c63d4351c7752a769cdc9a1bfcf79ffd140e0e6a,onnx_tf/handlers/backend/unpool_mixin.py,UnpoolMixin,_unpool,#Any#Any#Any#Any#Any#,126

Before Change

      batch_range = tf.reshape(
          tf.range(tf.cast(output_shape[0], tf.int64), dtype=ind.dtype),
          shape=[input_shape[0], 1, 1, 1])
      b = tf.ones_like(ind) * batch_range
      b1 = tf.reshape(b, [flat_input_size, 1])
      ind_ = tf.reshape(ind, [flat_input_size, 1])
      ind_ = tf.concat([b1, ind_], 1)

      ret = tf.scatter_nd(

After Change

      input_shape = tf.shape(pool)

      flat_input_size = tf.reduce_prod(input_shape)
      flat_output_shape = [tf.reduce_prod(output_shape)]

      pool_ = tf.reshape(pool, [flat_input_size])
      ind_ = tf.reshape(ind, [flat_input_size, 1])

Instances