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+/*
+ * 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 "support/tflite/kernels/register.h"
+#include "tensorflow/contrib/lite/model.h"
+#include "tensorflow/contrib/lite/builtin_op_data.h"
+
+#include <iostream>
+
+using namespace tflite;
+using namespace tflite::ops::builtin;
+
+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;
+}