def _init(self):
logger.info("[{}]: agent init, isTrain={}".format(self._agentIdent, self._isTrain))
self._episodeCount = -1
from tensorpack.utils.utils import get_rng
self._rng = get_rng(self)
from tensorpack.utils.stats import StatCounter
self.reset_stat()
self.rwd_counter = StatCounter()
self._memorySaver = None
save_dir = self._kwargs.pop('save_dir', None)
if save_dir is not None:
self._memorySaver = MemorySaver(save_dir,
self._kwargs.pop('max_save_item', 3),
self._kwargs.pop('min_save_score', None),
)
self.restart_episode()
pass
def __init__(self,
predictor_io_names,
player,
state_shape,
batch_size,
memory_size, init_memory_size,
init_exploration,
update_frequency, history_len):
"""
Args:
predictor_io_names (tuple of list of str): input/output names to
predict Q value from state.
player (RLEnvironment): the player.
state_shape (tuple): h, w, c
history_len (int): length of history frames to concat. Zero-filled
initial frames.
update_frequency (int): number of new transitions to add to memory
after sampling a batch of transitions for training.
"""
assert len(state_shape) == 3, state_shape
init_memory_size = int(init_memory_size)
for k, v in locals().items():
if k != 'self':
setattr(self, k, v)
self.exploration = init_exploration
self.num_actions = player.action_space.n
logger.info("Number of Legal actions: {}".format(self.num_actions))
self.rng = get_rng(self)
self._init_memory_flag = threading.Event() # tell if memory has been initialized
# a queue to receive notifications to populate memory
self._populate_job_queue = queue.Queue(maxsize=5)
self.mem = ReplayMemory(memory_size, state_shape, history_len)
self._current_ob = self.player.reset()
self._player_scores = StatCounter()
self._current_game_score = StatCounter()
def __init__(self,
directory=None,
viz=False,
task=False,
files_list=None,
screen_dims=(27, 27, 27),
history_length=20,
multiscale=True,
max_num_frames=0,
saveGif=False,
saveVideo=False):
"""
:param train_directory: environment or game name
:param viz: visualization
set to 0 to disable
set to +ve number to be the delay between frames to show
set to a string to be the directory for storing frames
:param screen_dims: shape of the frame cropped from the image to feed
it to dqn (d,w,h) - defaults (27,27,27)
:param nullop_start: start with random number of null ops
:param location_history_length: consider lost of lives as end of
episode (useful for training)
:max_num_frames: maximum numbe0r of frames per episode.
"""
# ######################################################################
# ## generate evaluation results from 19 different points
# ## save results in csv file
# self.csvfile = 'DuelDoubleDQN_multiscale_brain_mri_point_pc_ROI_45_45_45_midl2018.csv'
# if not train:
# with open(self.csvfile, 'w') as outcsv:
# fields = ["filename", "dist_error"]
# writer = csv.writer(outcsv)
# writer.writerow(map(lambda x: x, fields))
#
# x = [0.5,0.25,0.75]
# y = [0.5,0.25,0.75]
# z = [0.5,0.25,0.75]
# self.start_points = []
# for combination in itertools.product(x, y, z):
# if 0.5 in combination: self.start_points.append(combination)
# self.start_points = itertools.cycle(self.start_points)
# self.count_points = 0
# self.total_loc = []
# ######################################################################
super(MedicalPlayer, self).__init__()
# inits stat counters
self.reset_stat()
# counter to limit number of steps per episodes
self.cnt = 0
# maximum number of frames (steps) per episodes
self.max_num_frames = max_num_frames
# stores information: terminal, score, distError
self.info = None
# option to save display as gif
self.saveGif = saveGif
self.saveVideo = saveVideo
# training flag
self.task = task
# image dimension (2D/3D)
self.screen_dims = screen_dims
self.dims = len(self.screen_dims)
# multi-scale agent
self.multiscale = multiscale
# init env dimensions
if self.dims == 2:
self.width, self.height = screen_dims
else:
self.width, self.height, self.depth = screen_dims
with _ALE_LOCK:
self.rng = get_rng(self)
# visualization setup
if isinstance(viz, six.string_types): # check if viz is a string
assert os.path.isdir(viz), viz
viz = 0
if isinstance(viz, int):
viz = float(viz)
self.viz = viz
if self.viz and isinstance(self.viz, float):
self.viewer = None
self.gif_buffer = []
# stat counter to store current score or accumlated reward
self.current_episode_score = StatCounter()
# get action space and minimal action set
self.action_space = spaces.Discrete(6) # change number actions here
self.actions = self.action_space.n
self.observation_space = spaces.Box(low=0,
high=255,
shape=self.screen_dims,
dtype=np.uint8)
# history buffer for storing last locations to check oscilations
self._history_length = history_length
self._loc_history = [(0, ) * self.dims] * self._history_length
self._qvalues_history = [(0, ) * self.actions] * self._history_length
# initialize rectangle limits from input image coordinates
self.rectangle = Rectangle(0, 0, 0, 0, 0, 0)
# add your data loader here
if self.task == 'play':
self.files = filesListBrainMRLandmark(files_list,
returnLandmarks=False)
else:
self.files = filesListBrainMRLandmark(files_list,
returnLandmarks=True)
# prepare file sampler
self.filepath = None
self.sampled_files = self.files.sample_circular()
# reset buffer, terminal, counters, and init new_random_game
self._restart_episode()
def reset_state(self):
""" Reset the RNG """
self.rng = get_rng(self)
super(lymphomaBase, self).reset_state()
def __init__(self, pipe_c2s, pipe_s2c, model):
super(MySimulatorMaster, self).__init__(pipe_c2s, pipe_s2c)
self.M = model
self.queue = queue.Queue(maxsize=BATCH_SIZE * 8 * 2)
from tensorpack.utils.utils import get_rng
self._rng = get_rng(self)
def __init__(self):
self.rng = get_rng(self)
def __init__(self, pipe_c2s, pipe_s2c, model):
super(MySimulatorMaster, self).__init__(pipe_c2s, pipe_s2c)
self.M = model
self.queue = queue.Queue(maxsize=BATCH_SIZE * 8 * 2)
from tensorpack.utils.utils import get_rng
self._rng = get_rng(self)
def __init__(self,
data_dir='/home/Pearl/quantm/QuadRand/data/SNEMI/',
viz=0,
frame_skip=8,
nullop_start=30,
max_num_frames=0):
"""
Args:
data_dir: path to the image
frame_skip: skip every k frames and repeat the action
viz: visualization to be done.
Set to 0 to disable.
Set to a positive number to be the delay between frames to show.
Set to a string to be a directory to store frames.
nullop_start: start with random number of null ops.
live_losts_as_eoe: consider lost of lives as end of episode. Useful for training.
max_num_frames: maximum number of frames per episode.
"""
super(ImagePlayer, self).__init__()
# Read the image here
import glob, skimage.io, cv2
from natsort import natsorted
self.imageFiles = natsorted(
glob.glob(os.path.join(data_dir, 'images/*.tif')))
self.labelFiles = natsorted(
glob.glob(os.path.join(data_dir, 'labels/*.tif')))
# self.images = cv2.imread(self.imageFiles[idx], cv2.IMREAD_GRAYSCALE).astype(np.uint8)
# self.labels = cv2.imread(self.labelFiles[idx], cv2.IMREAD_GRAYSCALE).astype(np.uint8)
print(self.imageFiles)
print(self.labelFiles)
self.images = []
self.labels = []
for imageFile in self.imageFiles:
self.images.append(skimage.io.imread(imageFile))
for labelFile in self.labelFiles:
self.labels.append(skimage.io.imread(labelFile)) # 3x100x1024x1024
# self.images = np.array(self.images)
# self.labels = np.array(self.labels)
self.image = None
self.label = None
self.estim = None
self.heap = None
self.root = None
# avoid simulator bugs: https://github.com/mgbellemare/Arcade-Learning-Environment/issues/86
with _SAFE_LOCK:
self.rng = get_rng(self)
# viz setup
if isinstance(viz, six.string_types):
assert os.path.isdir(viz), viz
viz = 0
if isinstance(viz, int):
viz = float(viz)
self.viz = viz
if self.viz and isinstance(self.viz, float):
self.windowname = os.path.basename(data_dir)
cv2.namedWindow(self.windowname)
self.width = DIMX
self.height = DIMY
self.actions = [0, 1, 2, 3, 4, 5,
6] # Modify here {0, 1, 2, 3, 4, 5, 6: bg, fg, split}
self.frame_skip = frame_skip
self.nullop_start = nullop_start
self.action_space = spaces.Discrete(len(self.actions))
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.height, self.width, 1),
dtype=np.uint8)
self._restart_episode()
def __init__(self,
rom_file,
viz=0,
frame_skip=4,
nullop_start=30,
live_lost_as_eoe=True,
max_num_frames=0):
"""
Args:
rom_file: path to the rom
frame_skip: skip every k frames and repeat the action
viz: visualization to be done.
Set to 0 to disable.
Set to a positive number to be the delay between frames to show.
Set to a string to be a directory to store frames.
nullop_start: start with random number of null ops.
live_losts_as_eoe: consider lost of lives as end of episode. Useful for training.
max_num_frames: maximum number of frames per episode.
"""
super(AtariPlayer, self).__init__()
if not os.path.isfile(rom_file) and '/' not in rom_file:
rom_file = get_dataset_path('atari_rom', rom_file)
assert os.path.isfile(rom_file), \
"rom {} not found. Please download at {}".format(rom_file, ROM_URL)
try:
ALEInterface.setLoggerMode(ALEInterface.Logger.Error)
except AttributeError:
if execute_only_once():
logger.warn("You're not using latest ALE")
# avoid simulator bugs: https://github.com/mgbellemare/Arcade-Learning-Environment/issues/86
with _ALE_LOCK:
self.ale = ALEInterface()
self.rng = get_rng(self)
self.ale.setInt(b"random_seed", self.rng.randint(0, 30000))
self.ale.setInt(b"max_num_frames_per_episode", max_num_frames)
self.ale.setBool(b"showinfo", False)
self.ale.setInt(b"frame_skip", 1)
self.ale.setBool(b'color_averaging', False)
# manual.pdf suggests otherwise.
self.ale.setFloat(b'repeat_action_probability', 0.0)
# viz setup
if isinstance(viz, six.string_types):
assert os.path.isdir(viz), viz
self.ale.setString(b'record_screen_dir', viz)
viz = 0
if isinstance(viz, int):
viz = float(viz)
self.viz = viz
if self.viz and isinstance(self.viz, float):
self.windowname = os.path.basename(rom_file)
cv2.startWindowThread()
cv2.namedWindow(self.windowname)
self.ale.loadROM(rom_file.encode('utf-8'))
self.width, self.height = self.ale.getScreenDims()
self.actions = self.ale.getMinimalActionSet()
self.live_lost_as_eoe = live_lost_as_eoe
self.frame_skip = frame_skip
self.nullop_start = nullop_start
self.action_space = spaces.Discrete(len(self.actions))
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.height, self.width),
dtype=np.uint8)
self._restart_episode()
def reset_state(self):
self.rng = get_rng(self)
print self.is_training
def __init__(self,
directory=None,
viz=False,
task=False,
files_list=None,
file_type="brain",
landmark_ids=None,
screen_dims=(27, 27, 27),
history_length=28,
multiscale=True,
max_num_frames=0,
saveGif=False,
saveVideo=False,
agents=1,
oscillations_allowed=4,
logger=None):
"""
:param train_directory: environment or game name
:param viz: visualization
set to 0 to disable
set to +ve number to be the delay between frames to show
set to a string to be the directory for storing frames
:param screen_dims: shape of the frame cropped from the image to feed
it to dqn (d,w,h) - defaults (27,27,27)
:param nullop_start: start with random number of null ops
:param location_history_length: consider lost of lives as end of
episode (useful for training)
:max_num_frames: maximum numbe0r of frames per episode.
"""
super(MedicalPlayer, self).__init__()
self.agents = agents
self.oscillations_allowed = oscillations_allowed
self.logger = logger
# inits stat counters
self.reset_stat()
# counter to limit number of steps per episodes
self.cnt = 0
# maximum number of frames (steps) per episodes
self.max_num_frames = max_num_frames
# stores information: terminal, score, distError
self.info = None
# option to save display as gif
self.saveGif = saveGif
self.saveVideo = saveVideo
# training flag
self.task = task
# image dimension (2D/3D)
self.screen_dims = screen_dims
self.dims = len(self.screen_dims)
# multi-scale agent
self.multiscale = multiscale
# init env dimensions
if self.dims == 2:
self.width, self.height = screen_dims
else:
self.width, self.height, self.depth = screen_dims
with _ALE_LOCK:
self.rng = get_rng(self)
# visualization setup
if isinstance(viz, six.string_types): # check if viz is a string
assert os.path.isdir(viz), viz
viz = 0
if isinstance(viz, int):
viz = float(viz)
self.viz = viz
if self.viz and isinstance(self.viz, float):
self.viewer = None
self.gif_buffer = []
# stat counter to store current score or accumlated reward
self.current_episode_score = [StatCounter()] * self.agents
# get action space and minimal action set
self.action_space = spaces.Discrete(6) # change number actions here
self.actions = self.action_space.n
self.observation_space = spaces.Box(low=0,
high=255,
shape=self.screen_dims,
dtype=np.uint8)
# history buffer for storing last locations to check oscilations
self._history_length = history_length
self._loc_history = [[(0, ) * self.dims
for _ in range(self._history_length)]
for _ in range(self.agents)]
self._qvalues_history = [[(0, ) * self.actions
for _ in range(self._history_length)]
for _ in range(self.agents)]
# initialize rectangle limits from input image coordinates
self.rectangle = [Rectangle(0, 0, 0, 0, 0, 0)] * int(self.agents)
returnLandmarks = (self.task != 'play')
# add your data loader here
if file_type == "brain":
self.files = filesListBrainMRLandmark(files_list, returnLandmarks,
self.agents)
elif file_type == "cardiac":
self.files = filesListCardioLandmark(files_list, returnLandmarks,
self.agents)
elif file_type == "fetal":
self.files = filesListFetalUSLandmark(files_list, returnLandmarks,
self.agents)
# prepare file sampler
self.filepath = None
self.sampled_files = self.files.sample_circular(landmark_ids)
# reset buffer, terminal, counters, and init new_random_game
self._restart_episode()
def __init__(self, num):
super(DiscreteActionSpace, self).__init__()
self.num = num
self.rng = get_rng(self)
def __init__(self,
directory=None,
files_list=None,
viz=False,
train=False,
screen_dims=(27, 27, 27),
spacing=(1, 1, 1),
nullop_start=30,
history_length=30,
max_num_frames=0,
saveGif=False,
saveVideo=False):
"""
:param train_directory: environment or game name
:param viz: visualization
set to 0 to disable
set to +ve number to be the delay between frames to show
set to a string to be the directory for storing frames
:param screen_dims: shape of the frame cropped from the image to feed
it to dqn (d,w,h) - defaults (27,27,27)
:param nullop_start: start with random number of null ops
:param history_length: consider lost of lives as end of
episode (useful for training)
"""
super(MedicalPlayer, self).__init__()
self.reset_stat()
self._supervised = False
self._init_action_angle_step = 8
self._init_action_dist_step = 4
## save results in csv file
# brain_adult_spacing_multi_3_actionh_8_4_unsupervised_duel_double_smooth_batch_32_layers_8_fold_1_model_2000000
self.csvfile = 'dummy.csv'
if not train:
with open(self.csvfile, 'w') as outcsv:
fields = ["filename", "dist_error", "angle_error"]
writer = csv.writer(outcsv)
writer.writerow(map(lambda x: x, fields))
# read files from directory - add your data loader here
self.files = filesListBrainMRPlane(directory, files_list)
# prepare file sampler
self.sampled_files = self.files.sample_circular()
self.filepath = None
# maximum number of frames (steps) per episodes
self.cnt = 0
self.max_num_frames = max_num_frames
# stores information: terminal, score, distError
self.info = None
# option to save display as gif
self.saveGif = saveGif
self.saveVideo = saveVideo
# training flag
self.train = train
# image dimension (2D/3D)
self._plane_size = screen_dims
self.dims = len(self._plane_size)
if self.dims == 2:
self.width, self.height = self._plane_size
else:
self.width, self.height, self.depth = self._plane_size
# plane sampling spacings
self.init_spacing = np.array(spacing)
# stat counter to store current score or accumlated reward
self.current_episode_score = StatCounter()
# get action space and minimal action set
self.action_space = spaces.Discrete(8) # change number actions here
self.actions = self.action_space.n
self.observation_space = spaces.Box(low=0,
high=255,
shape=self._plane_size)
# history buffer for storing last locations to check oscillations
self._history_length = history_length
# circular buffer to store plane parameters history [4,history_length]
self._plane_history = deque(maxlen=self._history_length)
self._bestq_history = deque(maxlen=self._history_length)
self._dist_history = deque(maxlen=self._history_length)
self._dist_history_params = deque(maxlen=self._history_length)
self._dist_supervised_history = deque(maxlen=self._history_length)
# self._loc_history = [(0,) * self.dims] * self._history_length
self._loc_history = [(0, ) * 4] * self._history_length
self._qvalues_history = [(0, ) * self.actions] * self._history_length
self._qvalues = [
0,
] * self.actions
with _ALE_LOCK:
self.rng = get_rng(self)
# visualization setup
if isinstance(viz, six.string_types): # check if viz is a string
assert os.path.isdir(viz), viz
viz = 0
if isinstance(viz, int):
viz = float(viz)
self.viz = viz
if self.viz and isinstance(self.viz, float):
self.viewer = None
self.gif_buffer = []
self._restart_episode()
def __init__(self, rom_file, viz=0,
frame_skip=4, nullop_start=30,
live_lost_as_eoe=True, max_num_frames=0):
"""
Args:
rom_file: path to the rom
frame_skip: skip every k frames and repeat the action
viz: visualization to be done.
Set to 0 to disable.
Set to a positive number to be the delay between frames to show.
Set to a string to be a directory to store frames.
nullop_start: start with random number of null ops.
live_losts_as_eoe: consider lost of lives as end of episode. Useful for training.
max_num_frames: maximum number of frames per episode.
"""
super(AtariPlayer, self).__init__()
if not os.path.isfile(rom_file) and '/' not in rom_file:
rom_file = get_dataset_path('atari_rom', rom_file)
assert os.path.isfile(rom_file), \
"rom {} not found. Please download at {}".format(rom_file, ROM_URL)
try:
ALEInterface.setLoggerMode(ALEInterface.Logger.Error)
except AttributeError:
if execute_only_once():
logger.warn("You're not using latest ALE")
# avoid simulator bugs: https://github.com/mgbellemare/Arcade-Learning-Environment/issues/86
with _ALE_LOCK:
self.ale = ALEInterface()
self.rng = get_rng(self)
self.ale.setInt(b"random_seed", self.rng.randint(0, 30000))
self.ale.setInt(b"max_num_frames_per_episode", max_num_frames)
self.ale.setBool(b"showinfo", False)
self.ale.setInt(b"frame_skip", 1)
self.ale.setBool(b'color_averaging', False)
# manual.pdf suggests otherwise.
self.ale.setFloat(b'repeat_action_probability', 0.0)
# viz setup
if isinstance(viz, six.string_types):
assert os.path.isdir(viz), viz
self.ale.setString(b'record_screen_dir', viz)
viz = 0
if isinstance(viz, int):
viz = float(viz)
self.viz = viz
if self.viz and isinstance(self.viz, float):
self.windowname = os.path.basename(rom_file)
cv2.startWindowThread()
cv2.namedWindow(self.windowname)
self.ale.loadROM(rom_file.encode('utf-8'))
self.width, self.height = self.ale.getScreenDims()
self.actions = self.ale.getMinimalActionSet()
self.live_lost_as_eoe = live_lost_as_eoe
self.frame_skip = frame_skip
self.nullop_start = nullop_start
self.action_space = spaces.Discrete(len(self.actions))
self.observation_space = spaces.Box(
low=0, high=255, shape=(self.height, self.width))
self._restart_episode()
for k in range(self.frame_skip):
if k == self.frame_skip - 1:
self.last_raw_screen = self._grab_raw_image()
r += self.ale.act(self.actions[act])
newlives = self.ale.lives()
if self.ale.game_over() or \
(self.live_lost_as_eoe and newlives < oldlives):
break
isOver = self.ale.game_over()
if self.live_lost_as_eoe:
isOver = isOver or newlives < oldlives
info = {'ale.lives': newlives}
return self._current_state(), r, isOver, info
if __name__ == '__main__':
import sys
a = AtariPlayer(sys.argv[1], viz=0.03)
num = a.action_space.n
rng = get_rng(num)
while True:
act = rng.choice(range(num))
state, reward, isOver, info = a.step(act)
if isOver:
print(info)
a.reset()
print("Reward:", reward)
def reset_state(self):
self.rng = get_rng(self)
for k in range(self.frame_skip):
if k == self.frame_skip - 1:
self.last_raw_screen = self._grab_raw_image()
r += self.ale.act(self.actions[act])
newlives = self.ale.lives()
if self.ale.game_over() or \
(self.live_lost_as_eoe and newlives < oldlives):
break
isOver = self.ale.game_over()
if self.live_lost_as_eoe:
isOver = isOver or newlives < oldlives
info = {'ale.lives': newlives}
return self._current_state(), r, isOver, info
if __name__ == '__main__':
import sys
a = AtariPlayer(sys.argv[1], viz=0.03)
num = a.action_space.n
rng = get_rng(num)
while True:
act = rng.choice(range(num))
state, reward, isOver, info = a.step(act)
if isOver:
print(info)
a.reset()
print("Reward:", reward)
for i in xrange(x):
for j in xrange(y):
im = image[i:i + xo, j:j + yo]
if im.shape == obj.shape:
if method == 'absolute':
diff = float(np.sum(im != obj)) / obj.size
elif method == 'relative':
diff = np.sum(abs(im - obj) / 255.0) / obj.size
if diff < max_diff:
answers.append((i, j))
return answers
if __name__ == '__main__':
player = get_player()
rng = get_rng()
task = 'detect'
import matplotlib.pyplot as plt
if task == 'save':
for i in xrange(50):
#random_action = rng.choice(range(NUM_ACTIONS))
player.action(1)
# Original image: (210 * 160 * 3) by print player.current_state().shape
# print player.current_state().shape
#if i == 25:
# player.restart_episode()
file_name = 'freeway/' + ENV_NAME + '_' + str(i)
X = player.current_state()
np.save(file_name, X)
def __init__(self, wmin, hmin, wmax=None, hmax=None):
self.rng = get_rng()
super().__init__(wmin, hmin, wmax, hmax)
def __init__(self, directory=None, viz=False, task=False, files_list=None,
observation_dims=(27, 27, 27), multiscale=False, # FIXME automatic dimensions
max_num_frames=0, saveGif=False, saveVideo=False): # FIXME hardcoded max num frames!
"""
:param train_directory: environment or game name
:param viz: visualization
set to 0 to disable
set to +ve number to be the delay between frames to show
set to a string to be the directory for storing frames
:param observation_dims: shape of the frame cropped from the image to feed
it to dqn (d,w,h) - defaults (27,27,27)
:param nullop_start: start with random number of null ops
:param location_history_length: consider lost of lives as end of
episode (useful for training)
:max_num_frames: maximum number of frames per episode.
"""
super(Brain_Env, self).__init__()
# inits stat counters
self.reset_stat()
# counter to limit number of steps per episodes
self.cnt = 0
# maximum number of frames (steps) per episodes
self.max_num_frames = max_num_frames
# stores information: terminal, score, distError
self.info = None
# option to save display as gif
self.saveGif = saveGif
self.saveVideo = saveVideo
# training flag
self.task = task
# image dimension (2D/3D)
self.observation_dims = observation_dims
self.dims = len(self.observation_dims)
# multi-scale agent
self.multiscale = multiscale
# FIXME force multiscale false for now
self.multiscale = False
# init env dimensions
if self.dims == 2:
self.width, self.height = observation_dims
elif self.dims == 3:
self.width, self.height, self.depth = observation_dims
else:
raise ValueError
with THREAD_LOCKER:
self.rng = get_rng(self)
self.viz = viz
print("viz {} gif {} video {}".format(self.viz, self.saveGif, self.saveVideo))
# get action space and minimal action set
self.action_space = spaces.Discrete(6) # change number actions here
self.actions = self.action_space.n
self.observation_space = spaces.Box(low=-1., high=1.,
shape=self.observation_dims,
dtype=np.uint8)
# history buffer for storing last locations to check oscilations
# TODO initialize _observation_bounds limits from input image coordinates
# -1 to compensate for 0 indexing
self._observation_bounds = ObservationBounds(0,
self.observation_dims[0] - 1,
0,
self.observation_dims[1] - 1,
0,
self.observation_dims[2] - 1)
self.files = FilesListCubeNPY(directory, files_list)
# self.files = filesListFetalUSLandmark(directory,files_list)
# self.files = filesListCardioMRLandmark(directory,files_list)
# prepare file sampler
self.filepath = None
self.file_sampler = self.files.sample_circular() # returns generator
# reset buffer, terminal, counters, and init new_random_game
# we put this here so that init_player in DQN.py doesn't try to update_history
self._clear_history() # init arrays
self._restart_episode()
assert (np.shape(self._state) == self.observation_dims)
# test jaccard
assert np.isclose(jaccard(self.original_state, self.original_state)[0], 1)