def set_bounds(tensor_spec: dm_env_rpc_pb2.TensorSpec, minimum, maximum):
"""Modifies `tensor_spec` to have its inclusive bounds set.
Packs `minimum` in to `tensor_spec.min` and `maximum` in to `tensor_spec.max`.
Args:
tensor_spec: An instance of a dm_env_rpc TensorSpec proto. It should
already have its `name`, `dtype` and `shape` attributes set.
minimum: The minimum value that elements in the described tensor can obtain.
A scalar, iterable of scalars, or None. If None, `min` will be cleared on
`tensor_spec`.
maximum: The maximum value that elements in the described tensor can obtain.
A scalar, iterable of scalars, or None. If None, `max` will be cleared on
`tensor_spec`.
"""
np_type = tensor_utils.data_type_to_np_type(tensor_spec.dtype)
if not issubclass(np_type, np.number):
raise ValueError(f'TensorSpec has non-numeric type "{np_type}".')
np_type_bounds = _np_range_info(np_type)
has_min = minimum is not None
has_max = maximum is not None
if has_min:
minimum = np.asarray(minimum)
if minimum.size != 1 and minimum.shape != tuple(tensor_spec.shape):
raise ValueError(
f'minimum has shape {minimum.shape}, which is incompatible with '
f"tensor_spec {tensor_spec.name}'s shape {tensor_spec.shape}.")
if has_max:
maximum = np.asarray(maximum)
if maximum.size != 1 and maximum.shape != tuple(tensor_spec.shape):
raise ValueError(
f'maximum has shape {maximum.shape}, which is incompatible with '
f"tensor_spec {tensor_spec.name}'s shape {tensor_spec.shape}.")
if ((has_min and not _can_cast(minimum, np_type)) or
(has_max and not _can_cast(maximum, np_type))):
raise ValueError(
_BOUNDS_CANNOT_BE_SAFELY_CAST_TO_DTYPE.format(
name=tensor_spec.name,
minimum=minimum,
maximum=maximum,
dtype=dm_env_rpc_pb2.DataType.Name(tensor_spec.dtype)))
if (has_min and has_max and np.any(maximum < minimum)):
raise ValueError('TensorSpec "{}" has min {} larger than max {}.'.format(
tensor_spec.name, minimum, maximum))
packer = tensor_utils.get_packer(np_type)
if has_min:
packer.pack(tensor_spec.min, minimum)
else:
tensor_spec.ClearField('min')
if has_max:
packer.pack(tensor_spec.max, maximum)
else:
tensor_spec.ClearField('max')
def test_can_unpack(self):
packer = tensor_utils.get_packer(np.int32)
tensor = dm_env_rpc_pb2.Tensor()
tensor.int32s.array[:] = [1, 2, 3]
np.testing.assert_array_equal([1, 2, 3], packer.unpack(tensor))
def test_can_pack(self):
packer = tensor_utils.get_packer(np.int32)
tensor = dm_env_rpc_pb2.Tensor()
packer.pack(tensor, np.asarray([1, 2, 3]))
self.assertEqual([1, 2, 3], tensor.int32s.array)
def test_cannot_get_packer_for_invalid_type(self):
with self.assertRaisesRegex(TypeError, 'complex64'):
tensor_utils.get_packer(np.complex64)