def forward(self, x: torch.Tensor):
"""
Args:
x (torch.Tensor): any input tensor where the first dim is the batch-dim.
Returns:
EmptyEmitterSet: An empty EmitterSet
"""
return EmptyEmitterSet(xy_unit=self.xy_unit, px_size=self.px_size)
def forward(self, features: torch.Tensor):
"""
Forward the feature map through the post processing and return an EmitterSet or a list of EmitterSets.
For the input features we use the following convention:
0 - Detection channel
1 - Photon channel
2 - 'x' channel
3 - 'y' channel
4 - 'z' channel
5 - Background channel
Expecting x and y channels in nano-metres.
Args:
features (torch.Tensor): Features of size :math:`(N, C, H, W)`
Returns:
EmitterSet or list of EmitterSets: Specified by return_format argument, EmitterSet in nano metres.
"""
if self.skip_if(features):
return EmptyEmitterSet(xy_unit=self.xy_unit, px_size=self.px_size)
if features.dim() != 4:
raise ValueError(
"Wrong dimensionality. Needs to be N x C x H x W.")
features = features[:, self.
pphotxyzbg_mapping] # change channel order if needed
p = features[:, [0], :, :]
features = features[:, 1:, :, :] # phot, x, y, z, bg
p_out, feat_out = self._forward_raw_impl(p, features)
feature_list, prob_final, frame_ix = self._frame2emitter(
p_out, feat_out)
frame_ix = frame_ix.squeeze()
return EmitterSet(xyz=feature_list[:, 1:4],
phot=feature_list[:, 0],
frame_ix=frame_ix,
prob=prob_final,
bg=feature_list[:, 4],
xy_unit=self.xy_unit,
px_size=self.px_size)
def test_empty_emitterset():
em = EmptyEmitterSet()
assert 0 == len(em)