from typing import (Sequence, Optional)
from textwrap import wrap
import torch
import numpy as np
import matplotlib.pyplot as plt
from rastervision.pytorch_learner.dataset.visualizer import Visualizer # NOQA
from rastervision.pytorch_learner.utils import (plot_channel_groups,
channel_groups_to_imgs)
[docs]class RegressionVisualizer(Visualizer):
"""Plots samples from image regression 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:
# display targets as a horizontal bar plot
bars_gt = label_ax.barh(
y=class_names, width=y, color='lightgray', edgecolor='black')
# show values on the end of bars
label_ax.bar_label(bars_gt, fmt='%.3f', padding=3)
label_ax.set_title('Ground truth')
else:
# display targets and predictions as a grouped horizontal bar plot
bar_thickness = 0.35
y_tick_locs = np.arange(len(class_names))
bars_gt = label_ax.barh(
y=y_tick_locs + bar_thickness / 2,
width=y,
height=bar_thickness,
color='lightgray',
edgecolor='black',
label='true')
bars_pred = label_ax.barh(
y=y_tick_locs - bar_thickness / 2,
width=z,
height=bar_thickness,
color=plt.get_cmap('tab10')(0),
edgecolor='black',
label='pred')
# show values on the end of bars
label_ax.bar_label(bars_gt, fmt='%.3f', padding=3)
label_ax.bar_label(bars_pred, fmt='%.3f', padding=3)
label_ax.set_yticks(ticks=y_tick_locs, labels=class_names)
label_ax.legend(
ncol=2, loc='lower center', bbox_to_anchor=(0.5, 1.0))
label_ax.xaxis.grid(linestyle='--', alpha=1)
label_ax.set_xlabel('Target value')
label_ax.spines['right'].set_visible(False)
label_ax.get_yaxis().tick_left()
[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