Compute Library
 22.11
impl.cpp
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25 namespace arm_compute
26 {
27 namespace cpu
28 {
29 void bounding_box_transform_qsymm16(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window)
30 
31 {
32  const size_t num_classes = deltas->info()->tensor_shape()[0] >> 2;
33  const size_t deltas_width = deltas->info()->tensor_shape()[0];
34  const int img_h = std::floor(bbinfo.img_height() / bbinfo.scale() + 0.5f);
35  const int img_w = std::floor(bbinfo.img_width() / bbinfo.scale() + 0.5f);
36 
37  const auto scale_after = (bbinfo.apply_scale() ? bbinfo.scale() : 1.f);
38  const auto scale_before = bbinfo.scale();
39  const auto offset = (bbinfo.correct_transform_coords() ? 1.f : 0.f);
40 
41  auto pred_ptr = reinterpret_cast<uint16_t *>(pred_boxes->buffer() + pred_boxes->info()->offset_first_element_in_bytes());
42  auto delta_ptr = reinterpret_cast<uint8_t *>(deltas->buffer() + deltas->info()->offset_first_element_in_bytes());
43 
44  const auto boxes_qinfo = boxes->info()->quantization_info().uniform();
45  const auto deltas_qinfo = deltas->info()->quantization_info().uniform();
46  const auto pred_qinfo = pred_boxes->info()->quantization_info().uniform();
47 
48  Iterator box_it(boxes, window);
49  execute_window_loop(window, [&](const Coordinates & id)
50  {
51  const auto ptr = reinterpret_cast<uint16_t *>(box_it.ptr());
52  const auto b0 = dequantize_qasymm16(*ptr, boxes_qinfo);
53  const auto b1 = dequantize_qasymm16(*(ptr + 1), boxes_qinfo);
54  const auto b2 = dequantize_qasymm16(*(ptr + 2), boxes_qinfo);
55  const auto b3 = dequantize_qasymm16(*(ptr + 3), boxes_qinfo);
56  const float width = (b2 / scale_before) - (b0 / scale_before) + 1.f;
57  const float height = (b3 / scale_before) - (b1 / scale_before) + 1.f;
58  const float ctr_x = (b0 / scale_before) + 0.5f * width;
59  const float ctr_y = (b1 / scale_before) + 0.5f * height;
60  for(size_t j = 0; j < num_classes; ++j)
61  {
62  // Extract deltas
63  const size_t delta_id = id.y() * deltas_width + 4u * j;
64  const float dx = dequantize_qasymm8(delta_ptr[delta_id], deltas_qinfo) / bbinfo.weights()[0];
65  const float dy = dequantize_qasymm8(delta_ptr[delta_id + 1], deltas_qinfo) / bbinfo.weights()[1];
66  float dw = dequantize_qasymm8(delta_ptr[delta_id + 2], deltas_qinfo) / bbinfo.weights()[2];
67  float dh = dequantize_qasymm8(delta_ptr[delta_id + 3], deltas_qinfo) / bbinfo.weights()[3];
68  // Clip dw and dh
69  dw = std::min(dw, bbinfo.bbox_xform_clip());
70  dh = std::min(dh, bbinfo.bbox_xform_clip());
71  // Determine the predictions
72  const float pred_ctr_x = dx * width + ctr_x;
73  const float pred_ctr_y = dy * height + ctr_y;
74  const float pred_w = std::exp(dw) * width;
75  const float pred_h = std::exp(dh) * height;
76  // Store the prediction into the output tensor
77  pred_ptr[delta_id] = quantize_qasymm16(scale_after * utility::clamp<float>(pred_ctr_x - 0.5f * pred_w, 0.f, img_w - 1.f), pred_qinfo);
78  pred_ptr[delta_id + 1] = quantize_qasymm16(scale_after * utility::clamp<float>(pred_ctr_y - 0.5f * pred_h, 0.f, img_h - 1.f), pred_qinfo);
79  pred_ptr[delta_id + 2] = quantize_qasymm16(scale_after * utility::clamp<float>(pred_ctr_x + 0.5f * pred_w - offset, 0.f, img_w - 1.f), pred_qinfo);
80  pred_ptr[delta_id + 3] = quantize_qasymm16(scale_after * utility::clamp<float>(pred_ctr_y + 0.5f * pred_h - offset, 0.f, img_h - 1.f), pred_qinfo);
81  }
82  },
83  box_it);
84 }
85 
86 template <typename T>
87 void bounding_box_transform(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window)
88 {
89  const size_t num_classes = deltas->info()->tensor_shape()[0] >> 2;
90  const size_t deltas_width = deltas->info()->tensor_shape()[0];
91  const int img_h = std::floor(bbinfo.img_height() / bbinfo.scale() + 0.5f);
92  const int img_w = std::floor(bbinfo.img_width() / bbinfo.scale() + 0.5f);
93 
94  const auto scale_after = (bbinfo.apply_scale() ? T(bbinfo.scale()) : T(1));
95  const auto scale_before = T(bbinfo.scale());
96  ARM_COMPUTE_ERROR_ON(scale_before <= 0);
97  const auto offset = (bbinfo.correct_transform_coords() ? T(1.f) : T(0.f));
98 
99  auto pred_ptr = reinterpret_cast<T *>(pred_boxes->buffer() + pred_boxes->info()->offset_first_element_in_bytes());
100  auto delta_ptr = reinterpret_cast<T *>(deltas->buffer() + deltas->info()->offset_first_element_in_bytes());
101 
102  Iterator box_it(boxes, window);
103  execute_window_loop(window, [&](const Coordinates & id)
104  {
105  const auto ptr = reinterpret_cast<T *>(box_it.ptr());
106  const auto b0 = *ptr;
107  const auto b1 = *(ptr + 1);
108  const auto b2 = *(ptr + 2);
109  const auto b3 = *(ptr + 3);
110  const T width = (b2 / scale_before) - (b0 / scale_before) + T(1.f);
111  const T height = (b3 / scale_before) - (b1 / scale_before) + T(1.f);
112  const T ctr_x = (b0 / scale_before) + T(0.5f) * width;
113  const T ctr_y = (b1 / scale_before) + T(0.5f) * height;
114  for(size_t j = 0; j < num_classes; ++j)
115  {
116  // Extract deltas
117  const size_t delta_id = id.y() * deltas_width + 4u * j;
118  const T dx = delta_ptr[delta_id] / T(bbinfo.weights()[0]);
119  const T dy = delta_ptr[delta_id + 1] / T(bbinfo.weights()[1]);
120  T dw = delta_ptr[delta_id + 2] / T(bbinfo.weights()[2]);
121  T dh = delta_ptr[delta_id + 3] / T(bbinfo.weights()[3]);
122  // Clip dw and dh
123  dw = std::min(dw, T(bbinfo.bbox_xform_clip()));
124  dh = std::min(dh, T(bbinfo.bbox_xform_clip()));
125  // Determine the predictions
126  const T pred_ctr_x = dx * width + ctr_x;
127  const T pred_ctr_y = dy * height + ctr_y;
128  const T pred_w = std::exp(dw) * width;
129  const T pred_h = std::exp(dh) * height;
130  // Store the prediction into the output tensor
131  pred_ptr[delta_id] = scale_after * utility::clamp<T>(pred_ctr_x - T(0.5f) * pred_w, T(0), T(img_w - 1));
132  pred_ptr[delta_id + 1] = scale_after * utility::clamp<T>(pred_ctr_y - T(0.5f) * pred_h, T(0), T(img_h - 1));
133  pred_ptr[delta_id + 2] = scale_after * utility::clamp<T>(pred_ctr_x + T(0.5f) * pred_w - offset, T(0), T(img_w - 1));
134  pred_ptr[delta_id + 3] = scale_after * utility::clamp<T>(pred_ctr_y + T(0.5f) * pred_h - offset, T(0), T(img_h - 1));
135  }
136  },
137  box_it);
138 }
139 
140 template void bounding_box_transform<float>(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window);
141 
142 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
143 template void bounding_box_transform<float16_t>(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window);
144 #endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
145 } // namespace cpu
146 } // namespace arm_compute
__global uchar * offset(const Image *img, int x, int y)
Get the pointer position of a Image.
Definition: helpers.h:1084
void bounding_box_transform_qsymm16(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window)
Definition: impl.cpp:29
float dequantize_qasymm8(uint8_t value, const INFO_TYPE &qinfo)
Dequantize a value given an unsigned 8-bit asymmetric quantization scheme.
#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
Interface for CPU tensor.
Definition: ITensor.h:36
template void bounding_box_transform< float >(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window)
Copyright (c) 2017-2022 Arm Limited.
float dequantize_qasymm16(uint16_t value, const UniformQuantizationInfo &qinfo)
Dequantize a value given a 16-bit asymmetric quantization scheme.
void bounding_box_transform(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window)
Definition: impl.cpp:87
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
Coordinates of an item.
Definition: Coordinates.h:37
virtual uint8_t * buffer() const =0
Interface to be implemented by the child class to return a pointer to CPU memory. ...
UniformQuantizationInfo uniform() const
Return per layer quantization info.
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
constexpr uint8_t * ptr() const
Return a pointer to the current pixel.
Definition: Helpers.inl:139
Bounding Box Transform information class.
Definition: Types.h:1572
virtual QuantizationInfo quantization_info() const =0
Get the quantization settings (scale and offset) of the tensor.
virtual size_t offset_first_element_in_bytes() const =0
The offset from the beginning of the memory allocation to the first element of the tensor...
std::array< float, 4 > weights() const
Definition: Types.h:1593
void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators)
Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...
Definition: Helpers.inl:77
Iterator updated by execute_window_loop for each window element.
Definition: Helpers.h:46
Describe a multidimensional execution window.
Definition: Window.h:39
uint16_t quantize_qasymm16(float value, const UniformQuantizationInfo &qinfo, RoundingPolicy rounding_policy=RoundingPolicy::TO_NEAREST_UP)
Quantize a value given a 16-bit asymmetric quantization scheme.