Compute Library
 20.08
CLCropResize.cpp
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25 
28 
29 #include <cstddef>
30 
31 namespace arm_compute
32 {
33 namespace
34 {
35 inline void configure_crop(const ICLTensor *input, ICLTensor *crop_boxes, ICLTensor *box_ind, ICLTensor *output, uint32_t crop_box_ind, Coordinates &start, Coordinates &end, uint32_t &batch_index)
36 {
37  batch_index = *(reinterpret_cast<int32_t *>(box_ind->ptr_to_element(Coordinates(crop_box_ind))));
38 
39  // _crop_box_ind is used to index crop_boxes and retrieve the appropriate crop box.
40  // The crop box is specified by normalized coordinates [y0, x0, y1, x1].
41  const float x0 = *reinterpret_cast<const float *>(crop_boxes->ptr_to_element(Coordinates(1, crop_box_ind)));
42  const float y0 = *reinterpret_cast<const float *>(crop_boxes->ptr_to_element(Coordinates(0, crop_box_ind)));
43  const float x1 = *reinterpret_cast<const float *>(crop_boxes->ptr_to_element(Coordinates(3, crop_box_ind)));
44  const float y1 = *reinterpret_cast<const float *>(crop_boxes->ptr_to_element(Coordinates(2, crop_box_ind)));
45  // The normalized coordinates are scaled to retrieve the floating point image coordinates which are rounded to integers.
46  start = Coordinates(std::floor(x0 * (input->info()->tensor_shape()[1] - 1) + 0.5f),
47  std::floor(y0 * (input->info()->tensor_shape()[2] - 1) + 0.5f));
48  end = Coordinates(std::floor(x1 * (input->info()->tensor_shape()[1] - 1) + 0.5f),
49  std::floor(y1 * (input->info()->tensor_shape()[2] - 1) + 0.5f));
50  const TensorShape out_shape(input->info()->tensor_shape()[0], static_cast<uint32_t>(abs(end[0] - start[0])) + 1, static_cast<uint32_t>(abs(end[1] - start[1])) + 1);
51  output->info()->set_tensor_shape(out_shape);
52 }
53 } // namespace
54 
56  : _input(nullptr), _boxes(nullptr), _box_ind(nullptr), _output(nullptr), _num_boxes(0), _method(), _extrapolation_value(0), _scale(), _copy(), _crop_results(), _scaled_results(), _internal_kernels()
57 {
58 }
59 
61  Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value)
62 {
63  ARM_COMPUTE_RETURN_ERROR_ON(crop_size.x <= 0 || crop_size.y <= 0);
65  ARM_COMPUTE_RETURN_ERROR_ON(boxes->tensor_shape()[0] != 4);
66  ARM_COMPUTE_RETURN_ERROR_ON(boxes->tensor_shape()[1] != box_ind->tensor_shape()[0]);
67  TensorInfo temp_info;
68  ARM_COMPUTE_RETURN_ON_ERROR(CLCropKernel::validate(input->clone().get(), &temp_info, { 0, 0 }, { 1, 1 }, input->dimension(3) - 1, extrapolation_value));
69  if(output->total_size() > 0)
70  {
73  TensorShape out_shape(input->tensor_shape()[0], crop_size.x, crop_size.y, boxes->tensor_shape()[1]);
75  }
76  return Status{};
77 }
78 
79 void CLCropResize::configure(const ICLTensor *input, ICLTensor *boxes, ICLTensor *box_ind, ICLTensor *output, Coordinates2D crop_size,
80  InterpolationPolicy method, float extrapolation_value)
81 {
82  configure(CLKernelLibrary::get().get_compile_context(), input, boxes, box_ind, output, crop_size, method, extrapolation_value);
83 }
84 
85 void CLCropResize::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *boxes, ICLTensor *box_ind, ICLTensor *output, Coordinates2D crop_size,
86  InterpolationPolicy method, float extrapolation_value)
87 {
88  ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, boxes, box_ind);
89  ARM_COMPUTE_ERROR_THROW_ON(CLCropResize::validate(input->info(), boxes->info(), box_ind->info(), output->info(), crop_size, method, extrapolation_value));
90 
91  TensorShape output_shape = TensorShape(input->info()->tensor_shape()[0], crop_size.x, crop_size.y, boxes->info()->tensor_shape()[1]);
93 
94  _num_boxes = boxes->info()->tensor_shape()[1];
95  TensorShape out_shape(input->info()->tensor_shape()[0], crop_size.x, crop_size.y);
96 
97  _input = input;
98  _boxes = boxes;
99  _box_ind = box_ind;
100  _output = output;
101  _method = method;
102  _extrapolation_value = extrapolation_value;
103 
104  // For each crop box:
105  // - The initial cropped image is produced as specified by boxes[i] from the 3D image input[box_ind[i]].
106  // Possibly using a CLCropKernel and up to four CLMemsetKernels.
107  // - A tensor is required to hold this initial cropped image.
108  // - A scale function is used to resize the cropped image to the size specified by crop_size.
109  // - A tensor is required to hold the final scaled image before it is copied into the 4D output
110  // that will hold all final cropped and scaled 3D images using CLCopyKernel.
111 
112  // The contents of _boxes and _box_ind are required to calculate the shape
113  // of the initial cropped image and thus are required to configure the
114  // kernels used for cropping and scaling.
115  _boxes->map(CLScheduler::get().queue());
116  _box_ind->map(CLScheduler::get().queue());
117  for(unsigned int num_box = 0; num_box < _num_boxes; ++num_box)
118  {
119  auto crop_tensor = support::cpp14::make_unique<CLTensor>();
120  TensorInfo crop_result_info(1, DataType::F32);
121  crop_result_info.set_data_layout(DataLayout::NHWC);
122  crop_tensor->allocator()->init(crop_result_info);
123  _crop_results.emplace_back(std::move(crop_tensor));
124 
125  auto scale_tensor = support::cpp14::make_unique<CLTensor>();
126  TensorInfo scaled_result_info(out_shape, 1, DataType::F32);
127  scaled_result_info.set_data_layout(DataLayout::NHWC);
128  scale_tensor->allocator()->init(scaled_result_info);
129  _scaled_results.emplace_back(std::move(scale_tensor));
130 
131  // Size of the crop box in _boxes has to be given before the configure
132  uint32_t batch_index;
133  Coordinates start{};
134  Coordinates end{};
135  configure_crop(_input, _boxes, _box_ind, _crop_results[num_box].get(), num_box, start, end, batch_index);
136 
137  auto scale_kernel = support::cpp14::make_unique<CLScale>();
138  scale_kernel->configure(compile_context, _crop_results[num_box].get(), _scaled_results[num_box].get(), ScaleKernelInfo{ _method, BorderMode::CONSTANT, PixelValue(_extrapolation_value), SamplingPolicy::TOP_LEFT });
139  _scale.emplace_back(std::move(scale_kernel));
140 
142  win.set(3, Window::Dimension(num_box, num_box + 1, 1));
143 
144  auto copy_kernel = support::cpp14::make_unique<CLCopyKernel>();
145  copy_kernel->configure(compile_context, _scaled_results[num_box].get(), _output, PaddingList(), &win);
146  _copy.emplace_back(std::move(copy_kernel));
147 
148  _crop_results[num_box]->allocator()->allocate();
149  _scaled_results[num_box]->allocator()->allocate();
150 
151  bool is_width_flipped = end[0] < start[0];
152  bool is_height_flipped = end[1] < start[1];
153  /** The number of rows out of bounds at the start and end of _crop_results[num_box].get(). */
154  std::array<int32_t, 2> rows_out_of_bounds{ 0 };
155  /** The number of columns out of bounds at the start and end of _crop_results[num_box].get(). */
156  std::array<int32_t, 2> cols_out_of_bounds{ 0 };
157  if(is_height_flipped)
158  {
159  rows_out_of_bounds[0] = start[1] >= static_cast<int32_t>(_input->info()->dimension(2)) ? std::min(start[1] - _input->info()->dimension(2) + 1, _crop_results[num_box].get()->info()->dimension(2)) : 0;
160  rows_out_of_bounds[1] = end[1] < 0 ? std::min(-end[1], static_cast<int32_t>(_crop_results[num_box].get()->info()->dimension(2))) : 0;
161  }
162  else
163  {
164  rows_out_of_bounds[0] = start[1] < 0 ? std::min(-start[1], static_cast<int32_t>(_crop_results[num_box].get()->info()->dimension(2))) : 0;
165  rows_out_of_bounds[1] = end[1] >= static_cast<int32_t>(_input->info()->dimension(2)) ? std::min(end[1] - _input->info()->dimension(2) + 1, _crop_results[num_box].get()->info()->dimension(2)) : 0;
166  }
167  if(is_width_flipped)
168  {
169  cols_out_of_bounds[0] = start[0] >= static_cast<int32_t>(_input->info()->dimension(1)) ? std::min(start[0] - _input->info()->dimension(1) + 1, _crop_results[num_box].get()->info()->dimension(1)) : 0;
170  cols_out_of_bounds[1] = end[0] < 0 ? std::min(-end[0], static_cast<int32_t>(_crop_results[num_box].get()->info()->dimension(1))) : 0;
171  }
172  else
173  {
174  cols_out_of_bounds[0] = start[0] < 0 ? std::min(-start[0], static_cast<int32_t>(_crop_results[num_box].get()->info()->dimension(1))) : 0;
175  cols_out_of_bounds[1] = end[0] >= static_cast<int32_t>(_input->info()->dimension(1)) ? std::min(end[0] - _input->info()->dimension(1) + 1, _crop_results[num_box].get()->info()->dimension(1)) : 0;
176  }
177 
178  Window full_window = calculate_max_window(*_crop_results[num_box].get()->info());
179 
180  // Full _crop_results[num_box].get() window:
181  // --------------------------------
182  // | Out of bounds |
183  // | rows before |
184  // |------------------------------|
185  // | Out of | In | Out of |
186  // | bounds | bounds | bounds |
187  // | cols | elements | cols |
188  // | before | copied | after |
189  // | | from input | |
190  // |------------------------------|
191  // | Out of bounds |
192  // | rows after |
193  // |------------------------------|
194  // Use a separate _crop_results[num_box].get() window for each section of the full _crop_results[num_box].get() window.
195  // Fill all _crop_results[num_box].get() rows that have no elements that are within the input bounds
196  // with the extrapolation value using memset.
197  // First for the rows before the in bounds rows.
198  if(rows_out_of_bounds[0] > 0)
199  {
200  Window slice_fill_rows_before(full_window);
201  slice_fill_rows_before.set(2, Window::Dimension(0, rows_out_of_bounds[0], 1));
202  auto kernel = arm_compute::support::cpp14::make_unique<CLMemsetKernel>();
203  kernel->configure(compile_context, _crop_results[num_box].get(), extrapolation_value, &slice_fill_rows_before);
204  _internal_kernels.push_back(std::move(kernel));
205  }
206 
207  Window slice_in(full_window);
208  slice_in.set(2, Window::Dimension(rows_out_of_bounds[0], _crop_results[num_box].get()->info()->dimension(2) - rows_out_of_bounds[1], 1));
209  slice_in.set(1, Window::Dimension(cols_out_of_bounds[0], _crop_results[num_box].get()->info()->dimension(1) - cols_out_of_bounds[1], 1));
210 
211  int rows_in_bounds = static_cast<int32_t>(_crop_results[num_box].get()->info()->dimension(2)) - rows_out_of_bounds[0] - rows_out_of_bounds[1];
212  if(rows_in_bounds > 0)
213  {
214  // Fill all elements that share a row with an in bounds element with the extrapolation value.
215  if(cols_out_of_bounds[0] > 0)
216  {
217  Window slice_fill_cols_before(slice_in);
218  slice_fill_cols_before.set(1, Window::Dimension(0, cols_out_of_bounds[0], 1));
219  auto kernel = arm_compute::support::cpp14::make_unique<CLMemsetKernel>();
220  kernel->configure(compile_context, _crop_results[num_box].get(), extrapolation_value, &slice_fill_cols_before);
221  _internal_kernels.push_back(std::move(kernel));
222  }
223 
224  if(cols_out_of_bounds[1] > 0)
225  {
226  Window slice_fill_cols_after(slice_in);
227  slice_fill_cols_after.set(1, Window::Dimension(_crop_results[num_box].get()->info()->dimension(1) - cols_out_of_bounds[1], _crop_results[num_box].get()->info()->dimension(1), 1));
228  auto kernel = arm_compute::support::cpp14::make_unique<CLMemsetKernel>();
229  kernel->configure(compile_context, _crop_results[num_box].get(), extrapolation_value, &slice_fill_cols_after);
230  _internal_kernels.push_back(std::move(kernel));
231  }
232 
233  // Copy all elements within the input bounds from the input tensor.
234  int cols_in_bounds = static_cast<int32_t>(_crop_results[num_box].get()->info()->dimension(1)) - cols_out_of_bounds[0] - cols_out_of_bounds[1];
235  if(cols_in_bounds > 0)
236  {
237  Coordinates2D start_in{ is_width_flipped ? start[0] - cols_out_of_bounds[0] : start[0] + cols_out_of_bounds[0],
238  is_height_flipped ? start[1] - rows_out_of_bounds[0] : start[1] + rows_out_of_bounds[0] };
239  Coordinates2D end_in{ is_width_flipped ? start_in.x - cols_in_bounds + 1 : start_in.x + cols_in_bounds - 1,
240  is_height_flipped ? start_in.y - rows_in_bounds + 1 : start_in.y + rows_in_bounds - 1 };
241  auto kernel = arm_compute::support::cpp14::make_unique<CLCropKernel>();
242 
243  kernel->configure(compile_context, _input, _crop_results[num_box].get(), start_in, end_in, batch_index, extrapolation_value, &slice_in);
244  _internal_kernels.push_back(std::move(kernel));
245  }
246  }
247 
248  // Fill all rows after the in bounds elements with the extrapolation value.
249  if(rows_out_of_bounds[1] > 0)
250  {
251  Window slice_fill_rows_after(full_window);
252  slice_fill_rows_after.set(2, Window::Dimension(_crop_results[num_box].get()->info()->dimension(2) - rows_out_of_bounds[1], _crop_results[num_box].get()->info()->dimension(2), 1));
253  auto kernel = arm_compute::support::cpp14::make_unique<CLMemsetKernel>();
254  kernel->configure(compile_context, _crop_results[num_box].get(), extrapolation_value, &slice_fill_rows_after);
255  _internal_kernels.push_back(std::move(kernel));
256  }
257  }
258  _boxes->unmap(CLScheduler::get().queue());
259  _box_ind->unmap(CLScheduler::get().queue());
261 }
262 
264 {
265  ARM_COMPUTE_ERROR_ON_MSG(_output == nullptr, "Unconfigured function");
266 
267  for(unsigned int i = 0; i < _internal_kernels.size(); ++i)
268  {
270  }
271 
273  for(auto &kernel : _scale)
274  {
275  kernel->run();
276  }
278  for(auto &kernel : _copy)
279  {
280  CLScheduler::get().enqueue(*kernel, true);
281  }
283 }
284 } // namespace arm_compute
Class describing the value of a pixel for any image format.
Definition: PixelValue.h:34
InterpolationPolicy
Interpolation method.
Definition: Types.h:369
std::vector< std::unique_ptr< CLTensor > > _crop_results
Definition: CLCropResize.h:128
CLCropResize()
Default constructor.
void map(cl::CommandQueue &q, bool blocking=true)
Enqueue a map operation of the allocated buffer on the given queue.
Definition: ICLTensor.cpp:35
Shape of a tensor.
Definition: TensorShape.h:39
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
std::vector< PaddingInfo > PaddingList
List of padding information.
Definition: Types.h:458
static CLScheduler & get()
Access the scheduler singleton.
Definition: CLScheduler.cpp:99
static Status validate(const ITensorInfo *input, const ITensorInfo *output, Coordinates2D start, Coordinates2D end, uint32_t batch_index, float extrapolation_value=0, Window *output_window=nullptr)
Static function to check if given info will lead to a valid configuration of CLStridedSliceKernel.
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(...)
Definition: Validate.h:494
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
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
std::vector< std::unique_ptr< CLCopyKernel > > _copy
Definition: CLCropResize.h:127
Status class.
Definition: Error.h:52
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(t,...)
Definition: Validate.h:694
#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
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
int32_t x
X coordinates.
Definition: Types.h:442
std::vector< std::unique_ptr< CLTensor > > _scaled_results
Definition: CLCropResize.h:129
InterpolationPolicy _method
Definition: CLCropResize.h:123
ITensorInfo & set_data_layout(const DataLayout &data_layout) override
Set the data layout of the tensor.
Definition: TensorInfo.cpp:378
int32_t y
Y coordinates.
Definition: Types.h:443
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(...)
Definition: Validate.h:288
void unmap(cl::CommandQueue &q)
Enqueue an unmap operation of the allocated and mapped buffer on the given queue.
Definition: ICLTensor.cpp:40
#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg)
Definition: Error.h:456
Coordinates of an item.
Definition: Coordinates.h:37
static Status validate(const ITensorInfo *input, ITensorInfo *boxes, ITensorInfo *box_ind, const ITensorInfo *output, Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value)
Static function to check if given info will lead to a valid configuration of NESlice.
void run() override
Run the kernels contained in the function.
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor's metadata.
Samples are taken at pixel top left corner.
const ICLTensor * _input
Definition: CLCropResize.h:118
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
Output values are determined by averaging the source pixels whose areas fall under the area of the de...
void enqueue(ICLKernel &kernel, bool flush=true)
Schedule the execution of the passed kernel if possible.
CLCompileContext class.
void sync()
Blocks until all commands in the associated command queue have finished.
Definition: CLScheduler.cpp:67
std::vector< std::unique_ptr< ICLKernel > > _internal_kernels
Definition: CLCropResize.h:131
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:161
Interface for OpenCL tensor.
Definition: ICLTensor.h:42
virtual size_t total_size() const =0
Returns the total size of the tensor in bytes.
Coordinate type.
Definition: Types.h:440
Num samples, height, width, channels.
void configure(const ICLTensor *input, ICLTensor *boxes, ICLTensor *box_ind, ICLTensor *output, Coordinates2D crop_size, InterpolationPolicy method=InterpolationPolicy::BILINEAR, float extrapolation_value=0)
Configure kernel.
std::vector< std::unique_ptr< CLScale > > _scale
Definition: CLCropResize.h:126
Store the tensor's metadata.
Definition: TensorInfo.h:45
Describe a multidimensional execution window.
Definition: Window.h:39