def on_epoch_end(self, data: Data) -> None:
for key in self.inputs:
if key in data:
imgs = data[key]
if isinstance(imgs, ImgData):
fig = imgs.paint_numpy(dpi=96)
plt.imshow(fig[0])
plt.axis('off')
plt.tight_layout()
plt.show()
else:
for idx, img in enumerate(imgs):
show_image(img, title="{}_{}".format(key, idx))
plt.show()
def _save_images(self, data: Data):
for key in self.inputs:
if key in data:
imgs = data[key]
im_path = os.path.join(self.save_dir,
"{}_{}_epoch_{}.png".format(key, self.system.mode, self.system.epoch_idx))
if isinstance(imgs, ImgData):
f = imgs.paint_figure()
plt.savefig(im_path, dpi=self.dpi, bbox_inches="tight")
plt.close(f)
print("FastEstimator-ImageSaver: saved image to {}".format(im_path))
elif isinstance(imgs, Summary):
visualize_logs([imgs], save_path=im_path, dpi=self.dpi, verbose=False)
print("FastEstimator-ImageSaver: saved image to {}".format(im_path))
elif isinstance(imgs, (list, tuple)) and all([isinstance(img, Summary) for img in imgs]):
visualize_logs(imgs, save_path=im_path, dpi=self.dpi, verbose=False)
print("FastEstimator-ImageSaver: saved image to {}".format(im_path))
else:
for idx, img in enumerate(imgs):
f = show_image(img, title=key)
im_path = os.path.join(
self.save_dir,
"{}_{}_epoch_{}_elem_{}.png".format(key, self.system.mode, self.system.epoch_idx, idx))
plt.savefig(im_path, dpi=self.dpi, bbox_inches="tight")
plt.close(f)
print("FastEstimator-ImageSaver: saved image to {}".format(im_path))
def on_epoch_end(self, data: Data) -> None:
for key in self.inputs:
if key in data:
imgs = data[key]
if isinstance(imgs, ImgData):
f = imgs.paint_figure()
im_path = os.path.join(
self.save_dir,
"{}_{}_epoch_{}.png".format(key, self.system.mode,
self.system.epoch_idx))
plt.savefig(im_path, dpi=self.dpi, bbox_inches="tight")
plt.close(f)
print("FastEstimator-ImageSaver: saved image to {}".format(
im_path))
else:
for idx, img in enumerate(imgs):
f = show_image(img, title=key)
im_path = os.path.join(
self.save_dir, "{}_{}_epoch_{}_elem_{}.png".format(
key, self.system.mode, self.system.epoch_idx,
idx))
plt.savefig(im_path, dpi=self.dpi, bbox_inches="tight")
plt.close(f)
print("FastEstimator-ImageSaver: saved image to {}".
format(im_path))
def _display_images(self, data: Data) -> None:
"""A method to render images to the screen.
Args:
data: Data possibly containing images to render.
"""
for key in self.inputs:
if key in data:
imgs = data[key]
if isinstance(imgs, ImgData):
fig = imgs.paint_numpy(dpi=96)
plt.imshow(fig[0])
plt.axis('off')
plt.tight_layout()
plt.show()
elif isinstance(imgs, Summary):
visualize_logs([imgs])
elif isinstance(imgs, (list, tuple)) and all(
[isinstance(img, Summary) for img in imgs]):
visualize_logs(imgs)
else:
for idx, img in enumerate(imgs):
show_image(img, title="{}_{}".format(key, idx))
plt.show()
def paint_figure(self,
height_gap: int = 100,
min_height: int = 200,
width_gap: int = 50,
min_width: int = 200,
dpi: int = 96,
save_path: Optional[str] = None) -> plt.Figure:
"""Visualize the current ImgData entries in a matplotlib figure.
```python
d = fe.util.ImgData(y=tf.ones((4,)), x=0.5*tf.ones((4, 32, 32, 3)))
fig = d.paint_figure()
plt.show()
```
Args:
height_gap: How much space to put between each row.
min_height: The minimum height of a row.
width_gap: How much space to put between each column.
min_width: The minimum width of a column.
dpi: The resolution of the image to display.
save_path: If provided, the figure will be saved to the given path.
Returns:
The handle to the generated matplotlib figure.
"""
total_width = self._total_width(gap=width_gap, min_width=min_width)
total_height = self._total_height(gap=height_gap,
min_height=min_height)
fig = plt.figure(figsize=(total_width / dpi, total_height / dpi),
dpi=dpi)
grid = self._to_grid()
# TODO - elements with batch size = 1 should be laid out in a grid like for plotting
for row_idx, (start_height, end_height) in enumerate(
self._heights(gap=height_gap, min_height=min_height)):
row = grid[row_idx]
batch_size = self._batch_size(row_idx)
gs = GridSpec(nrows=batch_size,
ncols=total_width,
figure=fig,
left=0.0,
right=1.0,
bottom=start_height / total_height,
top=end_height / total_height,
hspace=0.05,
wspace=0.0)
for batch_idx in range(batch_size):
for col_idx, width in enumerate(
self._widths(row=row_idx,
gap=width_gap,
min_width=min_width)):
ax = fig.add_subplot(gs[batch_idx, width[0]:width[1]])
img_stack = [elem[batch_idx] for elem in row[col_idx][1]]
for idx, img in enumerate(img_stack):
show_image(img,
axis=ax,
fig=fig,
title=row[col_idx][0] if
(batch_idx == 0 and idx == 0) else None,
stack_depth=idx,
color_map=self.colormap)
if save_path:
plt.savefig(save_path, dpi=dpi, bbox_inches="tight")
return fig