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| <div class="section" id="fine-tune-with-pretrained-models"> |
| <span id="fine-tune-with-pretrained-models"></span><h1>Fine-tune with Pretrained Models<a class="headerlink" href="#fine-tune-with-pretrained-models" title="Permalink to this headline">¶</a></h1> |
| <p>Many of the exciting deep learning algorithms for computer vision require |
| massive datasets for training. The most popular benchmark dataset, |
| <a class="reference external" href="http://www.image-net.org/">ImageNet</a>, for example, contains one million images |
| from one thousand categories. But for any practical problem, we typically have |
| access to comparatively small datasets. In these cases, if we were to train a |
| neural network’s weights from scratch, starting from random initialized |
| parameters, we would overfit the training set badly.</p> |
| <p>One approach to get around this problem is to first pretrain a deep net on a |
| large-scale dataset, like ImageNet. Then, given a new dataset, we can start |
| with these pretrained weights when training on our new task. This process is |
| commonly called <em>fine-tuning</em>. There are a number of variations of fine-tuning. |
| Sometimes, the initial neural network is used only as a <em>feature extractor</em>. |
| That means that we freeze every layer prior to the output layer and simply learn |
| a new output layer. In <a class="reference external" href="https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb">another document</a>, we explained how to |
| do this kind of feature extraction. Another approach is to update all of |
| the network’s weights for the new task, and that’s the approach we demonstrate in |
| this document.</p> |
| <p>To fine-tune a network, we must first replace the last fully-connected layer |
| with a new one that outputs the desired number of classes. We initialize its |
| weights randomly. Then we continue training as normal. Sometimes it’s common to |
| use a smaller learning rate based on the intuition that we may already be close |
| to a good result.</p> |
| <p>In this demonstration, we’ll fine-tune a model pretrained on ImageNet to the |
| smaller caltech-256 dataset. Following this example, you can fine-tune to other |
| datasets, even for strikingly different applications such as face |
| identification.</p> |
| <p>We will show that, even with simple hyper-parameters setting, we can match and |
| even outperform state-of-the-art results on caltech-256.</p> |
| <table border="1" class="docutils"> |
| <colgroup> |
| <col width="50%"/> |
| <col width="50%"/> |
| </colgroup> |
| <thead valign="bottom"> |
| <tr class="row-odd"><th class="head">Network</th> |
| <th class="head">Accuracy</th> |
| </tr> |
| </thead> |
| <tbody valign="top"> |
| <tr class="row-even"><td>Resnet-50</td> |
| <td>77.4%</td> |
| </tr> |
| <tr class="row-odd"><td>Resnet-152</td> |
| <td>86.4%</td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="section" id="prepare-data"> |
| <span id="prepare-data"></span><h2>Prepare data<a class="headerlink" href="#prepare-data" title="Permalink to this headline">¶</a></h2> |
| <p>We follow the standard protocol to sample 60 images from each class as the |
| training set, and the rest for the validation set. We resize images into 256x256 |
| size and pack them into the rec file. The scripts to prepare the data is as |
| following.</p> |
| <div class="highlight-sh"><div class="highlight"><pre><span></span>wget http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tar |
| tar -xf 256_ObjectCategories.tar |
| |
| mkdir -p caltech_256_train_60 |
| <span class="k">for</span> i in 256_ObjectCategories/*<span class="p">;</span> <span class="k">do</span> |
| <span class="nv">c</span><span class="o">=</span><span class="sb">`</span>basename <span class="nv">$i</span><span class="sb">`</span> |
| mkdir -p caltech_256_train_60/<span class="nv">$c</span> |
| <span class="k">for</span> j in <span class="sb">`</span>ls <span class="nv">$i</span>/*.jpg <span class="p">|</span> shuf <span class="p">|</span> head -n <span class="m">60</span><span class="sb">`</span><span class="p">;</span> <span class="k">do</span> |
| mv <span class="nv">$j</span> caltech_256_train_60/<span class="nv">$c</span>/ |
| <span class="k">done</span> |
| <span class="k">done</span> |
| |
| python ~/mxnet/tools/im2rec.py --list True --recursive True caltech-256-60-train caltech_256_train_60/ |
| python ~/mxnet/tools/im2rec.py --list True --recursive True caltech-256-60-val 256_ObjectCategories/ |
| python ~/mxnet/tools/im2rec.py --resize <span class="m">256</span> --quality <span class="m">90</span> --num-thread <span class="m">16</span> caltech-256-60-val 256_ObjectCategories/ |
| python ~/mxnet/tools/im2rec.py --resize <span class="m">256</span> --quality <span class="m">90</span> --num-thread <span class="m">16</span> caltech-256-60-train caltech_256_train_60/ |
| </pre></div> |
| </div> |
| <p>The following code downloads the pregenerated rec files. It may take a few minutes.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">urllib</span> |
| <span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="n">url</span><span class="p">):</span> |
| <span class="n">filename</span> <span class="o">=</span> <span class="n">url</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"/"</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span> |
| <span class="n">urllib</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span> |
| <span class="n">download</span><span class="p">(</span><span class="s1">'http://data.mxnet.io/data/caltech-256/caltech-256-60-train.rec'</span><span class="p">)</span> |
| <span class="n">download</span><span class="p">(</span><span class="s1">'http://data.mxnet.io/data/caltech-256/caltech-256-60-val.rec'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Next, we define the function which returns the data iterators:</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span> |
| |
| <span class="k">def</span> <span class="nf">get_iterators</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">data_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</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="n">train</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span> |
| <span class="n">path_imgrec</span> <span class="o">=</span> <span class="s1">'./caltech-256-60-train.rec'</span><span class="p">,</span> |
| <span class="n">data_name</span> <span class="o">=</span> <span class="s1">'data'</span><span class="p">,</span> |
| <span class="n">label_name</span> <span class="o">=</span> <span class="s1">'softmax_label'</span><span class="p">,</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">data_shape</span> <span class="o">=</span> <span class="n">data_shape</span><span class="p">,</span> |
| <span class="n">shuffle</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span> |
| <span class="n">rand_crop</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span> |
| <span class="n">rand_mirror</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span> |
| <span class="n">val</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span> |
| <span class="n">path_imgrec</span> <span class="o">=</span> <span class="s1">'./caltech-256-60-val.rec'</span><span class="p">,</span> |
| <span class="n">data_name</span> <span class="o">=</span> <span class="s1">'data'</span><span class="p">,</span> |
| <span class="n">label_name</span> <span class="o">=</span> <span class="s1">'softmax_label'</span><span class="p">,</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">data_shape</span> <span class="o">=</span> <span class="n">data_shape</span><span class="p">,</span> |
| <span class="n">rand_crop</span> <span class="o">=</span> <span class="bp">False</span><span class="p">,</span> |
| <span class="n">rand_mirror</span> <span class="o">=</span> <span class="bp">False</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>We then download a pretrained 50-layer ResNet model and load it into memory. Note |
| that if <code class="docutils literal"><span class="pre">load_checkpoint</span></code> reports an error, we can remove the downloaded files |
| and try <code class="docutils literal"><span class="pre">get_model</span></code> again.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_model</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span> |
| <span class="n">download</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'-symbol.json'</span><span class="p">)</span> |
| <span class="n">download</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'-</span><span class="si">%04d</span><span class="s1">.params'</span> <span class="o">%</span> <span class="p">(</span><span class="n">epoch</span><span class="p">,))</span> |
| |
| <span class="n">get_model</span><span class="p">(</span><span class="s1">'http://data.mxnet.io/models/imagenet/resnet/50-layers/resnet-50'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="s1">'resnet-50'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="train"> |
| <span id="train"></span><h2>Train<a class="headerlink" href="#train" title="Permalink to this headline">¶</a></h2> |
| <p>We first define a function which replaces the last fully-connected layer for a given network.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_fine_tune_model</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="n">layer_name</span><span class="o">=</span><span class="s1">'flatten0'</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> symbol: the pretrained network symbol</span> |
| <span class="sd"> arg_params: the argument parameters of the pretrained model</span> |
| <span class="sd"> num_classes: the number of classes for the fine-tune datasets</span> |
| <span class="sd"> layer_name: the layer name before the last fully-connected layer</span> |
| <span class="sd"> """</span> |
| <span class="n">all_layers</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">all_layers</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">'_output'</span><span class="p">]</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc1'</span><span class="p">)</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">SoftmaxOutput</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">)</span> |
| <span class="n">new_args</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">({</span><span class="n">k</span><span class="p">:</span><span class="n">arg_params</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">arg_params</span> <span class="k">if</span> <span class="s1">'fc1'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">k</span><span class="p">})</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">new_args</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Now we create a module. We first call <code class="docutils literal"><span class="pre">init_params</span></code> to randomly initialize parameters, next use <code class="docutils literal"><span class="pre">set_params</span></code> to replace all parameters except for the last fully-connected layer with pretrained model.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span> |
| <span class="n">head</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%(asctime)-15s</span><span class="s1"> </span><span class="si">%(message)s</span><span class="s1">'</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="n">head</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">num_gpus</span><span class="p">):</span> |
| <span class="n">devs</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_gpus</span><span class="p">)]</span> |
| <span class="n">mod</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">mod</span><span class="o">.</span><span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="o">=</span><span class="n">symbol</span><span class="p">,</span> <span class="n">context</span><span class="o">=</span><span class="n">devs</span><span class="p">)</span> |
| <span class="n">mod</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> |
| <span class="n">num_epoch</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> |
| <span class="n">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span> |
| <span class="n">aux_params</span><span class="o">=</span><span class="n">aux_params</span><span class="p">,</span> |
| <span class="n">allow_missing</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> |
| <span class="n">batch_end_callback</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">callback</span><span class="o">.</span><span class="n">Speedometer</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> |
| <span class="n">kvstore</span><span class="o">=</span><span class="s1">'device'</span><span class="p">,</span> |
| <span class="n">optimizer</span><span class="o">=</span><span class="s1">'sgd'</span><span class="p">,</span> |
| <span class="n">optimizer_params</span><span class="o">=</span><span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span><span class="mf">0.01</span><span class="p">},</span> |
| <span class="n">initializer</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Xavier</span><span class="p">(</span><span class="n">rnd_type</span><span class="o">=</span><span class="s1">'gaussian'</span><span class="p">,</span> <span class="n">factor_type</span><span class="o">=</span><span class="s2">"in"</span><span class="p">,</span> <span class="n">magnitude</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> |
| <span class="n">eval_metric</span><span class="o">=</span><span class="s1">'acc'</span><span class="p">)</span> |
| <span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">mod</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">metric</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Then we can start training. We use AWS EC2 g2.8xlarge, which has 8 GPUs.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">num_classes</span> <span class="o">=</span> <span class="mi">256</span> |
| <span class="n">batch_per_gpu</span> <span class="o">=</span> <span class="mi">16</span> |
| <span class="n">num_gpus</span> <span class="o">=</span> <span class="mi">8</span> |
| |
| <span class="p">(</span><span class="n">new_sym</span><span class="p">,</span> <span class="n">new_args</span><span class="p">)</span> <span class="o">=</span> <span class="n">get_fine_tune_model</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span> |
| |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_per_gpu</span> <span class="o">*</span> <span class="n">num_gpus</span> |
| <span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span> <span class="o">=</span> <span class="n">get_iterators</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> |
| <span class="n">mod_score</span> <span class="o">=</span> <span class="n">fit</span><span class="p">(</span><span class="n">new_sym</span><span class="p">,</span> <span class="n">new_args</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">num_gpus</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">mod_score</span> <span class="o">></span> <span class="mf">0.77</span><span class="p">,</span> <span class="s2">"Low training accuracy."</span> |
| </pre></div> |
| </div> |
| <p>You will see that, after only 8 epochs, we can get 78% validation accuracy. This |
| matches the state-of-the-art results training on caltech-256 alone, |
| e.g. <a class="reference external" href="http://www.robots.ox.ac.uk/~vgg/research/deep_eval/">VGG</a>.</p> |
| <p>Next, we try to use another pretrained model. This model was trained on the |
| complete Imagenet dataset, which is 10x larger than the Imagenet 1K classes |
| version, and uses a 3x deeper Resnet architecture.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">get_model</span><span class="p">(</span><span class="s1">'http://data.mxnet.io/models/imagenet-11k/resnet-152/resnet-152'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="s1">'resnet-152'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="p">(</span><span class="n">new_sym</span><span class="p">,</span> <span class="n">new_args</span><span class="p">)</span> <span class="o">=</span> <span class="n">get_fine_tune_model</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span> |
| <span class="n">mod_score</span> <span class="o">=</span> <span class="n">fit</span><span class="p">(</span><span class="n">new_sym</span><span class="p">,</span> <span class="n">new_args</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">num_gpus</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">mod_score</span> <span class="o">></span> <span class="mf">0.86</span><span class="p">,</span> <span class="s2">"Low training accuracy."</span> |
| </pre></div> |
| </div> |
| <p>As can be seen, even for a single data epoch, it reaches 83% validation |
| accuracy. After 8 epoches, the validation accuracy increases to 86.4%.</p> |
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| <h3><a href="../index.html">Table Of Contents</a></h3> |
| <ul> |
| <li><a class="reference internal" href="#">Fine-tune with Pretrained Models</a><ul> |
| <li><a class="reference internal" href="#prepare-data">Prepare data</a></li> |
| <li><a class="reference internal" href="#train">Train</a></li> |
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