4ac4dca40d8a123e90f2cf2d4a81d07d70a0b45d,onnx_tf/handlers/backend/scatter_elements.py,ScatterElements,version_11,#Any#Any#,11

Before Change

    updates = kwargs["tensor_dict"][node.inputs[2]]

    // Calculate shape of the tensorflow version of indices tensor.
    sparsified_dense_idx_shape = updates.get_shape().as_list()

    // Move on to convert ONNX indices to tensorflow indices in 2 steps:
    //
    // Step 1:

After Change

    // check are there any indices are out of bounds
    result = cls.chk_idx_out_of_bounds_along_axis(data, axis, indices)
    msg = "ScatterElements indices are out of bounds, please double check the indices and retry."
    with tf.control_dependencies(
        [tf.compat.v1.assert_equal(result, True, message=msg)]):
      // process negative indices
      indices = cls.process_neg_idx_along_axis(data, axis, indices)

      // Calculate shape of the tensorflow version of indices tensor.
      sparsified_dense_idx_shape = tf_shape(updates)

      // Move on to convert ONNX indices to tensorflow indices in 2 steps:
      //
      // Step 1:
      //   What would the index tensors look like if updates are all
      //   dense? In other words, produce a coordinate tensor for updates:
      //
      //   coordinate[i, j, k ...] = [i, j, k ...]
      //   where the shape of "coordinate" tensor is same as that of updates.
      //
      // Step 2:
      //   But the coordinate tensor needs some correction because coord
      //   vector at position axis is wrong (since we assumed update is dense,
      //   but it is not at the axis specified).
      //   So we update coordinate vector tensor elements at psotion=axis with
      //   the sparse coordinate indices.

      idx_tensors_per_axis = tf.meshgrid(
          *list(
              map(lambda x: tf.range(x, dtype=tf.dtypes.int64),
                  sparsified_dense_idx_shape)),
          indexing="ij")
      idx_tensors_per_axis[axis] = indices
      dim_expanded_idx_tensors_per_axis = list(
          map(lambda x: tf.expand_dims(x, axis=-1), idx_tensors_per_axis))
      coordinate = tf.concat(dim_expanded_idx_tensors_per_axis, axis=-1)

      // Now the coordinate tensor is in the shape
      // [updates.shape, updates.rank]
      // we need it to flattened into the shape:
      // [product(updates.shape), updates.rank]
      indices = tf.reshape(coordinate, [-1, tf.rank(data)])
      updates = tf.reshape(updates, [-1])

      return [tf.tensor_scatter_nd_update(data, indices, updates)]

Instances