def __init__(self, name, task):
self.name = name
self.task = task
self.total_iter = 0
self.num_rotations = 24
self.descriptor_dim = 16
self.pixel_size = 0.003125
self.input_shape = (320, 160, 6)
self.camera_config = cameras.RealSenseD415.CONFIG
self.models_dir = os.path.join('checkpoints', self.name)
self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]])
self.pick_model = Attention(
input_shape=self.input_shape,
num_rotations=1,
preprocess=self.preprocess,
lite=True)
self.place_model = Attention(
input_shape=self.input_shape,
num_rotations=1,
preprocess=self.preprocess,
lite=True)
self.match_model = Matching(
input_shape=self.input_shape,
descriptor_dim=self.descriptor_dim,
num_rotations=self.num_rotations,
preprocess=self.preprocess,
lite=True)
def __init__(self, name, task, n_rotations=36):
super().__init__(name, task, n_rotations)
self.attention = Attention(in_shape=self.in_shape,
n_rotations=1,
preprocess=utils.preprocess)
self.transport = Attention(in_shape=self.in_shape,
n_rotations=self.n_rotations,
preprocess=utils.preprocess)
def __init__(self, name, task):
super().__init__(name, task)
self.attention_model = Attention(input_shape=self.input_shape,
num_rotations=1,
preprocess=self.preprocess)
self.transport_model = Attention(input_shape=self.input_shape,
num_rotations=self.num_rotations,
preprocess=self.preprocess)
def __init__(self, name, task):
super().__init__(name, task)
self.attention_model = Attention(input_shape=self.input_shape,
num_rotations=1,
preprocess=self.preprocess)
self.transport_model = Transport(input_shape=self.input_shape,
num_rotations=self.num_rotations,
crop_size=self.crop_size,
preprocess=self.preprocess,
per_pixel_loss=False,
six_dof=False)
self.rpz_model = Transport(input_shape=self.input_shape,
num_rotations=self.num_rotations,
crop_size=self.crop_size,
preprocess=self.preprocess,
per_pixel_loss=False,
six_dof=True)
self.transport_model.set_bounds_pixel_size(self.bounds,
self.pixel_size)
self.rpz_model.set_bounds_pixel_size(self.bounds, self.pixel_size)
self.six_dof = True
self.p0_pixel_error = tf.keras.metrics.Mean(name='p0_pixel_error')
self.p1_pixel_error = tf.keras.metrics.Mean(name='p1_pixel_error')
self.p0_theta_error = tf.keras.metrics.Mean(name='p0_theta_error')
self.p1_theta_error = tf.keras.metrics.Mean(name='p1_theta_error')
self.metrics = [
self.p0_pixel_error, self.p1_pixel_error, self.p0_theta_error,
self.p1_theta_error
]
def __init__(self, name, task, n_rotations=36):
super().__init__(name, task, n_rotations)
self.attention = Attention(in_shape=self.in_shape,
n_rotations=1,
preprocess=utils.preprocess)
self.transport = TransportPerPixelLoss(in_shape=self.in_shape,
n_rotations=self.n_rotations,
crop_size=self.crop_size,
preprocess=utils.preprocess)
def __init__(self, name, task, num_rotations=36):
super().__init__(name, task, num_rotations, use_goal_image=True)
self.attention_model = Attention(input_shape=self.input_shape,
num_rotations=1,
preprocess=self.preprocess)
self.transport_model = TransportGoal(input_shape=self.input_shape,
num_rotations=self.num_rotations,
crop_size=self.crop_size,
preprocess=self.preprocess)
def __init__(self, name, task):
super().__init__(name, task)
self.attention_model = Attention(input_shape=self.input_shape,
num_rotations=1,
preprocess=self.preprocess)
self.transport_model = Transport(input_shape=self.input_shape,
num_rotations=self.num_rotations,
crop_size=self.crop_size,
preprocess=self.preprocess,
per_pixel_loss=True)
def __init__(self, name, task, num_rotations=24):
# (Oct 26) set attn_no_targ=False, and that should be all we need along w/shape ...
super().__init__(name, task, num_rotations, use_goal_image=True, attn_no_targ=False)
# (Oct 26) Stack the goal image for the Attention module -- model cannot pick properly otherwise.
a_shape = (self.input_shape[0], self.input_shape[1], int(self.input_shape[2] * 2))
self.attention_model = Attention(input_shape=a_shape,
num_rotations=1,
preprocess=self.preprocess)
self.transport_model = TransportGoal(input_shape=self.input_shape,
num_rotations=self.num_rotations,
crop_size=self.crop_size,
preprocess=self.preprocess)
def __init__(self, name, task, num_rotations=24):
super().__init__(name, task, num_rotations, use_goal_image=True, attn_no_targ=False)
# We stack the goal image for both modules.
in_shape = (self.input_shape[0], self.input_shape[1], int(self.input_shape[2] * 2))
self.attention_model = Attention(input_shape=in_shape,
num_rotations=1,
preprocess=self.preprocess)
self.transport_model = Transport(input_shape=in_shape,
num_rotations=self.num_rotations,
crop_size=self.crop_size,
preprocess=self.preprocess,
per_pixel_loss=False, # NOTE: see docs above.
use_goal_image=True)
def __init__(self, name, task, n_rotations=36):
super().__init__(name, task, n_rotations)
# Stack the goal image for the vanilla Transport module.
t_shape = (self.in_shape[0], self.in_shape[1],
int(self.in_shape[2] * 2))
self.attention = Attention(in_shape=self.in_shape,
n_rotations=1,
preprocess=utils.preprocess)
self.transport = Transport(in_shape=t_shape,
n_rotations=self.n_rotations,
crop_size=self.crop_size,
preprocess=utils.preprocess,
per_pixel_loss=False,
use_goal_image=True)