def _compute_full_miou(self):
if self._full_vote_miou is not None:
return
has_prediction = self._test_area.prediction_count > 0
log.info(
"Computing full res mIoU, we have predictions for %.2f%% of the points."
% (torch.sum(has_prediction) /
(1.0 * has_prediction.shape[0]) * 100))
self._test_area = self._test_area.to("cpu")
# Full res interpolation
full_pred = knn_interpolate(
self._test_area.votes[has_prediction],
self._test_area.pos[has_prediction],
self._test_area.pos,
k=1,
)
# Full res pred
c = ConfusionMatrix(self._num_classes)
c.count_predicted_batch(self._test_area.y.numpy(),
torch.argmax(full_pred, 1).numpy())
self._full_vote_miou = c.get_average_intersection_union() * 100
def _predict_full_res(self):
""" Predict full resolution results based on votes """
has_prediction = self._vote_counts > 0
votes = self._votes[has_prediction].div(self._vote_counts[has_prediction].unsqueeze(-1))
# Upsample and predict
full_pred = knn_interpolate(votes, self._raw_data.pos[has_prediction], self._raw_data.pos, k=self._k)
self._full_res_preds = full_pred
def forward(self, x, x_sub, pos, pos_sub, batch=None, batch_sub=None):
# transform low-res features and reduce the number of features
x_sub = self.mlp_sub(x_sub)
# interpolate low-res feats to high-res points
x_interpolated = knn_interpolate(x_sub, pos_sub, pos, k=3,
batch_x=batch_sub, batch_y=batch)
x = self.mlp(x) + x_interpolated
return x
def _predict_full_res(self):
""" Predict full resolution results based on votes """
for id_scan in self._votes:
has_prediction = self._vote_counts[id_scan] > 0
self._votes[id_scan][has_prediction] /= self._vote_counts[id_scan][has_prediction].unsqueeze(-1)
# Upsample and predict
full_pred = knn_interpolate(
self._votes[id_scan][has_prediction],
self._raw_datas[id_scan].pos[has_prediction],
self._raw_datas[id_scan].pos,
k=1,
)
self._full_preds[id_scan] = full_pred.argmax(-1)
def upsample(self, y, coarse_nodes, coarse_batch, fine):
fine_nodes = fine.x[:, :2]
y = knn_interpolate(y.cpu(), coarse_nodes[:, :2].cpu(), fine_nodes.cpu(),
coarse_batch.cpu(), fine.batch.cpu(), k=3).to(y.device)
return y