def testSamplingContainmentSetMinusMultiple(self):
base = distribs.Continuous('x', 2, 10)
hold_out = distribs.Mixture([
distribs.Discrete('x', [1, 4, 6]),
distribs.Discrete('x', [3, 8, 9, 12]),
distribs.Continuous('x', 3, 5),
])
contained = [{'x': value} for value in [2.5, 5.5, 7, 9.5]]
not_contained = [{'x': value} for value in [4, 6, 9, 11]]
d = distribs.SetMinus(base, hold_out)
test_sampling_and_containment(self, d, contained, not_contained)
def testCubeWithTunnel(self):
cube = distribs.Product([
distribs.Continuous('x', 0, 1),
distribs.Continuous('y', 0, 1),
distribs.Continuous('z', 0, 1),
])
tunnel = distribs.Product([
distribs.Continuous('x', 0.25, 0.75),
distribs.Continuous('y', 0.25, 0.75),
])
cube_with_tunnel = distribs.SetMinus(cube, tunnel)
contained = [{
'x': 0.2,
'y': 0.2,
'z': 0.2
}, {
'x': 0.2,
'y': 0.2,
'z': 0.5
}, {
'x': 0.2,
'y': 0.5,
'z': 0.5
}]
not_contained = [
{
'x': 0.5,
'y': 0.5,
'z': 0.5
},
{
'x': 0.5,
'y': 0.5,
'z': 0.2
},
]
test_sampling_and_containment(self, cube_with_tunnel, contained,
not_contained)
import os
from spriteworld import factor_distributions as distribs
from spriteworld import sprite_generators
from spriteworld import tasks
from spriteworld.configs.cobra import common
TERMINATE_DISTANCE = 0.075
NUM_TARGETS = 1
NUM_DISTRACTORS = 1
MODES_TARGET_POSITIONS = {
'train':
distribs.SetMinus(
distribs.Product((
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
)),
distribs.Product((
distribs.Continuous('x', 0.5, 0.9),
distribs.Continuous('y', 0.5, 0.9),
)),
),
'test':
distribs.Product((
distribs.Continuous('x', 0.5, 0.9),
distribs.Continuous('y', 0.5, 0.9),
)),
}
def get_config(mode='train'):
"""Generate environment config.
def get_config(mode='train'):
"""Generate environment config.
Args:
mode: Unused task mode.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
kwargs to environment.Environment.
"""
# Factor distributions common to all objects.
common_factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Continuous('angle', 0, 360, dtype='int32'),
])
# train/test split for goal-finding object scales and clustering object colors
goal_finding_scale_test = distribs.Continuous('scale', 0.08, 0.12)
green_blue_colors = distribs.Product([
distribs.Continuous('c1', 64, 256, dtype='int32'),
distribs.Continuous('c2', 64, 256, dtype='int32'),
])
if mode == 'train':
goal_finding_scale = distribs.SetMinus(
distribs.Continuous('scale', 0.05, 0.15),
goal_finding_scale_test,
)
cluster_colors = distribs.Product([
distribs.Continuous('c0', 128, 256, dtype='int32'),
green_blue_colors
])
elif mode == 'test':
goal_finding_scale = goal_finding_scale_test
cluster_colors = distribs.Product([
distribs.Continuous('c0', 0, 128, dtype='int32'), green_blue_colors
])
else:
raise ValueError(
'Invalid mode {}. Mode must be "train" or "test".'.format(mode))
# Create clustering sprite generators
sprite_gen_list = []
cluster_shapes = [
distribs.Discrete('shape', [s])
for s in ['triangle', 'square', 'pentagon']
]
for shape in cluster_shapes:
factors = distribs.Product([
common_factors,
cluster_colors,
shape,
distribs.Continuous('scale', 0.08, 0.12),
])
sprite_gen_list.append(
sprite_generators.generate_sprites(factors, num_sprites=2))
# Create goal-finding sprite generators
goal_finding_colors = [
distribs.Product([
distribs.Continuous('c0', 192, 256, dtype='int32'),
distribs.Continuous('c1', 0, 128, dtype='int32'),
distribs.Continuous('c2', 64, 128, dtype='int32'),
]),
distribs.Product([
distribs.Continuous('c0', 0, 128, dtype='int32'),
distribs.Continuous('c1', 192, 256, dtype='int32'),
distribs.Continuous('c2', 64, 128, dtype='int32'),
])
]
# Goal positions corresponding to the colors in goal_finding_colors
goal_finding_positions = [(0., 0.5), (1., 0.5)]
goal_finding_shapes = distribs.Discrete('shape', ['spoke_4', 'star_4'])
for colors in goal_finding_colors:
factors = distribs.Product([
common_factors,
goal_finding_scale,
goal_finding_shapes,
colors,
])
sprite_gen_list.append(
sprite_generators.generate_sprites(
factors, num_sprites=lambda: np.random.randint(1, 3)))
# Create distractor sprite generator
distractor_factors = distribs.Product([
common_factors,
distribs.Discrete('shape', ['circle']),
distribs.Continuous('c0', 64, 256, dtype='uint8'),
distribs.Continuous('c1', 64, 256, dtype='uint8'),
distribs.Continuous('c2', 64, 256, dtype='uint8'),
distribs.Continuous('scale', 0.08, 0.12),
])
sprite_gen_list.append(
sprite_generators.generate_sprites(
distractor_factors, num_sprites=lambda: np.random.randint(0, 3)))
# Concat clusters into single scene to generate
sprite_gen = sprite_generators.chain_generators(*sprite_gen_list)
# Randomize sprite ordering to eliminate any task information from occlusions
sprite_gen = sprite_generators.shuffle(sprite_gen)
# Create the combined task of goal-finding and clustering
task_list = []
task_list.append(
tasks.Clustering(cluster_shapes, terminate_bonus=0., reward_range=10.))
for colors, goal_pos in zip(goal_finding_colors, goal_finding_positions):
goal_finding_task = tasks.FindGoalPosition(distribs.Product(
[colors, goal_finding_shapes]),
goal_position=goal_pos,
weights_dimensions=(1, 0),
terminate_distance=0.15,
raw_reward_multiplier=30)
task_list.append(goal_finding_task)
task = tasks.MetaAggregated(task_list,
reward_aggregator='sum',
termination_criterion='all')
renderers = {
'image':
spriteworld_renderers.PILRenderer(image_size=(64, 64), anti_aliasing=5)
}
config = {
'task': task,
'action_space': action_spaces.SelectMove(scale=0.5),
'renderers': renderers,
'init_sprites': sprite_gen,
'max_episode_length': 50,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
def testCornerUnion(self):
square_0 = distribs.Product([
distribs.Continuous('x', 0, 3),
distribs.Continuous('y', 0, 3),
])
hold_out_0 = distribs.Product([
distribs.Continuous('x', 1, 3),
distribs.Continuous('y', 0, 2),
])
square_1 = distribs.Product([
distribs.Continuous('x', 2, 5),
distribs.Continuous('y', 0, 3),
])
hold_out_1 = distribs.Product([
distribs.Continuous('x', 2, 4),
distribs.Continuous('y', 1, 3),
])
corner_0 = distribs.SetMinus(square_0, hold_out_0)
corner_1 = distribs.SetMinus(square_1, hold_out_1)
corner_union = distribs.Mixture([corner_0, corner_1])
contained = [
{
'x': 0.5,
'y': 0.5
},
{
'x': 0.5,
'y': 2.5
},
{
'x': 2.5,
'y': 2.5
},
{
'x': 2.5,
'y': 0.5
},
{
'x': 4.5,
'y': 0.5
},
{
'x': 4.5,
'y': 2.5
},
]
not_contained = [
{
'x': 1.5,
'y': 0.5
},
{
'x': 1.5,
'y': 1.5
},
{
'x': 2.5,
'y': 1.5
},
{
'x': 3.5,
'y': 1.5
},
{
'x': 3.5,
'y': 2.5
},
]
test_sampling_and_containment(self, corner_union, contained,
not_contained)
def testRaisesError(self):
d_0 = distribs.Continuous('x', 0, 2)
d_1 = distribs.Continuous('y', 1, 3)
with self.assertRaises(ValueError):
distribs.SetMinus(d_0, d_1)
def testSamplingContainmentSetMinusTwo(self, d_0, d_1, contained,
not_contained):
d = distribs.SetMinus(d_0, d_1)
contained = [{'x': value} for value in contained]
not_contained = [{'x': value} for value in not_contained]
test_sampling_and_containment(self, d, contained, not_contained)