COMPMID-925: Enabling OpenCL tuner in the graph examples
Change-Id: I4fe501281f527e20e8fdd0253d59ea2c4629056b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/120354
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/examples/graph_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp
new file mode 100644
index 0000000..29daeff
--- /dev/null
+++ b/examples/graph_mobilenet_qasymm8.cpp
@@ -0,0 +1,229 @@
+/*
+ * 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/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+using namespace arm_compute;
+using namespace arm_compute::graph;
+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 )
+ */
+class GraphMobileNetQASYMM8Example : public utils::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 int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ TargetHint target_hint = set_target_hint(int_target_hint);
+
+ // Parse arguments
+ if(argc < 2)
+ {
+ // Print help
+ std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels]\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]\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]\n\n";
+ std::cout << "No text file with labels provided: skipping output accessor\n\n";
+ }
+ else
+ {
+ data_path = argv[2];
+ input = argv[3];
+ label = argv[4];
+ }
+
+ // Initialize graph
+ graph.graph_init(int_target_hint == 2);
+
+ graph << target_hint
+ << arm_compute::graph::Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, 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, WeightsInfo(),
+ conv_weights_quant_info.at(0),
+ mid_quant_info)
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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))
+ << 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, WeightsInfo(), conv_weights_quant_info.at(1))
+ << ReshapeLayer(TensorShape(1001U))
+ << SoftmaxLayer()
+ << arm_compute::graph::Tensor(get_output_accessor(label, 5));
+ }
+ void do_run() override
+ {
+ // Run graph
+ graph.run();
+ }
+
+private:
+ Graph graph{};
+
+ /** 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 + "_";
+ SubGraph sg;
+
+ 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,
+ true,
+ 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, WeightsInfo(),
+ 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 )
+ */
+int main(int argc, char **argv)
+{
+ return utils::run_example<GraphMobileNetQASYMM8Example>(argc, argv);
+}