322 lines
16 KiB
C++
322 lines
16 KiB
C++
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/****************************************************************************
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*
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* Copyright (c) 2020 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include <algorithm>
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#include <iomanip>
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#include <iostream>
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#include <tuple>
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#include <vector>
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#include "lenet_asymu8_weights.h"
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/operation.h"
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#include "tim/vx/ops/activations.h"
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#include "tim/vx/ops/conv2d.h"
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#include "tim/vx/ops/fullyconnected.h"
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#include "tim/vx/ops/pool2d.h"
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#include "tim/vx/ops/softmax.h"
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#include "tim/vx/tensor.h"
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#include "custom_softmax.h"
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std::vector<uint8_t> input_data = {
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0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 2, 0, 0, 8, 0,
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3, 0, 7, 0, 2, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 3, 1, 1, 0, 14, 0, 0, 3, 0,
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2, 4, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 5, 0, 4, 0, 0,
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0, 0, 10, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 6, 5, 0, 2, 0, 9, 0, 12, 2, 0, 5, 1, 0,
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0, 2, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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3, 0, 33, 0, 0, 155, 186, 55, 17, 22, 0, 0, 3, 9, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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2, 0, 167, 253, 255, 235, 255, 240, 134, 36, 0, 6, 1, 4, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 87,
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240, 251, 254, 254, 237, 255, 252, 191, 27, 0, 0, 5, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 19, 226, 255, 235,
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255, 255, 254, 242, 255, 255, 68, 12, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 4, 1, 58, 254, 255, 158, 0, 2,
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47, 173, 253, 247, 255, 65, 4, 1, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 162, 240, 248, 92, 8, 0, 13, 0,
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88, 249, 244, 148, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 17, 64, 244, 255, 210, 0, 0, 1, 2, 0, 52, 223,
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255, 223, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 144, 245, 255, 142, 0, 4, 9, 0, 6, 0, 37, 222, 226,
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42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 73,
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255, 243, 104, 0, 0, 0, 0, 11, 0, 0, 0, 235, 242, 101, 4,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 133, 245, 226,
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12, 4, 15, 0, 0, 0, 0, 24, 0, 235, 246, 41, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 236, 245, 152, 0, 10,
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0, 0, 0, 0, 6, 0, 28, 227, 239, 1, 6, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 227, 240, 53, 4, 0, 0, 24,
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0, 1, 0, 8, 181, 249, 177, 0, 2, 0, 0, 0, 0, 4, 0,
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6, 1, 5, 0, 0, 87, 246, 219, 14, 0, 0, 2, 0, 10, 7,
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0, 134, 255, 249, 104, 4, 0, 0, 0, 0, 0, 8, 0, 3, 0,
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0, 0, 4, 89, 255, 228, 0, 11, 0, 8, 14, 0, 0, 100, 250,
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248, 236, 0, 0, 8, 0, 0, 0, 0, 5, 0, 2, 0, 0, 2,
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6, 68, 250, 228, 6, 6, 0, 0, 1, 0, 140, 240, 253, 238, 51,
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31, 0, 3, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 26,
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215, 255, 119, 0, 21, 1, 40, 156, 233, 244, 239, 103, 0, 6, 6,
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0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 225, 251,
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240, 141, 118, 139, 222, 244, 255, 249, 112, 17, 0, 0, 8, 3, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 84, 245, 255, 247,
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255, 249, 255, 255, 249, 132, 11, 0, 9, 3, 1, 1, 0, 0, 0,
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0, 2, 0, 0, 1, 0, 0, 6, 1, 0, 166, 236, 255, 255, 248,
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249, 248, 72, 0, 0, 16, 0, 16, 0, 4, 0, 0, 0, 0, 0,
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0, 0, 6, 0, 0, 4, 0, 0, 20, 106, 126, 188, 190, 112, 28,
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0, 21, 0, 1, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0,
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};
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template <typename T>
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static void printTopN(const T* prob, int outputCount, int topNum) {
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std::vector<std::tuple<int, T>> data;
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for (int i = 0; i < outputCount; i++) {
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data.push_back(std::make_tuple(i, prob[i]));
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}
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std::sort(data.begin(), data.end(),
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[](auto& a, auto& b) { return std::get<1>(a) > std::get<1>(b); });
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std::cout << " --- Top" << topNum << " ---" << std::endl;
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for (int i = 0; i < topNum; i++) {
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std::cout << std::setw(3) << std::get<0>(data[i]) << ": " << std::fixed
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<< std::setprecision(6) << std::get<1>(data[i]) << std::endl;
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}
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}
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int main(int argc, char** argv) {
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(void) argc, (void) argv;
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auto context = tim::vx::Context::Create();
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auto graph = context->CreateGraph();
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tim::vx::ShapeType input_shape({28, 28, 1, 1});
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tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 0.00390625f,
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0);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape,
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tim::vx::TensorAttribute::INPUT, input_quant);
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auto input = graph->CreateTensor(input_spec);
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tim::vx::ShapeType conv1_weight_shape({5, 5, 1, 20});
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tim::vx::Quantization conv1_weighteight_quant(tim::vx::QuantType::ASYMMETRIC,
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0.00336234f, 119);
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tim::vx::TensorSpec conv1_weighteight_spec(
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tim::vx::DataType::UINT8, conv1_weight_shape,
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tim::vx::TensorAttribute::CONSTANT, conv1_weighteight_quant);
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auto conv1_weight =
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graph->CreateTensor(conv1_weighteight_spec, &lenet_weights[0]);
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tim::vx::ShapeType conv1_bias_shape({20});
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tim::vx::Quantization conv1_bias_quant(tim::vx::QuantType::ASYMMETRIC,
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1.313e-05f, 0);
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tim::vx::TensorSpec conv1_bias_spec(
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tim::vx::DataType::INT32, conv1_bias_shape,
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tim::vx::TensorAttribute::CONSTANT, conv1_bias_quant);
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auto conv1_bias = graph->CreateTensor(conv1_bias_spec, &lenet_weights[500]);
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tim::vx::Quantization conv1_output_quant(tim::vx::QuantType::ASYMMETRIC,
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0.01928069f, 140);
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tim::vx::TensorSpec conv1_output_spec(tim::vx::DataType::UINT8, {},
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tim::vx::TensorAttribute::TRANSIENT,
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conv1_output_quant);
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auto conv1_output = graph->CreateTensor(conv1_output_spec);
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tim::vx::Quantization pool1_output_quant(tim::vx::QuantType::ASYMMETRIC,
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0.01928069f, 140);
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tim::vx::TensorSpec pool1_output_spec(tim::vx::DataType::UINT8, {},
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tim::vx::TensorAttribute::TRANSIENT,
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pool1_output_quant);
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auto pool1_output = graph->CreateTensor(pool1_output_spec);
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tim::vx::ShapeType conv2_weight_shape({5, 5, 20, 50});
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tim::vx::Quantization conv2_weight_quant(tim::vx::QuantType::ASYMMETRIC,
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0.0011482f, 128);
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tim::vx::TensorSpec conv2_weight_spec(
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tim::vx::DataType::UINT8, conv2_weight_shape,
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tim::vx::TensorAttribute::CONSTANT, conv2_weight_quant);
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auto conv2_weight =
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graph->CreateTensor(conv2_weight_spec, &lenet_weights[580]);
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tim::vx::ShapeType conv2_bias_shape({50});
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tim::vx::Quantization conv2_bias_quant(tim::vx::QuantType::ASYMMETRIC,
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2.214e-05f, 0);
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tim::vx::TensorSpec conv2_bias_spec(
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tim::vx::DataType::INT32, conv2_bias_shape,
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tim::vx::TensorAttribute::CONSTANT, conv2_bias_quant);
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auto conv2_bias = graph->CreateTensor(conv2_bias_spec, &lenet_weights[25580]);
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tim::vx::Quantization conv2_output_quant(tim::vx::QuantType::ASYMMETRIC,
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0.04075872f, 141);
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tim::vx::TensorSpec conv2_output_spec(tim::vx::DataType::UINT8, {},
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tim::vx::TensorAttribute::TRANSIENT,
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conv2_output_quant);
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auto conv2_output = graph->CreateTensor(conv2_output_spec);
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tim::vx::Quantization pool2_output_quant(tim::vx::QuantType::ASYMMETRIC,
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0.04075872f, 141);
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tim::vx::TensorSpec pool2_output_spec(tim::vx::DataType::UINT8, {},
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tim::vx::TensorAttribute::TRANSIENT,
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pool2_output_quant);
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auto pool2_output = graph->CreateTensor(pool2_output_spec);
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tim::vx::ShapeType fc3_weight_shape({800, 500});
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tim::vx::Quantization fc3_weight_quant(tim::vx::QuantType::ASYMMETRIC,
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0.00073548f, 130);
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tim::vx::TensorSpec fc3_weight_spec(
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tim::vx::DataType::UINT8, fc3_weight_shape,
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tim::vx::TensorAttribute::CONSTANT, fc3_weight_quant);
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auto fc3_weight = graph->CreateTensor(fc3_weight_spec, &lenet_weights[25780]);
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tim::vx::ShapeType fc3_bias_shape({500});
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tim::vx::Quantization fc3_bias_quant(tim::vx::QuantType::ASYMMETRIC,
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2.998e-05f, 0);
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tim::vx::TensorSpec fc3_bias_spec(tim::vx::DataType::INT32, fc3_bias_shape,
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tim::vx::TensorAttribute::CONSTANT,
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fc3_bias_quant);
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auto fc3_bias = graph->CreateTensor(fc3_bias_spec, &lenet_weights[425780]);
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tim::vx::Quantization fc3_output_quant(tim::vx::QuantType::ASYMMETRIC,
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0.01992089f, 0);
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tim::vx::TensorSpec fc3_output_spec(tim::vx::DataType::UINT8, {},
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tim::vx::TensorAttribute::TRANSIENT,
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fc3_output_quant);
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auto fc3_output = graph->CreateTensor(fc3_output_spec);
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tim::vx::Quantization relu_output_quant(tim::vx::QuantType::ASYMMETRIC,
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0.01992089f, 0);
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tim::vx::TensorSpec relu_output_spec(tim::vx::DataType::UINT8, {},
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tim::vx::TensorAttribute::TRANSIENT,
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relu_output_quant);
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auto relu_output = graph->CreateTensor(relu_output_spec);
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tim::vx::ShapeType fc4_weight_shape({500, 10});
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tim::vx::Quantization fc4_weight_quant(tim::vx::QuantType::ASYMMETRIC,
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0.00158043f, 135);
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tim::vx::TensorSpec fc4_weight_spec(
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tim::vx::DataType::UINT8, fc4_weight_shape,
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tim::vx::TensorAttribute::CONSTANT, fc4_weight_quant);
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auto fc4_weight =
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graph->CreateTensor(fc4_weight_spec, &lenet_weights[427780]);
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tim::vx::ShapeType fc4_bias_shape({10});
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tim::vx::Quantization fc4_bias_quant(tim::vx::QuantType::ASYMMETRIC,
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3.148e-05f, 0);
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tim::vx::TensorSpec fc4_bias_spec(tim::vx::DataType::INT32, fc4_bias_shape,
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tim::vx::TensorAttribute::CONSTANT,
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fc4_bias_quant);
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auto fc4_bias = graph->CreateTensor(fc4_bias_spec, &lenet_weights[432780]);
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tim::vx::Quantization fc4_output_quant(tim::vx::QuantType::ASYMMETRIC,
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0.06251489f, 80);
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tim::vx::TensorSpec fc4_output_spec(tim::vx::DataType::UINT8, {10,1},
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tim::vx::TensorAttribute::TRANSIENT,
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fc4_output_quant);
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auto fc4_output = graph->CreateTensor(fc4_output_spec);
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tim::vx::ShapeType output_shape({10, 1});
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto output = graph->CreateTensor(output_spec);
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auto conv1 = graph->CreateOperation<tim::vx::ops::Conv2d>(
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conv1_weight_shape[3], tim::vx::PadType::VALID,
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std::array<uint32_t, 2>({5, 5}), std::array<uint32_t, 2>({1, 1}),
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std::array<uint32_t, 2>({1, 1}));
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(*conv1)
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.BindInputs({input, conv1_weight, conv1_bias})
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.BindOutputs({conv1_output});
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auto pool1 = graph->CreateOperation<tim::vx::ops::Pool2d>(
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tim::vx::PoolType::MAX, tim::vx::PadType::NONE,
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std::array<uint32_t, 2>({2, 2}), std::array<uint32_t, 2>({2, 2}));
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(*pool1).BindInputs({conv1_output}).BindOutputs({pool1_output});
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auto conv2 = graph->CreateOperation<tim::vx::ops::Conv2d>(
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conv2_weight_shape[3], tim::vx::PadType::VALID,
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std::array<uint32_t, 2>({5, 5}), std::array<uint32_t, 2>({1, 1}),
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std::array<uint32_t, 2>({1, 1}));
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(*conv2)
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.BindInputs({pool1_output, conv2_weight, conv2_bias})
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.BindOutputs({conv2_output});
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auto pool2 = graph->CreateOperation<tim::vx::ops::Pool2d>(
|
||
|
|
tim::vx::PoolType::MAX, tim::vx::PadType::NONE,
|
||
|
|
std::array<uint32_t, 2>({2, 2}), std::array<uint32_t, 2>({2, 2}));
|
||
|
|
(*pool2).BindInputs({conv2_output}).BindOutputs({pool2_output});
|
||
|
|
|
||
|
|
auto fc3 = graph->CreateOperation<tim::vx::ops::FullyConnected>(
|
||
|
|
2, fc3_weight_shape[1]);
|
||
|
|
(*fc3)
|
||
|
|
.BindInputs({pool2_output, fc3_weight, fc3_bias})
|
||
|
|
.BindOutputs({fc3_output});
|
||
|
|
|
||
|
|
auto relu = graph->CreateOperation<tim::vx::ops::Relu>();
|
||
|
|
(*relu).BindInput(fc3_output).BindOutput(relu_output);
|
||
|
|
|
||
|
|
auto fc4 = graph->CreateOperation<tim::vx::ops::FullyConnected>(
|
||
|
|
0, fc4_weight_shape[1]);
|
||
|
|
(*fc4)
|
||
|
|
.BindInputs({relu_output, fc4_weight, fc4_bias})
|
||
|
|
.BindOutputs({fc4_output});
|
||
|
|
|
||
|
|
tim::vx::ops::CustomSoftmax::ParamTuple tuple_list(fc4_output_spec.GetElementNum(),
|
||
|
|
fc4_output_quant.ZeroPoints()[0],
|
||
|
|
fc4_output_quant.Scales()[0]);
|
||
|
|
auto softmax = graph->CreateOperation<tim::vx::ops::CustomSoftmax>(tuple_list);
|
||
|
|
(*softmax).BindInput(fc4_output).BindOutput(output);
|
||
|
|
|
||
|
|
if (!graph->Compile()) {
|
||
|
|
std::cout << "Compile graph fail." << std::endl;
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
if (!input->CopyDataToTensor(input_data.data(), input_data.size())) {
|
||
|
|
std::cout << "Copy input data fail." << std::endl;
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
if (!graph->Run()) {
|
||
|
|
std::cout << "Run graph fail." << std::endl;
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
std::vector<float> output_data;
|
||
|
|
output_data.resize(1 * 10);
|
||
|
|
if (!output->CopyDataFromTensor(output_data.data())) {
|
||
|
|
std::cout << "Copy output data fail." << std::endl;
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
printTopN(output_data.data(), output_data.size(), 5);
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|