| /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},
|
