def get_config(mode='train'):
"""Generate environment config.
Args:
mode: 'train' or 'test'.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
kwargs to environment.Environment.
"""
# Select clusters to use, and their c0 factor distribution.
c0_clusters = [CLUSTERS_DISTS[cluster] for cluster in MODES[mode]]
print('Clustering task: {}, #sprites: {}'.format(MODES[mode],
NUM_SPRITES_PER_CLUSTER))
other_factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['square', 'triangle', 'circle']),
distribs.Discrete('scale', [0.13]),
distribs.Continuous('c1', 0.3, 1.),
distribs.Continuous('c2', 0.9, 1.),
])
# Generate the sprites to be used in this task, by combining Hue with the
# other factors.
sprite_factors = [
distribs.Product((other_factors, c0)) for c0 in c0_clusters
]
# Convert to sprites, generating the appropriate number per cluster.
sprite_gen_per_cluster = [
sprite_generators.generate_sprites(factors,
num_sprites=NUM_SPRITES_PER_CLUSTER)
for factors in sprite_factors
]
# Concat clusters into single scene to generate.
sprite_gen = sprite_generators.chain_generators(*sprite_gen_per_cluster)
# Randomize sprite ordering to eliminate any task information from occlusions
sprite_gen = sprite_generators.shuffle(sprite_gen)
# Clustering task will define rewards
task = tasks.Clustering(c0_clusters,
terminate_bonus=0.,
reward_range=10.,
sparse_reward=True)
config = {
'task': task,
'action_space': common.noisy_action_space(),
'renderers': common.renderers(),
'init_sprites': sprite_gen,
'max_episode_length': MAX_EPISODE_LENGTH,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
class IntersectionTest(parameterized.TestCase):
"""Runs tests for Intersection of distributions."""
@parameterized.named_parameters(
('ContinuousContinuous', distribs.Continuous(
'x', 0, 2), distribs.Continuous('x', 1, 3), [1.5], [0.5, 2.5]),
('DiscreteDiscrete', distribs.Discrete(
'x', [0, 1]), distribs.Discrete('x', [1, 2]), [1], [0, 2]),
('DiscreteContinuous', distribs.Discrete(
'x', [1, 3]), distribs.Continuous('x', 0, 2), [1], [0.5, 1.5, 3]),
)
def testSamplingContainmentIntersectionTwo(self, d_0, d_1, contained,
not_contained):
d = distribs.Intersection((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)
def testSamplingContainmentIntersectionMultiple(self):
dists = [
distribs.Discrete('x', [1, 2.5, 3, 4, 6]),
distribs.Discrete('x', [1, 2.5, 3, 12]),
distribs.Continuous('x', 0, 5),
distribs.Continuous('x', 2, 10),
]
contained = [2.5, 3]
not_contained = [1, 4, 8]
d = distribs.Intersection(dists)
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)
def testRaisesError(self):
d_0 = distribs.Continuous('x', 0, 1)
d_1 = distribs.Continuous('x', 2, 3)
d = distribs.Intersection((d_0, d_1))
with self.assertRaises(ValueError):
d.sample()
def testIndexForSampling(self):
d_0 = distribs.Continuous('x', 0, 2)
d_1 = distribs.Discrete('x', [1, 3])
d = distribs.Intersection((d_0, d_1), index_for_sampling=1)
d.sample()
with self.assertRaises(ValueError):
d = distribs.Intersection((d_0, d_1), index_for_sampling=0)
d.sample()
def testKeys(self):
d_0 = distribs.Product(
(distribs.Continuous('x', 0, 2), distribs.Continuous('y', 0, 1)))
d_1 = distribs.Product(
(distribs.Continuous('x', 0, 1), distribs.Continuous('y', 0, 0.5)))
d_2 = distribs.Continuous('x', 0.4, 0.6)
distribs.Intersection((d_0, d_1))
with self.assertRaises(ValueError):
distribs.Intersection((d_0, d_2))
def testProbs(self):
c_0 = distribs.Discrete('x', [0])
c_1 = distribs.Discrete('x', [1])
d_0 = distribs.Mixture([c_0, c_1], probs=(0.3, 0.7))
d_1 = distribs.Mixture([c_0, c_1], probs=(0.0, 1.0))
for _ in range(5):
self.assertTrue(d_0.contains(d_0.sample()))
for _ in range(5):
self.assertEqual(d_1.sample(), {'x': 1})
def get_config(mode='train'):
"""Generate environment config.
Args:
mode: 'train' or 'test'.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
kwargs to environment.Environment.
"""
shared_factors = distribs.Product([
distribs.Discrete('shape', ['square', 'triangle', 'circle']),
distribs.Discrete('scale', [0.13]),
distribs.Continuous('c1', 0.3, 1.),
distribs.Continuous('c2', 0.9, 1.),
])
target_hue = distribs.Continuous('c0', 0., 0.4)
distractor_hue = distribs.Continuous('c0', 0.5, 0.9)
target_factors = distribs.Product([
MODES_TARGET_POSITIONS[mode],
target_hue,
shared_factors,
])
distractor_factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distractor_hue,
shared_factors,
])
target_sprite_gen = sprite_generators.generate_sprites(
target_factors, num_sprites=NUM_TARGETS)
distractor_sprite_gen = sprite_generators.generate_sprites(
distractor_factors, num_sprites=NUM_DISTRACTORS)
sprite_gen = sprite_generators.chain_generators(target_sprite_gen,
distractor_sprite_gen)
# Randomize sprite ordering to eliminate any task information from occlusions
sprite_gen = sprite_generators.shuffle(sprite_gen)
task = tasks.FindGoalPosition(filter_distrib=target_hue,
terminate_distance=TERMINATE_DISTANCE)
config = {
'task': task,
'action_space': common.action_space(),
'renderers': common.renderers(),
'init_sprites': sprite_gen,
'max_episode_length': 20,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
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 get_config(mode=None):
"""Generate environment config.
Args:
mode: Unused task mode.
Returns:
config: Dictionary defining task/environment configuration. Can be fed
as kwargs to physics_environment.PhysicsEnvironment.
"""
# Factor distributions for the sprites.
factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['circle', 'square', 'triangle']),
distribs.Discrete('scale', [0.1]),
distribs.Continuous('c0', 0, 1),
distribs.Continuous('c1', 0.5, 1.),
distribs.Discrete('c2', [1.]),
distribs.Continuous('x_vel', -0.03, 0.03),
distribs.Continuous('y_vel', -0.03, 0.03),
distribs.Continuous('mass', 0.5, 2.0),
])
sprite_gen = generate_sprites.generate_sprites(
factors, num_sprites=lambda: np.random.randint(4, 8))
# The collisions are simulated by applying an invisible rigid circular shell
# around each sprite. The shell_radius of 0.08 is eye-balled to look
# reasonable.
force = forces.SymmetricShellCollision(shell_radius=0.08)
graph_generator = graph_generators.LowerTriangular(force=force)
renderers = {
'image':
spriteworld_renderers.PILRenderer(
image_size=(64, 64), anti_aliasing=5)
}
config = {
'graph_generators': (graph_generator,),
'renderers': renderers,
'init_sprites': sprite_gen,
'episode_length': 30,
'bounce_off_walls': True,
'physics_steps_per_env_step': 10,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
def testSamplingContainmentIntersectionMultiple(self):
dists = [
distribs.Discrete('x', [1, 2.5, 3, 4, 6]),
distribs.Discrete('x', [1, 2.5, 3, 12]),
distribs.Continuous('x', 0, 5),
distribs.Continuous('x', 2, 10),
]
contained = [2.5, 3]
not_contained = [1, 4, 8]
d = distribs.Intersection(dists)
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)
class MixtureTest(parameterized.TestCase):
"""Runs tests for Mixture of distributions."""
@parameterized.named_parameters(
('DisjointContinuous', distribs.Continuous('x', 0, 1),
distribs.Continuous('x', 2, 3), [0.5, 2.5], [1.5, 3.5]),
('OverlappingContinuous', distribs.Continuous('x', 0, 2),
distribs.Continuous('x', 1, 3), [0.5, 2.5], [-0.5, 3.5]),
('DisjointDiscrete', distribs.Discrete(
'x', [0, 1]), distribs.Discrete('x', [2, 3]), [1, 2], [-1, 4]),
('OverlappingDiscrete', distribs.Discrete(
'x', [0, 1]), distribs.Discrete('x', [1, 2]), [0, 1, 2], [-1, 3]),
('ContinuousDiscrete', distribs.Continuous(
'x', 0, 2), distribs.Discrete('x', [1, 3]), [0.5, 3], [2.5]),
)
def testSamplingContainmentMixtureTwo(self, c_0, c_1, contained,
not_contained):
d = distribs.Mixture((c_0, c_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)
def testSamplingContainmentMixtureMultiple(self):
dists = [
distribs.Continuous('x', 0, 2),
distribs.Continuous('x', 1, 5),
distribs.Continuous('x', 9, 12),
distribs.Discrete('x', [7, 10]),
distribs.Discrete('x', [14]),
]
contained = [0.5, 4, 11, 7, 14]
not_contained = [5.5, 6, 8, 13]
d = distribs.Mixture(dists)
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)
def testRaisesError(self):
c_0 = distribs.Discrete('x', [0])
c_1 = distribs.Discrete('y', [1])
with self.assertRaises(ValueError):
distribs.Mixture((c_0, c_1))
def testProbs(self):
c_0 = distribs.Discrete('x', [0])
c_1 = distribs.Discrete('x', [1])
d_0 = distribs.Mixture([c_0, c_1], probs=(0.3, 0.7))
d_1 = distribs.Mixture([c_0, c_1], probs=(0.0, 1.0))
for _ in range(5):
self.assertTrue(d_0.contains(d_0.sample()))
for _ in range(5):
self.assertEqual(d_1.sample(), {'x': 1})
def testSamplingContainmentMixtureMultiple(self):
dists = [
distribs.Continuous('x', 0, 2),
distribs.Continuous('x', 1, 5),
distribs.Continuous('x', 9, 12),
distribs.Discrete('x', [7, 10]),
distribs.Discrete('x', [14]),
]
contained = [0.5, 4, 11, 7, 14]
not_contained = [5.5, 6, 8, 13]
d = distribs.Mixture(dists)
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)
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 physics_environment.PhysicsEnvironment.
"""
# Factor distributions for the sprites.
factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['circle']),
distribs.Discrete('scale', [0.1]),
distribs.Continuous('c0', 0, 1),
distribs.Discrete('c1', [1.]),
distribs.Discrete('c2', [1.]),
distribs.Continuous('x_vel', -0.03, 0.03),
distribs.Continuous('y_vel', -0.03, 0.03),
distribs.Discrete('mass', [1]),
])
num_sprites = _NUM_SPRITES[mode]
sprite_gen = generate_sprites.generate_sprites(
factors, num_sprites=num_sprites)
graph_generator = graph_generators.FullyConnected(force=forces.NoForce)
renderers = {
'image':
spriteworld_renderers.PILRenderer(
image_size=(64, 64), anti_aliasing=5)
}
config = {
'graph_generators': (graph_generator,),
'renderers': renderers,
'init_sprites': sprite_gen,
'episode_length': 20,
'bounce_off_walls': False,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
def get_config(mode=None):
"""Generate environment config.
Args:
mode: Unused task mode.
Returns:
config: Dictionary defining task/environment configuration. Can be fed
as kwargs to physics_environment.PhysicsEnvironment.
"""
# Factor distributions for the sprites.
factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['circle', 'square', 'triangle']),
distribs.Discrete('scale', [0.1]),
distribs.Continuous('c0', 0, 1),
distribs.Continuous('c1', 0.5, 1.),
distribs.Discrete('c2', [1.]),
distribs.Continuous('x_vel', -0.03, 0.03),
distribs.Continuous('y_vel', -0.03, 0.03),
distribs.Continuous('mass', 0.5, 2.0),
])
sprite_gen = generate_sprites.generate_sprites(factors, num_sprites=4)
force = forces.Spring(spring_constant=0.03, spring_equilibrium=0.25)
graph_generator = graph_generators.FullyConnected(force=force)
renderers = {
'image':
spriteworld_renderers.PILRenderer(image_size=(64, 64), anti_aliasing=5)
}
config = {
'graph_generators': (graph_generator, ),
'renderers': renderers,
'init_sprites': sprite_gen,
'episode_length': 30,
'bounce_off_walls': False,
'physics_steps_per_env_step': 10,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
def get_config(mode='train'):
"""Generate environment config.
Args:
mode: 'train' or 'test'.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
kwargs to environment.Environment.
"""
factors = distribs.Product([
MODES_SHAPES[mode],
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('scale', [0.2]),
distribs.Discrete('c0', [0.9, 0.55, 0.27], probs=[0.333, 0.334,
0.333]),
distribs.Discrete('c1', [0.6]),
distribs.Continuous('c2', 0.9, 1.),
])
sprite_gen = sprite_generators.generate_sprites(factors,
num_sprites=NUM_TARGETS,
fix_colors=True)
# Randomize sprite ordering to eliminate any task information from occlusions
sprite_gen = sprite_generators.shuffle(sprite_gen)
task = tasks.FindGoalPosition(terminate_distance=TERMINATE_DISTANCE,
sparse_reward=True)
config = {
'task':
task,
'action_space':
common.noisy_action_space(MOTION_STD_DEV, PROPORTIONAL_MOTION_NOISE,
None),
'renderers':
common.renderers(),
'init_sprites':
sprite_gen,
'max_episode_length':
60,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
def testIndexForSampling(self):
d_0 = distribs.Continuous('x', 0, 2)
d_1 = distribs.Discrete('x', [1, 3])
d = distribs.Intersection((d_0, d_1), index_for_sampling=1)
d.sample()
with self.assertRaises(ValueError):
d = distribs.Intersection((d_0, d_1), index_for_sampling=0)
d.sample()
def testkeys(self):
dists = [
distribs.Discrete('x', [1, 2.5, 3, 12]),
distribs.Continuous('y', 0, 5),
distribs.Continuous('z', 2, 10),
]
d = distribs.Product(dists)
self.assertEqual(d.keys, set(('x', 'y', 'z')))
def get_config(mode=None):
"""Generate environment config.
Args:
mode: Unused.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
kwargs to environment.Environment.
"""
del mode # No train/test split for pure exploration
factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['square', 'triangle', 'circle']),
distribs.Discrete('scale', [0.13]),
distribs.Continuous('c0', 0., 1.),
distribs.Continuous('c1', 0.3, 1.),
distribs.Continuous('c2', 0.9, 1.),
])
num_sprites = lambda: np.random.randint(1, 7)
sprite_gen = sprite_generators.generate_sprites(factors,
num_sprites=num_sprites)
task = tasks.NoReward()
config = {
'task': task,
'action_space': common.action_space(),
'renderers': common.renderers(),
'init_sprites': sprite_gen,
'max_episode_length': 10,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
class SetMinusTest(parameterized.TestCase):
"""Runs tests for SetMinus of distributions."""
@parameterized.named_parameters(
('ContinuousContinuous', distribs.Continuous(
'x', 0, 2), distribs.Continuous('x', 1, 3), [0.5], [1.5]),
('DiscreteDiscrete', distribs.Discrete(
'x', [0, 1]), distribs.Discrete('x', [1, 2]), [0], [1]),
('DiscreteContinuous', distribs.Discrete(
'x', [1, 3]), distribs.Continuous('x', 0, 2), [3], [1]),
('ContinuousDiscrete', distribs.Continuous(
'x', 0, 2), distribs.Discrete('x', [1, 3]), [0.5, 1.5], [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)
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 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 testSamplingContainmentProductMultiple(self):
dists = [
distribs.Discrete('x', [1, 2.5, 3, 4, 6]),
distribs.Discrete('y', [1, 2.5, 3, 12]),
distribs.Continuous('z', 0, 5),
distribs.Continuous('w', 2, 10),
]
contained = [{'x': 2.5, 'y': 12, 'z': 3.5, 'w': 9}]
not_contained = [
{
'x': 2.5,
'y': 12,
'z': 3.5,
'w': 1
},
{
'x': 3.5,
'y': 12,
'z': 3.5,
'w': 9
},
]
d = distribs.Product(dists)
test_sampling_and_containment(self, d, contained, not_contained)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from spriteworld import factor_distributions as distribs
from spriteworld import sprite_generators
from spriteworld import tasks
from spriteworld.configs.cobra import common
import numpy as np
TERMINATE_DISTANCE = 0.075
NUM_TARGETS = 2 #np.random.choice([2])
MODES_SHAPES = {
'train':
distribs.Discrete('shape', ['square', 'circle', 'triangle'],
probs=[0.55, 0.25, 0.2]),
'test':
distribs.Discrete('shape', ['triangle', 'circle']),
}
MOTION_STD_DEV = np.array([0, 0, 0.075, 0.075])
PROPORTIONAL_MOTION_NOISE = 0
def get_config(mode='train'):
"""Generate environment config.
Args:
mode: 'train' or 'test'.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
def get_config(mode=None):
"""Generate environment config.
Args:
mode: Unused task mode.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
kwargs to environment.Environment.
"""
del mode
shared_factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['square', 'triangle', 'circle']),
distribs.Discrete('scale', [0.13]),
distribs.Continuous('c1', 0.3, 1.),
distribs.Continuous('c2', 0.9, 1.),
])
target_hue = distribs.Continuous('c0', 0., 0.4)
distractor_hue = distribs.Continuous('c0', 0.5, 0.9)
target_factors = distribs.Product([
target_hue,
shared_factors,
])
distractor_factors = distribs.Product([
distractor_hue,
shared_factors,
])
target_sprite_gen = sprite_generators.generate_sprites(
target_factors, num_sprites=NUM_TARGETS)
distractor_sprite_gen = sprite_generators.generate_sprites(
distractor_factors, num_sprites=NUM_DISTRACTORS)
sprite_gen = sprite_generators.chain_generators(target_sprite_gen,
distractor_sprite_gen)
# Randomize sprite ordering to eliminate any task information from occlusions
sprite_gen = sprite_generators.shuffle(sprite_gen)
# Create the agent body
agent_body_factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['circle']),
distribs.Discrete('scale', [0.07]),
distribs.Discrete('c0', [1.]),
distribs.Discrete('c1', [0.]),
distribs.Discrete('c2', [1.]),
])
agent_body_gen = sprite_generators.generate_sprites(agent_body_factors,
num_sprites=1)
sprite_gen = sprite_generators.chain_generators(sprite_gen, agent_body_gen)
task = tasks.FindGoalPosition(filter_distrib=target_hue,
terminate_distance=TERMINATE_DISTANCE)
renderers = {
'image':
spriteworld_renderers.PILRenderer(image_size=(64, 64),
anti_aliasing=5,
color_to_rgb=color_maps.hsv_to_rgb)
}
config = {
'task': task,
'action_space': action_spaces.Embodied(step_size=0.05),
'renderers': renderers,
'init_sprites': sprite_gen,
'max_episode_length': 50,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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
MODES_SHAPES = {
'train': distribs.Discrete('shape', ['square']),
'test': distribs.Discrete('shape', ['triangle', 'circle']),
}
def get_config(mode='train'):
"""Generate environment config.
Args:
mode: 'train' or 'test'.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
kwargs to environment.Environment.
"""
def get_config(mode=None):
"""Generate environment config.
Args:
mode: Unused task mode.
Returns:
config: Dictionary defining task/environment configuration. Can be fed
as kwargs to physics_environment.PhysicsEnvironment.
"""
# Factor distribution for the fixed center star
center_factors = distribs.Product([
distribs.Discrete('x', [0.5]),
distribs.Discrete('y', [0.5]),
distribs.Discrete('shape', ['circle']),
distribs.Discrete('scale', [0.15]),
distribs.Discrete('c0', [3.]),
distribs.Discrete('c1', [1.]),
distribs.Discrete('c2', [1.]),
distribs.Continuous('mass', 1, 3),
])
center_sprite_gen = generate_sprites.generate_sprites(center_factors,
num_sprites=1)
# Factor distributions for the orbiting sprites
orbit_factors = distribs.Product([
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['star_4', 'star_5', 'star_6']),
distribs.Discrete('scale', [0.08]),
distribs.Continuous('c0', 0, 1),
distribs.Continuous('c1', 0.5, 1.),
distribs.Discrete('c2', [1.]),
distribs.Continuous('x_vel', -0.03, 0.03),
distribs.Continuous('y_vel', -0.03, 0.03),
distribs.Discrete('mass', [1]),
])
orbit_sprite_gen = generate_sprites.generate_sprites(orbit_factors,
num_sprites=4)
sprite_gen = sprite_generators.chain_generators(center_sprite_gen,
orbit_sprite_gen)
force = forces.Gravity(gravity_constant=-0.0001,
distance_for_max_force=0.05)
adjacency_matrix = {
(0, 1): force,
(0, 2): force,
(0, 3): force,
(0, 4): force,
}
graph_generator = graph_generators.AdjacencyMatrix(
adjacency_matrix=adjacency_matrix, symmetric=False)
renderers = {
'image':
spriteworld_renderers.PILRenderer(image_size=(64, 64), anti_aliasing=5)
}
config = {
'graph_generators': (graph_generator, ),
'renderers': renderers,
'init_sprites': sprite_gen,
'episode_length': 30,
'bounce_off_walls': True,
'physics_steps_per_env_step': 10,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return 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 load_data(self):
# download if not avaiable
file_path = os.path.join(self.path, self.fname)
if not os.path.exists(file_path):
os.makedirs(self.path, exist_ok=True)
print(f'file not found, downloading from {self.url} ...')
from urllib import request
url = self.url
request.urlretrieve(url, file_path)
with open(file_path) as data:
self.csv_dict = load_csv(data, sequence=self.sequence_len)
self.orig_num = [32, 32, 6, 40, 4, 1, 1, 1]
self.dsprites = {
'x':
np.linspace(0.2, 0.8, self.orig_num[0]),
'y':
np.linspace(0.2, 0.8, self.orig_num[1]),
'scale':
np.linspace(0, 0.5, self.orig_num[2] + 1)[1:],
'angle':
np.linspace(0, 360, self.orig_num[3], dtype=np.int,
endpoint=False),
'shape': ['square', 'triangle', 'star_4', 'spoke_4'],
'c0': [1.],
'c1': [1.],
'c2': [1.]
}
distributions = []
for key in self.dsprites.keys():
distributions.append(distribs.Discrete(key, self.dsprites[key]))
self.factor_dist = distribs.Product(distributions)
self.renderer = spriteworld_renderers.PILRenderer(image_size=(64, 64),
anti_aliasing=5,
color_to_rgb=rgb)
if self.area_filter:
keep_idxes = []
print(len(self.csv_dict['x']))
for i in range(self.sequence_len):
x = pd.Series(np.array(self.csv_dict['area'])[:, i])
keep_idxes.append(
x.between(x.quantile(self.area_filter / 2),
x.quantile(1 - (self.area_filter / 2))))
for k in self.csv_dict.keys():
y = pd.Series(self.csv_dict[k])
self.csv_dict[k] = np.array(
[x for x in y[np.logical_and(*keep_idxes)]])
print(len(self.csv_dict['x']))
if self.natural_discrete:
num_bins = self.orig_num[:3]
self.lab_encs = {}
print('num_bins', num_bins)
for i, key in enumerate(['x', 'y', 'area']):
count, bin_edges = np.histogram(np.array(
self.csv_dict[key]).flatten().tolist(),
bins=num_bins[i])
bin_left, bin_right = bin_edges[:-1], bin_edges[1:]
bin_centers = bin_left + (bin_right - bin_left) / 2
new_data = []
old_shape = np.array(self.csv_dict[key]).shape
lab_enc = preprocessing.LabelEncoder()
if key == 'area':
self.lab_encs['scale'] = lab_enc.fit(
np.sqrt(bin_centers / (64**2)))
else:
self.lab_encs[key] = lab_enc.fit(bin_centers / 64)
for j in range(self.sequence_len):
differences = (
np.array(self.csv_dict[key])[:, j].reshape(1, -1) -
bin_centers.reshape(-1, 1))
new_data.append([
bin_centers[x]
for x in np.abs(differences).argmin(axis=0)
])
self.csv_dict[key] = np.swapaxes(new_data, 0, 1)
assert old_shape == np.array(self.csv_dict[key]).shape
assert len(np.unique(np.array(
self.csv_dict[key]).flatten())) == num_bins[i]
for i, key in enumerate(['angle', 'shape', 'c0', 'c1', 'c2']):
lab_enc = preprocessing.LabelEncoder()
self.lab_encs[key] = lab_enc.fit(self.dsprites[key])
assert self.lab_encs.keys() == self.dsprites.keys()
self.factor_sizes = [
len(np.unique(np.array(self.csv_dict['x']).flatten())),
len(np.unique(np.array(self.csv_dict['y']).flatten())),
len(np.unique(np.array(self.csv_dict['area']).flatten())), 40, 4,
1, 1, 1
]
print(self.factor_sizes)
self.latent_factor_indices = list(range(5))
self.num_factors = len(self.latent_factor_indices)
self.observation_factor_indices = [
i for i in range(self.num_factors)
if i not in self.latent_factor_indices
]
self.mapping = {'square': 0, 'triangle': 1, 'star_4': 2, 'spoke_4': 3}
# python2 python3
"""Tests for sprite_generators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from spriteworld import factor_distributions as distribs
from spriteworld import sprite
from spriteworld import sprite_generators
_distrib_0 = distribs.Product([
distribs.Discrete('x', [0.5]),
distribs.Discrete('y', [0.5]),
distribs.Discrete('shape', ['square', 'triangle']),
distribs.Discrete('c0', [255]),
distribs.Discrete('c1', [255]),
distribs.Discrete('c2', [255]),
])
_distrib_1 = distribs.Product([
distribs.Discrete('x', [0.5]),
distribs.Discrete('y', [0.5]),
distribs.Discrete('shape', ['hexagon', 'circle', 'star_5']),
distribs.Discrete('c0', [255]),
distribs.Discrete('c1', [255]),
distribs.Discrete('c2', [255]),
])
def testRaisesError(self):
c_0 = distribs.Discrete('x', [0])
c_1 = distribs.Discrete('y', [1])
with self.assertRaises(ValueError):
distribs.Mixture((c_0, c_1))
def get_config(mode='train'):
"""Generate environment config.
Args:
mode: 'train' or 'test'.
Returns:
config: Dictionary defining task/environment configuration. Can be fed as
kwargs to environment.Environment.
"""
# Create the subtasks and their corresponding sprite generators
subtasks = []
sprite_gen_per_subtask = []
for subtask in SUBTASKS:
subtasks.append(
tasks.FindGoalPosition(
filter_distrib=subtask['distrib'],
goal_position=subtask['goal_position'],
terminate_distance=TERMINATE_DISTANCE,
raw_reward_multiplier=RAW_REWARD_MULTIPLIER))
factors = distribs.Product((
subtask['distrib'],
distribs.Continuous('x', 0.1, 0.9),
distribs.Continuous('y', 0.1, 0.9),
distribs.Discrete('shape', ['square', 'triangle', 'circle']),
distribs.Discrete('scale', [0.13]),
distribs.Continuous('c1', 0.3, 1.),
distribs.Continuous('c2', 0.9, 1.),
))
sprite_gen_per_subtask.append(
sprite_generators.generate_sprites(factors, num_sprites=1))
# Consider all combinations of subtasks
subtask_combos = list(
itertools.combinations(np.arange(len(SUBTASKS)), NUM_TARGETS))
if mode == 'train':
# Randomly sample a combination of subtasks, holding one combination out
sprite_gen = sprite_generators.sample_generator([
sprite_generators.chain_generators(
*[sprite_gen_per_subtask[i] for i in c])
for c in subtask_combos[1:]
])
elif mode == 'test':
# Use the held-out subtask combination for testing
sprite_gen = sprite_generators.chain_generators(
*[sprite_gen_per_subtask[i] for i in subtask_combos[0]])
else:
raise ValueError('Invalide mode {}.'.format(mode))
# Randomize sprite ordering to eliminate any task information from occlusions
sprite_gen = sprite_generators.shuffle(sprite_gen)
task = tasks.MetaAggregated(subtasks,
reward_aggregator='sum',
termination_criterion='all')
config = {
'task': task,
'action_space': common.action_space(),
'renderers': common.renderers(),
'init_sprites': sprite_gen,
'max_episode_length': MAX_EPISODE_LENGTH,
'metadata': {
'name': os.path.basename(__file__),
'mode': mode
}
}
return config
class SelectionTest(parameterized.TestCase):
"""Runs tests for Selection of distributions."""
@parameterized.named_parameters(
(
'Continuous',
distribs.Continuous('x', 0, 2),
distribs.Continuous('x', 1, 3),
[{
'x': 1.5
}],
[{
'x': 0.5
}, {
'x': 2.5
}],
),
(
'Discrete',
distribs.Discrete('x', [0, 1]),
distribs.Discrete('x', [1, 2]),
[{
'x': 1
}],
[{
'x': 0
}, {
'x': 2
}],
),
(
'MultiDimensional',
distribs.Product(
(distribs.Discrete('x', [1, 2]), distribs.Discrete(
'y', [3, 4]))),
distribs.Discrete('x', [2]),
[{
'x': 2,
'y': 3
}],
[{
'x': 1,
'y': 3
}, {
'x': 2
}, {
'x': 2,
'y': 5
}],
),
)
def testSamplingContainmentSelection(self, d_base, d_filter, contained,
not_contained):
d = distribs.Selection(d_base, d_filter)
test_sampling_and_containment(self, d, contained, not_contained)
def testRaisesErrorFailedSampling(self):
d_base = distribs.Continuous('x', 0, 1)
d_filter = distribs.Continuous('x', 2, 3)
d = distribs.Selection(d_base, d_filter)
with self.assertRaises(ValueError):
d.sample()
def testKeys(self):
d_base = distribs.Product(
(distribs.Continuous('x', 0, 2), distribs.Continuous('y', 0, 1)))
d_filter_1 = distribs.Continuous('x', 0, 1)
d_filter_2 = distribs.Continuous('z', 0.4, 0.6)
distribs.Selection(d_base, d_filter_1)
with self.assertRaises(ValueError):
distribs.Selection(d_base, d_filter_2)
def testSamplingContainmentDiscrete(self, candidates, contained,
not_contained):
d = distribs.Discrete('x', candidates)
cont = [{'x': value} for value in contained]
not_cont = [{'x': value} for value in not_contained]
test_sampling_and_containment(self, d, cont, not_cont)
class ProductTest(parameterized.TestCase):
"""Runs tests for Product of distributions."""
@parameterized.named_parameters(
('ContinuousContinuous', distribs.Continuous(
'x', 0, 2), distribs.Continuous('y', 1, 3), [{
'x': 0.5,
'y': 2.5
}, {
'x': 1.5,
'y': 1.5
}], [{
'x': 0.5,
'y': 0.5
}, {
'x': 2.5,
'y': 1.5
}]),
('DiscreteDiscrete', distribs.Discrete(
'x', [0, 1]), distribs.Discrete('y', [1, 2]), [{
'x': 0,
'y': 2
}, {
'x': 1,
'y': 1
}], [{
'x': 1,
'y': 0
}, {
'x': 2,
'y': 2
}]),
('DiscreteContinuous', distribs.Discrete(
'x', [1, 3]), distribs.Continuous('y', 0, 2), [{
'x': 1,
'y': 1
}, {
'x': 3,
'y': 0.5
}], [{
'x': 2,
'y': 1
}, {
'x': 3,
'y': 3
}]),
)
def testSamplingContainmentProductTwo(self, d_0, d_1, contained,
not_contained):
d = distribs.Product((d_0, d_1))
test_sampling_and_containment(self, d, contained, not_contained)
def testSamplingContainmentProductMultiple(self):
dists = [
distribs.Discrete('x', [1, 2.5, 3, 4, 6]),
distribs.Discrete('y', [1, 2.5, 3, 12]),
distribs.Continuous('z', 0, 5),
distribs.Continuous('w', 2, 10),
]
contained = [{'x': 2.5, 'y': 12, 'z': 3.5, 'w': 9}]
not_contained = [
{
'x': 2.5,
'y': 12,
'z': 3.5,
'w': 1
},
{
'x': 3.5,
'y': 12,
'z': 3.5,
'w': 9
},
]
d = distribs.Product(dists)
test_sampling_and_containment(self, d, contained, not_contained)
def testRaisesError(self):
d_0 = distribs.Continuous('x', 0, 1)
d_1 = distribs.Continuous('x', 2, 3)
with self.assertRaises(ValueError):
distribs.Product((d_0, d_1))
def testkeys(self):
dists = [
distribs.Discrete('x', [1, 2.5, 3, 12]),
distribs.Continuous('y', 0, 5),
distribs.Continuous('z', 2, 10),
]
d = distribs.Product(dists)
self.assertEqual(d.keys, set(('x', 'y', 'z')))
