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@ -145,10 +145,15 @@ def batched_multiclass_nms(boxes, scores, background, score_threshold,
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lod.append(nmsed_num)
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if nmsed_num == 0: continue
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tmp_det_out = []
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for c, indices in nmsed_outs.items():
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for idx in indices:
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xmin, ymin, xmax, ymax = boxes[n][idx][:]
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det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])
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tmp_det_out.append(
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[c, scores[n][c][idx], xmin, ymin, xmax, ymax])
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sorted_det_out = sorted(
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tmp_det_out, key=lambda tup: tup[0], reverse=False)
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det_outs.extend(sorted_det_out)
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return det_outs, lod
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@ -210,7 +215,7 @@ class TestMulticlassNMSOp(OpTest):
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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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# In practical use, 0.0 < score_threshold < 1.0
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self.score_threshold = 2.0
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