<int>, ops::GatherOpKernel<double>);
+REGISTER_OP_CPU_KERNEL(gather_grad, ops::GatherGradientOpKernel<float>,
+                       ops::GatherGradientOpKernel<int>,
+                       ops::GatherGradientOpKernel<double>);

python/paddle/fluid/layers/detection.py

@@ -39,6 +39,7 @@ __all__ = [
     'detection_map',
     'rpn_target_assign',
     'anchor_generator',
+    'generate_proposal_labels',
     'generate_proposals',
 ]
 
@@ -1256,6 +1257,64 @@ def anchor_generator(input,
     return anchor, var
 
 
+def generate_proposal_labels(rpn_rois,
+                             gt_classes,
+                             gt_boxes,
+                             im_scales,
+                             batch_size_per_im=256,
+                             fg_fraction=0.25,
+                             fg_thresh=0.25,
+                             bg_thresh_hi=0.5,
+                             bg_thresh_lo=0.0,
+                             bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
+                             class_nums=None):
+    """
+    ** Generate proposal labels Faster-RCNN **
+    TODO(buxingyuan): Add Document
+    """
+
+    helper = LayerHelper('generate_proposal_labels', **locals())
+
+    rois = helper.create_tmp_variable(dtype=rpn_rois.dtype)
+    labels_int32 = helper.create_tmp_variable(dtype=gt_classes.dtype)
+    bbox_targets = helper.create_tmp_variable(dtype=rpn_rois.dtype)
+    bbox_inside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
+    bbox_outside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
+
+    helper.append_op(
+        type="generate_proposal_labels",
+        inputs={
+            'RpnRois': rpn_rois,
+            'GtClasses': gt_classes,
+            'GtBoxes': gt_boxes,
+            'ImScales': im_scales
+        },
+        outputs={
+            'Rois': rois,
+            'LabelsInt32': labels_int32,
+            'BboxTargets': bbox_targets,
+            'BboxInsideWeights': bbox_inside_weights,
+            'BboxOutsideWeights': bbox_outside_weights
+        },
+        attrs={
+            'batch_size_per_im': batch_size_per_im,
+            'fg_fraction': fg_fraction,
+            'fg_thresh': fg_thresh,
+            'bg_thresh_hi': bg_thresh_hi,
+            'bg_thresh_lo': bg_thresh_lo,
+            'bbox_reg_weights': bbox_reg_weights,
+            'class_nums': class_nums
+        })
+
+    rois.stop_gradient = True
+    labels_int32.stop_gradient = True
+    bbox_targets.stop_gradient = True
+    bbox_inside_weights.stop_gradient = True
+    bbox_outside_weights.stop_gradient = True
+
+    return rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights
+
+
 def generate_proposals(scores,
                        bbox_deltas,
                        im_info,

python/paddle/fluid/tests/test_detection.py

@@ -146,6 +146,55 @@ class TestAnchorGenerator(unittest.TestCase):
         assert anchor.shape[3] == 4
 
 
+class TestGenerateProposalLabels(unittest.TestCase):
+    def test_generate_proposal_labels(self):
+        rpn_rois = layers.data(
+            name='rpn_rois',
+            shape=[4, 4],
+            dtype='float32',
+            lod_level=1,
+            append_batch_size=False)
+        gt_classes = layers.data(
+            name='gt_classes',
+            shape=[6],
+            dtype='int32',
+            lod_level=1,
+            append_batch_size=False)
+        gt_boxes = layers.data(
+            name='gt_boxes',
+            shape=[6, 4],
+            dtype='float32',
+            lod_level=1,
+            append_batch_size=False)
+        im_scales = layers.data(
+            name='im_scales',
+            shape=[1],
+            dtype='float32',
+            lod_level=1,
+            append_batch_size=False)
+        class_nums = 5
+        rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights = fluid.layers.generate_proposal_labels(
+            rpn_rois=rpn_rois,
+            gt_classes=gt_classes,
+            gt_boxes=gt_boxes,
+            im_scales=im_scales,
+            batch_size_per_im=2,
+            fg_fraction=0.5,
+            fg_thresh=0.5,
+            bg_thresh_hi=0.5,
+            bg_thresh_lo=0.0,
+            bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
+            class_nums=class_nums)
+        assert rois.shape[1] == 4
+        assert rois.shape[0] == labels_int32.shape[0]
+        assert rois.shape[0] == bbox_targets.shape[0]
+        assert rois.shape[0] == bbox_inside_weights.shape[0]
+        assert rois.shape[0] == bbox_outside_weights.shape[0]
+        assert bbox_targets.shape[1] == 4 * class_nums
+        assert bbox_inside_weights.shape[1] == 4 * class_nums
+        assert bbox_outside_weights.shape[1] == 4 * class_nums
+
+
 class TestMultiBoxHead(unittest.TestCase):
     def test_multi_box_head(self):
         data_shape = [3, 224, 224]

python/paddle/fluid/tests/unittests/test_generate_proposal_labels.py

+#   Copyright (c) 2018 PaddlePaddle 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://w_idxw.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.
+
+import unittest
+import numpy as np
+import sys
+import math
+import paddle.fluid as fluid
+from op_test import OpTest
+
+
+def generate_proposal_labels_in_python(
+        rpn_rois, gt_classes, gt_boxes, im_scales, batch_size_per_im,
+        fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights,
+        class_nums):
+    rois = []
+    labels_int32 = []
+    bbox_targets = []
+    bbox_inside_weights = []
+    bbox_outside_weights = []
+    lod = []
+    assert len(rpn_rois) == len(
+        im_scales), 'batch size of rpn_rois and ground_truth is not matched'
+
+    for im_i in range(len(im_scales)):
+        frcn_blobs = _sample_rois(
+            rpn_rois[im_i], gt_classes[im_i], gt_boxes[im_i], im_scales[im_i],
+            batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi,
+            bg_thresh_lo, bbox_reg_weights, class_nums)
+
+        lod.append(frcn_blobs['rois'].shape[0])
+
+        rois.append(frcn_blobs['rois'])
+        labels_int32.append(frcn_blobs['labels_int32'])
+        bbox_targets.append(frcn_blobs['bbox_targets'])
+        bbox_inside_weights.append(frcn_blobs['bbox_inside_weights'])
+        bbox_outside_weights.append(frcn_blobs['bbox_outside_weights'])
+
+    return rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, lod
+
+
+def _sample_rois(rpn_rois, gt_classes, gt_boxes, im_scale, batch_size_per_im,
+                 fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo,
+                 bbox_reg_weights, class_nums):
+    rois_per_image = int(batch_size_per_im)
+    fg_rois_per_im = int(np.round(fg_fraction * rois_per_image))
+
+    # Roidb
+    inv_im_scale = 1. / im_scale
+    rpn_rois = rpn_rois * inv_im_scale
+
+    boxes = np.vstack([gt_boxes, rpn_rois])
+    gt_overlaps = np.zeros((boxes.shape[0], class_nums))
+    box_to_gt_ind_map = np.zeros((boxes.shape[0]), dtype=np.int32)
+    if len(gt_boxes) > 0:
+        proposal_to_gt_overlaps = _bbox_overlaps(boxes, gt_boxes)
+
+        overlaps_argmax = proposal_to_gt_overlaps.argmax(axis=1)
+        overlaps_max = proposal_to_gt_overlaps.max(axis=1)
+        # Boxes which with non-zero overlap with gt boxes
+        overlapped_boxes_ind = np.where(overlaps_max > 0)[0]
+        overlapped_boxes_gt_classes = gt_classes[overlaps_argmax[
+            overlapped_boxes_ind]]
+        gt_overlaps[overlapped_boxes_ind,
+                    overlapped_boxes_gt_classes] = overlaps_max[
+                        overlapped_boxes_ind]
+        box_to_gt_ind_map[overlapped_boxes_ind] = overlaps_argmax[
+            overlapped_boxes_ind]
+
+    max_overlaps = gt_overlaps.max(axis=1)
+    max_classes = gt_overlaps.argmax(axis=1)
+
+    # Foreground
+    fg_inds = np.where(max_overlaps >= fg_thresh)[0]
+    fg_rois_per_this_image = np.minimum(fg_rois_per_im, fg_inds.shape[0])
+    # Sample foreground if there are too many
+    if fg_inds.shape[0] > fg_rois_per_this_image:
+        fg_inds = np.random.choice(
+            fg_inds, size=fg_rois_per_this_image, replace=False)
+
+    # Background
+    bg_inds = np.where((max_overlaps < bg_thresh_hi) & (max_overlaps >=
+                                                        bg_thresh_lo))[0]
+    bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
+    bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,
+                                        bg_inds.shape[0])
+    # Sample background if there are too many
+    if bg_inds.shape[0] > bg_rois_per_this_image:
+        bg_inds = np.random.choice(
+            bg_inds, size=bg_rois_per_this_image, replace=False)
+
+    keep_inds = np.append(fg_inds, bg_inds)
+    sampled_labels = max_classes[keep_inds]
+    sampled_labels[fg_rois_per_this_image:] = 0
+    sampled_boxes = boxes[keep_inds]
+    sampled_gts = gt_boxes[box_to_gt_ind_map[keep_inds]]
+    sampled_gts[fg_rois_per_this_image:, :] = gt_boxes[0]
+
+    bbox_label_targets = _compute_targets(sampled_boxes, sampled_gts,
+                                          sampled_labels, bbox_reg_weights)
+    bbox_targets, bbox_inside_weights = _expand_bbox_targets(bbox_label_targets,
+                                                             class_nums)
+    bbox_outside_weights = np.array(
+        bbox_inside_weights > 0, dtype=bbox_inside_weights.dtype)
+
+    # Scale rois
+    sampled_rois = sampled_boxes * im_scale
+
+    # Faster RCNN blobs
+    frcn_blobs = dict(
+        rois=sampled_rois,
+        labels_int32=sampled_labels,
+        bbox_targets=bbox_targets,
+        bbox_inside_weights=bbox_inside_weights,
+        bbox_outside_weights=bbox_outside_weights)
+    return frcn_blobs
+
+
+def _bbox_overlaps(roi_boxes, gt_boxes):
+    w1 = np.maximum(roi_boxes[:, 2] - roi_boxes[:, 0] + 1, 0)
+    h1 = np.maximum(roi_boxes[:, 3] - roi_boxes[:, 1] + 1, 0)
+    w2 = np.maximum(gt_boxes[:, 2] - gt_boxes[:, 0] + 1, 0)
+    h2 = np.maximum(gt_boxes[:, 3] - gt_boxes[:, 1] + 1, 0)
+    area1 = w1 * h1
+    area2 = w2 * h2
+
+    overlaps = np.zeros((roi_boxes.shape[0], gt_boxes.shape[0]))
+    for ind1 in range(roi_boxes.shape[0]):
+        for ind2 in range(gt_boxes.shape[0]):
+            inter_x1 = np.maximum(roi_boxes[ind1, 0], gt_boxes[ind2, 0])
+            inter_y1 = np.maximum(roi_boxes[ind1, 1], gt_boxes[ind2, 1])
+            inter_x2 = np.minimum(roi_boxes[ind1, 2], gt_boxes[ind2, 2])
+            inter_y2 = np.minimum(roi_boxes[ind1, 3], gt_boxes[ind2, 3])
+            inter_w = np.maximum(inter_x2 - inter_x1 + 1, 0)
+            inter_h = np.maximum(inter_y2 - inter_y1 + 1, 0)
+            inter_area = inter_w * inter_h
+            iou = inter_area / (area1[ind1] + area2[ind2] - inter_area)
+            overlaps[ind1, ind2] = iou
+    return overlaps
+
+
+def _compute_targets(roi_boxes, gt_boxes, labels, bbox_reg_weights):
+    assert roi_boxes.shape[0] == gt_boxes.shape[0]
+    assert roi_boxes.shape[1] == 4
+    assert gt_boxes.shape[1] == 4
+
+    targets = np.zeros(roi_boxes.shape)
+    bbox_reg_weights = np.asarray(bbox_reg_weights)
+    targets = _box_to_delta(
+        ex_boxes=roi_boxes, gt_boxes=gt_boxes, weights=bbox_reg_weights)
+
+    return np.hstack([labels[:, np.newaxis], targets]).astype(
+        np.float32, copy=False)
+
+
+def _box_to_delta(ex_boxes, gt_boxes, weights):
+    ex_w = ex_boxes[:, 2] - ex_boxes[:, 0] + 1
+    ex_h = ex_boxes[:, 3] - ex_boxes[:, 1] + 1
+    ex_ctr_x = ex_boxes[:, 0] + 0.5 * ex_w
+    ex_ctr_y = ex_boxes[:, 1] + 0.5 * ex_h
+
+    gt_w = gt_boxes[:, 2] - gt_boxes[:, 0] + 1
+    gt_h = gt_boxes[:, 3] - gt_boxes[:, 1] + 1
+    gt_ctr_x = gt_boxes[:, 0] + 0.5 * gt_w
+    gt_ctr_y = gt_boxes[:, 1] + 0.5 * gt_h
+
+    dx = (gt_ctr_x - ex_ctr_x) / ex_w / weights[0]
+    dy = (gt_ctr_y - ex_ctr_y) / ex_h / weights[1]
+    dw = (np.log(gt_w / ex_w)) / ex_w / weights[2]
+    dh = (np.log(gt_h / ex_h)) / ex_h / weights[3]
+
+    targets = np.vstack([dx, dy, dw, dh]).transpose()
+    return targets
+
+
+def _expand_bbox_targets(bbox_targets_input, class_nums):
+    class_labels = bbox_targets_input[:, 0]
+    fg_inds = np.where(class_labels > 0)[0]
+
+    bbox_targets = np.zeros((class_labels.shape[0], 4 * class_nums))
+    bbox_inside_weights = np.zeros(bbox_targets.shape)
+    for ind in fg_inds:
+        class_label = int(class_labels[ind])
+        start_ind = class_label * 4
+        end_ind = class_label * 4 + 4
+        bbox_targets[ind, start_ind:end_ind] = bbox_targets_input[ind, 1:]
+        bbox_inside_weights[ind, start_ind:end_ind] = (1.0, 1.0, 1.0, 1.0)
+
+    return bbox_targets, bbox_inside_weights
+
+
+class TestGenerateProposalLabelsOp(OpTest):
+    def set_data(self):
+        self.init_test_params()
+        self.init_test_input()
+        self.init_test_output()
+        self.inputs = {
+            'RpnRois': (self.rpn_rois[0], self.rpn_rois_lod),
+            'GtClasses': (self.gt_classes[0], self.gts_lod),
+            'GtBoxes': (self.gt_boxes[0], self.gts_lod),
+            'ImScales': self.im_scales[0]
+        }
+        self.attrs = {
+            'batch_size_per_im': self.batch_size_per_im,
+            'fg_fraction': self.fg_fraction,
+            'fg_thresh': self.fg_thresh,
+            'bg_thresh_hi': self.bg_thresh_hi,
+            'bg_thresh_lo': self.bg_thresh_lo,
+            'bbox_reg_weights': self.bbox_reg_weights,
+            'class_nums': self.class_nums
+        }
+        self.outputs = {
+            'Rois': (self.rois[0], [self.lod]),
+            'LabelsInt32': (self.labels_int32[0], [self.lod]),
+            'BboxTargets': (self.bbox_targets[0], [self.lod]),
+            'BboxInsideWeights': (self.bbox_inside_weights[0], [self.lod]),
+            'BboxOutsideWeights': (self.bbox_outside_weights[0], [self.lod]),
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.op_type = 'generate_proposal_labels'
+        self.set_data()
+
+    def init_test_params(self):
+        self.batch_size_per_im = 10
+        self.fg_fraction = 1.0
+        self.fg_thresh = 0.5
+        self.bg_thresh_hi = 0.5
+        self.bg_thresh_lo = 0.0
+        self.bbox_reg_weights = [0.1, 0.1, 0.2, 0.2]
+        self.class_nums = 81
+
+    def init_test_input(self):
+        np.random.seed(0)
+        image_nums = 1
+        gt_nums = 6  # Keep same with batch_size_per_im for unittest
+        proposal_nums = self.batch_size_per_im - gt_nums
+        images_shape = []
+        self.im_scales = []
+        for i in range(image_nums):
+            images_shape.append(np.random.randint(200, size=2))
+            self.im_scales.append(np.ones((1)).astype(np.float32))
+
+        self.rpn_rois, self.rpn_rois_lod = _generate_proposals(images_shape,
+                                                               proposal_nums)
+        ground_truth, self.gts_lod = _generate_groundtruth(
+            images_shape, self.class_nums, gt_nums)
+        self.gt_classes = [gt['gt_classes'] for gt in ground_truth]
+        self.gt_boxes = [gt['boxes'] for gt in ground_truth]
+
+    def init_test_output(self):
+        self.rois, self.labels_int32, self.bbox_targets, \
+        self.bbox_inside_weights, self.bbox_outside_weights, \
+        self.lod = generate_proposal_labels_in_python(
+                self.rpn_rois, self.gt_classes, self.gt_boxes, self.im_scales,
+                self.batch_size_per_im, self.fg_fraction,
+                self.fg_thresh, self.bg_thresh_hi, self.bg_thresh_lo,
+                self.bbox_reg_weights, self.class_nums
+            )
+
+
+def _generate_proposals(images_shape, proposal_nums):
+    rpn_rois = []
+    rpn_rois_lod = []
+    num_proposals = 0
+    for i, image_shape in enumerate(images_shape):
+        proposals = _generate_boxes(image_shape, proposal_nums)
+        rpn_rois.append(proposals)
+        num_proposals += len(proposals)
+        rpn_rois_lod.append(num_proposals)
+    return rpn_rois, [rpn_rois_lod]
+
+
+def _generate_groundtruth(images_shape, class_nums, gt_nums):
+    ground_truth = []
+    gts_lod = []
+    num_gts = 0
+    for i, image_shape in enumerate(images_shape):
+        # Avoid background
+        gt_classes = np.random.randint(
+            low=1, high=class_nums, size=gt_nums).astype(np.int32)
+        gt_boxes = _generate_boxes(image_shape, gt_nums)
+        ground_truth.append(dict(gt_classes=gt_classes, boxes=gt_boxes))
+        num_gts += len(gt_classes)
+        gts_lod.append(num_gts)
+    return ground_truth, [gts_lod]
+
+
+def _generate_boxes(image_size, box_nums):
+    width = image_size[0]
+    height = image_size[1]
+    xywh = np.random.rand(box_nums, 4)
+    xy1 = xywh[:, [0, 1]] * image_size
+    wh = xywh[:, [2, 3]] * (image_size - xy1)
+    xy2 = xy1 + wh
+    boxes = np.hstack([xy1, xy2])
+    boxes[:, [0, 2]] = np.minimum(width - 1., np.maximum(0., boxes[:, [0, 2]]))
+    boxes[:, [1, 3]] = np.minimum(height - 1., np.maximum(0., boxes[:, [1, 3]]))
+    return boxes.astype(np.float32)
+
+
+if __name__ == '__main__':
+    unittest.main()