unbind-test.cc
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// sherpa-onnx/csrc/unbind-test.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/unbind.h"
#include "gtest/gtest.h"
#include "sherpa-onnx/csrc/cat.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
namespace sherpa_onnx {
TEST(Ubind, Test1DTensors) {
Ort::AllocatorWithDefaultOptions allocator;
std::array<int64_t, 1> shape{3};
Ort::Value v =
Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
float *p = v.GetTensorMutableData<float>();
for (int32_t i = 0; i != static_cast<int32_t>(shape[0]); ++i) {
p[i] = i;
}
auto ans = Unbind(allocator, &v, 0);
EXPECT_EQ(ans.size(), shape[0]);
for (int32_t i = 0; i != static_cast<int32_t>(shape[0]); ++i) {
EXPECT_EQ(ans[i].GetTensorData<float>()[0], p[i]);
}
Print1D(&v);
for (int32_t i = 0; i != static_cast<int32_t>(shape[0]); ++i) {
Print1D(&ans[i]);
}
// For Cat
std::vector<const Ort::Value *> vec(ans.size());
for (int32_t i = 0; i != static_cast<int32_t>(vec.size()); ++i) {
vec[i] = &ans[i];
}
Ort::Value v2 = Cat(allocator, vec, 0);
const float *p2 = v2.GetTensorData<float>();
for (int32_t i = 0; i != shape[0]; ++i) {
EXPECT_EQ(p[i], p2[i]);
}
}
TEST(Ubind, Test2DTensorsDim0) {
Ort::AllocatorWithDefaultOptions allocator;
std::array<int64_t, 2> shape{3, 2};
Ort::Value v =
Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
float *p = v.GetTensorMutableData<float>();
for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1]); ++i) {
p[i] = i;
}
auto ans = Unbind(allocator, &v, 0);
Print2D(&v);
for (int32_t i = 0; i != static_cast<int32_t>(shape[0]); ++i) {
Print2D(&ans[i]);
}
for (int32_t i = 0; i != static_cast<int32_t>(shape[0]); ++i) {
const float *pans = ans[i].GetTensorData<float>();
for (int32_t k = 0; k != static_cast<int32_t>(shape[1]); ++k, ++p) {
EXPECT_EQ(*p, pans[k]);
}
}
// For Cat
std::vector<const Ort::Value *> vec(ans.size());
for (int32_t i = 0; i != static_cast<int32_t>(vec.size()); ++i) {
vec[i] = &ans[i];
}
Ort::Value v2 = Cat(allocator, vec, 0);
Print2D(&v2);
p = v.GetTensorMutableData<float>();
const float *p2 = v2.GetTensorData<float>();
for (int32_t i = 0; i != shape[0] * shape[1]; ++i) {
EXPECT_EQ(p[i], p2[i]);
}
}
TEST(Ubind, Test2DTensorsDim1) {
Ort::AllocatorWithDefaultOptions allocator;
std::array<int64_t, 2> shape{3, 2};
Ort::Value v =
Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
float *p = v.GetTensorMutableData<float>();
for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1]); ++i) {
p[i] = i;
}
auto ans = Unbind(allocator, &v, 1);
Print2D(&v);
for (int32_t i = 0; i != static_cast<int32_t>(shape[1]); ++i) {
Print2D(&ans[i]);
}
// For Cat
std::vector<const Ort::Value *> vec(ans.size());
for (int32_t i = 0; i != static_cast<int32_t>(vec.size()); ++i) {
vec[i] = &ans[i];
}
Ort::Value v2 = Cat(allocator, vec, 1);
Print2D(&v2);
p = v.GetTensorMutableData<float>();
const float *p2 = v2.GetTensorData<float>();
for (int32_t i = 0; i != shape[0] * shape[1]; ++i) {
EXPECT_EQ(p[i], p2[i]);
}
}
TEST(Ubind, Test3DTensorsDim0) {
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>();
for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1] * shape[2]);
++i) {
p[i] = i;
}
auto ans = Unbind(allocator, &v, 0);
Print3D(&v);
for (int32_t i = 0; i != static_cast<int32_t>(shape[0]); ++i) {
Print3D(&ans[i]);
}
for (int32_t i = 0; i != static_cast<int32_t>(shape[0]); ++i) {
const float *pans = ans[i].GetTensorData<float>();
for (int32_t k = 0; k != static_cast<int32_t>(shape[1] * shape[2]);
++k, ++p) {
EXPECT_EQ(*p, pans[k]);
}
}
// For Cat
std::vector<const Ort::Value *> vec(ans.size());
for (int32_t i = 0; i != static_cast<int32_t>(vec.size()); ++i) {
vec[i] = &ans[i];
}
Ort::Value v2 = Cat(allocator, vec, 0);
Print3D(&v2);
p = v.GetTensorMutableData<float>();
const float *p2 = v2.GetTensorData<float>();
for (int32_t i = 0; i != shape[0] * shape[1] * shape[2]; ++i) {
EXPECT_EQ(p[i], p2[i]);
}
}
TEST(Ubind, Test3DTensorsDim1) {
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>();
for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1] * shape[2]);
++i) {
p[i] = i;
}
auto ans = Unbind(allocator, &v, 1);
Print3D(&v);
for (int32_t i = 0; i != static_cast<int32_t>(shape[1]); ++i) {
Print3D(&ans[i]);
}
// For Cat
std::vector<const Ort::Value *> vec(ans.size());
for (int32_t i = 0; i != static_cast<int32_t>(vec.size()); ++i) {
vec[i] = &ans[i];
}
Ort::Value v2 = Cat(allocator, vec, 1);
Print3D(&v2);
p = v.GetTensorMutableData<float>();
const float *p2 = v2.GetTensorData<float>();
for (int32_t i = 0; i != shape[0] * shape[1] * shape[2]; ++i) {
EXPECT_EQ(p[i], p2[i]);
}
}
TEST(Ubind, Test3DTensorsDim2) {
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>();
for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1] * shape[2]);
++i) {
p[i] = i;
}
auto ans = Unbind(allocator, &v, 2);
Print3D(&v);
for (int32_t i = 0; i != static_cast<int32_t>(shape[2]); ++i) {
Print3D(&ans[i]);
}
// For Cat
std::vector<const Ort::Value *> vec(ans.size());
for (int32_t i = 0; i != static_cast<int32_t>(vec.size()); ++i) {
vec[i] = &ans[i];
}
Ort::Value v2 = Cat(allocator, vec, 2);
Print3D(&v2);
p = v.GetTensorMutableData<float>();
const float *p2 = v2.GetTensorData<float>();
for (int32_t i = 0; i != shape[0] * shape[1] * shape[2]; ++i) {
EXPECT_EQ(p[i], p2[i]);
}
}
} // namespace sherpa_onnx