def sparse_conv_infeat_backprop(out_features_gradient, inp_features):
return mltest.run_op_grad(
ml,
ml.device,
True,
ml.ops.sparse_conv,
inp_features,
'',
out_features_gradient,
filters=filters,
inp_features=inp_features,
inp_importance=inp_importance,
neighbors_index=neighbors_index,
neighbors_kernel_index=neighbors_kernel_index,
neighbors_importance=neighbors_importance,
neighbors_row_splits=neighbors_row_splits,
**conv_attrs)
def conv_infeat_backprop(out_features_gradient, inp_features):
return mltest.run_op_grad(ml,
ml.device,
True,
ml.ops.continuous_conv,
inp_features,
'',
out_features_gradient,
filters=filters,
out_positions=out_positions,
extents=extent,
offset=offset,
inp_positions=inp_positions,
inp_features=inp_features,
inp_importance=inp_importance,
neighbors_index=neighbors_index,
neighbors_importance=neighbors_importance,
neighbors_row_splits=neighbors_row_splits,
**conv_attrs)
def sparse_conv_transpose_filter_backprop(out_features_gradient, filters):
return mltest.run_op_grad(
ml,
ml.device,
True,
ml.ops.sparse_conv_transpose,
filters,
'',
out_features_gradient,
filters=filters,
out_importance=inp_importance,
inp_features=y_arr,
inp_neighbors_index=neighbors_index,
inp_neighbors_importance_sum=neighbors_importance_sum,
inp_neighbors_row_splits=neighbors_row_splits,
neighbors_index=inv_neighbors_index,
neighbors_kernel_index=inv_neighbors_kernel_index,
neighbors_importance=inv_neighbors_importance,
neighbors_row_splits=inv_neighbors_row_splits,
**conv_attrs)
def conv_transpose_infeat_backprop(out_features_gradient, inp_features):
return mltest.run_op_grad(
ml,
ml.device,
True,
ml.ops.continuous_conv_transpose,
inp_features,
'',
out_features_gradient,
filters=filters.transpose([0, 1, 2, 4, 3]),
out_positions=inp_positions,
out_importance=inp_importance,
extents=extent,
offset=offset,
inp_positions=out_positions,
inp_features=inp_features,
inp_neighbors_index=neighbors_index,
inp_neighbors_importance_sum=neighbors_importance_sum,
inp_neighbors_row_splits=neighbors_row_splits,
neighbors_index=inverted_neighbors_index,
neighbors_importance=inverted_neighbors_importance,
neighbors_row_splits=inverted_neighbors_row_splits,
**conv_attrs)
def fn_grad(features_bp, features):
return mltest.run_op_grad(ml, ml.device, True, ml.ops.voxel_pooling,
features, 'pooled_features', features_bp,
positions, features, voxel_size, position_fn,
feature_fn)
