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<div class="title">GCFullyConnectedLayer.cpp</div> </div>
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<a href="_g_c_fully_connected_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>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2017-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<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>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<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>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_g_c_fully_connected_layer_8h.xhtml">arm_compute/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_validate_8h.xhtml">arm_compute/core/Validate.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_g_c_scheduler_8h.xhtml">arm_compute/runtime/GLES_COMPUTE/GCScheduler.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<span class="preprocessor">#include &lt;algorithm&gt;</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;</div><div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_fully_connected_layer_reshape_weights.xhtml#aa029e9740bc43eb3301316be76be3b7e"> 34</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_g_c_fully_connected_layer_reshape_weights.xhtml#aa029e9740bc43eb3301316be76be3b7e">GCFullyConnectedLayerReshapeWeights::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *input, <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *output)</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;{</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <span class="keyword">auto</span> k = arm_compute::support::cpp14::make_unique&lt;GCTransposeKernel&gt;();</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160; k-&gt;configure(input, output);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; _kernel = std::move(k);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;}</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#ac0ce422247b7eae1a1931b9717c86ba5"> 41</a></span>&#160;<a class="code" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#ac0ce422247b7eae1a1931b9717c86ba5">GCFullyConnectedLayer::GCFullyConnectedLayer</a>(std::shared_ptr&lt;IMemoryManager&gt; memory_manager)</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; : _memory_group(std::move(memory_manager)), _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _reshape_weights_output(),</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; _original_weights(nullptr), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false)</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;{</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160;}</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;<span class="keywordtype">void</span> GCFullyConnectedLayer::configure_conv_fc(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *output)</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160;{</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(1) != (input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) * input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2))));</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dt = input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>();</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <span class="comment">// If the fully connected layer is called after a convolution layer, the input tensor must be linearized</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="comment">// Initialize output tensor for im2col</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> shape_im2col;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(0, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) * input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) * input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(2));</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(1, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(3));</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(2, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(4));</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; shape_im2col.<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a9c54fb6cea3557692fe7c00c40bb40ad">set</a>(3, input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(5));</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; _im2col_output.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">init</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(shape_im2col, 1, dt));</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <span class="comment">// Configure im2col kernel</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; _memory_group.<a class="code" href="classarm__compute_1_1_memory_group_base.xhtml#ac1f67376afb7822f262a0174ef4a3104">manage</a>(&amp;_im2col_output);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; _im2col_kernel.<a class="code" href="classarm__compute_1_1_g_c_im2_col_kernel.xhtml#a2461b3d633deab1e051da8170c959b2a">configure</a>(input, &amp;_im2col_output, <a class="code" href="classarm__compute_1_1_size2_d.xhtml">Size2D</a>(1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0), <span class="keyword">false</span>);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="comment">// Configure matrix multiply kernel</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; _mm_kernel.<a class="code" href="classarm__compute_1_1_g_c_g_e_m_m_matrix_multiply_kernel.xhtml#ada3b192e7f6ec5a8950b57fae93e9166">configure</a>(&amp;_im2col_output, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output, 1.0f, <span class="keyword">false</span>);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; <span class="comment">// Allocate the output tensor for im2col once all the configure methods have been called</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; _im2col_output.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160;}</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160;<span class="keywordtype">void</span> GCFullyConnectedLayer::configure_fc_fc(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *output)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;{</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">dimension</a>(1));</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="comment">// Configure matrix multiply kernel</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; _mm_kernel.<a class="code" href="classarm__compute_1_1_g_c_g_e_m_m_matrix_multiply_kernel.xhtml#ada3b192e7f6ec5a8950b57fae93e9166">configure</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output, 1.0f, <span class="keyword">false</span>);</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;}</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160;</div><div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#a0effb36388142226203c802f31614634"> 82</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#a0effb36388142226203c802f31614634">GCFullyConnectedLayer::configure</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *biases, <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *output,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml">FullyConnectedLayerInfo</a> fc_info)</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;{</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, output);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_tensor_info.xhtml#a13622133d9b41900a6a3e8f89e59a78b">num_dimensions</a>() &gt; 2);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; _original_weights = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; _are_weights_reshaped = fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a11851649b6a7cd12ae25cf72b769cfb9">transpose_weights</a> ? fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a146a7be9d813ad80abb72a0bf6566cbc">are_weights_reshaped</a> : <span class="keyword">true</span>;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; _is_fc_after_conv = <span class="keyword">true</span>;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; _accumulate_biases = <span class="keyword">false</span>;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; <span class="keywordflow">if</span>(biases != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <a class="code" href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, biases);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; _accumulate_biases = <span class="keyword">true</span>;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="comment">// Configure accumulate biases kernel</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; _accumulate_biases_kernel.<a class="code" href="classarm__compute_1_1_g_c_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#aebd765d07fc0c9401ec83025aa0e13de">configure</a>(output, biases);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; }</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="comment">// With the Fully Connected layer we can have 4 different cases:</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <span class="comment">// 1) Convolution layer -&gt; Fully Connected layer without batches</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <span class="comment">// 2) Fully Connected layer -&gt; Fully Connected layer without batches</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="comment">// 3) Convolution layer -&gt; Fully Connected layer with batches</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; <span class="comment">// 4) Fully Connected layer -&gt; Fully Connected layer with batches</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_g_c_tensor.xhtml">IGCTensor</a> *weights_to_use = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; {</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; weights_to_use = &amp;_reshape_weights_output;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="comment">// Reshape the weights</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; _reshape_weights_kernel.<a class="code" href="classarm__compute_1_1_g_c_fully_connected_layer_reshape_weights.xhtml#aa029e9740bc43eb3301316be76be3b7e">configure</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>, &amp;_reshape_weights_output);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; }</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <span class="comment">// Check if we have a fully connected layer with batches</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> is_batched_fc_layer = output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(1) &gt; 1;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <span class="keywordflow">if</span>(is_batched_fc_layer)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; _is_fc_after_conv = (<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">TensorShape::num_max_dimensions</a> &gt;= 4) &amp;&amp; (std::equal(input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">cbegin</a>() + 3,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#afde273ebfa15fe83c690ad5cf6693c9f">cend</a>(),</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; output-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">cbegin</a>() + 1));</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; }</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; {</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; _is_fc_after_conv = input-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a1f4e725b8e1ea36b30e09dc08ae6961d">num_dimensions</a>() &gt; 1;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <span class="keywordflow">if</span>(_is_fc_after_conv)</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; {</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <span class="comment">// Fully Connected layer after a Convolution Layer without batches</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; configure_conv_fc(input, weights_to_use, output);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <span class="comment">// Fully Connected layer after a Fully Connected Layer without batches</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; configure_fc_fc(input, weights_to_use, output);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a4f87c5060cca01305f94a9d2f10e9d83">retain_internal_weights</a> &amp;&amp; _reshape_weights_output.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#acccf4173f8c67e6f93ea3353c9590c9e">gc_buffer</a>() == 0);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; _are_weights_reshaped = _are_weights_reshaped || fc_info.<a class="code" href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a4f87c5060cca01305f94a9d2f10e9d83">retain_internal_weights</a>;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160;}</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160;</div><div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb"> 149</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">GCFullyConnectedLayer::run</a>()</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160;{</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">prepare</a>();</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; <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="l00154"></a><span class="lineno"> 154</span>&#160;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <span class="comment">// Linearize input if it comes from a convolutional layer</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; <span class="keywordflow">if</span>(_is_fc_after_conv)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_im2col_kernel, <span class="keyword">false</span>);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <span class="keywordflow">if</span>(!_are_weights_reshaped || _is_fc_after_conv)</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; }</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <span class="comment">// Run matrix multiply</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_mm_kernel, !_accumulate_biases);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; <span class="comment">// Accumulate biases if provided</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; <span class="keywordflow">if</span>(_accumulate_biases)</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">memory_barrier</a>();</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; <a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">GCScheduler::get</a>().<a class="code" href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">dispatch</a>(_accumulate_biases_kernel);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; }</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160;}</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160;</div><div class="line"><a name="l00178"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77"> 178</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">GCFullyConnectedLayer::prepare</a>()</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160;{</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; <span class="comment">// Reshape of the weights (happens only once)</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; <span class="keywordflow">if</span>(!_are_weights_reshaped)</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(!_original_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">is_used</a>());</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <span class="comment">// Run reshape weights kernel and mark weights as unused</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; _reshape_weights_output.<a class="code" href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; _reshape_weights_kernel.<a class="code" href="classarm__compute_1_1_i_g_c_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">run</a>();</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; <span class="comment">// Mark original weights tensor as unused</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; _original_weights-&gt;<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">mark_as_unused</a>();</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; _are_weights_reshaped = <span class="keyword">true</span>;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1_g_c_fully_connected_layer_xhtml_a0effb36388142226203c802f31614634"><div class="ttname"><a href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#a0effb36388142226203c802f31614634">arm_compute::GCFullyConnectedLayer::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_fully_connected_layer_8cpp_source.xhtml#l00082">GCFullyConnectedLayer.cpp:82</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_im2_col_kernel_xhtml_a2461b3d633deab1e051da8170c959b2a"><div class="ttname"><a href="classarm__compute_1_1_g_c_im2_col_kernel.xhtml#a2461b3d633deab1e051da8170c959b2a">arm_compute::GCIm2ColKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, IGCTensor *output, const Size2D &amp;kernel_dims, const PadStrideInfo &amp;conv_info, bool has_bias, const Size2D &amp;dilation=Size2D(1U, 1U))</div><div class="ttdoc">Set the input and output of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_im2_col_kernel_8cpp_source.xhtml#l00067">GCIm2ColKernel.cpp:67</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="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_tensor_xhtml_ad45f0c01a0713dfb6bd7232c7f396fc4"><div class="ttname"><a href="classarm__compute_1_1_c_l_tensor.xhtml#ad45f0c01a0713dfb6bd7232c7f396fc4">arm_compute::CLTensor::info</a></div><div class="ttdeci">TensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_tensor_8cpp_source.xhtml#l00035">CLTensor.cpp:35</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div>
<div class="ttc" id="_validate_8h_xhtml_a5befbfaf6bc224eabc58b5e88b1de6d1"><div class="ttname"><a href="_validate_8h.xhtml#a5befbfaf6bc224eabc58b5e88b1de6d1">ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00543">Validate.h:543</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a66a29e27a51a13250143981b0ee4ad19"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a66a29e27a51a13250143981b0ee4ad19">arm_compute::GCScheduler::dispatch</a></div><div class="ttdeci">void dispatch(IGCKernel &amp;kernel, bool flush=true)</div><div class="ttdoc">Schedule the execution of the passed kernel if possible.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00069">GCScheduler.cpp:69</a></div></div>
<div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a4f87c5060cca01305f94a9d2f10e9d83"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a4f87c5060cca01305f94a9d2f10e9d83">arm_compute::FullyConnectedLayerInfo::retain_internal_weights</a></div><div class="ttdeci">bool retain_internal_weights</div><div class="ttdoc">Retain internal reshaped weights.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00798">Types.h:798</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_g_c_simple_function_xhtml_a92fe532c342ae2b07956a65520c05362"><div class="ttname"><a href="classarm__compute_1_1_i_g_c_simple_function.xhtml#a92fe532c342ae2b07956a65520c05362">arm_compute::IGCSimpleFunction::run</a></div><div class="ttdeci">void run() override final</div><div class="ttdoc">Run the kernels contained in the function.</div><div class="ttdef"><b>Definition:</b> <a href="_i_g_c_simple_function_8cpp_source.xhtml#l00037">IGCSimpleFunction.cpp:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a8813441b655b97c00139c6a5a6390e97"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a8813441b655b97c00139c6a5a6390e97">arm_compute::TensorInfo::dimension</a></div><div class="ttdeci">size_t dimension(size_t index) const override</div><div class="ttdoc">Return the size of the requested dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00223">TensorInfo.h:223</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="classarm__compute_1_1_i_tensor_xhtml_a209ea2ddfdfa80703799c92da8beb643"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a209ea2ddfdfa80703799c92da8beb643">arm_compute::ITensor::is_used</a></div><div class="ttdeci">bool is_used() const</div><div class="ttdoc">Flags if the tensor is used or not.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00162">ITensor.cpp:162</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_g_c_scheduler_xhtml_a2dcf87458fcfdfb5e9fdd369e0320d78"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a2dcf87458fcfdfb5e9fdd369e0320d78">arm_compute::GCScheduler::memory_barrier</a></div><div class="ttdeci">void memory_barrier()</div><div class="ttdoc">Defines a barrier ordering memory transactions.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00078">GCScheduler.cpp:78</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00337">Error.h:337</a></div></div>
<div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml">arm_compute::FullyConnectedLayerInfo</a></div><div class="ttdoc">Fully connected layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00793">Types.h:793</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_g_c_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_g_c_tensor.xhtml">arm_compute::IGCTensor</a></div><div class="ttdoc">Interface for GLES Compute tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_g_c_tensor_8h_source.xhtml#l00035">IGCTensor.h:35</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_af36143939a43fa124312e395975091ed"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#af36143939a43fa124312e395975091ed">arm_compute::ITensorAllocator::init</a></div><div class="ttdeci">void init(const TensorInfo &amp;input, size_t alignment=0)</div><div class="ttdoc">Initialize a tensor based on the passed TensorInfo.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_allocator_8cpp_source.xhtml#l00038">ITensorAllocator.cpp:38</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_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a9bc00234de9adf8c99a21eb1d7d494c2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a9bc00234de9adf8c99a21eb1d7d494c2">arm_compute::ITensor::mark_as_unused</a></div><div class="ttdeci">void mark_as_unused() const</div><div class="ttdoc">Marks a tensor as unused.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8cpp_source.xhtml#l00167">ITensor.cpp:167</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_scheduler_xhtml_a9c5f715748222ab9607cc52134b36b0b"><div class="ttname"><a href="classarm__compute_1_1_g_c_scheduler.xhtml#a9c5f715748222ab9607cc52134b36b0b">arm_compute::GCScheduler::get</a></div><div class="ttdeci">static GCScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_scheduler_8cpp_source.xhtml#l00062">GCScheduler.cpp:62</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml_a13622133d9b41900a6a3e8f89e59a78b"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml#a13622133d9b41900a6a3e8f89e59a78b">arm_compute::TensorInfo::num_dimensions</a></div><div class="ttdeci">size_t num_dimensions() const override</div><div class="ttdoc">The number of dimensions of the tensor (rank)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00244">TensorInfo.h:244</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_g_c_tensor_xhtml_acccf4173f8c67e6f93ea3353c9590c9e"><div class="ttname"><a href="classarm__compute_1_1_g_c_tensor.xhtml#acccf4173f8c67e6f93ea3353c9590c9e">arm_compute::GCTensor::gc_buffer</a></div><div class="ttdeci">GLuint gc_buffer() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's gles compute buffer id.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_tensor_8cpp_source.xhtml#l00054">GCTensor.cpp:54</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 &amp; tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_fully_connected_layer_xhtml_aa9b93ef660fc3c5b4b19d3fc7b891b77"><div class="ttname"><a href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#aa9b93ef660fc3c5b4b19d3fc7b891b77">arm_compute::GCFullyConnectedLayer::prepare</a></div><div class="ttdeci">void prepare() override</div><div class="ttdoc">Prepare the function for executing.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_fully_connected_layer_8cpp_source.xhtml#l00178">GCFullyConnectedLayer.cpp:178</a></div></div>
<div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a146a7be9d813ad80abb72a0bf6566cbc"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a146a7be9d813ad80abb72a0bf6566cbc">arm_compute::FullyConnectedLayerInfo::are_weights_reshaped</a></div><div class="ttdeci">bool are_weights_reshaped</div><div class="ttdoc">Reshape the weights tensor if false.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00797">Types.h:797</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_g_e_m_m_matrix_multiply_kernel_xhtml_ada3b192e7f6ec5a8950b57fae93e9166"><div class="ttname"><a href="classarm__compute_1_1_g_c_g_e_m_m_matrix_multiply_kernel.xhtml#ada3b192e7f6ec5a8950b57fae93e9166">arm_compute::GCGEMMMatrixMultiplyKernel::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed=true, const GEMMReshapeInfo &amp;reshape_info=GEMMReshapeInfo())</div><div class="ttdoc">Initialise the kernel's input, output and alpha.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_g_e_m_m_matrix_multiply_kernel_8cpp_source.xhtml#l00185">GCGEMMMatrixMultiplyKernel.cpp:185</a></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_g_c_fully_connected_layer_xhtml_ad1717410afd0be936c6213a63c8005fb"><div class="ttname"><a href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#ad1717410afd0be936c6213a63c8005fb">arm_compute::GCFullyConnectedLayer::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="_g_c_fully_connected_layer_8cpp_source.xhtml#l00149">GCFullyConnectedLayer.cpp:149</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00676">Types.h:676</a></div></div>
<div class="ttc" id="classarm__compute_1_1_i_tensor_allocator_xhtml_aa8a4946cd749d482dd996874d295af85"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">arm_compute::ITensorAllocator::allocate</a></div><div class="ttdeci">virtual void allocate()=0</div><div class="ttdoc">Interface to be implemented by the child class to allocate the tensor.</div></div>
<div class="ttc" id="_validate_8h_xhtml_aadf5c9cff86327b96d88d04649d9715e"><div class="ttname"><a href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00789">Validate.h:789</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_fully_connected_layer_reshape_weights_xhtml_aa029e9740bc43eb3301316be76be3b7e"><div class="ttname"><a href="classarm__compute_1_1_g_c_fully_connected_layer_reshape_weights.xhtml#aa029e9740bc43eb3301316be76be3b7e">arm_compute::GCFullyConnectedLayerReshapeWeights::configure</a></div><div class="ttdeci">void configure(const IGCTensor *input, IGCTensor *output)</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_fully_connected_layer_8cpp_source.xhtml#l00034">GCFullyConnectedLayer.cpp:34</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_afde273ebfa15fe83c690ad5cf6693c9f"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#afde273ebfa15fe83c690ad5cf6693c9f">arm_compute::Dimensions::cend</a></div><div class="ttdeci">std::array&lt; T, num_max_dimensions &gt;::const_iterator cend() const</div><div class="ttdoc">Returns a read-only (constant) iterator that points one past the last element in the dimension array.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00234">Dimensions.h:234</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a8c9efb4e1ad142d58d65af400f20217d"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a8c9efb4e1ad142d58d65af400f20217d">arm_compute::Dimensions::cbegin</a></div><div class="ttdeci">std::array&lt; T, num_max_dimensions &gt;::const_iterator cbegin() const</div><div class="ttdoc">Returns a read-only (constant) iterator that points to the first element in the dimension array.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00210">Dimensions.h:210</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="classarm__compute_1_1_size2_d_xhtml"><div class="ttname"><a href="classarm__compute_1_1_size2_d.xhtml">arm_compute::Size2D</a></div><div class="ttdoc">Class for specifying the size of an image or rectangle.</div><div class="ttdef"><b>Definition:</b> <a href="_size2_d_8h_source.xhtml#l00034">Size2D.h:34</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a64a08a9fec5aeee8650e7182b6d171d0"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">arm_compute::test::validation::weights</a></div><div class="ttdeci">CLTensor weights</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00180">ConvolutionLayer.cpp:180</a></div></div>
<div class="ttc" id="_g_c_scheduler_8h_xhtml"><div class="ttname"><a href="_g_c_scheduler_8h.xhtml">GCScheduler.h</a></div></div>
<div class="ttc" id="structarm__compute_1_1_fully_connected_layer_info_xhtml_a11851649b6a7cd12ae25cf72b769cfb9"><div class="ttname"><a href="structarm__compute_1_1_fully_connected_layer_info.xhtml#a11851649b6a7cd12ae25cf72b769cfb9">arm_compute::FullyConnectedLayerInfo::transpose_weights</a></div><div class="ttdeci">bool transpose_weights</div><div class="ttdoc">Transpose weights if true.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00796">Types.h:796</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 &amp; 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_g_c_g_e_m_m_matrix_accumulate_biases_kernel_xhtml_aebd765d07fc0c9401ec83025aa0e13de"><div class="ttname"><a href="classarm__compute_1_1_g_c_g_e_m_m_matrix_accumulate_biases_kernel.xhtml#aebd765d07fc0c9401ec83025aa0e13de">arm_compute::GCGEMMMatrixAccumulateBiasesKernel::configure</a></div><div class="ttdeci">void configure(IGCTensor *accum, const IGCTensor *biases)</div><div class="ttdoc">Set the accumulate buffer and the biases of the kernel.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_g_e_m_m_matrix_accumulate_biases_kernel_8cpp_source.xhtml#l00044">GCGEMMMatrixAccumulateBiasesKernel.cpp:44</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="_g_c_fully_connected_layer_8h_xhtml"><div class="ttname"><a href="_g_c_fully_connected_layer_8h.xhtml">GCFullyConnectedLayer.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1_g_c_fully_connected_layer_xhtml_ac0ce422247b7eae1a1931b9717c86ba5"><div class="ttname"><a href="classarm__compute_1_1_g_c_fully_connected_layer.xhtml#ac0ce422247b7eae1a1931b9717c86ba5">arm_compute::GCFullyConnectedLayer::GCFullyConnectedLayer</a></div><div class="ttdeci">GCFullyConnectedLayer(std::shared_ptr&lt; IMemoryManager &gt; memory_manager=nullptr)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_fully_connected_layer_8cpp_source.xhtml#l00041">GCFullyConnectedLayer.cpp:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a1b67d5b720119d50faa286c774579ecc"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a1b67d5b720119d50faa286c774579ecc">arm_compute::Dimensions&lt; size_t &gt;::num_max_dimensions</a></div><div class="ttdeci">static constexpr size_t num_max_dimensions</div><div class="ttdoc">Number of dimensions the tensor has.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00045">Dimensions.h:45</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_g_c_tensor_xhtml_a44d1d7d909047fe63f5f6c11a9849986"><div class="ttname"><a href="classarm__compute_1_1_g_c_tensor.xhtml#a44d1d7d909047fe63f5f6c11a9849986">arm_compute::GCTensor::allocator</a></div><div class="ttdeci">ITensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="_g_c_tensor_8cpp_source.xhtml#l00034">GCTensor.cpp:34</a></div></div>
<div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div>
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