Add Streaming zipformer (#50)
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Fangjun Kuang
254
sherpa-onnx/csrc/cat-test.cc
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254
sherpa-onnx/csrc/cat-test.cc
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// sherpa-onnx/csrc/cat-test.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/cat.h"
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#include "gtest/gtest.h"
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#include "sherpa-onnx/csrc/onnx-utils.h"
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namespace sherpa_onnx {
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TEST(Cat, Test1DTensors) {
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Ort::AllocatorWithDefaultOptions allocator;
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std::array<int64_t, 1> a_shape{3};
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std::array<int64_t, 1> b_shape{6};
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Ort::Value a = Ort::Value::CreateTensor<float>(allocator, a_shape.data(),
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a_shape.size());
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Ort::Value b = Ort::Value::CreateTensor<float>(allocator, b_shape.data(),
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b_shape.size());
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float *pa = a.GetTensorMutableData<float>();
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float *pb = b.GetTensorMutableData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(a_shape[0]); ++i) {
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pa[i] = i;
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}
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for (int32_t i = 0; i != static_cast<int32_t>(b_shape[0]); ++i) {
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pb[i] = i + 10;
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}
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Ort::Value ans = Cat(allocator, {&a, &b}, 0);
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const float *pans = ans.GetTensorData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(a_shape[0]); ++i) {
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EXPECT_EQ(pa[i], pans[i]);
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}
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for (int32_t i = 0; i != static_cast<int32_t>(b_shape[0]); ++i) {
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EXPECT_EQ(pb[i], pans[i + a_shape[0]]);
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}
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Print1D(&a);
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Print1D(&b);
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Print1D(&ans);
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}
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TEST(Cat, Test2DTensorsDim0) {
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Ort::AllocatorWithDefaultOptions allocator;
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std::array<int64_t, 2> a_shape{2, 3};
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std::array<int64_t, 2> b_shape{4, 3};
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Ort::Value a = Ort::Value::CreateTensor<float>(allocator, a_shape.data(),
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a_shape.size());
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Ort::Value b = Ort::Value::CreateTensor<float>(allocator, b_shape.data(),
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b_shape.size());
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float *pa = a.GetTensorMutableData<float>();
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float *pb = b.GetTensorMutableData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(a_shape[0] * a_shape[1]); ++i) {
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pa[i] = i;
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}
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for (int32_t i = 0; i != static_cast<int32_t>(b_shape[0] * b_shape[1]); ++i) {
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pb[i] = i + 10;
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}
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Ort::Value ans = Cat(allocator, {&a, &b}, 0);
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const float *pans = ans.GetTensorData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(a_shape[0] * a_shape[1]); ++i) {
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EXPECT_EQ(pa[i], pans[i]);
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}
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for (int32_t i = 0; i != static_cast<int32_t>(b_shape[0] * b_shape[1]); ++i) {
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EXPECT_EQ(pb[i], pans[i + a_shape[0] * a_shape[1]]);
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}
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Print2D(&a);
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Print2D(&b);
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Print2D(&ans);
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}
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TEST(Cat, Test2DTensorsDim1) {
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Ort::AllocatorWithDefaultOptions allocator;
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std::array<int64_t, 2> a_shape{4, 3};
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std::array<int64_t, 2> b_shape{4, 2};
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Ort::Value a = Ort::Value::CreateTensor<float>(allocator, a_shape.data(),
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a_shape.size());
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Ort::Value b = Ort::Value::CreateTensor<float>(allocator, b_shape.data(),
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b_shape.size());
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float *pa = a.GetTensorMutableData<float>();
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float *pb = b.GetTensorMutableData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(a_shape[0] * a_shape[1]); ++i) {
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pa[i] = i;
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}
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for (int32_t i = 0; i != static_cast<int32_t>(b_shape[0] * b_shape[1]); ++i) {
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pb[i] = i + 10;
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}
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Ort::Value ans = Cat(allocator, {&a, &b}, 1);
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const float *pans = ans.GetTensorData<float>();
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for (int32_t r = 0; r != static_cast<int32_t>(a_shape[0]); ++r) {
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for (int32_t i = 0; i != static_cast<int32_t>(a_shape[1]);
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++i, ++pa, ++pans) {
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EXPECT_EQ(*pa, *pans);
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}
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for (int32_t i = 0; i != static_cast<int32_t>(b_shape[1]);
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++i, ++pb, ++pans) {
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EXPECT_EQ(*pb, *pans);
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}
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}
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Print2D(&a);
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Print2D(&b);
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Print2D(&ans);
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}
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TEST(Cat, Test3DTensorsDim0) {
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Ort::AllocatorWithDefaultOptions allocator;
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std::array<int64_t, 3> a_shape{2, 3, 2};
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std::array<int64_t, 3> b_shape{4, 3, 2};
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Ort::Value a = Ort::Value::CreateTensor<float>(allocator, a_shape.data(),
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a_shape.size());
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Ort::Value b = Ort::Value::CreateTensor<float>(allocator, b_shape.data(),
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b_shape.size());
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float *pa = a.GetTensorMutableData<float>();
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float *pb = b.GetTensorMutableData<float>();
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for (int32_t i = 0;
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i != static_cast<int32_t>(a_shape[0] * a_shape[1] * a_shape[2]); ++i) {
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pa[i] = i;
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}
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for (int32_t i = 0;
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i != static_cast<int32_t>(b_shape[0] * b_shape[1] * b_shape[2]); ++i) {
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pb[i] = i + 10;
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}
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Ort::Value ans = Cat(allocator, {&a, &b}, 0);
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const float *pans = ans.GetTensorData<float>();
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for (int32_t i = 0;
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i != static_cast<int32_t>(a_shape[0] * a_shape[1] * a_shape[2]); ++i) {
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EXPECT_EQ(pa[i], pans[i]);
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}
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for (int32_t i = 0;
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i != static_cast<int32_t>(b_shape[0] * b_shape[1] * b_shape[2]); ++i) {
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EXPECT_EQ(pb[i], pans[i + a_shape[0] * a_shape[1] * a_shape[2]]);
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}
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Print3D(&a);
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Print3D(&b);
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Print3D(&ans);
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}
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TEST(Cat, Test3DTensorsDim1) {
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Ort::AllocatorWithDefaultOptions allocator;
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std::array<int64_t, 3> a_shape{2, 2, 3};
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std::array<int64_t, 3> b_shape{2, 4, 3};
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Ort::Value a = Ort::Value::CreateTensor<float>(allocator, a_shape.data(),
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a_shape.size());
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Ort::Value b = Ort::Value::CreateTensor<float>(allocator, b_shape.data(),
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b_shape.size());
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float *pa = a.GetTensorMutableData<float>();
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float *pb = b.GetTensorMutableData<float>();
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for (int32_t i = 0;
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i != static_cast<int32_t>(a_shape[0] * a_shape[1] * a_shape[2]); ++i) {
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pa[i] = i;
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}
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for (int32_t i = 0;
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i != static_cast<int32_t>(b_shape[0] * b_shape[1] * b_shape[2]); ++i) {
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pb[i] = i + 10;
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}
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Ort::Value ans = Cat(allocator, {&a, &b}, 1);
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const float *pans = ans.GetTensorData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(a_shape[0]); ++i) {
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for (int32_t k = 0; k != static_cast<int32_t>(a_shape[1] * a_shape[2]);
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++k, ++pa, ++pans) {
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EXPECT_EQ(*pa, *pans);
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}
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for (int32_t k = 0; k != static_cast<int32_t>(b_shape[1] * b_shape[2]);
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++k, ++pb, ++pans) {
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EXPECT_EQ(*pb, *pans);
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}
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}
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Print3D(&a);
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Print3D(&b);
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Print3D(&ans);
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}
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TEST(Cat, Test3DTensorsDim2) {
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Ort::AllocatorWithDefaultOptions allocator;
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std::array<int64_t, 3> a_shape{2, 3, 4};
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std::array<int64_t, 3> b_shape{2, 3, 5};
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Ort::Value a = Ort::Value::CreateTensor<float>(allocator, a_shape.data(),
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a_shape.size());
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Ort::Value b = Ort::Value::CreateTensor<float>(allocator, b_shape.data(),
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b_shape.size());
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float *pa = a.GetTensorMutableData<float>();
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float *pb = b.GetTensorMutableData<float>();
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for (int32_t i = 0;
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i != static_cast<int32_t>(a_shape[0] * a_shape[1] * a_shape[2]); ++i) {
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pa[i] = i;
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}
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for (int32_t i = 0;
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i != static_cast<int32_t>(b_shape[0] * b_shape[1] * b_shape[2]); ++i) {
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pb[i] = i + 10;
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}
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Ort::Value ans = Cat(allocator, {&a, &b}, 2);
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const float *pans = ans.GetTensorData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(a_shape[0] * a_shape[1]); ++i) {
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for (int32_t k = 0; k != static_cast<int32_t>(a_shape[2]);
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++k, ++pa, ++pans) {
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EXPECT_EQ(*pa, *pans);
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}
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for (int32_t k = 0; k != static_cast<int32_t>(b_shape[2]);
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++k, ++pb, ++pans) {
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EXPECT_EQ(*pb, *pans);
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}
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}
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Print3D(&a);
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Print3D(&b);
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Print3D(&ans);
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}
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} // namespace sherpa_onnx
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