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fix: temporal supervision shift in XYZ loss mask uses t+1 instead of t #52

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fix: temporal supervision shift in XYZ loss mask uses t+1 instead of t #52

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4 changes: 2 additions & 2 deletions NDP-HNN/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,10 +31,10 @@ def train_model(model,
#--- forward one snapshot
state, pred_xyz, inc_logits = model(data, state)

#--- mask nodes that are alive at next time step (t+1)
#--- mask nodes that are alive at current time step (t)
t = int(data.t[0].item())
mask_next = torch.tensor(
[birth_times[c] <= (t + 1) for c in cells],
[birth_times[c] <= t for c in cells],
dtype=torch.bool, device=device
)
target_xyz = torch.tensor(birth_feat[:, :3], device=device)[mask_next]
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