arm_compute v18.05
diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp
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+++ b/examples/graph_resnext50.cpp
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+/*
+ * Copyright (c) 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::utils;
+using namespace arm_compute::graph::frontend;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement ResNeXt50 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_in, [optional] npy_out, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ */
+class GraphResNeXt50Example : public Example
+{
+public:
+ void do_setup(int argc, char **argv) override
+ {
+ std::string data_path; /* Path to the trainable data */
+ std::string npy_in; /* Input npy data */
+ std::string npy_out; /* Output npy data */
+
+ // 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_in] [npy_out] [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_in] [npy_out] [fast_math_hint]\n\n";
+ std::cout << "No data folder provided: using random values\n\n";
+ }
+ else if(argc == 3)
+ {
+ data_path = argv[2];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [npy_in] [npy_out] [fast_math_hint]\n\n";
+ std::cout << "No input npy file provided: using random values\n\n";
+ }
+ else if(argc == 4)
+ {
+ data_path = argv[2];
+ npy_in = argv[3];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [npy_out] [fast_math_hint]\n\n";
+ std::cout << "No output npy file provided: skipping output accessor\n\n";
+ }
+ else if(argc == 5)
+ {
+ data_path = argv[2];
+ npy_in = argv[3];
+ npy_out = 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];
+ npy_in = argv[3];
+ npy_out = argv[4];
+ fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+ }
+
+ graph << target_hint
+ << fast_math_hint
+ << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
+ get_input_accessor(npy_in))
+ << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"),
+ get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy"))
+ .set_name("bn_data/Scale")
+ << ConvolutionLayer(
+ 7U, 7U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"),
+ PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR))
+ .set_name("conv0/Convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0");
+
+ add_residual_block(data_path, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1);
+ add_residual_block(data_path, 512, 2, 4, 2);
+ add_residual_block(data_path, 1024, 3, 6, 2);
+ add_residual_block(data_path, 2048, 4, 3, 2);
+
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1")
+ << FlattenLayer().set_name("predictions/Reshape")
+ << OutputLayer(get_npy_output_accessor(npy_out, TensorShape(2048U), DataType::F32));
+
+ // 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, "ResNeXt50" };
+
+ void add_residual_block(const std::string &data_path, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1)
+ {
+ for(unsigned int i = 0; i < num_units; ++i)
+ {
+ std::stringstream unit_path_ss;
+ unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_";
+ std::string unit_path = unit_path_ss.str();
+
+ std::stringstream unit_name_ss;
+ unit_name_ss << "stage" << stage << "/unit" << (i + 1) << "/";
+ std::string unit_name = unit_name_ss.str();
+
+ PadStrideInfo pad_grouped_conv(1, 1, 1, 1);
+ if(i == 0)
+ {
+ pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, DimensionRoundingType::FLOOR);
+ }
+
+ SubStream right(graph);
+ right << ConvolutionLayer(
+ 1U, 1U, base_depth / 2,
+ get_weights_accessor(data_path, unit_path + "conv1_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(unit_name + "conv1/convolution")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
+
+ << ConvolutionLayer(
+ 3U, 3U, base_depth / 2,
+ get_weights_accessor(data_path, unit_path + "conv2_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ pad_grouped_conv, 32)
+ .set_name(unit_name + "conv2/convolution")
+ << ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"),
+ get_weights_accessor(data_path, unit_path + "bn2_add.npy"))
+ .set_name(unit_name + "conv1/Scale")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu")
+
+ << ConvolutionLayer(
+ 1U, 1U, base_depth,
+ get_weights_accessor(data_path, unit_path + "conv3_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv3_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(unit_name + "conv3/convolution");
+
+ SubStream left(graph);
+ if(i == 0)
+ {
+ left << ConvolutionLayer(
+ 1U, 1U, base_depth,
+ get_weights_accessor(data_path, unit_path + "sc_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0))
+ .set_name(unit_name + "sc/convolution")
+ << ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"),
+ get_weights_accessor(data_path, unit_path + "sc_bn_add.npy"))
+ .set_name(unit_name + "sc/scale");
+ }
+
+ graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add");
+ graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
+ }
+ }
+};
+
+/** Main program for ResNeXt50
+ *
+ * @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_in, [optional] npy_out )
+ */
+int main(int argc, char **argv)
+{
+ return arm_compute::utils::run_example<GraphResNeXt50Example>(argc, argv);
+}