Merge pull request #7953 from qingqing01/multiclass_nms_op
Add multi-class non-maximum suppression operator.emailweixu-patch-1
qingqing01
7 years ago
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#Unless required by applicable law or agreed to in writing, software
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#distributed under the License is distributed on an "AS IS" BASIS,
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#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#See the License for the specific language governing permissions and
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#limitations under the License.
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import unittest
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import numpy as np
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import copy
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from op_test import OpTest
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def iou(box_a, box_b):
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"""Apply intersection-over-union overlap between box_a and box_b
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"""
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xmin_a = min(box_a[0], box_a[2])
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ymin_a = min(box_a[1], box_a[3])
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xmax_a = max(box_a[0], box_a[2])
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ymax_a = max(box_a[1], box_a[3])
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xmin_b = min(box_b[0], box_b[2])
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ymin_b = min(box_b[1], box_b[3])
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xmax_b = max(box_b[0], box_b[2])
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ymax_b = max(box_b[1], box_b[3])
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area_a = (ymax_a - ymin_a) * (xmax_a - xmin_a)
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area_b = (ymax_b - ymin_b) * (xmax_b - xmin_b)
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if area_a <= 0 and area_b <= 0:
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return 0.0
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xa = max(xmin_a, xmin_b)
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ya = max(ymin_a, ymin_b)
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xb = min(xmax_a, xmax_b)
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yb = min(ymax_a, ymax_b)
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inter_area = max(xb - xa, 0.0) * max(yb - ya, 0.0)
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box_a_area = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1])
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box_b_area = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1])
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iou_ratio = inter_area / (area_a + area_b - inter_area)
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return iou_ratio
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def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
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"""Apply non-maximum suppression at test time to avoid detecting too many
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overlapping bounding boxes for a given object.
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Args:
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boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
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scores: (tensor) The class predscores for the img, Shape:[num_priors].
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score_threshold: (float) The confidence thresh for filtering low
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confidence boxes.
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nms_threshold: (float) The overlap thresh for suppressing unnecessary
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boxes.
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top_k: (int) The maximum number of box preds to consider.
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eta: (float) The parameter for adaptive NMS.
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Return:
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The indices of the kept boxes with respect to num_priors.
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"""
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all_scores = copy.deepcopy(scores)
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all_scores = all_scores.flatten()
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selected_indices = np.argwhere(all_scores > score_threshold)
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selected_indices = selected_indices.flatten()
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all_scores = all_scores[selected_indices]
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sorted_indices = np.argsort(-all_scores, axis=0, kind='mergesort')
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sorted_scores = all_scores[sorted_indices]
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if top_k > -1 and top_k < sorted_indices.shape[0]:
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sorted_indices = sorted_indices[:top_k]
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sorted_scores = sorted_scores[:top_k]
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selected_indices = []
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adaptive_threshold = nms_threshold
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for i in range(sorted_scores.shape[0]):
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idx = sorted_indices[i]
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keep = True
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for k in range(len(selected_indices)):
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if keep:
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kept_idx = selected_indices[k]
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overlap = iou(boxes[idx], boxes[kept_idx])
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keep = True if overlap <= adaptive_threshold else False
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else:
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break
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if keep:
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selected_indices.append(idx)
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if keep and eta < 1 and adaptive_threshold > 0.5:
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adaptive_threshold *= eta
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return selected_indices
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def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
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nms_top_k, keep_top_k):
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class_num = scores.shape[0]
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priorbox_num = scores.shape[1]
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selected_indices = {}
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num_det = 0
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for c in range(class_num):
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if c == background: continue
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indices = nms(boxes, scores[c], score_threshold, nms_threshold,
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nms_top_k)
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selected_indices[c] = indices
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num_det += len(indices)
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if keep_top_k > -1 and num_det > keep_top_k:
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score_index = []
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for c, indices in selected_indices.iteritems():
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for idx in indices:
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score_index.append((scores[c][idx], c, idx))
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sorted_score_index = sorted(
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score_index, key=lambda tup: tup[0], reverse=True)
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sorted_score_index = sorted_score_index[:keep_top_k]
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selected_indices = {}
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for _, c, _ in sorted_score_index:
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selected_indices[c] = []
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for s, c, idx in sorted_score_index:
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selected_indices[c].append(idx)
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num_det = keep_top_k
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return selected_indices, num_det
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def batched_multiclass_nms(boxes, scores, background, score_threshold,
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nms_threshold, nms_top_k, keep_top_k):
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batch_size = scores.shape[0]
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det_outs = []
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lod = [0]
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for n in range(batch_size):
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nmsed_outs, nmsed_num = multiclass_nms(boxes, scores[n], background,
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score_threshold, nms_threshold,
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nms_top_k, keep_top_k)
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lod.append(lod[-1] + nmsed_num)
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if nmsed_num == 0: continue
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for c, indices in nmsed_outs.iteritems():
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for idx in indices:
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xmin, ymin, xmax, ymax = boxes[idx][:]
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det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])
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return det_outs, lod
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class TestMulticlassNMSOp(OpTest):
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def set_argument(self):
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self.score_threshold = 0.01
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def setUp(self):
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self.set_argument()
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N = 7
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M = 1200
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C = 21
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BOX_SIZE = 4
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background = 0
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nms_threshold = 0.3
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nms_top_k = 400
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keep_top_k = 200
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score_threshold = self.score_threshold
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scores = np.random.random((N * M, C)).astype('float32')
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def softmax(x):
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shiftx = x - np.max(x).clip(-64.)
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exps = np.exp(shiftx)
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return exps / np.sum(exps)
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scores = np.apply_along_axis(softmax, 1, scores)
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scores = np.reshape(scores, (N, M, C))
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scores = np.transpose(scores, (0, 2, 1))
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boxes = np.random.random((M, BOX_SIZE)).astype('float32')
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boxes[:, 0:2] = boxes[:, 0:2] * 0.5
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boxes[:, 2:4] = boxes[:, 2:4] * 0.5 + 0.5
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nmsed_outs, lod = batched_multiclass_nms(boxes, scores, background,
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score_threshold, nms_threshold,
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nms_top_k, keep_top_k)
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nmsed_outs = [-1] if not nmsed_outs else nmsed_outs
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nmsed_outs = np.array(nmsed_outs).astype('float32')
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self.op_type = 'multiclass_nms'
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self.inputs = {'BBoxes': boxes, 'Scores': scores}
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self.outputs = {'Out': (nmsed_outs, [lod])}
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self.attrs = {
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'background_label': 0,
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'nms_threshold': nms_threshold,
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'nms_top_k': nms_top_k,
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'keep_top_k': keep_top_k,
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'score_threshold': score_threshold,
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'nms_eta': 1.0,
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}
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def test_check_output(self):
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self.check_output()
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class TestMulticlassNMSOpNoOutput(TestMulticlassNMSOp):
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def set_argument(self):
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# Here set 2.0 to test the case there is no outputs.
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# In practical use, 0.0 < score_threshold < 1.0
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self.score_threshold = 2.0
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class TestIOU(unittest.TestCase):
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def test_iou(self):
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box1 = np.array([4.0, 3.0, 7.0, 5.0]).astype('float32')
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box2 = np.array([3.0, 4.0, 6.0, 8.0]).astype('float32')
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expt_output = np.array([2.0 / 16.0]).astype('float32')
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calc_output = np.array([iou(box1, box2)]).astype('float32')
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self.assertTrue(np.allclose(calc_output, expt_output))
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if __name__ == '__main__':
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unittest.main()
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