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smart_augmentation/higher/doc/html/namespaceutils.html
Antoine Harlé b89dac9084 Changes since Teledyne
2024-08-20 11:53:35 +02:00

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<div class="title">utils Namespace Reference</div> </div>
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Functions</h2></td></tr>
<tr class="memitem:aaf9c453130b45dce2d49276ec2a8b016"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespaceutils.html#aaf9c453130b45dce2d49276ec2a8b016">print_graph</a> (PyTorch_obj, fig_name='graph')</td></tr>
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<tr class="memitem:a669b01f2e6565bb22c2d6e14de4a0406"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespaceutils.html#a669b01f2e6565bb22c2d6e14de4a0406">plot_resV2</a> (log, fig_name='res', param_names=None)</td></tr>
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<tr class="memitem:a88479ed8126ca655160ac16d90262d54"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespaceutils.html#a88479ed8126ca655160ac16d90262d54">plot_compare</a> (filenames, fig_name='res')</td></tr>
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<tr class="memitem:ac48855b80376b93d7ce0c38104be7a3f"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespaceutils.html#ac48855b80376b93d7ce0c38104be7a3f">viz_sample_data</a> (imgs, labels, fig_name='data_sample', weight_labels=None)</td></tr>
<tr class="separator:ac48855b80376b93d7ce0c38104be7a3f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a715e732754b790c8fb72f6e73e2e7a2e"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespaceutils.html#a715e732754b790c8fb72f6e73e2e7a2e">print_torch_mem</a> (add_info='')</td></tr>
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<tr class="memitem:aeb66bb0a666aba819993b695608ae02b"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespaceutils.html#aeb66bb0a666aba819993b695608ae02b">clip_norm</a> (tensors, max_norm, norm_type=2)</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><pre class="fragment">Utilties function.</pre> </div><h2 class="groupheader">Function Documentation</h2>
<a id="aeb66bb0a666aba819993b695608ae02b"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aeb66bb0a666aba819993b695608ae02b">&#9670;&nbsp;</a></span>clip_norm()</h2>
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<td class="memname">def utils.clip_norm </td>
<td>(</td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>tensors</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>max_norm</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>norm_type</em> = <code>2</code>&#160;</td>
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<pre class="fragment">Clips norm of passed tensors.
The norm is computed over all tensors together, as if they were
concatenated into a single vector. Clipped tensors are returned.
See: https://github.com/facebookresearch/higher/issues/18
Args:
tensors (Iterable[Tensor]): an iterable of Tensors or a
single Tensor to be normalized.
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
Returns:
Clipped (List[Tensor]) tensors.
</pre>
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<h2 class="memtitle"><span class="permalink"><a href="#a88479ed8126ca655160ac16d90262d54">&#9670;&nbsp;</a></span>plot_compare()</h2>
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<td class="memname">def utils.plot_compare </td>
<td>(</td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>filenames</em>, </td>
</tr>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>fig_name</em> = <code>'res'</code>&#160;</td>
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<td>)</td>
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<pre class="fragment">Save a visual graph comparing trainings stats.
Args:
filenames (list[Strings]): Relative paths to the logs (JSON files).
fig_name (string): Relative path where to save the graph. (default: res)
</pre>
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<h2 class="memtitle"><span class="permalink"><a href="#a669b01f2e6565bb22c2d6e14de4a0406">&#9670;&nbsp;</a></span>plot_resV2()</h2>
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<td class="memname">def utils.plot_resV2 </td>
<td>(</td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>log</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>fig_name</em> = <code>'res'</code>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>param_names</em> = <code>None</code>&#160;</td>
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<pre class="fragment">Save a visual graph of the logs.
Args:
log (dict): Logs of the training generated by most of train_utils.
fig_name (string): Relative path where to save the graph. (default: res)
param_names (list): Labels for the parameters. (default: None)
</pre>
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<h2 class="memtitle"><span class="permalink"><a href="#aaf9c453130b45dce2d49276ec2a8b016">&#9670;&nbsp;</a></span>print_graph()</h2>
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<td class="memname">def utils.print_graph </td>
<td>(</td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>PyTorch_obj</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>fig_name</em> = <code>'graph'</code>&#160;</td>
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<td></td>
<td>)</td>
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<pre class="fragment">Save the computational graph.
Args:
PyTorch_obj (Tensor): End of the graph. Commonly, the loss tensor to get the whole graph.
fig_name (string): Relative path where to save the graph. (default: graph)
</pre>
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<a id="a715e732754b790c8fb72f6e73e2e7a2e"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a715e732754b790c8fb72f6e73e2e7a2e">&#9670;&nbsp;</a></span>print_torch_mem()</h2>
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<td class="memname">def utils.print_torch_mem </td>
<td>(</td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>add_info</em> = <code>''</code></td><td>)</td>
<td></td>
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<pre class="fragment">Print informations on PyTorch memory usage.
Args:
add_info (string): Prefix added before the print. (default: None)
</pre>
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<a id="ac48855b80376b93d7ce0c38104be7a3f"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ac48855b80376b93d7ce0c38104be7a3f">&#9670;&nbsp;</a></span>viz_sample_data()</h2>
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<td class="memname">def utils.viz_sample_data </td>
<td>(</td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>imgs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>labels</em>, </td>
</tr>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>fig_name</em> = <code>'data_sample'</code>, </td>
</tr>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">&#160;</td>
<td class="paramname"><em>weight_labels</em> = <code>None</code>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
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<pre class="fragment">Save data samples.
Args:
imgs (Tensor): Batch of image to sample from. Intended to contain at least 25 images.
labels (Tensor): Labels of the images.
fig_name (string): Relative path where to save the graph. (default: data_sample)
weight_labels (Tensor): Weights associated to each labels. (default: None)
</pre>
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