Compute Library
 21.05
CLReductionOperationKernel Class Reference

Interface for the reduction operation kernel. More...

#include <CLReductionOperationKernel.h>

Collaboration diagram for CLReductionOperationKernel:
[legend]

Public Member Functions

 CLReductionOperationKernel ()
 Default constructor. More...
 
 CLReductionOperationKernel (const CLReductionOperationKernel &)=delete
 Prevent instances of this class from being copied (As this class contains pointers) More...
 
CLReductionOperationKerneloperator= (const CLReductionOperationKernel &)=delete
 Prevent instances of this class from being copied (As this class contains pointers) More...
 
 CLReductionOperationKernel (CLReductionOperationKernel &&)=default
 Allow instances of this class to be moved. More...
 
CLReductionOperationKerneloperator= (CLReductionOperationKernel &&)=default
 Allow instances of this class to be moved. More...
 
 ~CLReductionOperationKernel ()=default
 Default destructor. More...
 
void configure (const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op)
 Set the input and output tensors. More...
 
void configure (const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op)
 Set the input and output tensors. More...
 
void run (const Window &window, cl::CommandQueue &queue) override
 Enqueue the OpenCL kernel to process the given window on the passed OpenCL command queue. More...
 
- Public Member Functions inherited from ICLKernel
 ICLKernel ()
 Constructor. More...
 
cl::Kernel & kernel ()
 Returns a reference to the OpenCL kernel of this object. 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...
 
virtual void run_op (ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 Enqueue the OpenCL kernel to process the given window on the passed OpenCL command queue. 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...
 
virtual BorderSize border_size () const
 The size of the border for that kernel. 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 *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
 Static function to check if given info will lead to a valid configuration of CLReductionOperationKernel. More...
 
- Static Public Member Functions inherited from ICLKernel
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

Interface for the reduction operation kernel.

Definition at line 36 of file CLReductionOperationKernel.h.

Constructor & Destructor Documentation

◆ CLReductionOperationKernel() [1/3]

Default constructor.

Definition at line 75 of file CLReductionOperationKernel.cpp.

76  : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE)
77 {
78 }

◆ CLReductionOperationKernel() [2/3]

Prevent instances of this class from being copied (As this class contains pointers)

◆ CLReductionOperationKernel() [3/3]

Allow instances of this class to be moved.

◆ ~CLReductionOperationKernel()

Default destructor.

Member Function Documentation

◆ configure() [1/2]

void configure ( const ICLTensor input,
ICLTensor output,
unsigned int  axis,
ReductionOperation  op 
)

Set the input and output tensors.

Parameters
[in]inputSource tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/S32/F16/F32.
[out]outputDestination tensor. Data types and data layouts supported: Same as input. Output will have the same number of dimensions as input.
[in]axisAxis along which to reduce. Supported reduction axis : 0,1,2,3
[in]opReduction operation to perform. Operations supported: MEAN_SUM, PROD, SUM_SQUARE, SUM, MIN, MAX

Definition at line 80 of file CLReductionOperationKernel.cpp.

81 {
82  configure(CLKernelLibrary::get().get_compile_context(), input, output, axis, op);
83 }
static CLKernelLibrary & get()
Access the KernelLibrary singleton.
void configure(const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op)
Set the input and output tensors.

References CLKernelLibrary::get(), and arm_compute::test::validation::input.

◆ configure() [2/2]

void configure ( const CLCompileContext compile_context,
const ICLTensor input,
ICLTensor output,
unsigned int  axis,
ReductionOperation  op 
)

Set the input and output tensors.

Parameters
[in]compile_contextThe compile context to be used.
[in]inputSource tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/S32/F16/F32.
[out]outputDestination tensor. Data types and data layouts supported: Same as input. Output will have the same number of dimensions as input.
[in]axisAxis along which to reduce. Supported reduction axis : 0,1,2,3
[in]opReduction operation to perform. Operations supported: MEAN_SUM, PROD, SUM_SQUARE, SUM, MIN, MAX

Definition at line 85 of file CLReductionOperationKernel.cpp.

86 {
88 
89  ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op));
90 
91  auto padding_info = get_padding_info({ input, output });
92 
93  _input = input;
94  _output = output;
95  _reduction_axis = axis;
96  _op = op;
97 
98  const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, true);
99  auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).reset_padding().set_is_resizable(true));
100 
101  // Set build options
102  CLBuildOptions build_opts;
103  DataType data_type = input->info()->data_type();
104  std::string data_type_promoted{};
105 
107  {
108  data_type_promoted = "int";
109  }
110  else
111  {
112  data_type_promoted = get_cl_type_from_data_type(data_type);
113  }
114 
115  const unsigned int width = input->info()->dimension(0) * input->info()->num_channels();
116  unsigned int vec_size = (is_data_type_quantized(input->info()->data_type()) && (axis == 0)) ? 1 : 16;
117  vec_size = adjust_vec_size(vec_size, width);
118  const unsigned int vec_size_leftover = width % vec_size;
119 
120  build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
121  build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted);
122  build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size));
123  build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(vec_size_leftover));
124  build_opts.add_option_if(is_data_type_float(data_type), "-DFLOAT_DATA_TYPE");
125  build_opts.add_option_if(op == ReductionOperation::SUM_SQUARE, "-DSUM_SQUARE");
126  build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DMEAN");
127  build_opts.add_option_if(op == ReductionOperation::SUM, "-DSUM");
128  build_opts.add_option_if(op == ReductionOperation::PROD, "-DPROD");
129  build_opts.add_option_if(op == ReductionOperation::MIN, "-DMIN");
130  build_opts.add_option_if(op == ReductionOperation::MAX, "-DMAX");
131  build_opts.add_option_if(is_data_type_quantized(data_type), "-DOFFSET=" + support::cpp11::to_string(input->info()->quantization_info().uniform().offset));
132  build_opts.add_option_if(is_data_type_quantized(data_type), "-DSCALE=" + float_to_string_with_full_precision(input->info()->quantization_info().uniform().scale));
133 
134  switch(op)
135  {
137  build_opts.add_option(("-DOPERATION=square_sum"));
138  break;
141  build_opts.add_option(("-DOPERATION=sum"));
142  break;
145  break;
147  build_opts.add_option(("-DOPERATION=product"));
148  break;
149  default:
150  ARM_COMPUTE_ERROR("Unsupported reduction operation");
151  }
152 
153  // Create kernel
154  std::string kernel_axis_name;
155  const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis);
156 
157  switch(axis)
158  {
159  case 0:
160  {
161  build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(width));
162  kernel_axis_name = ((is_serial_op) ? "non_parallel_x" : "x");
163  }
164  break;
165  case 1:
166  build_opts.add_option("-DHEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
167  kernel_axis_name = "y";
168  break;
169  case 2:
170  build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
171  kernel_axis_name = "z";
172  break;
173  case 3:
174  build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
175  build_opts.add_option("-DBATCH=" + support::cpp11::to_string(input->info()->dimension(3)));
176  kernel_axis_name = "w";
177  break;
178  default:
179  ARM_COMPUTE_ERROR("Not supported");
180  }
181  _kernel = create_kernel(compile_context, "reduction_operation_" + kernel_axis_name, build_opts.options());
182 
183  // Configure kernel window
184  Window win = calculate_max_window(*input->info(), Steps(vec_size));
185  win.set(Window::DimX, Window::Dimension(win.x().start(), win.x().end() * _input->info()->num_channels(), win.x().step()));
186  ICLKernel::configure_internal(win);
187 
189 }
bool is_data_type_quantized(DataType dt)
Check if a given data type is of quantized type.
Definition: Utils.h:967
bool needs_serialized_reduction(ReductionOperation op, DataType dt, unsigned int axis)
Check if the given reduction operation should be handled in a serial way.
Definition: Utils.cpp:429
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
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:489
#define ARM_COMPUTE_ERROR(msg)
Print the given message then throw an std::runtime_error.
Definition: Error.h:352
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
const DataType data_type
Definition: Im2Col.cpp:150
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:403
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
std::string float_to_string_with_full_precision(float val)
Create a string with the float in full precision.
Definition: Utils.h:1061
TensorShape compute_reduced_shape(const TensorShape &input, unsigned int axis, bool keep_dims=true)
Calculate the reduced shape of a tensor given an axis.
std::string get_cl_type_from_data_type(const DataType &dt)
Translates a tensor data type to the appropriate OpenCL type.
Definition: CLHelpers.cpp:37
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...
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
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:504
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:157
unsigned int adjust_vec_size(unsigned int vec_size, size_t dim0)
Returns the adjusted vector size in case it is less than the input's first dimension,...
Definition: Utils.h:1157
DataType
Available data types.
Definition: Types.h:77
bool is_data_type_float(DataType dt)
Check if a given data type is of floating point type.
Definition: Utils.h:947

References arm_compute::adjust_vec_size(), ARM_COMPUTE_ERROR, ARM_COMPUTE_ERROR_ON, ARM_COMPUTE_ERROR_ON_NULLPTR, ARM_COMPUTE_ERROR_THROW_ON, arm_compute::auto_init_if_empty(), arm_compute::calculate_max_window(), arm_compute::misc::shape_calculator::compute_reduced_shape(), arm_compute::create_kernel(), arm_compute::test::validation::data_type, Window::DimX, arm_compute::float_to_string_with_full_precision(), arm_compute::get_cl_type_from_data_type(), arm_compute::get_padding_info(), arm_compute::has_padding_changed(), ITensor::info(), arm_compute::test::validation::input, arm_compute::is_data_type_float(), arm_compute::is_data_type_quantized(), arm_compute::MAX, arm_compute::MEAN_SUM, arm_compute::MIN, arm_compute::needs_serialized_reduction(), arm_compute::test::validation::output_shape, arm_compute::PROD, Window::set(), arm_compute::SUM, arm_compute::SUM_SQUARE, arm_compute::support::cpp11::to_string(), and arm_compute::validate_arguments().

◆ operator=() [1/2]

CLReductionOperationKernel& operator= ( const CLReductionOperationKernel )
delete

Prevent instances of this class from being copied (As this class contains pointers)

◆ operator=() [2/2]

Allow instances of this class to be moved.

◆ run()

void run ( 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]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 197 of file CLReductionOperationKernel.cpp.

198 {
201 
202  const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis);
203  switch(_reduction_axis)
204  {
205  case 0:
206  {
207  // We use parallel reduction only in non quantized types
208  if(is_serial_op)
209  {
210  // Get first input and output slices
211  Window window_in{ window };
212  window_in.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0)));
213 
214  Window out_window{ window };
215  out_window.set(Window::DimX, Window::Dimension(0, 0, 0));
216 
217  Window in_slice = window_in.first_slice_window_1D();
218  Window out_slice = out_window.first_slice_window_1D();
219 
220  do
221  {
222  unsigned int idx = 0;
223  add_1D_tensor_argument(idx, _input, in_slice);
224  add_1D_tensor_argument(idx, _output, out_slice);
225  enqueue(queue, *this, in_slice);
226  }
227  while(window_in.slide_window_slice_1D(in_slice) && out_window.slide_window_slice_1D(out_slice));
228  }
229  else
230  {
231  // Set out window
232  bool has_collapsed = true;
233  Window window_in = window.collapse_if_possible(window, 2, &has_collapsed);
234  ARM_COMPUTE_ERROR_ON(!has_collapsed);
235 
236  Window window_out = window_in;
237  window_out.set(0, Window::Dimension());
238 
239  unsigned int idx = 0;
240  add_3D_tensor_argument(idx, _input, window_in);
241  add_3D_tensor_argument(idx, _output, window_out);
242  enqueue(queue, *this, window_in);
243  }
244  }
245  break;
246  case 1:
247  {
248  // Get first input and output slices
249  Window window_in{ window };
250  window_in.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
251  Window in_slice = window_in.first_slice_window_2D();
252  Window out_slice = window.first_slice_window_2D();
253 
254  do
255  {
256  unsigned int idx = 0;
257  add_2D_tensor_argument(idx, _input, in_slice);
258  add_2D_tensor_argument(idx, _output, out_slice);
259  enqueue(queue, *this, in_slice);
260  }
261  while(window_in.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
262  }
263  break;
264  case 2:
265  {
266  // Get first input and output slices
267  Window window_in{ window };
268  window_in.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
269  Window in_slice = window_in.first_slice_window_3D();
270  Window out_slice = window.first_slice_window_3D();
271 
272  do
273  {
274  unsigned int idx = 0;
275  add_3D_tensor_argument(idx, _input, in_slice);
276  add_3D_tensor_argument(idx, _output, out_slice);
277  enqueue(queue, *this, in_slice);
278  }
279  while(window_in.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice));
280  }
281  break;
282  case 3:
283  {
284  // Get first input and output slices
285  Window window_in{ window };
286  window_in.set(3, Window::Dimension(0, 1, 1));
287  Window in_slice = window_in.first_slice_window_4D();
288  Window out_slice = window.first_slice_window_4D();
289 
290  do
291  {
292  unsigned int idx = 0;
293  add_4D_tensor_argument(idx, _input, in_slice);
294  add_4D_tensor_argument(idx, _output, out_slice);
295  enqueue(queue, *this, in_slice);
296  }
297  while(window_in.slide_window_slice_4D(in_slice) && window.slide_window_slice_4D(out_slice));
298  }
299  break;
300  default:
301  ARM_COMPUTE_ERROR("Not supported");
302  }
303 }
Window first_slice_window_2D() const
First 2D slice of the window.
Definition: Window.h:283
bool needs_serialized_reduction(ReductionOperation op, DataType dt, unsigned int axis)
Check if the given reduction operation should be handled in a serial way.
Definition: Utils.cpp:429
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
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
#define ARM_COMPUTE_ERROR(msg)
Print the given message then throw an std::runtime_error.
Definition: Error.h:352
virtual DataType data_type() const =0
Data type used for each element of the tensor.
#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
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.
Definition: ICLKernel.h:172
bool slide_window_slice_2D(Window &slice) const
Slide the passed 2D window slice.
Definition: Window.h:323
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
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
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor's metadata.
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
bool slide_window_slice_3D(Window &slice) const
Slide the passed 3D window slice.
Definition: Window.h:335
#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)
Definition: Validate.h:915
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
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.
Definition: ICLKernel.h:148
static constexpr size_t DimZ
Alias for dimension 2 also known as Z dimension.
Definition: Window.h:47
Window first_slice_window_4D() const
First 4D slice of the window.
Definition: Window.h:299
bool slide_window_slice_4D(Window &slice) const
Slide the passed 4D window slice.
Definition: Window.h:347
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.
Definition: ICLKernel.h:124
Window first_slice_window_3D() const
First 3D slice of the window.
Definition: Window.h:291
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.
Definition: ICLKernel.h:182
#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)
Definition: Validate.h:201

References ICLKernel::add_1D_tensor_argument(), ICLKernel::add_2D_tensor_argument(), ICLKernel::add_3D_tensor_argument(), ICLKernel::add_4D_tensor_argument(), ARM_COMPUTE_ERROR, ARM_COMPUTE_ERROR_ON, ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW, ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL, Window::collapse_if_possible(), ITensorInfo::data_type(), ITensorInfo::dimension(), Window::DimX, Window::DimY, Window::DimZ, arm_compute::enqueue(), Window::first_slice_window_1D(), Window::first_slice_window_2D(), Window::first_slice_window_3D(), Window::first_slice_window_4D(), ITensor::info(), arm_compute::needs_serialized_reduction(), Window::set(), Window::slide_window_slice_2D(), Window::slide_window_slice_3D(), Window::slide_window_slice_4D(), and IKernel::window().

◆ validate()

Status validate ( const ITensorInfo input,
const ITensorInfo output,
unsigned int  axis,
ReductionOperation  op 
)
static

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

Parameters
[in]inputSource tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/S32/F16/F32.
[in]outputDestination tensor info. Data types and data layouts supported: Same as input. Output will have the same number of dimensions as input.
[in]axisAxis along which to reduce. Supported reduction axis : 0,1,2,3
[in]opReduction operation to perform. Operations supported: MEAN_SUM, PROD, SUM_SQUARE, SUM, MIN, MAX
Returns
a status

Definition at line 191 of file CLReductionOperationKernel.cpp.

192 {
194  return Status{};
195 }
#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 *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)

References ARM_COMPUTE_RETURN_ON_ERROR, arm_compute::test::validation::input, and arm_compute::validate_arguments().

Referenced by CLReductionOperation::validate().


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