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| <a href="space__to__batch_8cl.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software withoutput restriction, including withoutput limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KOUTD, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, OUTCLUDOUTG BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONOUTFROUTGEMENT. OUT NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER OUT AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISOUTG FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR OUT CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALOUTGS OUT THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml">helpers.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#if defined(BATCH_SIZE) && defined(DATA_TYPE) && defined(WIDTH_IN) && defined(HEIGHT_IN)</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="comment">/** Calculate the space to batch conversion.</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment"> *</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment"> * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment"> * @note The block shape tensor rank must be passed at compile time using -DBLOCK_SHAPE_DIM. e.g. -DBLOCK_SHAPE_DIM=2</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="comment"> *</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="comment"> * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="comment"> * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="comment"> * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="comment"> * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="comment"> * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="comment"> * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="comment"> * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="comment"> * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source image</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="comment"> * @param[in] paddings_ptr Pointer to the second source image. Supported data types: S32</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment"> * @param[in] paddings_stride_x Stride of the paddinds tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment"> * @param[in] paddings_step_x paddings_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment"> * @param[in] paddings_stride_y Stride of the paddinds tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment"> * @param[in] paddings_step_y paddings_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment"> * @param[in] paddingse_offset_first_element_in_bytes The offset of the first element in the second source image</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment"> * @param[in] block_shape_ptr Pointer to the block shape tensor. Supported data types: S32</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="comment"> * @param[in] block_shape_stride_x Stride of the block shape tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment"> * @param[in] block_shape_step_x block_shape_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="comment"> * @param[in] block_shape_stride_y Stride of the block shape tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="comment"> * @param[in] block_shape_step_y block_shape_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="comment"> * @param[in] block_shape_offset_first_element_in_bytes The offset of the first element in the block shapetensor</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment"> * @param[in] batch_id The output tensor batch id</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="comment"> * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="comment"> * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="comment"> * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment"> * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="comment"> * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="comment"> * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="comment"> * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="comment"> * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="comment"> */</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> __kernel <span class="keywordtype">void</span> space_to_batch_nchw(</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>),</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a22f42fcf2077d951271df83b55c1a71a">IMAGE_DECLARATION</a>(paddings),</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(block_shape),</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_id,</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(output))</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="struct_tensor4_d.xhtml">Tensor4D</a> in = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a302e05cc5f90bd76a9d0812c4be8b5eb">CONVERT_TO_TENSOR4D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, 0);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="struct_image.xhtml">Image</a> pad = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a4334a4a76f8e9628c0fb9e1acf616e2a">CONVERT_TO_IMAGE_STRUCT_NO_STEP</a>(paddings);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="struct_vector.xhtml">Vector</a> block = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(block_shape);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> out = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a31c8c760f08fb1a331b16b7c204321dc">CONVERT_TO_TENSOR3D_STRUCT</a>(output);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_left_x = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&pad, 0, 0));</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_right_x = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&pad, 1, 0));</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_left_y = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&pad, 0, 1));</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_right_y = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&pad, 1, 1));</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordtype">int</span> block_x = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&block, 0));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordtype">int</span> block_y = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&block, 1));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_x = get_global_id(0);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_y = get_global_id(1);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> z = get_global_id(2);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pos_x = out_x * block_x + ((batch_id / BATCH_IN) % block_x);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pos_y = out_y * block_y + ((batch_id / BATCH_IN) / block_x);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">if</span>(((pos_y >= pad_left_y) && (pos_y < pad_left_y + HEIGHT_IN) && (pos_x >= pad_left_x) && (pos_x < pad_left_x + WIDTH_IN)))</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a> = batch_id % BATCH_IN;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_x = pos_x - pad_left_x;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_y = pos_y - pad_left_y;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)out.<a class="code" href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a>) = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#ad442fb5ec8be1fff97f543150de5d822">tensor4D_offset</a>(&in, in_x, in_y, z, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> }<span class="comment"></span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="comment">/** Calculate the space to batch conversion. (NHWC)</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="comment"> *</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="comment"> * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="comment"> * @note The block shape tensor rank must be passed at compile time using -DBLOCK_SHAPE_DIM. e.g. -DBLOCK_SHAPE_DIM=2</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="comment"> *</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> <span class="comment"> * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="comment"> * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="comment"> * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="comment"> * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="comment"> * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="comment"> * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="comment"> * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="comment"> * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source image</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="comment"> * @param[in] paddings_ptr Pointer to the second source image. Supported data types: S32</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="comment"> * @param[in] paddings_stride_x Stride of the paddinds tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="comment"> * @param[in] paddings_step_x paddings_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment"> * @param[in] paddings_stride_y Stride of the paddinds tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment"> * @param[in] paddings_step_y paddings_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="comment"> * @param[in] paddingse_offset_first_element_in_bytes The offset of the first element in the second source image</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment"> * @param[in] block_shape_ptr Pointer to the block shape tensor. Supported data types: S32</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="comment"> * @param[in] block_shape_stride_x Stride of the block shape tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="comment"> * @param[in] block_shape_step_x block_shape_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="comment"> * @param[in] block_shape_stride_y Stride of the block shape tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="comment"> * @param[in] block_shape_step_y block_shape_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="comment"> * @param[in] block_shape_offset_first_element_in_bytes The offset of the first element in the block shapetensor</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="comment"> * @param[in] batch_id The output tensor batch id</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> <span class="comment"> * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="comment"> * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="comment"> * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="comment"> * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="comment"> * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="comment"> * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="comment"> * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="comment"> * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="comment"> */</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> __kernel <span class="keywordtype">void</span> space_to_batch_nhwc(</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>),</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a22f42fcf2077d951271df83b55c1a71a">IMAGE_DECLARATION</a>(paddings),</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(block_shape),</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_id,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(output))</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <a class="code" href="struct_tensor4_d.xhtml">Tensor4D</a> in = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a302e05cc5f90bd76a9d0812c4be8b5eb">CONVERT_TO_TENSOR4D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, 0);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="struct_image.xhtml">Image</a> pad = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a4334a4a76f8e9628c0fb9e1acf616e2a">CONVERT_TO_IMAGE_STRUCT_NO_STEP</a>(paddings);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="struct_vector.xhtml">Vector</a> block = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(block_shape);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> out = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a31c8c760f08fb1a331b16b7c204321dc">CONVERT_TO_TENSOR3D_STRUCT</a>(output);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_left_x = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&pad, 0, 0));</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_right_x = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&pad, 1, 0));</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_left_y = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&pad, 0, 1));</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pad_right_y = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&pad, 1, 1));</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordtype">int</span> block_x = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&block, 0));</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordtype">int</span> block_y = *((__global <span class="keywordtype">int</span> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&block, 1));</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_x = get_global_id(1);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_y = get_global_id(2);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> z = get_global_id(0);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pos_x = out_x * block_x + ((batch_id / BATCH_IN) % block_x);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pos_y = out_y * block_y + ((batch_id / BATCH_IN) / block_x);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordflow">if</span>(((pos_y >= pad_left_y) && (pos_y < pad_left_y + HEIGHT_IN) && (pos_x >= pad_left_x) && (pos_x < pad_left_x + WIDTH_IN)))</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a> = batch_id % BATCH_IN;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_x = pos_x - pad_left_x;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_y = pos_y - pad_left_y;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)out.<a class="code" href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a>) = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#ad442fb5ec8be1fff97f543150de5d822">tensor4D_offset</a>(&in, z, in_x, in_y, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> }</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="preprocessor">#endif // defined(BATCH_SIZE) && defined(DATA_TYPE) && defined(WIDTH_IN) && defined(HEIGHT_IN)</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="preprocessor">#if defined(BATCH_SIZE) && defined(DATA_TYPE) && defined(BLOCK_SHAPE_X) && defined(BLOCK_SHAPE_Y) && defined(PAD_LEFT_X) && defined(PAD_RIGHT_X) && defined(PAD_LEFT_Y) && defined(PAD_RIGHT_Y) && defined(WIDTH_IN) && defined(HEIGHT_IN)</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="comment">/** Calculate the space to batch conversion.</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="comment"> *</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="comment"> * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="comment"> * @note The input tensor batch size must be passed at compile time using -DBATCH_SIZE. e.g. -DBATCH_SIZE=2</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="comment"> * @note The block shape x must be passed at compile time using -DBLOCK_SHAPE_X. e.g. -DBLOCK_SHAPE_X=2</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="comment"> * @note The block shape y must be passed at compile time using -DBLOCK_SHAPE_Y. e.g. -DBLOCK_SHAPE_Y=2</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="comment"> * @note The starting pad value of x must be passed at compile time using -DPAD_LEFT_X. e.g. -DPAD_LEFT_X=2</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="comment"> * @note The ending pad value of x must be passed at compile time using -DPAD_RIGHT_X. e.g. -DPAD_RIGHT_X=2</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="comment"> * @note The starting pad value of y must be passed at compile time using -DPAD_LEFT_Y. e.g. -DPAD_LEFT_Y=2</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> <span class="comment"> * @note The ending pad value of y must be passed at compile time using -DPAD_RIGHT_Y. e.g. -DPAD_RIGHT_X=2</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> <span class="comment"> *</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> <span class="comment"> * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> <span class="comment"> * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> <span class="comment"> * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> <span class="comment"> * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="comment"> * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="comment"> * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="comment"> * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="comment"> * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source image</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="comment"> * @param[in] batch_id The output tensor batch id</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> <span class="comment"> * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="comment"> * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="comment"> * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment"> * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment"> * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment"> * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="comment"> * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="comment"> * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="comment"> */</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> __kernel <span class="keywordtype">void</span> space_to_batch_static_nchw(</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>),</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_id,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(output))</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> {</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="struct_tensor4_d.xhtml">Tensor4D</a> in = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a302e05cc5f90bd76a9d0812c4be8b5eb">CONVERT_TO_TENSOR4D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, 0);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> out = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a31c8c760f08fb1a331b16b7c204321dc">CONVERT_TO_TENSOR3D_STRUCT</a>(output);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keywordtype">int</span> block_x = BLOCK_SHAPE_X;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordtype">int</span> block_y = BLOCK_SHAPE_Y;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_x = get_global_id(0);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_y = get_global_id(1);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> z = get_global_id(2);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pos_x = out_x * block_x + ((batch_id / BATCH_IN) % block_x);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pos_y = out_y * block_y + ((batch_id / BATCH_IN) / block_x);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">if</span>(pos_y >= PAD_LEFT_Y && pos_y < PAD_LEFT_Y + HEIGHT_IN && pos_x >= PAD_LEFT_X && pos_x < PAD_LEFT_X + WIDTH_IN)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a> = batch_id % BATCH_IN;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_x = pos_x - PAD_LEFT_X;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_y = pos_y - PAD_LEFT_Y;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)out.<a class="code" href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a>) = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#ad442fb5ec8be1fff97f543150de5d822">tensor4D_offset</a>(&in, in_x, in_y, z, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>));</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> }<span class="comment"></span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="comment">/** Calculate the space to batch conversion. (NHWC)</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="comment"> *</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="comment"> * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="comment"> * @note The input tensor batch size must be passed at compile time using -DBATCH_SIZE. e.g. -DBATCH_SIZE=2</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="comment"> * @note The block shape x must be passed at compile time using -DBLOCK_SHAPE_X. e.g. -DBLOCK_SHAPE_X=2</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="comment"> * @note The block shape y must be passed at compile time using -DBLOCK_SHAPE_Y. e.g. -DBLOCK_SHAPE_Y=2</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="comment"> * @note The starting pad value of x must be passed at compile time using -DPAD_LEFT_X. e.g. -DPAD_LEFT_X=2</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="comment"> * @note The ending pad value of x must be passed at compile time using -DPAD_RIGHT_X. e.g. -DPAD_RIGHT_X=2</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="comment"> * @note The starting pad value of y must be passed at compile time using -DPAD_LEFT_Y. e.g. -DPAD_LEFT_Y=2</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="comment"> * @note The ending pad value of y must be passed at compile time using -DPAD_RIGHT_Y. e.g. -DPAD_RIGHT_X=2</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="comment"> *</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="comment"> * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> <span class="comment"> * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="comment"> * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="comment"> * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> <span class="comment"> * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="comment"> * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="comment"> * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="comment"> * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source image</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="comment"> * @param[in] batch_id The output tensor batch id</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <span class="comment"> * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="comment"> * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="comment"> * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="comment"> * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="comment"> * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="comment"> * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="comment"> * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="comment"> * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="comment"> */</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> __kernel <span class="keywordtype">void</span> space_to_batch_static_nhwc(</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>),</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch_id,</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(output))</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> {</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <a class="code" href="struct_tensor4_d.xhtml">Tensor4D</a> in = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a302e05cc5f90bd76a9d0812c4be8b5eb">CONVERT_TO_TENSOR4D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">input</a>, 0);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> out = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a31c8c760f08fb1a331b16b7c204321dc">CONVERT_TO_TENSOR3D_STRUCT</a>(output);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordtype">int</span> block_x = BLOCK_SHAPE_X;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordtype">int</span> block_y = BLOCK_SHAPE_Y;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_x = get_global_id(1);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> out_y = get_global_id(2);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> z = get_global_id(0);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pos_x = out_x * block_x + ((batch_id / BATCH_IN) % block_x);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> pos_y = out_y * block_y + ((batch_id / BATCH_IN) / block_x);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">if</span>(pos_y >= PAD_LEFT_Y && pos_y < PAD_LEFT_Y + HEIGHT_IN && pos_x >= PAD_LEFT_X && pos_x < PAD_LEFT_X + WIDTH_IN)</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  {</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a> = batch_id % BATCH_IN;</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_x = pos_x - PAD_LEFT_X;</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> in_y = pos_y - PAD_LEFT_Y;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> </div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)out.<a class="code" href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a>) = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#ad442fb5ec8be1fff97f543150de5d822">tensor4D_offset</a>(&in, z, in_x, in_y, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>));</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  }</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> }</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> <span class="preprocessor">#endif // defined(BATCH_SIZE) && defined(DATA_TYPE) && defined(BLOCK_SHAPE_X) && defined(BLOCK_SHAPE_Y) && defined(PAD_LEFT_X) && defined(PAD_RIGHT_X) && defined(PAD_LEFT_Y) && defined(PAD_RIGHT_Y) && defined(WIDTH_IN) && defined(HEIGHT_IN)</span></div><div class="ttc" id="struct_vector_xhtml"><div class="ttname"><a href="struct_vector.xhtml">Vector</a></div><div class="ttdoc">Structure to hold Vector information.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00341">helpers.h:341</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a009469e4d9b8fce3b6d5e97d2077827d"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a></div><div class="ttdeci">__global uchar * offset(const Image *img, int x, int y)</div><div class="ttdoc">Get the pointer position of a Image.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00510">helpers.h:510</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1a367830ae09bf6138df822888ec1d71"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">arm_compute::test::validation::w</a></div><div class="ttdeci">SimpleTensor< float > w</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_d_f_t_8cpp_source.xhtml#l00156">DFT.cpp:156</a></div></div> |
| <div class="ttc" id="convolution3x3_8cl_xhtml_afb8c72ce35c4a1f4a2588d6573e54aa1"><div class="ttname"><a href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a></div><div class="ttdeci">#define DATA_TYPE</div><div class="ttdef"><b>Definition:</b> <a href="convolution3x3_8cl_source.xhtml#l00027">convolution3x3.cl:27</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a22f42fcf2077d951271df83b55c1a71a"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a22f42fcf2077d951271df83b55c1a71a">IMAGE_DECLARATION</a></div><div class="ttdeci">#define IMAGE_DECLARATION(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00275">helpers.h:275</a></div></div> |
| <div class="ttc" id="struct_tensor3_d_xhtml"><div class="ttname"><a href="struct_tensor3_d.xhtml">Tensor3D</a></div><div class="ttdoc">Structure to hold 3D tensor information.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00358">helpers.h:358</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a8fcf2ddd9a1d58b1b280f5c0aed71845"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a8fcf2ddd9a1d58b1b280f5c0aed71845">arm_compute::test::validation::input</a></div><div class="ttdeci">auto input</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_l_s_t_m_layer_quantized_8cpp_source.xhtml#l00487">LSTMLayerQuantized.cpp:487</a></div></div> |
| <div class="ttc" id="struct_tensor4_d_xhtml"><div class="ttname"><a href="struct_tensor4_d.xhtml">Tensor4D</a></div><div class="ttdoc">Structure to hold 4D tensor information.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00368">helpers.h:368</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a302e05cc5f90bd76a9d0812c4be8b5eb"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a302e05cc5f90bd76a9d0812c4be8b5eb">CONVERT_TO_TENSOR4D_STRUCT_NO_STEP</a></div><div class="ttdeci">#define CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(name, mod_size)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00337">helpers.h:337</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a4334a4a76f8e9628c0fb9e1acf616e2a"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a4334a4a76f8e9628c0fb9e1acf616e2a">CONVERT_TO_IMAGE_STRUCT_NO_STEP</a></div><div class="ttdeci">#define CONVERT_TO_IMAGE_STRUCT_NO_STEP(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00314">helpers.h:314</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a40a6eb9f2a7712f08d6bb8ff6c9e6ca7"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a></div><div class="ttdeci">#define VECTOR_DECLARATION(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00269">helpers.h:269</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_ad442fb5ec8be1fff97f543150de5d822"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#ad442fb5ec8be1fff97f543150de5d822">tensor4D_offset</a></div><div class="ttdeci">__global const uchar * tensor4D_offset(const Tensor4D *tensor, int x, int y, int z, int w)</div><div class="ttdoc">Get the pointer position of a Tensor4D.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00535">helpers.h:535</a></div></div> |
| <div class="ttc" id="struct_image_xhtml"><div class="ttname"><a href="struct_image.xhtml">Image</a></div><div class="ttdoc">Structure to hold Image information.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00349">helpers.h:349</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a31c8c760f08fb1a331b16b7c204321dc"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a31c8c760f08fb1a331b16b7c204321dc">CONVERT_TO_TENSOR3D_STRUCT</a></div><div class="ttdeci">#define CONVERT_TO_TENSOR3D_STRUCT(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00326">helpers.h:326</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a7e4940407322d6f0ccb8b6b86b856019"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a></div><div class="ttdeci">__global const uchar * vector_offset(const Vector *vec, int x)</div><div class="ttdoc">Get the pointer position of a Vector.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00499">helpers.h:499</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml">helpers.h</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a481bdc6d61b3df9dcdbdb244f0f97790"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a></div><div class="ttdeci">#define TENSOR4D_DECLARATION(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00293">helpers.h:293</a></div></div> |
| <div class="ttc" id="struct_tensor3_d_xhtml_acf52c23cbd7424606c10a606524e3e32"><div class="ttname"><a href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">Tensor3D::ptr</a></div><div class="ttdeci">__global uchar * ptr</div><div class="ttdoc">Pointer to the starting postion of the buffer.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00360">helpers.h:360</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a6b83038822d1ae7ab619b684ed3b7fc0"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a></div><div class="ttdeci">#define TENSOR3D_DECLARATION(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00283">helpers.h:283</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a64d779f80eeb923e0ab2313433f7b40b"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a></div><div class="ttdeci">#define CONVERT_TO_VECTOR_STRUCT_NO_STEP(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00308">helpers.h:308</a></div></div> |
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