Compute Library
 22.11
ClPool3dKernel Class Reference

Interface for the pooling layer kernel. More...

#include <ClPool3dKernel.h>

Collaboration diagram for ClPool3dKernel:
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Public Member Functions

 ClPool3dKernel ()
 
 ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE (ClPool3dKernel)
 
void configure (const ClCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *dst, const Pooling3dLayerInfo &pool_info)
 Configure kernel for a given list of arguments. 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...
 
- 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...
 
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 *src, const ITensorInfo *dst, const Pooling3dLayerInfo &pool_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

Interface for the pooling layer kernel.

Definition at line 38 of file ClPool3dKernel.h.

Constructor & Destructor Documentation

◆ ClPool3dKernel()

Definition at line 91 of file ClPool3dKernel.cpp.

References arm_compute::POOL.

92 {
93  _type = CLKernelType::POOL;
94 }
Pool CL kernel type.
Definition: CLTypes.h:87

Member Function Documentation

◆ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE()

ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE ( ClPool3dKernel  )

◆ configure()

void configure ( const ClCompileContext compile_context,
const ITensorInfo src,
ITensorInfo dst,
const Pooling3dLayerInfo pool_info 
)

Configure kernel for a given list of arguments.

Note
Asymmetric padding is not supported when dimension rounding type == CEIL.
Parameters
[in]compile_contextThe compile context to be used.
[in]srcSource tensor info. Data types supported: F16/F32/QASYMM8/QASYMM8_SIGNED
[out]dstDestination tensor info. Data types supported: same as src.
[in]pool_infoContains pooling operation information described in Pooling3dLayerInfo.

Definition at line 96 of file ClPool3dKernel.cpp.

References CLBuildOptions::add_option(), CLBuildOptions::add_option_if(), arm_compute::adjust_vec_size(), ARM_COMPUTE_ERROR_ON, ARM_COMPUTE_ERROR_ON_NULLPTR, ARM_COMPUTE_ERROR_THROW_ON, arm_compute::auto_init_if_empty(), arm_compute::BATCHES, arm_compute::calculate_max_window(), arm_compute::CHANNEL, ICloneable< T >::clone(), arm_compute::misc::shape_calculator::compute_pool3d_shape(), arm_compute::create_kernel(), ITensorInfo::data_layout(), arm_compute::test::validation::data_type, ITensorInfo::data_type(), Size3D::depth, arm_compute::DEPTH, ITensorInfo::dimension(), Pooling3dLayerInfo::exclude_padding, arm_compute::F16, arm_compute::F32, arm_compute::float_to_string_with_full_precision(), Pooling3dLayerInfo::fp_mixed_precision, Padding3D::front, arm_compute::get_cl_type_from_data_type(), arm_compute::get_data_layout_dimension_index(), arm_compute::get_min_max(), arm_compute::get_padding_info(), arm_compute::has_padding_changed(), Size3D::height, arm_compute::HEIGHT, arm_compute::is_data_type_quantized(), arm_compute::is_data_type_quantized_asymmetric(), Pooling3dLayerInfo::is_global_pooling, kernel_name, Padding3D::left, arm_compute::lower_string(), arm_compute::support::cpp11::lowest(), arm_compute::MAX, UniformQuantizationInfo::offset, CLBuildOptions::options(), Pooling3dLayerInfo::padding, Pooling3dLayerInfo::pool_size, Pooling3dLayerInfo::pool_type, ITensorInfo::quantization_info(), arm_compute::S32, UniformQuantizationInfo::scale, arm_compute::test::validation::src, Pooling3dLayerInfo::stride, arm_compute::string_from_data_layout(), arm_compute::string_from_data_type(), arm_compute::string_from_pooling_type(), ITensorInfo::tensor_shape(), arm_compute::support::cpp11::to_string(), Padding3D::top, QuantizationInfo::uniform(), arm_compute::cpu::kernels::validate_arguments(), Size3D::width, arm_compute::WIDTH, Size3D::x(), Size3D::y(), and Size3D::z().

97 {
100  auto padding_info = get_padding_info({ src, dst });
101 
102  // Auto init if empty
103  TensorShape out_shape = compute_pool3d_shape(src->tensor_shape(), pool_info);
104  auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape));
105 
106  // Set instance variables
107  _pool_info = pool_info;
108  _data_layout = src->data_layout();
109 
110  _num_elems_processed_per_iteration = (dst->data_type() == DataType::F32) ? 2 : 4;
111  _num_elems_processed_per_iteration = adjust_vec_size(_num_elems_processed_per_iteration, dst->dimension(0));
112 
113  const PoolingType pool_type = pool_info.pool_type;
116  const int idx_depth = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::DEPTH);
117  const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
118  const int idx_batch_size = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
119  const int pool_size_x = pool_info.is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
120  const int pool_size_y = pool_info.is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
121  const int pool_size_z = pool_info.is_global_pooling ? src->dimension(idx_depth) : pool_info.pool_size.depth;
122  const bool exclude_padding = pool_info.exclude_padding;
123  const int pool_stride_x = pool_info.stride.x();
124  const int pool_stride_y = pool_info.stride.y();
125  const int pool_stride_z = pool_info.stride.z();
126  const int pool_pad_top = pool_info.padding.top;
127  const int pool_pad_left = pool_info.padding.left;
128  const int pool_pad_front = pool_info.padding.front;
129  const DataType data_type = src->data_type();
130 
131  // Set build options
132  CLBuildOptions build_opts;
133  build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration));
134  build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
135  build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type));
136  build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x));
137  build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y));
138  build_opts.add_option("-DSTRIDE_Z=" + support::cpp11::to_string(pool_stride_z));
139  build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_left));
140  build_opts.add_option("-DPAD_Y=" + support::cpp11::to_string(pool_pad_top));
141  build_opts.add_option("-DPAD_Z=" + support::cpp11::to_string(pool_pad_front));
142  build_opts.add_option("-DPOOL_SIZE_X=" + support::cpp11::to_string(pool_size_x));
143  build_opts.add_option("-DPOOL_SIZE_Y=" + support::cpp11::to_string(pool_size_y));
144  build_opts.add_option("-DPOOL_SIZE_Z=" + support::cpp11::to_string(pool_size_z));
145  build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
146  build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
147  build_opts.add_option("-DSRC_DEPTH=" + support::cpp11::to_string(src->dimension(idx_depth)));
148 
149  // If datatype is quantized add relevant parameters
150  if(is_data_type_quantized_asymmetric(data_type) && src->quantization_info() != dst->quantization_info())
151  {
152  const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
153  const UniformQuantizationInfo oq_info = dst->quantization_info().uniform();
154 
155  build_opts.add_option("-DOFFSET_IN1=" + float_to_string_with_full_precision(iq_info.offset));
156  build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset));
157  build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq_info.scale));
158  build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale));
159  }
160 
161  // Set the initial value for the pooling operation accordingly with the data type
162  if(pool_type == PoolingType::MAX)
163  {
164  if(is_data_type_quantized(data_type))
165  {
166  PixelValue type_min{};
167  std::tie(type_min, std::ignore) = get_min_max(data_type);
168  build_opts.add_option("-DINITIAL_VALUE=" + support::cpp11::to_string(type_min.get<int32_t>()));
169  }
170  else
171  {
172  build_opts.add_option("-DINITIAL_VALUE=" + float_to_string_with_full_precision(std::numeric_limits<float>::lowest()));
173  }
174  }
175  else
176  {
177  // Pool AVG and Pool L2 initial value
178  build_opts.add_option("-DINITIAL_VALUE=0");
179  }
180  // Create kernel
181  // Floating point mixed precision is support on F16 only
182  const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX;
183 
184  // Wider accumulation is required to avoid accuracy loss
185  // Case 1: Floating point mixed precision (fp16 src data and fp32 accumulation)
186  DataType acc_data_type = data_type;
187  if(use_fp_mixed_precision)
188  {
189  acc_data_type = DataType::F32;
190  }
191  else if(is_data_type_quantized(data_type) && pool_type != PoolingType::MAX) // Use S32 for avg pooling to allow for integer division
192  {
193  acc_data_type = DataType::S32;
194  }
195 
196  build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(acc_data_type));
197  build_opts.add_option_if(use_fp_mixed_precision, "-DFP_MIXED_PRECISION");
198  build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
199  build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height)));
200  build_opts.add_option("-DDST_DEPTH=" + support::cpp11::to_string(dst->dimension(idx_depth)));
201  build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(idx_channel)));
202  build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(dst->dimension(idx_batch_size)));
203  build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration));
204 
205  // if datatype is quantized use quantized kernel function
206  std::string kernel_name = (is_data_type_quantized_asymmetric(data_type) ? "pooling_3d_layer_MxN_ndhwc_quantized" : "pooling_3d_layer_MxN_ndhwc");
207  _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
208 
209  // Configure kernel window
210  Window win = calculate_max_window(*dst, Steps(_num_elems_processed_per_iteration));
211  ICLKernel::configure_internal(win);
212 
213  // Set config_id for enabling LWS tuning
214  _config_id = "pooling_layer_3d";
215  _config_id += lower_string(string_from_data_type(data_type));
216  _config_id += "_";
217  _config_id += lower_string(string_from_data_layout(_data_layout));
218  _config_id += "_";
219  _config_id += support::cpp11::to_string(dst->dimension(idx_width));
220  _config_id += "_";
221  _config_id += support::cpp11::to_string(dst->dimension(idx_height));
222  _config_id += "_";
223  _config_id += support::cpp11::to_string(dst->dimension(idx_channel));
224  _config_id += "_";
225  _config_id += lower_string(string_from_data_layout(src->data_layout()));
226 
228 }
bool is_data_type_quantized(DataType dt)
Check if a given data type is of quantized type.
Definition: Utils.h:1030
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
std::string to_string(T &&value)
Convert integer and float values to string.
1 channel, 1 F32 per channel
#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
TensorShape compute_pool3d_shape(const TensorShape &src, Pooling3dLayerInfo pool3d_info)
Calculate the output pool3d shape of a tensor.
std::string lower_string(const std::string &val)
Lower a given string.
Definition: Utils.cpp:353
Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info)
SimpleTensor< float > src
Definition: DFT.cpp:155
1 channel, 1 F16 per channel
1 channel, 1 S32 per channel
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:404
const std::string & string_from_data_type(DataType dt)
Convert a data type identity into a string.
Definition: Utils.cpp:135
std::string float_to_string_with_full_precision(float val)
Create a string with the float in full precision.
Definition: Utils.h:1124
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:603
bool is_data_type_quantized_asymmetric(DataType dt)
Check if a given data type is of asymmetric quantized type.
Definition: Utils.h:1052
PoolingType
Available pooling types.
Definition: Types.h:557
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
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:588
#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&#39;s first dimension, getting rounded down to its closest valid vector size.
Definition: Utils.h:1222
std::string kernel_name
DataType
Available data types.
Definition: Types.h:79
const std::string & string_from_pooling_type(PoolingType type)
Translates a given pooling type to a string.
Definition: Utils.cpp:225
std::tuple< PixelValue, PixelValue > get_min_max(DataType dt)
Compute the mininum and maximum values a data type can take.
Definition: Utils.h:564

◆ 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 236 of file ClPool3dKernel.cpp.

References arm_compute::ACL_DST_0, arm_compute::ACL_SRC, ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW, ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL, Window::collapse_if_possible(), Window::DimZ, arm_compute::enqueue(), ITensorPack::get_const_tensor(), ITensorPack::get_tensor(), and IKernel::window().

237 {
240 
241  const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
242  auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0));
243 
244  // Collapse 3D window
245  Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
246 
247  // Set CL kernel arguments
248  unsigned int idx = 0;
249  // Passing of the window not needed, as the steps are not used for the pool3d kernel
251  add_5D_tensor_argument(idx, dst, window);
252  enqueue(queue, *this, window_collapsed, lws_hint());
253 }
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:383
SimpleTensor< float > src
Definition: DFT.cpp:155
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
#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
void add_5D_tensor_argument(unsigned int &idx, const ICLTensor *tensor, const Window &window)
Add the passed 5D tensor&#39;s parameters to the object&#39;s kernel&#39;s arguments starting from the index idx...
Definition: ICLKernel.h:246
#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)
Definition: Validate.h:201

◆ validate()

Status validate ( const ITensorInfo src,
const ITensorInfo dst,
const Pooling3dLayerInfo pool_info 
)
static

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

Similar to ClPool3dKernel::configure()

Returns
a status

Definition at line 230 of file ClPool3dKernel.cpp.

References ARM_COMPUTE_RETURN_ON_ERROR, and arm_compute::cpu::kernels::validate_arguments().

Referenced by ClPool3d::validate().

231 {
233  return Status{};
234 }
#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

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