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)