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Video Classification with Transformers
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document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> <span class="k-location-slug-pointer">►</span> <a href='/examples/'>Code examples</a> / <a href='/examples/vision/'>Computer Vision</a> / Video Classification with Transformers </div> <div class='k-content'> <h1 id="video-classification-with-transformers">Video Classification with Transformers</h1> <p><strong>Author:</strong> <a href="https://twitter.com/RisingSayak">Sayak Paul</a><br> <strong>Date created:</strong> 2021/06/08<br> <strong>Last modified:</strong> 2023/22/07<br> <strong>Description:</strong> Training a video classifier with hybrid transformers.</p> <div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div> <p><img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/video_transformers.ipynb"><strong>View in Colab</strong></a> <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/examples/vision/video_transformers.py"><strong>GitHub source</strong></a></p> <p>This example is a follow-up to the <a href="https://keras.io/examples/vision/video_classification/">Video Classification with a CNN-RNN Architecture</a> example. This time, we will be using a Transformer-based model (<a href="https://arxiv.org/abs/1706.03762">Vaswani et al.</a>) to classify videos. You can follow <a href="https://livebook.manning.com/book/deep-learning-with-python-second-edition/chapter-11">this book chapter</a> in case you need an introduction to Transformers (with code). After reading this example, you will know how to develop hybrid Transformer-based models for video classification that operate on CNN feature maps.</p> <div class="codehilite"><pre><span></span><code><span class="err">!</span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">q</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">tensorflow</span><span class="o">/</span><span class="n">docs</span> </code></pre></div> <hr /> <h2 id="data-collection">Data collection</h2> <p>As done in the <a href="https://keras.io/examples/vision/video_classification/">predecessor</a> to this example, we will be using a subsampled version of the <a href="https://www.crcv.ucf.edu/data/UCF101.php">UCF101 dataset</a>, a well-known benchmark dataset. In case you want to operate on a larger subsample or even the entire dataset, please refer to <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">this notebook</a>.</p> <div class="codehilite"><pre><span></span><code><span class="err">!</span><span class="n">wget</span> <span class="o">-</span><span class="n">q</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">sayakpaul</span><span class="o">/</span><span class="n">Action</span><span class="o">-</span><span class="n">Recognition</span><span class="o">-</span><span class="ow">in</span><span class="o">-</span><span class="n">TensorFlow</span><span class="o">/</span><span class="n">releases</span><span class="o">/</span><span class="n">download</span><span class="o">/</span><span class="n">v1</span><span class="mf">.0.0</span><span class="o">/</span><span class="n">ucf101_top5</span><span class="o">.</span><span class="n">tar</span><span class="o">.</span><span class="n">gz</span> <span class="err">!</span><span class="n">tar</span> <span class="o">-</span><span class="n">xf</span> <span class="n">ucf101_top5</span><span class="o">.</span><span class="n">tar</span><span class="o">.</span><span class="n">gz</span> </code></pre></div> <hr /> <h2 id="setup">Setup</h2> <div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">os</span> <span class="kn">import</span> <span class="nn">keras</span> <span class="kn">from</span> <span class="nn">keras</span> <span class="kn">import</span> <span class="n">layers</span> <span class="kn">from</span> <span class="nn">keras.applications.densenet</span> <span class="kn">import</span> <span class="n">DenseNet121</span> <span class="kn">from</span> <span class="nn">tensorflow_docs.vis</span> <span class="kn">import</span> <span class="n">embed</span> <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">imageio</span> <span class="kn">import</span> <span class="nn">cv2</span> </code></pre></div> <hr /> <h2 id="define-hyperparameters">Define hyperparameters</h2> <div class="codehilite"><pre><span></span><code><span class="n">MAX_SEQ_LENGTH</span> <span class="o">=</span> <span class="mi">20</span> <span class="n">NUM_FEATURES</span> <span class="o">=</span> <span class="mi">1024</span> <span class="n">IMG_SIZE</span> <span class="o">=</span> <span class="mi">128</span> <span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">5</span> </code></pre></div> <hr /> <h2 id="data-preparation">Data preparation</h2> <p>We will mostly be following the same data preparation steps in this example, except for the following changes:</p> <ul> <li>We reduce the image size to 128x128 instead of 224x224 to speed up computation.</li> <li>Instead of using a pre-trained <a href="https://arxiv.org/abs/1512.00567">InceptionV3</a> network, we use a pre-trained <a href="http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.pdf">DenseNet121</a> for feature extraction.</li> <li>We directly pad shorter videos to length <code>MAX_SEQ_LENGTH</code>.</li> </ul> <p>First, let's load up the <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html">DataFrames</a>.</p> <div class="codehilite"><pre><span></span><code><span class="n">train_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"train.csv"</span><span class="p">)</span> <span class="n">test_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"test.csv"</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Total videos for training: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">train_df</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Total videos for testing: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">test_df</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="n">center_crop_layer</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="n">IMG_SIZE</span><span class="p">,</span> <span class="n">IMG_SIZE</span><span class="p">)</span> <span class="k">def</span> <span class="nf">crop_center</span><span class="p">(</span><span class="n">frame</span><span class="p">):</span> <span class="n">cropped</span> <span class="o">=</span> <span class="n">center_crop_layer</span><span class="p">(</span><span class="n">frame</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="n">cropped</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">convert_to_numpy</span><span class="p">(</span><span class="n">cropped</span><span class="p">)</span> <span class="n">cropped</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">cropped</span><span class="p">)</span> <span class="k">return</span> <span class="n">cropped</span> <span class="c1"># Following method is modified from this tutorial:</span> <span class="c1"># https://www.tensorflow.org/hub/tutorials/action_recognition_with_tf_hub</span> <span class="k">def</span> <span class="nf">load_video</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">max_frames</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">offload_to_cpu</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="n">cap</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">VideoCapture</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="n">frames</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">try</span><span class="p">:</span> <span class="k">while</span> <span class="kc">True</span><span class="p">:</span> <span class="n">ret</span><span class="p">,</span> <span class="n">frame</span> <span class="o">=</span> <span class="n">cap</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">ret</span><span class="p">:</span> <span class="k">break</span> <span class="n">frame</span> <span class="o">=</span> <span class="n">frame</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span> <span class="n">frame</span> <span class="o">=</span> <span class="n">crop_center</span><span class="p">(</span><span class="n">frame</span><span class="p">)</span> <span class="k">if</span> <span class="n">offload_to_cpu</span> <span class="ow">and</span> <span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">backend</span><span class="p">()</span> <span class="o">==</span> <span class="s2">"torch"</span><span class="p">:</span> <span class="n">frame</span> <span class="o">=</span> <span class="n">frame</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">"cpu"</span><span class="p">)</span> <span class="n">frames</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">frame</span><span class="p">)</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">frames</span><span class="p">)</span> <span class="o">==</span> <span class="n">max_frames</span><span class="p">:</span> <span class="k">break</span> <span class="k">finally</span><span class="p">:</span> <span class="n">cap</span><span class="o">.</span><span class="n">release</span><span class="p">()</span> <span class="k">if</span> <span class="n">offload_to_cpu</span> <span class="ow">and</span> <span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">backend</span><span class="p">()</span> <span class="o">==</span> <span class="s2">"torch"</span><span class="p">:</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">frame</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">"cpu"</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">frame</span> <span class="ow">in</span> <span class="n">frames</span><span class="p">])</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">frames</span><span class="p">)</span> <span class="k">def</span> <span class="nf">build_feature_extractor</span><span class="p">():</span> <span class="n">feature_extractor</span> <span class="o">=</span> <span class="n">DenseNet121</span><span class="p">(</span> <span class="n">weights</span><span class="o">=</span><span class="s2">"imagenet"</span><span class="p">,</span> <span class="n">include_top</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pooling</span><span class="o">=</span><span class="s2">"avg"</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">IMG_SIZE</span><span class="p">,</span> <span class="n">IMG_SIZE</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">)</span> <span class="n">preprocess_input</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">applications</span><span class="o">.</span><span class="n">densenet</span><span class="o">.</span><span class="n">preprocess_input</span> <span class="n">inputs</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">Input</span><span class="p">((</span><span class="n">IMG_SIZE</span><span class="p">,</span> <span class="n">IMG_SIZE</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="n">preprocessed</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">feature_extractor</span><span class="p">(</span><span class="n">preprocessed</span><span class="p">)</span> <span class="k">return</span> <span class="n">keras</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"feature_extractor"</span><span class="p">)</span> <span class="n">feature_extractor</span> <span class="o">=</span> <span class="n">build_feature_extractor</span><span class="p">()</span> <span class="c1"># Label preprocessing with StringLookup.</span> <span class="n">label_processor</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">StringLookup</span><span class="p">(</span> <span class="n">num_oov_indices</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">vocabulary</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">train_df</span><span class="p">[</span><span class="s2">"tag"</span><span class="p">]),</span> <span class="n">mask_token</span><span class="o">=</span><span class="kc">None</span> <span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="n">label_processor</span><span class="o">.</span><span class="n">get_vocabulary</span><span class="p">())</span> <span class="k">def</span> <span class="nf">prepare_all_videos</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">root_dir</span><span class="p">):</span> <span class="n">num_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="n">video_paths</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"video_name"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"tag"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">label_processor</span><span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="kc">None</span><span class="p">])</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="c1"># `frame_features` are what we will feed to our sequence model.</span> <span class="n">frame_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">num_samples</span><span class="p">,</span> <span class="n">MAX_SEQ_LENGTH</span><span class="p">,</span> <span class="n">NUM_FEATURES</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"float32"</span> <span class="p">)</span> <span class="c1"># For each video.</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">path</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">video_paths</span><span class="p">):</span> <span class="c1"># Gather all its frames and add a batch dimension.</span> <span class="n">frames</span> <span class="o">=</span> <span class="n">load_video</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">root_dir</span><span class="p">,</span> <span class="n">path</span><span class="p">))</span> <span class="c1"># Pad shorter videos.</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">frames</span><span class="p">)</span> <span class="o"><</span> <span class="n">MAX_SEQ_LENGTH</span><span class="p">:</span> <span class="n">diff</span> <span class="o">=</span> <span class="n">MAX_SEQ_LENGTH</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">frames</span><span class="p">)</span> <span class="n">padding</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">diff</span><span class="p">,</span> <span class="n">IMG_SIZE</span><span class="p">,</span> <span class="n">IMG_SIZE</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="n">frames</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">frames</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span> <span class="n">frames</span> <span class="o">=</span> <span class="n">frames</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="c1"># Initialize placeholder to store the features of the current video.</span> <span class="n">temp_frame_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">MAX_SEQ_LENGTH</span><span class="p">,</span> <span class="n">NUM_FEATURES</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"float32"</span> <span class="p">)</span> <span class="c1"># Extract features from the frames of the current video.</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">frames</span><span class="p">):</span> <span class="n">video_length</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">length</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">MAX_SEQ_LENGTH</span><span class="p">,</span> <span class="n">video_length</span><span class="p">)</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">length</span><span class="p">):</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="p">:])</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">:</span> <span class="n">temp_frame_features</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">feature_extractor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span> <span class="n">batch</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="p">)</span> <span class="k">else</span><span class="p">:</span> <span class="n">temp_frame_features</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="n">frame_features</span><span class="p">[</span><span class="n">idx</span><span class="p">,]</span> <span class="o">=</span> <span class="n">temp_frame_features</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span> <span class="k">return</span> <span class="n">frame_features</span><span class="p">,</span> <span class="n">labels</span> </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><span></span><code>Total videos for training: 594 Total videos for testing: 224 ['CricketShot', 'PlayingCello', 'Punch', 'ShavingBeard', 'TennisSwing'] </code></pre></div> </div> <p>Calling <code>prepare_all_videos()</code> on <code>train_df</code> and <code>test_df</code> takes ~20 minutes to complete. For this reason, to save time, here we download already preprocessed NumPy arrays:</p> <div class="codehilite"><pre><span></span><code><span class="err">!!</span><span class="n">wget</span> <span class="o">-</span><span class="n">q</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">git</span><span class="o">.</span><span class="n">io</span><span class="o">/</span><span class="n">JZmf4</span> <span class="o">-</span><span class="n">O</span> <span class="n">top5_data_prepared</span><span class="o">.</span><span class="n">tar</span><span class="o">.</span><span class="n">gz</span> <span class="err">!!</span><span class="n">tar</span> <span class="o">-</span><span class="n">xf</span> <span class="n">top5_data_prepared</span><span class="o">.</span><span class="n">tar</span><span class="o">.</span><span class="n">gz</span> </code></pre></div> <div class="codehilite"><pre><span></span><code><span class="n">train_data</span><span class="p">,</span> <span class="n">train_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"train_data.npy"</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"train_labels.npy"</span><span class="p">)</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">test_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"test_data.npy"</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"test_labels.npy"</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Frame features in train set: </span><span class="si">{</span><span class="n">train_data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><span></span><code>[] Frame features in train set: (594, 20, 1024) </code></pre></div> </div> <hr /> <h2 id="building-the-transformerbased-model">Building the Transformer-based model</h2> <p>We will be building on top of the code shared in <a href="https://livebook.manning.com/book/deep-learning-with-python-second-edition/chapter-11">this book chapter</a> of <a href="https://www.manning.com/books/deep-learning-with-python">Deep Learning with Python (Second ed.)</a> by François Chollet.</p> <p>First, self-attention layers that form the basic blocks of a Transformer are order-agnostic. Since videos are ordered sequences of frames, we need our Transformer model to take into account order information. We do this via <strong>positional encoding</strong>. We simply embed the positions of the frames present inside videos with an <a href="https://keras.io/api/layers/core_layers/embedding"><code>Embedding</code> layer</a>. We then add these positional embeddings to the precomputed CNN feature maps.</p> <div class="codehilite"><pre><span></span><code><span class="k">class</span> <span class="nc">PositionalEmbedding</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Layer</span><span class="p">):</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">position_embeddings</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span> <span class="n">input_dim</span><span class="o">=</span><span class="n">sequence_length</span><span class="p">,</span> <span class="n">output_dim</span><span class="o">=</span><span class="n">output_dim</span> <span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">sequence_length</span> <span class="o">=</span> <span class="n">sequence_length</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span> <span class="o">=</span> <span class="n">output_dim</span> <span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">):</span> <span class="bp">self</span><span class="o">.</span><span class="n">position_embeddings</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">input_shape</span><span class="p">)</span> <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span> <span class="c1"># The inputs are of shape: `(batch_size, frames, num_features)`</span> <span class="n">inputs</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_dtype</span><span class="p">)</span> <span class="n">length</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">inputs</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="n">positions</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="n">length</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="n">embedded_positions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">position_embeddings</span><span class="p">(</span><span class="n">positions</span><span class="p">)</span> <span class="k">return</span> <span class="n">inputs</span> <span class="o">+</span> <span class="n">embedded_positions</span> </code></pre></div> <p>Now, we can create a subclassed layer for the Transformer.</p> <div class="codehilite"><pre><span></span><code><span class="k">class</span> <span class="nc">TransformerEncoder</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Layer</span><span class="p">):</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="n">dense_dim</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span> <span class="o">=</span> <span class="n">embed_dim</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense_dim</span> <span class="o">=</span> <span class="n">dense_dim</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span> <span class="o">=</span> <span class="n">num_heads</span> <span class="bp">self</span><span class="o">.</span><span class="n">attention</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">MultiHeadAttention</span><span class="p">(</span> <span class="n">num_heads</span><span class="o">=</span><span class="n">num_heads</span><span class="p">,</span> <span class="n">key_dim</span><span class="o">=</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.3</span> <span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense_proj</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span> <span class="p">[</span> <span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">dense_dim</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">keras</span><span class="o">.</span><span class="n">activations</span><span class="o">.</span><span class="n">gelu</span><span class="p">),</span> <span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">embed_dim</span><span class="p">),</span> <span class="p">]</span> <span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">layernorm_1</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">LayerNormalization</span><span class="p">()</span> <span class="bp">self</span><span class="o">.</span><span class="n">layernorm_2</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">LayerNormalization</span><span class="p">()</span> <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="n">attention_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">attention</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">attention_mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span> <span class="n">proj_input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layernorm_1</span><span class="p">(</span><span class="n">inputs</span> <span class="o">+</span> <span class="n">attention_output</span><span class="p">)</span> <span class="n">proj_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense_proj</span><span class="p">(</span><span class="n">proj_input</span><span class="p">)</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">layernorm_2</span><span class="p">(</span><span class="n">proj_input</span> <span class="o">+</span> <span class="n">proj_output</span><span class="p">)</span> </code></pre></div> <hr /> <h2 id="utility-functions-for-training">Utility functions for training</h2> <div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">get_compiled_model</span><span class="p">(</span><span class="n">shape</span><span class="p">):</span> <span class="n">sequence_length</span> <span class="o">=</span> <span class="n">MAX_SEQ_LENGTH</span> <span class="n">embed_dim</span> <span class="o">=</span> <span class="n">NUM_FEATURES</span> <span class="n">dense_dim</span> <span class="o">=</span> <span class="mi">4</span> <span class="n">num_heads</span> <span class="o">=</span> <span class="mi">1</span> <span class="n">classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">label_processor</span><span class="o">.</span><span class="n">get_vocabulary</span><span class="p">())</span> <span class="n">inputs</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> <span class="n">x</span> <span class="o">=</span> <span class="n">PositionalEmbedding</span><span class="p">(</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"frame_position_embedding"</span> <span class="p">)(</span><span class="n">inputs</span><span class="p">)</span> <span class="n">x</span> <span class="o">=</span> <span class="n">TransformerEncoder</span><span class="p">(</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">dense_dim</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"transformer_layer"</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span> <span class="n">x</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">GlobalMaxPooling1D</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span> <span class="n">x</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"softmax"</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span> <span class="n">model</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span> <span class="n">optimizer</span><span class="o">=</span><span class="s2">"adam"</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s2">"sparse_categorical_crossentropy"</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">"accuracy"</span><span class="p">],</span> <span class="p">)</span> <span class="k">return</span> <span class="n">model</span> <span class="k">def</span> <span class="nf">run_experiment</span><span class="p">():</span> <span class="n">filepath</span> <span class="o">=</span> <span class="s2">"/tmp/video_classifier.weights.h5"</span> <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ModelCheckpoint</span><span class="p">(</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">save_weights_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span> <span class="p">)</span> <span class="n">model</span> <span class="o">=</span> <span class="n">get_compiled_model</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">,</span> <span class="n">validation_split</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">EPOCHS</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpoint</span><span class="p">],</span> <span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">filepath</span><span class="p">)</span> <span class="n">_</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Test accuracy: </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">accuracy</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s2">%"</span><span class="p">)</span> <span class="k">return</span> <span class="n">model</span> </code></pre></div> <hr /> <h2 id="model-training-and-inference">Model training and inference</h2> <div class="codehilite"><pre><span></span><code><span class="n">trained_model</span> <span class="o">=</span> <span class="n">run_experiment</span><span class="p">()</span> </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><span></span><code>Epoch 1/5 16/16 ━━━━━━━━━━━━━━━━━━━━ 0s 160ms/step - accuracy: 0.5286 - loss: 2.6762 Epoch 1: val_loss improved from inf to 7.75026, saving model to /tmp/video_classifier.weights.h5 16/16 ━━━━━━━━━━━━━━━━━━━━ 7s 272ms/step - accuracy: 0.5387 - loss: 2.6139 - val_accuracy: 0.0000e+00 - val_loss: 7.7503 Epoch 2/5 15/16 ━━━━━━━━━━━━━━━━━━[37m━━ 0s 4ms/step - accuracy: 0.9396 - loss: 0.2264 Epoch 2: val_loss improved from 7.75026 to 1.96635, saving model to /tmp/video_classifier.weights.h5 16/16 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9406 - loss: 0.2186 - val_accuracy: 0.4000 - val_loss: 1.9664 Epoch 3/5 14/16 ━━━━━━━━━━━━━━━━━[37m━━━ 0s 4ms/step - accuracy: 0.9823 - loss: 0.0384 Epoch 3: val_loss did not improve from 1.96635 16/16 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9822 - loss: 0.0391 - val_accuracy: 0.3667 - val_loss: 3.7076 Epoch 4/5 15/16 ━━━━━━━━━━━━━━━━━━[37m━━ 0s 4ms/step - accuracy: 0.9825 - loss: 0.0681 Epoch 4: val_loss did not improve from 1.96635 16/16 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.9831 - loss: 0.0674 - val_accuracy: 0.4222 - val_loss: 3.7957 Epoch 5/5 15/16 ━━━━━━━━━━━━━━━━━━[37m━━ 0s 4ms/step - accuracy: 1.0000 - loss: 0.0035 Epoch 5: val_loss improved from 1.96635 to 1.56071, saving model to /tmp/video_classifier.weights.h5 16/16 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 1.0000 - loss: 0.0033 - val_accuracy: 0.6333 - val_loss: 1.5607 7/7 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9286 - loss: 0.4434 Test accuracy: 89.29% </code></pre></div> </div> <p><strong>Note</strong>: This model has ~4.23 Million parameters, which is way more than the sequence model (99918 parameters) we used in the prequel of this example. This kind of Transformer model works best with a larger dataset and a longer pre-training schedule.</p> <div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">prepare_single_video</span><span class="p">(</span><span class="n">frames</span><span class="p">):</span> <span class="n">frame_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">MAX_SEQ_LENGTH</span><span class="p">,</span> <span class="n">NUM_FEATURES</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"float32"</span><span class="p">)</span> <span class="c1"># Pad shorter videos.</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">frames</span><span class="p">)</span> <span class="o"><</span> <span class="n">MAX_SEQ_LENGTH</span><span class="p">:</span> <span class="n">diff</span> <span class="o">=</span> <span class="n">MAX_SEQ_LENGTH</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">frames</span><span class="p">)</span> <span class="n">padding</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">diff</span><span class="p">,</span> <span class="n">IMG_SIZE</span><span class="p">,</span> <span class="n">IMG_SIZE</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="n">frames</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">frames</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span> <span class="n">frames</span> <span class="o">=</span> <span class="n">frames</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="c1"># Extract features from the frames of the current video.</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">frames</span><span class="p">):</span> <span class="n">video_length</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="n">length</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">MAX_SEQ_LENGTH</span><span class="p">,</span> <span class="n">video_length</span><span class="p">)</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">length</span><span class="p">):</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="p">:])</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">:</span> <span class="n">frame_features</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">feature_extractor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:])</span> <span class="k">else</span><span class="p">:</span> <span class="n">frame_features</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="k">return</span> <span class="n">frame_features</span> <span class="k">def</span> <span class="nf">predict_action</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> <span class="n">class_vocab</span> <span class="o">=</span> <span class="n">label_processor</span><span class="o">.</span><span class="n">get_vocabulary</span><span class="p">()</span> <span class="n">frames</span> <span class="o">=</span> <span class="n">load_video</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">"test"</span><span class="p">,</span> <span class="n">path</span><span class="p">),</span> <span class="n">offload_to_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">frame_features</span> <span class="o">=</span> <span class="n">prepare_single_video</span><span class="p">(</span><span class="n">frames</span><span class="p">)</span> <span class="n">probabilities</span> <span class="o">=</span> <span class="n">trained_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">frame_features</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="n">plot_x_axis</span><span class="p">,</span> <span class="n">plot_y_axis</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">probabilities</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span> <span class="n">plot_x_axis</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">class_vocab</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="n">plot_y_axis</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">probabilities</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" </span><span class="si">{</span><span class="n">class_vocab</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">probabilities</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">5.2f</span><span class="si">}</span><span class="s2">%"</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">plot_x_axis</span><span class="p">,</span> <span class="n">plot_y_axis</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">plot_x_axis</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"class_label"</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Probability"</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> <span class="k">return</span> <span class="n">frames</span> <span class="c1"># This utility is for visualization.</span> <span class="c1"># Referenced from:</span> <span class="c1"># https://www.tensorflow.org/hub/tutorials/action_recognition_with_tf_hub</span> <span class="k">def</span> <span class="nf">to_gif</span><span class="p">(</span><span class="n">images</span><span class="p">):</span> <span class="n">converted_images</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span> <span class="n">imageio</span><span class="o">.</span><span class="n">mimsave</span><span class="p">(</span><span class="s2">"animation.gif"</span><span class="p">,</span> <span class="n">converted_images</span><span class="p">,</span> <span class="n">fps</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> <span class="k">return</span> <span class="n">embed</span><span class="o">.</span><span class="n">embed_file</span><span class="p">(</span><span class="s2">"animation.gif"</span><span class="p">)</span> <span class="n">test_video</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">test_df</span><span class="p">[</span><span class="s2">"video_name"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Test video path: </span><span class="si">{</span><span class="n">test_video</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="n">test_frames</span> <span class="o">=</span> <span class="n">predict_action</span><span class="p">(</span><span class="n">test_video</span><span class="p">)</span> <span class="n">to_gif</span><span class="p">(</span><span class="n">test_frames</span><span class="p">[:</span><span class="n">MAX_SEQ_LENGTH</span><span class="p">])</span> </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><span></span><code>Test video path: v_ShavingBeard_g03_c02.avi 1/1 ━━━━━━━━━━━━━━━━━━━━ 20s 20s/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 557ms/step ShavingBeard: 100.00% Punch: 0.00% CricketShot: 0.00% TennisSwing: 0.00% PlayingCello: 0.00% </code></pre></div> </div> <p><img alt="png" src="/img/examples/vision/video_transformers/video_transformers_23_1.png" /></p> <p><img src="data:image/gif;base64,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"/></p> <p>The performance of our model is far from optimal, because it was trained on a small dataset.</p> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#video-classification-with-transformers'>Video Classification with Transformers</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#data-collection'>Data collection</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#setup'>Setup</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#define-hyperparameters'>Define hyperparameters</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#data-preparation'>Data preparation</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#building-the-transformerbased-model'>Building the Transformer-based model</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#utility-functions-for-training'>Utility functions for training</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#model-training-and-inference'>Model training and inference</a> </div> </div> </div> </div> </div> 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