transpose-test.cc
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// sherpa-onnx/csrc/transpose-test.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/transpose.h"
#include <numeric>
#include "gtest/gtest.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
namespace sherpa_onnx {
TEST(Tranpose, Tranpose01) {
Ort::AllocatorWithDefaultOptions allocator;
std::array<int64_t, 3> shape{3, 2, 5};
Ort::Value v =
Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
float *p = v.GetTensorMutableData<float>();
std::iota(p, p + shape[0] * shape[1] * shape[2], 0);
auto ans = Transpose01(allocator, &v);
auto v2 = Transpose01(allocator, &ans);
Print3D(&v);
Print3D(&ans);
Print3D(&v2);
const float *q = v2.GetTensorData<float>();
for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1] * shape[2]);
++i) {
EXPECT_EQ(p[i], q[i]);
}
}
TEST(Tranpose, Tranpose12) {
Ort::AllocatorWithDefaultOptions allocator;
std::array<int64_t, 3> shape{3, 2, 5};
Ort::Value v =
Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
float *p = v.GetTensorMutableData<float>();
std::iota(p, p + shape[0] * shape[1] * shape[2], 0);
auto ans = Transpose12(allocator, &v);
auto v2 = Transpose12(allocator, &ans);
Print3D(&v);
Print3D(&ans);
Print3D(&v2);
const float *q = v2.GetTensorData<float>();
for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1] * shape[2]);
++i) {
EXPECT_EQ(p[i], q[i]);
}
}
} // namespace sherpa_onnx