.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "usage_examples/lenet_style/plot_higher_resolution_lenet_style.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_usage_examples_lenet_style_plot_higher_resolution_lenet_style.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_usage_examples_lenet_style_plot_higher_resolution_lenet_style.py:

Higher Resolution
=======================================

``lenet_view``'s box size is mainly controlled by ``scale_xy`` (each stacked plane's spatial
width/height). Unlike ``flow_view``, its ``scale_z`` already defaults to ``1`` rather than
``0.1``, so the per-slice depth is rarely the bottleneck here - ``scale_xy`` is the lever that
actually makes each plane bigger and easier to read. ``scale_z`` is instead capped by
``max_channels`` (default ``100``): a layer with more channels than that gets its slice count
clamped, not scaled further.

Conv2d is orange, BatchNorm2d is green, and ReLU is sky blue.

.. GENERATED FROM PYTHON SOURCE LINES 13-53

.. code-block:: Python


    from collections import defaultdict

    import matplotlib.pyplot as plt
    import torch
    import visualtorch
    from torch import nn


    class ResidualBlock(nn.Module):
        """A classic ResNet-style block with a plain identity shortcut."""

        def __init__(self, channels: int) -> None:
            super().__init__()
            self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
            self.bn1 = nn.BatchNorm2d(channels)
            self.relu = nn.ReLU()
            self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
            self.bn2 = nn.BatchNorm2d(channels)

        def forward(self, x: torch.Tensor) -> torch.Tensor:
            """Define the forward pass, with a skip connection around conv1/bn1/relu/conv2/bn2."""
            identity = x
            out = self.relu(self.bn1(self.conv1(x)))
            out = self.bn2(self.conv2(out))
            out = out + identity
            return self.relu(out)


    model = ResidualBlock(channels=8)

    input_shape = (1, 8, 16, 16)

    color_map: dict = defaultdict(dict)
    color_map[nn.Conv2d]["fill"] = "#E69F00"
    color_map[nn.BatchNorm2d]["fill"] = "#009E73"
    color_map[nn.ReLU]["fill"] = "#56B4E9"

    dpi = 150  # rendered at 2x this in the final doc build (savefig.dpi=300 in conf.py)








.. GENERATED FROM PYTHON SOURCE LINES 54-57

Default
-------
At the default ``scale_xy=1``, each stacked plane is fairly small.

.. GENERATED FROM PYTHON SOURCE LINES 57-66

.. code-block:: Python


    img_default = visualtorch.render(model, input_shape, style="lenet", color_map=color_map)

    plt.figure(figsize=(img_default.width / dpi, img_default.height / dpi), dpi=dpi)
    plt.imshow(img_default)
    plt.axis("off")
    plt.tight_layout()
    plt.show()




.. image-sg:: /usage_examples/lenet_style/images/sphx_glr_plot_higher_resolution_lenet_style_001.png
   :alt: plot higher resolution lenet style
   :srcset: /usage_examples/lenet_style/images/sphx_glr_plot_higher_resolution_lenet_style_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 67-70

Higher Resolution
-----------------
Raising ``scale_xy`` grows each plane's spatial extent for a clearer, more substantial result.

.. GENERATED FROM PYTHON SOURCE LINES 70-78

.. code-block:: Python


    img_hires = visualtorch.render(model, input_shape, style="lenet", color_map=color_map, scale_xy=3)

    plt.figure(figsize=(img_hires.width / dpi, img_hires.height / dpi), dpi=dpi)
    plt.imshow(img_hires)
    plt.axis("off")
    plt.tight_layout()
    plt.show()



.. image-sg:: /usage_examples/lenet_style/images/sphx_glr_plot_higher_resolution_lenet_style_002.png
   :alt: plot higher resolution lenet style
   :srcset: /usage_examples/lenet_style/images/sphx_glr_plot_higher_resolution_lenet_style_002.png
   :class: sphx-glr-single-img






.. _sphx_glr_download_usage_examples_lenet_style_plot_higher_resolution_lenet_style.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_higher_resolution_lenet_style.ipynb <plot_higher_resolution_lenet_style.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_higher_resolution_lenet_style.py <plot_higher_resolution_lenet_style.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_higher_resolution_lenet_style.zip <plot_higher_resolution_lenet_style.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_