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Diffstat (limited to 'contrib/labs/tflite_examples/src/conv.cpp')
-rw-r--r-- | contrib/labs/tflite_examples/src/conv.cpp | 330 |
1 files changed, 0 insertions, 330 deletions
diff --git a/contrib/labs/tflite_examples/src/conv.cpp b/contrib/labs/tflite_examples/src/conv.cpp deleted file mode 100644 index e517da9f3..000000000 --- a/contrib/labs/tflite_examples/src/conv.cpp +++ /dev/null @@ -1,330 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "tflite/ext/kernels/register.h" -#include "tensorflow/contrib/lite/model.h" -#include "tensorflow/contrib/lite/builtin_op_data.h" - -#include <iostream> - -using namespace tflite; -using namespace nnfw::tflite; - -namespace vector -{ - -template <typename T> struct View -{ - virtual ~View() = default; - - virtual int32_t size(void) const = 0; - virtual T at(uint32_t off) const = 0; -}; -} - -namespace feature -{ - -struct Shape -{ - int32_t C; - int32_t H; - int32_t W; -}; - -template <typename T> struct View -{ - virtual ~View() = default; - - virtual const Shape &shape(void) const = 0; - virtual T at(uint32_t ch, uint32_t row, uint32_t col) const = 0; -}; -} - -namespace kernel -{ - -struct Shape -{ - int32_t N; - int32_t C; - int32_t H; - int32_t W; -}; - -template <typename T> struct View -{ - virtual ~View() = default; - - virtual const Shape &shape(void) const = 0; - virtual T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const = 0; -}; -} - -const int32_t N = 1; -const int32_t C = 2; - -class SampleBiasObject final : public vector::View<float> -{ -public: - SampleBiasObject() : _size(N) - { - // DO NOTHING - } - -public: - int32_t size(void) const override { return _size; } - - float at(uint32_t off) const override { return 0.0f; } - -private: - int32_t _size; -}; - -class SampleFeatureObject final : public feature::View<float> -{ -public: - SampleFeatureObject() - { - _shape.C = C; - _shape.H = 3; - _shape.W = 4; - - const uint32_t size = _shape.C * _shape.H * _shape.W; - - for (uint32_t off = 0; off < size; ++off) - { - _value.emplace_back(off); - } - - assert(_value.size() == size); - } - -public: - const feature::Shape &shape(void) const override { return _shape; }; - - float at(uint32_t ch, uint32_t row, uint32_t col) const override - { - return _value.at(ch * _shape.H * _shape.W + row * _shape.W + col); - } - -public: - float &at(uint32_t ch, uint32_t row, uint32_t col) - { - return _value.at(ch * _shape.H * _shape.W + row * _shape.W + col); - } - -private: - feature::Shape _shape; - std::vector<float> _value; -}; - -class SampleKernelObject final : public kernel::View<float> -{ -public: - SampleKernelObject() - { - _shape.N = N; - _shape.C = C; - _shape.H = 3; - _shape.W = 4; - - const uint32_t size = _shape.N * _shape.C * _shape.H * _shape.W; - - for (uint32_t off = 0; off < size; ++off) - { - _value.emplace_back(off); - } - - assert(_value.size() == size); - } - -public: - const kernel::Shape &shape(void) const override { return _shape; }; - - float at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override - { - return _value.at(nth * _shape.C * _shape.H * _shape.W + ch * _shape.H * _shape.W + - row * _shape.W + col); - } - -private: - kernel::Shape _shape; - std::vector<float> _value; -}; - -int main(int argc, char **argv) -{ - const SampleFeatureObject ifm; - const SampleKernelObject kernel; - const SampleBiasObject bias; - - const int32_t IFM_C = ifm.shape().C; - const int32_t IFM_H = ifm.shape().H; - const int32_t IFM_W = ifm.shape().W; - - const int32_t KER_N = kernel.shape().N; - const int32_t KER_C = kernel.shape().C; - const int32_t KER_H = kernel.shape().H; - const int32_t KER_W = kernel.shape().W; - - const int32_t OFM_C = kernel.shape().N; - const int32_t OFM_H = (IFM_H - KER_H) + 1; - const int32_t OFM_W = (IFM_W - KER_W) + 1; - - // Assumption on this example - assert(IFM_C == KER_C); - assert(KER_N == bias.size()); - - // Comment from 'context.h' - // - // Parameters for asymmetric quantization. Quantized values can be converted - // back to float using: - // real_value = scale * (quantized_value - zero_point); - // - // Q: Is this necessary? - TfLiteQuantizationParams quantization; - - quantization.scale = 1; - quantization.zero_point = 0; - - Interpreter interp; - - // On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N) - interp.AddTensors(5); - - // Configure OFM - interp.SetTensorParametersReadWrite(0, kTfLiteFloat32 /* type */, "output" /* name */, - {1 /*N*/, OFM_H, OFM_W, OFM_C} /* dims */, quantization); - - // Configure IFM - interp.SetTensorParametersReadWrite(1, kTfLiteFloat32 /* type */, "input" /* name */, - {1 /*N*/, IFM_H, IFM_W, IFM_C} /* dims */, quantization); - - // Configure Filter - const uint32_t kernel_size = KER_N * KER_C * KER_H * KER_W; - float kernel_data[kernel_size] = { - 0.0f, - }; - - // Fill kernel data in NHWC order - { - uint32_t off = 0; - - for (uint32_t nth = 0; nth < KER_N; ++nth) - { - for (uint32_t row = 0; row < KER_H; ++row) - { - for (uint32_t col = 0; col < KER_W; ++col) - { - for (uint32_t ch = 0; ch < KER_C; ++ch) - { - const auto value = kernel.at(nth, ch, row, col); - kernel_data[off++] = value; - } - } - } - } - - assert(kernel_size == off); - } - - interp.SetTensorParametersReadOnly( - 2, kTfLiteFloat32 /* type */, "filter" /* name */, {KER_N, KER_H, KER_W, KER_C} /* dims */, - quantization, reinterpret_cast<const char *>(kernel_data), sizeof(kernel_data)); - - // Configure Bias - const uint32_t bias_size = bias.size(); - float bias_data[bias_size] = { - 0.0f, - }; - - // Fill bias data - for (uint32_t off = 0; off < bias.size(); ++off) - { - bias_data[off] = bias.at(off); - } - - interp.SetTensorParametersReadOnly(3, kTfLiteFloat32 /* type */, "bias" /* name */, - {bias.size()} /* dims */, quantization, - reinterpret_cast<const char *>(bias_data), sizeof(bias_data)); - - // Add Convolution Node - // - // NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free - // So, param should be allocated with malloc - TfLiteConvParams *param = reinterpret_cast<TfLiteConvParams *>(malloc(sizeof(TfLiteConvParams))); - - param->padding = kTfLitePaddingValid; - param->stride_width = 1; - param->stride_height = 1; - param->activation = kTfLiteActRelu; - - // Run Convolution and store its result into Tensor #0 - // - Read IFM from Tensor #1 - // - Read Filter from Tensor #2, - // - Read Bias from Tensor #3 - interp.AddNodeWithParameters({1, 2, 3}, {0}, nullptr, 0, reinterpret_cast<void *>(param), - BuiltinOpResolver().FindOp(BuiltinOperator_CONV_2D, 1)); - - // Set Tensor #1 as Input #0, and Tensor #0 as Output #0 - interp.SetInputs({1}); - interp.SetOutputs({0}); - - // Let's use NNAPI (if possible) - interp.UseNNAPI(true); - - // Allocate Tensor - interp.AllocateTensors(); - - // Fill IFM data in HWC order - { - uint32_t off = 0; - - for (uint32_t row = 0; row < ifm.shape().H; ++row) - { - for (uint32_t col = 0; col < ifm.shape().W; ++col) - { - for (uint32_t ch = 0; ch < ifm.shape().C; ++ch) - { - const auto value = ifm.at(ch, row, col); - interp.typed_input_tensor<float>(0)[off++] = value; - } - } - } - } - - // Let's Rock-n-Roll! - interp.Invoke(); - - // Print OFM - { - uint32_t off = 0; - - for (uint32_t row = 0; row < OFM_H; ++row) - { - for (uint32_t col = 0; col < OFM_W; ++col) - { - for (uint32_t ch = 0; ch < kernel.shape().N; ++ch) - { - std::cout << interp.typed_output_tensor<float>(0)[off++] << std::endl; - } - } - } - } - - return 0; -} |