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<h1>Source code for mxnet.gluon.model_zoo.vision</h1><div class="highlight"><pre>
<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
<span class="c1"># distributed with this work for additional information</span>
<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
<span class="c1"># "License"); you may not use this file except in compliance</span>
<span class="c1"># with the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
<span class="c1"># software distributed under the License is distributed on an</span>
<span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
<span class="c1"># KIND, either express or implied. See the License for the</span>
<span class="c1"># specific language governing permissions and limitations</span>
<span class="c1"># under the License.</span>
<span class="c1"># coding: utf-8</span>
<span class="c1"># pylint: disable=wildcard-import, arguments-differ</span>
<span class="sa">r</span><span class="sd">"""Module for pre-defined neural network models.</span>
<span class="sd">This module contains definitions for the following model architectures:</span>
<span class="sd">- `AlexNet`_</span>
<span class="sd">- `DenseNet`_</span>
<span class="sd">- `Inception V3`_</span>
<span class="sd">- `ResNet V1`_</span>
<span class="sd">- `ResNet V2`_</span>
<span class="sd">- `SqueezeNet`_</span>
<span class="sd">- `VGG`_</span>
<span class="sd">- `MobileNet`_</span>
<span class="sd">You can construct a model with random weights by calling its constructor:</span>
<span class="sd">.. code::</span>
<span class="sd"> from mxnet.gluon.model_zoo import vision</span>
<span class="sd"> resnet18 = vision.resnet18_v1()</span>
<span class="sd"> alexnet = vision.alexnet()</span>
<span class="sd"> squeezenet = vision.squeezenet1_0()</span>
<span class="sd"> densenet = vision.densenet_161()</span>
<span class="sd">We provide pre-trained models for all the models except ResNet V2.</span>
<span class="sd">These can constructed by passing ``pretrained=True``:</span>
<span class="sd">.. code::</span>
<span class="sd"> from mxnet.gluon.model_zoo import vision</span>
<span class="sd"> resnet18 = vision.resnet18_v1(pretrained=True)</span>
<span class="sd"> alexnet = vision.alexnet(pretrained=True)</span>
<span class="sd">Pretrained models are converted from torchvision.</span>
<span class="sd">All pre-trained models expect input images normalized in the same way,</span>
<span class="sd">i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W),</span>
<span class="sd">where N is the batch size, and H and W are expected to be at least 224.</span>
<span class="sd">The images have to be loaded in to a range of [0, 1] and then normalized</span>
<span class="sd">using ``mean = [0.485, 0.456, 0.406]`` and ``std = [0.229, 0.224, 0.225]``.</span>
<span class="sd">The transformation should preferrably happen at preprocessing. You can use</span>
<span class="sd">``mx.image.color_normalize`` for such transformation::</span>
<span class="sd"> image = image/255</span>
<span class="sd"> normalized = mx.image.color_normalize(image,</span>
<span class="sd"> mean=mx.nd.array([0.485, 0.456, 0.406]),</span>
<span class="sd"> std=mx.nd.array([0.229, 0.224, 0.225]))</span>
<span class="sd">.. _AlexNet: https://arxiv.org/abs/1404.5997</span>
<span class="sd">.. _DenseNet: https://arxiv.org/abs/1608.06993</span>
<span class="sd">.. _Inception V3: http://arxiv.org/abs/1512.00567</span>
<span class="sd">.. _ResNet V1: https://arxiv.org/abs/1512.03385</span>
<span class="sd">.. _ResNet V2: https://arxiv.org/abs/1512.03385</span>
<span class="sd">.. _SqueezeNet: https://arxiv.org/abs/1602.07360</span>
<span class="sd">.. _VGG: https://arxiv.org/abs/1409.1556</span>
<span class="sd">.. _MobileNet: https://arxiv.org/abs/1704.04861</span>
<span class="sd">"""</span>
<span class="kn">from</span> <span class="nn">.alexnet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.densenet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.inception</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.resnet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.squeezenet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.vgg</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.mobilenet</span> <span class="k">import</span> <span class="o">*</span>
<div class="viewcode-block" id="get_model"><a class="viewcode-back" href="../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.get_model">[docs]</a><span class="k">def</span> <span class="nf">get_model</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Returns a pre-defined model by name</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of the model.</span>
<span class="sd"> pretrained : bool</span>
<span class="sd"> Whether to load the pretrained weights for model.</span>
<span class="sd"> classes : int</span>
<span class="sd"> Number of classes for the output layer.</span>
<span class="sd"> ctx : Context, default CPU</span>
<span class="sd"> The context in which to load the pretrained weights.</span>
<span class="sd"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> HybridBlock</span>
<span class="sd"> The model.</span>
<span class="sd"> """</span>
<span class="n">models</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'resnet18_v1'</span><span class="p">:</span> <span class="n">resnet18_v1</span><span class="p">,</span>
<span class="s1">'resnet34_v1'</span><span class="p">:</span> <span class="n">resnet34_v1</span><span class="p">,</span>
<span class="s1">'resnet50_v1'</span><span class="p">:</span> <span class="n">resnet50_v1</span><span class="p">,</span>
<span class="s1">'resnet101_v1'</span><span class="p">:</span> <span class="n">resnet101_v1</span><span class="p">,</span>
<span class="s1">'resnet152_v1'</span><span class="p">:</span> <span class="n">resnet152_v1</span><span class="p">,</span>
<span class="s1">'resnet18_v2'</span><span class="p">:</span> <span class="n">resnet18_v2</span><span class="p">,</span>
<span class="s1">'resnet34_v2'</span><span class="p">:</span> <span class="n">resnet34_v2</span><span class="p">,</span>
<span class="s1">'resnet50_v2'</span><span class="p">:</span> <span class="n">resnet50_v2</span><span class="p">,</span>
<span class="s1">'resnet101_v2'</span><span class="p">:</span> <span class="n">resnet101_v2</span><span class="p">,</span>
<span class="s1">'resnet152_v2'</span><span class="p">:</span> <span class="n">resnet152_v2</span><span class="p">,</span>
<span class="s1">'vgg11'</span><span class="p">:</span> <span class="n">vgg11</span><span class="p">,</span>
<span class="s1">'vgg13'</span><span class="p">:</span> <span class="n">vgg13</span><span class="p">,</span>
<span class="s1">'vgg16'</span><span class="p">:</span> <span class="n">vgg16</span><span class="p">,</span>
<span class="s1">'vgg19'</span><span class="p">:</span> <span class="n">vgg19</span><span class="p">,</span>
<span class="s1">'vgg11_bn'</span><span class="p">:</span> <span class="n">vgg11_bn</span><span class="p">,</span>
<span class="s1">'vgg13_bn'</span><span class="p">:</span> <span class="n">vgg13_bn</span><span class="p">,</span>
<span class="s1">'vgg16_bn'</span><span class="p">:</span> <span class="n">vgg16_bn</span><span class="p">,</span>
<span class="s1">'vgg19_bn'</span><span class="p">:</span> <span class="n">vgg19_bn</span><span class="p">,</span>
<span class="s1">'alexnet'</span><span class="p">:</span> <span class="n">alexnet</span><span class="p">,</span>
<span class="s1">'densenet121'</span><span class="p">:</span> <span class="n">densenet121</span><span class="p">,</span>
<span class="s1">'densenet161'</span><span class="p">:</span> <span class="n">densenet161</span><span class="p">,</span>
<span class="s1">'densenet169'</span><span class="p">:</span> <span class="n">densenet169</span><span class="p">,</span>
<span class="s1">'densenet201'</span><span class="p">:</span> <span class="n">densenet201</span><span class="p">,</span>
<span class="s1">'squeezenet1.0'</span><span class="p">:</span> <span class="n">squeezenet1_0</span><span class="p">,</span>
<span class="s1">'squeezenet1.1'</span><span class="p">:</span> <span class="n">squeezenet1_1</span><span class="p">,</span>
<span class="s1">'inceptionv3'</span><span class="p">:</span> <span class="n">inception_v3</span><span class="p">,</span>
<span class="s1">'mobilenet1.0'</span><span class="p">:</span> <span class="n">mobilenet1_0</span><span class="p">,</span>
<span class="s1">'mobilenet0.75'</span><span class="p">:</span> <span class="n">mobilenet0_75</span><span class="p">,</span>
<span class="s1">'mobilenet0.5'</span><span class="p">:</span> <span class="n">mobilenet0_5</span><span class="p">,</span>
<span class="s1">'mobilenet0.25'</span><span class="p">:</span> <span class="n">mobilenet0_25</span>
<span class="p">}</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Model </span><span class="si">%s</span><span class="s1"> is not supported. Available options are</span><span class="se">\n\t</span><span class="si">%s</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="s1">'</span><span class="se">\n\t</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">models</span><span class="o">.</span><span class="n">keys</span><span class="p">()))))</span>
<span class="k">return</span> <span class="n">models</span><span class="p">[</span><span class="n">name</span><span class="p">](</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
</pre></div>
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