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| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
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| <li class="toctree-l6"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| |
| <div class="section" id="vision-transforms"> |
| <h1>vision.transforms<a class="headerlink" href="#vision-transforms" title="Permalink to this headline">¶</a></h1> |
| <p>Gluon provides pre-defined vision transformation and data augmentation functions in the <a class="reference internal" href="#module-mxnet.gluon.data.vision.transforms" title="mxnet.gluon.data.vision.transforms"><code class="xref py py-mod docutils literal notranslate"><span class="pre">mxnet.gluon.data.vision.transforms</span></code></a> |
| module.</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.data.vision.transforms.Compose" title="mxnet.gluon.data.vision.transforms.Compose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.Compose</span></code></a></p></td> |
| <td><p>Sequentially composes multiple transforms.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.data.vision.transforms.Cast" title="mxnet.gluon.data.vision.transforms.Cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.Cast</span></code></a></p></td> |
| <td><p>Cast inputs to a specific data type</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.ToTensor</span></code></p></td> |
| <td><p>Converts an image NDArray or batch of image NDArray to a tensor NDArray.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.Normalize</span></code></p></td> |
| <td><p>Normalize an tensor of shape (C x H x W) or (N x C x H x W) with mean and standard deviation.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomResizedCrop</span></code></p></td> |
| <td><p>Crop the input image with random scale and aspect ratio.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.CenterCrop</span></code></p></td> |
| <td><p>Crops the image <cite>src</cite> to the given <cite>size</cite> by trimming on all four sides and preserving the center of the image.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.Resize</span></code></p></td> |
| <td><p>Resize an image or a batch of image NDArray to the given size.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomFlipLeftRight</span></code></p></td> |
| <td><p>Randomly flip the input image left to right with a probability of p(0.5 by default).</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomFlipTopBottom</span></code></p></td> |
| <td><p>Randomly flip the input image top to bottom with a probability of p(0.5 by default).</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomBrightness</span></code></p></td> |
| <td><p>Randomly jitters image brightness with a factor chosen from <cite>[max(0, 1 - brightness), 1 + brightness]</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomContrast</span></code></p></td> |
| <td><p>Randomly jitters image contrast with a factor chosen from <cite>[max(0, 1 - contrast), 1 + contrast]</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomSaturation</span></code></p></td> |
| <td><p>Randomly jitters image saturation with a factor chosen from <cite>[max(0, 1 - saturation), 1 + saturation]</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomHue</span></code></p></td> |
| <td><p>Randomly jitters image hue with a factor chosen from <cite>[max(0, 1 - hue), 1 + hue]</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomColorJitter</span></code></p></td> |
| <td><p>Randomly jitters the brightness, contrast, saturation, and hue of an image.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transforms.RandomLighting</span></code></p></td> |
| <td><p>Add AlexNet-style PCA-based noise to an image.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="section" id="module-mxnet.gluon.data.vision.transforms"> |
| <span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#module-mxnet.gluon.data.vision.transforms" title="Permalink to this headline">¶</a></h2> |
| <p>Vision transforms.</p> |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast"> |
| <em class="property">class </em><code class="sig-name descname">Cast</code><span class="sig-paren">(</span><em class="sig-param">dtype='float32'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/mxnet/gluon/data/vision/transforms.html#Cast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p>Cast inputs to a specific data type</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em>, </em><em>default 'float32'</em>) – The target data type, in string or <cite>numpy.dtype</cite>.</p> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with arbitrary shape and dtype.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with the same shape as <cite>data</cite> and data type as dtype.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.apply"> |
| <code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.collect_params"> |
| <code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.collect_params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its |
| children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code> |
| which match some given regular expressions.</p> |
| <p>For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, |
| ‘fc.bias’]:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'conv1.weight|conv1.bias|fc.weight|fc.bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done |
| using regular expressions:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">'.*weight|.*bias'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>The selected <code class="xref py py-class docutils literal notranslate"><span class="pre">Dict</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.export"> |
| <code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.export" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Export HybridBlock to json format that can be loaded by |
| <cite>gluon.SymbolBlock.imports</cite> or the C++ interface.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When there are only one input, it will have name <cite>data</cite>. When there |
| Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path</strong> (<em>str</em><em> or </em><em>None</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite> |
| will be created, where xxxx is the 4 digits epoch number. |
| If None, do not export to file but return Python Symbol object and |
| corresponding dictionary of parameters.</p></li> |
| <li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li> |
| <li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>symbol_filename</strong> (<em>str</em>) – Filename to which model symbols were saved, including <cite>path</cite> prefix.</p></li> |
| <li><p><strong>params_filename</strong> (<em>str</em>) – Filename to which model parameters were saved, including <cite>path</cite> prefix.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/mxnet/gluon/data/vision/transforms.html#Cast.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides the forward computation. Arguments must be |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.numpy.ndarray</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.hybridize"> |
| <code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.hybridize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Activates or deactivates <code class="xref py py-class docutils literal notranslate"><span class="pre">HybridBlock</span></code> s recursively. Has no effect on |
| non-hybrid children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>active</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether to turn hybrid on or off.</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.infer_shape"> |
| <code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.infer_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers shape of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.infer_type"> |
| <code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.infer_type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Infers data type of Parameters from inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">device=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../../../../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>. |
| Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</p></li> |
| <li><p><strong>device</strong> (<a class="reference internal" href="../../../../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a><em> or </em><em>list of Device</em>) – Keeps a copy of Parameters on one or many device(s).</p></li> |
| <li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.load"> |
| <code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model saved using the <cite>save</cite> API</p> |
| <p>Reconfigures a model using the saved configuration. This function |
| does not regenerate the model architecture. It resets each Block’s |
| parameter UUIDs as they were when saved in order to match the names of the |
| saved parameters.</p> |
| <p>This function assumes the Blocks in the model were created in the same |
| order they were when the model was saved. This is because each Block is |
| uniquely identified by Block class name and a unique ID in order (since |
| its an OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph (Symbol & inputs) and settings are |
| restored if it had been hybridized before saving.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for loading this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.load_dict"> |
| <code class="sig-name descname">load_dict</code><span class="sig-paren">(</span><em class="sig-param">param_dict</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.load_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from dict</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>param_dict</strong> (<em>dict</em>) – Dictionary containing model parameters</p></li> |
| <li><p><strong>device</strong> (<a class="reference internal" href="../../../../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a><em>, </em><em>optional</em>) – Device context on which the memory is allocated. Default is |
| <cite>mxnet.device.current_device()</cite>.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represented in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this dict.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.load_parameters"> |
| <code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">device=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.load_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</p></li> |
| <li><p><strong>device</strong> (<a class="reference internal" href="../../../../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a><em> or </em><em>list of Device</em><em>, </em><em>default cpu</em><em>(</em><em>)</em>) – Device(s) to initialize loaded parameters on.</p></li> |
| <li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li> |
| <li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not |
| present in this Block.</p></li> |
| <li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype |
| provided by the Parameter if any.</p></li> |
| <li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’} |
| Only valid if cast_dtype=True, specify the source of the dtype for casting |
| the parameters</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.optimize_for"> |
| <code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">partition_if_dynamic=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.optimize_for" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Partitions the current HybridBlock and optimizes it for a given backend |
| without executing a forward pass. Modifies the HybridBlock in-place.</p> |
| <p>Immediately partitions a HybridBlock using the specified backend. Combines |
| the work done in the hybridize API with part of the work done in the forward |
| pass without calling the CachedOp. Can be used in place of hybridize, |
| afterwards <cite>export</cite> can be called or inference can be run. See README.md in |
| example/extensions/lib_subgraph/README.md for more details.</p> |
| <p class="rubric">Examples</p> |
| <p># partition and then export to file |
| block.optimize_for(x, backend=’myPart’) |
| block.export(‘partitioned’)</p> |
| <p># partition and then run inference |
| block.optimize_for(x, backend=’myPart’) |
| block(x)</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li> |
| <li><p><strong>*args</strong> (<a class="reference internal" href="../../../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li> |
| <li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li> |
| <li><p><strong>backend_opts</strong> (<em>dict of user-specified options to pass to the backend for partitioning</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| <li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – clears any previous optimizations</p></li> |
| <li><p><strong>partition_if_dynamic</strong> (<em>bool</em><em>, </em><em>default False</em>) – whether to partition the graph when dynamic shape op exists</p></li> |
| <li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li> |
| <li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also |
| set static_alloc to True. Change of input shapes is still allowed |
| but slower.</p></li> |
| <li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li> |
| <li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li> |
| <li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during backward pass.</p></li> |
| <li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.params"> |
| <em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.params" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its |
| children’s parameters).</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.register_forward_hook"> |
| <code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.register_forward_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward hook on the block.</p> |
| <p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.data.vision.transforms.Cast.forward" title="mxnet.gluon.data.vision.transforms.Cast.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.register_forward_pre_hook"> |
| <code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers a forward pre-hook on the block.</p> |
| <p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.data.vision.transforms.Cast.forward" title="mxnet.gluon.data.vision.transforms.Cast.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. |
| It should not modify the input or output.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -> None</cite>.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.register_op_hook"> |
| <code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.register_op_hook" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install op hook for block recursively.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Function called to inspect the values of the intermediate outputs |
| of blocks after hybridization. It takes 3 parameters: |
| name of the tensor being inspected (str) |
| name of the operator producing or consuming that tensor (str) |
| tensor being inspected (NDArray).</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">HybridBlock.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save the model architecture and parameters to load again later</p> |
| <p>Saves the model architecture as a nested dictionary where each Block |
| in the model is a dictionary and its children are sub-dictionaries.</p> |
| <p>Each Block is uniquely identified by Block class name and a unique ID. |
| We save each Block’s parameter UUID to restore later in order to match |
| the saved parameters.</p> |
| <p>Recursively traverses a Block’s children in order (since its an |
| OrderedDict) and uses the unique ID to denote that specific Block.</p> |
| <p>Assumes that the model is created in an identical order every time. |
| If the model is not able to be recreated deterministically do not |
| use this set of APIs to save/load your model.</p> |
| <p>For HybridBlocks, the cached_graph is saved (Symbol & inputs) if |
| it has already been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – The prefix to use in filenames for saving this model: |
| <prefix>-model.json and <prefix>-model.params</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.save_parameters"> |
| <code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.save_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save parameters to file.</p> |
| <p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this |
| method only saves parameters, not model structure. If you want to save |
| model structures, please use <code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li> |
| <li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block |
| contains multiple sub-blocks that share parameters, each of the |
| shared parameters will be separately saved for every sub-block.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.setattr"> |
| <code class="sig-name descname">setattr</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.setattr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an attribute to a new value for all Parameters.</p> |
| <p>For example, set grad_req to null if you don’t need gradient w.r.t a |
| model’s Parameters:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'null'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>or change the learning rate multiplier:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'lr_mult'</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the attribute.</p></li> |
| <li><p><strong>value</strong> (<em>valid type for attribute name</em>) – The new value for the attribute.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.share_parameters"> |
| <code class="sig-name descname">share_parameters</code><span class="sig-paren">(</span><em class="sig-param">shared</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.share_parameters" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Share parameters recursively inside the model.</p> |
| <p>For example, if you want <code class="docutils literal notranslate"><span class="pre">dense1</span></code> to share <code class="docutils literal notranslate"><span class="pre">dense0</span></code>’s weights, you can do:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dense0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span> |
| <span class="n">dense1</span><span class="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">dense0</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>which equals to</dt><dd><p>dense1.weight = dense0.weight |
| dense1.bias = dense0.bias</p> |
| </dd> |
| </dl> |
| <p>Note that unlike the <cite>load_parameters</cite> or <cite>load_dict</cite> functions, |
| <cite>share_parameters</cite> results in the <cite>Parameter</cite> object being shared (or |
| tied) between the models, whereas <cite>load_parameters</cite> or <cite>load_dict</cite> only |
| set the value of the data dictionary of a model. If you call |
| <cite>load_parameters</cite> or <cite>load_dict</cite> after <cite>share_parameters</cite>, the loaded |
| value will be reflected in all networks that use the shared (or tied) |
| <cite>Parameter</cite> object.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>shared</strong> (<em>Dict</em>) – Dict of the shared parameters.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>this block</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.summary"> |
| <code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.summary" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Print the summary of the model’s output and parameters.</p> |
| <p>The network must have been initialized, and must not have been hybridized.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only |
| <a class="reference internal" href="../../../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.Cast.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Cast.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets all Parameters’ gradient buffer to 0.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.vision.transforms.Compose"> |
| <em class="property">class </em><code class="sig-name descname">Compose</code><span class="sig-paren">(</span><em class="sig-param">transforms</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/mxnet/gluon/data/vision/transforms.html#Compose"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.Compose" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.nn.basic_layers.Sequential</span></code></p> |
| <p>Sequentially composes multiple transforms.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>transforms</strong> (<em>list of transform Blocks.</em>) – The list of transforms to be composed.</p> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with shape of the first transform Block requires.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with shape of the last transform Block produces.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">300</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">()])</span> |
| <span class="gp">>>> </span><span class="n">image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">transformer</span><span class="p">(</span><span class="n">image</span><span class="p">)</span> |
| <span class="go"><NDArray 3x256x256 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.vision.transforms.HybridCompose"> |
| <em class="property">class </em><code class="sig-name descname">HybridCompose</code><span class="sig-paren">(</span><em class="sig-param">transforms</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/mxnet/gluon/data/vision/transforms.html#HybridCompose"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.HybridCompose" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.nn.basic_layers.HybridSequential</span></code></p> |
| <p>Sequentially composes multiple transforms. This is the Hybrid version of Compose.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>transforms</strong> (<em>list of transform Blocks.</em>) – The list of transforms to be composed.</p> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor with shape of the first transform Block requires.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: output tensor with shape of the last transform Block produces.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">HybridCompose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">300</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">()])</span> |
| <span class="gp">>>> </span><span class="n">image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">transformer</span><span class="p">(</span><span class="n">image</span><span class="p">)</span> |
| <span class="go"><NDArray 3x256x256 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.vision.transforms.HybridRandomApply"> |
| <em class="property">class </em><code class="sig-name descname">HybridRandomApply</code><span class="sig-paren">(</span><em class="sig-param">transforms</em>, <em class="sig-param">p=0.5</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/mxnet/gluon/data/vision/transforms.html#HybridRandomApply"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.HybridRandomApply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.nn.basic_layers.HybridSequential</span></code></p> |
| <p>Apply a list of transformations randomly given probability</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>transforms</strong> – List of transformations which must be HybridBlocks.</p></li> |
| <li><p><strong>p</strong> (<em>float</em>) – Probability of applying the transformations.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: transformed image.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.HybridRandomApply.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/mxnet/gluon/data/vision/transforms.html#HybridRandomApply.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.HybridRandomApply.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides the forward computation. Arguments must be |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.numpy.ndarray</span></code>.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.data.vision.transforms.RandomApply"> |
| <em class="property">class </em><code class="sig-name descname">RandomApply</code><span class="sig-paren">(</span><em class="sig-param">transforms</em>, <em class="sig-param">p=0.5</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/mxnet/gluon/data/vision/transforms.html#RandomApply"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.RandomApply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.nn.basic_layers.Sequential</span></code></p> |
| <p>Apply a list of transformations randomly given probability</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>transforms</strong> – List of transformations.</p></li> |
| <li><p><strong>p</strong> (<em>float</em>) – Probability of applying the transformations.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="simple"> |
| <dt>Inputs:</dt><dd><ul class="simple"> |
| <li><p><strong>data</strong>: input tensor.</p></li> |
| </ul> |
| </dd> |
| <dt>Outputs:</dt><dd><ul class="simple"> |
| <li><p><strong>out</strong>: transformed image.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.data.vision.transforms.RandomApply.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/mxnet/gluon/data/vision/transforms.html#RandomApply.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.data.vision.transforms.RandomApply.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to implement forward computation using <code class="xref py py-class docutils literal notranslate"><span class="pre">NDArray</span></code>. Only |
| accepts positional arguments.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>*args</strong> (<em>list of NDArray</em>) – Input tensors.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| </div> |
| </div> |
| |
| |
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| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">vision.transforms</a><ul> |
| <li><a class="reference internal" href="#module-mxnet.gluon.data.vision.transforms">API Reference</a></li> |
| </ul> |
| </li> |
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