arm_compute v18.11
diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.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/CommonGraphOptions.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+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 MobileNetV2's network using the Compute Library's graph API */
+class GraphMobilenetV2Example : public Example
+{
+public:
+ GraphMobilenetV2Example()
+ : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV2")
+ {
+ }
+ GraphMobilenetV2Example(const GraphMobilenetV2Example &) = delete;
+ GraphMobilenetV2Example &operator=(const GraphMobilenetV2Example &) = delete;
+ GraphMobilenetV2Example(GraphMobilenetV2Example &&) = default; // NOLINT
+ GraphMobilenetV2Example &operator=(GraphMobilenetV2Example &&) = default; // NOLINT
+ ~GraphMobilenetV2Example() override = default;
+
+ bool do_setup(int argc, char **argv) override
+ {
+ // Parse arguments
+ cmd_parser.parse(argc, argv);
+
+ // Consume common parameters
+ common_params = consume_common_graph_parameters(common_opts);
+
+ // Return when help menu is requested
+ if(common_params.help)
+ {
+ cmd_parser.print_help(argv[0]);
+ return false;
+ }
+
+ // Checks
+ ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+ ARM_COMPUTE_EXIT_ON_MSG(common_params.data_type == DataType::F16 && common_params.target == Target::NEON, "F16 NEON not supported for this graph");
+
+ // Print parameter values
+ std::cout << common_params << std::endl;
+
+ // Create model path
+ std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/";
+
+ // Create input descriptor
+ const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
+ TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+ // Create a preprocessor object
+ std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+ // Get trainable parameters data path
+ std::string data_path = common_params.data_path;
+
+ // Add model path to data path
+ if(!data_path.empty())
+ {
+ data_path += model_path;
+ }
+
+ // Create graph
+ graph << common_params.target
+ << DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
+ << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
+ << ConvolutionLayer(3U, 3U, 32U,
+ get_weights_accessor(data_path, "Conv_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL))
+ .set_name("Conv")
+ << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"),
+ 0.0010000000474974513f)
+ .set_name("Conv/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name("Conv/Relu6");
+
+ get_expanded_conv(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1));
+ get_expanded_conv(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
+ get_expanded_conv(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
+ get_expanded_conv(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
+ get_expanded_conv(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), true);
+ get_expanded_conv(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
+ get_expanded_conv(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true);
+ get_expanded_conv(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), true);
+
+ graph << ConvolutionLayer(1U, 1U, 1280U,
+ get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv_1")
+ << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"),
+ 0.0010000000474974513f)
+ .set_name("Conv_1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name("Conv_1/Relu6")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool")
+ << 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("Predictions/Reshape")
+ << SoftmaxLayer().set_name("Predictions/Softmax")
+ << OutputLayer(get_output_accessor(common_params, 5));
+
+ // Finalize graph
+ GraphConfig config;
+ config.num_threads = common_params.threads;
+ config.use_tuner = common_params.enable_tuner;
+ config.tuner_file = common_params.tuner_file;
+
+ graph.finalize(common_params.target, config);
+
+ return true;
+ }
+
+ void do_run() override
+ {
+ // Run graph
+ graph.run();
+ }
+
+private:
+ CommandLineParser cmd_parser;
+ CommonGraphOptions common_opts;
+ CommonGraphParams common_params;
+ Stream graph;
+
+ void get_expanded_conv(const std::string &data_path, std::string &¶m_path,
+ unsigned int input_channels, unsigned int output_channels,
+ PadStrideInfo dwc_pad_stride_info,
+ bool has_expand = false, bool is_residual = false, unsigned int expansion_size = 6)
+ {
+ std::string total_path = param_path + "_";
+ SubStream left(graph);
+
+ // Add expand node
+ if(has_expand)
+ {
+ left << ConvolutionLayer(1U, 1U, input_channels * expansion_size,
+ get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/expand/Conv2D")
+ << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, total_path + "expand_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, total_path + "expand_BatchNorm_beta.npy"),
+ 0.0010000000474974513f)
+ .set_name(param_path + "/expand/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name(param_path + "/expand/Relu6");
+ }
+
+ // Add depthwise node
+ left << 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(param_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"),
+ 0.0010000000474974513f)
+ .set_name(param_path + "/depthwise/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name(param_path + "/depthwise/Relu6");
+
+ // Add project node
+ left << ConvolutionLayer(1U, 1U, output_channels,
+ get_weights_accessor(data_path, total_path + "project_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/project/Conv2D")
+ << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, total_path + "project_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, total_path + "project_BatchNorm_beta.npy"),
+ 0.0010000000474974513)
+ .set_name(param_path + "/project/BatchNorm");
+
+ if(is_residual)
+ {
+ // Add residual node
+ SubStream right(graph);
+ graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add");
+ }
+ else
+ {
+ graph.forward_tail(left.tail_node());
+ }
+ }
+};
+
+/** Main program for MobileNetV2
+ *
+ * Model is based on:
+ * https://arxiv.org/abs/1801.04381
+ * "MobileNetV2: Inverted Residuals and Linear Bottlenecks"
+ * Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
+ *
+ * @note To list all the possible arguments execute the binary appended with the --help option
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments
+ */
+int main(int argc, char **argv)
+{
+ return arm_compute::utils::run_example<GraphMobilenetV2Example>(argc, argv);
+}