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<h1 id="tensorflow-efficientnet-condconv">(Tensorflow) EfficientNet CondConv</h1>
<p><strong>EfficientNet</strong> is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a <em>compound coefficient</em>. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use <span class="arithmatex"><span class="MathJax_Preview">2^N</span><script type="math/tex">2^N</script></span> times more computational resources, then we can simply increase the network depth by <span class="arithmatex"><span class="MathJax_Preview">\alpha ^ N</span><script type="math/tex">\alpha ^ N</script></span>, width by <span class="arithmatex"><span class="MathJax_Preview">\beta ^ N</span><script type="math/tex">\beta ^ N</script></span>, and image size by <span class="arithmatex"><span class="MathJax_Preview">\gamma ^ N</span><script type="math/tex">\gamma ^ N</script></span>, where <span class="arithmatex"><span class="MathJax_Preview">\alpha, \beta, \gamma</span><script type="math/tex">\alpha, \beta, \gamma</script></span> are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient <span class="arithmatex"><span class="MathJax_Preview">\phi</span><script type="math/tex">\phi</script></span> to uniformly scales network width, depth, and resolution in a principled way.</p>
<p>The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.</p>
<p>The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of <a href="https://paperswithcode.com/method/mobilenetv2">MobileNetV2</a>, in addition to squeeze-and-excitation blocks.</p>
<p>This collection of models amends EfficientNet by adding <a href="https://paperswithcode.com/method/condconv">CondConv</a> convolutions.</p>
<p>The weights from this model were ported from <a href="https://github.com/tensorflow/tpu">Tensorflow/TPU</a>.</p>
<h2 id="how-do-i-use-this-model-on-an-image">How do I use this model on an image?</h2>
<p>To load a pretrained model:</p>
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">timm</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;tf_efficientnet_cc_b0_4e&#39;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
</code></pre></div>
<p>To load and preprocess the image:
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">urllib</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">timm.data</span> <span class="kn">import</span> <span class="n">resolve_data_config</span>
<span class="kn">from</span> <span class="nn">timm.data.transforms_factory</span> <span class="kn">import</span> <span class="n">create_transform</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">resolve_data_config</span><span class="p">({},</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">create_transform</span><span class="p">(</span><span class="o">**</span><span class="n">config</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="p">(</span><span class="s2">&quot;https://github.com/pytorch/hub/raw/master/images/dog.jpg&quot;</span><span class="p">,</span> <span class="s2">&quot;dog.jpg&quot;</span><span class="p">)</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">request</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">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">&#39;RGB&#39;</span><span class="p">)</span>
<span class="n">tensor</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># transform and add batch dimension</span>
</code></pre></div></p>
<p>To get the model predictions:
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
<span class="n">probabilities</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">probabilities</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># prints: torch.Size([1000])</span>
</code></pre></div></p>
<p>To get the top-5 predictions class names:
<div class="highlight"><pre><span></span><code><span class="c1"># Get imagenet class mappings</span>
<span class="n">url</span><span class="p">,</span> <span class="n">filename</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;imagenet_classes.txt&quot;</span><span class="p">)</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">request</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="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;imagenet_classes.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()]</span>
<span class="c1"># Print top categories per image</span>
<span class="n">top5_prob</span><span class="p">,</span> <span class="n">top5_catid</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">probabilities</span><span class="p">,</span> <span class="mi">5</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">top5_prob</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">categories</span><span class="p">[</span><span class="n">top5_catid</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">top5_prob</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
<span class="c1"># prints class names and probabilities like:</span>
<span class="c1"># [(&#39;Samoyed&#39;, 0.6425196528434753), (&#39;Pomeranian&#39;, 0.04062102362513542), (&#39;keeshond&#39;, 0.03186424449086189), (&#39;white wolf&#39;, 0.01739676296710968), (&#39;Eskimo dog&#39;, 0.011717947199940681)]</span>
</code></pre></div></p>
<p>Replace the model name with the variant you want to use, e.g. <code>tf_efficientnet_cc_b0_4e</code>. You can find the IDs in the model summaries at the top of this page.</p>
<p>To extract image features with this model, follow the <a href="https://rwightman.github.io/pytorch-image-models/feature_extraction/">timm feature extraction examples</a>, just change the name of the model you want to use.</p>
<h2 id="how-do-i-finetune-this-model">How do I finetune this model?</h2>
<p>You can finetune any of the pre-trained models just by changing the classifier (the last layer).
<div class="highlight"><pre><span></span><code><span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;tf_efficientnet_cc_b0_4e&#39;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">NUM_FINETUNE_CLASSES</span><span class="p">)</span>
</code></pre></div>
To finetune on your own dataset, you have to write a training loop or adapt <a href="https://github.com/rwightman/pytorch-image-models/blob/master/train.py">timm's training
script</a> to use your dataset.</p>
<h2 id="how-do-i-train-this-model">How do I train this model?</h2>
<p>You can follow the <a href="https://rwightman.github.io/pytorch-image-models/scripts/">timm recipe scripts</a> for training a new model afresh.</p>
<h2 id="citation">Citation</h2>
<div class="highlight"><pre><span></span><code><span class="nc">@article</span><span class="p">{</span><span class="nl">DBLP:journals/corr/abs-1904-04971</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Brandon Yang and</span>
<span class="s"> Gabriel Bender and</span>
<span class="s"> Quoc V. Le and</span>
<span class="s"> Jiquan Ngiam}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Soft Conditional Computation}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">journal</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{CoRR}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">volume</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{abs/1904.04971}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2019}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{http://arxiv.org/abs/1904.04971}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">archivePrefix</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{arXiv}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">eprint</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{1904.04971}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">timestamp</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Thu, 25 Apr 2019 13:55:01 +0200}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">biburl</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{https://dblp.org/rec/journals/corr/abs-1904-04971.bib}</span><span class="p">,</span><span class="w"></span>
<span class="w"> </span><span class="na">bibsource</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{dblp computer science bibliography, https://dblp.org}</span><span class="w"></span>
<span class="p">}</span><span class="w"></span>
</code></pre></div>
<!--
Type: model-index
Collections:
- Name: TF EfficientNet CondConv
Paper:
Title: 'CondConv: Conditionally Parameterized Convolutions for Efficient Inference'
URL: https://paperswithcode.com/paper/soft-conditional-computation
Models:
- Name: tf_efficientnet_cc_b0_4e
In Collection: TF EfficientNet CondConv
Metadata:
FLOPs: 224153788
Parameters: 13310000
File Size: 53490940
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- CondConv
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_cc_b0_4e
LR: 0.256
Epochs: 350
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 2048
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1561
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.32%
Top 5 Accuracy: 93.32%
- Name: tf_efficientnet_cc_b0_8e
In Collection: TF EfficientNet CondConv
Metadata:
FLOPs: 224158524
Parameters: 24010000
File Size: 96287616
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- CondConv
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_cc_b0_8e
LR: 0.256
Epochs: 350
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 2048
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1572
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.91%
Top 5 Accuracy: 93.65%
- Name: tf_efficientnet_cc_b1_8e
In Collection: TF EfficientNet CondConv
Metadata:
FLOPs: 370427824
Parameters: 39720000
File Size: 159206198
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- CondConv
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_cc_b1_8e
LR: 0.256
Epochs: 350
Crop Pct: '0.882'
Momentum: 0.9
Batch Size: 2048
Image Size: '240'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1584
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.33%
Top 5 Accuracy: 94.37%
-->
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