diff options
| author | Sang-Hoon Park <sang-hoon.park@arm.com> | 2020-01-15 14:44:04 +0000 |
|---|---|---|
| committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2020-01-28 16:05:27 +0000 |
| commit | 11fedda86532cf632b9a3ae4b0f57e85f2a7c4f4 (patch) | |
| tree | 6fd8003a38fe9baa262696754bdd5cb1d1595947 | |
| parent | 6c89ffac750010cb9335794defe8a366c04db937 (diff) | |
| download | ComputeLibrary-11fedda86532cf632b9a3ae4b0f57e85f2a7c4f4.tar.gz | |
COMPMID-2985 add data_layout to PoolingLayerInfo
- use data layout from PoolingLayerInfo if it's available
- deprecate constructors without data_layout
- (3RDPARTY_UPDATE) modify examples and test suites to give data layout
Change-Id: Ie9ae8cc4837c339ff69a16a816110be704863c2d
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2603
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
31 files changed, 321 insertions, 168 deletions
diff --git a/3rdparty b/3rdparty -Subproject 2249a99abb8ea5c622e15e299c7aebad0c9643f +Subproject 10027ddd13d7fbfec016407137c00213f6c4c63 diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h index 4cec63bff9..a4e629ce6c 100644 --- a/arm_compute/core/Types.h +++ b/arm_compute/core/Types.h @@ -29,6 +29,7 @@ #include "arm_compute/core/Size2D.h" #include "arm_compute/core/Strides.h" #include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/utils/misc/Macros.h" #include "support/Half.h" #include <cmath> @@ -1211,10 +1212,16 @@ struct PoolingLayerInfo { /** Default Constructor */ PoolingLayerInfo() - : pool_type(PoolingType::MAX), pool_size(Size2D()), pad_stride_info(PadStrideInfo()), exclude_padding(false), is_global_pooling(false), fp_mixed_precision(false) + : pool_type(PoolingType::MAX), + pool_size(Size2D()), + data_layout(DataLayout::UNKNOWN), + pad_stride_info(PadStrideInfo()), + exclude_padding(false), + is_global_pooling(false), + fp_mixed_precision(false) { } - /** Default Constructor + /** Constructor * * @param[in] pool_type Pooling type @ref PoolingType. * @param[in] pool_size Pooling size, in elements, across x and y. @@ -1224,15 +1231,22 @@ struct PoolingLayerInfo * Defaults to false; * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy. */ + ARM_COMPUTE_DEPRECATED_REL_REPLACE(20.02, PoolingLayerInfo(PoolingType, unsigned int, DataLayout, PadStrideInfo, bool, bool)) explicit PoolingLayerInfo(PoolingType pool_type, unsigned int pool_size, PadStrideInfo pad_stride_info = PadStrideInfo(), bool exclude_padding = false, bool fp_mixed_precision = false) - : pool_type(pool_type), pool_size(Size2D(pool_size, pool_size)), pad_stride_info(pad_stride_info), exclude_padding(exclude_padding), is_global_pooling(false), fp_mixed_precision(fp_mixed_precision) + : pool_type(pool_type), + pool_size(Size2D(pool_size, pool_size)), + data_layout(DataLayout::UNKNOWN), + pad_stride_info(pad_stride_info), + exclude_padding(exclude_padding), + is_global_pooling(false), + fp_mixed_precision(fp_mixed_precision) { } - /** Default Constructor + /** Constructor * * @param[in] pool_type Pooling type @ref PoolingType. * @param[in] pool_size Pooling size, in elements, across x and y. @@ -1242,27 +1256,114 @@ struct PoolingLayerInfo * Defaults to false; * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy. */ + ARM_COMPUTE_DEPRECATED_REL_REPLACE(20.02, PoolingLayerInfo(PoolingType, Size2D, DataLayout, PadStrideInfo, bool, bool)) explicit PoolingLayerInfo(PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info = PadStrideInfo(), bool exclude_padding = false, bool fp_mixed_precision = false) - : pool_type(pool_type), pool_size(pool_size), pad_stride_info(pad_stride_info), exclude_padding(exclude_padding), is_global_pooling(false), fp_mixed_precision(fp_mixed_precision) + : pool_type(pool_type), + pool_size(pool_size), + data_layout(DataLayout::UNKNOWN), + pad_stride_info(pad_stride_info), + exclude_padding(exclude_padding), + is_global_pooling(false), + fp_mixed_precision(fp_mixed_precision) { } - /** Default Constructor + /** Constructor * * @note This constructor is used for global pooling * * @param[in] pool_type Pooling type @ref PoolingType. */ + ARM_COMPUTE_DEPRECATED_REL_REPLACE(20.02, PoolingLayerInfo(PoolingType, DataLayout)) explicit PoolingLayerInfo(PoolingType pool_type) - : pool_type(pool_type), pool_size(Size2D()), pad_stride_info(PadStrideInfo(1, 1, 0, 0)), exclude_padding(false), is_global_pooling(true), fp_mixed_precision(false) + : pool_type(pool_type), + pool_size(Size2D()), + data_layout(DataLayout::UNKNOWN), + pad_stride_info(PadStrideInfo(1, 1, 0, 0)), + exclude_padding(false), + is_global_pooling(true), + fp_mixed_precision(false) + { + } + + /** Constructor + * + * @param[in] pool_type Pooling type @ref PoolingType. + * @param[in] pool_size Pooling size, in elements, across x and y. + * @param[in] data_layout Data layout used by the layer @ref DataLayout + * @param[in] pad_stride_info (Optional) Padding and stride information @ref PadStrideInfo + * @param[in] exclude_padding (Optional) Strategy when accounting padding in calculations. + * True will exclude padding while false will not (Used in AVG/L2 pooling to determine the pooling area). + * Defaults to false; + * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy. + */ + explicit PoolingLayerInfo(PoolingType pool_type, + unsigned int pool_size, + DataLayout data_layout, + PadStrideInfo pad_stride_info = PadStrideInfo(), + bool exclude_padding = false, + bool fp_mixed_precision = false) + : pool_type(pool_type), + pool_size(Size2D(pool_size, pool_size)), + data_layout(data_layout), + pad_stride_info(pad_stride_info), + exclude_padding(exclude_padding), + is_global_pooling(false), + fp_mixed_precision(fp_mixed_precision) + { + } + + /** Constructor + * + * @param[in] pool_type Pooling type @ref PoolingType. + * @param[in] pool_size Pooling size, in elements, across x and y. + * @param[in] data_layout Data layout used by the layer @ref DataLayout + * @param[in] pad_stride_info (Optional) Padding and stride information @ref PadStrideInfo + * @param[in] exclude_padding (Optional) Strategy when accounting padding in calculations. + * True will exclude padding while false will not (Used in AVG/L2 pooling to determine the pooling area). + * Defaults to false; + * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy. + */ + explicit PoolingLayerInfo(PoolingType pool_type, + Size2D pool_size, + DataLayout data_layout, + PadStrideInfo pad_stride_info = PadStrideInfo(), + bool exclude_padding = false, + bool fp_mixed_precision = false) + : pool_type(pool_type), + pool_size(pool_size), + data_layout(data_layout), + pad_stride_info(pad_stride_info), + exclude_padding(exclude_padding), + is_global_pooling(false), + fp_mixed_precision(fp_mixed_precision) + { + } + + /** Constructor + * + * @note This constructor is used for global pooling + * + * @param[in] pool_type Pooling type @ref PoolingType. + * @param[in] data_layout Data layout used by the layer @ref DataLayout + */ + explicit PoolingLayerInfo(PoolingType pool_type, DataLayout data_layout) + : pool_type(pool_type), + pool_size(Size2D()), + data_layout(data_layout), + pad_stride_info(PadStrideInfo(1, 1, 0, 0)), + exclude_padding(false), + is_global_pooling(true), + fp_mixed_precision(false) { } PoolingType pool_type; Size2D pool_size; + DataLayout data_layout; PadStrideInfo pad_stride_info; bool exclude_padding; bool is_global_pooling; diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp index 79d02f6ba5..0adc6a8e93 100644 --- a/examples/graph_alexnet.cpp +++ b/examples/graph_alexnet.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -69,8 +69,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(227U, 227U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -87,7 +88,7 @@ public: .set_name("conv1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu1") << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm1") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") // Layer 2 << ConvolutionLayer( 5U, 5U, 256U, @@ -97,7 +98,7 @@ public: .set_name("conv2") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu2") << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm2") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") // Layer 3 << ConvolutionLayer( 3U, 3U, 384U, @@ -122,7 +123,7 @@ public: PadStrideInfo(1, 1, 1, 1), 2) .set_name("conv5") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu5") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") // Layer 6 << FullyConnectedLayer( 4096U, diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp index b768d282ef..84a10ffce1 100644 --- a/examples/graph_googlenet.cpp +++ b/examples/graph_googlenet.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -69,8 +69,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -85,7 +86,7 @@ public: PadStrideInfo(2, 2, 3, 3)) .set_name("conv1/7x7_s2") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/relu_7x7") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1/3x3_s2") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1/3x3_s2") << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("pool1/norm1") << ConvolutionLayer( 1U, 1U, 64U, @@ -102,19 +103,19 @@ public: .set_name("conv2/3x3") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/relu_3x3") << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("conv2/norm2") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool2/3x3_s2"); + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool2/3x3_s2"); graph << get_inception_node(data_path, "inception_3a", weights_layout, 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U).set_name("inception_3a/concat"); graph << get_inception_node(data_path, "inception_3b", weights_layout, 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U).set_name("inception_3b/concat"); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3/3x3_s2"); + graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3/3x3_s2"); graph << get_inception_node(data_path, "inception_4a", weights_layout, 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U).set_name("inception_4a/concat"); graph << get_inception_node(data_path, "inception_4b", weights_layout, 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U).set_name("inception_4b/concat"); graph << get_inception_node(data_path, "inception_4c", weights_layout, 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U).set_name("inception_4c/concat"); graph << get_inception_node(data_path, "inception_4d", weights_layout, 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U).set_name("inception_4d/concat"); graph << get_inception_node(data_path, "inception_4e", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U).set_name("inception_4e/concat"); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4/3x3_s2"); + graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4/3x3_s2"); graph << get_inception_node(data_path, "inception_5a", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U).set_name("inception_5a/concat"); graph << get_inception_node(data_path, "inception_5b", weights_layout, 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U).set_name("inception_5b/concat"); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5/7x7_s1") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, operation_layout, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5/7x7_s1") << FullyConnectedLayer( 1000U, get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy", weights_layout), @@ -195,7 +196,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_5x5"); SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))).set_name(param_path + "/pool") + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))).set_name(param_path + "/pool") << ConvolutionLayer( 1U, 1U, d_filt, get_weights_accessor(data_path, total_path + "pool_proj_w.npy", weights_layout), diff --git a/examples/graph_inception_resnet_v1.cpp b/examples/graph_inception_resnet_v1.cpp index 89e44edc33..599aa5c8ac 100644 --- a/examples/graph_inception_resnet_v1.cpp +++ b/examples/graph_inception_resnet_v1.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -97,8 +97,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f); // Create input descriptor - const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 3U, 1U), DataLayout::NCHW, common_params.data_layout); - TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -146,7 +147,7 @@ public: .set_name("Conv2d_2b_3x3/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") // MaxPool_3a_3x3 - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool") // Conv2d_3b_1x1 << ConvolutionLayer(1U, 1U, 80U, get_weights_accessor(data_path, "Conv2d_3b_1x1_weights.npy", weights_layout), @@ -201,7 +202,7 @@ public: block8_repeat(data_path, weights_layout, 1, 1.f, false); // Logits tail - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a_8x8") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a_8x8") << FlattenLayer().set_name("Logits/Flatten") << FullyConnectedLayer( 128U, @@ -592,7 +593,7 @@ private: // Branch 2 SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3"); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3"); // Concatenate graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat"); @@ -695,7 +696,7 @@ private: // Branch 3 SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3"); + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3"); // Concatenate graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat"); diff --git a/examples/graph_inception_resnet_v2.cpp b/examples/graph_inception_resnet_v2.cpp index 424884f16f..d2f6e1d576 100644 --- a/examples/graph_inception_resnet_v2.cpp +++ b/examples/graph_inception_resnet_v2.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -79,8 +79,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f); // 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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -128,7 +129,7 @@ public: .set_name("Conv2d_2b_3x3/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") // MaxPool_3a_3x3 - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool") // Conv2d_3b_1x1 << ConvolutionLayer(1U, 1U, 80U, get_weights_accessor(data_path, "Conv2d_3b_1x1_weights.npy", weights_layout), @@ -156,7 +157,7 @@ public: .set_name("Conv2d_4a_3x3/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") // MaxPool_5a_3x3 - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("MaxPool_5a_3x3/MaxPool"); + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("MaxPool_5a_3x3/MaxPool"); block_mixed_5b(data_path, weights_layout); block35_repeat(data_path, weights_layout, 10); @@ -179,7 +180,7 @@ public: 0.0010000000474974513f) .set_name("Conv2d_7b_1x1/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_7b_1x1/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a_8x8") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a_8x8") << FlattenLayer().set_name("Logits/Flatten") << FullyConnectedLayer( 1001U, @@ -298,7 +299,7 @@ private: // Branch 3 SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name("Mixed_5b/Branch_3/AvgPool_0a_3x3") + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name("Mixed_5b/Branch_3/AvgPool_0a_3x3") << ConvolutionLayer(1U, 1U, 64U, get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), @@ -374,7 +375,7 @@ private: // Branch 2 SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3"); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3"); // Concatenate graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat"); @@ -477,7 +478,7 @@ private: // Branch 3 SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3"); + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3"); // Concatenate graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat"); diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp index d49561ae38..03d5dff9be 100644 --- a/examples/graph_inception_v3.cpp +++ b/examples/graph_inception_v3.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -65,8 +65,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); // 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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -115,7 +116,7 @@ public: .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, 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", weights_layout), @@ -145,7 +146,7 @@ public: .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool"); + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool"); graph << get_inception_node_A(data_path, "Mixed_5b", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), 32U) @@ -183,7 +184,7 @@ public: 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") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, operation_layout, 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", weights_layout), get_weights_accessor(data_path, @@ -328,7 +329,9 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu"); SubStream i_d(graph); - 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") + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, 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", weights_layout), @@ -413,7 +416,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu"); SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); } @@ -558,7 +561,9 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu"); SubStream i_d(graph); - 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") + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, 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", weights_layout), @@ -671,7 +676,7 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu"); SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); } @@ -824,7 +829,9 @@ private: i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat"); SubStream i_d(graph); - 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") + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, 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", weights_layout), diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp index a322b2268d..7893930993 100644 --- a/examples/graph_inception_v4.cpp +++ b/examples/graph_inception_v4.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -65,8 +65,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); // 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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -135,7 +136,7 @@ public: graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7b").set_name("Mixed_7b/concat"); graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7c").set_name("Mixed_7c/concat"); graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7d").set_name("Mixed_7d/concat"); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a/AvgPool") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a/AvgPool") << FlattenLayer().set_name("Logits/Flatten") << FullyConnectedLayer( 1001U, @@ -188,7 +189,9 @@ private: std::string total_path = "/cnn_data/inceptionv4_model/Mixed_3a_"; SubStream i_a(graph); - i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_3a/Branch_0/MaxPool_0a_3x3/MaxPool"); + i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), + true)) + .set_name("Mixed_3a/Branch_0/MaxPool_0a_3x3/MaxPool"); SubStream i_b(graph); i_b << ConvolutionLayer(3U, 3U, 96U, @@ -301,7 +304,9 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/Relu"); SubStream i_b(graph); - i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_5a/Branch_1/MaxPool_1a_3x3/MaxPool"); + i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), + true)) + .set_name("Mixed_5a/Branch_1/MaxPool_1a_3x3/MaxPool"); return ConcatLayer(std::move(i_a), std::move(i_b)); } @@ -383,7 +388,9 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu"); SubStream i_d(graph); - 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") + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), + true)) + .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") << ConvolutionLayer(1U, 1U, 96U, get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) @@ -452,7 +459,9 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Relu"); SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3/MaxPool"); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), + true)) + .set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3/MaxPool"); return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); } @@ -567,7 +576,9 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu"); SubStream i_d(graph); - 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") + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), + true)) + .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") << ConvolutionLayer(1U, 1U, 128U, get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) @@ -658,7 +669,9 @@ private: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Relu"); SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_2/MaxPool_1a_3x3/MaxPool"); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), + true)) + .set_name("Mixed_7a/Branch_2/MaxPool_1a_3x3/MaxPool"); return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); } @@ -811,7 +824,9 @@ private: i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat"); SubStream i_d(graph); - 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") + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), + true)) + .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") << ConvolutionLayer(1U, 1U, 256U, get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index 9936ea52ae..7b475c2c03 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -66,8 +66,9 @@ public: unsigned int batches = 4; /** Number of batches */ // Create input descriptor - const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U, batches), DataLayout::NCHW, common_params.data_layout); - TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U, batches), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -82,14 +83,14 @@ public: get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name("conv1") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") << ConvolutionLayer( 5U, 5U, 50U, get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name("conv2") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") << FullyConnectedLayer( 500U, get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout), diff --git a/examples/graph_mnist.cpp b/examples/graph_mnist.cpp index eb66138df4..56d5c96282 100644 --- a/examples/graph_mnist.cpp +++ b/examples/graph_mnist.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 ARM Limited. + * Copyright (c) 2019-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -70,8 +70,9 @@ public: } // Create input descriptor - const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U), DataLayout::NCHW, common_params.data_layout); - TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); const QuantizationInfo in_quant_info = QuantizationInfo(0.003921568859368563f, 0); @@ -106,7 +107,7 @@ public: PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(1).first, conv_quant_info.at(1).second) .set_name("conv1") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool1") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool1") << ConvolutionLayer( 3U, 3U, 32U, @@ -115,7 +116,7 @@ public: PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(2).first, conv_quant_info.at(2).second) .set_name("conv2") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool2") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool2") << FullyConnectedLayer( 10U, diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp index 5a39dc0ffe..23653c52c8 100644 --- a/examples/graph_mobilenet.cpp +++ b/examples/graph_mobilenet.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -166,7 +166,7 @@ private: graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a") << ConvolutionLayer( 1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), @@ -269,7 +269,7 @@ private: point_weights_quant_info.at(11)); graph << get_dwsc_node_qasymm(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12), point_weights_quant_info.at(12)) - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a") << ConvolutionLayer( 1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp index 337d68524c..1f5b0ec1ac 100644 --- a/examples/graph_mobilenet_v2.cpp +++ b/examples/graph_mobilenet_v2.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -188,7 +188,7 @@ private: .set_name("Conv_1/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) .set_name("Conv_1/Relu6") - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool") << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), @@ -398,7 +398,7 @@ private: PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(1)) .set_name("Conv_1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv_1/Relu6") - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool") << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp index 84355131d0..7af058e042 100644 --- a/examples/graph_resnet50.cpp +++ b/examples/graph_resnet50.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -67,8 +67,9 @@ public: 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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -90,14 +91,14 @@ public: 0.0000100099996416f) .set_name("conv1/BatchNorm") << 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"); + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool"); 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") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool5") << ConvolutionLayer( 1U, 1U, 1000U, get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy", weights_layout), @@ -220,7 +221,7 @@ private: else if(middle_stride > 1) { SubStream left(graph); - left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool"); + left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, common_params.data_layout, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool"); graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add"); } diff --git a/examples/graph_resnet_v2_50.cpp b/examples/graph_resnet_v2_50.cpp index e2325151bc..7d6b9aa3fd 100644 --- a/examples/graph_resnet_v2_50.cpp +++ b/examples/graph_resnet_v2_50.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -70,8 +70,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); // 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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -85,7 +86,7 @@ public: get_weights_accessor(data_path, "conv1_biases.npy", weights_layout), PadStrideInfo(2, 2, 3, 3)) .set_name("conv1/convolution") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool"); + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool"); add_residual_block(data_path, "block1", weights_layout, 64, 3, 2); add_residual_block(data_path, "block2", weights_layout, 128, 4, 2); @@ -100,7 +101,7 @@ public: 0.000009999999747378752f) .set_name("postnorm/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("postnorm/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool5") << ConvolutionLayer( 1U, 1U, 1001U, get_weights_accessor(data_path, "logits_weights.npy", weights_layout), @@ -174,7 +175,7 @@ private: { if(middle_stride != 1) { - shortcut << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool"); + shortcut << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, common_params.data_layout, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool"); } } else diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp index 4e505a05e5..2c50594b0c 100644 --- a/examples/graph_resnext50.cpp +++ b/examples/graph_resnext50.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -65,8 +65,9 @@ public: 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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -84,14 +85,14 @@ public: 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"); + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); 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") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool1") << FlattenLayer().set_name("predictions/Reshape") << OutputLayer(get_npy_output_accessor(common_params.labels, TensorShape(2048U), DataType::F32)); diff --git a/examples/graph_shufflenet.cpp b/examples/graph_shufflenet.cpp index 0a67f5873a..0b977982b5 100644 --- a/examples/graph_shufflenet.cpp +++ b/examples/graph_shufflenet.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -81,8 +81,9 @@ public: } // 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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -107,7 +108,7 @@ public: 1e-5f) .set_name("Conv1/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv1/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 1, 1))).set_name("pool1/MaxPool"); + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 1, 1))).set_name("pool1/MaxPool"); // Stage 2 add_residual_block(data_path, DataLayout::NCHW, 0U /* unit */, 112U /* depth */, 2U /* stride */); @@ -131,7 +132,7 @@ public: add_residual_block(data_path, DataLayout::NCHW, 14U /* unit */, 544U /* depth */, 1U /* stride */); add_residual_block(data_path, DataLayout::NCHW, 15U /* unit */, 544U /* depth */, 1U /* stride */); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("predictions/AvgPool") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("predictions/AvgPool") << FlattenLayer().set_name("predictions/Reshape") << FullyConnectedLayer( 1000U, @@ -181,7 +182,7 @@ private: if(stride == 2) { - right_ss << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(2, 2, 1, 1))).set_name(unit_name + "/pool_1/AveragePool"); + right_ss << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(2, 2, 1, 1))).set_name(unit_name + "/pool_1/AveragePool"); dwc_info = PadStrideInfo(2, 2, 1, 1); } diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp index 234def150d..35fceb4e98 100644 --- a/examples/graph_squeezenet.cpp +++ b/examples/graph_squeezenet.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -66,8 +66,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -82,7 +83,7 @@ public: PadStrideInfo(2, 2, 0, 0)) .set_name("conv1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1") << ConvolutionLayer( 1U, 1U, 16U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy", weights_layout), @@ -107,7 +108,7 @@ public: .set_name("fire4/squeeze1x1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire4/relu_squeeze1x1"); graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U).set_name("fire4/concat"); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4") << ConvolutionLayer( 1U, 1U, 32U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy", weights_layout), @@ -140,7 +141,7 @@ public: .set_name("fire8/squeeze1x1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire8/relu_squeeze1x1"); graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U).set_name("fire8/concat"); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool8") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool8") << ConvolutionLayer( 1U, 1U, 64U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy", weights_layout), @@ -156,7 +157,7 @@ public: PadStrideInfo(1, 1, 0, 0)) .set_name("conv10") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10") - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool10") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool10") << FlattenLayer().set_name("flatten") << SoftmaxLayer().set_name("prob") << OutputLayer(get_output_accessor(common_params, 5)); diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp index b43c8ffdad..f648b6337d 100644 --- a/examples/graph_squeezenet_v1_1.cpp +++ b/examples/graph_squeezenet_v1_1.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -66,8 +66,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(227U, 227U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -82,7 +83,7 @@ public: PadStrideInfo(2, 2, 0, 0)) .set_name("conv1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1") << ConvolutionLayer( 1U, 1U, 16U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy", weights_layout), @@ -99,7 +100,7 @@ public: .set_name("fire3/squeeze1x1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire3/relu_squeeze1x1"); graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U).set_name("fire3/concat"); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3") << ConvolutionLayer( 1U, 1U, 32U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_w.npy", weights_layout), @@ -116,7 +117,7 @@ public: .set_name("fire5/squeeze1x1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire5/relu_squeeze1x1"); graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U).set_name("fire5/concat"); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5") << ConvolutionLayer( 1U, 1U, 48U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_w.npy", weights_layout), @@ -156,7 +157,7 @@ public: PadStrideInfo(1, 1, 0, 0)) .set_name("conv10") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10") - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool10") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool10") << FlattenLayer().set_name("flatten") << SoftmaxLayer().set_name("prob") << OutputLayer(get_output_accessor(common_params, 5)); diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp index 2c7f614f64..f6996dadd5 100644 --- a/examples/graph_vgg16.cpp +++ b/examples/graph_vgg16.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -76,8 +76,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -102,7 +103,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv1_2") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") // Layer 3 << ConvolutionLayer( 3U, 3U, 128U, @@ -119,7 +120,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv2_2") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") // Layer 5 << ConvolutionLayer( 3U, 3U, 256U, @@ -144,7 +145,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv3_3") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool3") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3") // Layer 8 << ConvolutionLayer( 3U, 3U, 512U, @@ -169,7 +170,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv4_3") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool4") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4") // Layer 11 << ConvolutionLayer( 3U, 3U, 512U, @@ -194,7 +195,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv5_3") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") // Layer 14 << FullyConnectedLayer( 4096U, diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp index e3733d859c..f9f5c213d5 100644 --- a/examples/graph_vgg19.cpp +++ b/examples/graph_vgg19.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -75,8 +75,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(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); + const auto operation_layout = common_params.data_layout; + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; @@ -99,7 +100,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv1_2") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") // Layer 2 << ConvolutionLayer( 3U, 3U, 128U, @@ -115,7 +116,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv2_2") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") // Layer 3 << ConvolutionLayer( 3U, 3U, 256U, @@ -145,7 +146,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv3_4") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_4/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool3") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3") // Layer 4 << ConvolutionLayer( 3U, 3U, 512U, @@ -175,7 +176,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv4_4") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_4/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool4") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4") // Layer 5 << ConvolutionLayer( 3U, 3U, 512U, @@ -205,7 +206,7 @@ public: PadStrideInfo(1, 1, 1, 1)) .set_name("conv5_4") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_4/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") // Layer 6 << FullyConnectedLayer( 4096U, diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp index 6f26af7af4..ee6f46d28b 100644 --- a/examples/neon_cnn.cpp +++ b/examples/neon_cnn.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2018 ARM Limited. + * Copyright (c) 2016-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -131,6 +131,8 @@ public: const TensorShape out_shape_softmax(out_shape_fc0.x()); out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32)); + constexpr auto data_layout = DataLayout::NCHW; + /* -----------------------End: [Initialize tensors] */ /* [Configure functions] */ @@ -142,7 +144,7 @@ public: act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max - pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); + pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, data_layout, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); // in:16x16x8: 3x3 convolution, 16 output features maps (OFM) conv1->configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */)); @@ -151,7 +153,7 @@ public: act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average - pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); + pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, data_layout, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); // in:8x8x16, out:128 fc0->configure(&out_pool1, &weights2, &biases2, &out_fc0); diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp index 2c45df8d4d..1d9aa33171 100644 --- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp +++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp @@ -80,7 +80,7 @@ std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITenso ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Get data layout - const DataLayout data_layout = input->data_layout(); + const DataLayout data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->data_layout() : pool_info.data_layout; const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); @@ -179,7 +179,7 @@ void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, _input = input; _output = output; _pool_info = pool_info; - _data_layout = input->info()->data_layout(); + _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->info()->data_layout() : pool_info.data_layout; int pool_stride_x = 0; int pool_stride_y = 0; diff --git a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp index 75b33f2e90..36116d20ec 100644 --- a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp +++ b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp @@ -163,7 +163,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen // Output auto inizialitation if not yet initialized auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_pool_shape(*input, pool_info))); - DataLayout data_layout = input->data_layout(); + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->data_layout() : pool_info.data_layout; unsigned int num_elems_read_per_iteration = 0; unsigned int num_elems_horizontal_window = 0; int pool_stride_x = 0; @@ -403,9 +403,9 @@ void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, cons const int pool_stride_x = pad_stride_info.stride().first; // Get data layout - const DataLayout data_layout = input->info()->data_layout(); - const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->info()->data_layout() : pool_info.data_layout; + const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); // Update pool size in case of global pooling const Size2D pool_size( @@ -1932,9 +1932,9 @@ Status NEPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInf unsigned int pool_size_y = 0; // Get data layout - const DataLayout data_layout = input->data_layout(); - const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->data_layout() : pool_info.data_layout; + const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); pool_size_x = is_global_pooling ? input->dimension(idx_width) : pool_info.pool_size.width; pool_size_y = is_global_pooling ? input->dimension(idx_height) : pool_info.pool_size.height; diff --git a/src/runtime/CL/functions/CLPoolingLayer.cpp b/src/runtime/CL/functions/CLPoolingLayer.cpp index 00e5447875..883183492d 100644 --- a/src/runtime/CL/functions/CLPoolingLayer.cpp +++ b/src/runtime/CL/functions/CLPoolingLayer.cpp @@ -49,7 +49,11 @@ void CLPoolingLayer::configure(ICLTensor *input, ICLTensor *output, const Poolin { pixel_value = PixelValue(0, data_type, input->info()->quantization_info()); } - switch(input->info()->data_layout()) + + // Data layout + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->info()->data_layout() : pool_info.data_layout; + + switch(data_layout) { case DataLayout::NCHW: border_mode = (PoolingType::MAX == pool_info.pool_type) ? BorderMode::REPLICATE : BorderMode::CONSTANT; diff --git a/src/runtime/NEON/functions/NEPoolingLayer.cpp b/src/runtime/NEON/functions/NEPoolingLayer.cpp index e1619188d1..6b87189c2b 100644 --- a/src/runtime/NEON/functions/NEPoolingLayer.cpp +++ b/src/runtime/NEON/functions/NEPoolingLayer.cpp @@ -41,7 +41,7 @@ void NEPoolingLayer::configure(ITensor *input, ITensor *output, const PoolingLay _is_global_pooling_layer = (input->info()->dimension(0) == pool_info.pool_size.width) && (input->info()->dimension(1) == pool_info.pool_size.height); // Get data layout - _data_layout = input->info()->data_layout(); + _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->info()->data_layout() : pool_info.data_layout; // Configure pooling kernel _pooling_layer_kernel.configure(input, output, pool_info); diff --git a/tests/datasets/PoolingLayerDataset.h b/tests/datasets/PoolingLayerDataset.h index 363aabad96..56fcdc3d0f 100644 --- a/tests/datasets/PoolingLayerDataset.h +++ b/tests/datasets/PoolingLayerDataset.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -106,10 +106,10 @@ public: PoolingLayerDatasetSpecial() { // Special cases - add_config(TensorShape(60U, 52U, 3U, 2U), PoolingLayerInfo(PoolingType::AVG, Size2D(100, 100), PadStrideInfo(5, 5, 50, 50), true)); + add_config(TensorShape(60U, 52U, 3U, 2U), PoolingLayerInfo(PoolingType::AVG, Size2D(100, 100), DataLayout::NCHW, PadStrideInfo(5, 5, 50, 50), true)); // Asymmetric padding - add_config(TensorShape(112U, 112U, 32U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))); - add_config(TensorShape(14U, 14U, 832U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))); + add_config(TensorShape(112U, 112U, 32U), PoolingLayerInfo(PoolingType::MAX, 3, DataLayout::NCHW, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))); + add_config(TensorShape(14U, 14U, 832U), PoolingLayerInfo(PoolingType::MAX, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))); } }; } // namespace datasets diff --git a/tests/validation/CL/PoolingLayer.cpp b/tests/validation/CL/PoolingLayer.cpp index 262cea3338..e9d756d9a2 100644 --- a/tests/validation/CL/PoolingLayer.cpp +++ b/tests/validation/CL/PoolingLayer.cpp @@ -105,14 +105,14 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32), TensorInfo(TensorShape(1U, 1U, 5U), 1, DataType::F32), })), - framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)), - PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)), - PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(1, 1, 2, 0)), - PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(1, 1, 0, 2)), - PoolingLayerInfo(PoolingType::L2, 3, PadStrideInfo(1, 1, 0, 0)), - PoolingLayerInfo(PoolingType::AVG), - PoolingLayerInfo(PoolingType::MAX), - PoolingLayerInfo(PoolingType::AVG), + framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 2, 0)), + PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 2)), + PoolingLayerInfo(PoolingType::L2, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW), + PoolingLayerInfo(PoolingType::MAX, DataLayout::NCHW), + PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW), })), framework::dataset::make("Expected", { false, false, false, false, false, true, false, true })), input_info, output_info, pool_info, expected) diff --git a/tests/validation/GLES_COMPUTE/PoolingLayer.cpp b/tests/validation/GLES_COMPUTE/PoolingLayer.cpp index 7679007a82..e50748095f 100644 --- a/tests/validation/GLES_COMPUTE/PoolingLayer.cpp +++ b/tests/validation/GLES_COMPUTE/PoolingLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -73,7 +73,16 @@ framework::dataset::make("OutputInfo", })), framework::dataset::make("PoolInfo", { - PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)), PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(1, 1, 2, 0)), PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(1, 1, 0, 2)), PoolingLayerInfo(PoolingType::L2, 3, PadStrideInfo(1, 1, 0, 0)), PoolingLayerInfo(PoolingType::AVG), PoolingLayerInfo(PoolingType::MAX), PoolingLayerInfo(PoolingType::AVG), + PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 2, 0)), + PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 2)), + PoolingLayerInfo(PoolingType::L2, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW), + PoolingLayerInfo(PoolingType::MAX, DataLayout::NCHW), + PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW), })), framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, true })), input_info, output_info, pool_info, expected) diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp index 041e60607a..c6b7e92804 100644 --- a/tests/validation/NEON/PoolingLayer.cpp +++ b/tests/validation/NEON/PoolingLayer.cpp @@ -94,13 +94,13 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32), TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), })), - framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)), - PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)), - PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(1, 1, 2, 0)), - PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(1, 1, 0, 2)), - PoolingLayerInfo(PoolingType::AVG), - PoolingLayerInfo(PoolingType::MAX), - PoolingLayerInfo(PoolingType::AVG), + framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), + PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 2, 0)), + PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 2)), + PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW), + PoolingLayerInfo(PoolingType::MAX, DataLayout::NCHW), + PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW), })), framework::dataset::make("Expected", { false, false, false, false, true, false, false, true })), input_info, output_info, pool_info, expected) diff --git a/tests/validation/fixtures/PoolingLayerFixture.h b/tests/validation/fixtures/PoolingLayerFixture.h index 350b0d51e8..ec186564b7 100644 --- a/tests/validation/fixtures/PoolingLayerFixture.h +++ b/tests/validation/fixtures/PoolingLayerFixture.h @@ -138,7 +138,7 @@ public: template <typename...> void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout) { - PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding), + PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, data_layout, pad_stride_info, exclude_padding), data_type, data_layout); } }; @@ -150,7 +150,7 @@ public: template <typename...> void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout, bool fp_mixed_precision = false) { - PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding, fp_mixed_precision), + PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, data_layout, pad_stride_info, exclude_padding, fp_mixed_precision), data_type, data_layout); } }; @@ -162,7 +162,7 @@ public: template <typename...> void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout = DataLayout::NCHW) { - PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding), + PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, data_layout, pad_stride_info, exclude_padding), data_type, data_layout); } }; @@ -185,7 +185,7 @@ public: template <typename...> void setup(TensorShape shape, PoolingType pool_type, DataType data_type, DataLayout data_layout = DataLayout::NCHW) { - PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type), data_type, data_layout); + PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, data_layout), data_type, data_layout); } }; diff --git a/utils/TypePrinter.h b/utils/TypePrinter.h index 4390381a69..1831733ec0 100644 --- a/utils/TypePrinter.h +++ b/utils/TypePrinter.h @@ -1621,6 +1621,7 @@ inline std::string to_string(const PoolingLayerInfo &info) { std::stringstream str; str << "{Type=" << info.pool_type << "," + << "DataLayout=" << info.data_layout << "," << "IsGlobalPooling=" << info.is_global_pooling; if(!info.is_global_pooling) { |