Compute Library
 20.08
CLIm2ColKernel.cpp
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25 
32 #include "arm_compute/core/Error.h"
35 #include "arm_compute/core/Types.h"
38 #include "support/StringSupport.h"
39 
40 #include <cmath>
41 #include <tuple>
42 #include <utility>
43 
44 namespace arm_compute
45 {
46 using namespace misc::shape_calculator;
47 
48 namespace
49 {
50 struct Im2ColConfiguration
51 {
52  std::string kernel_name{};
53  std::set<std::string> build_options{};
56 };
57 
58 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
59  unsigned int num_groups)
60 {
61  const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
62 
67  ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
71  ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(channel_idx) % num_groups) != 0);
72 
73  // Since there's no implicit padding added, check the total input spatial dimensions (with conv paddings) are big enough for the kernel dimensions
74  const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
75  const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
76  const unsigned total_width = input->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right();
77  const unsigned total_height = input->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom();
78  ARM_COMPUTE_RETURN_ERROR_ON((total_width < kernel_dims.width) || (total_height < kernel_dims.height));
79 
80  if(output->total_size() > 0)
81  {
82  const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, num_groups == 1, num_groups));
83  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
86  }
87 
88  return Status{};
89 }
90 
91 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
93 {
95 
96  // Output tensor auto initialization if not yet initialized
97  TensorShape expected_output_shape = compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, num_groups == 1, num_groups);
98 
99  auto_init_if_empty(*output, input->clone()->set_tensor_shape(expected_output_shape));
100 
101  const DataLayout data_layout = input->data_layout();
104  const unsigned int input_width = input->dimension(width_idx);
105  const unsigned int input_height = input->dimension(height_idx);
106 
107  // Configure the execute window based on the selected optimal OpenCL kernel
108  bool window_changed = false;
109  Window win;
110 
112  {
114 
115  const int xin_start = 0;
116  const int xin_end = input->dimension(0);
117  const int yin_start = 0;
118  const int yin_end = input->dimension(1);
119 
120  const int xout_start = 0;
121  const int xout_end = output->dimension(0);
122  const int yout_start = 0;
123  const int yout_end = output->dimension(1);
124 
125  AccessWindowStatic input_access(input, xin_start, yin_start, xin_end, yin_end);
126  AccessWindowStatic output_access(output, xout_start, yout_start, xout_end, yout_end);
127  window_changed = window_changed || update_window_and_padding(win, input_access, output_access);
128  }
129  else
130  {
132  {
133  const BorderSize border(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
135  Steps(num_elems_processed_per_iteration * conv_info.stride().first, conv_info.stride().second));
136  AccessWindowStatic input_access(input,
137  -border.left,
138  -border.top,
139  ceil_to_multiple(input_width + border.right, kernel_dims.width * num_elems_processed_per_iteration),
140  input_height + border.bottom);
141  window_changed = window_changed || update_window_and_padding(win, input_access);
142  }
143  else
144  {
145  // For the generic case, CLIm2ColKernel doesn't need padding (we do not read out-of-bounds elements) so
146  // update_window_and_padding() can be skipped
147  win = calculate_max_window(*input, Steps());
148  }
149  }
150  output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
151  // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
152  win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
153 
154  Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
155  return std::make_pair(err, win);
156 }
157 
158 Im2ColConfiguration configure_opencl_kernel(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, unsigned int num_groups)
159 {
160  const DataLayout data_layout = input->data_layout();
161  const DataType data_type = input->data_type();
165  const unsigned int input_width = input->dimension(width_idx);
166  const unsigned int input_height = input->dimension(height_idx);
167  const unsigned int input_channel = input->dimension(channel_idx);
168 
169  const std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
170 
171  // Im2Col configuration
172  std::string kernel_name = "im2col_generic_";
173  CLBuildOptions build_opts;
174  unsigned int num_elems_processed_per_iteration = 1;
175  bool is_padding_required_nchw = false;
176  const UniformQuantizationInfo qinfo = input->quantization_info().uniform();
177 
178  build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
179  build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->element_size()));
180  build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
181  build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
182  build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(convolved_dims.first));
183  build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(convolved_dims.second));
184  build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
185  build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
186  build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
187  build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
188  build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
189  build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
190  build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width));
191  build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height));
192  build_opts.add_option("-DSRC_DEPTH=" + support::cpp11::to_string(input_channel));
193  build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
194  build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
195  build_opts.add_option_if(num_groups > 1, "-DNUM_GROUPS=" + support::cpp11::to_string(num_groups));
196  build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(qinfo.offset), "-DPAD_VALUE=0");
197  build_opts.add_option_if(has_bias, "-DHAS_BIAS");
198 
200  {
201  num_elems_processed_per_iteration = std::min(2U, input_channel);
202  is_padding_required_nchw = false;
203 
204  // Only the 3x3 and 9x9 cases are optimized for NHWC
205  if(kernel_dims == Size2D(3U, 3U))
206  {
207  kernel_name = "im2col3x3_";
208  }
209  else if(kernel_dims == Size2D(9U, 9U))
210  {
211  kernel_name = "im2col9x9_";
212  }
213 
214  // Get boundary vector (the first/last vector with potentially a partial vector size) size
215  // If input_channel is a multiple of num_elems_processed_per_iteration, the boundary vec size is the (full) vector size
216  // otherwise, the boundary vec size is the (partial) remainder vector size
217  const unsigned int vec_size = num_elems_processed_per_iteration;
218  const unsigned int partial_vec_size = input_channel % vec_size;
219  const unsigned int boundary_vec_size = vec_size - ((vec_size - partial_vec_size) % vec_size);
220  build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vec_size));
221  build_opts.add_option("-DBOUNDARY_VECTOR_SIZE=" + support::cpp11::to_string(boundary_vec_size));
222  }
223  else
224  {
225  if(dilation == Size2D(1U, 1U))
226  {
227  const bool squared_im2col = kernel_dims.width == kernel_dims.height;
228  if(squared_im2col)
229  {
230  // Check if we can run an optimized im2col for NCHW
231  switch(kernel_dims.width)
232  {
233  case 1:
234  // Optimized im2col1x1 if stride_x = 1 and conv_info.has_padding() = false
235  if(conv_info.stride().first == 1 && !conv_info.has_padding())
236  {
237  kernel_name = "im2col1x1_stridex1_";
240  }
241  break;
242  case 3:
243  kernel_name = "im2col3x3_";
246  break;
247  case 5:
248  kernel_name = "im2col5x5_";
251  break;
252  case 11:
253  // Optimized im2col11x11 if pad_x = pad_y = 0
254  if(!conv_info.has_padding())
255  {
256  kernel_name = "im2col11x11_padx0_pady0_";
259  }
260  break;
261  default:
262  kernel_name = "im2col_generic_";
264  is_padding_required_nchw = false;
265  break;
266  }
267  }
268  else if(kernel_dims.width > 1 && !conv_info.has_padding())
269  {
270  kernel_name = "im2col_generic_padx0_pady0_";
272  is_padding_required_nchw = false;
273 
274  // Optimized im2col is performed using one or more vector operations with the specified vector size
275  // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
276  // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
277  // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
278  // Using the vector size of 8, however, may be faster.
279  // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
280  // is used instead.)
281  const size_t vector_size = std::min(static_cast<size_t>(4), kernel_dims.width);
282  const size_t width_mod_vector_size = kernel_dims.width % vector_size;
283  build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
284  build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
285  }
286  }
287  }
288 
289  // Append the data layout to the kernel_name
291 
292  Im2ColConfiguration im2col_config;
293  im2col_config.kernel_name = kernel_name;
294  im2col_config.build_options = build_opts.options();
295  im2col_config.num_elems_processed_per_iteration = num_elems_processed_per_iteration;
296  im2col_config.is_padding_required_nchw = is_padding_required_nchw;
297 
298  return im2col_config;
299 }
300 } // namespace
301 
303  : _input(nullptr), _output(nullptr), _data_layout(DataLayout::UNKNOWN), _convolved_dims(), _num_elems_processed_per_iteration(1), _kernel_dims(), _conv_info(), _num_groups()
304 {
305 }
306 
307 void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
308  unsigned int num_groups)
309 {
310  configure(CLKernelLibrary::get().get_compile_context(), input, output, kernel_dims, conv_info, has_bias, dilation, num_groups);
311 }
312 
313 void CLIm2ColKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias,
314  const Size2D &dilation,
315  unsigned int num_groups)
316 {
319 
320  _data_layout = input->info()->data_layout();
321 
324  const unsigned int input_width = input->info()->dimension(width_idx);
325  const unsigned int input_height = input->info()->dimension(height_idx);
326 
327  // Select and configure the optimal OpenCL kernel to run.
328  // This function returns the OpenCL kernel's name, the arguments to pass at compile time, the number of elements processed per iteration
329  // and the padding requirement flag
330  Im2ColConfiguration im2col_config = configure_opencl_kernel(input->info(), kernel_dims, conv_info, has_bias, dilation, num_groups);
331 
332  // Create kernel
333  _kernel = create_kernel(compile_context, im2col_config.kernel_name, im2col_config.build_options);
334 
335  _input = input;
336  _output = output;
337  _convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
338  _num_elems_processed_per_iteration = im2col_config.num_elems_processed_per_iteration;
339  _kernel_dims = kernel_dims; // Only needed by the Tuner
340  _conv_info = conv_info; // Only needed by the Tuner
342 
343  // Configure kernel window
344  auto win_config = validate_and_configure_window(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration,
345  im2col_config.is_padding_required_nchw, num_groups);
346  ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
347  ICLKernel::configure_internal(win_config.second);
348 
349  // Set config_id for enabling LWS tuning
350  _config_id = im2col_config.kernel_name;
351  _config_id += "_";
352  _config_id += lower_string(string_from_data_type(input->info()->data_type()));
353  _config_id += "_";
354  _config_id += support::cpp11::to_string(num_groups);
355  _config_id += "_";
356  _config_id += support::cpp11::to_string(output->info()->dimension(0));
357  _config_id += "_";
358  _config_id += support::cpp11::to_string(output->info()->dimension(1));
359  _config_id += "_";
361 }
362 
363 Status CLIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
364  unsigned int num_groups)
365 {
367  Im2ColConfiguration im2col_config = configure_opencl_kernel(input, kernel_dims, conv_info, has_bias, dilation, num_groups);
368  ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration,
369  im2col_config.is_padding_required_nchw, num_groups)
370  .first);
371  return Status{};
372 }
373 
374 void CLIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
375 {
378 
379  // Get initial windows
380  // Collapse in order to have (SRC_DEPTH * BATCH_SIZE) on the 3rd dimension
382  window_collapsed.set_dimension_step(Window::DimZ, 1);
383 
384  Window window_output;
385  window_output.use_tensor_dimensions(_output->info()->tensor_shape());
386 
387  const Window first_slice_3d = window_collapsed.first_slice_window_3D();
388 
389  Window slice = first_slice_3d;
390  Window slice_in = first_slice_3d;
391  Window slice_out = window_output.first_slice_window_2D();
392 
394  {
395  const Window tmp_win = window.collapse_if_possible(ICLKernel::window(), 3);
396  const int num_batches = tmp_win[3].end();
397 
398  slice.set(1, Window::Dimension(0, static_cast<int>(_output->info()->tensor_shape()[1]), 1));
399  slice.set(2, Window::Dimension(0, static_cast<int>(num_batches), 1));
400  }
401  else
402  {
404  slice.set(1, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
405  // Note: In case of NCHW the 3rd dimension is already set collapsing the input window
406  }
407 
408  // Setup input slice
409  // The dimensions of the input are increased within the OpenCL kernel
410  slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
411  slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
412  slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
413 
414  // Setup output slice
415  // The dimensions of the output are increased within the OpenCL kernel
416  slice_out.set(Window::DimX, Window::Dimension(0, 0, 0));
417  slice_out.set(Window::DimY, Window::Dimension(0, 0, 0));
418 
420  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input->info()->strides_in_bytes()[3]));
421  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[((_num_groups == 1) ? 2 : 3)]));
422  do
423  {
424  unsigned int idx = 0;
425  add_3D_tensor_argument(idx, _input, slice_in);
426  if(_num_groups == 1)
427  {
428  add_2D_tensor_argument(idx, _output, slice_out);
429  }
430  else
431  {
432  add_3D_tensor_argument(idx, _output, slice_out);
433  }
434  enqueue(queue, *this, slice, lws_hint());
435  }
436  while(window_collapsed.slide_window_slice_3D(slice) && window_output.slide_window_slice_2D(slice_out) && window_collapsed.slide_window_slice_3D(slice_in));
437 }
438 } // namespace arm_compute
bool is_data_type_quantized(DataType dt)
Check if a given data type is of quantized type.
Definition: Utils.h:1121
Window first_slice_window_2D() const
First 2D slice of the window.
Definition: Window.h:281
const std::vector< int32_t > & offset() const
Offset vector accessor.
#define ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(tensor)
Definition: CLValidate.h:34
void configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U), unsigned int num_groups=1)
Set the input and output of the kernel.
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
const DataLayout data_layout
Definition: Im2Col.cpp:146
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
std::pair< unsigned int, unsigned int > _convolved_dims
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:39
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:545
cl::NDRange lws_hint() const
Return the Local-Workgroup-Size hint.
Definition: ICLKernel.h:263
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
std::string to_string(T &&value)
Convert integer and float values to string.
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:792
1 channel, 1 F32 per channel
static CLKernelLibrary & get()
Access the KernelLibrary singleton.
Store the tensor's metadata.
Definition: ITensorInfo.h:40
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Describe one of the image's dimensions with a start, end and step.
Definition: Window.h:75
Status class.
Definition: Error.h:52
std::string lower_string(const std::string &val)
Lower a given string.
Definition: Utils.cpp:326
#define ARM_COMPUTE_RETURN_ERROR_ON(cond)
If the condition is true, an error is returned.
Definition: Error.h:296
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps=Steps(), bool skip_border=false, BorderSize border_size=BorderSize())
Calculate the maximum window for a given tensor shape and border setting.
Definition: Helpers.cpp:28
void run(const Window &window, cl::CommandQueue &queue) override
Enqueue the OpenCL kernel to process the given window on the passed OpenCL command queue.
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:159
void use_tensor_dimensions(const TensorShape &shape, size_t first_dimension=Window::DimX)
Use the tensor's dimensions to fill the window dimensions.
Definition: Window.inl:276
bool slide_window_slice_2D(Window &slice) const
Slide the passed 2D window slice.
Definition: Window.h:321
Copyright (c) 2017-2020 Arm Limited.
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...
Definition: Helpers.inl:207
size_t height
Height of the image region or rectangle.
Definition: Size2D.h:90
1 channel, 1 F16 per channel
std::pair< unsigned int, unsigned int > scaled_dimensions(int width, int height, int kernel_width, int kernel_height, const PadStrideInfo &pad_stride_info, const Size2D &dilation=Size2D(1U, 1U))
Returns expected width and height of output scaled tensor depending on dimensions rounding mode.
Definition: Utils.cpp:395
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
const std::string & string_from_data_type(DataType dt)
Convert a data type identity into a string.
Definition: Utils.cpp:135
static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U), unsigned int num_groups=1)
Static function to check if given info will lead to a valid configuration of CLIm2ColKernel.
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
bool update_window_and_padding(Window &win, Ts &&... patterns)
Update window and padding size for each of the access patterns.
Definition: Helpers.h:437
static constexpr unsigned int num_arguments_per_3D_tensor()
Returns the number of arguments enqueued per 3D tensor object.
Definition: ICLKernel.h:201
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_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)
Definition: Validate.h:443
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
auto ceil_to_multiple(S value, T divisor) -> decltype(((value+divisor - 1)/divisor) *divisor)
Computes the smallest number larger or equal to value that is a multiple of divisor.
Definition: Utils.h:67
quantized, asymmetric fixed-point 8-bit number unsigned
const unsigned int num_groups
Definition: Im2Col.cpp:148
std::set< std::string > build_options
std::string kernel_name
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
virtual std::unique_ptr< T > clone() const =0
Provide a clone of the current object of class T.
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor's metadata.
Padding and stride information class.
Definition: Types.h:689
#define ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(f, w)
Definition: Validate.h:183
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
static constexpr unsigned int num_arguments_per_2D_tensor()
Returns the number of arguments enqueued per 2D tensor object.
Definition: ICLKernel.h:193
bool slide_window_slice_3D(Window &slice) const
Slide the passed 3D window slice.
Definition: Window.h:333
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(...)
Definition: Validate.h:610
CLCompileContext class.
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:163
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
void set_dimension_step(size_t dimension, int step)
Set the step of a given dimension.
Definition: Window.inl:167
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:135
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:161
Interface for OpenCL tensor.
Definition: ICLTensor.h:42
bool is_padding_required_nchw
const std::string & string_from_data_layout(DataLayout dl)
Convert a data layout identity into a string.
Definition: Utils.cpp:123
#define ARM_COMPUTE_CREATE_ERROR(error_code, msg)
Creates an error with a given message.
Definition: Error.h:159
size_t width
Width of the image region or rectangle.
Definition: Size2D.h:89
static constexpr size_t DimZ
Alias for dimension 2 also known as Z dimension.
Definition: Window.h:47
Class for specifying the size of an image or rectangle.
Definition: Size2D.h:34
const QuantizationInfo qinfo
Definition: Im2Col.cpp:150
Num samples, height, width, channels.
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
unsigned int _num_elems_processed_per_iteration
unsigned int num_elems_processed_per_iteration
quantized, asymmetric fixed-point 8-bit number signed
virtual const Strides & strides_in_bytes() const =0
The strides in bytes for accessing each dimension of the tensor.
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:332
Window first_slice_window_3D() const
First 3D slice of the window.
Definition: Window.h:289
DataType
Available data types.
Definition: Types.h:77
DataLayout
[DataLayout enum definition]
Definition: Types.h:120
TensorShape compute_im2col_conv_shape(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, bool batch_size_on_z, unsigned int num_groups=1)
Calculate the im2col output shape of a tensor.
Describe a multidimensional execution window.
Definition: Window.h:39
#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)
Definition: Validate.h:941
SimpleTensor< T > slice(const SimpleTensor< T > &src, Coordinates starts, Coordinates ends)
CLIm2ColKernel()
Default constructor.