Compute Library
 22.11
list.h
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24 #ifndef SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
25 #define SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
26 
27 #include "arm_compute/core/Types.h"
33 
34 namespace arm_compute
35 {
36 namespace cpu
37 {
38 template <typename T>
39 void directconv3d_float_neon_ndhwc(const ITensor *src0, const ITensor *src1, const ITensor *src2, ITensor *dst, const Conv3dInfo &conv_info, const Window &window)
40 {
41  const ITensor *src = src0;
42  const ITensor *weights = src1;
43  const ITensor *biases = src2;
44 
46  using vector_type = typename vtype::type;
47  using tag_type = typename vtype::tag_type;
48  constexpr int num_elems_read_per_iteration = 16 / sizeof(T);
49 
50  // Scalar quantities (N D H W Cin)
51  const int element_size = src->info()->element_size();
52  const int input_stride_w = src->info()->strides_in_bytes().y() / element_size;
53  const int input_stride_h = src->info()->strides_in_bytes().z() / element_size;
54  const int input_stride_d = src->info()->strides_in_bytes()[3] / element_size;
55  const int input_stride_n = src->info()->strides_in_bytes()[4] / element_size;
56  const int input_dim_w = src->info()->dimension(1);
57  const int input_dim_h = src->info()->dimension(2);
58  const int input_dim_d = src->info()->dimension(3);
59 
60  // Kernel info (D H W Cin Cout)
61  const unsigned int kernel_stride_w = weights->info()->strides_in_bytes()[2] / element_size;
62  const unsigned int kernel_stride_h = weights->info()->strides_in_bytes()[3] / element_size;
63  const unsigned int kernel_stride_d = weights->info()->strides_in_bytes()[4] / element_size;
64  const int kernel_dim_w = weights->info()->dimension(2);
65  const int kernel_dim_h = weights->info()->dimension(3);
66  const int kernel_dim_d = weights->info()->dimension(4);
67 
68  // Convolution padding and stride
69  const int conv_pad_top = conv_info.padding.top;
70  const int conv_pad_left = conv_info.padding.left;
71  const int conv_pad_front = conv_info.padding.front;
72  const int conv_stride_w = conv_info.stride.width;
73  const int conv_stride_h = conv_info.stride.height;
74  const int conv_stride_d = conv_info.stride.depth;
75 
76  // Setup input window for the output iterator
77  Window window_out = window;
78  window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
79 
80  // Setup input window for the weights iterator
81  Window window_w = calculate_max_window(*weights->info(), Steps());
82  window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
83  window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
84  window_w.set(Window::DimW, Window::Dimension(0, 1, 1));
85  window_w.set(4, Window::Dimension(0, 1, 1));
86 
87  Iterator out(dst, window_out);
88  Iterator wei(weights, window_w);
89 
90  const T *biases_ptr = nullptr;
91  if(biases != nullptr)
92  {
93  biases_ptr = reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes());
94  }
95  execute_window_loop(window_out, [&](const Coordinates & id)
96  {
97  // We are computing the theoretical input starting points
98  const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
99  const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
100  const int in_d_start_t = static_cast<int>(id[3]) * conv_stride_d - conv_pad_front;
101  const int in_w_end_t = in_w_start_t + kernel_dim_w;
102  const int in_h_end_t = in_h_start_t + kernel_dim_h;
103  const int in_d_end_t = in_d_start_t + kernel_dim_d;
104 
105  // We are computing the valid initial and ending input points by checking the borders
106  const int in_w_start = std::max(in_w_start_t, 0);
107  const int in_h_start = std::max(in_h_start_t, 0);
108  const int in_d_start = std::max(in_d_start_t, 0);
109  const int in_w_end = std::min(in_w_end_t, input_dim_w);
110  const int in_h_end = std::min(in_h_end_t, input_dim_h);
111  const int in_d_end = std::min(in_d_end_t, input_dim_d);
112 
113  // We use the input points to select the valid weight points to use
114  const int wei_w_start = in_w_start - in_w_start_t;
115  const int wei_h_start = in_h_start - in_h_start_t;
116  const int wei_d_start = in_d_start - in_d_start_t;
117  const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end);
118  const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
119  const int wei_d_end = kernel_dim_d - (in_d_end_t - in_d_end);
120 
121  const int index_c_out_end = weights->info()->dimension(0);
122  const int index_c_in_end = weights->info()->dimension(1);
123  const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[4] * input_stride_n;
124 
125  execute_window_loop(window_w, [&](const Coordinates & id_w)
126  {
127  /*
128  * This is the loop in the weights, and it goes along OFM (output feature map)
129  */
130  const auto weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
131  T out_temp = static_cast<T>(0);
132  T *out_ptr = reinterpret_cast<T *>(out.ptr());
133  for(int index_wei_d = wei_d_start, index_in_d = in_d_start; index_wei_d < wei_d_end; ++index_wei_d, ++index_in_d)
134  {
135  const auto in_ptr_d = in_ptr_start + index_in_d * input_stride_d;
136  const auto weights_ptr_d = weights_ptr_start + index_wei_d * kernel_stride_d;
137  for(int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; ++index_wei_h, ++index_in_h)
138  {
139  const T *const in_ptr_row = in_ptr_d + index_in_h * input_stride_h;
140  const T *const weights_ptr_row = weights_ptr_d + index_wei_h * kernel_stride_h;
141  for(int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end; ++index_wei_w, ++index_in_w)
142  {
143  const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w;
144  const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w;
145  int index_c_in = 0;
146  vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
147  vector_type w_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
148  for(; index_c_in <= index_c_in_end - num_elems_read_per_iteration;
149  index_c_in += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration)
150  {
151  const auto src_vec = wrapper::vloadq(in_ptr_mover);
152  //Load Cin weights
153  for(int k = 0; k < num_elems_read_per_iteration; ++k, weights_ptr_mover += index_c_out_end)
154  {
155  w_vec = wrapper::vsetlane(*weights_ptr_mover, w_vec, k);
156  }
157  out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
158  }
159  out_temp += vreduce(out_temp_vec);
160  for(; index_c_in < index_c_in_end; ++index_c_in, ++in_ptr_mover, weights_ptr_mover += index_c_out_end)
161  {
162  const auto src_val = *(in_ptr_mover);
163  const auto w_val = *(weights_ptr_mover);
164  out_temp += src_val * w_val;
165  }
166  }
167  }
168  }
169  *(reinterpret_cast<T *>(out_ptr + id_w[0])) = (biases_ptr != nullptr) ? out_temp + biases_ptr[id_w[0]] : out_temp;
170  },
171  wei);
172  },
173  out);
174 }
175 
176 } // namespace cpu
177 } // namespace arm_compute
178 #endif // SRC_CORE_NEON_KERNELS_CONV3D_LIST_H
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
Descriptor used by the 3d Convolution function.
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
uint8x16_t vloadq(const uint8_t *ptr)
Definition: load.h:58
Describe one of the image&#39;s dimensions with a start, end and step.
Definition: Window.h:79
const size_t conv_pad_top
Definition: impl.cpp:60
decltype(strategy::transforms) typedef type
Interface for CPU tensor.
Definition: ITensor.h:36
SimpleTensor< float > src
Definition: DFT.cpp:155
Copyright (c) 2017-2022 Arm Limited.
const size_t conv_pad_left
Definition: impl.cpp:59
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
size_t front
Padding across the depth dimenstion on the front, in elements.
Definition: Types.h:820
Class to describe a number of elements in each dimension.
Definition: Steps.h:40
T z() const
Alias to access the size of the third dimension.
Definition: Dimensions.h:97
size_t height
Height of the 3D shape or object.
Definition: Size3D.h:93
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. ...
Create the appropriate SIMD vector given its type and size in terms of bits.
Definition: traits.h:92
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
size_t top
Padding across the height dimenstion on the top, in elements.
Definition: Types.h:818
constexpr uint8_t * ptr() const
Return a pointer to the current pixel.
Definition: Helpers.inl:139
size_t left
Padding across the width dimenstion on the left, in elements.
Definition: Types.h:816
size_t width
Width of the 3D shape or object.
Definition: Size3D.h:92
virtual size_t element_size() const =0
Element size in bytes calculated as data_size() * num_channels()
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
uint8x8_t vsetlane(const uint8_t value, const uint8x8_t vector, const unsigned int lane)
Definition: setlane.h:91
static constexpr size_t DimW
Alias for dimension 3 also known as W dimension.
Definition: Window.h:49
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...
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
size_t depth
Depth of the 3D shape or object.
Definition: Size3D.h:94
float vreduce(const float32x4_t &v)
Reduce a vector to be a scalar by accumulating all lanes in the vector.
Definition: NEMath.inl:458
static constexpr size_t DimZ
Alias for dimension 2 also known as Z dimension.
Definition: Window.h:47
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
T y() const
Alias to access the size of the second dimension.
Definition: Dimensions.h:92
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
Iterator updated by execute_window_loop for each window element.
Definition: Helpers.h:46
Describe a multidimensional execution window.
Definition: Window.h:39
void directconv3d_float_neon_ndhwc(const ITensor *src0, const ITensor *src1, const ITensor *src2, ITensor *dst, const Conv3dInfo &conv_info, const Window &window)
Definition: list.h:39