def __init__(self, venv, directory, record_video_trigger, video_length=200):
"""
# Arguments
venv: VecEnv to wrap
directory: Where to save videos
record_video_trigger:
Function that defines when to start recording.
The function takes the current number of step,
and returns whether we should start recording or not.
video_length: Length of recorded video
"""
VecEnvWrapper.__init__(self, venv)
self.record_video_trigger = record_video_trigger
self.video_recorder = None
self.directory = os.path.abspath(directory)
if not os.path.exists(self.directory): os.mkdir(self.directory)
self.file_prefix = "vecenv"
self.file_infix = '{}'.format(os.getpid())
self.step_id = 0
self.video_length = video_length
self.recording = False
self.recorded_frames = 0
def __init__(self, venv, mode, paths, clip=10., epsilon=1e-8):
VecEnvWrapper.__init__(self, venv)
# TODO: Find a more elegant way for controlling wrapper activation in environments.
# self.training = True if mode == "train" else False
self.training = False if mode in ["test", "eval", "custom"] else True
self.paths = paths
# Check all entries
for path in self.paths:
# Works only for vector observations, not for images
if (not len(self.observation_space.spaces[path].shape)
== 1) and (isinstance(self.observation_space.spaces[path],
spaces.Box)):
raise TypeError(
"VecNormalizeObsDict supports 1d (vector) Box observation spaces. Whereas it is {}d for '{}'."
.format(len(self.observation_space.spaces[path].shape),
path))
# Create normalizers
self.normalizers = {}
for path in self.paths:
self.normalizers[path] = Normalizer(
shape=self.observation_space.spaces[path].shape,
clip=clip,
eps=epsilon)
self.observation_space.spaces[path].low = np.full_like(
self.observation_space.spaces[path].low, -clip)
self.observation_space.spaces[path].high = np.full_like(
self.observation_space.spaces[path].high, +clip)
def __init__(self, venv, mode, path, nstack, axis=-1):
"""
* ``Axis = 0``: If images are already transposed using :class:`~digideep.environment.wrappers.normal.WrapperTransposeImage`,
then use ``axis=0`` to stack images on the first dimension which is channels. This will be compatible with PyTorch.
* ``Axis = -1``: If images are NOT transposed and their shape is like ``(W,H,3)``, then stack images on the last axis, which
is again the channels. This is compatible with the OpenAI's defaults.
"""
self.venv = venv
self.path = path
self.axis = axis
self.nstack = nstack
observation_space = venv.observation_space
wos = observation_space.spaces[self.path] # wrapped ob space
## This shape0 is in [1,3].
self.axis_dim = wos.shape[axis]
low = np.repeat(wos.low, self.nstack, axis=self.axis)
high = np.repeat(wos.high, self.nstack, axis=self.axis)
# This serves as a queue type history for past observations.
self.stacked_obs = np.zeros((venv.num_envs, ) + low.shape,
dtype=low.dtype)
mod_observation_space = spaces.Box(
low=low,
high=high,
dtype=observation_space.spaces[self.path].dtype)
observation_space.spaces[self.path] = mod_observation_space
VecEnvWrapper.__init__(self, venv, observation_space=observation_space)
def __init__(self, venv, mode, clip=10., gamma=0.99, epsilon=1e-8):
VecEnvWrapper.__init__(self, venv)
self.training = True if mode == "train" else False
# self.training = False if mode in ["test", "eval"] else True
self.normalizer = Normalizer(shape=(), clip=clip, eps=epsilon)
self.ret = np.zeros(self.num_envs)
self.gamma = gamma
def __init__(self, venv):
VecEnvWrapper.__init__(self, venv)
def __init__(self, venv):
VecEnvWrapper.__init__(self, venv)
# NOTE: This wrapper also flattens the observation space (nested observation spaces will be flattened).
self.observation_space = spaces.Dict(
_flatten_space(self.observation_space))
def close(self):
VecEnvWrapper.close(self)
self.close_video_recorder()
def __init__(self,
venv,
mode,
session_state=None,
threshold=0.,
interval=100,
info_keys=["/rand"],
obs_keys=["/parameters"],
num_workers=None):
VecEnvWrapper.__init__(self, venv)
self.session_state = session_state
self.eprets = None
self.eplens = None
self.tstart = time.time()
# Observations are one step old, so should be the infos!
self.infos = None
self.obs = None
if self.session_state:
self.filename = self.session_state['path_session']
# print(">>>> Success filename set to:", self.filename)
else:
self.filename = ""
self.results_writer = None
self.num_workers = num_workers
self.threshold = threshold
self.interval = interval
self.key_root_infos = info_keys
self.key_root_obs = obs_keys
self.stats = {
"episodes": 0,
"episodes_failure": 0,
# "scatter":{
# "offset_noise_2d": Scatter("Offset Noise 2D")
# },
"scatter": {
"offset_noise_2d": [[], []],
"controller_thre": [],
"controller_gain": [],
"randomized_duration": [],
"r": [],
"subject_id": [],
"starting_id": []
},
"histogram": {
# "controller_thre": Histogram("Controller Threshold", limit=[0.05,0.25], bins=4),
# "controller_gain": Histogram("Controller Gain", limit=[0,10], bins=10),
# "randomized_duration": Histogram("Randomized Duration", limit=[0.0,10.0], bins=10),
# "r": Histogram("Rewards", limit=[0,55], bins=11),
# "subject_id": Histogram("Subject ID", categories=["01","02","03","04","05","06","07","08","09"]),
"rotation_style":
Histogram("Rotation Style",
categories=["nl", "nr", "fl", "fr"]),
# "starting_id": Histogram("Starting ID", categories=["01","02","03","04","05","06","07","08","09","10","11","12","13","14","15","16"]),
"worker_id":
Histogram("Worker ID", categories=list(range(num_workers)))
}
}