plenoptic.plot.metamer_representation_error

plenoptic.plot.metamer_representation_error(metamer, batch_idx=0, iteration=None, ylim=None, ax=None, as_rgb=False, **kwargs)

Plot representation error showing how close we are to convergence.

We plot _representation_error(metamer, iteration). For more details, see plenoptic.plot.plot_representation.

Parameters:

• metamer (Metamer) – Metamer object whose synthesized metamer we want to display.

• batch_idx (int (default: 0)) – Which index to take from the batch dimension.

• iteration (int | None (default: None)) – Which iteration to display. If None, we show the most recent one. Negative values are also allowed. If iteration!=None and metamer.store_progress>1 (that is, the metamer was not cached on every iteration), then we show the cached metamer from the nearest iteration.

• ylim (tuple[float, float] | None | Literal[False] (default: None)) – If ylim is None, we sets the axes’ y-limits to be (-y_max, y_max), where y_max=np.abs(data).max(). If it’s False, we do nothing. If a tuple, we use that range.

• ax (Axes | None (default: None)) – Pre-existing axes for plot. If None, we call matplotlib.pyplot.gca.

• as_rgb (bool (default: False)) – The representation can be image-like with multiple channels, and we have no way to determine whether it should be represented as an RGB image or not, so the user must set this flag to tell us. It will be ignored if the response doesn’t look image-like or if the model has its own plot_representation_error() method. Else, it will be passed to imshow, see that methods docstring for details.

• **kwargs (Any) – Passed to metamer.model.forward.

Return type:

list[Axes]

Returns:

axes – List of created axes.

Raises:

IndexError – If iteration takes an illegal value.

Warns:

UserWarning – If the iteration for the metamer used to compute the error is not the same as the argument iteration (because e.g., you set iteration=3 but metamer.store_progress=2).

See also

plot_representation

Function used by this one to plot representation.

synthesis_status

Create a figure combining this with other axis-level plots to summarize synthesis status at a given iteration.

synthesis_animate

Create a video animating this and other axis-level plots changing over the course of synthesis.

Examples

>>> import plenoptic as po
>>> import torch
>>> img = po.data.einstein()
>>> model = po.models.Gaussian(30).eval()
>>> po.remove_grad(model)
>>> met = po.Metamer(img, model)
>>> met.to(torch.float64)
>>> met.load(po.data.fetch_data("example_metamer_gaussian.pt"))
>>> po.plot.metamer_representation_error(met)
[<Axes: title=...Representation error...>]

(png, hires.png, pdf)

Plot on an existing axis:

>>> import matplotlib.pyplot
>>> fig, axes = plt.subplots(1, 2, figsize=(8, 4))
>>> po.plot.metamer_representation_error(met, ax=axes[1])
[<Axes: title=...Representation error...>]

(png, hires.png, pdf)

The function uses plot_representation, which switches between imshow and stem_plot based on the shape of the model’s output:

>>> # Flatten the last two dimensions of the output, so it looks like a vector.
>>> class TestModel(po.models.Gaussian):
...     def __init__(self, *args, **kwargs):
...         super().__init__(*args, **kwargs)
...
...     def forward(self, x):
...         return super().forward(x).flatten(-2)
>>> model = TestModel(30).eval()
>>> po.remove_grad(model)
>>> met = po.Metamer(img, model)
>>> met.to(torch.float64)
>>> met.synthesize(5)
>>> po.plot.metamer_representation_error(met)
[<Axes: title=...Representation error...>]

(png, hires.png, pdf)

If model has its own plot_representation method, this function will use it, potentially creating multiple axes (see plot_representation ):

>>> img = po.data.reptile_skin()
>>> model = po.models.PortillaSimoncelli(img.shape[-2:])
>>> met = po.MetamerCTF(img, model, po.loss.l2_norm)
>>> met.to(torch.float64)
>>> met.load(po.data.fetch_data("example_metamerCTF_ps.pt"))
>>> po.plot.metamer_representation_error(met)
[<Axes: ...>, ..., <Axes: ...>]

(png, hires.png, pdf)

If plotting on an existing axis, this function will sub-divide that axis as needed:

>>> fig, axes = plt.subplots(1, 2, figsize=(8, 4))
>>> po.plot.synthesis_imshow(met, ax=axes[0])
<Axes: title=...Metamer[0] [iteration=150]...>
>>> po.plot.metamer_representation_error(met, ax=axes[1])
[<Axes: ...>, ..., <Axes: ...>]

(png, hires.png, pdf)