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Diffstat (limited to 'examples/graph_mobilenet_qasymm8.cpp')
| -rw-r--r-- | examples/graph_mobilenet_qasymm8.cpp | 239 |
1 files changed, 0 insertions, 239 deletions
diff --git a/examples/graph_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp deleted file mode 100644 index 2801209985..0000000000 --- a/examples/graph_mobilenet_qasymm8.cpp +++ /dev/null @@ -1,239 +0,0 @@ -/* - * Copyright (c) 2017-2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "arm_compute/graph.h" -#include "support/ToolchainSupport.h" -#include "utils/GraphUtils.h" -#include "utils/Utils.h" - -#include <cstdlib> - -using namespace arm_compute; -using namespace arm_compute::utils; -using namespace arm_compute::graph::frontend; -using namespace arm_compute::graph_utils; - -/** Example demonstrating how to implement QASYMM8 MobileNet's network using the Compute Library's graph API - * - * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) - */ -class GraphMobileNetQASYMM8Example : public Example -{ -public: - void do_setup(int argc, char **argv) override - { - std::string data_path; /* Path to the trainable data */ - std::string input; /* Image data */ - std::string label; /* Label data */ - - // Quantization info taken from the AndroidNN QASYMM8 MobileNet example - const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128); - const QuantizationInfo mid_quant_info = QuantizationInfo(0.0784313753247f, 128); - - const std::vector<QuantizationInfo> conv_weights_quant_info = - { - QuantizationInfo(0.031778190285f, 156), // conv0 - QuantizationInfo(0.00604454148561f, 66) // conv14 - }; - - const std::vector<QuantizationInfo> depth_weights_quant_info = - { - QuantizationInfo(0.254282623529f, 129), // dwsc1 - QuantizationInfo(0.12828284502f, 172), // dwsc2 - QuantizationInfo(0.265911251307f, 83), // dwsc3 - QuantizationInfo(0.0985597148538f, 30), // dwsc4 - QuantizationInfo(0.0631204470992f, 54), // dwsc5 - QuantizationInfo(0.0137207424268f, 141), // dwsc6 - QuantizationInfo(0.0817828401923f, 125), // dwsc7 - QuantizationInfo(0.0393880493939f, 164), // dwsc8 - QuantizationInfo(0.211694166064f, 129), // dwsc9 - QuantizationInfo(0.158015936613f, 103), // dwsc10 - QuantizationInfo(0.0182712618262f, 137), // dwsc11 - QuantizationInfo(0.0127998134121f, 134), // dwsc12 - QuantizationInfo(0.299285322428f, 161) // dwsc13 - }; - - const std::vector<QuantizationInfo> point_weights_quant_info = - { - QuantizationInfo(0.0425766184926f, 129), // dwsc1 - QuantizationInfo(0.0250773020089f, 94), // dwsc2 - QuantizationInfo(0.015851572156f, 93), // dwsc3 - QuantizationInfo(0.0167811904103f, 98), // dwsc4 - QuantizationInfo(0.00951790809631f, 135), // dwsc5 - QuantizationInfo(0.00999817531556f, 128), // dwsc6 - QuantizationInfo(0.00590536883101f, 126), // dwsc7 - QuantizationInfo(0.00576109671965f, 133), // dwsc8 - QuantizationInfo(0.00830461271107f, 142), // dwsc9 - QuantizationInfo(0.0152327232063f, 72), // dwsc10 - QuantizationInfo(0.00741417845711f, 125), // dwsc11 - QuantizationInfo(0.0135628981516f, 142), // dwsc12 - QuantizationInfo(0.0338749065995f, 140) // dwsc13 - }; - - // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON - const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; - Target target_hint = set_target_hint(target); - FastMathHint fast_math_hint = FastMathHint::DISABLED; - - // Parse arguments - if(argc < 2) - { - // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels] [fast_math_hint]\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 2) - { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels] [fast_math_hint]\n\n"; - std::cout << "No input provided: using random values\n\n"; - } - else if(argc == 4) - { - data_path = argv[2]; - input = argv[3]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; - std::cout << "No text file with labels provided: skipping output accessor\n\n"; - } - else if(argc == 5) - { - data_path = argv[2]; - input = argv[3]; - label = argv[4]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; - std::cout << "No fast math info provided: disabling fast math\n\n"; - } - else - { - data_path = argv[2]; - input = argv[3]; - label = argv[4]; - fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; - } - - graph << target_hint - << DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method - << fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::QASYMM8, in_quant_info), - get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input)) - << ConvolutionLayer( - 3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_weights.npy"), - get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_bias.npy"), - PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), - 1, conv_weights_quant_info.at(0), mid_quant_info) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)); - graph << get_dwsc_node(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0)); - graph << get_dwsc_node(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1), - point_weights_quant_info.at(1)); - graph << get_dwsc_node(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2), - point_weights_quant_info.at(2)); - graph << get_dwsc_node(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3), - point_weights_quant_info.at(3)); - graph << get_dwsc_node(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4), - point_weights_quant_info.at(4)); - graph << get_dwsc_node(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5), - point_weights_quant_info.at(5)); - graph << get_dwsc_node(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6), - point_weights_quant_info.at(6)); - graph << get_dwsc_node(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7), - point_weights_quant_info.at(7)); - graph << get_dwsc_node(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8), - point_weights_quant_info.at(8)); - graph << get_dwsc_node(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9), - point_weights_quant_info.at(9)); - graph << get_dwsc_node(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10), - point_weights_quant_info.at(10)); - graph << get_dwsc_node(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11), - point_weights_quant_info.at(11)); - graph << get_dwsc_node(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12), - point_weights_quant_info.at(12)) - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) - << ConvolutionLayer( - 1U, 1U, 1001U, - get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_weights.npy"), - get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_bias.npy"), - PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1)) - << ReshapeLayer(TensorShape(1001U)) - << SoftmaxLayer() - << OutputLayer(get_output_accessor(label, 5)); - - // Finalize graph - GraphConfig config; - config.use_tuner = (target == 2); - graph.finalize(target_hint, config); - } - void do_run() override - { - // Run graph - graph.run(); - } - -private: - Stream graph{ 0, "MobileNetV1_QASYMM8" }; - - /** This function produces a depthwise separable convolution node (i.e. depthwise + pointwise layers) with ReLU6 activation after each layer. - * - * @param[in] data_path Path to trainable data folder - * @param[in] param_path Prefix of specific set of weights/biases data - * @param[in] conv_filt Filters depths for pointwise convolution - * @param[in] dwc_pad_stride_info PadStrideInfo for depthwise convolution - * @param[in] conv_pad_stride_info PadStrideInfo for pointwise convolution - * @param[in] depth_weights_quant_info QuantizationInfo for depthwise convolution's weights - * @param[in] point_weights_quant_info QuantizationInfo for pointwise convolution's weights - * - * @return The complete dwsc node - */ - BranchLayer get_dwsc_node(const std::string &data_path, std::string &¶m_path, - const unsigned int conv_filt, - PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info, - QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info) - { - std::string total_path = "/cnn_data/mobilenet_qasymm8_model/" + param_path + "_"; - SubStream sg(graph); - - sg << DepthwiseConvolutionLayer( - 3U, 3U, - get_weights_accessor(data_path, total_path + "depthwise_weights.npy"), - get_weights_accessor(data_path, total_path + "depthwise_bias.npy"), - dwc_pad_stride_info, depth_weights_quant_info) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)) - << ConvolutionLayer( - 1U, 1U, conv_filt, - get_weights_accessor(data_path, total_path + "pointwise_weights.npy"), - get_weights_accessor(data_path, total_path + "pointwise_bias.npy"), - conv_pad_stride_info, 1, point_weights_quant_info) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)); - - return BranchLayer(std::move(sg)); - } -}; -/** Main program for MobileNetQASYMM8 - * - * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) - */ -int main(int argc, char **argv) -{ - return arm_compute::utils::run_example<GraphMobileNetQASYMM8Example>(argc, argv); -} |