Compute Library
 21.02
NENormalizationLayerKernel.cpp
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2  * Copyright (c) 2017-2021 Arm Limited.
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25 
26 #include "arm_compute/core/Error.h"
29 #include "arm_compute/core/Utils.h"
33 #include "src/core/CPP/Validate.h"
35 #include "src/core/NEON/NEMath.h"
40 
41 namespace arm_compute
42 {
43 namespace
44 {
45 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, const NormalizationLayerInfo &norm_info)
46 {
47  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_squared, output);
50 
53  ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd");
54 
55  // Checks performed when output is configured
56  if(output->total_size() != 0)
57  {
61  }
62 
63  return Status{};
64 }
65 
66 } // namespace
67 
69  : _func(nullptr), _input(nullptr), _input_squared(nullptr), _output(nullptr), _norm_info(NormType::IN_MAP_1D)
70 {
71 }
72 
73 void NENormalizationLayerKernel::configure(const ITensor *input, const ITensor *input_squared, ITensor *output, NormalizationLayerInfo norm_info)
74 {
75  ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_squared, output);
76  // Output tensor auto initialization if not yet initialized
77  auto_init_if_empty(*output->info(), *input->info());
78 
79  // Perform validation step
80  ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), input_squared->info(), output->info(), norm_info));
81 
82  const unsigned int norm_idx = get_normalization_dimension_index(input->info()->data_layout(), norm_info);
83 
84  _input = input;
85  _input_squared = input_squared;
86  _output = output;
87  _norm_info = norm_info;
88 
89  switch(_input->info()->data_type())
90  {
91  case DataType::F32:
92  {
93  switch(norm_idx)
94  {
95  case 0:
96  {
97  if(norm_info.type() == NormType::IN_MAP_2D)
98  {
99  _func = &NENormalizationLayerKernel::normalize_float<float, 4, 0, true>;
100  }
101  else
102  {
103  _func = &NENormalizationLayerKernel::normalize_float<float, 4, 0, false>;
104  }
105  break;
106  }
107  case 1:
108  if(norm_info.type() == NormType::IN_MAP_2D)
109  {
110  _func = &NENormalizationLayerKernel::normalize_float<float, 4, 1, true>;
111  }
112  else
113  {
114  _func = &NENormalizationLayerKernel::normalize_float<float, 4, 1, false>;
115  }
116  break;
117  case 2:
118  _func = &NENormalizationLayerKernel::normalize_float<float, 4, 2, false>;
119  break;
120  default:
121  break;
122  }
123  break;
124  }
125 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
126  case DataType::F16:
127  {
128  switch(norm_idx)
129  {
130  case 0:
131  {
132  if(norm_info.type() == NormType::IN_MAP_2D)
133  {
134  _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 0, true>;
135  }
136  else
137  {
138  _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 0, false>;
139  }
140  break;
141  }
142  case 1:
143  if(norm_info.type() == NormType::IN_MAP_2D)
144  {
145  _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 1, true>;
146  }
147  else
148  {
149  _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 1, false>;
150  }
151  break;
152  case 2:
153  _func = &NENormalizationLayerKernel::normalize_float<float16_t, 8, 2, false>;
154  break;
155  default:
156  break;
157  }
158  break;
159  }
160 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
161  default:
162  ARM_COMPUTE_ERROR("NOT SUPPORTED!");
163  }
164 
165  // Configure kernel window
166  Window win = calculate_max_window(*input->info(), Steps());
167  Coordinates coord;
168  coord.set_num_dimensions(output->info()->num_dimensions());
169  output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));
170  INEKernel::configure(win);
171 }
172 
173 template <typename T, unsigned int S, unsigned int dim, bool do_2D_norm>
174 void NENormalizationLayerKernel::normalize_float(const Window &window)
175 {
176  /** Neon vector tag type. */
177  using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
178 
179  Window win(window);
180  win.set(Window::DimX, Window::Dimension(0, 1, 1));
181 
182  const auto window_start_x = static_cast<int>(window.x().start());
183  const auto window_end_x = static_cast<int>(window.x().end());
184  const int window_step_x = S;
185 
186  Iterator input(_input, win);
187  Iterator input_squared(_input_squared, win);
188  Iterator output(_output, win);
189 
190  const int dim_y = _input->info()->data_layout() == DataLayout::NCHW ? 1 : 2;
191  const int radius = _norm_info.norm_size() / 2;
192  const int input_squared_stride_x = _input_squared->info()->strides_in_bytes()[0];
193  const int input_squared_stride_slice = _input_squared->info()->strides_in_bytes()[dim];
194  const int input_squared_stride_row = _input_squared->info()->strides_in_bytes()[dim_y];
195 
196  const int max_right = _input->info()->dimension(dim) - 1;
197  const int max_bottom = _input->info()->dimension(dim_y) - 1;
198 
199  const auto coeff_vec = wrapper::vdup_n(static_cast<T>(_norm_info.scale_coeff()), ExactTagType{});
200  const auto beta_vec = wrapper::vdup_n(static_cast<T>(_norm_info.beta()), ExactTagType{});
201  const auto kappa_vec = wrapper::vdup_n(static_cast<T>(_norm_info.kappa()), ExactTagType{});
202 
203  auto sequential_normalization = [&](const int x, const Coordinates & id, const int current_row, const int first_row, const int last_row, const T * input_ptr, const uint8_t *input_squared_start_ptr,
204  T * output_ptr)
205  {
206  const int current_slice = dim == 0 ? x : id[dim];
207  const int first_slice = std::max(current_slice - radius, 0);
208  const int last_slice = std::min(current_slice + radius, max_right);
209 
210  const uint8_t *const input_squared_x_ptr = input_squared_start_ptr + x * input_squared_stride_x;
211  // Accumulate 2D In-Map values
212  auto accu = static_cast<T>(0.f);
213  for(int j = first_row; j <= last_row; ++j)
214  {
215  // Compute row displacement
216  const uint8_t *const input_squared_ptr = input_squared_x_ptr + (j - current_row) * input_squared_stride_row;
217  for(int i = first_slice; i <= last_slice; ++i)
218  {
219  accu += *reinterpret_cast<const T *>(input_squared_ptr + (i - current_slice) * input_squared_stride_slice);
220  }
221  }
222 
223  // Normalize
224  const auto normalized = std::pow(accu * static_cast<T>(_norm_info.scale_coeff()) + static_cast<T>(_norm_info.kappa()), _norm_info.beta());
225  const auto normalized_pixel = (*(input_ptr + x)) / normalized;
226  *(output_ptr + x) = normalized_pixel;
227  };
228 
229  execute_window_loop(win, [&](const Coordinates & id)
230  {
231  const auto input_ptr = reinterpret_cast<const T *>(input.ptr());
232  auto output_ptr = reinterpret_cast<T *>(output.ptr());
233 
234  // Get range to normalize
235  const int current_row = do_2D_norm ? id[dim_y] : 0;
236  const int first_row = do_2D_norm ? std::max(current_row - radius, 0) : 0;
237  const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0;
238 
239  int x = window_start_x;
240  // Compute serially starting elements for the case x dimension is width
241  for(; x < radius && x < window_end_x && dim == 0; ++x)
242  {
243  sequential_normalization(x, id, current_row, first_row, last_row, input_ptr, input_squared.ptr(), output_ptr);
244  }
245 
246  // Compute vectorized
247  for(; x <= window_end_x - window_step_x - radius; x += window_step_x)
248  {
249  const int current_slice = dim == 0 ? x : id[dim];
250  const int first_slice = std::max(current_slice - radius, 0);
251  const int last_slice = std::min(current_slice + radius, max_right);
252 
253  const uint8_t *const input_squared_x_ptr = input_squared.ptr() + x * input_squared_stride_x;
254  // Accumulate 2D In-Map values
255  auto accu = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
256  for(int j = first_row; j <= last_row; ++j)
257  {
258  // Compute row displacement
259  const uint8_t *const input_squared_ptr = input_squared_x_ptr + (j - current_row) * input_squared_stride_row;
260  for(int i = first_slice; i <= last_slice; ++i)
261  {
262  accu = wrapper::vadd(accu, wrapper::vloadq(reinterpret_cast<const T *>(input_squared_ptr + (i - current_slice) * input_squared_stride_slice)));
263  }
264  }
265 
266  // Normalize
267  const auto normalized = wrapper::vpow(wrapper::vmla(kappa_vec, coeff_vec, accu), beta_vec);
268  const auto normalized_pixel = wrapper::vmul(wrapper::vloadq(input_ptr + x), wrapper::vinv(normalized));
269  wrapper::vstore(reinterpret_cast<T *>(output_ptr + x), normalized_pixel);
270  }
271 
272  // Compute left-over elements
273  for(; x < window_end_x; ++x)
274  {
275  sequential_normalization(x, id, current_row, first_row, last_row, input_ptr, input_squared.ptr(), output_ptr);
276  }
277  },
278  input, input_squared, output);
279 }
280 
281 Status NENormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, const NormalizationLayerInfo norm_info)
282 {
283  ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, input_squared, output, norm_info));
284 
285  return Status{};
286 }
287 
289 {
290  ARM_COMPUTE_UNUSED(info);
293  ARM_COMPUTE_ERROR_ON(_func == nullptr);
294 
295  // Run function
296  (this->*_func)(window);
297 }
298 } // namespace arm_compute
static Status validate(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, NormalizationLayerInfo norm_info)
Static function to check if given info will lead to a valid configuration of NENormalizationLayerKern...
float scale_coeff() const
Return the scaling factor of the normalization function.
Definition: Types.h:1711
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
float kappa() const
Get the kappa value.
Definition: Types.h:1685
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(...)
Definition: Validate.h:494
#define ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(tensor)
Definition: Validate.h:108
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
#define ARM_COMPUTE_ERROR(msg)
Print the given message then throw an std::runtime_error.
Definition: Error.h:352
uint32_t norm_size() const
Get the normalization size.
Definition: Types.h:1670
NormType type() const
Get the normalization type.
Definition: Types.h:1665
uint8x16_t vloadq(const uint8_t *ptr)
Definition: load.h:58
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
uint8x8_t vadd(const uint8x8_t &a, const uint8x8_t &b)
Definition: add.h:39
1 channel, 1 F32 per channel
Normalization Layer Information class.
Definition: Types.h:1647
#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
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
float32x2_t vinv(const float32x2_t &a)
Definition: inv.h:47
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Describe one of the image&#39;s dimensions with a start, end and step.
Definition: Window.h:77
float32x4_t vpow(const float32x4_t &a, const float32x4_t &b)
Definition: pow.h:40
Status class.
Definition: Error.h:52
Interface for Neon tensor.
Definition: ITensor.h:36
Copyright (c) 2017-2021 Arm Limited.
virtual void set_valid_region(const ValidRegion &valid_region)=0
Set the valid region of the tensor.
1 channel, 1 F16 per channel
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:163
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
#define ARM_COMPUTE_UNUSED(...)
To avoid unused variables warnings.
Definition: Error.h:152
Create the appropriate Neon vector given its type and size in terms of elements.
Definition: traits.h:48
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
Normalization applied within the same map in 1D region.
Class to describe a number of elements in each dimension.
Definition: Steps.h:40
Coordinates of an item.
Definition: Coordinates.h:37
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 ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
float beta() const
Get the beta value.
Definition: Types.h:1680
constexpr uint8_t * ptr() const
Return a pointer to the current pixel.
Definition: Helpers.inl:139
void run(const Window &window, const ThreadInfo &info) override
Execute the kernel on the passed window.
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)
Definition: Validate.h:941
Num samples, channels, height, width.
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
uint8x8_t vmul(const uint8x8_t &a, const uint8x8_t &b)
Definition: mul.h:39
Information about executing thread and CPU.
Definition: CPPTypes.h:235
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)
Definition: Validate.h:443
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:545
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:792
void configure(const ITensor *input, const ITensor *input_squared, ITensor *output, NormalizationLayerInfo norm_info)
Set the input and output tensors.
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
void vstore(uint8_t *ptr, uint8x8_t val)
Definition: store.h:39
#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)
If the condition is true, an error is returned.
Definition: Error.h:244
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:161
uint8x8_t vdup_n(uint8_t value, traits::vector_64_tag)
Definition: dup_n.h:41
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
void set_num_dimensions(size_t num_dimensions)
Set number of dimensions.
Definition: Dimensions.h:149
Includes all wrapper headers at once.
virtual const Strides & strides_in_bytes() const =0
The strides in bytes for accessing each dimension of the tensor.
uint8x8_t vmla(const uint8x8_t &a, const uint8x8_t &b, const uint8x8_t &c)
Definition: mla.h:46
Container for valid region of a window.
Definition: Types.h:188
constexpr int end() const
Return the end of the dimension.
Definition: Window.h:99
Iterator updated by execute_window_loop for each window element.
Definition: Helpers.h:46
unsigned int get_normalization_dimension_index(DataLayout layout, const NormalizationLayerInfo &info)
Calculate the normalization dimension index for a given normalization type.
constexpr int start() const
Return the start of the dimension.
Definition: Window.h:94
NormType
The normalization type used for the normalization layer.
Definition: Types.h:569
Describe a multidimensional execution window.
Definition: Window.h:39
Normalization applied within the same map in 2D region.
#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)
Definition: Validate.h:205
virtual DataLayout data_layout() const =0
Get the data layout of the tensor.
constexpr const Dimension & x() const
Alias to access the first dimension of the window.
Definition: Window.h:145