80060c276d
Please see https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/nemo/index.html for a list of pre-trained CTC models from NeMo.
63 lines
1.5 KiB
C++
63 lines
1.5 KiB
C++
// sherpa-onnx/csrc/transpose-test.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/transpose.h"
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#include <numeric>
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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(Tranpose, Tranpose01) {
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Ort::AllocatorWithDefaultOptions allocator;
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std::array<int64_t, 3> shape{3, 2, 5};
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Ort::Value v =
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Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
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float *p = v.GetTensorMutableData<float>();
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std::iota(p, p + shape[0] * shape[1] * shape[2], 0);
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auto ans = Transpose01(allocator, &v);
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auto v2 = Transpose01(allocator, &ans);
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Print3D(&v);
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Print3D(&ans);
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Print3D(&v2);
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const float *q = v2.GetTensorData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1] * shape[2]);
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++i) {
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EXPECT_EQ(p[i], q[i]);
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}
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}
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TEST(Tranpose, Tranpose12) {
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Ort::AllocatorWithDefaultOptions allocator;
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std::array<int64_t, 3> shape{3, 2, 5};
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Ort::Value v =
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Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
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float *p = v.GetTensorMutableData<float>();
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std::iota(p, p + shape[0] * shape[1] * shape[2], 0);
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auto ans = Transpose12(allocator, &v);
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auto v2 = Transpose12(allocator, &ans);
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Print3D(&v);
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Print3D(&ans);
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Print3D(&v2);
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const float *q = v2.GetTensorData<float>();
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for (int32_t i = 0; i != static_cast<int32_t>(shape[0] * shape[1] * shape[2]);
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++i) {
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EXPECT_EQ(p[i], q[i]);
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}
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}
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} // namespace sherpa_onnx
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