def bottleneck_images(s, L):
assert s.dim() == 4, s.shape
_assert_contains_symbol_indices(s, L)
s = s.float().div(L)
return [
image_summaries.to_image(s[:, c, ...]) for c in range(s.shape[1])
]
def save_img(self, img, filename, convert_to_image=True):
"""
:param img: image tensor, in {0, ..., 255}
:param filename: output filename
:param convert_to_image: if True, call to_image on img, otherwise assume this has already been done.
:return:
"""
if convert_to_image:
img = to_image(img.type(torch.uint8))
out_p = self.get_save_p(filename)
Image.fromarray(img).save(out_p)
return out_p
def _save(self, saver, x, filename, convert):
# if self.trim:
# t = self.trim
# x = x[..., t:-t, t:-t]
if isinstance(filename, tuple):
x, filename = self._unpack(x, filename)
if x is None:
return None, None
if convert:
x = to_image(x.type(torch.uint8))
print('*** Saving', filename)
out_p = self.get_save_p(filename)
saver(x, out_p)
return x, filename
def _to_image_summary_safe(tag, tensor):
tag = _clean_tag(tag)
img = make_image_summary(to_image(tensor))
return Summary(value=[Summary.Value(tag=tag, image=img)])