experiments/STT-tensorflow
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Remove uses of rand_r in quantize/dequantize op_test.

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rand_r is not available on windows.

PiperOrigin-RevId: 289270961
Change-Id: I94a287bbcf213fd1656d8e34636c23339db2b219
This commit is contained in:
Gunhan Gulsoy 2020-01-11 14:30:37 -08:00 committed by TensorFlower Gardener
parent 6aaa640cec
commit eb068fbfca
2 changed files with 7 additions and 2 deletions

4
tensorflow/core/kernels/dequantize_op_test.cc
View File

@ -15,6 +15,7 @@ limitations under the License.
#include <functional>
#include <memory>
#include <random>
#include <vector>
#include "tensorflow/cc/ops/array_ops.h"
@ -128,6 +129,7 @@ class DequantizeOpTest : public OpsTestBase {
std::vector<T> ScalePerSliceAlongAxis(std::vector<int64> dims, int axis,
const std::vector<T>& data) {
uint32 seed = 123;
std::minstd_rand rng(seed);
int64 out_size = 1;
for (int dim : dims) {
out_size *= dim;
@ -139,7 +141,7 @@ class DequantizeOpTest : public OpsTestBase {
std::vector<T> out(out_size);
int num_slices = (axis == -1) ? 1 : dims[axis];
for (int out_idx = 0; out_idx < out_size; ++out_idx) {
int in_idx = rand_r(&seed) % data.size();
int in_idx = rng() % data.size();
T multiplier = ((out_idx / minor_size) % num_slices) + 1;
out[out_idx] = data[in_idx] * multiplier;
}

5
tensorflow/core/kernels/quantize_op_test.cc
View File

@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <random>
#include "tensorflow/core/framework/fake_input.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/tensor.h"
@ -61,6 +63,7 @@ template <typename T>
std::vector<T> ScalePerSliceAlongAxis(std::vector<int64> dims, int axis,
const std::vector<T>& data) {
uint32 seed = 123;
std::minstd_rand rng(seed);
int64 out_size = 1;
for (int dim : dims) {
out_size *= dim;
@ -72,7 +75,7 @@ std::vector<T> ScalePerSliceAlongAxis(std::vector<int64> dims, int axis,
std::vector<T> out(out_size);
int num_slices = (axis == -1) ? 1 : dims[axis];
for (int out_idx = 0; out_idx < out_size; ++out_idx) {
int in_idx = rand_r(&seed) % data.size();
int in_idx = rng() % data.size();
T multiplier = ((out_idx / minor_size) % num_slices) + 1;
out[out_idx] = data[in_idx] * multiplier;
}