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[CPU] Reducenode handles negative input (master) (#5494)

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Yury Gaydaychuk 2021-05-14 13:05:22 +03:00 committed by GitHub
parent 0bac48d5ba
commit 79e197218e
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 8 additions and 4 deletions
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6
inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
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@ -1881,13 +1881,13 @@ inline void MKLDNNReduceNode::init_dst_data(uint8_t *out_ptr, size_t dst_size) {
case ReduceMax:
if (output_prec == Precision::FP32) {
auto out_p = reinterpret_cast<float *>(out_ptr);
parallel_for(dst_size / dst_data_size, [&](size_t i) { out_p[i] = std::numeric_limits<float>::min(); });
parallel_for(dst_size / dst_data_size, [&](size_t i) { out_p[i] = std::numeric_limits<float>::lowest(); });
} else if (output_prec == Precision::I32) {
auto out_p = reinterpret_cast<int32_t *>(out_ptr);
parallel_for(dst_size / dst_data_size, [&](size_t i) { out_p[i] = std::numeric_limits<int32_t>::min(); });
} else if (output_prec == Precision::BF16) {
auto out_p = reinterpret_cast<bfloat16_t*>(out_ptr);
parallel_for(dst_size / dst_data_size, [&](size_t i) { out_p[i] = std::numeric_limits<bfloat16_t>::min(); });
parallel_for(dst_size / dst_data_size, [&](size_t i) { out_p[i] = std::numeric_limits<bfloat16_t>::lowest(); });
} else if (output_prec == Precision::U8) {
auto out_p = reinterpret_cast<uint8_t *>(out_ptr);
parallel_for(dst_size / dst_data_size, [&](size_t i) { out_p[i] = std::numeric_limits<uint8_t>::min(); });
@ -1972,7 +1972,7 @@ inline void MKLDNNReduceNode::reduce_ref(const float *in_ptr, float *out_ptr) {
reduce_ref_process(in_ptr, out_ptr, 0, [](float old, float y)->float { return old + expf(y); });
break;
case ReduceMax:
reduce_ref_process(in_ptr, out_ptr, std::numeric_limits<float>::min(),
reduce_ref_process(in_ptr, out_ptr, std::numeric_limits<float>::lowest(),
[](float x, float y)->float { return x > y ? x : y; });
break;
case ReduceMean:

6
inference-engine/tests/functional/shared_test_classes/src/single_layer/reduce_ops.cpp
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@ -78,7 +78,11 @@ InferenceEngine::Blob::Ptr ReduceOpsLayerTest::GenerateInput(const InferenceEngi
auto td = info.getTensorDesc();
auto blob = make_blob_with_precision(td);
blob->allocate();
CommonTestUtils::fill_data_random_float<InferenceEngine::Precision::FP32>(blob, 5, 0, 1000);
if (reductionType == ngraph::helpers::ReductionType::Max) {
CommonTestUtils::fill_data_random_float<InferenceEngine::Precision::FP32>(blob, 5, -5, 1000);
} else {
CommonTestUtils::fill_data_random_float<InferenceEngine::Precision::FP32>(blob, 5, 0, 1000);
}
return blob;
}