Fix numeric_limits::min()->lowest() and clang-format
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736eba374e
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178ed66d97
@ -498,10 +498,10 @@ Status DoNMS(OpKernelContext* context, const Tensor& boxes,
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original_boxes, sorted_boxes));
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int limited_num_boxes = num_boxes;
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// filter boxes by scores if nms v3
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if (score_threshold > std::numeric_limits<float>::min()) {
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if (score_threshold > std::numeric_limits<float>::lowest()) {
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GreaterThanCubOp score_limit(score_threshold);
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TF_RETURN_IF_ERROR(CountIf(context, d_sorted_scores.flat<float>().data(),
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score_limit, num_boxes, &limited_num_boxes));
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score_limit, num_boxes, &limited_num_boxes));
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if (limited_num_boxes == 0) {
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Tensor* output_indices = nullptr;
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VLOG(1) << "Number of boxes above score threshold " << score_threshold
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@ -510,8 +510,8 @@ Status DoNMS(OpKernelContext* context, const Tensor& boxes,
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context->allocate_output(0, TensorShape({0}), &output_indices));
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return Status::OK();
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} else {
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VLOG(2) << "Number of boxes above threshold=" << score_threshold
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<< " is " << limited_num_boxes;
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VLOG(2) << "Number of boxes above threshold=" << score_threshold << " is "
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<< limited_num_boxes;
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}
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}
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int num_to_keep = 0;
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@ -580,8 +580,8 @@ class NonMaxSuppressionV2GPUOp : public OpKernel {
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// otherwise tests fail!
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if (num_boxes == 0) {
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Tensor* output_indices = nullptr;
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OP_REQUIRES_OK(context,
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context->allocate_output(0, TensorShape({0}), &output_indices));
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OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({0}),
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&output_indices));
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return;
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}
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const int64_t output_size = max_output_size.scalar<int>()();
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@ -589,7 +589,7 @@ class NonMaxSuppressionV2GPUOp : public OpKernel {
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context,
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DoNMS(context, boxes, scores, output_size, iou_threshold_val,
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/*score_threshold is float min if score threshold is disabled*/
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std::numeric_limits<float>::min()));
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std::numeric_limits<float>::lowest()));
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}
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};
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