experiments/STT-tensorflow
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Qualify calls to some functions from <cmath>.

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PiperOrigin-RevId: 247789928
This commit is contained in:
A. Unique TensorFlower 2019-05-11 19:09:55 -07:00 committed by TensorFlower Gardener
parent 8bec6bfadf
commit 696a6a0be2
1 changed files with 11 additions and 9 deletions
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16
tensorflow/lite/kernels/internal/reference/reference_ops.h
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@ -1578,7 +1578,7 @@ inline void ConcatenationWithScaling(const ConcatenationParams& params,
const float bias = -input_zeropoint[i] * scale;
for (int j = 0; j < copy_size; ++j) {
const int32_t value =
static_cast<int32_t>(round(input_ptr[j] * scale + bias)) +
static_cast<int32_t>(std::round(input_ptr[j] * scale + bias)) +
output_zeropoint;
output_ptr[j] =
static_cast<uint8_t>(std::max(std::min(255, value), 0));
@ -1689,7 +1689,7 @@ void PackWithScaling(const PackParams& params,
auto input_ptr = input_data[i];
for (int j = 0; j < copy_size; ++j) {
const int32_t value =
static_cast<int32_t>(round(input_ptr[j] * scale + bias)) +
static_cast<int32_t>(std::round(input_ptr[j] * scale + bias)) +
output_zeropoint;
output_ptr[j] =
static_cast<uint8_t>(std::max(std::min(255, value), 0));
@ -3151,7 +3151,7 @@ inline void Exp(const T* input_data, const size_t num_elements,
T* output_data) {
gemmlowp::ScopedProfilingLabel label("Exp");
for (size_t idx = 0; idx < num_elements; ++idx) {
output_data[idx] = exp(input_data[idx]);
output_data[idx] = std::exp(input_data[idx]);
}
}
@ -3422,10 +3422,10 @@ inline void Mean(const tflite::MeanParams& op_params,
temp_value = temp_value / num_elements_in_axis;
if (ordinary_mean) {
output_data[Offset(output_shape, out_b, 0, 0, out_d)] =
static_cast<uint8_t>(round(temp_value));
static_cast<uint8_t>(std::round(temp_value));
} else {
output_data[Offset(output_shape, out_b, 0, 0, out_d)] =
static_cast<uint8_t>(round(temp_value * scale + bias)) +
static_cast<uint8_t>(std::round(temp_value * scale + bias)) +
output_zero_point;
}
}
@ -3492,7 +3492,8 @@ inline bool QuantizedMeanOrSum(const T* input_data, int32 input_zero_point,
// TODO(b/116341117): Eliminate float and do this completely in 8bit.
const float bias = -input_zero_point * scale * num_elements_in_axis + 0.5;
for (size_t idx = 0; idx < num_outputs; ++idx) {
const U value = static_cast<U>(round(temp_sum[idx] * scale + bias)) +
const U value =
static_cast<U>(std::round(temp_sum[idx] * scale + bias)) +
output_zero_point;
output_data[idx] = static_cast<T>(value);
}
@ -3503,7 +3504,8 @@ inline bool QuantizedMeanOrSum(const T* input_data, int32 input_zero_point,
static_cast<float>(num_elements_in_axis);
// Convert to float value.
output_data[idx] = static_cast<T>(round(float_mean * scale + bias)) +
output_data[idx] =
static_cast<T>(std::round(float_mean * scale + bias)) +
output_zero_point;
}
}