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_alexnet.cpp | 27 +++--- examples/graph_googlenet.cpp | 51 ++++++------ examples/graph_inception_v3.cpp | 129 +++++++++++++++-------------- examples/graph_inception_v4.cpp | 163 +++++++++++++++++++------------------ examples/graph_mobilenet.cpp | 2 +- examples/graph_resnet50.cpp | 35 ++++---- examples/graph_resnext50.cpp | 32 +++++--- examples/graph_squeezenet.cpp | 54 ++++++------ examples/graph_squeezenet_v1_1.cpp | 58 +++++++------ 9 files changed, 298 insertions(+), 253 deletions(-) (limited to 'examples') diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp index 63e7b16128..944a435c3b 100644 --- a/examples/graph_alexnet.cpp +++ b/examples/graph_alexnet.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; @@ -72,14 +71,20 @@ public: const std::array mean_rgb{ { 122.68f, 116.67f, 104.01f } }; std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(227U, 227U, 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(227U, 227U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params, std::move(preprocessor))) + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) // Layer 1 << ConvolutionLayer( 11U, 11U, 96U, - get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"), PadStrideInfo(4, 4, 0, 0)) .set_name("conv1") @@ -89,7 +94,7 @@ public: // Layer 2 << ConvolutionLayer( 5U, 5U, 256U, - get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"), PadStrideInfo(1, 1, 2, 2), 2) .set_name("conv2") @@ -99,7 +104,7 @@ public: // Layer 3 << ConvolutionLayer( 3U, 3U, 384U, - get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv3") @@ -107,7 +112,7 @@ public: // Layer 4 << ConvolutionLayer( 3U, 3U, 384U, - get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"), + get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"), PadStrideInfo(1, 1, 1, 1), 2) .set_name("conv4") @@ -115,7 +120,7 @@ public: // Layer 5 << ConvolutionLayer( 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"), + get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"), PadStrideInfo(1, 1, 1, 1), 2) .set_name("conv5") @@ -124,21 +129,21 @@ public: // Layer 6 << FullyConnectedLayer( 4096U, - get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"), + get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy")) .set_name("fc6") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu6") // Layer 7 << FullyConnectedLayer( 4096U, - get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"), + get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy")) .set_name("fc7") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu7") // Layer 8 << FullyConnectedLayer( 1000U, - get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"), + get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy")) .set_name("fc8") // Softmax diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp index 4497dbd470..d5bd0c0552 100644 --- a/examples/graph_googlenet.cpp +++ b/examples/graph_googlenet.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; @@ -72,13 +71,19 @@ public: const std::array mean_rgb{ { 122.68f, 116.67f, 104.01f } }; std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + // 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 weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params, std::move(preprocessor))) + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) << ConvolutionLayer( 7U, 7U, 64U, - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"), PadStrideInfo(2, 2, 3, 3)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) @@ -86,33 +91,33 @@ public: << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) << ConvolutionLayer( 1U, 1U, 64U, - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer( 3U, 3U, 192U, - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"), PadStrideInfo(1, 1, 1, 1)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); - graph << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U); - graph << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U); + graph << get_inception_node(data_path, "inception_3a", weights_layout, 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U); + graph << get_inception_node(data_path, "inception_3b", weights_layout, 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U); graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); - graph << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U); - graph << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U); - graph << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U); - graph << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U); - graph << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U); + graph << get_inception_node(data_path, "inception_4a", weights_layout, 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U); + graph << get_inception_node(data_path, "inception_4b", weights_layout, 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U); + graph << get_inception_node(data_path, "inception_4c", weights_layout, 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U); + graph << get_inception_node(data_path, "inception_4d", weights_layout, 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U); + graph << get_inception_node(data_path, "inception_4e", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U); graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); - graph << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U); - graph << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U); + graph << get_inception_node(data_path, "inception_5a", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U); + graph << get_inception_node(data_path, "inception_5b", weights_layout, 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U); graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))) << FullyConnectedLayer( 1000U, - get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy")) << SoftmaxLayer() << OutputLayer(get_output_accessor(common_params, 5)); @@ -139,7 +144,7 @@ private: CommonGraphParams common_params; Stream graph; - BranchLayer get_inception_node(const std::string &data_path, std::string &¶m_path, + BranchLayer get_inception_node(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, unsigned int a_filt, std::tuple b_filters, std::tuple c_filters, @@ -149,7 +154,7 @@ private: SubStream i_a(graph); i_a << ConvolutionLayer( 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "1x1_w.npy"), + get_weights_accessor(data_path, total_path + "1x1_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); @@ -157,13 +162,13 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"), + get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer( 3U, 3U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "3x3_w.npy"), + get_weights_accessor(data_path, total_path + "3x3_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "3x3_b.npy"), PadStrideInfo(1, 1, 1, 1)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); @@ -171,13 +176,13 @@ private: SubStream i_c(graph); i_c << ConvolutionLayer( 1U, 1U, std::get<0>(c_filters), - get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"), + get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer( 5U, 5U, std::get<1>(c_filters), - get_weights_accessor(data_path, total_path + "5x5_w.npy"), + get_weights_accessor(data_path, total_path + "5x5_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "5x5_b.npy"), PadStrideInfo(1, 1, 2, 2)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); @@ -186,7 +191,7 @@ private: i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))) << ConvolutionLayer( 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "pool_proj_w.npy"), + get_weights_accessor(data_path, total_path + "pool_proj_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "pool_proj_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); 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") diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp index 4e405923fc..b61acfcb3f 100644 --- a/examples/graph_inception_v4.cpp +++ b/examples/graph_inception_v4.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; @@ -71,13 +70,19 @@ 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)) // Conv2d_1a_3x3 << ConvolutionLayer(3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), @@ -87,7 +92,7 @@ public: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) // Conv2d_2a_3x3 << ConvolutionLayer(3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"), @@ -97,7 +102,7 @@ public: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) // Conv2d_2b_3x3 << ConvolutionLayer(3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"), @@ -106,35 +111,35 @@ public: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_mixed_3a(data_path); - graph << get_mixed_4a(data_path); - graph << get_mixed_5a(data_path); + graph << get_mixed_3a(data_path, weights_layout); + graph << get_mixed_4a(data_path, weights_layout); + graph << get_mixed_5a(data_path, weights_layout); // 4 inception A blocks - graph << get_inceptionA_block(data_path, "Mixed_5b"); - graph << get_inceptionA_block(data_path, "Mixed_5c"); - graph << get_inceptionA_block(data_path, "Mixed_5d"); - graph << get_inceptionA_block(data_path, "Mixed_5e"); + graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5b"); + graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5c"); + graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5d"); + graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5e"); // reduction A block - graph << get_reductionA_block(data_path); + graph << get_reductionA_block(data_path, weights_layout); // 7 inception B blocks - graph << get_inceptionB_block(data_path, "Mixed_6b"); - graph << get_inceptionB_block(data_path, "Mixed_6c"); - graph << get_inceptionB_block(data_path, "Mixed_6d"); - graph << get_inceptionB_block(data_path, "Mixed_6e"); - graph << get_inceptionB_block(data_path, "Mixed_6f"); - graph << get_inceptionB_block(data_path, "Mixed_6g"); - graph << get_inceptionB_block(data_path, "Mixed_6h"); + graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6b"); + graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6c"); + graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6d"); + graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6e"); + graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6f"); + graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6g"); + graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6h"); // reduction B block - graph << get_reductionB_block(data_path); + graph << get_reductionB_block(data_path, weights_layout); // 3 inception C blocks - graph << get_inceptionC_block(data_path, "Mixed_7b"); - graph << get_inceptionC_block(data_path, "Mixed_7c"); - graph << get_inceptionC_block(data_path, "Mixed_7d"); + graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7b"); + graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7c"); + graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7d"); graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) << FlattenLayer() << FullyConnectedLayer( 1001U, - get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_weights.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_biases.npy")) << SoftmaxLayer() << OutputLayer(get_output_accessor(common_params, 5)); @@ -162,7 +167,7 @@ private: Stream graph; private: - BranchLayer get_mixed_3a(const std::string &data_path) + BranchLayer get_mixed_3a(const std::string &data_path, DataLayout weights_layout) { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_3a_"; @@ -171,7 +176,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer(3U, 3U, 96U, - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_variance.npy"), @@ -183,13 +188,13 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b)); } - BranchLayer get_mixed_4a(const std::string &data_path) + BranchLayer get_mixed_4a(const std::string &data_path, DataLayout weights_layout) { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_4a_"; SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 64U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -198,7 +203,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 96U, - 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(1, 1, 0, 0)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), @@ -209,7 +214,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 64U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -218,7 +223,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(7U, 1U, 64U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), @@ -227,7 +232,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(1U, 7U, 64U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), @@ -236,7 +241,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 96U, - 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(1, 1, 0, 0)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), @@ -248,13 +253,13 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b)); } - BranchLayer get_mixed_5a(const std::string &data_path) + BranchLayer get_mixed_5a(const std::string &data_path, DataLayout weights_layout) { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_5a_"; SubStream i_a(graph); i_a << ConvolutionLayer(3U, 3U, 192U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), @@ -269,13 +274,13 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b)); } - BranchLayer get_inceptionA_block(const std::string &data_path, std::string &¶m_path) + BranchLayer get_inceptionA_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path) { std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 96U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -286,7 +291,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 64U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -295,7 +300,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 96U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), @@ -306,7 +311,7 @@ private: SubStream i_c(graph); i_c << ConvolutionLayer(1U, 1U, 64U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -315,7 +320,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 96U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), @@ -324,7 +329,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 96U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), @@ -336,7 +341,7 @@ private: SubStream i_d(graph); i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) << ConvolutionLayer(1U, 1U, 96U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), @@ -348,13 +353,13 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); } - BranchLayer get_reductionA_block(const std::string &data_path) + BranchLayer get_reductionA_block(const std::string &data_path, DataLayout weights_layout) { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_6a_"; SubStream i_a(graph); i_a << ConvolutionLayer(3U, 3U, 384U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), @@ -365,7 +370,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 192U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -374,7 +379,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 224U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), @@ -383,7 +388,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 256U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), @@ -398,13 +403,13 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); } - BranchLayer get_inceptionB_block(const std::string &data_path, std::string &¶m_path) + BranchLayer get_inceptionB_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path) { std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 384U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -415,7 +420,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 192U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -424,7 +429,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(7U, 1U, 224U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), @@ -433,7 +438,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(1U, 7U, 256U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), @@ -444,7 +449,7 @@ private: SubStream i_c(graph); i_c << ConvolutionLayer(1U, 1U, 192U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -453,7 +458,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(1U, 7U, 192U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"), @@ -462,7 +467,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(7U, 1U, 224U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"), @@ -471,7 +476,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(1U, 7U, 224U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"), @@ -480,7 +485,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(7U, 1U, 256U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"), @@ -492,7 +497,7 @@ private: SubStream i_d(graph); i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) << ConvolutionLayer(1U, 1U, 128U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), @@ -504,13 +509,13 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); } - BranchLayer get_reductionB_block(const std::string &data_path) + BranchLayer get_reductionB_block(const std::string &data_path, DataLayout weights_layout) { std::string total_path = "/cnn_data/inceptionv4_model/Mixed_7a_"; SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 192U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -519,7 +524,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 192U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), @@ -530,7 +535,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer(1U, 1U, 256U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -539,7 +544,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(7U, 1U, 256U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), @@ -548,7 +553,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(1U, 7U, 320U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), @@ -557,7 +562,7 @@ private: 0.001f) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer(3U, 3U, 320U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), @@ -572,13 +577,13 @@ private: return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); } - BranchLayer get_inceptionC_block(const std::string &data_path, std::string &¶m_path) + BranchLayer get_inceptionC_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path) { std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; SubStream i_a(graph); i_a << ConvolutionLayer(1U, 1U, 256U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), @@ -590,7 +595,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 1U, 1U, 384U, - 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)) << BatchNormalizationLayer( @@ -604,7 +609,7 @@ private: SubStream i_b1(i_b); i_b1 << ConvolutionLayer( 3U, 1U, 256U, - 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)) << BatchNormalizationLayer( @@ -618,7 +623,7 @@ private: SubStream i_b2(i_b); i_b2 << ConvolutionLayer( 1U, 3U, 256U, - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 1)) << BatchNormalizationLayer( @@ -635,7 +640,7 @@ private: SubStream i_c(graph); i_c << ConvolutionLayer( 1U, 1U, 384U, - 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)) << BatchNormalizationLayer( @@ -647,7 +652,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer( 1U, 3U, 448U, - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 1)) << BatchNormalizationLayer( @@ -659,7 +664,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer( 3U, 1U, 512U, - 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)) << BatchNormalizationLayer( @@ -673,7 +678,7 @@ private: SubStream i_c1(i_c); i_c1 << ConvolutionLayer( 3U, 1U, 256U, - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 0)) << BatchNormalizationLayer( @@ -687,7 +692,7 @@ private: SubStream i_c2(i_c); i_c2 << ConvolutionLayer( 1U, 3U, 256U, - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_weights.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 1)) << BatchNormalizationLayer( @@ -704,7 +709,7 @@ private: SubStream i_d(graph); i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) << ConvolutionLayer(1U, 1U, 256U, - 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)) << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp index a747b3cd11..d182844530 100644 --- a/examples/graph_mobilenet.cpp +++ b/examples/graph_mobilenet.cpp @@ -85,7 +85,7 @@ public: // Set graph hints graph << common_params.target - << DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method + << DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method << common_params.fast_math_hint; // Create core graph diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp index 58f36f6ae4..0ad719a2ca 100644 --- a/examples/graph_resnet50.cpp +++ b/examples/graph_resnet50.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; @@ -72,13 +71,20 @@ public: const std::array mean_rgb{ { 122.68f, 116.67f, 104.01f } }; std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb, false /* Do not convert to BGR */); + + // 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 weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params, std::move(preprocessor), false /* Do not convert to BGR */)) + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false /* Do not convert to BGR */)) << ConvolutionLayer( 7U, 7U, 64U, - get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 3, 3)) .set_name("conv1/convolution") @@ -92,15 +98,15 @@ public: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu") << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool"); - add_residual_block(data_path, "block1", 64, 3, 2); - add_residual_block(data_path, "block2", 128, 4, 2); - add_residual_block(data_path, "block3", 256, 6, 2); - add_residual_block(data_path, "block4", 512, 3, 1); + add_residual_block(data_path, "block1", weights_layout, 64, 3, 2); + add_residual_block(data_path, "block2", weights_layout, 128, 4, 2); + add_residual_block(data_path, "block3", weights_layout, 256, 6, 2); + add_residual_block(data_path, "block4", weights_layout, 512, 3, 1); graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5") << ConvolutionLayer( 1U, 1U, 1000U, - get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name("logits/convolution") @@ -129,7 +135,8 @@ private: CommonGraphParams common_params; Stream graph; - void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride) + void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout, + unsigned int base_depth, unsigned int num_units, unsigned int stride) { for(unsigned int i = 0; i < num_units; ++i) { @@ -151,7 +158,7 @@ private: SubStream right(graph); right << ConvolutionLayer( 1U, 1U, base_depth, - get_weights_accessor(data_path, unit_path + "conv1_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "conv1/convolution") @@ -166,7 +173,7 @@ private: << ConvolutionLayer( 3U, 3U, base_depth, - get_weights_accessor(data_path, unit_path + "conv2_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(middle_stride, middle_stride, 1, 1)) .set_name(unit_name + "conv2/convolution") @@ -181,7 +188,7 @@ private: << ConvolutionLayer( 1U, 1U, base_depth * 4, - get_weights_accessor(data_path, unit_path + "conv3_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "conv3/convolution") @@ -198,7 +205,7 @@ private: SubStream left(graph); left << ConvolutionLayer( 1U, 1U, base_depth * 4, - get_weights_accessor(data_path, unit_path + "shortcut_weights.npy"), + get_weights_accessor(data_path, unit_path + "shortcut_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "shortcut/convolution") diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp index c0e9b9f22a..e7ef013f17 100644 --- a/examples/graph_resnext50.cpp +++ b/examples/graph_resnext50.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; @@ -68,26 +67,32 @@ public: // Get trainable parameters data path std::string data_path = common_params.data_path; + // 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 weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params)) + << InputLayer(input_descriptor, get_input_accessor(common_params)) << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"), get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy")) .set_name("bn_data/Scale") << ConvolutionLayer( 7U, 7U, 64U, - get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) .set_name("conv0/Convolution") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu") << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); - add_residual_block(data_path, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); - add_residual_block(data_path, 512, 2, 4, 2); - add_residual_block(data_path, 1024, 3, 6, 2); - add_residual_block(data_path, 2048, 4, 3, 2); + add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); + add_residual_block(data_path, weights_layout, 512, 2, 4, 2); + add_residual_block(data_path, weights_layout, 1024, 3, 6, 2); + add_residual_block(data_path, weights_layout, 2048, 4, 3, 2); graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1") << FlattenLayer().set_name("predictions/Reshape") @@ -116,7 +121,8 @@ private: CommonGraphParams common_params; Stream graph; - void add_residual_block(const std::string &data_path, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1) + void add_residual_block(const std::string &data_path, DataLayout weights_layout, + unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1) { for(unsigned int i = 0; i < num_units; ++i) { @@ -137,7 +143,7 @@ private: SubStream right(graph); right << ConvolutionLayer( 1U, 1U, base_depth / 2, - get_weights_accessor(data_path, unit_path + "conv1_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), get_weights_accessor(data_path, unit_path + "conv1_biases.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "conv1/convolution") @@ -145,7 +151,7 @@ private: << ConvolutionLayer( 3U, 3U, base_depth / 2, - get_weights_accessor(data_path, unit_path + "conv2_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout), std::unique_ptr(nullptr), pad_grouped_conv, 32) .set_name(unit_name + "conv2/convolution") @@ -156,7 +162,7 @@ private: << ConvolutionLayer( 1U, 1U, base_depth, - get_weights_accessor(data_path, unit_path + "conv3_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout), get_weights_accessor(data_path, unit_path + "conv3_biases.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "conv3/convolution"); @@ -166,7 +172,7 @@ private: { left << ConvolutionLayer( 1U, 1U, base_depth, - get_weights_accessor(data_path, unit_path + "sc_weights.npy"), + get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0)) .set_name(unit_name + "sc/convolution") diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp index 9439ab4343..b539a9bc34 100644 --- a/examples/graph_squeezenet.cpp +++ b/examples/graph_squeezenet.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; @@ -72,78 +71,84 @@ public: const std::array mean_rgb{ { 122.68f, 116.67f, 104.01f } }; std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + // 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 weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params, std::move(preprocessor))) + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) << ConvolutionLayer( 7U, 7U, 96U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"), PadStrideInfo(2, 2, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) << ConvolutionLayer( 1U, 1U, 16U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire2", 64U, 64U); + graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U); graph << ConvolutionLayer( 1U, 1U, 16U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire3", 64U, 64U); + graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U); graph << ConvolutionLayer( 1U, 1U, 32U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire4", 128U, 128U); + graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U); graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) << ConvolutionLayer( 1U, 1U, 32U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire5", 128U, 128U); + graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U); graph << ConvolutionLayer( 1U, 1U, 48U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire6", 192U, 192U); + graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U); graph << ConvolutionLayer( 1U, 1U, 48U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire7", 192U, 192U); + graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U); graph << ConvolutionLayer( 1U, 1U, 64U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire8", 256U, 256U); + graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U); graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) << ConvolutionLayer( 1U, 1U, 64U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire9", 256U, 256U); + graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U); graph << ConvolutionLayer( 1U, 1U, 1000U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) @@ -174,13 +179,14 @@ private: CommonGraphParams common_params; Stream graph; - BranchLayer get_expand_fire_node(const std::string &data_path, std::string &¶m_path, unsigned int expand1_filt, unsigned int expand3_filt) + BranchLayer get_expand_fire_node(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, + unsigned int expand1_filt, unsigned int expand3_filt) { std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_"; SubStream i_a(graph); i_a << ConvolutionLayer( 1U, 1U, expand1_filt, - get_weights_accessor(data_path, total_path + "expand1x1_w.npy"), + get_weights_accessor(data_path, total_path + "expand1x1_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "expand1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); @@ -188,7 +194,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 3U, 3U, expand3_filt, - get_weights_accessor(data_path, total_path + "expand3x3_w.npy"), + get_weights_accessor(data_path, total_path + "expand3x3_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "expand3x3_b.npy"), PadStrideInfo(1, 1, 1, 1)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp index ba7ee774a7..c0b5ff212d 100644 --- a/examples/graph_squeezenet_v1_1.cpp +++ b/examples/graph_squeezenet_v1_1.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; @@ -72,80 +71,86 @@ public: const std::array mean_rgb{ { 122.68f, 116.67f, 104.01f } }; std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + // 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 weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params, std::move(preprocessor))) - << ConvolutionMethod::DIRECT + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) + << ConvolutionMethod::Direct << ConvolutionLayer( 3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_b.npy"), PadStrideInfo(2, 2, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) - << ConvolutionMethod::DEFAULT + << ConvolutionMethod::Default << ConvolutionLayer( 1U, 1U, 16U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire2", 64U, 64U); + graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U); graph << ConvolutionLayer( 1U, 1U, 16U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire3", 64U, 64U); + graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U); graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) << ConvolutionLayer( 1U, 1U, 32U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire4", 128U, 128U); + graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U); graph << ConvolutionLayer( 1U, 1U, 32U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire5", 128U, 128U); + graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U); graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) << ConvolutionLayer( 1U, 1U, 48U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire6", 192U, 192U); + graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U); graph << ConvolutionLayer( 1U, 1U, 48U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire7", 192U, 192U); + graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U); graph << ConvolutionLayer( 1U, 1U, 64U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire8", 256U, 256U); + graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U); graph << ConvolutionLayer( 1U, 1U, 64U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph << get_expand_fire_node(data_path, "fire9", 256U, 256U); + graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U); graph << ConvolutionLayer( 1U, 1U, 1000U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) @@ -176,13 +181,14 @@ private: CommonGraphParams common_params; Stream graph; - BranchLayer get_expand_fire_node(const std::string &data_path, std::string &¶m_path, unsigned int expand1_filt, unsigned int expand3_filt) + BranchLayer get_expand_fire_node(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, + unsigned int expand1_filt, unsigned int expand3_filt) { std::string total_path = "/cnn_data/squeezenet_v1_1_model/" + param_path + "_"; SubStream i_a(graph); i_a << ConvolutionLayer( 1U, 1U, expand1_filt, - get_weights_accessor(data_path, total_path + "expand1x1_w.npy"), + get_weights_accessor(data_path, total_path + "expand1x1_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "expand1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); @@ -190,7 +196,7 @@ private: SubStream i_b(graph); i_b << ConvolutionLayer( 3U, 3U, expand3_filt, - get_weights_accessor(data_path, total_path + "expand3x3_w.npy"), + get_weights_accessor(data_path, total_path + "expand3x3_w.npy", weights_layout), get_weights_accessor(data_path, total_path + "expand3x3_b.npy"), PadStrideInfo(1, 1, 1, 1)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); 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