def test_min_visual_size():
# Make sure each EncoderType has an entry in MIS_RESOLUTION_FOR_ENCODER
assert set(
ModelUtils.MIN_RESOLUTION_FOR_ENCODER.keys()) == set(EncoderType)
for encoder_type in EncoderType:
with tf.Graph().as_default():
good_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type]
good_res = CameraResolution(width=good_size,
height=good_size,
num_channels=3)
vis_input = ModelUtils.create_visual_input(good_res,
"test_min_visual_size")
ModelUtils._check_resolution_for_encoder(vis_input, encoder_type)
enc_func = ModelUtils.get_encoder_for_type(encoder_type)
enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False)
# Anything under the min size should raise an exception. If not, decrease the min size!
with pytest.raises(Exception):
with tf.Graph().as_default():
bad_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[
encoder_type] - 1
bad_res = CameraResolution(width=bad_size,
height=bad_size,
num_channels=3)
vis_input = ModelUtils.create_visual_input(
bad_res, "test_min_visual_size")
with pytest.raises(UnityTrainerException):
# Make sure we'd hit a friendly error during model setup time.
ModelUtils._check_resolution_for_encoder(
vis_input, encoder_type)
enc_func = ModelUtils.get_encoder_for_type(encoder_type)
enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False)
def group_spec_to_brain_parameters(
name: str, group_spec: AgentGroupSpec
) -> BrainParameters:
vec_size = np.sum(
[shape[0] for shape in group_spec.observation_shapes if len(shape) == 1]
)
vis_sizes = [shape for shape in group_spec.observation_shapes if len(shape) == 3]
cam_res = [CameraResolution(s[0], s[1], s[2]) for s in vis_sizes]
a_size: List[int] = []
if group_spec.is_action_discrete():
a_size += list(group_spec.discrete_action_branches)
vector_action_space_type = 0
else:
a_size += [group_spec.action_size]
vector_action_space_type = 1
return BrainParameters(
name, int(vec_size), cam_res, a_size, [], vector_action_space_type
)
def make_brain_parameters(
discrete_action: bool = False,
visual_inputs: int = 0,
brain_name: str = "RealFakeBrain",
vec_obs_size: int = 6,
) -> BrainParameters:
resolutions = [
CameraResolution(width=30, height=40, num_channels=3)
for _ in range(visual_inputs)
]
return BrainParameters(
vector_observation_space_size=vec_obs_size,
camera_resolutions=resolutions,
vector_action_space_size=[2],
vector_action_descriptions=["", ""],
vector_action_space_type=int(not discrete_action),
brain_name=brain_name,
)
def group_spec_to_brain_parameters(
name: str, group_spec
) -> BrainParameters:
vec_size = np.sum(
[shape[0] for shape in group_spec['observation_shapes'] if len(shape) == 1]
)
vis_sizes = [shape for shape in group_spec['observation_shapes'] if len(shape) == 3]
cam_res = [CameraResolution(s[0], s[1], s[2]) for s in vis_sizes]
a_size: List[int] = []
if group_spec['action_type'] == 'DISCRETE':
a_size += list(group_spec['action_shape'])
vector_action_space_type = 0
else:
a_size += [group_spec.action_size]
vector_action_space_type = 1
return BrainParameters(
brain_name=name, vector_observation_space_size=int(vec_size), camera_resolutions=cam_res,
vector_action_space_size=a_size, vector_action_descriptions=[],
vector_action_space_type=vector_action_space_type
)
def create_mock_brainparams(
number_visual_observations=0,
vector_action_space_type="continuous",
vector_observation_space_size=3,
vector_action_space_size=None,
):
"""
Creates a mock BrainParameters object with parameters.
"""
# Avoid using mutable object as default param
if vector_action_space_size is None:
vector_action_space_size = [2]
mock_brain = mock.Mock()
mock_brain.return_value.number_visual_observations = number_visual_observations
mock_brain.return_value.vector_action_space_type = vector_action_space_type
mock_brain.return_value.vector_observation_space_size = (
vector_observation_space_size
)
camrez = CameraResolution(height=84, width=84, num_channels=3)
mock_brain.return_value.camera_resolutions = [camrez] * number_visual_observations
mock_brain.return_value.vector_action_space_size = vector_action_space_size
mock_brain.return_value.brain_name = "MockBrain"
return mock_brain()