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-rw-r--r--contrib/labs/tflite_examples/src/conv.cpp330
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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;
-}
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