/frameworks/ml/nn/tools/test_generator/tests/P_vts_operands/ |
stdout.txt.expect | 7 .dimensions = {3,4}, 16 .dimensions = {3,4}, 25 .dimensions = {3,4}, 34 .dimensions = {1, 2, 3}, 43 .dimensions = {}, 52 .dimensions = {3,4}, 61 .dimensions = {3,4},
|
/frameworks/ml/nn/common/ |
Utils.cpp | 203 uint32_t sizeOfData(OperandType type, const std::vector<uint32_t>& dimensions) { 212 for (auto d : dimensions) { 258 // Validates the type. The used dimensions can be underspecified. 263 if (type.dimensions[i] == 0) { 264 LOG(ERROR) << tag << " OperandType invalid dimensions[" << i 265 << "] = " << type.dimensions[i]; 318 if (!validOperandIndexes(operand.dimensions, mDimensions)) { 399 argument.dimensions.size() != 0) { 411 uint32_t rank = argument.dimensions.size(); 413 if (rank != operand.dimensions.size()) [all...] |
/external/eigen/unsupported/test/ |
cxx11_tensor_reduction_sycl.cpp | 39 float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); 45 sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); 74 float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); 75 float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); 80 sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); 82 sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); 112 float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); 113 float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); 118 sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); 120 sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)) [all...] |
/external/eigen/unsupported/Eigen/CXX11/src/Tensor/ |
TensorConvolution.h | 30 array<Index, NumDims> dimensions = input_dims; local 36 dimensions[index] = result_dim; 46 outputStrides[i] = outputStrides[i-1] * dimensions[i-1]; 53 outputStrides[i] = outputStrides[i + 1] * dimensions[i + 1]; 59 array<Index, NumDims> tmp = dimensions; 69 cudaOutputDimensions[index] = dimensions[indices[i]]; 79 cudaOutputDimensions[written] = dimensions[i]; 218 template<typename Dimensions, typename InputXprType, typename KernelXprType> 219 struct traits<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> > 240 template<typename Dimensions, typename InputXprType, typename KernelXprType 375 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } function in struct:Eigen::TensorEvaluator 795 EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_dimensions; } function in struct:Eigen::TensorEvaluator [all...] |
TensorSyclExtractFunctors.h | 37 typedef typename Evaluator::Dimensions Dimensions; 38 const Dimensions m_dimensions; 39 const Dimensions& dimensions() const { return m_dimensions; } function in struct:Eigen::TensorSycl::internal::FunctorExtractor 41 : m_dimensions(expr.dimensions()) {} 157 typedef typename Eigen::internal::conditional<Evaluator::NumOutputDims==0, DSizes<typename Evaluator::Index, 1>, typename Evaluator::Dimensions >::type Dimensions; 158 const Dimensions m_dimensions; 159 const Dimensions& dimensions() const { return m_dimensions; function in struct:Eigen::TensorSycl::internal::FunctorExtractor [all...] |
TensorConcatenation.h | 114 static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value; 115 static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value; 116 typedef DSizes<Index, NumDims> Dimensions; 135 const Dimensions& lhs_dims = m_leftImpl.dimensions(); 136 const Dimensions& rhs_dims = m_rightImpl.dimensions(); 177 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } function in struct:Eigen::TensorEvaluator 213 const Dimensions& left_dims = m_leftImpl.dimensions() [all...] |
/developers/build/prebuilts/gradle/HdrViewfinder/Application/src/main/java/com/example/android/hdrviewfinder/ |
ViewfinderProcessor.java | 50 public ViewfinderProcessor(RenderScript rs, Size dimensions) { 52 yuvTypeBuilder.setX(dimensions.getWidth()); 53 yuvTypeBuilder.setY(dimensions.getHeight()); 61 rgbTypeBuilder.setX(dimensions.getWidth()); 62 rgbTypeBuilder.setY(dimensions.getHeight()); 76 mHdrTask = new ProcessingTask(mInputHdrAllocation, dimensions.getWidth()/2, true);
|
/developers/samples/android/media/HdrViewfinder/Application/src/main/java/com/example/android/hdrviewfinder/ |
ViewfinderProcessor.java | 50 public ViewfinderProcessor(RenderScript rs, Size dimensions) { 52 yuvTypeBuilder.setX(dimensions.getWidth()); 53 yuvTypeBuilder.setY(dimensions.getHeight()); 61 rgbTypeBuilder.setX(dimensions.getWidth()); 62 rgbTypeBuilder.setY(dimensions.getHeight()); 76 mHdrTask = new ProcessingTask(mInputHdrAllocation, dimensions.getWidth()/2, true);
|
/frameworks/layoutlib/bridge/src/android/graphics/ |
RoundRectangle.java | 64 assert cornerDimensions.length == 8 : "The array of corner dimensions must have eight " + 72 float[] dimensions = cornerDimensions.clone(); local 74 for (int i = 0; i < dimensions.length; i += 2) { 75 if (dimensions[i] < 0 || dimensions[i + 1] < 0) { 76 dimensions[i] = 0; 77 dimensions[i + 1] = 0; 81 double topCornerWidth = (dimensions[0] + dimensions[2]) / 2d; 82 double bottomCornerWidth = (dimensions[4] + dimensions[6]) / 2d [all...] |
/external/glide/library/src/main/java/com/bumptech/glide/load/resource/gif/ |
GifDrawableTransformation.java | 28 // to end up with the right dimensions. Since our transformations may arbitrarily modify the dimensions of 30 // transformed dimensions will be so that our drawable can report the correct intrinsic width and height.
|
/external/skia/src/core/ |
SkColorLookUpTable.h | 33 * with fInputChannels input dimensions and kOutputChannels output dimensions. 55 * Performs tetrahedral interpolation with 3 input and 3 output dimensions.
|
SkLinearBitmapPipeline.h | 68 PointProcessorInterface* createTiler(SampleProcessorInterface* next, SkISize dimensions, 73 SampleProcessorInterface* next, SkShader::TileMode yMode, SkISize dimensions, 78 SkISize dimensions,
|
/frameworks/ml/nn/common/operations/ |
HashtableLookup.cpp | 45 const int num_rows = value_->shape().dimensions[0]; 46 const int row_bytes = sizeOfData(value_->type, value_->dimensions) / num_rows; 49 for (int i = 0; i < static_cast<int>(lookup_->shape().dimensions[0]); i++) {
|
/frameworks/ml/nn/runtime/test/generated/vts_models/ |
depth_to_space_float_1.model.cpp | 7 .dimensions = {1, 1, 1, 8}, 16 .dimensions = {}, 25 .dimensions = {1, 2, 2, 2},
|
depth_to_space_float_2.model.cpp | 7 .dimensions = {1, 2, 2, 4}, 16 .dimensions = {}, 25 .dimensions = {1, 4, 4, 1},
|
depth_to_space_float_3.model.cpp | 7 .dimensions = {1, 2, 2, 8}, 16 .dimensions = {}, 25 .dimensions = {1, 4, 4, 2},
|
depth_to_space_quant8_1.model.cpp | 7 .dimensions = {1, 1, 1, 8}, 16 .dimensions = {}, 25 .dimensions = {1, 2, 2, 2},
|
depth_to_space_quant8_2.model.cpp | 7 .dimensions = {1, 2, 2, 4}, 16 .dimensions = {}, 25 .dimensions = {1, 4, 4, 1},
|
embedding_lookup.model.cpp | 7 .dimensions = {3}, 16 .dimensions = {3, 2, 4}, 25 .dimensions = {3, 2, 4},
|
reshape.model.cpp | 7 .dimensions = {1, 1, 3, 3}, 16 .dimensions = {1}, 25 .dimensions = {9},
|
reshape_quant8.model.cpp | 7 .dimensions = {1, 1, 3, 3}, 16 .dimensions = {1}, 25 .dimensions = {9},
|
reshape_quant8_weights_as_inputs.model.cpp | 7 .dimensions = {1, 1, 3, 3}, 16 .dimensions = {1}, 25 .dimensions = {9},
|
reshape_weights_as_inputs.model.cpp | 7 .dimensions = {1, 1, 3, 3}, 16 .dimensions = {1}, 25 .dimensions = {9},
|
softmax_float_1.model.cpp | 7 .dimensions = {1, 4}, 16 .dimensions = {}, 25 .dimensions = {1, 4},
|
softmax_float_2.model.cpp | 7 .dimensions = {2, 5}, 16 .dimensions = {}, 25 .dimensions = {2, 5},
|