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  <section id="slideflow-simclr">
<h1>slideflow.simclr<a class="headerlink" href="#slideflow-simclr" title="Permalink to this heading">¶</a></h1>
<p>This module contains utility functions for training a SimCLR model. Please see
<a class="reference internal" href="../ssl/#simclr-ssl"><span class="std std-ref">Self-Supervised Learning (SSL)</span></a> for more information on the high-level API and recommended use.</p>
<dl class="py function">
<dt class="sig sig-object py" id="slideflow.simclr.get_args">
<span class="sig-name descname"><span class="pre">get_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/slideflow/simclr/simclr/tf2/utils/#get_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#slideflow.simclr.get_args" title="Permalink to this definition">¶</a></dt>
<dd><p>Configure a <code class="docutils literal notranslate"><span class="pre">SimCLR_Args</span></code> object for training SimCLR.</p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>**kwargs</strong> – Please see the <a class="reference internal" href="#slideflow.simclr.SimCLR_Args" title="slideflow.simclr.SimCLR_Args"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.simclr.SimCLR_Args</span></code></a> documentation
for information on available parameters.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>slideflow.simclr.SimCLR_Args</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="slideflow.simclr.load">
<span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_pretrained</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/slideflow/simclr/simclr/tf2/#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#slideflow.simclr.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Load a SavedModel or checkpoint for inference.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Path to saved model.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Tensorflow SimCLR model.</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="slideflow.simclr.load_model_args">
<span class="sig-name descname"><span class="pre">load_model_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/slideflow/simclr/simclr/tf2/utils/#load_model_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#slideflow.simclr.load_model_args" title="Permalink to this definition">¶</a></dt>
<dd><p>Load args.json associated with a given SimCLR model or checkpoint.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>model_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Path to SimCLR model or checkpoint.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Dictionary of contents of args.json file. If file is not found and
<cite>ignore_missing</cite> is False, will return None. If <cite>ignore_missing</cite> is
True, will raise an OSError.</p>
</dd>
<dt class="field-odd">Raises<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#OSError" title="(in Python v3.12)"><strong>OSError</strong></a> – If args.json cannot be found and <cite>ignore_missing</cite> is False.</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="slideflow.simclr.run_simclr">
<span class="sig-name descname"><span class="pre">run_simclr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">builder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_tpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tpu_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tpu_zone</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gcp_project</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/slideflow/simclr/simclr/tf2/#run_simclr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#slideflow.simclr.run_simclr" title="Permalink to this definition">¶</a></dt>
<dd><p>Train a SimCLR model.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>simCLR_args</strong> (<em>SimpleNamespace</em>) – SimCLR arguments, as provided by
<a class="reference internal" href="#slideflow.simclr.get_args" title="slideflow.simclr.get_args"><code class="xref py py-func docutils literal notranslate"><span class="pre">slideflow.simclr.get_args()</span></code></a>.</p></li>
<li><p><strong>builder</strong> (<a class="reference internal" href="#slideflow.simclr.DatasetBuilder" title="slideflow.simclr.DatasetBuilder"><em>DatasetBuilder</em></a><em>, </em><em>optional</em>) – Builder for preparing SimCLR input
pipelines. If None, will build using TensorflowDatasets and
<cite>simclr_args.dataset</cite>.</p></li>
<li><p><strong>model_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Model directory for training.</p></li>
<li><p><strong>cache_dataset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to cache the entire dataset in memory. If
the dataset is ImageNet, this is a very bad idea, but for smaller datasets
it can improve performance</p></li>
<li><p><strong>checkpoint_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Loading from the given checkpoint for fine-tuning if
a finetuning checkpoint does not already exist in model_dir</p></li>
<li><p><strong>use_tpu</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to run on TPU.</p></li>
<li><p><strong>tpu_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The Cloud TPU to use for training. This should be either the
name used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470
url</p></li>
<li><p><strong>tpu_zone</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – GCE zone where the Cloud TPU is located in. If not
specified, we will attempt to automatically detect the GCE project from
metadata</p></li>
<li><p><strong>gcp_project</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Project name for the Cloud TPU-enabled project. If not
specified, we will attempt to automatically detect the GCE project from
metadata</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="slideflow.simclr.SimCLR">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SimCLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/slideflow/simclr/simclr/tf2/model/#SimCLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#slideflow.simclr.SimCLR" title="Permalink to this definition">¶</a></dt>
<dd><p>Resnet model with projection or supervised layer.</p>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="slideflow.simclr.SimCLR_Args">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SimCLR_Args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.075</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sqrt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warmup_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'validation'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'imagenet2012'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pretrain'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lineareval_while_pretraining</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_init_logits_layer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fine_tune_after_block</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_checkpoint_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temperature</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_head_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nonlinear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_out_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_proj_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ft_proj_selector</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">width_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resnet_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sk_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">se_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">224</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_jitter_strength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_blur</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stain_augment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/slideflow/simclr/simclr/tf2/utils/#SimCLR_Args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#slideflow.simclr.SimCLR_Args" title="Permalink to this definition">¶</a></dt>
<dd><p>SimCLR arguments.</p>
<dl class="simple">
<dt>A class containg all default - if not overwritten at initialization -</dt><dd><p>SimCLR arguments.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learning_rate</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Initial learning rate per batch size of 256.</p></li>
<li><p><strong>learning_rate_scaling</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – How to scale the learning rate as a
function of batch size. ‘linear’ or ‘sqrt’.</p></li>
<li><p><strong>warmup_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of epochs of warmup.</p></li>
<li><p><strong>weight_decay</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Amount of weight decay to use.</p></li>
<li><p><strong>batch_norm_decay</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Batch norm decay parameter.</p></li>
<li><p><strong>train_batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Batch size for training.</p></li>
<li><p><strong>train_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Split for training</p></li>
<li><p><strong>train_epoch</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of epochs to train for.</p></li>
<li><p><strong>train_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of steps to train for. If provided, overrides
train_epochs.</p></li>
<li><p><strong>eval_steps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of steps to eval for. If not provided, evals
over entire dataset.</p></li>
<li><p><strong>eval_batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Batch size for eval.</p></li>
<li><p><strong>checkpoint_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of epochs between
checkpoints/summaries.</p></li>
<li><p><strong>checkpoint_steps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of steps between checkpoints/summaries.
If provided, overrides checkpoint_epochs.</p></li>
<li><p><strong>eval_split</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Split for evaluation.</p></li>
<li><p><strong>dataset</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Name of a dataset.</p></li>
<li><p><strong>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Whether to perform training or evaluation. ‘train’,
‘eval’, or ‘train_then_eval’</p></li>
<li><p><strong>train_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The train mode controls different objectives and
trainable components.</p></li>
<li><p><strong>lineareval_while_pretraining</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to finetune supervised
head while pretraining. ‘pretrain’ or ‘finetune’</p></li>
<li><p><strong>zero_init_logits_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, zero initialize layers after
avg_pool for supervised learning.</p></li>
<li><p><strong>fine_tune_after_block</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The layers after which block that we will
fine-tune. -1 means fine-tuning everything. 0 means fine-tuning
after stem block. 4 means fine-tuning just the linear head.</p></li>
<li><p><strong>master</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Address/name of the TensorFlow master to use.
By default, use an in-process master.</p></li>
<li><p><strong>data_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Directory where dataset is stored.</p></li>
<li><p><strong>optimizer</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Optimizer to use. ‘momentum’, ‘adam’, ‘lars’</p></li>
<li><p><strong>momentum</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum parameter.</p></li>
<li><p><strong>keep_checkpoint_max</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Maximum number of checkpoints to keep.</p></li>
<li><p><strong>temperature</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Temperature parameter for contrastive loss.</p></li>
<li><p><strong>hidden_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Temperature parameter for contrastive loss.</p></li>
<li><p><strong>proj_head_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – How the head projection is done. ‘none’,
‘linear’, ‘nonlinear’</p></li>
<li><p><strong>proj_out_dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of head projection dimension.</p></li>
<li><p><strong>num_proj_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of non-linear head layers.</p></li>
<li><p><strong>ft_proj_selector</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Which layer of the projection head to use
during fine-tuning. 0 means no projection head, and -1 means the
final layer.</p></li>
<li><p><strong>global_bn</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to aggregate BN statistics across
distributed cores.</p></li>
<li><p><strong>width_multiplier</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Multiplier to change width of network.</p></li>
<li><p><strong>resnet_depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth of ResNet.</p></li>
<li><p><strong>sk_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – If it is bigger than 0, it will enable SK.
Recommendation: 0.0625.</p></li>
<li><p><strong>se_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – If it is bigger than 0, it will enable SE.</p></li>
<li><p><strong>image_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Input image size.</p></li>
<li><p><strong>color_jitter_strength</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The strength of color jittering.</p></li>
<li><p><strong>use_blur</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether or not to use Gaussian blur for augmentation
during pretraining.</p></li>
<li><p><strong>num_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of classes for the supervised head.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="slideflow.simclr.DatasetBuilder">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">DatasetBuilder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_dts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_dts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_dts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps_per_epoch_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalizer_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/slideflow/simclr/simclr/tf2/data/#DatasetBuilder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#slideflow.simclr.DatasetBuilder" title="Permalink to this definition">¶</a></dt>
<dd><p>Build a training/validation dataset pipeline for SimCLR.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>train_dts</strong> (<em>sf.Dataset</em><em>, </em><em>optional</em>) – Training dataset.</p></li>
<li><p><strong>val_dts</strong> (<em>sf.Dataset</em><em>, </em><em>optional</em>) – Optional validation dataset.</p></li>
<li><p><strong>test_dts</strong> (<em>sf.Dataset</em><em>, </em><em>optional</em>) – Optional held-out test set.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments<span class="colon">:</span></dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Labels for training the supervised head.
Can be a name of an outcome (str) or a dict mapping slide names
to labels.</p></li>
<li><p><strong>val_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – Optional keyword arguments for
generating a validation dataset from <code class="docutils literal notranslate"><span class="pre">train_dts</span></code> via
<code class="docutils literal notranslate"><span class="pre">train_dts.split()</span></code>. Incompatible with <code class="docutils literal notranslate"><span class="pre">val_dts</span></code>.</p></li>
<li><p><strong>steps_per_epoch_override</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Override the number
of steps per epoch.</p></li>
<li><p><strong>dataset_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – Keyword arguments passed to the
<a class="reference internal" href="../dataset/#slideflow.Dataset.tensorflow" title="slideflow.Dataset.tensorflow"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Dataset.tensorflow()</span></code></a> method when creating
the input pipeline.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</section>


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