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| <a href="_n_e_softmax_layer_8cpp.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) 2017-2019 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 without restriction, including without 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 KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING 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 NONINFRINGEMENT. IN 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 IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 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="_n_e_softmax_layer_8h.xhtml">arm_compute/runtime/NEON/functions/NESoftmaxLayer.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">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_n_e_softmax_layer_kernel_8h.xhtml">arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_shape_calculator_8h.xhtml">arm_compute/core/utils/misc/ShapeCalculator.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_n_e_scheduler_8h.xhtml">arm_compute/runtime/NEON/NEScheduler.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="utils_2_type_printer_8h.xhtml">utils/TypePrinter.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <cfloat></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> {</div><div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a302bd66eed7126e9c7b0eb6bc3314ac3"> 36</a></span> <a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a302bd66eed7126e9c7b0eb6bc3314ac3">NESoftmaxLayer::NESoftmaxLayer</a>(std::shared_ptr<IMemoryManager> memory_manager)</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  : _memory_group(std::move(memory_manager)), _max_kernel(), _softmax_kernel(), _flat_or_reshape_kernel_ptr(nullptr), _fill_border_kernel(), _reshape_kernel(), _max(), _tmp(), _input_flattened(),</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  _output_flattened(), _needs_flattening(false)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> }</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keywordtype">void</span> NESoftmaxLayer::configure_reshape_input_kernel(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, <span class="keywordtype">size_t</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>)</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// Flatten the input</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_flatten = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad16b366db486fec63b6d962937ec4545">misc::shape_calculator::compute_softmax_shape</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// Initialize the flat input</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  _input_flattened.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_is_resizable(<span class="keyword">true</span>).reset_padding().set_tensor_shape(shape_flatten));</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// If we need to flatten the input, we can use NEFlattenKernel or NEReshapeKernel</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// If flattening on the third axes, we use NEFlattenKernel.</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// In all other cases we have to use NEReshapeKernel</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> != 3)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">auto</span> reshape_kernel_ptr = support::cpp14::make_unique<NEReshapeLayerKernel>();</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  reshape_kernel_ptr->configure(input, &_input_flattened);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  _flat_or_reshape_kernel_ptr = std::move(reshape_kernel_ptr);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">auto</span> flatten_kernel_ptr = support::cpp14::make_unique<NEFlattenLayerKernel>();</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  flatten_kernel_ptr->configure(input, &_input_flattened);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  _flat_or_reshape_kernel_ptr = std::move(flatten_kernel_ptr);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// We need to init the output tensor here. Indeed, the reshape kernel expects</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// both tensors to be already initialized</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), *input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>());</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#aa4aee9a6c9abb0cfbcbf1727de23070c"> 71</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#aa4aee9a6c9abb0cfbcbf1727de23070c">NESoftmaxLayer::configure</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output, <span class="keywordtype">float</span> beta, <span class="keywordtype">size_t</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="comment">// Perform validation step</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input, output);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(<a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a85db282920d24cd0f4dca6a439201a43">NESoftmaxLayer::validate</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), beta, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>));</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="comment">// We don't need flattening only in the case the input is 2D and axis is 1</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  _needs_flattening = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> != 1;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="comment">// If we are dealing with a 4D tensor, we will:</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// - Flatten the input, so that we end up with a [width*height*depth] * batches 2D tensor</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">// - Execute all the pipeline (reduction + normalization) on the flattened tensor</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="comment">// - Reshape the flattened output into the real output</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">if</span>(_needs_flattening)</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="comment">// Add to the memory manager _input_flattened</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_input_flattened);</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="comment">// Configure _flatten_kernel and _input_flattened</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  configure_reshape_input_kernel(input, output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  }</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">// We want to deal with a 2D input. Either it is the flattened version of the original input (4D case)</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">// or it is the original input case (2D case)</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input_2D = (_needs_flattening ? &_input_flattened : input);</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">// Create intermediate tensors shapes</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae008e90eb6906fa3526213bc860f6cc5">input_info</a> = input_2D-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->reset_padding().set_is_resizable(<span class="keyword">true</span>);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> tmp_data_type = <a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">is_data_type_quantized_asymmetric</a>(input_2D-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>()) ? <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a> : input_2D-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>();</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> tensor_info_tmp(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae008e90eb6906fa3526213bc860f6cc5">input_info</a>.clone()->set_data_type(tmp_data_type));</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Init intermediate tensors</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> max_sum_shape = input_2D-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  max_sum_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, 1);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  _max.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ae008e90eb6906fa3526213bc860f6cc5">input_info</a>.clone()->set_tensor_shape(max_sum_shape));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  _tmp.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">init</a>(tensor_info_tmp);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// Manage intermediate buffers</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_max);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_tmp);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">// Configure Kernels</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  _max_kernel.<a class="code" href="classarm__compute_1_1_n_e_logits1_d_max_kernel.xhtml#a83a344e60eb7db895953a942abf16628">configure</a>(input_2D, &_max);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordflow">if</span>(_needs_flattening)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// Add to the memory manager _output_flattened</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&_output_flattened);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// The normalization kernel stores the result in a flat output tensor</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  _softmax_kernel.<a class="code" href="classarm__compute_1_1_n_e_logits1_d_softmax_kernel.xhtml#ad7a5f63cd7550fd2712d6c0e1e677419">configure</a>(input_2D, &_max, &_output_flattened, beta, &_tmp);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  _input_flattened.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="comment">// Reshape the flat output into the requested (4D) output</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  _reshape_kernel.<a class="code" href="classarm__compute_1_1_n_e_reshape_layer_kernel.xhtml#a83a344e60eb7db895953a942abf16628">configure</a>(&_output_flattened, output);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// Allocate the intermediate flat tensors</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  _output_flattened.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  }</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  {</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="comment">// Softmax 2D case</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  _fill_border_kernel.<a class="code" href="classarm__compute_1_1_n_e_fill_border_kernel.xhtml#a12f5fc5a4fc18544922aebb0fcbf4eb6">configure</a>(input_2D, _max_kernel.<a class="code" href="classarm__compute_1_1_n_e_logits1_d_max_kernel.xhtml#a423f9a45a52983b4de5e2b347f4369c7">border_size</a>(), <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  _softmax_kernel.<a class="code" href="classarm__compute_1_1_n_e_logits1_d_softmax_kernel.xhtml#ad7a5f63cd7550fd2712d6c0e1e677419">configure</a>(input_2D, &_max, output, beta, &_tmp);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// Allocate intermediate buffers</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  _max.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  _tmp.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</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> </div><div class="line"><a name="l00141"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a85db282920d24cd0f4dca6a439201a43"> 141</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a85db282920d24cd0f4dca6a439201a43">NESoftmaxLayer::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keywordtype">float</span> beta, <span class="keywordtype">size_t</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="comment">// Perform validation step</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a>(input, output);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() > 4, <span class="stringliteral">"Only up to 4 dimensions are supported"</span>);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(beta);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(axis < 1 || input->num_dimensions() < <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="comment">// Create intermediate tensor info</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> tmp_data_type = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>();</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> tensor_info_tmp(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_data_type(tmp_data_type).set_is_resizable(<span class="keyword">true</span>));</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>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> max_sum_shape = input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  max_sum_shape.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, 1);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> tensor_info_max_sum(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">quantization_info</a>()).set_is_resizable(<span class="keyword">true</span>));</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> dont_care;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> needs_flattening = (<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> != 1);</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>(needs_flattening)</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> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_flatten = <a class="code" href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad16b366db486fec63b6d962937ec4545">misc::shape_calculator::compute_softmax_shape</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a>);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> tensor_info_flat(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>()->set_tensor_shape(shape_flatten).set_is_resizable(<span class="keyword">true</span>));</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">axis</a> != 3)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_reshape_layer_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEReshapeLayerKernel::validate</a>(input, &tensor_info_flat));</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="keywordflow">else</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>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_flatten_layer_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NEFlattenLayerKernel::validate</a>(input, &tensor_info_flat));</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  }</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  }</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_logits1_d_max_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">NELogits1DMaxKernel::validate</a>(input, &tensor_info_max_sum));</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="classarm__compute_1_1_n_e_logits1_d_softmax_kernel.xhtml#ae9a64d99f08581b961e1ac5a5fc46af8">NELogits1DSoftmaxKernel::validate</a>(&tensor_info_tmp, &tensor_info_max_sum, output, beta, &dont_care));</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 181</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">NESoftmaxLayer::run</a>()</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="classarm__compute_1_1_memory_group_resource_scope.xhtml">MemoryGroupResourceScope</a> scope_mg(_memory_group);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keywordflow">if</span>(_needs_flattening)</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(_flat_or_reshape_kernel_ptr.get(), <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  }</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_fill_border_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_max_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_softmax_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordflow">if</span>(_needs_flattening)</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  {</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">NEScheduler::get</a>().<a class="code" href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">schedule</a>(&_reshape_kernel, <a class="code" href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">Window::DimY</a>);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  }</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> }</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="_n_e_softmax_layer_kernel_8h_xhtml"><div class="ttname"><a href="_n_e_softmax_layer_kernel_8h.xhtml">NESoftmaxLayerKernel.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a1f4e725b8e1ea36b30e09dc08ae6961d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">arm_compute::ITensorInfo::num_dimensions</a></div><div class="ttdeci">virtual size_t num_dimensions() const =0</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3fc6adad84b23f10d54d5a7b6928f872"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3fc6adad84b23f10d54d5a7b6928f872">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo &sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_softmax_layer_xhtml_a302bd66eed7126e9c7b0eb6bc3314ac3"><div class="ttname"><a href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a302bd66eed7126e9c7b0eb6bc3314ac3">arm_compute::NESoftmaxLayer::NESoftmaxLayer</a></div><div class="ttdeci">NESoftmaxLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_8cpp_source.xhtml#l00036">NESoftmaxLayer.cpp:36</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00193">Error.h:193</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1misc_1_1shape__calculator_xhtml_ad16b366db486fec63b6d962937ec4545"><div class="ttname"><a href="namespacearm__compute_1_1misc_1_1shape__calculator.xhtml#ad16b366db486fec63b6d962937ec4545">arm_compute::misc::shape_calculator::compute_softmax_shape</a></div><div class="ttdeci">TensorShape compute_softmax_shape(const ITensorInfo *input, size_t axis=1)</div><div class="ttdoc">Calculate the softmax output shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_shape_calculator_8h_source.xhtml#l00569">ShapeCalculator.h:569</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00327">Error.h:327</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_fill_border_kernel_xhtml_a12f5fc5a4fc18544922aebb0fcbf4eb6"><div class="ttname"><a href="classarm__compute_1_1_n_e_fill_border_kernel.xhtml#a12f5fc5a4fc18544922aebb0fcbf4eb6">arm_compute::NEFillBorderKernel::configure</a></div><div class="ttdeci">void configure(ITensor *tensor, BorderSize border_size, BorderMode border_mode, const PixelValue &constant_border_value=PixelValue())</div><div class="ttdoc">Initialise the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_fill_border_kernel_8cpp_source.xhtml#l00098">NEFillBorderKernel.cpp:98</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a></div><div class="ttdoc">Interface for NEON tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00036">ITensor.h:36</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00201">Helpers.inl:201</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_logits1_d_softmax_kernel_xhtml_ae9a64d99f08581b961e1ac5a5fc46af8"><div class="ttname"><a href="classarm__compute_1_1_n_e_logits1_d_softmax_kernel.xhtml#ae9a64d99f08581b961e1ac5a5fc46af8">arm_compute::NELogits1DSoftmaxKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *max, const ITensorInfo *output, const float beta, const ITensorInfo *tmp)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NELogits1DSoftmaxKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_kernel_8cpp_source.xhtml#l00798">NESoftmaxLayerKernel.cpp:798</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_logits1_d_softmax_kernel_xhtml_ad7a5f63cd7550fd2712d6c0e1e677419"><div class="ttname"><a href="classarm__compute_1_1_n_e_logits1_d_softmax_kernel.xhtml#ad7a5f63cd7550fd2712d6c0e1e677419">arm_compute::NELogits1DSoftmaxKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_kernel_8cpp_source.xhtml#l00761">NESoftmaxLayerKernel.cpp:761</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_base_xhtml_ac1f67376afb7822f262a0174ef4a3104"><div class="ttname"><a href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">arm_compute::MemoryGroupBase::manage</a></div><div class="ttdeci">void manage(TensorType *obj)</div><div class="ttdoc">Sets a object to be managed by the given memory group.</div><div class="ttdef"><b>Definition:</b> <a href="_memory_group_base_8h_source.xhtml#l00102">MemoryGroupBase.h:102</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_softmax_layer_xhtml_aa4aee9a6c9abb0cfbcbf1727de23070c"><div class="ttname"><a href="classarm__compute_1_1_n_e_softmax_layer.xhtml#aa4aee9a6c9abb0cfbcbf1727de23070c">arm_compute::NESoftmaxLayer::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, float beta=1.0f, size_t axis=1)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_8cpp_source.xhtml#l00071">NESoftmaxLayer.cpp:71</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ae008e90eb6906fa3526213bc860f6cc5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ae008e90eb6906fa3526213bc860f6cc5">arm_compute::test::validation::input_info</a></div><div class="ttdeci">input_info</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_winograd_8cpp_source.xhtml#l00330">Winograd.cpp:330</a></div></div> |
| <div class="ttc" id="utils_2_type_printer_8h_xhtml"><div class="ttname"><a href="utils_2_type_printer_8h.xhtml">TypePrinter.h</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a86084036bd3851575ef871ad5bf079a7"><div class="ttname"><a href="_error_8h.xhtml#a86084036bd3851575ef871ad5bf079a7">ARM_COMPUTE_RETURN_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond,...)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00214">Error.h:214</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="_shape_calculator_8h_xhtml"><div class="ttname"><a href="_shape_calculator_8h.xhtml">ShapeCalculator.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_logits1_d_max_kernel_xhtml_a83a344e60eb7db895953a942abf16628"><div class="ttname"><a href="classarm__compute_1_1_n_e_logits1_d_max_kernel.xhtml#a83a344e60eb7db895953a942abf16628">arm_compute::NELogits1DMaxKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_kernel_8cpp_source.xhtml#l00437">NESoftmaxLayerKernel.cpp:437</a></div></div> |
| <div class="ttc" id="_n_e_scheduler_8h_xhtml"><div class="ttname"><a href="_n_e_scheduler_8h.xhtml">NEScheduler.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a3f3e1a3200223e6a304a533b1016e749"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a3f3e1a3200223e6a304a533b1016e749">arm_compute::ITensorInfo::quantization_info</a></div><div class="ttdeci">virtual QuantizationInfo quantization_info() const =0</div><div class="ttdoc">Get the quantization settings (scale and offset) of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_reshape_layer_kernel_xhtml_a83a344e60eb7db895953a942abf16628"><div class="ttname"><a href="classarm__compute_1_1_n_e_reshape_layer_kernel.xhtml#a83a344e60eb7db895953a942abf16628">arm_compute::NEReshapeLayerKernel::configure</a></div><div class="ttdeci">void configure(const ITensor *input, ITensor *output)</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_reshape_layer_kernel_8cpp_source.xhtml#l00075">NEReshapeLayerKernel.cpp:75</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01030">Utils.h:1030</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aff911654521523937ff24372a870b89f"><div class="ttname"><a href="_validate_8h.xhtml#aff911654521523937ff24372a870b89f">ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00163">Validate.h:163</a></div></div> |
| <div class="ttc" id="_n_e_softmax_layer_8h_xhtml"><div class="ttname"><a href="_n_e_softmax_layer_8h.xhtml">NESoftmaxLayer.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_window_xhtml_ad2d402364fa822b0b7775081291eeca9"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#ad2d402364fa822b0b7775081291eeca9">arm_compute::Window::DimY</a></div><div class="ttdeci">static constexpr size_t DimY</div><div class="ttdoc">Alias for dimension 1 also known as Y dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00045">Window.h:45</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_memory_group_resource_scope_xhtml"><div class="ttname"><a href="classarm__compute_1_1_memory_group_resource_scope.xhtml">arm_compute::MemoryGroupResourceScope</a></div><div class="ttdoc">Memory group resources scope handling class.</div><div class="ttdef"><b>Definition:</b> <a href="_i_memory_group_8h_source.xhtml#l00046">IMemoryGroup.h:46</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_accc088009d44c521706aa98d6387ee21"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#accc088009d44c521706aa98d6387ee21">arm_compute::test::validation::axis</a></div><div class="ttdeci">axis</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_stack_layer_8cpp_source.xhtml#l00226">StackLayer.cpp:226</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_scheduler_xhtml_a4e58f95544bd5ac6559a421671bd9842"><div class="ttname"><a href="classarm__compute_1_1_i_scheduler.xhtml#a4e58f95544bd5ac6559a421671bd9842">arm_compute::IScheduler::schedule</a></div><div class="ttdeci">virtual void schedule(ICPPKernel *kernel, const Hints &hints)=0</div><div class="ttdoc">Runs the kernel in the same thread as the caller synchronously.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_logits1_d_max_kernel_xhtml_a423f9a45a52983b4de5e2b347f4369c7"><div class="ttname"><a href="classarm__compute_1_1_n_e_logits1_d_max_kernel.xhtml#a423f9a45a52983b4de5e2b347f4369c7">arm_compute::NELogits1DMaxKernel::border_size</a></div><div class="ttdeci">BorderSize border_size() const override</div><div class="ttdoc">The size of the border for that kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_kernel_8cpp_source.xhtml#l00432">NESoftmaxLayerKernel.cpp:432</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa"><div class="ttname"><a href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">arm_compute::BorderMode::REPLICATE</a></div><div class="ttdoc">Pixels outside the image are assumed to have the same value as the closest image pixel.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_softmax_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_n_e_softmax_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::NESoftmaxLayer::run</a></div><div class="ttdeci">void run() override</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_8cpp_source.xhtml#l00181">NESoftmaxLayer.cpp:181</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_softmax_layer_xhtml_a85db282920d24cd0f4dca6a439201a43"><div class="ttname"><a href="classarm__compute_1_1_n_e_softmax_layer.xhtml#a85db282920d24cd0f4dca6a439201a43">arm_compute::NESoftmaxLayer::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, float beta=1.0f, size_t axis=1)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NESoftmaxLayer.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_8cpp_source.xhtml#l00141">NESoftmaxLayer.cpp:141</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_reshape_layer_kernel_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_reshape_layer_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NEReshapeLayerKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEReshapeLayerKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_reshape_layer_kernel_8cpp_source.xhtml#l00092">NEReshapeLayerKernel.cpp:92</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a9c54fb6cea3557692fe7c00c40bb40ad"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">arm_compute::TensorShape::set</a></div><div class="ttdeci">TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true)</div><div class="ttdoc">Accessor to set the value of one of the dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00078">TensorShape.h:78</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_logits1_d_max_kernel_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_logits1_d_max_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NELogits1DMaxKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NELogits1DMaxKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_softmax_layer_kernel_8cpp_source.xhtml#l00476">NESoftmaxLayerKernel.cpp:476</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_flatten_layer_kernel_xhtml_a968b23a6ef327fcfb5b99d58e3fbe883"><div class="ttname"><a href="classarm__compute_1_1_n_e_flatten_layer_kernel.xhtml#a968b23a6ef327fcfb5b99d58e3fbe883">arm_compute::NEFlattenLayerKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output)</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEFlattenLayerKernel.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_flatten_layer_kernel_8cpp_source.xhtml#l00097">NEFlattenLayerKernel.cpp:97</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00074">Types.h:74</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_scheduler_xhtml_a0d63ca713bab377aabcfb63c192b8429"><div class="ttname"><a href="classarm__compute_1_1_scheduler.xhtml#a0d63ca713bab377aabcfb63c192b8429">arm_compute::Scheduler::get</a></div><div class="ttdeci">static IScheduler & get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_scheduler_8cpp_source.xhtml#l00096">Scheduler.cpp:96</a></div></div> |
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