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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">## @package concat</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"># Module caffe2.python.layers.concat</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> 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> (</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>  ModelLayer,</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> )</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="keyword">from</span> future.utils <span class="keyword">import</span> viewitems</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">import</span> numpy <span class="keyword">as</span> np</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> <span class="keyword">import</span> logging</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> logger = logging.getLogger(__name__)</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"><a class="line" href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html"> 18</a></span> <span class="keyword">class </span><a class="code" href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html">Concat</a>(ModelLayer):</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  <span class="stringliteral">"""</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="stringliteral"> Construct Concat layer</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="stringliteral"> Assume that first dimension is batch,</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="stringliteral"></span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="stringliteral"> Example:</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="stringliteral"></span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="stringliteral"> embedding_dim = 64</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="stringliteral"> input_record = self.new_record(schema.Struct(</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="stringliteral"> ('input1', schema.Scalar((np.float32, (embedding_dim, )))),</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="stringliteral"> ('input2', schema.Scalar((np.float32, (embedding_dim, )))),</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="stringliteral"> ('input3', schema.Scalar((np.float32, (embedding_dim, )))),</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="stringliteral"> ))</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="stringliteral"></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="stringliteral"> output = self.model.Concat(input_record)</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="stringliteral"> self.assertEqual(</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="stringliteral"> schema.Scalar((np.float32, ((len(input_record.fields) * embedding_dim, )))),</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="stringliteral"> output</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="stringliteral"> )</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="stringliteral"></span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="stringliteral"> # Note that in Concat layer we assume first dimension is batch.</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="stringliteral"> # so input is B * embedding_dim</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="stringliteral"> # add_axis=1 make it B * 1 * embedding_dim</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="stringliteral"> # Concat on axis=1 make it B * N * embedding_dim</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="stringliteral"></span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="stringliteral"> output = self.model.Concat(input_record, axis=1, add_axis=1)</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="stringliteral"> self.assertEqual(</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="stringliteral"> schema.Scalar((np.float32, ((len(input_record.fields), embedding_dim)))),</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="stringliteral"> output</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="stringliteral"> )</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="stringliteral"> """</span></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="keyword">def </span>__init__(self, model, input_record, axis=1, add_axis=0,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  name=<span class="stringliteral">'concat'</span>, **kwargs):</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  super(Concat, self).__init__(model, name, input_record, **kwargs)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#a0f3033354dd4d1f2bfafb4f8760a6b94">axis</a> = axis</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#ae9d1038bcc498ec6f6f895e6604af2d7">add_axis</a> = add_axis</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keyword">assert</span> <span class="keywordflow">not</span> (axis == 0 <span class="keywordflow">and</span> add_axis == 1), \</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="stringliteral">"It's not allowed to add axis=0"</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">assert</span> isinstance(input_record, <a class="code" href="classcaffe2_1_1python_1_1schema_1_1_struct.html">schema.Struct</a>),\</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="stringliteral">"Incorrect input type. Excpected Struct, but received: {0}"</span>.\</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  format(input_record)</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>  shapes = []</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">for</span> field_name, field_type <span class="keywordflow">in</span> viewitems(input_record.fields):</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keyword">assert</span> isinstance(field_type, <a class="code" href="classcaffe2_1_1python_1_1schema_1_1_scalar.html">schema.Scalar</a>),\</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="stringliteral">"Incorrect input type for {}. Excpected Scalar, but got: {}"</span>.\</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  format(field_name, field_type)</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment"># Assume that first dimension is batch, so actual axis in shape is</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment"># axis - 1</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  shape = list(field_type.field_type().shape)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">if</span> add_axis:</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  shape.insert(axis - 1, 1)</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">assert</span> len(shape) >= axis,\</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="stringliteral">"Concat expects that limited dimensions of the input tensor"</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  shapes.append(shape)</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  logger.info(<span class="stringliteral">'Concat Layer input shapes: '</span> + str(shapes))</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span> axis == 0:</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#a67131b6e423e16650ae838ecd49e63c5">output_schema</a> = schema.from_blob_list(</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  input_record[0],</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  [self.get_next_blob_reference(<span class="stringliteral">'output'</span>)]</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  )</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">return</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  concat_dim = 0</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">for</span> shape <span class="keywordflow">in</span> shapes:</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  concat_dim += shape[axis - 1]</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  shape[axis - 1] = 0</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">assert</span> shape == shapes[0],\</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="stringliteral">"Shapes {0} and {1} are not compatible for Concat"</span>.\</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  format(shape, shapes[0])</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  output_dims = shapes[0]</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  output_dims[axis - 1] = concat_dim</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>  logger.info(<span class="stringliteral">'Concat Layer output_dims: '</span> + str(output_dims))</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#a67131b6e423e16650ae838ecd49e63c5">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="l00095"></a><span class="lineno"> 95</span>  (np.float32, output_dims),</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  self.get_next_blob_reference(<span class="stringliteral">'output'</span>))</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">def </span>add_ops(self, net):</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  net.Concat(</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  self.input_record.field_blobs(),</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>  self.output_schema.field_blobs()[0],</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  self.output_schema.field_blobs()[0] + <span class="stringliteral">"_concat_dims"</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  ],</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  axis=self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#a0f3033354dd4d1f2bfafb4f8760a6b94">axis</a>,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  add_axis=self.<a class="code" href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#ae9d1038bcc498ec6f6f895e6604af2d7">add_axis</a>,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  )</div><div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat_html_a67131b6e423e16650ae838ecd49e63c5"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#a67131b6e423e16650ae838ecd49e63c5">caffe2.python.layers.concat.Concat.output_schema</a></div><div class="ttdeci">output_schema</div><div class="ttdef"><b>Definition:</b> <a href="concat_8py_source.html#l00077">concat.py:77</a></div></div>
<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat_html_a0f3033354dd4d1f2bfafb4f8760a6b94"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#a0f3033354dd4d1f2bfafb4f8760a6b94">caffe2.python.layers.concat.Concat.axis</a></div><div class="ttdeci">axis</div><div class="ttdef"><b>Definition:</b> <a href="concat_8py_source.html#l00053">concat.py:53</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_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_1concat_1_1_concat_html"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html">caffe2.python.layers.concat.Concat</a></div><div class="ttdef"><b>Definition:</b> <a href="concat_8py_source.html#l00018">concat.py:18</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>
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<div class="ttc" id="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat_html_ae9d1038bcc498ec6f6f895e6604af2d7"><div class="ttname"><a href="classcaffe2_1_1python_1_1layers_1_1concat_1_1_concat.html#ae9d1038bcc498ec6f6f895e6604af2d7">caffe2.python.layers.concat.Concat.add_axis</a></div><div class="ttdeci">add_axis</div><div class="ttdef"><b>Definition:</b> <a href="concat_8py_source.html#l00054">concat.py:54</a></div></div>
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