Compute Library
 22.05
ClWinogradInputTransformKernel Class Reference

OpenCL kernel to perform Winograd input transform. More...

#include <ClWinogradInputTransformKernel.h>

Collaboration diagram for ClWinogradInputTransformKernel:
[legend]

Public Member Functions

 ClWinogradInputTransformKernel ()
 
 ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE (ClWinogradInputTransformKernel)
 
void configure (const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const WinogradInfo &winograd_info)
 Set the input and output of the kernel. More...
 
void run_op (ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override
 Enqueue the OpenCL kernel to process the given window on the passed OpenCL command queue. More...
 
BorderSize border_size () const override
 The size of the border for that kernel. More...
 
- Public Member Functions inherited from ICLKernel
 ICLKernel ()
 Constructor. More...
 
cl::Kernel & kernel ()
 Returns a reference to the OpenCL kernel of this object. More...
 
CLKernelType type () const
 Returns the CL kernel type. More...
 
template<typename T >
void add_1D_array_argument (unsigned int &idx, const ICLArray< T > *array, const Strides &strides, unsigned int num_dimensions, const Window &window)
 Add the passed 1D array's parameters to the object's kernel's arguments starting from the index idx. More...
 
void add_1D_tensor_argument (unsigned int &idx, const ICLTensor *tensor, const Window &window)
 Add the passed 1D tensor's parameters to the object's kernel's arguments starting from the index idx. More...
 
void add_1D_tensor_argument_if (bool cond, unsigned int &idx, const ICLTensor *tensor, const Window &window)
 Add the passed 1D tensor's parameters to the object's kernel's arguments starting from the index idx if the condition is true. More...
 
void add_2D_tensor_argument (unsigned int &idx, const ICLTensor *tensor, const Window &window)
 Add the passed 2D tensor's parameters to the object's kernel's arguments starting from the index idx. More...
 
void add_2D_tensor_argument_if (bool cond, unsigned int &idx, const ICLTensor *tensor, const Window &window)
 Add the passed 2D tensor's parameters to the object's kernel's arguments starting from the index idx if the condition is true. More...
 
void add_3D_tensor_argument (unsigned int &idx, const ICLTensor *tensor, const Window &window)
 Add the passed 3D tensor's parameters to the object's kernel's arguments starting from the index idx. More...
 
void add_4D_tensor_argument (unsigned int &idx, const ICLTensor *tensor, const Window &window)
 Add the passed 4D tensor's parameters to the object's kernel's arguments starting from the index idx. More...
 
void add_5D_tensor_argument (unsigned int &idx, const ICLTensor *tensor, const Window &window)
 Add the passed 5D tensor's parameters to the object's kernel's arguments starting from the index idx. More...
 
void add_3d_tensor_nhw_argument (unsigned int &idx, const ICLTensor *tensor)
 Add the passed NHW 3D tensor's parameters to the object's kernel's arguments by passing strides, dimensions and the offset to the first valid element in bytes. More...
 
void add_4d_tensor_nhwc_argument (unsigned int &idx, const ICLTensor *tensor)
 Add the passed NHWC 4D tensor's parameters to the object's kernel's arguments by passing strides, dimensions and the offset to the first valid element in bytes. More...
 
virtual void run (const Window &window, cl::CommandQueue &queue)
 Enqueue the OpenCL kernel to process the given window on the passed OpenCL command queue. More...
 
virtual void run_composite_op (ITensorPack &tensors, const Window &window, cl::CommandQueue &queue, const experimental::dynamic_fusion::ClExecutionDescriptor &exec_desc)
 The execution is carried out through run_op method. But the run_op method needs to be extended to include ClExecutionDescriptor as now LWS GWS tuning will be separated from the IKernel. More...
 
template<typename T >
void add_argument (unsigned int &idx, T value)
 Add the passed parameters to the object's kernel's arguments starting from the index idx. More...
 
void set_lws_hint (const cl::NDRange &lws_hint)
 Set the Local-Workgroup-Size hint. More...
 
cl::NDRange lws_hint () const
 Return the Local-Workgroup-Size hint. More...
 
void set_wbsm_hint (const cl_int &wbsm_hint)
 Set the workgroup batch size modifier hint. More...
 
cl_int wbsm_hint () const
 Return the workgroup batch size modifier hint. More...
 
const std::string & config_id () const
 Get the configuration ID. More...
 
void set_target (GPUTarget target)
 Set the targeted GPU architecture. More...
 
void set_target (cl::Device &device)
 Set the targeted GPU architecture according to the CL device. More...
 
GPUTarget get_target () const
 Get the targeted GPU architecture. More...
 
size_t get_max_workgroup_size ()
 Get the maximum workgroup size for the device the CLKernelLibrary uses. More...
 
template<unsigned int dimension_size>
void add_tensor_argument (unsigned &idx, const ICLTensor *tensor, const Window &window)
 
template<typename T , unsigned int dimension_size>
void add_array_argument (unsigned &idx, const ICLArray< T > *array, const Strides &strides, unsigned int num_dimensions, const Window &window)
 Add the passed array's parameters to the object's kernel's arguments starting from the index idx. More...
 
- Public Member Functions inherited from IKernel
 IKernel ()
 Constructor. More...
 
virtual ~IKernel ()=default
 Destructor. More...
 
virtual bool is_parallelisable () const
 Indicates whether or not the kernel is parallelisable. More...
 
const Windowwindow () const
 The maximum window the kernel can be executed on. More...
 
bool is_window_configured () const
 Function to check if the embedded window of this kernel has been configured. More...
 

Static Public Member Functions

static Status validate (const ITensorInfo *src, const ITensorInfo *dst, const WinogradInfo &winograd_info)
 Static function to check if given info will lead to a valid configuration. More...
 
- Static Public Member Functions inherited from ICLKernel
static constexpr unsigned int num_arguments_per_3d_tensor_nhw ()
 Returns the number of arguments enqueued per NHW 3D Tensor object. More...
 
static constexpr unsigned int num_arguments_per_4d_tensor_nhwc ()
 Returns the number of arguments enqueued per NHWC 4D Tensor object. More...
 
static constexpr unsigned int num_arguments_per_1D_array ()
 Returns the number of arguments enqueued per 1D array object. More...
 
static constexpr unsigned int num_arguments_per_1D_tensor ()
 Returns the number of arguments enqueued per 1D tensor object. More...
 
static constexpr unsigned int num_arguments_per_2D_tensor ()
 Returns the number of arguments enqueued per 2D tensor object. More...
 
static constexpr unsigned int num_arguments_per_3D_tensor ()
 Returns the number of arguments enqueued per 3D tensor object. More...
 
static constexpr unsigned int num_arguments_per_4D_tensor ()
 Returns the number of arguments enqueued per 4D tensor object. More...
 
static cl::NDRange gws_from_window (const Window &window)
 Get the global work size given an execution window. More...
 

Detailed Description

OpenCL kernel to perform Winograd input transform.

Definition at line 39 of file ClWinogradInputTransformKernel.h.

Constructor & Destructor Documentation

◆ ClWinogradInputTransformKernel()

Definition at line 103 of file ClWinogradInputTransformKernel.cpp.

References arm_compute::WINOGRAD.

104 {
105  _type = CLKernelType::WINOGRAD;
106 }
Convolution using Winograd.

Member Function Documentation

◆ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE()

ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE ( ClWinogradInputTransformKernel  )

◆ border_size()

BorderSize border_size ( ) const
overridevirtual

The size of the border for that kernel.

Returns
The width in number of elements of the border.

Reimplemented from IKernel.

Definition at line 108 of file ClWinogradInputTransformKernel.cpp.

109 {
110  return _border_size;
111 }

◆ configure()

void configure ( const ClCompileContext compile_context,
ITensorInfo src,
ITensorInfo dst,
const WinogradInfo winograd_info 
)

Set the input and output of the kernel.

Note
Winograd input transform supports the following configurations for NCWH data layout F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3), F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3), F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
Winograd input transform supports the following configurations for NHWC data layout F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3), F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)

Strides: only unit strides

Parameters
[in]compile_contextThe compile context to be used.
[in]srcThe input tensor info to transform. Data types supported: F16/F32
[in]dstThe output tensor info. The shape for this tensor can be calculated using the utility function compute_winograd_input_transform_shape. Data types supported: Same as input
[in]winograd_infoContains Winograd's information described in WinogradInfo.

Definition at line 113 of file ClWinogradInputTransformKernel.cpp.

References CLBuildOptions::add_option(), CLBuildOptions::add_option_if(), ARM_COMPUTE_ERROR_ON, ARM_COMPUTE_ERROR_ON_NULLPTR, ARM_COMPUTE_ERROR_THROW_ON, arm_compute::auto_init_if_empty(), ICloneable< T >::clone(), arm_compute::compute_winograd_convolution_tiles(), arm_compute::misc::shape_calculator::compute_winograd_input_transform_shape(), WinogradInfo::convolution_info, arm_compute::create_kernel(), ITensorInfo::data_layout(), ITensorInfo::data_type(), ITensorInfo::dimension(), arm_compute::get_cl_type_from_data_type(), arm_compute::get_data_layout_dimension_index(), arm_compute::get_padding_info(), arm_compute::has_padding_changed(), Size2D::height, arm_compute::HEIGHT, kernel_name, WinogradInfo::kernel_size, arm_compute::lower_string(), arm_compute::NCHW, arm_compute::NHWC, CLBuildOptions::options(), arm_compute::test::validation::output_shape, WinogradInfo::output_tile_size, ITensorInfo::padding(), arm_compute::test::validation::src, arm_compute::string_from_data_layout(), ITensorInfo::tensor_shape(), Size2D::to_string(), arm_compute::support::cpp11::to_string(), TensorShape::total_size_upper(), arm_compute::upper_string(), arm_compute::cpu::kernels::validate_and_configure_window(), arm_compute::cpu::kernels::validate_arguments(), Size2D::width, and arm_compute::WIDTH.

114 {
117 
118  auto padding_info = get_padding_info({ src, dst });
119 
120  const PadStrideInfo conv_info = winograd_info.convolution_info;
121  const Size2D output_tile_size = winograd_info.output_tile_size;
122  const Size2D kernel_size = winograd_info.kernel_size;
123 
124  _data_layout = src->data_layout();
125 
126  const size_t idx_w = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
127  const size_t idx_h = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
128 
129  // Compute the number of output tiles along the x and y direction of size "output_tile_size"
130  const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(src->dimension(idx_w), src->dimension(idx_h)),
131  kernel_size,
132  output_tile_size,
133  conv_info);
134 
135  _num_tiles_x = num_tiles.width;
136  _num_tiles_y = num_tiles.height;
137 
139 
140  // Output auto initialization if not yet initialized
141  auto_init_if_empty(*dst, src->clone()->set_tensor_shape(output_shape));
142 
143  ARM_COMPUTE_ERROR_ON(_num_tiles_x * _num_tiles_y != static_cast<int>(dst->dimension(1)));
144  const size_t total_batches = src->tensor_shape().total_size_upper(3);
145 
146  CLBuildOptions build_opts;
147  if(_data_layout == DataLayout::NHWC)
148  {
149  build_opts.add_option("-DNHWC");
150  _src_width = src->dimension(idx_w);
151  _src_height = src->dimension(idx_h);
152  build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
153  build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
154  build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
155  build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
156  build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src->data_type()));
157  build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
158  build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
159  }
160  else
161  {
162  build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(_num_tiles_x));
163  build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
164  build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
165  build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
166  build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
167  build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src->data_type()));
168  build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
169  build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
170  build_opts.add_option_if(total_batches > 1, "-DSRC_DEPTH=" + support::cpp11::to_string(src->dimension(2)));
171  }
172 
173  // Create kernel
174  std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string();
175 
176  // Get the maximum dimension from the tile size
177  const unsigned int tile_max_dim = std::max(output_tile_size.width, output_tile_size.height);
178 
179  // Check optimized kernel if output_dims == 2x2
180  if((tile_max_dim == 2) && (_data_layout == DataLayout::NCHW))
181  {
182  _step_z = (src->dimension(2) % 2) != 0 ? 1 : 2;
183  }
184 
185  // Append stepz and data layout
186  kernel_name += "_stepz";
187  kernel_name += support::cpp11::to_string(_step_z);
188  kernel_name += "_" + lower_string(string_from_data_layout(_data_layout));
189 
190  // A macro guard to compile ONLY the kernel of interest
191  build_opts.add_option("-D" + upper_string(kernel_name));
192  _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
193 
194  // Create window and update padding
195  auto win_config = validate_and_configure_window(src, dst, winograd_info);
196  ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
197  IClKernel::configure_internal(win_config.second, cl::NDRange(1, 1, 8));
198 
199  _border_size = BorderSize(src->padding());
200 
201  ARM_COMPUTE_ERROR_ON((src->data_layout() == DataLayout::NHWC) && has_padding_changed(padding_info));
202 
203  _config_id = kernel_name;
204  _config_id += support::cpp11::to_string(src->dimension(0));
205  _config_id += "_";
206  _config_id += support::cpp11::to_string(src->dimension(1));
207  _config_id += "_";
208  _config_id += support::cpp11::to_string(src->dimension(2));
209  _config_id += "_";
210  _config_id += support::cpp11::to_string(conv_info.pad_left());
211  _config_id += "_";
212  _config_id += support::cpp11::to_string(conv_info.pad_top());
213  _config_id += "_";
214  _config_id += lower_string(string_from_data_layout(_data_layout));
215 }
TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)
Calculate the winograd input transform shape.
std::string to_string(T &&value)
Convert integer and float values to string.
#define ARM_COMPUTE_ERROR_ON(cond)
If the condition is true then an error message is printed and an exception thrown.
Definition: Error.h:466
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, const Size2D &kernel_size, const Size2D &output_tile_size, const PadStrideInfo &conv_info)
Calculate the number of output tiles required by Winograd Convolution layer.
Definition: Helpers.h:227
std::string lower_string(const std::string &val)
Lower a given string.
Definition: Utils.cpp:351
Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info)
SimpleTensor< float > src
Definition: DFT.cpp:155
std::string upper_string(const std::string &val)
Raise a given string to upper case.
Definition: Utils.cpp:358
cl::Kernel create_kernel(const CLCompileContext &ctx, const std::string &kernel_name, const std::set< std::string > &build_opts=std::set< std::string >())
Creates an opencl kernel using a compile context.
Definition: CLHelpers.cpp:391
std::string get_cl_type_from_data_type(const DataType &dt)
Translates a tensor data type to the appropriate OpenCL type.
Definition: CLHelpers.cpp:39
bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())
Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...
bool has_padding_changed(const std::unordered_map< const ITensorInfo *, PaddingSize > &padding_map)
Check if the previously stored padding info has changed after configuring a kernel.
Definition: Utils.cpp:601
Num samples, channels, height, width.
std::pair< Status, Window > validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst)
const std::string & string_from_data_layout(DataLayout dl)
Convert a data layout identity into a string.
Definition: Utils.cpp:123
size_t get_data_layout_dimension_index(const DataLayout &data_layout, const DataLayoutDimension &data_layout_dimension)
Get the index of the given dimension.
Definition: Helpers.inl:193
Num samples, height, width, channels.
std::unordered_map< const ITensorInfo *, PaddingSize > get_padding_info(std::initializer_list< const ITensorInfo *> infos)
Stores padding information before configuring a kernel.
Definition: Utils.cpp:586
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:157
std::string kernel_name

◆ run_op()

void run_op ( ITensorPack tensors,
const Window window,
cl::CommandQueue &  queue 
)
overridevirtual

Enqueue the OpenCL kernel to process the given window on the passed OpenCL command queue.

Note
The queue is not flushed by this method, and therefore the kernel will not have been executed by the time this method returns.
Parameters
[in]tensorsA vector containing the tensors to operato on.
[in]windowRegion on which to execute the kernel. (Must be a valid region of the window returned by window()).
[in,out]queueCommand queue on which to enqueue the kernel.

Reimplemented from ICLKernel.

Definition at line 225 of file ClWinogradInputTransformKernel.cpp.

References arm_compute::ACL_DST, arm_compute::ACL_SRC, ICLKernel::add_3D_tensor_argument(), ICLKernel::add_4D_tensor_argument(), ARM_COMPUTE_ERROR_ON, ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW, ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL, arm_compute::CHANNEL, Window::collapse_if_possible(), Window::DimZ, arm_compute::test::validation::dst, arm_compute::mlgo::parser::end(), arm_compute::enqueue(), Window::first_slice_window_3D(), ITensorPack::get_const_tensor(), arm_compute::get_data_layout_dimension_index(), ITensorPack::get_tensor(), arm_compute::HEIGHT, ICLKernel::lws_hint(), arm_compute::NHWC, ICLKernel::num_arguments_per_3D_tensor(), Window::set(), Window::shape(), arm_compute::test::validation::reference::slice(), Window::slide_window_slice_3D(), arm_compute::test::validation::src, TensorShape::total_size_upper(), arm_compute::WIDTH, and IKernel::window().

226 {
229 
230  auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
231  auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
232 
233  const size_t idx_w = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
234  const size_t idx_h = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
235  const size_t idx_c = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
236  const size_t total_batches = window.shape().total_size_upper(3);
237 
238  // Collapse window
239  Window window_collapsed = window.collapse_if_possible(IClKernel::window(), Window::DimZ);
240 
241  if(_data_layout == DataLayout::NHWC)
242  {
243  Window slice = window_collapsed.first_slice_window_3D();
244  slice.set(1, Window::Dimension(0, _num_tiles_x * _num_tiles_y, 1));
245  slice.set(2, Window::Dimension(0, total_batches, 1));
246 
247  unsigned int idx = 0;
248  add_4D_tensor_argument(idx, src, slice);
249  add_4D_tensor_argument(idx, dst, slice);
250  _kernel.setArg<cl_uint>(idx++, _src_width);
251  _kernel.setArg<cl_uint>(idx++, _src_height);
252  _kernel.setArg<cl_uint>(idx++, _num_tiles_x);
253  _kernel.setArg<cl_uint>(idx++, _num_tiles_y);
254  enqueue(queue, *this, slice, lws_hint());
255  }
256  else
257  {
258  Window slice = window_collapsed.first_slice_window_3D();
259  slice.set(idx_w, Window::Dimension(0, _num_tiles_x, 1));
260  slice.set(idx_h, Window::Dimension(0, _num_tiles_y, 1));
261 
262  ARM_COMPUTE_ERROR_ON(((slice[idx_c].end() - slice[idx_c].start()) % _step_z) != 0);
263  slice.set(idx_c, Window::Dimension(slice[idx_c].start(), slice[idx_c].end(), _step_z));
264 
265  unsigned int idx = 2 * num_arguments_per_3D_tensor();
266  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src->info()->strides_in_bytes()[3]));
267  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[3]));
268 
269  do
270  {
271  unsigned int idx = 0;
272  add_3D_tensor_argument(idx, src, slice);
273  add_3D_tensor_argument(idx, dst, slice);
274 
275  enqueue(queue, *this, slice, lws_hint());
276  }
277  while(window_collapsed.slide_window_slice_3D(slice));
278  }
279 }
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
void enqueue(cl::CommandQueue &queue, ICLKernel &kernel, const Window &window, const cl::NDRange &lws_hint=CLKernelLibrary::get().default_ndrange(), bool use_dummy_work_items=false)
Add the kernel to the command queue with the given window.
Definition: ICLKernel.cpp:32
cl::NDRange lws_hint() const
Return the Local-Workgroup-Size hint.
Definition: ICLKernel.h:384
#define ARM_COMPUTE_ERROR_ON(cond)
If the condition is true then an error message is printed and an exception thrown.
Definition: Error.h:466
size_t total_size_upper(size_t dimension) const
Collapses given dimension and above.
Definition: TensorShape.h:182
void add_3D_tensor_argument(unsigned int &idx, const ICLTensor *tensor, const Window &window)
Add the passed 3D tensor&#39;s parameters to the object&#39;s kernel&#39;s arguments starting from the index idx...
Definition: ICLKernel.h:227
SimpleTensor< float > src
Definition: DFT.cpp:155
static constexpr unsigned int num_arguments_per_3D_tensor()
Returns the number of arguments enqueued per 3D tensor object.
Definition: ICLKernel.h:314
Window collapse_if_possible(const Window &full_window, size_t first, size_t last, bool *has_collapsed=nullptr) const
Collapse the dimensions between first and last if possible.
Definition: Window.inl:68
void end(TokenStream &in, bool &valid)
Definition: MLGOParser.cpp:290
#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)
Definition: Validate.h:915
static constexpr size_t DimZ
Alias for dimension 2 also known as Z dimension.
Definition: Window.h:47
size_t get_data_layout_dimension_index(const DataLayout &data_layout, const DataLayoutDimension &data_layout_dimension)
Get the index of the given dimension.
Definition: Helpers.inl:193
Num samples, height, width, channels.
TensorShape shape() const
Return the shape of the window in number of steps.
Definition: Window.inl:284
void add_4D_tensor_argument(unsigned int &idx, const ICLTensor *tensor, const Window &window)
Add the passed 4D tensor&#39;s parameters to the object&#39;s kernel&#39;s arguments starting from the index idx...
Definition: ICLKernel.h:237
#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)
Definition: Validate.h:201
SimpleTensor< T > slice(const SimpleTensor< T > &src, Coordinates starts, Coordinates ends)

◆ validate()

Status validate ( const ITensorInfo src,
const ITensorInfo dst,
const WinogradInfo winograd_info 
)
static

Static function to check if given info will lead to a valid configuration.

Similar to ClWinogradInputTransformKernel::configure()

Returns
a status

Definition at line 217 of file ClWinogradInputTransformKernel.cpp.

References ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR, ARM_COMPUTE_RETURN_ON_ERROR, ICloneable< T >::clone(), arm_compute::cpu::kernels::validate_and_configure_window(), and arm_compute::cpu::kernels::validate_arguments().

218 {
221  ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), winograd_info).first);
222  return Status{};
223 }
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info)
SimpleTensor< float > src
Definition: DFT.cpp:155
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:159
std::pair< Status, Window > validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst)

The documentation for this class was generated from the following files: