Compute Library
 21.02
NEGenerateProposalsLayer.cpp
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25 
26 #include "arm_compute/core/Types.h"
32 
33 namespace arm_compute
34 {
35 NEGenerateProposalsLayer::NEGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager)
36  : _memory_group(memory_manager),
37  _permute_deltas(),
38  _flatten_deltas(),
39  _permute_scores(),
40  _flatten_scores(),
41  _compute_anchors(nullptr),
42  _bounding_box(),
43  _pad(),
44  _dequantize_anchors(),
45  _dequantize_deltas(),
46  _quantize_all_proposals(),
47  _cpp_nms(memory_manager),
48  _is_nhwc(false),
49  _is_qasymm8(false),
50  _deltas_permuted(),
51  _deltas_flattened(),
52  _deltas_flattened_f32(),
53  _scores_permuted(),
54  _scores_flattened(),
55  _all_anchors(),
56  _all_anchors_f32(),
57  _all_proposals(),
58  _all_proposals_quantized(),
59  _keeps_nms_unused(),
60  _classes_nms_unused(),
61  _proposals_4_roi_values(),
62  _all_proposals_to_use(nullptr),
63  _num_valid_proposals(nullptr),
64  _scores_out(nullptr)
65 {
66 }
67 
69 
70 void NEGenerateProposalsLayer::configure(const ITensor *scores, const ITensor *deltas, const ITensor *anchors, ITensor *proposals, ITensor *scores_out, ITensor *num_valid_proposals,
72 {
73  ARM_COMPUTE_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals);
74  ARM_COMPUTE_ERROR_THROW_ON(NEGenerateProposalsLayer::validate(scores->info(), deltas->info(), anchors->info(), proposals->info(), scores_out->info(), num_valid_proposals->info(), info));
75 
76  _is_nhwc = scores->info()->data_layout() == DataLayout::NHWC;
77  const DataType scores_data_type = scores->info()->data_type();
78  _is_qasymm8 = scores_data_type == DataType::QASYMM8;
79  const int num_anchors = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::CHANNEL));
80  const int feat_width = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::WIDTH));
81  const int feat_height = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::HEIGHT));
82  const int total_num_anchors = num_anchors * feat_width * feat_height;
83  const int pre_nms_topN = info.pre_nms_topN();
84  const int post_nms_topN = info.post_nms_topN();
85  const size_t values_per_roi = info.values_per_roi();
86 
87  const QuantizationInfo scores_qinfo = scores->info()->quantization_info();
88  const DataType rois_data_type = (_is_qasymm8) ? DataType::QASYMM16 : scores_data_type;
89  const QuantizationInfo rois_qinfo = (_is_qasymm8) ? QuantizationInfo(0.125f, 0) : scores->info()->quantization_info();
90 
91  // Compute all the anchors
92  _memory_group.manage(&_all_anchors);
93  _compute_anchors = std::make_unique<NEComputeAllAnchorsKernel>();
94  _compute_anchors->configure(anchors, &_all_anchors, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale()));
95 
96  const TensorShape flatten_shape_deltas(values_per_roi, total_num_anchors);
97  _deltas_flattened.allocator()->init(TensorInfo(flatten_shape_deltas, 1, scores_data_type, deltas->info()->quantization_info()));
98 
99  // Permute and reshape deltas
100  _memory_group.manage(&_deltas_flattened);
101  if(!_is_nhwc)
102  {
103  _memory_group.manage(&_deltas_permuted);
104  _permute_deltas.configure(deltas, &_deltas_permuted, PermutationVector{ 2, 0, 1 });
105  _flatten_deltas.configure(&_deltas_permuted, &_deltas_flattened);
106  _deltas_permuted.allocator()->allocate();
107  }
108  else
109  {
110  _flatten_deltas.configure(deltas, &_deltas_flattened);
111  }
112 
113  const TensorShape flatten_shape_scores(1, total_num_anchors);
114  _scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, scores_data_type, scores_qinfo));
115 
116  // Permute and reshape scores
117  _memory_group.manage(&_scores_flattened);
118  if(!_is_nhwc)
119  {
120  _memory_group.manage(&_scores_permuted);
121  _permute_scores.configure(scores, &_scores_permuted, PermutationVector{ 2, 0, 1 });
122  _flatten_scores.configure(&_scores_permuted, &_scores_flattened);
123  _scores_permuted.allocator()->allocate();
124  }
125  else
126  {
127  _flatten_scores.configure(scores, &_scores_flattened);
128  }
129 
130  Tensor *anchors_to_use = &_all_anchors;
131  Tensor *deltas_to_use = &_deltas_flattened;
132  if(_is_qasymm8)
133  {
134  _all_anchors_f32.allocator()->init(TensorInfo(_all_anchors.info()->tensor_shape(), 1, DataType::F32));
135  _deltas_flattened_f32.allocator()->init(TensorInfo(_deltas_flattened.info()->tensor_shape(), 1, DataType::F32));
136  _memory_group.manage(&_all_anchors_f32);
137  _memory_group.manage(&_deltas_flattened_f32);
138  // Dequantize anchors to float
139  _dequantize_anchors.configure(&_all_anchors, &_all_anchors_f32);
140  _all_anchors.allocator()->allocate();
141  anchors_to_use = &_all_anchors_f32;
142  // Dequantize deltas to float
143  _dequantize_deltas.configure(&_deltas_flattened, &_deltas_flattened_f32);
144  _deltas_flattened.allocator()->allocate();
145  deltas_to_use = &_deltas_flattened_f32;
146  }
147  // Bounding box transform
148  _memory_group.manage(&_all_proposals);
149  BoundingBoxTransformInfo bbox_info(info.im_width(), info.im_height(), 1.f);
150  _bounding_box.configure(anchors_to_use, &_all_proposals, deltas_to_use, bbox_info);
151  deltas_to_use->allocator()->allocate();
152  anchors_to_use->allocator()->allocate();
153 
154  _all_proposals_to_use = &_all_proposals;
155  if(_is_qasymm8)
156  {
157  _memory_group.manage(&_all_proposals_quantized);
158  // Requantize all_proposals to QASYMM16 with 0.125 scale and 0 offset
159  _all_proposals_quantized.allocator()->init(TensorInfo(_all_proposals.info()->tensor_shape(), 1, DataType::QASYMM16, QuantizationInfo(0.125f, 0)));
160  _quantize_all_proposals.configure(&_all_proposals, &_all_proposals_quantized);
161  _all_proposals.allocator()->allocate();
162  _all_proposals_to_use = &_all_proposals_quantized;
163  }
164  // The original layer implementation first selects the best pre_nms_topN anchors (thus having a lightweight sort)
165  // that are then transformed by bbox_transform. The boxes generated are then fed into a non-sorting NMS operation.
166  // Since we are reusing the NMS layer and we don't implement any CL/sort, we let NMS do the sorting (of all the input)
167  // and the filtering
168  const int scores_nms_size = std::min<int>(std::min<int>(post_nms_topN, pre_nms_topN), total_num_anchors);
169  const float min_size_scaled = info.min_size() * info.im_scale();
170  _memory_group.manage(&_classes_nms_unused);
171  _memory_group.manage(&_keeps_nms_unused);
172 
173  // Note that NMS needs outputs preinitialized.
174  auto_init_if_empty(*scores_out->info(), TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo);
175  auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, rois_data_type, rois_qinfo);
176  auto_init_if_empty(*num_valid_proposals->info(), TensorShape(1), 1, DataType::U32);
177 
178  // Initialize temporaries (unused) outputs
179  _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo));
180  _keeps_nms_unused.allocator()->init(*scores_out->info());
181 
182  // Save the output (to map and unmap them at run)
183  _scores_out = scores_out;
184  _num_valid_proposals = num_valid_proposals;
185 
186  _memory_group.manage(&_proposals_4_roi_values);
187 
188  const BoxNMSLimitInfo box_nms_info(0.0f, info.nms_thres(), scores_nms_size, false, NMSType::LINEAR, 0.5f, 0.001f, true, min_size_scaled, info.im_width(), info.im_height());
189  _cpp_nms.configure(&_scores_flattened /*scores_in*/,
190  _all_proposals_to_use /*boxes_in,*/,
191  nullptr /* batch_splits_in*/,
192  scores_out /* scores_out*/,
193  &_proposals_4_roi_values /*boxes_out*/,
194  &_classes_nms_unused /*classes*/,
195  nullptr /*batch_splits_out*/,
196  &_keeps_nms_unused /*keeps*/,
197  num_valid_proposals /* keeps_size*/,
198  box_nms_info);
199 
200  _keeps_nms_unused.allocator()->allocate();
201  _classes_nms_unused.allocator()->allocate();
202  _all_proposals_to_use->allocator()->allocate();
203  _scores_flattened.allocator()->allocate();
204 
205  // Add the first column that represents the batch id. This will be all zeros, as we don't support multiple images
206  _pad.configure(&_proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } });
207  _proposals_4_roi_values.allocator()->allocate();
208 }
209 
210 Status NEGenerateProposalsLayer::validate(const ITensorInfo *scores, const ITensorInfo *deltas, const ITensorInfo *anchors, const ITensorInfo *proposals, const ITensorInfo *scores_out,
211  const ITensorInfo *num_valid_proposals, const GenerateProposalsInfo &info)
212 {
213  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals);
218 
219  const int num_anchors = scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::CHANNEL));
220  const int feat_width = scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::WIDTH));
221  const int feat_height = scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::HEIGHT));
222  const int num_images = scores->dimension(3);
223  const int total_num_anchors = num_anchors * feat_width * feat_height;
224  const int values_per_roi = info.values_per_roi();
225 
226  const bool is_qasymm8 = scores->data_type() == DataType::QASYMM8;
227 
228  ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1);
229 
230  if(is_qasymm8)
231  {
233  const UniformQuantizationInfo anchors_qinfo = anchors->quantization_info().uniform();
234  ARM_COMPUTE_RETURN_ERROR_ON(anchors_qinfo.scale != 0.125f);
235  }
236 
237  TensorInfo all_anchors_info(anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
238  ARM_COMPUTE_RETURN_ON_ERROR(NEComputeAllAnchorsKernel::validate(anchors, &all_anchors_info, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale())));
239 
240  TensorInfo deltas_permuted_info = deltas->clone()->set_tensor_shape(TensorShape(values_per_roi * num_anchors, feat_width, feat_height)).set_is_resizable(true);
241  TensorInfo scores_permuted_info = scores->clone()->set_tensor_shape(TensorShape(num_anchors, feat_width, feat_height)).set_is_resizable(true);
242  if(scores->data_layout() == DataLayout::NHWC)
243  {
244  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(deltas, &deltas_permuted_info);
245  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(scores, &scores_permuted_info);
246  }
247  else
248  {
249  ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(deltas, &deltas_permuted_info, PermutationVector{ 2, 0, 1 }));
250  ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(scores, &scores_permuted_info, PermutationVector{ 2, 0, 1 }));
251  }
252 
253  TensorInfo deltas_flattened_info(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
254  ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayer::validate(&deltas_permuted_info, &deltas_flattened_info));
255 
256  TensorInfo scores_flattened_info(scores->clone()->set_tensor_shape(TensorShape(1, total_num_anchors)).set_is_resizable(true));
257  TensorInfo proposals_4_roi_values(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
258 
259  ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayer::validate(&scores_permuted_info, &scores_flattened_info));
260 
261  TensorInfo *proposals_4_roi_values_to_use = &proposals_4_roi_values;
262  TensorInfo proposals_4_roi_values_quantized(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
263  proposals_4_roi_values_quantized.set_data_type(DataType::QASYMM16).set_quantization_info(QuantizationInfo(0.125f, 0));
264  if(is_qasymm8)
265  {
266  TensorInfo all_anchors_f32_info(anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32));
267  ARM_COMPUTE_RETURN_ON_ERROR(NEDequantizationLayer::validate(&all_anchors_info, &all_anchors_f32_info));
268 
269  TensorInfo deltas_flattened_f32_info(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32));
270  ARM_COMPUTE_RETURN_ON_ERROR(NEDequantizationLayer::validate(&deltas_flattened_info, &deltas_flattened_f32_info));
271 
272  TensorInfo proposals_4_roi_values_f32(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32));
273  ARM_COMPUTE_RETURN_ON_ERROR(NEBoundingBoxTransform::validate(&all_anchors_f32_info, &proposals_4_roi_values_f32, &deltas_flattened_f32_info,
274  BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f)));
275 
276  ARM_COMPUTE_RETURN_ON_ERROR(NEQuantizationLayer::validate(&proposals_4_roi_values_f32, &proposals_4_roi_values_quantized));
277  proposals_4_roi_values_to_use = &proposals_4_roi_values_quantized;
278  }
279  else
280  {
281  ARM_COMPUTE_RETURN_ON_ERROR(NEBoundingBoxTransform::validate(&all_anchors_info, &proposals_4_roi_values, &deltas_flattened_info,
282  BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f)));
283  }
284 
285  ARM_COMPUTE_RETURN_ON_ERROR(NEPadLayer::validate(proposals_4_roi_values_to_use, proposals, PaddingList{ { 1, 0 } }));
286 
287  if(num_valid_proposals->total_size() > 0)
288  {
289  ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->num_dimensions() > 1);
290  ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->dimension(0) > 1);
292  }
293 
294  if(proposals->total_size() > 0)
295  {
297  ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(0) != size_t(values_per_roi) + 1);
298  ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(1) != size_t(total_num_anchors));
299  if(is_qasymm8)
300  {
302  const UniformQuantizationInfo proposals_qinfo = proposals->quantization_info().uniform();
303  ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.scale != 0.125f);
304  ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.offset != 0);
305  }
306  else
307  {
309  }
310  }
311 
312  if(scores_out->total_size() > 0)
313  {
314  ARM_COMPUTE_RETURN_ERROR_ON(scores_out->num_dimensions() > 1);
315  ARM_COMPUTE_RETURN_ERROR_ON(scores_out->dimension(0) != size_t(total_num_anchors));
317  }
318 
319  return Status{};
320 }
321 
323 {
324  // Acquire all the temporaries
325  MemoryGroupResourceScope scope_mg(_memory_group);
326 
327  // Compute all the anchors
328  NEScheduler::get().schedule(_compute_anchors.get(), Window::DimY);
329 
330  // Transpose and reshape the inputs
331  if(!_is_nhwc)
332  {
333  _permute_deltas.run();
334  _permute_scores.run();
335  }
336 
337  _flatten_deltas.run();
338  _flatten_scores.run();
339 
340  if(_is_qasymm8)
341  {
342  _dequantize_anchors.run();
343  _dequantize_deltas.run();
344  }
345 
346  // Build the boxes
347  _bounding_box.run();
348 
349  if(_is_qasymm8)
350  {
351  _quantize_all_proposals.run();
352  }
353 
354  // Non maxima suppression
355  _cpp_nms.run();
356 
357  // Add dummy batch indexes
358  _pad.run();
359 }
360 } // namespace arm_compute
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
Generate Proposals Information class.
Definition: Types.h:1352
Shape of a tensor.
Definition: TensorShape.h:39
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(t,...)
Definition: Validate.h:746
~NEGenerateProposalsLayer()
Default destructor.
void run() override final
Run the kernels contained in the function.
quantized, symmetric fixed-point 16-bit number
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(...)
Definition: Validate.h:494
void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo &sub_info)
Shares the same backing memory with another tensor allocator, while the tensor info might be differen...
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:481
static Status validate(const ITensorInfo *input, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of NEReshapeLayer.
BoxWithNonMaximaSuppressionLimit Information class.
Definition: Types.h:626
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
virtual DataType data_type() const =0
Data type used for each element of the tensor.
void run() override
Run the kernels contained in the function.
void configure(const ITensor *input, ITensor *output)
Configure the kernel.
1 channel, 1 F32 per channel
ITensorInfo & set_data_type(DataType data_type) override
Set the data type to the specified value.
Definition: TensorInfo.cpp:321
void run() override
Run the kernels contained in the function.
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm)
Static function to check if given info will lead to a valid configuration of NEPermute.
Definition: NEPermute.cpp:59
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Quantization info when assuming per layer quantization.
quantized, asymmetric fixed-point 16-bit number
static Status validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info)
Static function to check if given info will lead to a valid configuration of NEComputeAllAnchorsKerne...
Status class.
Definition: Error.h:52
#define ARM_COMPUTE_RETURN_ERROR_ON(cond)
If the condition is true, an error is returned.
Definition: Error.h:296
Interface for Neon tensor.
Definition: ITensor.h:36
static Status validate(const ITensorInfo *scores, const ITensorInfo *deltas, const ITensorInfo *anchors, const ITensorInfo *proposals, const ITensorInfo *scores_out, const ITensorInfo *num_valid_proposals, const GenerateProposalsInfo &info)
Static function to check if given info will lead to a valid configuration of NEGenerateProposalsLayer...
Copyright (c) 2017-2021 Arm Limited.
1 channel, 1 F16 per channel
TensorAllocator * allocator()
Return a pointer to the tensor&#39;s allocator.
Definition: Tensor.cpp:48
ITensorInfo * info() const override
Interface to be implemented by the child class to return the tensor&#39;s metadata.
Definition: Tensor.cpp:33
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:163
void manage(IMemoryManageable *obj) override
Sets a object to be managed by the given memory group.
Definition: MemoryGroup.h:79
Quantization information.
static Status validate(const ITensorInfo *input, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of NEDequantizationLayer.
1 channel, 1 U32 per channel
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
void configure(const ITensor *scores, const ITensor *deltas, const ITensor *anchors, ITensor *proposals, ITensor *scores_out, ITensor *num_valid_proposals, const GenerateProposalsInfo &info)
Set the input and output tensors.
static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding, const PixelValue constant_value=PixelValue(), const PaddingMode mode=PaddingMode::CONSTANT)
Static function to check if given info will lead to a valid configuration of NEPadLayer.
Definition: NEPadLayer.cpp:206
quantized, asymmetric fixed-point 8-bit number unsigned
void allocate() override
Allocate size specified by TensorInfo of CPU memory.
UniformQuantizationInfo uniform() const
Return per layer quantization info.
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...
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&#39;s metadata.
Basic implementation of the tensor interface.
Definition: Tensor.h:37
virtual ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info)=0
Set the quantization settings (scale and offset) of the tensor.
Bounding Box Transform information class.
Definition: Types.h:1483
void configure(const ITensor *input, ITensor *output)
Set the input and output tensors.
virtual QuantizationInfo quantization_info() const =0
Get the quantization settings (scale and offset) of the tensor.
Num samples, channels, height, width.
void configure(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, const BoundingBoxTransformInfo &info)
Set the input and output tensors.
NEGenerateProposalsLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
Default constructor.
Strides of an item in bytes.
Definition: Strides.h:37
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
void run() override
Run the kernels contained in the function.
Definition: NEPermute.cpp:67
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
Memory group resources scope handling class.
Definition: IMemoryGroup.h:82
virtual size_t total_size() const =0
Returns the total size of the tensor in bytes.
virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0
Runs the kernel in the same thread as the caller synchronously.
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)
Definition: Validate.h:443
static Status validate(const ITensorInfo *boxes, const ITensorInfo *pred_boxes, const ITensorInfo *deltas, const BoundingBoxTransformInfo &info)
Static function to check if given info will lead to a valid configuration of NEBoundingBoxTransform.
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:545
ComputeAnchors information class.
Definition: Types.h:1433
Num samples, height, width, channels.
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:792
void run() override
Run the kernels contained in the function.
Definition: NEPadLayer.cpp:250
void configure(ITensor *input, ITensor *output, const PaddingList &padding, const PixelValue constant_value=PixelValue(), const PaddingMode mode=PaddingMode::CONSTANT)
Initialize the function.
Definition: NEPadLayer.cpp:167
void configure(const ITensor *input, ITensor *output)
Initialise the kernel&#39;s inputs and outputs.
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:161
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:45
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
static Status validate(const ITensorInfo *input, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of NEQuantizationLayer.
void configure(const ITensor *input, ITensor *output, const PermutationVector &perm)
Configure the permute Neon kernel.
Definition: NEPermute.cpp:49
DataType
Available data types.
Definition: Types.h:77
void run() override
Run the kernels contained in the function.
virtual DataLayout data_layout() const =0
Get the data layout of the tensor.
static IScheduler & get()
Access the scheduler singleton.
Definition: Scheduler.cpp:94
void configure(const ITensor *scores_in, const ITensor *boxes_in, const ITensor *batch_splits_in, ITensor *scores_out, ITensor *boxes_out, ITensor *classes, ITensor *batch_splits_out=nullptr, ITensor *keeps=nullptr, ITensor *keeps_size=nullptr, const BoxNMSLimitInfo info=BoxNMSLimitInfo())
Configure the BoxWithNonMaximaSuppressionLimit CPP kernel.