class TSToGASF(Transform):
r"""Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Gramian Angular Summation Field.
It requires either input to be previously normalized between -1 and 1 or set range to (-1, 1)"""
order = 98
def __init__(self, size=224, cmap=None, range=None, **kwargs):
self.size, self.cmap, self.range = size, cmap, range
self.encoder = GramianAngularField(image_size=1.,
sample_range=self.range,
method='s',
**kwargs)
def encodes(self, o: TSTensor):
bs, *_, seq_len = o.shape
size = ifnone(self.size, seq_len)
if size != seq_len:
o = F.interpolate(o.reshape(-1, 1, seq_len),
size=size,
mode='linear',
align_corners=False)[:, 0]
else:
o = o.reshape(-1, seq_len)
output = self.encoder.fit_transform(o.cpu().numpy()).reshape(
bs, -1, size, size) / 2 + .5
if self.cmap and output.shape[1] == 1:
output = TSImage(plt.get_cmap(
self.cmap)(output)[..., :3]).squeeze(1).permute(0, 3, 1, 2)
else:
output = TSImage(output)
return output.to(device=o.device)
def __init__(self, size=224, cmap=None, range=None, **kwargs):
self.size, self.cmap, self.range = size, cmap, range
self.encoder = GramianAngularField(image_size=1.,
sample_range=self.range,
method='s',
**kwargs)