| /* |
| * Copyright (c) 2018-2020 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); |
| cmd_parser.validate(); |
| |
| // 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; |
| } |
| |
| // Print parameter values |
| std::cout << common_params << std::endl; |
| |
| // 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); |
| |
| // Set graph hints |
| graph << common_params.target |
| << common_params.fast_math_hint; |
| |
| // Create core graph |
| if(arm_compute::is_data_type_float(common_params.data_type)) |
| { |
| create_graph_float(input_descriptor); |
| } |
| else |
| { |
| create_graph_qasymm8(input_descriptor); |
| } |
| // Create common tail |
| graph << 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_mode = common_params.tuner_mode; |
| 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; |
| |
| private: |
| enum class IsResidual |
| { |
| Yes, |
| No |
| }; |
| |
| enum class HasExpand |
| { |
| Yes, |
| No |
| }; |
| |
| private: |
| void create_graph_float(TensorDescriptor &input_descriptor) |
| { |
| // Create model path |
| const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/"; |
| |
| // 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; |
| } |
| |
| graph << 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_float(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1)); |
| get_expanded_conv_float(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); |
| get_expanded_conv_float(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes); |
| |
| 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, common_params.data_layout)).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"); |
| } |
| |
| void get_expanded_conv_float(const std::string &data_path, std::string &¶m_path, |
| unsigned int input_channels, unsigned int output_channels, |
| PadStrideInfo dwc_pad_stride_info, |
| HasExpand has_expand = HasExpand::No, IsResidual is_residual = IsResidual::No, |
| unsigned int expansion_size = 6) |
| { |
| std::string total_path = param_path + "_"; |
| SubStream left(graph); |
| |
| // Add expand node |
| if(has_expand == HasExpand::Yes) |
| { |
| 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 == IsResidual::Yes) |
| { |
| // 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()); |
| } |
| } |
| |
| void create_graph_qasymm8(TensorDescriptor &input_descriptor) |
| { |
| // Create model path |
| const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_quantized_model/"; |
| |
| // 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; |
| } |
| |
| const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128); |
| const QuantizationInfo mid_quant_info = QuantizationInfo(0.023528477177023888f, 128); |
| |
| const std::vector<QuantizationInfo> conv_weights_quant_info = |
| { |
| QuantizationInfo(0.03396892547607422f, 122), // Conv |
| QuantizationInfo(0.005167067516595125f, 125), // Conv1 |
| QuantizationInfo(0.0016910821432247758f, 113) // Conv2d_1c_1x1 |
| }; |
| |
| // Pointwise expand convolution quantization info |
| const std::vector<QuantizationInfo> pwc_q = |
| { |
| QuantizationInfo(0.254282623529f, 129), // expand_0 (Dummy) |
| QuantizationInfo(0.009758507832884789f, 127), // expand_1 |
| QuantizationInfo(0.0036556976847350597f, 144), // expand_2 |
| QuantizationInfo(0.0029988749884068966f, 104), // expand_3 |
| QuantizationInfo(0.0019244228024035692f, 128), // expand_4 |
| QuantizationInfo(0.0013649158645421267f, 135), // expand_5 |
| QuantizationInfo(0.0019170437008142471f, 127), // expand_6 |
| QuantizationInfo(0.0015538912266492844f, 125), // expand_7 |
| QuantizationInfo(0.0014702979242429137f, 134), // expand_8 |
| QuantizationInfo(0.0013733493397012353f, 127), // expand_9 |
| QuantizationInfo(0.0016282502328976989f, 131), // expand_10 |
| QuantizationInfo(0.0016309921629726887f, 134), // expand_11 |
| QuantizationInfo(0.0018258779309689999f, 138), // expand_12 |
| QuantizationInfo(0.0013828007504343987f, 123), // expand_13 |
| QuantizationInfo(0.0020222084131091833f, 135), // expand_14 |
| QuantizationInfo(0.04281935095787048f, 102), // expand_15 |
| QuantizationInfo(0.002046825597062707f, 135) // expand_16 |
| }; |
| // Depthwise expand convolution quantization info |
| const std::vector<QuantizationInfo> dwc_q = |
| { |
| QuantizationInfo(0.3436955213546753f, 165), // expand_0 |
| QuantizationInfo(0.020969120785593987f, 109), // expand_1 |
| QuantizationInfo(0.16981913149356842f, 52), // expand_2 |
| QuantizationInfo(0.017202870920300484f, 143), // expand_3 |
| QuantizationInfo(0.06525065749883652f, 118), // expand_4 |
| QuantizationInfo(0.07909784466028214f, 95), // expand_5 |
| QuantizationInfo(0.010087885893881321f, 127), // expand_6 |
| QuantizationInfo(0.06092711538076401f, 110), // expand_7 |
| QuantizationInfo(0.052407849580049515f, 133), // expand_8 |
| QuantizationInfo(0.04077887907624245f, 155), // expand_9 |
| QuantizationInfo(0.031107846647500992f, 143), // expand_10 |
| QuantizationInfo(0.07080810517072678f, 66), // expand_11 |
| QuantizationInfo(0.07448793947696686f, 159), // expand_12 |
| QuantizationInfo(0.01525793131440878f, 92), // expand_13 |
| QuantizationInfo(0.04166752099990845f, 147), // expand_14 |
| QuantizationInfo(0.04281935095787048f, 102), // expand_15 |
| QuantizationInfo(0.16456253826618195, 201) // expand_16 |
| }; |
| // Project convolution quantization info |
| const std::vector<QuantizationInfo> prwc_q = |
| { |
| QuantizationInfo(0.03737175464630127f, 140), // expand_0 |
| QuantizationInfo(0.0225360207259655f, 156), // expand_1 |
| QuantizationInfo(0.02740888111293316f, 122), // expand_2 |
| QuantizationInfo(0.016844693571329117f, 111), // expand_3 |
| QuantizationInfo(0.019062912091612816f, 146), // expand_4 |
| QuantizationInfo(0.018293123692274094f, 128), // expand_5 |
| QuantizationInfo(0.014601286500692368f, 147), // expand_6 |
| QuantizationInfo(0.016782939434051514f, 124), // expand_7 |
| QuantizationInfo(0.012898261658847332f, 125), // expand_8 |
| QuantizationInfo(0.019561484456062317f, 144), // expand_9 |
| QuantizationInfo(0.007436311338096857f, 129), // expand_10 |
| QuantizationInfo(0.00838223285973072f, 136), // expand_11 |
| QuantizationInfo(0.023982593789696693f, 154), // expand_12 |
| QuantizationInfo(0.009447949007153511f, 140), // expand_13 |
| QuantizationInfo(0.00789870135486126f, 139), // expand_14 |
| QuantizationInfo(0.03697410225868225f, 131), // expand_15 |
| QuantizationInfo(0.008009289391338825f, 111) // expand_16 |
| }; |
| |
| graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info), |
| get_weights_accessor(data_path, common_params.image)) |
| << ConvolutionLayer( |
| 3U, 3U, 32U, |
| get_weights_accessor(data_path, "Conv_weights.npy"), |
| get_weights_accessor(data_path, "Conv_bias.npy"), |
| PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), |
| 1, conv_weights_quant_info.at(0), mid_quant_info) |
| .set_name("Conv") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv/Relu6") |
| << DepthwiseConvolutionLayer(3U, 3U, |
| get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_weights.npy"), |
| get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_biases.npy"), |
| PadStrideInfo(1, 1, 1, 1), 1, dwc_q.at(0)) |
| .set_name("expanded_conv/depthwise/depthwise") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("expanded_conv/depthwise/Relu6") |
| << ConvolutionLayer(1U, 1U, 16U, |
| get_weights_accessor(data_path, "expanded_conv_project_weights.npy"), |
| get_weights_accessor(data_path, "expanded_conv_project_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0), 1, prwc_q.at(0)) |
| .set_name("expanded_conv/project/Conv2D"); |
| |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_1", IsResidual::No, 96U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), |
| pwc_q.at(1), dwc_q.at(1), prwc_q.at(1)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_2", IsResidual::Yes, 144U, 24U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(2), dwc_q.at(2), prwc_q.at(2)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_3", IsResidual::No, 144U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), |
| pwc_q.at(3), dwc_q.at(3), prwc_q.at(3)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_4", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(4), dwc_q.at(4), prwc_q.at(4)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_5", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(5), dwc_q.at(5), prwc_q.at(5)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_6", IsResidual::No, 192U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), |
| pwc_q.at(6), dwc_q.at(6), prwc_q.at(6)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_7", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(7), dwc_q.at(7), prwc_q.at(7)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_8", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(8), dwc_q.at(8), prwc_q.at(8)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_9", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(9), dwc_q.at(9), prwc_q.at(9)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_10", IsResidual::No, 384U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(10), dwc_q.at(10), prwc_q.at(10)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_11", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(11), dwc_q.at(11), prwc_q.at(11)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_12", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(12), dwc_q.at(12), prwc_q.at(12)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_13", IsResidual::No, 576U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), |
| pwc_q.at(13), dwc_q.at(13), prwc_q.at(13)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_14", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(14), dwc_q.at(14), prwc_q.at(14)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_15", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(15), dwc_q.at(15), prwc_q.at(15)); |
| get_expanded_conv_qasymm8(data_path, "expanded_conv_16", IsResidual::No, 960U, 320U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(16), dwc_q.at(16), prwc_q.at(16)); |
| |
| graph << ConvolutionLayer(1U, 1U, 1280U, |
| get_weights_accessor(data_path, "Conv_1_weights.npy"), |
| get_weights_accessor(data_path, "Conv_1_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(1)) |
| .set_name("Conv_1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv_1/Relu6") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool") |
| << 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), 1, conv_weights_quant_info.at(2)) |
| .set_name("Logits/Conv2d_1c_1x1"); |
| } |
| |
| void get_expanded_conv_qasymm8(const std::string &data_path, std::string &¶m_path, IsResidual is_residual, |
| unsigned int input_channels, unsigned int output_channels, |
| PadStrideInfo dwc_pad_stride_info, |
| const QuantizationInfo &pwi, const QuantizationInfo &dwi, const QuantizationInfo &pji) |
| { |
| std::string total_path = param_path + "_"; |
| |
| SubStream left(graph); |
| left << ConvolutionLayer(1U, 1U, input_channels, |
| get_weights_accessor(data_path, total_path + "project_weights.npy"), |
| get_weights_accessor(data_path, total_path + "project_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0), 1, pwi) |
| .set_name(param_path + "/Conv2D") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/Conv2D/Relu6") |
| << DepthwiseConvolutionLayer(3U, 3U, |
| get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"), |
| get_weights_accessor(data_path, total_path + "depthwise_depthwise_biases.npy"), |
| dwc_pad_stride_info, 1, dwi) |
| .set_name(param_path + "/depthwise/depthwise") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/depthwise/Relu6") |
| << ConvolutionLayer(1U, 1U, output_channels, |
| get_weights_accessor(data_path, total_path + "project_weights.npy"), |
| get_weights_accessor(data_path, total_path + "project_biases.npy"), |
| PadStrideInfo(1, 1, 0, 0), 1, pji) |
| .set_name(param_path + "/project/Conv2D"); |
| |
| if(is_residual == IsResidual::Yes) |
| { |
| // 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 |
| * |
| * Provenance: https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz |
| * |
| * @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); |
| } |
