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develop/doc/_sources/api/v2/fluid/layers.rst.txt

@@ -505,6 +505,11 @@ swish
 ..  autofunction:: paddle.v2.fluid.layers.swish
     :noindex:
 
+im2sequence
+------
+..  autofunction:: paddle.v2.fluid.layers.im2sequence
+    :noindex:
+
 edit_distance
 ---------------
 ..  autofunction:: paddle.v2.fluid.layers.edit_distance_error

develop/doc/api/v2/fluid/layers.html

@@ -3388,6 +3388,110 @@ Duplicable: False  Optional: False</li>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="im2sequence">
+<h2>im2sequence<a class="headerlink" href="#im2sequence" title="Permalink to this headline">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">im2sequence</code><span class="sig-paren">(</span><em>input</em>, <em>filter_size=1</em>, <em>stride=1</em>, <em>padding=0</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
+<dd><p>Extracts image patches from the input tensor to form a tensor of shape
+{input.batch_size * output_height * output_width, filter_size_H *
+filter_size_W * input.channels} which is similar with im2col.
+This op use filter / kernel to scan images and convert these images to
+sequences. After expanding, the number of time step are
+output_height * output_width for an image, in which output_height and
+output_width are calculated by below equation:</p>
+<div class="math">
+\[output\_size = 1 +             (2 * padding + img\_size - block\_size + stride - 1) / stride\]</div>
+<p>And the dimension of each time step is block_y * block_x * input.channels.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<em>Variable</em>) &#8211; The input should be a tensor in NCHW format.</li>
+<li><strong>filter_size</strong> (<em>int|tuple|None</em>) &#8211; The filter size. If filter_size is a tuple,
+it must contain two integers, (filter_size_H, filter_size_W).
+Otherwise, the filter will be a square.</li>
+<li><strong>stride</strong> (<em>int|tuple</em>) &#8211; The stride size. If stride is a tuple, it must
+contain two integers, (stride_H, stride_W). Otherwise, the
+stride_H = stride_W = stride. Default: stride = 1.</li>
+<li><strong>padding</strong> (<em>int|tuple</em>) &#8211; The padding size. If padding is a tuple, it can
+contain two integers like (padding_H, padding_W) which means
+padding_up = padding_down = padding_H and
+padding_left = padding_right = padding_W. Or it can use
+(padding_up, padding_left, padding_down, padding_right) to indicate
+paddings of four direction. Otherwise, a scalar padding means
+padding_up = padding_down = padding_left = padding_right = padding
+Default: padding = 0.</li>
+<li><strong>name</strong> (<em>int</em>) &#8211; The name of this layer. It is optional.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The output is a LoDTensor with shape
+{input.batch_size * output_height * output_width,
+filter_size_H * filter_size_W * input.channels}.
+If we regard output as a matrix, each row of this matrix is
+a step of a sequence.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">output</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p>Examples:</p>
+<p>As an example:</p>
+<blockquote>
+<div><div class="highlight-text"><div class="highlight"><pre><span></span>Given:
+
+x = [[[[ 6.  2.  1.]
+       [ 8.  3.  5.]
+       [ 0.  2.  6.]]
+
+      [[ 2.  4.  4.]
+       [ 6.  3.  0.]
+       [ 6.  4.  7.]]]
+
+     [[[ 6.  7.  1.]
+       [ 5.  7.  9.]
+       [ 2.  4.  8.]]
+
+      [[ 1.  2.  1.]
+       [ 1.  3.  5.]
+       [ 9.  0.  8.]]]]
+
+x.dims = {2, 2, 3, 3}
+
+And:
+
+filter = [2, 2]
+stride = [1, 1]
+padding = [0, 0]
+
+Then:
+
+output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
+               [ 2.  1.  3.  5.  4.  4.  3.  0.]
+               [ 8.  3.  0.  2.  6.  3.  6.  4.]
+               [ 3.  5.  2.  6.  3.  0.  4.  7.]
+               [ 6.  7.  5.  7.  1.  2.  1.  3.]
+               [ 7.  1.  7.  9.  2.  1.  3.  5.]
+               [ 5.  7.  2.  4.  1.  3.  9.  0.]
+               [ 7.  9.  4.  8.  3.  5.  0.  8.]]
+
+output.dims = {8, 9}
+
+output.lod = [[0, 4, 8]]
+</pre></div>
+</div>
+<p>The simple usage is:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">im2sequence</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">filter_size</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+</pre></div>
+</div>
+</div></blockquote>
+</dd></dl>
+
 </div>
 <div class="section" id="edit-distance">
 <h2>edit_distance<a class="headerlink" href="#edit-distance" title="Permalink to this headline">¶</a></h2>

develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt

@@ -505,6 +505,11 @@ swish
 ..  autofunction:: paddle.v2.fluid.layers.swish
     :noindex:
 
+im2sequence
+------
+..  autofunction:: paddle.v2.fluid.layers.im2sequence
+    :noindex:
+
 edit_distance
 ---------------
 ..  autofunction:: paddle.v2.fluid.layers.edit_distance_error

develop/doc_cn/api/v2/fluid/layers.html

@@ -3407,6 +3407,110 @@ Duplicable: False  Optional: False</li>
 </table>
 </dd></dl>
 
+</div>
+<div class="section" id="im2sequence">
+<h2>im2sequence<a class="headerlink" href="#im2sequence" title="永久链接至标题">¶</a></h2>
+<dl class="function">
+<dt>
+<code class="descclassname">paddle.v2.fluid.layers.</code><code class="descname">im2sequence</code><span class="sig-paren">(</span><em>input</em>, <em>filter_size=1</em>, <em>stride=1</em>, <em>padding=0</em>, <em>name=None</em><span class="sig-paren">)</span></dt>
+<dd><p>Extracts image patches from the input tensor to form a tensor of shape
+{input.batch_size * output_height * output_width, filter_size_H *
+filter_size_W * input.channels} which is similar with im2col.
+This op use filter / kernel to scan images and convert these images to
+sequences. After expanding, the number of time step are
+output_height * output_width for an image, in which output_height and
+output_width are calculated by below equation:</p>
+<div class="math">
+\[output\_size = 1 +             (2 * padding + img\_size - block\_size + stride - 1) / stride\]</div>
+<p>And the dimension of each time step is block_y * block_x * input.channels.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">参数:</th><td class="field-body"><ul class="first simple">
+<li><strong>input</strong> (<em>Variable</em>) &#8211; The input should be a tensor in NCHW format.</li>
+<li><strong>filter_size</strong> (<em>int|tuple|None</em>) &#8211; The filter size. If filter_size is a tuple,
+it must contain two integers, (filter_size_H, filter_size_W).
+Otherwise, the filter will be a square.</li>
+<li><strong>stride</strong> (<em>int|tuple</em>) &#8211; The stride size. If stride is a tuple, it must
+contain two integers, (stride_H, stride_W). Otherwise, the
+stride_H = stride_W = stride. Default: stride = 1.</li>
+<li><strong>padding</strong> (<em>int|tuple</em>) &#8211; The padding size. If padding is a tuple, it can
+contain two integers like (padding_H, padding_W) which means
+padding_up = padding_down = padding_H and
+padding_left = padding_right = padding_W. Or it can use
+(padding_up, padding_left, padding_down, padding_right) to indicate
+paddings of four direction. Otherwise, a scalar padding means
+padding_up = padding_down = padding_left = padding_right = padding
+Default: padding = 0.</li>
+<li><strong>name</strong> (<em>int</em>) &#8211; The name of this layer. It is optional.</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">返回:</th><td class="field-body"><p class="first">The output is a LoDTensor with shape
+{input.batch_size * output_height * output_width,
+filter_size_H * filter_size_W * input.channels}.
+If we regard output as a matrix, each row of this matrix is
+a step of a sequence.</p>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">返回类型:</th><td class="field-body"><p class="first last">output</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p>Examples:</p>
+<p>As an example:</p>
+<blockquote>
+<div><div class="highlight-text"><div class="highlight"><pre><span></span>Given:
+
+x = [[[[ 6.  2.  1.]
+       [ 8.  3.  5.]
+       [ 0.  2.  6.]]
+
+      [[ 2.  4.  4.]
+       [ 6.  3.  0.]
+       [ 6.  4.  7.]]]
+
+     [[[ 6.  7.  1.]
+       [ 5.  7.  9.]
+       [ 2.  4.  8.]]
+
+      [[ 1.  2.  1.]
+       [ 1.  3.  5.]
+       [ 9.  0.  8.]]]]
+
+x.dims = {2, 2, 3, 3}
+
+And:
+
+filter = [2, 2]
+stride = [1, 1]
+padding = [0, 0]
+
+Then:
+
+output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
+               [ 2.  1.  3.  5.  4.  4.  3.  0.]
+               [ 8.  3.  0.  2.  6.  3.  6.  4.]
+               [ 3.  5.  2.  6.  3.  0.  4.  7.]
+               [ 6.  7.  5.  7.  1.  2.  1.  3.]
+               [ 7.  1.  7.  9.  2.  1.  3.  5.]
+               [ 5.  7.  2.  4.  1.  3.  9.  0.]
+               [ 7.  9.  4.  8.  3.  5.  0.  8.]]
+
+output.dims = {8, 9}
+
+output.lod = [[0, 4, 8]]
+</pre></div>
+</div>
+<p>The simple usage is:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">output</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">im2sequence</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">filter_size</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+</pre></div>
+</div>
+</div></blockquote>
+</dd></dl>
+
 </div>
 <div class="section" id="edit-distance">
 <h2>edit_distance<a class="headerlink" href="#edit-distance" title="永久链接至标题">¶</a></h2>