from typing import (Sequence, Optional)
from textwrap import wrap
import torch
from rastervision.pytorch_learner.dataset.visualizer import Visualizer # NOQA
from rastervision.pytorch_learner.utils import (plot_channel_groups,
channel_groups_to_imgs)
[docs]class ClassificationVisualizer(Visualizer):
"""Plots samples from image classification Datasets."""
[docs] def plot_xyz(self,
axs: Sequence,
x: torch.Tensor,
y: int,
z: Optional[int] = None) -> None:
channel_groups = self.get_channel_display_groups(x.shape[1])
img_axes = axs[:-1]
label_ax = axs[-1]
# plot image
imgs = channel_groups_to_imgs(x, channel_groups)
plot_channel_groups(img_axes, imgs, channel_groups)
# plot label
class_names = self.class_names
class_names = ['-\n-'.join(wrap(c, width=8)) for c in class_names]
if z is None:
# just display the class name as text
class_name = class_names[y]
label_ax.text(
.5,
.5,
class_name,
ha='center',
va='center',
fontdict={
'size': 24,
'family': 'sans-serif'
})
label_ax.set_xlim((0, 1))
label_ax.set_ylim((0, 1))
label_ax.axis('off')
else:
# display predicted class probabilities as a horizontal bar plot
# legend: green = ground truth, dark-red = wrong prediction,
# light-gray = other. In case predicted class matches ground truth,
# only one bar will be green and the others will be light-gray.
class_probabilities = z.softmax(dim=-1)
class_index_pred = z.argmax(dim=-1)
class_index_gt = y
bar_colors = ['lightgray'] * len(z)
if class_index_pred == class_index_gt:
bar_colors[class_index_pred] = 'green'
else:
bar_colors[class_index_pred] = 'darkred'
bar_colors[class_index_gt] = 'green'
label_ax.barh(
y=class_names,
width=class_probabilities,
color=bar_colors,
edgecolor='black')
label_ax.set_xlim((0, 1))
label_ax.xaxis.grid(linestyle='--', alpha=1)
label_ax.set_xlabel('Probability')
label_ax.set_title('Prediction')
[docs] def get_plot_ncols(self, **kwargs) -> int:
x = kwargs['x']
nb_img_channels = x.shape[1]
ncols = len(self.get_channel_display_groups(nb_img_channels)) + 1
return ncols