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<div class="title">adaptive_weight.py</div> </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment"># @package adaptive_weight</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"># Module caffe2.fb.python.layers.adaptive_weight</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="keyword">from</span> __future__ <span class="keyword">import</span> absolute_import</div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="keyword">from</span> __future__ <span class="keyword">import</span> division</div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="keyword">from</span> __future__ <span class="keyword">import</span> print_function</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="keyword">from</span> __future__ <span class="keyword">import</span> unicode_literals</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="keyword">from</span> <a class="code" href="namespacecaffe2_1_1python.html">caffe2.python</a> <span class="keyword">import</span> core, schema</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="keyword">from</span> <a class="code" href="namespacecaffe2_1_1python_1_1layers_1_1layers.html">caffe2.python.layers.layers</a> <span class="keyword">import</span> ModelLayer</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="keyword">import</span> numpy <span class="keyword">as</span> np</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="stringliteral">'''</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="stringliteral">Implementation of adaptive weighting: https://arxiv.org/pdf/1705.07115.pdf</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="stringliteral">'''</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"><a class="line" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html"> 16</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html">AdaptiveWeight</a>(<a class="code" href="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer.html">ModelLayer</a>):</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keyword">def </span>__init__(</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  self,</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  model,</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  input_record,</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  name=<span class="stringliteral">'adaptive_weight'</span>,</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  optimizer=<span class="keywordtype">None</span>,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  weights=<span class="keywordtype">None</span>,</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  **kwargs</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>  super(AdaptiveWeight,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  self).__init__(model, name, input_record, **kwargs)</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a008fc83163e927890cb2f369177e1175">output_schema</a> = <a class="code" href="classcaffe2_1_1python_1_1schema_1_1_scalar.html">schema.Scalar</a>(</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  np.float32, self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer.html#afa1a900475c7b729a2d964dc33a76a2d">get_next_blob_reference</a>(<span class="stringliteral">'adaptive_weight'</span>)</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  )</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#adeb0ac17b151ac30ea33f1737ae5f208">data</a> = self.input_record.field_blobs()</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a1d5f4db3947e649e1321f019e2f7f501">num</a> = len(self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#adeb0ac17b151ac30ea33f1737ae5f208">data</a>)</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="comment"># mu_i = log(sigma_i^2)</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">if</span> weights <span class="keywordflow">is</span> <span class="keywordtype">None</span>:</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment"># mu_i is set such that all initial weights are 1. / num</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  initializer = (<span class="stringliteral">'ConstantFill'</span>, {<span class="stringliteral">'value'</span>: np.log(self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a1d5f4db3947e649e1321f019e2f7f501">num</a> / 2.)})</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">else</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keyword">assert</span> len(weights) == self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a1d5f4db3947e649e1321f019e2f7f501">num</a></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  weights = np.array(weights).astype(np.float32)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  values = np.log(1. / 2. / weights)</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  initializer = (</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="stringliteral">'GivenTensorFill'</span>, {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="stringliteral">'values'</span>: values,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="stringliteral">'dtype'</span>: core.DataType.FLOAT</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  }</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> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a51301a8e485296a62a9f818cd9e56d31">mu</a> = self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer.html#a4a38cc38d554962035fd272810615f26">create_param</a>(</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  param_name=<span class="stringliteral">'mu'</span>,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  shape=[self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a1d5f4db3947e649e1321f019e2f7f501">num</a>],</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  initializer=initializer,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  optimizer=optimizer,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  )</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">def </span>concat_data(self, net):</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  reshaped = [</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  net.NextScopedBlob(<span class="stringliteral">'reshaped_data_%d'</span> % i) <span class="keywordflow">for</span> i <span class="keywordflow">in</span> range(self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a1d5f4db3947e649e1321f019e2f7f501">num</a>)</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="comment"># coerce shape for single real values</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">for</span> i <span class="keywordflow">in</span> range(self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a1d5f4db3947e649e1321f019e2f7f501">num</a>):</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  net.Reshape(</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  [self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#adeb0ac17b151ac30ea33f1737ae5f208">data</a>[i]],</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  [reshaped[i], net.NextScopedBlob(<span class="stringliteral">'new_shape_%d'</span> % i)],</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  shape=[1]</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>  concated = net.NextScopedBlob(<span class="stringliteral">'concated_data'</span>)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  net.Concat(</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  reshaped, [concated, net.NextScopedBlob(<span class="stringliteral">'concated_new_shape'</span>)],</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  axis=0</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  )</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">return</span> concated</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="keyword">def </span>compute_adaptive_sum(self, x, net):</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  mu_exp = net.NextScopedBlob(<span class="stringliteral">'mu_exp'</span>)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  net.Exp(self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a51301a8e485296a62a9f818cd9e56d31">mu</a>, mu_exp)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  mu_exp_double = net.NextScopedBlob(<span class="stringliteral">'mu_exp_double'</span>)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  net.Scale(mu_exp, mu_exp_double, scale=2.0)</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  weighted_x = net.NextScopedBlob(<span class="stringliteral">'weighted_x'</span>)</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  net.Div([x, mu_exp_double], weighted_x)</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  weighted_elements = net.NextScopedBlob(<span class="stringliteral">'weighted_elements'</span>)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  net.Add([weighted_x, self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a51301a8e485296a62a9f818cd9e56d31">mu</a>], weighted_elements)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  net.SumElements(weighted_elements, self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a008fc83163e927890cb2f369177e1175">output_schema</a>())</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">def </span>add_ops(self, net):</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  data = self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#aa44e375c9fdb3ac0cf4333dd3c76cfe5">concat_data</a>(net)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#adf69325cf0df5de32e5875748849e7ec">compute_adaptive_sum</a>(data, net)</div><div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer_html"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer.html">caffe2.python.layers.layers.ModelLayer</a></div><div class="ttdef"><b>Definition:</b> <a href="layers_8py_source.html#l00195">layers.py:195</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer_html_afa1a900475c7b729a2d964dc33a76a2d"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer.html#afa1a900475c7b729a2d964dc33a76a2d">caffe2.python.layers.layers.ModelLayer.get_next_blob_reference</a></div><div class="ttdeci">def get_next_blob_reference(self, name)</div><div class="ttdef"><b>Definition:</b> <a href="layers_8py_source.html#l00346">layers.py:346</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight_html_aa44e375c9fdb3ac0cf4333dd3c76cfe5"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#aa44e375c9fdb3ac0cf4333dd3c76cfe5">caffe2.python.layers.adaptive_weight.AdaptiveWeight.concat_data</a></div><div class="ttdeci">def concat_data(self, net)</div><div class="ttdef"><b>Definition:</b> <a href="adaptive__weight_8py_source.html#l00055">adaptive_weight.py:55</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight_html_a1d5f4db3947e649e1321f019e2f7f501"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a1d5f4db3947e649e1321f019e2f7f501">caffe2.python.layers.adaptive_weight.AdaptiveWeight.num</a></div><div class="ttdeci">num</div><div class="ttdef"><b>Definition:</b> <a href="adaptive__weight_8py_source.html#l00032">adaptive_weight.py:32</a></div></div>
<div class="ttc" id="namespacecaffe2_1_1python_html"><div class="ttname"><a href="namespacecaffe2_1_1python.html">caffe2.python</a></div><div class="ttdef"><b>Definition:</b> <a href="python_2____init_____8py_source.html#l00001">__init__.py:1</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight_html_a51301a8e485296a62a9f818cd9e56d31"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a51301a8e485296a62a9f818cd9e56d31">caffe2.python.layers.adaptive_weight.AdaptiveWeight.mu</a></div><div class="ttdeci">mu</div><div class="ttdef"><b>Definition:</b> <a href="adaptive__weight_8py_source.html#l00048">adaptive_weight.py:48</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight_html_a008fc83163e927890cb2f369177e1175"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#a008fc83163e927890cb2f369177e1175">caffe2.python.layers.adaptive_weight.AdaptiveWeight.output_schema</a></div><div class="ttdeci">output_schema</div><div class="ttdef"><b>Definition:</b> <a href="adaptive__weight_8py_source.html#l00028">adaptive_weight.py:28</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight_html"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html">caffe2.python.layers.adaptive_weight.AdaptiveWeight</a></div><div class="ttdef"><b>Definition:</b> <a href="adaptive__weight_8py_source.html#l00016">adaptive_weight.py:16</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1schema_1_1_scalar_html"><div class="ttname"><a href="classcaffe2_1_1python_1_1schema_1_1_scalar.html">caffe2.python.schema.Scalar</a></div><div class="ttdef"><b>Definition:</b> <a href="schema_8py_source.html#l00579">schema.py:579</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight_html_adeb0ac17b151ac30ea33f1737ae5f208"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#adeb0ac17b151ac30ea33f1737ae5f208">caffe2.python.layers.adaptive_weight.AdaptiveWeight.data</a></div><div class="ttdeci">data</div><div class="ttdef"><b>Definition:</b> <a href="adaptive__weight_8py_source.html#l00031">adaptive_weight.py:31</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer_html_a4a38cc38d554962035fd272810615f26"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1layers_1_1_model_layer.html#a4a38cc38d554962035fd272810615f26">caffe2.python.layers.layers.ModelLayer.create_param</a></div><div class="ttdeci">def create_param(self, param_name, shape, initializer, optimizer, ps_param=None, regularizer=None)</div><div class="ttdef"><b>Definition:</b> <a href="layers_8py_source.html#l00331">layers.py:331</a></div></div>
<div class="ttc" id="namespacecaffe2_1_1python_1_1layers_1_1layers_html"><div class="ttname"><a href="namespacecaffe2_1_1python_1_1layers_1_1layers.html">caffe2.python.layers.layers</a></div><div class="ttdef"><b>Definition:</b> <a href="layers_8py_source.html#l00001">layers.py:1</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight_html_adf69325cf0df5de32e5875748849e7ec"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1adaptive__weight_1_1_adaptive_weight.html#adf69325cf0df5de32e5875748849e7ec">caffe2.python.layers.adaptive_weight.AdaptiveWeight.compute_adaptive_sum</a></div><div class="ttdeci">def compute_adaptive_sum(self, x, net)</div><div class="ttdef"><b>Definition:</b> <a href="adaptive__weight_8py_source.html#l00073">adaptive_weight.py:73</a></div></div>
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