From e2220551b7a64b929650ba9a60529c31e70c13c5 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 20 Jul 2018 13:23:44 +0100 Subject: COMPMID-1367: Enable NHWC in graph examples Change-Id: Iabc54a3a1bdcd46a9a921cda39c7c85fef672b72 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141449 Reviewed-by: Giorgio Arena Reviewed-by: Anthony Barbier Tested-by: Jenkins --- examples/graph_inception_v3.cpp | 129 +++++++++++++++++++++------------------- 1 file changed, 67 insertions(+), 62 deletions(-) (limited to 'examples/graph_inception_v3.cpp') diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp index 67f4e3cacf..c41b0a808e 100644 --- a/examples/graph_inception_v3.cpp +++ b/examples/graph_inception_v3.cpp @@ -60,7 +60,6 @@ public: // Checks ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!"); - ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC, "Unsupported data layout!"); // Print parameter values std::cout << common_params << std::endl; @@ -71,12 +70,18 @@ public: // Create a preprocessor object std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(); + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 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 weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params, std::move(preprocessor), false)) + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) << ConvolutionLayer(3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) .set_name("Conv2d_1a_3x3/convolution") << BatchNormalizationLayer(get_weights_accessor(data_path, @@ -89,7 +94,7 @@ public: .set_name("Conv2d_1a_3x3/BatchNorm/batchnorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu") << ConvolutionLayer(3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name("Conv2d_2a_3x3/convolution") << BatchNormalizationLayer(get_weights_accessor(data_path, @@ -103,7 +108,7 @@ public: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu") << ConvolutionLayer(3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) .set_name("Conv2d_2b_3x3/convolution") << BatchNormalizationLayer(get_weights_accessor(data_path, @@ -119,7 +124,7 @@ public: << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool") << ConvolutionLayer(1U, 1U, 80U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name("Conv2d_3b_1x1/convolution") << BatchNormalizationLayer(get_weights_accessor(data_path, @@ -133,7 +138,7 @@ public: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu") << ConvolutionLayer(3U, 3U, 192U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name("Conv2d_4a_3x3/convolution") << BatchNormalizationLayer(get_weights_accessor(data_path, @@ -148,45 +153,45 @@ public: << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool"); - graph << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + graph << get_inception_node_A(data_path, "Mixed_5b", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), 32U) .set_name("Mixed_5b/concat"); - graph << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + graph << get_inception_node_A(data_path, "Mixed_5c", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), 64U, true) .set_name("Mixed_5c/concat"); - graph << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + graph << get_inception_node_A(data_path, "Mixed_5d", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), 64U) .set_name("Mixed_5d/concat"); - graph << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat"); + graph << get_inception_node_B(data_path, "Mixed_6a", weights_layout, 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat"); - graph << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U), + graph << get_inception_node_C(data_path, "Mixed_6b", weights_layout, 192U, std::make_tuple(128U, 128U, 192U), std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U) .set_name("Mixed_6b/concat"); - graph << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U), + graph << get_inception_node_C(data_path, "Mixed_6c", weights_layout, 192U, std::make_tuple(160U, 160U, 192U), std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) .set_name("Mixed_6c/concat"); - graph << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U), + graph << get_inception_node_C(data_path, "Mixed_6d", weights_layout, 192U, std::make_tuple(160U, 160U, 192U), std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) .set_name("Mixed_6d/concat"); - graph << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U), + graph << get_inception_node_C(data_path, "Mixed_6e", weights_layout, 192U, std::make_tuple(192U, 192U, 192U), std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U) .set_name("Mixed_6e/concat"); - graph << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U), + graph << get_inception_node_D(data_path, "Mixed_7a", weights_layout, std::make_tuple(192U, 320U), std::make_tuple(192U, 192U, 192U, 192U)) .set_name("Mixed_7a/concat"); - graph << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U), + graph << get_inception_node_E(data_path, "Mixed_7b", weights_layout, 320U, std::make_tuple(384U, 384U, 384U), std::make_tuple(448U, 384U, 384U, 384U), 192U) .set_name("Mixed_7b/concat"); - graph << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U), + graph << get_inception_node_E(data_path, "Mixed_7c", weights_layout, 320U, std::make_tuple(384U, 384U, 384U), std::make_tuple(448U, 384U, 384U, 384U), 192U, true) .set_name("Mixed_7c/concat"); graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool") << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"), + "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"), PadStrideInfo(1, 1, 0, 0)) @@ -218,7 +223,7 @@ private: Stream graph; private: - BranchLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path, + BranchLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, unsigned int a_filt, std::tuple b_filters, std::tuple c_filters, @@ -239,7 +244,7 @@ private: SubStream i_a(graph); i_a << ConvolutionLayer( 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") @@ -255,7 +260,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/convolution") @@ -269,7 +274,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/Relu") << ConvolutionLayer( 5U, 5U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 2, 2)) .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution") @@ -285,7 +290,7 @@ private: SubStream i_c(graph); i_c << ConvolutionLayer( 1U, 1U, std::get<0>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") @@ -299,7 +304,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") << ConvolutionLayer( 3U, 3U, std::get<1>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") @@ -313,7 +318,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu") << ConvolutionLayer( 3U, 3U, std::get<2>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/convolution") @@ -330,7 +335,7 @@ private: i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") << ConvolutionLayer( 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") @@ -346,7 +351,7 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); } - BranchLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, + BranchLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, unsigned int a_filt, std::tuple b_filters) { @@ -354,7 +359,7 @@ private: SubStream i_a(graph); i_a << ConvolutionLayer( 3U, 3U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution") @@ -370,7 +375,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") @@ -384,7 +389,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") << ConvolutionLayer( 3U, 3U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/convolution") @@ -398,7 +403,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu") << ConvolutionLayer( 3U, 3U, std::get<2>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution") @@ -417,7 +422,7 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); } - BranchLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, + BranchLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, unsigned int a_filt, std::tuple b_filters, std::tuple c_filters, @@ -427,7 +432,7 @@ private: SubStream i_a(graph); i_a << ConvolutionLayer( 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") @@ -443,7 +448,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") @@ -457,7 +462,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") << ConvolutionLayer( 7U, 1U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 3, 0)) .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") @@ -471,7 +476,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") << ConvolutionLayer( 1U, 7U, std::get<2>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 3)) .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") @@ -487,7 +492,7 @@ private: SubStream i_c(graph); i_c << ConvolutionLayer( 1U, 1U, std::get<0>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") @@ -501,7 +506,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") << ConvolutionLayer( 1U, 7U, std::get<1>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 3)) .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/convolution") @@ -515,7 +520,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu") << ConvolutionLayer( 7U, 1U, std::get<2>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 3, 0)) .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/convolution") @@ -529,7 +534,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu") << ConvolutionLayer( 1U, 7U, std::get<3>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 3)) .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/convolution") @@ -543,7 +548,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu") << ConvolutionLayer( 7U, 1U, std::get<4>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 3, 0)) .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/convolution") @@ -560,7 +565,7 @@ private: i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") << ConvolutionLayer( 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") @@ -576,15 +581,15 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); } - BranchLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, - std::tuple a_filters, + BranchLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, + std::tuple a_filters, std::tuple b_filters) { std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; SubStream i_a(graph); i_a << ConvolutionLayer( 1U, 1U, std::get<0>(a_filters), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") @@ -598,7 +603,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu") << ConvolutionLayer( 3U, 3U, std::get<1>(a_filters), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/convolution") @@ -614,7 +619,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") @@ -628,7 +633,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") << ConvolutionLayer( 7U, 1U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 3, 0)) .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") @@ -642,7 +647,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") << ConvolutionLayer( 1U, 7U, std::get<2>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 3)) .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") @@ -656,7 +661,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu") << ConvolutionLayer( 3U, 3U, std::get<3>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/convolution") @@ -675,7 +680,7 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); } - BranchLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, + BranchLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, unsigned int a_filt, std::tuple b_filters, std::tuple c_filters, @@ -693,7 +698,7 @@ private: SubStream i_a(graph); i_a << ConvolutionLayer( 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") @@ -709,7 +714,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") @@ -725,7 +730,7 @@ private: SubStream i_b1(i_b); i_b1 << ConvolutionLayer( 3U, 1U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 0)) .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/convolution") @@ -741,7 +746,7 @@ private: SubStream i_b2(i_b); i_b2 << ConvolutionLayer( 1U, 3U, std::get<2>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 1)) .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/convolution") @@ -760,7 +765,7 @@ private: SubStream i_c(graph); i_c << ConvolutionLayer( 1U, 1U, std::get<0>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") @@ -774,7 +779,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") << ConvolutionLayer( 3U, 3U, std::get<1>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") @@ -790,7 +795,7 @@ private: SubStream i_c1(i_c); i_c1 << ConvolutionLayer( 3U, 1U, std::get<2>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 0)) .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/convolution") @@ -806,7 +811,7 @@ private: SubStream i_c2(i_c); i_c2 << ConvolutionLayer( 1U, 3U, std::get<3>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 1)) .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/convolution") @@ -826,7 +831,7 @@ private: i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") << ConvolutionLayer( 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") -- cgit v1.2.1