def predict(self, X, n_horizon):
seq = X
for i in range(n_horizon):
seq_pred = self.model(seq)
x_new = seq_pred[:, -1, :]
seq = torch.cat((seq, torch.atleast_3d(x_new)), 1)
return seq
def __call__(self, images: Union[Tensor, NDArrayR], *,
labels: Union[Tensor, NDArrayR]) -> Tensor:
"""Apply the transformation.
:param images: Greyscale images to be colorized. Expected to be unnormalized (in the range [0, 255]).
:param labels: Indexes (0-9) indicating the gaussian distribution from which to sample each image's color.
:returns: Images converted to RGB.
"""
if isinstance(images, np.ndarray):
images = torch.as_tensor(images, dtype=torch.float32)
if isinstance(labels, np.ndarray):
labels = torch.as_tensor(labels, dtype=torch.long)
# Add a singular channel dimension if one isn't already there.
images = cast(Tensor, torch.atleast_3d(images))
if images.ndim == 3:
images = images.unsqueeze(1)
images = images.expand(-1, 3, -1, -1)
colors = self._sample_colors(self.palette[labels]).view(-1, 3, 1, 1)
if self.binarize:
images = (images > 127).float()
if self.background:
if self.black:
# colorful background, black digits
images_colorized = (1 - images) * colors
else:
# colorful background, white digits
images_colorized = images + colors
elif self.black:
# black background, colorful digits
images_colorized = images * colors
else:
# white background, colorful digits
images_colorized = 1 - images * (1 - colors)
if self.greyscale:
images_colorized = images_colorized.mean(dim=1, keepdim=True)
return images_colorized
def other_ops(self):
a = torch.randn(4)
b = torch.randn(4)
c = torch.randint(0, 8, (5, ), dtype=torch.int64)
e = torch.randn(4, 3)
f = torch.randn(4, 4, 4)
size = [0, 1]
dims = [0, 1]
return (
torch.atleast_1d(a),
torch.atleast_2d(a),
torch.atleast_3d(a),
torch.bincount(c),
torch.block_diag(a),
torch.broadcast_tensors(a),
torch.broadcast_to(a, (4)),
# torch.broadcast_shapes(a),
torch.bucketize(a, b),
torch.cartesian_prod(a),
torch.cdist(e, e),
torch.clone(a),
torch.combinations(a),
torch.corrcoef(a),
# torch.cov(a),
torch.cross(e, e),
torch.cummax(a, 0),
torch.cummin(a, 0),
torch.cumprod(a, 0),
torch.cumsum(a, 0),
torch.diag(a),
torch.diag_embed(a),
torch.diagflat(a),
torch.diagonal(e),
torch.diff(a),
torch.einsum("iii", f),
torch.flatten(a),
torch.flip(e, dims),
torch.fliplr(e),
torch.flipud(e),
torch.kron(a, b),
torch.rot90(e),
torch.gcd(c, c),
torch.histc(a),
torch.histogram(a),
torch.meshgrid(a),
torch.lcm(c, c),
torch.logcumsumexp(a, 0),
torch.ravel(a),
torch.renorm(e, 1, 0, 5),
torch.repeat_interleave(c),
torch.roll(a, 1, 0),
torch.searchsorted(a, b),
torch.tensordot(e, e),
torch.trace(e),
torch.tril(e),
torch.tril_indices(3, 3),
torch.triu(e),
torch.triu_indices(3, 3),
torch.vander(a),
torch.view_as_real(torch.randn(4, dtype=torch.cfloat)),
torch.view_as_complex(torch.randn(4, 2)),
torch.resolve_conj(a),
torch.resolve_neg(a),
)