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/*
* 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"
#include <cstdlib>
using namespace arm_compute::utils;
using namespace arm_compute::graph;
using namespace arm_compute::graph_utils;
/** 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 Example
{
public:
void do_setup(int argc, char **argv) override
{
std::string data_path; /* Path to the trainable data */
std::string image; /* Image data */
std::string label; /* Label data */
constexpr float mean = 0.f; /* Mean value to subtract from the channels */
constexpr float std = 255.f; /* Standard deviation value to divide from the channels */
// Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::GEMM;
// 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)");
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/";
// Parse arguments
if(argc < 2)
{
// Print help
std::cout << "Usage: " << argv[0] << " [target] [model] [path_to_data] [image] [labels]\n\n";
std::cout << "No model ID provided: using MobileNetV1_1.0_224\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] [path_to_data] [image] [labels]\n\n";
std::cout << "No model ID provided: using MobileNetV1_1.0_224\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] << " [path_to_data] [image] [labels]\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 4)
{
data_path = argv[3];
std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [image] [labels]\n\n";
std::cout << "No image provided: using random values\n\n";
}
else if(argc == 5)
{
data_path = argv[3];
image = argv[4];
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[3];
image = argv[4];
label = argv[5];
}
// Add model path to data path
if(!data_path.empty())
{
data_path += model_path;
}
graph << target_hint
<< convolution_hint
<< Tensor(TensorInfo(TensorShape(spatial_size, spatial_size, 3U, 1U), 1, DataType::F32),
get_input_accessor(image,
mean, mean, mean,
std, std, std, false /* Do not convert to BGR */))
<< ConvolutionLayer(
3U, 3U, 32U * depth_scale,
get_weights_accessor(data_path, "Conv2d_0_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
<< 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)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
<< get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
<< PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
<< ConvolutionLayer(
1U, 1U, 1001U,
get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
<< ReshapeLayer(TensorShape(1001U))
<< SoftmaxLayer()
<< Tensor(get_output_accessor(label, 5));
}
void do_run() override
{
// Run graph
graph.run();
}
private:
Graph graph{};
BranchLayer get_dwsc_node(const std::string &data_path, std::string &¶m_path,
unsigned int conv_filt,
PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
{
std::string total_path = param_path + "_";
SubGraph sg;
sg << DepthwiseConvolutionLayer(
3U, 3U,
get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
dwc_pad_stride_info,
true)
<< 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)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
<< ConvolutionLayer(
1U, 1U, conv_filt,
get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
conv_pad_stride_info)
<< 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)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
return BranchLayer(std::move(sg));
}
};
/** Main program for MobileNetV1
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL),
* [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160),
* [optional] Path to the weights folder,
* [optional] image,
* [optional] labels )
*/
int main(int argc, char **argv)
{
return arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv);
}
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