def test_render_with_input(self):
scene = simulator.simulate_task_with_input(self._task,
self._box_user_input,
steps=1).sceneList[0]
array = simulator.scene_to_raster(scene)
self.assertEqual(len(array.shape), 2)
self.assertEqual(array.shape[0], self._task.scene.height)
self.assertEqual(array.shape[1], self._task.scene.width)
def test_add_input_and_simulate_strided(self):
steps = 10
full_results = simulator.simulate_task_with_input(self._task,
self._box_user_input,
stride=1,
steps=steps)
strided_results = simulator.simulate_task_with_input(
self._task, self._box_user_input, stride=3, steps=steps)
self.assertEqual(len(full_results.sceneList), steps)
self.assertEqual(len(strided_results.sceneList), math.ceil(steps / 3))
self.assertEqual(len(full_results.solvedStateList), steps)
self.assertEqual(len(strided_results.solvedStateList),
math.ceil(steps / 3))
for i in range(0, steps, 3):
self.assertEqual(full_results.sceneList[i],
strided_results.sceneList[i // 3])
self.assertEqual(full_results.solvedStateList[i],
strided_results.solvedStateList[i // 3])
def test_add_input_and_simulate(self):
steps = 10
# Check simulate_task_with_input is identical to add_user_input_to_scene
# followed by simulate_task.
combined_results = simulator.simulate_task_with_input(
self._task, self._box_user_input, steps=steps)
task = copy.copy(self._task)
task.scene = simulator.add_user_input_to_scene(task.scene,
self._box_user_input)
bl_resuls = simulator.simulate_task(task, steps=steps)
self.assertEqual(combined_results, bl_resuls)
def test_add_input_and_ponies(self):
steps = 10
task_simulation = simulator.simulate_task_with_input(
self._task, self._ball_user_input, steps=steps, stride=1)
is_solved, had_occlusions, images, scenes = simulator.magic_ponies(
self._task,
self._ball_user_input,
steps=steps,
stride=1,
need_images=True,
need_featurized_objects=True)
self.assertEqual(is_solved, task_simulation.isSolution)
self.assertEqual(len(images), steps)
self.assertEqual(len(task_simulation.sceneList), steps)
self.assertEqual(
had_occlusions, task_simulation.sceneList[0].user_input_status ==
scene_if.UserInputStatus.HAD_OCCLUSIONS)
# Check images match target scenes
self.assertFalse(
np.array_equal(
images[0],
simulator.scene_to_raster(task_simulation.sceneList[-1])))
self.assertTrue((images[-1] == simulator.scene_to_raster(
task_simulation.sceneList[-1])).all())
# Test just images works
_, _, only_images, _ = simulator.magic_ponies(
self._task,
self._ball_user_input,
steps=steps,
stride=1,
need_images=True,
need_featurized_objects=False)
self.assertTrue(np.array_equal(images, only_images))
# Test just scenes works
_, _, _, only_scenes = simulator.magic_ponies(
self._task,
self._ball_user_input,
steps=steps,
stride=1,
need_images=False,
need_featurized_objects=True)
self.assertTrue(np.array_equal(scenes, only_scenes))
def test_add_input_and_ponies(self):
raise unittest.SkipTest
steps = 10
task_simulation = simulator.simulate_task_with_input(
self._task, self._box_user_input, steps=steps, stride=1)
is_solved, had_occlusions, images = simulator.magic_ponies(
self._task, self._box_compressed_user_input, steps=steps, stride=1)
self.assertEqual(is_solved, task_simulation.isSolution)
self.assertEqual(len(images), steps)
self.assertEqual(len(task_simulation.sceneList), steps)
self.assertEqual(
had_occlusions, task_simulation.sceneList[0].user_input_status ==
scene_if.UserInputStatus.HAD_OCCLUSIONS)
self.assertFalse((images[0] == simulator.scene_to_raster(
task_simulation.sceneList[-1])).all())
self.assertTrue((images[-1] == simulator.scene_to_raster(
task_simulation.sceneList[-1])).all())
# plt.imshow(phyre.vis.observations_to_float_rgb(initial_scene))
# plt.show()
from phyre import simulator as sim
actions = simulator.build_discrete_action_space(max_actions=1000)
action = actions[20] # Successful action for 3:28
action, is_valid = simulator.get_user_input(
action) # Phyre representation of the action
# If you want to brute force every solution until you get a success
# from phyre import SimulationStatus
# for action in actions:
# status = simulator.simulate_single(task_index, action, need_images=False)
# if status == SimulationStatus.SOLVED:
# Simulates task with the action, returning Thrift representations of intermediate images every frame
# Increase stride to decrease simulation time
result = sim.simulate_task_with_input(simulator.get_task(0), action, stride=1)
# Convert Thrift scene to array of pixels
images = [sim.scene_to_raster(scene) for scene in result.sceneList]
ims = [phyre.vis.observations_to_float_rgb(image) for image in images]
# Displays the simulation
delay = 0.0005 # Delay between frames
img = plt.imshow(ims[0])
for image in ims[1:]:
img.set_data(image)
plt.pause(delay)
plt.draw()
action_tier = phyre.eval_setup_to_action_tier(eval_setup)
simulator = phyre.initialize_simulator(tasks, action_tier)
task_index = 0
task_id = simulator.task_ids[task_index]
print("Task being simulated:", task_id)
# search for an action that solves this
actions = simulator.build_discrete_action_space(max_actions = 10000)
for action in actions:
status, _ = simulator.simulate_single(task_index, action, need_images=False)
if status == SimulationStatus.SOLVED:
print("Solution found:", action)
actual_action = action # for output later
action, is_valid = simulator.get_user_input(action)
result, user_input = sim.simulate_task_with_input(simulator.get_task(task_index), action, stride=1)
# get the initial scene
initial_scene = result.sceneList[0]
initial_scene_objects = viz.create_list_of_objects(initial_scene)
# add user input
viz.add_user_ball(initial_scene_objects, user_input)
# generate json format
initial_json = viz.initial_json(task_id, actual_action, initial_scene_objects)
initial_json = json.loads(initial_json.replace("'", '"'))
# convert json to csv
fp = OUTPUT_DIR + task_id.replace(':', '.') + ".csv"
viz.initial_csv(initial_json, fp)