53 lines
1.5 KiB
C++
53 lines
1.5 KiB
C++
// sherpa-onnx/csrc/pad-sequence.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/pad-sequence.h"
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#include <algorithm>
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#include <cassert>
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#include <vector>
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namespace sherpa_onnx {
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Ort::Value PadSequence(OrtAllocator *allocator,
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const std::vector<const Ort::Value *> &values,
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float padding_value) {
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int32_t batch_size = static_cast<int32_t>(values.size());
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std::vector<int64_t> shape0 =
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values[0]->GetTensorTypeAndShapeInfo().GetShape();
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assert(shape0.size() == 2);
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auto feature_dim = shape0[1];
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auto max_T = shape0[0];
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for (int32_t i = 1; i != batch_size; ++i) {
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auto shape = values[i]->GetTensorTypeAndShapeInfo().GetShape();
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assert(shape.size() == 2);
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assert(shape[1] == feature_dim);
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max_T = std::max(max_T, shape[0]);
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}
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std::array<int64_t, 3> ans_shape{batch_size, max_T, feature_dim};
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Ort::Value ans = Ort::Value::CreateTensor<float>(allocator, ans_shape.data(),
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ans_shape.size());
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float *dst = ans.GetTensorMutableData<float>();
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std::fill(dst, dst + batch_size * max_T * feature_dim, padding_value);
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for (const auto *v : values) {
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const float *src = v->GetTensorData<float>();
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auto shape = v->GetTensorTypeAndShapeInfo().GetShape();
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std::copy(src, src + shape[0] * shape[1], dst);
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dst += max_T * feature_dim;
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}
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return ans;
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// TODO(fangjun): Check that the returned value is correct.
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}
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} // namespace sherpa_onnx
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