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-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright (c) 2017 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 "Benchmark.h"
-
-#include <cstdlib>
-
-using namespace arm_compute::graph::frontend;
-
-inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void)
-{
- return get_accessor<InputAccessor>();
-}
-
-inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper)
-{
- return get_accessor<InputAccessor>();
-}
-
-inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW)
-{
- return get_accessor<InputAccessor>();
-}
-
-inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void)
-{
- return get_accessor<OutputAccessor>();
-}
-
-/** Example demonstrating how to implement 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), [optional] Path to the weights folder, [optional] image, [optional] labels )
- */
-class GraphMobilenetExample
-{
-public:
- void do_setup(int argc, char **argv)
- {
- std::string data_path; /* Path to the trainable data */
- std::string image; /* Image data */
- std::string label; /* Label 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);
- ConvolutionMethod convolution_hint = ConvolutionMethod::GEMM;
- DepthwiseConvolutionMethod depthwise_convolution_hint = DepthwiseConvolutionMethod::Optimized3x3;
- FastMathHint fast_math_hint = FastMathHint::Disabled;
-
- // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)
- int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0;
- ARM_COMPUTE_ERROR_ON_MSG(model_id > 1, "Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)");
- int layout_id = (argc > 3) ? std::strtol(argv[3], nullptr, 10) : 0;
- ARM_COMPUTE_ERROR_ON_MSG(layout_id > 1, "Invalid layout ID. Layout must be 0 (NCHW) or 1 (NHWC)");
-
- float depth_scale = (model_id == 0) ? 1.f : 0.75;
- unsigned int spatial_size = (model_id == 0) ? 224 : 160;
- std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";
- TensorDescriptor input_descriptor_nchw = TensorDescriptor(TensorShape(spatial_size, spatial_size, 3U, 1U), DataType::F32);
- TensorDescriptor input_descriptor_nhwc = TensorDescriptor(TensorShape(3U, spatial_size, spatial_size, 1U), DataType::F32).set_layout(DataLayout::NHWC);
- TensorDescriptor input_descriptor = (layout_id == 0) ? input_descriptor_nchw : input_descriptor_nhwc;
-
- // Parse arguments
- if(argc < 2)
- {
- // Print help
- std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
- std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
- std::cout << "No data layout provided: using NCHW\n\n";
- std::cout << "No data folder provided: using random values\n\n";
- }
- else if(argc == 2)
- {
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
- std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
- std::cout << "No data layout provided: using NCHW\n\n";
- std::cout << "No data folder provided: using random values\n\n";
- }
- else if(argc == 3)
- {
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
- std::cout << "No data layout provided: using NCHW\n\n";
- std::cout << "No data folder provided: using random values\n\n";
- }
- else if(argc == 4)
- {
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
- std::cout << "No data folder provided: using random values\n\n";
- }
- else if(argc == 5)
- {
- data_path = argv[4];
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels] [fast_math_hint]\n\n";
- std::cout << "No image provided: using random values\n\n";
- std::cout << "No text file with labels provided: skipping output accessor\n\n";
- }
- else if(argc == 6)
- {
- data_path = argv[4];
- image = argv[5];
- 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 == 7)
- {
- data_path = argv[4];
- image = argv[5];
- label = argv[6];
- 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[4];
- image = argv[5];
- label = argv[6];
- fast_math_hint = (std::strtol(argv[7], nullptr, 1) == 0) ? FastMathHint::Disabled : FastMathHint::Enabled;
- }
-
- // Add model path to data path
- if(!data_path.empty())
- {
- data_path += model_path;
- }
-
- graph << target_hint
- << convolution_hint
- << depthwise_convolution_hint
- << fast_math_hint
- << InputLayer(input_descriptor,
- get_input_accessor())
- << ConvolutionLayer(
- 3U, 3U, 32U * depth_scale,
- get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
- .set_name("Conv2d_0")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"),
- 0.001f)
- .set_name("Conv2d_0/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6");
- graph << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a")
- << ConvolutionLayer(
- 1U, 1U, 1001U,
- get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
- get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("Logits/Conv2d_1c_1x1")
- << ReshapeLayer(TensorShape(1001U)).set_name("Reshape")
- << SoftmaxLayer().set_name("Softmax")
- << OutputLayer(get_output_accessor());
-
- // Finalize graph
- GraphConfig config;
- config.use_tuner = (target == 2);
- graph.finalize(target_hint, config);
- }
- void do_run()
- {
- run_benchmark(graph);
- }
-
-private:
- Stream graph{ 0, "MobileNetV1" };
-
- ConcatLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
- unsigned int conv_filt,
- PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
- {
- std::string total_path = param_path + "_";
- SubStream sg(graph);
- sg << DepthwiseConvolutionLayer(
- 3U, 3U,
- get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- dwc_pad_stride_info)
- .set_name(total_path + "depthwise/depthwise")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
- 0.001f)
- .set_name(total_path + "depthwise/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6")
- << ConvolutionLayer(
- 1U, 1U, conv_filt,
- get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- conv_pad_stride_info)
- .set_name(total_path + "pointwise/Conv2D")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
- 0.001f)
- .set_name(total_path + "pointwise/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6");
-
- return ConcatLayer(std::move(sg));
- }
-};
-
-/** Main program for MobileNetV1
- *
- * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner),
- * [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160),
- * [optional] Path to the weights folder,
- * [optional] image,
- * [optional] labels,
- * [optional] data layout,
- * [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
- */
-int main(int argc, char **argv)
-{
- GraphMobilenetExample example;
-
- example.do_setup(argc, argv);
- example.do_run();
-
- return 0;
-}
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