| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2016 |
| // Mehdi Goli Codeplay Software Ltd. |
| // Ralph Potter Codeplay Software Ltd. |
| // Luke Iwanski Codeplay Software Ltd. |
| // Contact: <eigen@codeplay.com> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #define EIGEN_TEST_NO_LONGDOUBLE |
| #define EIGEN_TEST_NO_COMPLEX |
| |
| #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t |
| #define EIGEN_USE_SYCL |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| using Eigen::Tensor; |
| static const int DataLayout = ColMajor; |
| |
| template <typename DataType, typename IndexType> |
| static void test_simple_image_patch_sycl(const Eigen::SyclDevice& sycl_device) |
| { |
| IndexType sizeDim1 = 2; |
| IndexType sizeDim2 = 3; |
| IndexType sizeDim3 = 5; |
| IndexType sizeDim4 = 7; |
| array<IndexType, 4> tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; |
| array<IndexType, 4> tensorRowMajorRange = {{sizeDim4, sizeDim3, sizeDim2, sizeDim1}}; |
| Tensor<DataType, 4, DataLayout,IndexType> tensor_col_major(tensorColMajorRange); |
| Tensor<DataType, 4, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange); |
| tensor_col_major.setRandom(); |
| |
| DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType))); |
| DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange); |
| TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),(tensor_col_major.size())*sizeof(DataType)); |
| gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout(); |
| sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_col_major.size())*sizeof(DataType)); |
| |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(3)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(2)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(1)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(3), tensor_row_major.dimension(0)); |
| |
| // Single pixel patch: ColMajor |
| array<IndexType, 5> patchColMajorTensorRange={{sizeDim1, 1, 1, sizeDim2*sizeDim3, sizeDim4}}; |
| Tensor<DataType, 5, DataLayout,IndexType> single_patch_col_major(patchColMajorTensorRange); |
| size_t patchTensorBuffSize =single_patch_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_single_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_single_patch_col_major(gpu_data_single_patch_col_major, patchColMajorTensorRange); |
| gpu_single_patch_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(1, 1); |
| sycl_device.memcpyDeviceToHost(single_patch_col_major.data(), gpu_data_single_patch_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(0), 2); |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(1), 1); |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(2), 1); |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(3), 3*5); |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(4), 7); |
| |
| // Single pixel patch: RowMajor |
| array<IndexType, 5> patchRowMajorTensorRange={{sizeDim4, sizeDim2*sizeDim3, 1, 1, sizeDim1}}; |
| Tensor<DataType, 5, RowMajor,IndexType> single_patch_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =single_patch_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_single_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>> gpu_single_patch_row_major(gpu_data_single_patch_row_major, patchRowMajorTensorRange); |
| gpu_single_patch_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(1, 1); |
| sycl_device.memcpyDeviceToHost(single_patch_row_major.data(), gpu_data_single_patch_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(0), 7); |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(1), 3*5); |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(2), 1); |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(3), 1); |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(4), 2); |
| |
| for (IndexType i = 0; i < tensor_col_major.size(); ++i) { |
| // ColMajor |
| if (tensor_col_major.data()[i] != single_patch_col_major.data()[i]) { |
| std::cout << "Mismatch detected at index colmajor " << i << " : " |
| << tensor_col_major.data()[i] << " vs " << single_patch_col_major.data()[i] |
| << std::endl; |
| } |
| VERIFY_IS_EQUAL(single_patch_col_major.data()[i], tensor_col_major.data()[i]); |
| // RowMajor |
| if (tensor_row_major.data()[i] != single_patch_row_major.data()[i]) { |
| std::cout << "Mismatch detected at index row major" << i << " : " |
| << tensor_row_major.data()[i] << " vs " |
| << single_patch_row_major.data()[i] << std::endl; |
| } |
| VERIFY_IS_EQUAL(single_patch_row_major.data()[i], |
| tensor_row_major.data()[i]); |
| VERIFY_IS_EQUAL(tensor_col_major.data()[i], tensor_row_major.data()[i]); |
| VERIFY_IS_EQUAL(single_patch_col_major.data()[i], |
| single_patch_row_major.data()[i]); |
| } |
| |
| |
| // Entire image patch: ColMajor |
| patchColMajorTensorRange={{sizeDim1, sizeDim2, sizeDim3, sizeDim2*sizeDim3, sizeDim4}}; |
| Tensor<DataType, 5, DataLayout,IndexType> entire_image_patch_col_major(patchColMajorTensorRange); |
| patchTensorBuffSize =entire_image_patch_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_entire_image_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_entire_image_patch_col_major(gpu_data_entire_image_patch_col_major, patchColMajorTensorRange); |
| gpu_entire_image_patch_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(3, 5); |
| sycl_device.memcpyDeviceToHost(entire_image_patch_col_major.data(), gpu_data_entire_image_patch_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(0), 2); |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(1), 3); |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(2), 5); |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(3), 3*5); |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(4), 7); |
| |
| // Entire image patch: RowMajor |
| patchRowMajorTensorRange={{sizeDim4, sizeDim2*sizeDim3, sizeDim3, sizeDim2, sizeDim1}}; |
| Tensor<DataType, 5, RowMajor,IndexType> entire_image_patch_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =entire_image_patch_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_entire_image_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>> gpu_entire_image_patch_row_major(gpu_data_entire_image_patch_row_major, patchRowMajorTensorRange); |
| gpu_entire_image_patch_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(3, 5); |
| sycl_device.memcpyDeviceToHost(entire_image_patch_row_major.data(), gpu_data_entire_image_patch_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 7); |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 3*5); |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 5); |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(3), 3); |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(4), 2); |
| |
| for (IndexType i = 0; i < 3; ++i) { |
| for (IndexType j = 0; j < 5; ++j) { |
| IndexType patchId = i+3*j; |
| for (IndexType r = 0; r < 3; ++r) { |
| for (IndexType c = 0; c < 5; ++c) { |
| for (IndexType d = 0; d < 2; ++d) { |
| for (IndexType b = 0; b < 7; ++b) { |
| DataType expected_col_major = 0.0f; |
| DataType expected_row_major = 0.0f; |
| if (r-1+i >= 0 && c-2+j >= 0 && r-1+i < 3 && c-2+j < 5) { |
| expected_col_major = tensor_col_major(d, r-1+i, c-2+j, b); |
| expected_row_major = tensor_row_major(b, c-2+j, r-1+i, d); |
| } |
| // ColMajor |
| if (entire_image_patch_col_major(d, r, c, patchId, b) != expected_col_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(entire_image_patch_col_major(d, r, c, patchId, b), expected_col_major); |
| // RowMajor |
| if (entire_image_patch_row_major(b, patchId, c, r, d) != |
| expected_row_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j |
| << " r=" << r << " c=" << c << " d=" << d << " b=" << b |
| << std::endl; |
| } |
| VERIFY_IS_EQUAL(entire_image_patch_row_major(b, patchId, c, r, d), |
| expected_row_major); |
| // Check that ColMajor and RowMajor agree. |
| VERIFY_IS_EQUAL(expected_col_major, expected_row_major); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // 2D patch: ColMajor |
| patchColMajorTensorRange={{sizeDim1, 2, 2, sizeDim2*sizeDim3, sizeDim4}}; |
| Tensor<DataType, 5, DataLayout,IndexType> twod_patch_col_major(patchColMajorTensorRange); |
| patchTensorBuffSize =twod_patch_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_twod_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_twod_patch_col_major(gpu_data_twod_patch_col_major, patchColMajorTensorRange); |
| gpu_twod_patch_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(2, 2); |
| sycl_device.memcpyDeviceToHost(twod_patch_col_major.data(), gpu_data_twod_patch_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(0), 2); |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(1), 2); |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(2), 2); |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(3), 3*5); |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(4), 7); |
| |
| // 2D patch: RowMajor |
| patchRowMajorTensorRange={{sizeDim4, sizeDim2*sizeDim3, 2, 2, sizeDim1}}; |
| Tensor<DataType, 5, RowMajor,IndexType> twod_patch_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =twod_patch_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_twod_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>> gpu_twod_patch_row_major(gpu_data_twod_patch_row_major, patchRowMajorTensorRange); |
| gpu_twod_patch_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(2, 2); |
| sycl_device.memcpyDeviceToHost(twod_patch_row_major.data(), gpu_data_twod_patch_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 7); |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 3*5); |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2); |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(3), 2); |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(4), 2); |
| |
| |
| // Based on the calculation described in TensorTraits.h, padding happens to be 0. |
| IndexType row_padding = 0; |
| IndexType col_padding = 0; |
| IndexType stride = 1; |
| |
| for (IndexType i = 0; i < 3; ++i) { |
| for (IndexType j = 0; j < 5; ++j) { |
| IndexType patchId = i+3*j; |
| for (IndexType r = 0; r < 2; ++r) { |
| for (IndexType c = 0; c < 2; ++c) { |
| for (IndexType d = 0; d < 2; ++d) { |
| for (IndexType b = 0; b < 7; ++b) { |
| DataType expected_col_major = 0.0f; |
| DataType expected_row_major = 0.0f; |
| IndexType row_offset = r*stride + i - row_padding; |
| IndexType col_offset = c*stride + j - col_padding; |
| // ColMajor |
| if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_col_major.dimension(1) && col_offset < tensor_col_major.dimension(2)) { |
| expected_col_major = tensor_col_major(d, row_offset, col_offset, b); |
| } |
| if (twod_patch_col_major(d, r, c, patchId, b) != expected_col_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(twod_patch_col_major(d, r, c, patchId, b), expected_col_major); |
| |
| // RowMajor |
| if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_row_major.dimension(2) && col_offset < tensor_row_major.dimension(1)) { |
| expected_row_major = tensor_row_major(b, col_offset, row_offset, d); |
| |
| } |
| if (twod_patch_row_major(b, patchId, c, r, d) != expected_row_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(twod_patch_row_major(b, patchId, c, r, d), expected_row_major); |
| // Check that ColMajor and RowMajor agree. |
| VERIFY_IS_EQUAL(expected_col_major, expected_row_major); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| sycl_device.deallocate(gpu_data_col_major); |
| sycl_device.deallocate(gpu_data_row_major); |
| sycl_device.deallocate(gpu_data_single_patch_col_major); |
| sycl_device.deallocate(gpu_data_single_patch_row_major); |
| sycl_device.deallocate(gpu_data_entire_image_patch_col_major); |
| sycl_device.deallocate(gpu_data_entire_image_patch_row_major); |
| sycl_device.deallocate(gpu_data_twod_patch_col_major); |
| sycl_device.deallocate(gpu_data_twod_patch_row_major); |
| |
| } |
| |
| |
| // Verifies VALID padding (no padding) with incrementing values. |
| template <typename DataType, typename IndexType> |
| static void test_patch_padding_valid_sycl(const Eigen::SyclDevice& sycl_device){ |
| IndexType input_depth = 3; |
| IndexType input_rows = 3; |
| IndexType input_cols = 3; |
| IndexType input_batches = 1; |
| IndexType ksize = 2; // Corresponds to the Rows and Cols for tensor.extract_image_patches<>. |
| IndexType stride = 2; // Only same stride is supported. |
| |
| array<IndexType, 4> tensorColMajorRange = {{input_depth, input_rows, input_cols, input_batches}}; |
| array<IndexType, 4> tensorRowMajorRange = {{input_batches, input_cols, input_rows, input_depth}}; |
| Tensor<DataType, 4, DataLayout,IndexType> tensor_col_major(tensorColMajorRange); |
| Tensor<DataType, 4, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange); |
| |
| DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType))); |
| DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange); |
| TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),(tensor_col_major.size())*sizeof(DataType)); |
| gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout(); |
| sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_col_major.size())*sizeof(DataType)); |
| |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(3)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(2)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(1)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(3), tensor_row_major.dimension(0)); |
| |
| // Initializes tensor with incrementing numbers. |
| for (IndexType i = 0; i < tensor_col_major.size(); ++i) { |
| tensor_col_major.data()[i] = i + 1; |
| } |
| // ColMajor |
| array<IndexType, 5> patchColMajorTensorRange={{input_depth, ksize, ksize, 1, input_batches}}; |
| Tensor<DataType, 5, DataLayout,IndexType> result_col_major(patchColMajorTensorRange); |
| size_t patchTensorBuffSize =result_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_result_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_result_col_major(gpu_data_result_col_major, patchColMajorTensorRange); |
| gpu_result_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID); |
| sycl_device.memcpyDeviceToHost(result_col_major.data(), gpu_data_result_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(result_col_major.dimension(0), input_depth); // depth |
| VERIFY_IS_EQUAL(result_col_major.dimension(1), ksize); // kernel rows |
| VERIFY_IS_EQUAL(result_col_major.dimension(2), ksize); // kernel cols |
| VERIFY_IS_EQUAL(result_col_major.dimension(3), 1); // number of patches |
| VERIFY_IS_EQUAL(result_col_major.dimension(4), input_batches); // number of batches |
| |
| // RowMajor |
| array<IndexType, 5> patchRowMajorTensorRange={{input_batches, 1, ksize, ksize, input_depth }}; |
| Tensor<DataType, 5, RowMajor,IndexType> result_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =result_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_result_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>> gpu_result_row_major(gpu_data_result_row_major, patchRowMajorTensorRange); |
| gpu_result_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID); |
| sycl_device.memcpyDeviceToHost(result_row_major.data(), gpu_data_result_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(result_col_major.dimension(0), result_row_major.dimension(4)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(1), result_row_major.dimension(3)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(2), result_row_major.dimension(2)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(3), result_row_major.dimension(1)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(4), result_row_major.dimension(0)); |
| |
| // No padding is carried out. |
| IndexType row_padding = 0; |
| IndexType col_padding = 0; |
| |
| for (IndexType i = 0; (i+stride+ksize-1) < input_rows; i += stride) { // input rows |
| for (IndexType j = 0; (j+stride+ksize-1) < input_cols; j += stride) { // input cols |
| IndexType patchId = i+input_rows*j; |
| for (IndexType r = 0; r < ksize; ++r) { // patch rows |
| for (IndexType c = 0; c < ksize; ++c) { // patch cols |
| for (IndexType d = 0; d < input_depth; ++d) { // depth |
| for (IndexType b = 0; b < input_batches; ++b) { // batch |
| DataType expected_col_major = 0.0f; |
| DataType expected_row_major = 0.0f; |
| IndexType row_offset = r + i - row_padding; |
| IndexType col_offset = c + j - col_padding; |
| if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) { |
| expected_col_major = tensor_col_major(d, row_offset, col_offset, b); |
| expected_row_major = tensor_row_major(b, col_offset, row_offset, d); |
| } |
| // ColMajor |
| if (result_col_major(d, r, c, patchId, b) != expected_col_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(result_col_major(d, r, c, patchId, b), expected_col_major); |
| // RowMajor |
| if (result_row_major(b, patchId, c, r, d) != expected_row_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major); |
| // Check that ColMajor and RowMajor agree. |
| VERIFY_IS_EQUAL(expected_col_major, expected_row_major); |
| } |
| } |
| } |
| } |
| } |
| } |
| sycl_device.deallocate(gpu_data_col_major); |
| sycl_device.deallocate(gpu_data_row_major); |
| sycl_device.deallocate(gpu_data_result_col_major); |
| sycl_device.deallocate(gpu_data_result_row_major); |
| } |
| |
| // Verifies VALID padding (no padding) with the same value. |
| template <typename DataType, typename IndexType> |
| static void test_patch_padding_valid_same_value_sycl(const Eigen::SyclDevice& sycl_device){ |
| IndexType input_depth = 1; |
| IndexType input_rows = 5; |
| IndexType input_cols = 5; |
| IndexType input_batches = 2; |
| IndexType ksize = 3; // Corresponds to the Rows and Cols for tensor.extract_image_patches<>. |
| IndexType stride = 2; // Only same stride is supported. |
| // ColMajor |
| |
| array<IndexType, 4> tensorColMajorRange = {{input_depth, input_rows, input_cols, input_batches}}; |
| array<IndexType, 4> tensorRowMajorRange = {{input_batches, input_cols, input_rows, input_depth}}; |
| Tensor<DataType, 4, DataLayout,IndexType> tensor_col_major(tensorColMajorRange); |
| Tensor<DataType, 4, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange); |
| |
| DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType))); |
| DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange); |
| TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange); |
| gpu_col_major.device(sycl_device)=gpu_col_major.constant(11.0f); |
| gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout(); |
| sycl_device.memcpyDeviceToHost(tensor_col_major.data(), gpu_data_col_major, (tensor_col_major.size())*sizeof(DataType)); |
| sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_row_major.size())*sizeof(DataType)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(3)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(2)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(1)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(3), tensor_row_major.dimension(0)); |
| |
| array<IndexType, 5> patchColMajorTensorRange={{input_depth, ksize, ksize, 4, input_batches}}; |
| Tensor<DataType, 5, DataLayout,IndexType> result_col_major(patchColMajorTensorRange); |
| size_t patchTensorBuffSize =result_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_result_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_result_col_major(gpu_data_result_col_major, patchColMajorTensorRange); |
| gpu_result_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID); |
| sycl_device.memcpyDeviceToHost(result_col_major.data(), gpu_data_result_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(result_col_major.dimension(0), input_depth); // depth |
| VERIFY_IS_EQUAL(result_col_major.dimension(1), ksize); // kernel rows |
| VERIFY_IS_EQUAL(result_col_major.dimension(2), ksize); // kernel cols |
| VERIFY_IS_EQUAL(result_col_major.dimension(3), 4); // number of patches |
| VERIFY_IS_EQUAL(result_col_major.dimension(4), input_batches); // number of batches |
| |
| // RowMajor |
| array<IndexType, 5> patchRowMajorTensorRange={{input_batches, 4, ksize, ksize, input_depth }}; |
| Tensor<DataType, 5, RowMajor,IndexType> result_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =result_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_result_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>> gpu_result_row_major(gpu_data_result_row_major, patchRowMajorTensorRange); |
| gpu_result_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(ksize, ksize, stride, stride, 1, 1, PADDING_VALID); |
| sycl_device.memcpyDeviceToHost(result_row_major.data(), gpu_data_result_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(result_col_major.dimension(0), result_row_major.dimension(4)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(1), result_row_major.dimension(3)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(2), result_row_major.dimension(2)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(3), result_row_major.dimension(1)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(4), result_row_major.dimension(0)); |
| |
| // No padding is carried out. |
| IndexType row_padding = 0; |
| IndexType col_padding = 0; |
| |
| for (IndexType i = 0; (i+stride+ksize-1) <= input_rows; i += stride) { // input rows |
| for (IndexType j = 0; (j+stride+ksize-1) <= input_cols; j += stride) { // input cols |
| IndexType patchId = i+input_rows*j; |
| for (IndexType r = 0; r < ksize; ++r) { // patch rows |
| for (IndexType c = 0; c < ksize; ++c) { // patch cols |
| for (IndexType d = 0; d < input_depth; ++d) { // depth |
| for (IndexType b = 0; b < input_batches; ++b) { // batch |
| DataType expected_col_major = 0.0f; |
| DataType expected_row_major = 0.0f; |
| IndexType row_offset = r + i - row_padding; |
| IndexType col_offset = c + j - col_padding; |
| if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) { |
| expected_col_major = tensor_col_major(d, row_offset, col_offset, b); |
| expected_row_major = tensor_row_major(b, col_offset, row_offset, d); |
| } |
| // ColMajor |
| if (result_col_major(d, r, c, patchId, b) != expected_col_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(result_col_major(d, r, c, patchId, b), expected_col_major); |
| // RowMajor |
| if (result_row_major(b, patchId, c, r, d) != expected_row_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major); |
| // Check that ColMajor and RowMajor agree. |
| VERIFY_IS_EQUAL(expected_col_major, expected_row_major); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // Verifies SAME padding. |
| template <typename DataType, typename IndexType> |
| static void test_patch_padding_same_sycl(const Eigen::SyclDevice& sycl_device){ |
| IndexType input_depth = 3; |
| IndexType input_rows = 4; |
| IndexType input_cols = 2; |
| IndexType input_batches = 1; |
| IndexType ksize = 2; // Corresponds to the Rows and Cols for tensor.extract_image_patches<>. |
| IndexType stride = 2; // Only same stride is supported. |
| |
| // ColMajor |
| array<IndexType, 4> tensorColMajorRange = {{input_depth, input_rows, input_cols, input_batches}}; |
| array<IndexType, 4> tensorRowMajorRange = {{input_batches, input_cols, input_rows, input_depth}}; |
| Tensor<DataType, 4, DataLayout,IndexType> tensor_col_major(tensorColMajorRange); |
| Tensor<DataType, 4, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange); |
| |
| DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType))); |
| DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange); |
| TensorMap<Tensor<DataType, 4, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),(tensor_col_major.size())*sizeof(DataType)); |
| gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout(); |
| sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_col_major.size())*sizeof(DataType)); |
| |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(3)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(2)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(1)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(3), tensor_row_major.dimension(0)); |
| |
| // Initializes tensor with incrementing numbers. |
| for (IndexType i = 0; i < tensor_col_major.size(); ++i) { |
| tensor_col_major.data()[i] = i + 1; |
| } |
| |
| array<IndexType, 5> patchColMajorTensorRange={{input_depth, ksize, ksize, 2, input_batches}}; |
| Tensor<DataType, 5, DataLayout,IndexType> result_col_major(patchColMajorTensorRange); |
| size_t patchTensorBuffSize =result_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_result_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_result_col_major(gpu_data_result_col_major, patchColMajorTensorRange); |
| gpu_result_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(ksize, ksize, stride, stride, PADDING_SAME); |
| sycl_device.memcpyDeviceToHost(result_col_major.data(), gpu_data_result_col_major, patchTensorBuffSize); |
| |
| |
| VERIFY_IS_EQUAL(result_col_major.dimension(0), input_depth); // depth |
| VERIFY_IS_EQUAL(result_col_major.dimension(1), ksize); // kernel rows |
| VERIFY_IS_EQUAL(result_col_major.dimension(2), ksize); // kernel cols |
| VERIFY_IS_EQUAL(result_col_major.dimension(3), 2); // number of patches |
| VERIFY_IS_EQUAL(result_col_major.dimension(4), input_batches); // number of batches |
| |
| // RowMajor |
| |
| array<IndexType, 5> patchRowMajorTensorRange={{input_batches, 2, ksize, ksize, input_depth }}; |
| Tensor<DataType, 5, RowMajor,IndexType> result_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =result_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_result_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>> gpu_result_row_major(gpu_data_result_row_major, patchRowMajorTensorRange); |
| gpu_result_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(ksize, ksize, stride, stride, PADDING_SAME); |
| sycl_device.memcpyDeviceToHost(result_row_major.data(), gpu_data_result_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(result_col_major.dimension(0), result_row_major.dimension(4)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(1), result_row_major.dimension(3)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(2), result_row_major.dimension(2)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(3), result_row_major.dimension(1)); |
| VERIFY_IS_EQUAL(result_col_major.dimension(4), result_row_major.dimension(0)); |
| |
| // Based on the calculation described in TensorTraits.h, padding happens to be 0. |
| IndexType row_padding = 0; |
| IndexType col_padding = 0; |
| |
| for (IndexType i = 0; (i+stride+ksize-1) <= input_rows; i += stride) { // input rows |
| for (IndexType j = 0; (j+stride+ksize-1) <= input_cols; j += stride) { // input cols |
| IndexType patchId = i+input_rows*j; |
| for (IndexType r = 0; r < ksize; ++r) { // patch rows |
| for (IndexType c = 0; c < ksize; ++c) { // patch cols |
| for (IndexType d = 0; d < input_depth; ++d) { // depth |
| for (IndexType b = 0; b < input_batches; ++b) { // batch |
| DataType expected_col_major = 0.0f; |
| DataType expected_row_major = 0.0f; |
| IndexType row_offset = r*stride + i - row_padding; |
| IndexType col_offset = c*stride + j - col_padding; |
| if (row_offset >= 0 && col_offset >= 0 && row_offset < input_rows && col_offset < input_cols) { |
| expected_col_major = tensor_col_major(d, row_offset, col_offset, b); |
| expected_row_major = tensor_row_major(b, col_offset, row_offset, d); |
| } |
| // ColMajor |
| if (result_col_major(d, r, c, patchId, b) != expected_col_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(result_col_major(d, r, c, patchId, b), expected_col_major); |
| // RowMajor |
| if (result_row_major(b, patchId, c, r, d) != expected_row_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(result_row_major(b, patchId, c, r, d), expected_row_major); |
| // Check that ColMajor and RowMajor agree. |
| VERIFY_IS_EQUAL(expected_col_major, expected_row_major); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| |
| template <typename DataType, typename IndexType> |
| static void test_patch_no_extra_dim_sycl(const Eigen::SyclDevice& sycl_device){ |
| |
| IndexType sizeDim1 = 2; |
| IndexType sizeDim2 = 3; |
| IndexType sizeDim3 = 5; |
| |
| // ColMajor |
| array<IndexType, 3> tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3}}; |
| array<IndexType, 3> tensorRowMajorRange = {{sizeDim3, sizeDim2, sizeDim1}}; |
| Tensor<DataType, 3, DataLayout,IndexType> tensor_col_major(tensorColMajorRange); |
| tensor_col_major.setRandom(); |
| Tensor<DataType, 3, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange); |
| |
| DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType))); |
| DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 3, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange); |
| TensorMap<Tensor<DataType, 3, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),(tensor_col_major.size())*sizeof(DataType)); |
| gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout(); |
| sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_row_major.size())*sizeof(DataType)); |
| |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(0), tensor_row_major.dimension(2)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(1), tensor_row_major.dimension(1)); |
| VERIFY_IS_EQUAL(tensor_col_major.dimension(2), tensor_row_major.dimension(0)); |
| |
| |
| // Single pixel patch: ColMajor |
| array<IndexType, 4> patchColMajorTensorRange={{sizeDim1, 1, 1, sizeDim2*sizeDim3}}; |
| Tensor<DataType, 4, DataLayout,IndexType> single_patch_col_major(patchColMajorTensorRange); |
| size_t patchTensorBuffSize =single_patch_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_single_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_single_patch_col_major(gpu_data_single_patch_col_major, patchColMajorTensorRange); |
| gpu_single_patch_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(1, 1); |
| sycl_device.memcpyDeviceToHost(single_patch_col_major.data(), gpu_data_single_patch_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(0), sizeDim1); |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(1), 1); |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(2), 1); |
| VERIFY_IS_EQUAL(single_patch_col_major.dimension(3), sizeDim2*sizeDim3); |
| |
| // Single pixel patch: RowMajor |
| array<IndexType, 4> patchRowMajorTensorRange={{sizeDim2*sizeDim3, 1, 1, sizeDim1}}; |
| Tensor<DataType, 4, RowMajor,IndexType> single_patch_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =single_patch_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_single_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 4, RowMajor,IndexType>> gpu_single_patch_row_major(gpu_data_single_patch_row_major, patchRowMajorTensorRange); |
| gpu_single_patch_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(1, 1); |
| sycl_device.memcpyDeviceToHost(single_patch_row_major.data(), gpu_data_single_patch_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(0), sizeDim2*sizeDim3); |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(1), 1); |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(2), 1); |
| VERIFY_IS_EQUAL(single_patch_row_major.dimension(3), sizeDim1); |
| |
| for (IndexType i = 0; i < tensor_col_major.size(); ++i) { |
| // ColMajor |
| if (tensor_col_major.data()[i] != single_patch_col_major.data()[i]) { |
| std::cout << "Mismatch detected at index " << i << " : " << tensor_col_major.data()[i] << " vs " << single_patch_col_major.data()[i] << std::endl; |
| } |
| VERIFY_IS_EQUAL(single_patch_col_major.data()[i], tensor_col_major.data()[i]); |
| // RowMajor |
| if (tensor_row_major.data()[i] != single_patch_row_major.data()[i]) { |
| std::cout << "Mismatch detected at index " << i << " : " |
| << tensor_col_major.data()[i] << " vs " |
| << single_patch_row_major.data()[i] << std::endl; |
| } |
| VERIFY_IS_EQUAL(single_patch_row_major.data()[i], |
| tensor_row_major.data()[i]); |
| VERIFY_IS_EQUAL(tensor_col_major.data()[i], tensor_row_major.data()[i]); |
| VERIFY_IS_EQUAL(single_patch_col_major.data()[i], |
| single_patch_row_major.data()[i]); |
| } |
| |
| // Entire image patch: ColMajor |
| patchColMajorTensorRange={{sizeDim1, sizeDim2, sizeDim3, sizeDim2*sizeDim3}}; |
| Tensor<DataType, 4, DataLayout,IndexType> entire_image_patch_col_major(patchColMajorTensorRange); |
| patchTensorBuffSize =entire_image_patch_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_entire_image_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_entire_image_patch_col_major(gpu_data_entire_image_patch_col_major, patchColMajorTensorRange); |
| gpu_entire_image_patch_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(3, 5); |
| sycl_device.memcpyDeviceToHost(entire_image_patch_col_major.data(), gpu_data_entire_image_patch_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(0), 2); |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(1), 3); |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(2), 5); |
| VERIFY_IS_EQUAL(entire_image_patch_col_major.dimension(3), 3*5); |
| |
| // Entire image patch: RowMajor |
| patchRowMajorTensorRange={{sizeDim2*sizeDim3, sizeDim3, sizeDim2, sizeDim1}}; |
| Tensor<DataType, 4, RowMajor,IndexType> entire_image_patch_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =entire_image_patch_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_entire_image_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 4, RowMajor,IndexType>> gpu_entire_image_patch_row_major(gpu_data_entire_image_patch_row_major, patchRowMajorTensorRange); |
| gpu_entire_image_patch_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(3, 5); |
| sycl_device.memcpyDeviceToHost(entire_image_patch_row_major.data(), gpu_data_entire_image_patch_row_major, patchTensorBuffSize); |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(0), 3*5); |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(1), 5); |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(2), 3); |
| VERIFY_IS_EQUAL(entire_image_patch_row_major.dimension(3), 2); |
| |
| for (IndexType i = 0; i < 3; ++i) { |
| for (IndexType j = 0; j < 5; ++j) { |
| IndexType patchId = i+3*j; |
| for (IndexType r = 0; r < 3; ++r) { |
| for (IndexType c = 0; c < 5; ++c) { |
| for (IndexType d = 0; d < 2; ++d) { |
| DataType expected_col_major = 0.0f; |
| DataType expected_row_major = 0.0f; |
| if (r-1+i >= 0 && c-2+j >= 0 && r-1+i < 3 && c-2+j < 5) { |
| expected_col_major = tensor_col_major(d, r-1+i, c-2+j); |
| expected_row_major = tensor_row_major(c-2+j, r-1+i, d); |
| } |
| // ColMajor |
| if (entire_image_patch_col_major(d, r, c, patchId) != expected_col_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << std::endl; |
| } |
| VERIFY_IS_EQUAL(entire_image_patch_col_major(d, r, c, patchId), expected_col_major); |
| // RowMajor |
| if (entire_image_patch_row_major(patchId, c, r, d) != |
| expected_row_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << std::endl; |
| } |
| VERIFY_IS_EQUAL(entire_image_patch_row_major(patchId, c, r, d), |
| expected_row_major); |
| // Check that ColMajor and RowMajor agree. |
| VERIFY_IS_EQUAL(expected_col_major, expected_row_major); |
| } |
| } |
| } |
| } |
| } |
| |
| // 2D patch: ColMajor |
| patchColMajorTensorRange={{sizeDim1, 2, 2, sizeDim2*sizeDim3}}; |
| Tensor<DataType, 4, DataLayout,IndexType> twod_patch_col_major(patchColMajorTensorRange); |
| patchTensorBuffSize =twod_patch_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_twod_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_twod_patch_col_major(gpu_data_twod_patch_col_major, patchColMajorTensorRange); |
| gpu_twod_patch_col_major.device(sycl_device)=gpu_col_major.extract_image_patches(2, 2); |
| sycl_device.memcpyDeviceToHost(twod_patch_col_major.data(), gpu_data_twod_patch_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(0), 2); |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(1), 2); |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(2), 2); |
| VERIFY_IS_EQUAL(twod_patch_col_major.dimension(3), 3*5); |
| |
| // 2D patch: RowMajor |
| patchRowMajorTensorRange={{sizeDim2*sizeDim3, 2, 2, sizeDim1}}; |
| Tensor<DataType, 4, RowMajor,IndexType> twod_patch_row_major(patchRowMajorTensorRange); |
| patchTensorBuffSize =twod_patch_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_twod_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 4, RowMajor,IndexType>> gpu_twod_patch_row_major(gpu_data_twod_patch_row_major, patchRowMajorTensorRange); |
| gpu_twod_patch_row_major.device(sycl_device)=gpu_row_major.extract_image_patches(2, 2); |
| sycl_device.memcpyDeviceToHost(twod_patch_row_major.data(), gpu_data_twod_patch_row_major, patchTensorBuffSize); |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(0), 3*5); |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(1), 2); |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(2), 2); |
| VERIFY_IS_EQUAL(twod_patch_row_major.dimension(3), 2); |
| |
| // Based on the calculation described in TensorTraits.h, padding happens to be 0. |
| IndexType row_padding = 0; |
| IndexType col_padding = 0; |
| IndexType stride = 1; |
| |
| for (IndexType i = 0; i < 3; ++i) { |
| for (IndexType j = 0; j < 5; ++j) { |
| IndexType patchId = i+3*j; |
| for (IndexType r = 0; r < 2; ++r) { |
| for (IndexType c = 0; c < 2; ++c) { |
| for (IndexType d = 0; d < 2; ++d) { |
| DataType expected_col_major = 0.0f; |
| DataType expected_row_major = 0.0f; |
| IndexType row_offset = r*stride + i - row_padding; |
| IndexType col_offset = c*stride + j - col_padding; |
| // ColMajor |
| if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_col_major.dimension(1) && col_offset < tensor_col_major.dimension(2)) { |
| expected_col_major = tensor_col_major(d, row_offset, col_offset); |
| } |
| if (twod_patch_col_major(d, r, c, patchId) != expected_col_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << std::endl; |
| } |
| VERIFY_IS_EQUAL(twod_patch_col_major(d, r, c, patchId), expected_col_major); |
| // RowMajor |
| if (row_offset >= 0 && col_offset >= 0 && row_offset < tensor_row_major.dimension(1) && col_offset < tensor_row_major.dimension(0)) { |
| expected_row_major = tensor_row_major(col_offset, row_offset, d); |
| } |
| if (twod_patch_row_major(patchId, c, r, d) != expected_row_major) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << std::endl; |
| } |
| VERIFY_IS_EQUAL(twod_patch_row_major(patchId, c, r, d), expected_row_major); |
| // Check that ColMajor and RowMajor agree. |
| VERIFY_IS_EQUAL(expected_col_major, expected_row_major); |
| } |
| } |
| } |
| } |
| } |
| |
| sycl_device.deallocate(gpu_data_col_major); |
| sycl_device.deallocate(gpu_data_row_major); |
| sycl_device.deallocate(gpu_data_single_patch_col_major); |
| sycl_device.deallocate(gpu_data_single_patch_row_major); |
| sycl_device.deallocate(gpu_data_entire_image_patch_col_major); |
| sycl_device.deallocate(gpu_data_entire_image_patch_row_major); |
| sycl_device.deallocate(gpu_data_twod_patch_col_major); |
| sycl_device.deallocate(gpu_data_twod_patch_row_major); |
| } |
| |
| template <typename DataType, typename IndexType> |
| static void test_imagenet_patches_sycl(const Eigen::SyclDevice& sycl_device) |
| { |
| // Test the code on typical configurations used by the 'imagenet' benchmarks at |
| // https://github.com/soumith/convnet-benchmarks |
| // ColMajor |
| IndexType sizeDim1 = 3; |
| IndexType sizeDim2 = 128; |
| IndexType sizeDim3 = 128; |
| IndexType sizeDim4 = 16; |
| array<IndexType, 4> tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; |
| Tensor<DataType, 4, DataLayout,IndexType> l_in_col_major(tensorColMajorRange); |
| l_in_col_major.setRandom(); |
| |
| DataType* gpu_data_l_in_col_major = static_cast<DataType*>(sycl_device.allocate(l_in_col_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 4, ColMajor, IndexType>> gpu_l_in_col_major(gpu_data_l_in_col_major, tensorColMajorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_data_l_in_col_major, l_in_col_major.data(),(l_in_col_major.size())*sizeof(DataType)); |
| |
| array<IndexType, 5> patchTensorRange={{sizeDim1, 11, 11, sizeDim2*sizeDim3, sizeDim4}}; |
| Tensor<DataType, 5, DataLayout,IndexType> l_out_col_major(patchTensorRange); |
| size_t patchTensorBuffSize =l_out_col_major.size()*sizeof(DataType); |
| DataType* gpu_data_l_out_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_l_out_col_major(gpu_data_l_out_col_major, patchTensorRange); |
| gpu_l_out_col_major.device(sycl_device)=gpu_l_in_col_major.extract_image_patches(11, 11); |
| sycl_device.memcpyDeviceToHost(l_out_col_major.data(), gpu_data_l_out_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(0), sizeDim1); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(1), 11); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(2), 11); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(3), sizeDim2*sizeDim3); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(4), sizeDim4); |
| |
| // RowMajor |
| patchTensorRange={{sizeDim4, sizeDim2*sizeDim3, 11, 11, sizeDim1}}; |
| Tensor<DataType, 5, RowMajor,IndexType> l_out_row_major(patchTensorRange); |
| patchTensorBuffSize =l_out_row_major.size()*sizeof(DataType); |
| DataType* gpu_data_l_out_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>> gpu_l_out_row_major(gpu_data_l_out_row_major, patchTensorRange); |
| gpu_l_out_row_major.device(sycl_device)=gpu_l_in_col_major.swap_layout().extract_image_patches(11, 11); |
| sycl_device.memcpyDeviceToHost(l_out_row_major.data(), gpu_data_l_out_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(0), sizeDim4); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(1), sizeDim2*sizeDim3); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 11); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 11); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(4), sizeDim1); |
| |
| for (IndexType b = 0; b < 16; ++b) { |
| for (IndexType i = 0; i < 128; ++i) { |
| for (IndexType j = 0; j < 128; ++j) { |
| IndexType patchId = i+128*j; |
| for (IndexType c = 0; c < 11; ++c) { |
| for (IndexType r = 0; r < 11; ++r) { |
| for (IndexType d = 0; d < 3; ++d) { |
| DataType expected = 0.0f; |
| if (r-5+i >= 0 && c-5+j >= 0 && r-5+i < 128 && c-5+j < 128) { |
| expected = l_in_col_major(d, r-5+i, c-5+j, b); |
| } |
| // ColMajor |
| if (l_out_col_major(d, r, c, patchId, b) != expected) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(l_out_col_major(d, r, c, patchId, b), expected); |
| // RowMajor |
| if (l_out_row_major(b, patchId, c, r, d) != |
| expected) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j |
| << " r=" << r << " c=" << c << " d=" << d << " b=" << b |
| << std::endl; |
| } |
| VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), |
| expected); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // ColMajor |
| sycl_device.deallocate(gpu_data_l_in_col_major); |
| sycl_device.deallocate(gpu_data_l_out_col_major); |
| sizeDim1 = 16; |
| sizeDim2 = 64; |
| sizeDim3 = 64; |
| sizeDim4 = 32; |
| tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; |
| l_in_col_major.resize(tensorColMajorRange); |
| l_in_col_major.setRandom(); |
| gpu_data_l_in_col_major = static_cast<DataType*>(sycl_device.allocate(l_in_col_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 4, ColMajor, IndexType>>gpu_l_in_col_major_resize1(gpu_data_l_in_col_major, tensorColMajorRange); |
| |
| patchTensorRange={{sizeDim1, 9, 9, sizeDim2*sizeDim3, sizeDim4}}; |
| l_out_col_major.resize(patchTensorRange); |
| patchTensorBuffSize =l_out_col_major.size()*sizeof(DataType); |
| gpu_data_l_out_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>>gpu_l_out_col_major_resize1(gpu_data_l_out_col_major, patchTensorRange); |
| sycl_device.memcpyHostToDevice(gpu_data_l_in_col_major, l_in_col_major.data(),(l_in_col_major.size())*sizeof(DataType)); |
| gpu_l_out_col_major_resize1.device(sycl_device)=gpu_l_in_col_major_resize1.extract_image_patches(9, 9); |
| sycl_device.memcpyDeviceToHost(l_out_col_major.data(), gpu_data_l_out_col_major, patchTensorBuffSize); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(0), 16); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(1), 9); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(2), 9); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(3), 64*64); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(4), 32); |
| |
| // RowMajor |
| sycl_device.deallocate(gpu_data_l_out_row_major); |
| patchTensorRange={{sizeDim4, sizeDim2*sizeDim3, 9, 9 ,sizeDim1}}; |
| l_out_row_major.resize(patchTensorRange); |
| patchTensorBuffSize =l_out_row_major.size()*sizeof(DataType); |
| gpu_data_l_out_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>>gpu_l_out_row_major_resize1(gpu_data_l_out_row_major, patchTensorRange); |
| gpu_l_out_row_major_resize1.device(sycl_device)=gpu_l_in_col_major_resize1.swap_layout().extract_image_patches(9, 9); |
| sycl_device.memcpyDeviceToHost(l_out_row_major.data(), gpu_data_l_out_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 64*64); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 9); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 9); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 16); |
| |
| for (IndexType b = 0; b < 32; ++b) { |
| for (IndexType i = 0; i < 64; ++i) { |
| for (IndexType j = 0; j < 64; ++j) { |
| IndexType patchId = i+64*j; |
| for (IndexType c = 0; c < 9; ++c) { |
| for (IndexType r = 0; r < 9; ++r) { |
| for (IndexType d = 0; d < 16; ++d) { |
| DataType expected = 0.0f; |
| if (r-4+i >= 0 && c-4+j >= 0 && r-4+i < 64 && c-4+j < 64) { |
| expected = l_in_col_major(d, r-4+i, c-4+j, b); |
| } |
| // ColMajor |
| if (l_out_col_major(d, r, c, patchId, b) != expected) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(l_out_col_major(d, r, c, patchId, b), expected); |
| // RowMajor |
| if (l_out_row_major(b, patchId, c, r, d) != expected) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // ColMajor |
| |
| sycl_device.deallocate(gpu_data_l_in_col_major); |
| sycl_device.deallocate(gpu_data_l_out_col_major); |
| sizeDim1 = 32; |
| sizeDim2 = 16; |
| sizeDim3 = 16; |
| sizeDim4 = 32; |
| tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; |
| l_in_col_major.resize(tensorColMajorRange); |
| l_in_col_major.setRandom(); |
| gpu_data_l_in_col_major = static_cast<DataType*>(sycl_device.allocate(l_in_col_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 4, ColMajor, IndexType>>gpu_l_in_col_major_resize2(gpu_data_l_in_col_major, tensorColMajorRange); |
| |
| patchTensorRange={{sizeDim1, 7, 7, sizeDim2*sizeDim3, sizeDim4}}; |
| l_out_col_major.resize(patchTensorRange); |
| patchTensorBuffSize =l_out_col_major.size()*sizeof(DataType); |
| gpu_data_l_out_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>>gpu_l_out_col_major_resize2(gpu_data_l_out_col_major, patchTensorRange); |
| sycl_device.memcpyHostToDevice(gpu_data_l_in_col_major, l_in_col_major.data(),(l_in_col_major.size())*sizeof(DataType)); |
| gpu_l_out_col_major_resize2.device(sycl_device)=gpu_l_in_col_major_resize2.extract_image_patches(7, 7); |
| sycl_device.memcpyDeviceToHost(l_out_col_major.data(), gpu_data_l_out_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(0), 32); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(1), 7); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(2), 7); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(3), 16*16); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(4), 32); |
| |
| // RowMajor |
| sycl_device.deallocate(gpu_data_l_out_row_major); |
| patchTensorRange={{sizeDim4, sizeDim2*sizeDim3, 7, 7 ,sizeDim1}}; |
| l_out_row_major.resize(patchTensorRange); |
| patchTensorBuffSize =l_out_row_major.size()*sizeof(DataType); |
| gpu_data_l_out_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>>gpu_l_out_row_major_resize2(gpu_data_l_out_row_major, patchTensorRange); |
| gpu_l_out_row_major_resize2.device(sycl_device)=gpu_l_in_col_major_resize2.swap_layout().extract_image_patches(7, 7); |
| sycl_device.memcpyDeviceToHost(l_out_row_major.data(), gpu_data_l_out_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 16*16); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 7); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 7); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 32); |
| |
| for (IndexType b = 0; b < 32; ++b) { |
| for (IndexType i = 0; i < 16; ++i) { |
| for (IndexType j = 0; j < 16; ++j) { |
| IndexType patchId = i+16*j; |
| for (IndexType c = 0; c < 7; ++c) { |
| for (IndexType r = 0; r < 7; ++r) { |
| for (IndexType d = 0; d < 32; ++d) { |
| DataType expected = 0.0f; |
| if (r-3+i >= 0 && c-3+j >= 0 && r-3+i < 16 && c-3+j < 16) { |
| expected = l_in_col_major(d, r-3+i, c-3+j, b); |
| } |
| // ColMajor |
| if (l_out_col_major(d, r, c, patchId, b) != expected) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(l_out_col_major(d, r, c, patchId, b), expected); |
| // RowMajor |
| if (l_out_row_major(b, patchId, c, r, d) != expected) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // ColMajor |
| sycl_device.deallocate(gpu_data_l_in_col_major); |
| sycl_device.deallocate(gpu_data_l_out_col_major); |
| sizeDim1 = 64; |
| sizeDim2 = 13; |
| sizeDim3 = 13; |
| sizeDim4 = 32; |
| tensorColMajorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; |
| l_in_col_major.resize(tensorColMajorRange); |
| l_in_col_major.setRandom(); |
| gpu_data_l_in_col_major = static_cast<DataType*>(sycl_device.allocate(l_in_col_major.size()*sizeof(DataType))); |
| TensorMap<Tensor<DataType, 4, ColMajor, IndexType>>gpu_l_in_col_major_resize3(gpu_data_l_in_col_major, tensorColMajorRange); |
| |
| patchTensorRange={{sizeDim1, 3, 3, sizeDim2*sizeDim3, sizeDim4}}; |
| l_out_col_major.resize(patchTensorRange); |
| patchTensorBuffSize =l_out_col_major.size()*sizeof(DataType); |
| gpu_data_l_out_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout,IndexType>>gpu_l_out_col_major_resize3(gpu_data_l_out_col_major, patchTensorRange); |
| sycl_device.memcpyHostToDevice(gpu_data_l_in_col_major, l_in_col_major.data(),(l_in_col_major.size())*sizeof(DataType)); |
| gpu_l_out_col_major_resize3.device(sycl_device)=gpu_l_in_col_major_resize3.extract_image_patches(3, 3); |
| sycl_device.memcpyDeviceToHost(l_out_col_major.data(), gpu_data_l_out_col_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(0), 64); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(1), 3); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(2), 3); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(3), 13*13); |
| VERIFY_IS_EQUAL(l_out_col_major.dimension(4), 32); |
| |
| // RowMajor |
| sycl_device.deallocate(gpu_data_l_out_row_major); |
| patchTensorRange={{sizeDim4, sizeDim2*sizeDim3, 3, 3 ,sizeDim1}}; |
| l_out_row_major.resize(patchTensorRange); |
| patchTensorBuffSize =l_out_row_major.size()*sizeof(DataType); |
| gpu_data_l_out_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, RowMajor,IndexType>>gpu_l_out_row_major_resize3(gpu_data_l_out_row_major, patchTensorRange); |
| gpu_l_out_row_major_resize3.device(sycl_device)=gpu_l_in_col_major_resize3.swap_layout().extract_image_patches(3, 3); |
| sycl_device.memcpyDeviceToHost(l_out_row_major.data(), gpu_data_l_out_row_major, patchTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(0), 32); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(1), 13*13); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(2), 3); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(3), 3); |
| VERIFY_IS_EQUAL(l_out_row_major.dimension(4), 64); |
| |
| for (IndexType b = 0; b < 32; ++b) { |
| for (IndexType i = 0; i < 13; ++i) { |
| for (IndexType j = 0; j < 13; ++j) { |
| IndexType patchId = i+13*j; |
| for (IndexType c = 0; c < 3; ++c) { |
| for (IndexType r = 0; r < 3; ++r) { |
| for (IndexType d = 0; d < 64; ++d) { |
| DataType expected = 0.0f; |
| if (r-1+i >= 0 && c-1+j >= 0 && r-1+i < 13 && c-1+j < 13) { |
| expected = l_in_col_major(d, r-1+i, c-1+j, b); |
| } |
| // ColMajor |
| if (l_out_col_major(d, r, c, patchId, b) != expected) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(l_out_col_major(d, r, c, patchId, b), expected); |
| // RowMajor |
| if (l_out_row_major(b, patchId, c, r, d) != expected) { |
| std::cout << "Mismatch detected at index i=" << i << " j=" << j << " r=" << r << " c=" << c << " d=" << d << " b=" << b << std::endl; |
| } |
| VERIFY_IS_EQUAL(l_out_row_major(b, patchId, c, r, d), expected); |
| } |
| } |
| } |
| } |
| } |
| } |
| sycl_device.deallocate(gpu_data_l_in_col_major); |
| sycl_device.deallocate(gpu_data_l_out_col_major); |
| sycl_device.deallocate(gpu_data_l_out_row_major); |
| } |
| |
| |
| template<typename DataType, typename dev_Selector> void sycl_tensor_image_patch_test_per_device(dev_Selector s){ |
| QueueInterface queueInterface(s); |
| auto sycl_device = Eigen::SyclDevice(&queueInterface); |
| test_simple_image_patch_sycl<DataType, int64_t>(sycl_device); |
| test_patch_padding_valid_sycl<DataType, int64_t>(sycl_device); |
| test_patch_padding_valid_same_value_sycl<DataType, int64_t>(sycl_device); |
| test_patch_padding_same_sycl<DataType, int64_t>(sycl_device); |
| test_patch_no_extra_dim_sycl<DataType, int64_t>(sycl_device); |
| test_imagenet_patches_sycl<DataType, int64_t>(sycl_device); |
| } |
| EIGEN_DECLARE_TEST(cxx11_tensor_image_patch_sycl) |
| { |
| for (const auto& device :Eigen::get_sycl_supported_devices()) { |
| CALL_SUBTEST(sycl_tensor_image_patch_test_per_device<float>(device)); |
| } |
| } |
