def _eval_and_score_actions(cache,
model,
simulator,
num_actions,
batch_size,
observations,
action_paths=None):
actions = cache.action_array[:num_actions]
indices = np.random.RandomState(1).permutation(
len(observations))[:AUCCESS_EVAL_TASKS]
evaluator = phyre.Evaluator(
[simulator.task_ids[index] for index in indices])
for i, task_index in enumerate(indices):
scores = eval_actions(model,
actions,
batch_size,
observations[task_index],
action_path=action_paths[task_index]).tolist()
_, sorted_actions = zip(
*sorted(zip(scores, actions), key=lambda x: (-x[0], tuple(x[1]))))
for action in sorted_actions:
if (evaluator.get_attempts_for_task(i) >= phyre.MAX_TEST_ATTEMPTS):
break
status = simulator.simulate_action(task_index,
action,
need_images=False).status
evaluator.maybe_log_attempt(i, status)
return evaluator.get_aucess()
def eval(cls, state, task_ids, *args, **kwargs):
mem_template_aware = kwargs.pop('mem_template_aware')
evaluator = phyre.Evaluator(task_ids)
cache = state['cache']
train_statuses = state['simulation_statuses']
if mem_template_aware:
train_tpl_ids = frozenset(
x.split(':')[0] for x in state['train_task_ids'])
test_tpl_to_ids = collections.defaultdict(list)
for task_id in task_ids:
test_tpl_to_ids[task_id.split(':')[0]].append(task_id)
within_template = (
frozenset(test_tpl_to_ids) == frozenset(train_tpl_ids))
if within_template:
logging.info('Going to build sub-agent for each template id')
for tpl, task_ids in test_tpl_to_ids.items():
indices = [
i for i, task_id in enumerate(state['train_task_ids'])
if task_id.split(':')[0] == tpl
]
cls._eval(cache, train_statuses[indices], task_ids,
evaluator, *args, **kwargs)
else:
cls._eval(cache, train_statuses, task_ids, evaluator, *args,
**kwargs)
else:
cls._eval(cache, train_statuses, task_ids, evaluator, *args,
**kwargs)
return evaluator
def eval(self, state, task_ids, tier):
model = state['model']
cache = state['cache']
# NOTE: Current agent is only using the actions that are seen in the training set,
# though agent has the ability to rank the actions that are not seen in the training set
actions = state['cache'].action_array[:self.params['rank_size']]
model.cuda()
simulator = phyre.initialize_simulator(task_ids, tier)
observations = simulator.initial_scenes
evaluator = phyre.Evaluator(task_ids)
for task_index in range(len(task_ids)):
task_id = simulator.task_ids[task_index]
observation = observations[task_index]
scores = self.neural_model.eval_actions(
model, actions, self.params['eval_batch_size'], observation)
# Rank of the actions in descending order
action_order = np.argsort(-scores)
# Result of the actions are already stored in cache
statuses = cache.load_simulation_states(task_id)
for action_id in action_order:
if evaluator.get_attempts_for_task(
task_index) >= self.params['max_attempts_per_task']:
break
status = phyre.SimulationStatus(statuses[action_id])
evaluator.maybe_log_attempt(task_index, status)
return evaluator
def evaluate_simple_agent(tasks, tier):
"""Evaluates the random agent on the given tasks/tier.
Args:
tasks: A list of task instances (strings) in the split to evaluate.
tier: A string of the action tier.
Returns:
A Evaluator object updated with the results of all the siulations.
"""
# Create a simulator for the task and tier.
simulator = phyre.initialize_simulator(tasks, tier)
evaluator = phyre.Evaluator(tasks)
assert tuple(tasks) == simulator.task_ids
tasks_solved = 0
for task_index in tqdm(range(len(tasks)), desc='Evaluate tasks'):
domain = [{
'name': 'var1',
'type': 'continuous',
'domain': (0, 1)
}, {
'name': 'var2',
'type': 'continuous',
'domain': (0, 1)
}, {
'name': 'var3',
'type': 'continuous',
'domain': (0, 1)
}]
X_init = np.array([[0.5, .5, .5]])
eval_result = evalAction(X_init, simulator, task_index, evaluator)
Y_init = np.array([[eval_result['score']]])
X_step = X_init
Y_step = Y_init
solved_task = eval_result['solved']
while evaluator.get_attempts_for_task(
task_index) < phyre.MAX_TEST_ATTEMPTS and not solved_task:
bo_step = GPyOpt.methods.BayesianOptimization(
f=None,
domain=domain,
X=X_step,
Y=Y_step,
de_duplication=True,
acquisition_type='MPI',
model_type='sparseGP')
x_next = bo_step.suggest_next_locations()
eval_result = evalAction(x_next, simulator, task_index, evaluator)
X_step = np.vstack((X_step, x_next))
Y_step = np.vstack((Y_step, eval_result['score']))
#if eval_result['valid']:
# print(tasks[task_index],evaluator.get_attempts_for_task(task_index),x_next,eval_result)
if eval_result['solved']:
solved_task = True
print(tasks_solved, "Tasks solved out of ", len(tasks), "Total Tasks")
return evaluator
def real_eval(cls, cache, model, actions, task_ids, tier,
max_attempts_per_task, eval_batch_size, finetune_iterations,
refine_iterations, refine_loss, refine_lr):
# TODO: move to a flag.
finetune_lr = 1e-4
model.cuda()
simulator = phyre.initialize_simulator(task_ids, tier)
observations = simulator.initial_scenes
assert tuple(task_ids) == simulator.task_ids
logging.info('Ranking %d actions and simulating top %d', len(actions),
max_attempts_per_task)
if refine_iterations > 0:
logging.info(
'Will do refining for %d iterations with lr=%e and loss=%s',
refine_iterations, refine_lr, refine_loss)
evaluator = phyre.Evaluator(task_ids)
for task_index in tqdm.trange(len(task_ids)):
task_id = simulator.task_ids[task_index]
if refine_iterations > 0:
refined_actions = neural_agent.refine_actions(
model, actions, observations[task_index], refine_lr,
refine_iterations, eval_batch_size, refine_loss)
else:
refined_actions = actions
scores = neural_agent.eval_actions(model, refined_actions,
eval_batch_size,
observations[task_index])
# Order of descendig scores.
action_order = np.argsort(-scores)
if not refine_iterations > 0:
statuses = cache.load_simulation_states(task_id)
finetune_data = []
for action_id in action_order:
if evaluator.get_attempts_for_task(
task_index) >= max_attempts_per_task:
break
action = refined_actions[action_id]
if refine_iterations > 0:
status = simulator.simulate_action(
task_index,
action,
need_images=False,
need_scenes=False).status
else:
status = phyre.SimulationStatus(statuses[action_id])
finetune_data.append((task_index, status, action))
evaluator.maybe_log_attempt(task_index, status)
if evaluator.get_attempts_for_task(task_index) == 0:
logging.warning('Made 0 attempts for task %s', task_id)
if finetune_iterations > 0:
neural_agent.finetune(model, finetune_data, simulator,
finetune_lr, finetune_iterations)
return evaluator
def eval(cls, state, task_ids, max_attempts_per_task, **kwargs):
cache = state['cache']
evaluator = phyre.Evaluator(task_ids)
for i, task_id in enumerate(task_ids):
statuses = cache.load_simulation_states(task_id)
valid_statuses = statuses[
statuses != phyre.simulation_cache.INVALID]
for status in valid_statuses[:max_attempts_per_task]:
evaluator.maybe_log_attempt(i, status)
return evaluator
def evaluate_simple_agent(tasks, tier):
"""Evaluates the random agent on the given tasks/tier.
Args:
tasks: A list of task instances (strings) in the split to evaluate.
tier: A string of the action tier.
Returns:
A Evaluator object updated with the results of all the siulations.
"""
# Create a simulator for the task and tier.
simulator = phyre.initialize_simulator(tasks, tier)
evaluator = phyre.Evaluator(tasks)
assert tuple(tasks) == simulator.task_ids
tasks_solved = 0
for task_index in tqdm(range(len(tasks)), desc='Evaluate tasks'):
simFunc = partial(evalAction,
simulator=simulator,
task_index=task_index,
evaluator=evaluator)
space = {
'x': hp.uniform('x', 0, 1),
'y': hp.uniform('y', 0, 1),
'r': hp.uniform('r', 0, 1),
}
trials = Trials()
max_evals = 0
solved_task = False
while evaluator.get_attempts_for_task(
task_index) < phyre.MAX_TEST_ATTEMPTS and not solved_task:
max_evals += phyre.MAX_TEST_ATTEMPTS - evaluator.get_attempts_for_task(
task_index)
best = fmin(simFunc,
space=space,
algo=tpe.suggest,
max_evals=max_evals,
trials=trials,
rstate=random.seed(0),
show_progressbar=False)
counter = Counter(result['solved'] for result in trials.results)
solved_task = counter[True] > 0
if solved_task:
tasks_solved += 1
print(tasks_solved, "Tasks solved out of ", len(tasks), "Total Tasks")
return evaluator
def eval(cls, state: State, task_ids: TaskIds, max_attempts_per_task: int,
oracle_rank_size: int, **kwargs):
assert oracle_rank_size
cache = state['cache']
evaluator = phyre.Evaluator(task_ids)
for i, task_id in enumerate(task_ids):
statuses = cache.load_simulation_states(task_id)[:oracle_rank_size]
assert len(statuses) == oracle_rank_size, (len(statuses),
oracle_rank_size)
if (statuses == phyre.simulation_cache.SOLVED).any():
evaluator.maybe_log_attempt(i, phyre.SimulationStatus.SOLVED)
else:
evaluator.maybe_log_attempt(i,
phyre.SimulationStatus.NOT_SOLVED)
return evaluator
def evaluate_random_agent(tasks, tier):
# Create a simulator for the task and tier.
simulator = phyre.initialize_simulator(tasks, tier)
evaluator = phyre.Evaluator(tasks)
assert tuple(tasks) == simulator.task_ids
images = []
actions = []
for task_index in tqdm_notebook(range(len(tasks)), desc='Evaluate tasks'):
while evaluator.get_attempts_for_task(
task_index) < phyre.MAX_TEST_ATTEMPTS:
# Sample a random valid action from the simulator for the given action space.
action = simulator.sample()
# Simulate the given action and add the status from taking the action to the evaluator.
status = simulator.simulate_action(task_index,
action,
need_images=True)
stati = status.status
actions.append(action)
images.append(status.images)
evaluator.maybe_log_attempt(task_index, stati)
return evaluator, images, actions
def eval(cls, state: State, task_ids: TaskIds, max_attempts_per_task: int,
tier: str, **kwargs):
cache = state['cache']
evaluator = phyre.Evaluator(task_ids)
simulator = phyre.initialize_simulator(task_ids, tier)
assert tuple(task_ids) == simulator.task_ids
for i, task_id in enumerate(task_ids):
statuses = cache.load_simulation_states(task_id)
valid_mask = statuses != phyre.simulation_cache.INVALID
actions, statuses = cache.action_array[valid_mask], statuses[
valid_mask]
for action, status in zip(actions, statuses):
if evaluator.get_attempts_for_task(i) >= max_attempts_per_task:
break
if cls.in_prior(action, simulator._tasks[i].scene.bodies):
evaluator.maybe_log_attempt(i, status)
else:
print("Not enough actions in prior", task_id,
evaluator.get_attempts_for_task(i))
return evaluator
def simulate_result(chosen_action, chosen_score, model_number,
generation_number):
eval_setup = 'ball_cross_template'
fold_id = 0 # For simplicity, we will just use one fold for evaluation.
train_tasks, dev_tasks, test_tasks = phyre.get_fold(eval_setup, 0)
action_tier = phyre.eval_setup_to_action_tier(eval_setup)
tasks = dev_tasks[0:1]
simulator = phyre.initialize_simulator(tasks, action_tier)
evaluator = phyre.Evaluator(tasks)
# Simulate the given action and add the status from taking the action to the evaluator.
simulation_result = simulator.simulate_action(0,
chosen_action,
need_images=True,
need_featurized_objects=True)
simulation_score = sf.ScoreFunctionValue(simulation_result)
pair = np.array([chosen_action, simulation_score])
timestr = time.strftime("%Y%m%d-%H%M%S")
score_pair = [
chosen_score, simulation_score, model_number, generation_number
]
score_string = "ScoreLog" + timestr
path = "/home/kyra/Desktop/phyre/agents/Scores"
np.save(os.path.join(path, score_string), score_pair)
return pair, simulation_result
def _eval_and_score_actions(self, cache, model, data, num_actions,
batch_size, num_tasks):
""" Evaluate the AUCESS for the given data & model"""
_, _, _, simulator, observations = data
actions = cache.action_array[:num_actions]
indices = np.random.RandomState(1).permutation(
len(observations))[:num_tasks]
evaluator = phyre.Evaluator(
[simulator.task_ids[index] for index in indices])
for i, task_index in enumerate(indices):
scores = self.eval_actions(model, actions, batch_size,
observations[task_index]).tolist()
_, sorted_actions = zip(*sorted(
zip(scores, actions), key=lambda x: (-x[0], tuple(x[1]))))
for action in sorted_actions:
if (evaluator.get_attempts_for_task(i) >=
phyre.MAX_TEST_ATTEMPTS):
break
simulation = simulator.simulate_action(task_index,
action,
need_images=False)
evaluator.maybe_log_attempt(i, simulation.simulation_status)
return evaluator.get_aucess()
def get_auccess(solver, tasks, solve_noise=False, save_tries=False, brute=False):
if save_tries:
font = ImageFont.truetype("/usr/share/fonts/truetype/ubuntu/Ubuntu-R.ttf", 10)
eval_setup = 'ball_within_template'
sim = phyre.initialize_simulator(tasks, 'ball')
init_scenes = T.tensor([[cv2.resize((scene==channel).astype(float), (32,32)) for channel in range(2,7)] for scene in sim.initial_scenes]).float().flip(-2)
eva = phyre.Evaluator(tasks)
# Get Actions from solver:
if brute:
all_actions = solver.get_actions(tasks, init_scenes, brute =True)
else:
all_actions = solver.get_actions(tasks, init_scenes)
#L.info(list(zip(tasks, all_actions)))
#return 0
# Loop through actions
for t_idx, task in enumerate(tasks):
# Get 100 actions from solver
if solve_noise:
# expects one action for task
task_actions = [all_actions[t_idx]]
else:
# expects 100 actions for task
task_actions = all_actions[t_idx]
# Loop through actions
for j, action in enumerate(task_actions):
# Setting up visualization array
vis_wid = 64
vis_stack = T.zeros(6,10,vis_wid,vis_wid,3)
vis_count = 1
# Simulate action
res = sim.simulate_action(t_idx, action, need_featurized_objects=False)
# Refining if invalid Action
t = 0
temp = 1
base_action = action.copy()
L.info(base_action, 'base action')
# Checking for valid action
while res.status.is_invalid():
t += 1
action = base_action + (np.random.rand(3)-0.5)*0.05*temp
L.info(action, f"potential action for task {task}")
res = sim.simulate_action(t_idx, action, need_featurized_objects=False)
temp *= 1.01 if temp <5 else 1
#assert(t>500, "too many invalid tries")
L.info(action, 'valid action')
# Log first Attempt
eva.maybe_log_attempt(t_idx, res.status)
# Visualizing first attempt
if save_tries:
for i in range(min(len(res.images), 10)):
vis_stack[0,i] = T.tensor(cv2.resize(phyre.observations_to_uint8_rgb(res.images[i]), (vis_wid,vis_wid)))
# Collecting 100 Actions if solve noise
warning_flag = False
if solve_noise:
base_action = action
temp = 1
error = False
t = 0
delta_generator = action_delta_generator()
# Looping while less then 100 attempts
while eva.attempts_per_task_index[t_idx]<100:
# Searching for new action while not solved
if not res.status.is_solved():
""" OLD APPROACH
action = base_action + (np.random.rand(3)-0.5)*np.array([0.3,0.05,0.05])*temp
temp *= 1.01 if temp <5 else 1
"""
if t<1000:
action = base_action + delta_generator.__next__()
res = sim.simulate_action(t_idx, action, need_featurized_objects=False)
eva.maybe_log_attempt(t_idx, res.status)
t += 1
else:
if not warning_flag:
L.info(f"WARNING can't find valid action for {task}")
warning_flag = True
error = True
eva.maybe_log_attempt(t_idx, phyre.SimulationStatus.NOT_SOLVED)
# if solved -> repeating action
else:
if not warning_flag:
L.info(f"{task} solved after", eva.attempts_per_task_index[t_idx])
# Visualization
if save_tries and not error:
for i in range(min(len(res.images), 10)):
vis_stack[5,i] = T.tensor(cv2.resize(phyre.observations_to_uint8_rgb(res.images[i]), (vis_wid,vis_wid)))
warning_flag = True
eva.maybe_log_attempt(t_idx, res.status)
# Visualization
if save_tries and not error and not res.status.is_invalid() and t and vis_count<5:
for i in range(min(len(res.images), 10)):
vis_stack[vis_count,i] = T.tensor(cv2.resize(phyre.observations_to_uint8_rgb(res.images[i]), (vis_wid,vis_wid)))
vis_count +=1
if not warning_flag and not res.status.is_solved() and eva.attempts_per_task_index[t_idx]==100:
L.info(f"{task} not solved")
vis_batch(vis_stack, f'result/solver/pyramid', f"{task}_attempts")
# Not Solve Noise Case
else:
# Visualization
if save_tries and not res.status.is_invalid() and vis_count<5:
for i in range(min(len(res.images), 10)):
vis_stack[vis_count,i] = T.tensor(cv2.resize(phyre.observations_to_uint8_rgb(res.images[i]), (vis_wid,vis_wid)))
vis_count +=1
if res.status.is_solved():
L.info(f"{task} solved after", eva.attempts_per_task_index[t_idx])
vis_batch(vis_stack, f'result/solver/pyramid', f"{task}_attempts")
while eva.attempts_per_task_index[t_idx]<100:
eva.maybe_log_attempt(t_idx, res.status)
break
return eva.get_auccess()
T.cat((sub, T.ones(32, 1) * 0.5), dim=1)
for sub in X[inspect]),
dim=1))
#plt.imsave(f"result/flownet/{inspect}_init_scene.png", np.flip(batch[inspect][0], axis=0))
plt.imsave(f"result/flownet/{inspect}_action.png",
action_paths[inspect, 0])
plt.imsave(f"result/flownet/{inspect}_selection.png", B[inspect,
0])
gen_actions = []
for b in B[:, 0]:
gen_actions.append(pic_to_action_vector(b))
print("Extracted actions:\n", gen_actions)
# Feed actions into simulator
eva = phyre.Evaluator(tasks)
solved, valid, comb, avg_tries = dict(), dict(), dict(), dict()
for i, t in enumerate(tasks):
print(f"{i} solving {t}", end='\r')
if not (t[:5] in comb):
comb[t[:5]] = 0
valid[t[:5]] = 0
solved[t[:5]] = 0
avg_tries[t[:5]] = []
base_action = gen_actions[i]
# Random Agent Intercept:
#action = sim.sample()
res = sim.simulate_action(i, base_action)
alpha = 1
# 100 Tries Max:
while eva.get_attempts_for_task(i) < 100:
def solve(model, model2, save_images=False):
tasks = [
'00000:001', '00000:002', '00000:003', '00000:004', '00000:005',
'00001:001', '00001:002', '00001:003', '00001:004', '00001:005',
'00002:007', '00002:011', '00002:015', '00002:017', '00002:023',
'00003:000', '00003:001', '00003:002', '00003:003', '00003:004',
'00004:063', '00004:071', '00004:092', '00004:094', '00004:095'
]
tasks = json.load(open("most_tasks.txt", 'r'))
eval_setup = 'ball_within_template'
fold_id = 0 # For simplicity, we will just use one fold for evaluation.
train_tasks, dev_tasks, test_tasks = phyre.get_fold(eval_setup, fold_id)
print('Size of resulting splits:\n train:', len(train_tasks), '\n dev:',
len(dev_tasks), '\n test:', len(test_tasks))
tasks = train_tasks[:]
print("tasks:\n", tasks)
sim = phyre.initialize_simulator(tasks, 'ball')
init_scenes = sim.initial_scenes
X = T.tensor(format(init_scenes)).float()
print("Init Scenes Shape:\n", X.shape)
base_path = []
action_path = []
for i, t in enumerate(tasks):
while True:
action = sim.sample(i)
action[2] = 0.01
res = sim.simulate_action(i, action, stride=20)
if type(res.images) != type(None):
base_path.append(rollouts_to_channel([res.images], 2))
action_path.append(rollouts_to_channel([res.images], 1))
break
base_path = T.tensor(np.concatenate(base_path)).float()
action_path = T.tensor(np.concatenate(base_path)).float()
with T.no_grad():
Z = model(X)
A = model2(T.cat((X[:, 1:], base_path[:, None], Z), dim=1))
#B = model3(T.cat((X[:,1:], Y[:,None,2], Z, A), dim=1))
#B = extract_action(A, inspect=-2 if save_images else -1)
B = extract_action(action_path[:, None], inspect=-2 if save_images else -1)
# Saving Images:
if save_images:
for inspect in range(len(X)):
plt.imsave(
f"result/flownet/{inspect}_init.png",
T.cat(tuple(
T.cat((sub, T.ones(32, 1) * 0.5), dim=1)
for sub in X[inspect]),
dim=1))
plt.imsave(f"result/flownet/{inspect}_base.png",
base_path[inspect])
plt.imsave(f"result/flownet/{inspect}_target.png", Z[inspect, 0])
#plt.imsave(f"result/flownet/{inspect}_init_scene.png", np.flip(batch[inspect][0], axis=0))
plt.imsave(f"result/flownet/{inspect}_action.png", A[inspect, 0])
plt.imsave(f"result/flownet/{inspect}_selection.png", B[inspect,
0])
gen_actions = []
for b in B[:, 0]:
gen_actions.append(pic_to_values(b))
print(gen_actions)
# Feed actions into simulator
eva = phyre.Evaluator(tasks)
solved, valid, comb = dict(), dict(), dict()
for i, t in enumerate(tasks):
if not (t[:5] in comb):
comb[t[:5]] = 0
valid[t[:5]] = 0
solved[t[:5]] = 0
base_action = gen_actions[i]
# Random Agent Intercept:
#action = sim.sample()
res = sim.simulate_action(i, base_action)
tries = 0
alpha = 1
# 100 Tries Max:
while eva.get_attempts_for_task(i) < 100:
if not res.status.is_solved():
action = np.array(base_action) + np.random.randn(3) * np.array(
[0.1, 0.1, 0.1]) * alpha
res = sim.simulate_action(i, action)
subtries = 0
while subtries < 100 and res.status.is_invalid():
subtries += 1
action_var = np.array(action) + np.random.randn(
3) * np.array([0.05, 0.05, 0.05]) * alpha
res = sim.simulate_action(i, action_var)
eva.maybe_log_attempt(i, res.status)
alpha *= 1.01
else:
eva.maybe_log_attempt(i, res.status)
tries += 1
if save_images:
try:
for k, img in enumerate(res.images):
plt.imsave(f"result/flownet/{i}_{k}.png",
np.flip(img, axis=0))
pass
except Exception:
pass
#print(i, t, res.status.is_solved(), not res.status.is_invalid())
comb[t[:5]] = comb[t[:5]] + 1
if not res.status.is_invalid():
valid[t[:5]] = valid[t[:5]] + 1
if res.status.is_solved():
solved[t[:5]] = solved[t[:5]] + 1
# Prepare Plotting
print(eva.compute_all_metrics())
print(eva.get_auccess())
spacing = [1, 2, 3, 4]
fig, ax = plt.subplots(5, 5, sharey=True, sharex=True)
for i, t in enumerate(comb):
ax[i // 5, i % 5].bar(spacing, [
solved[t[:5]] /
(valid[t[:5]] if valid[t[:5]] else 1), solved[t[:5]] / comb[t[:5]],
valid[t[:5]] / comb[t[:5]], comb[t[:5]] / 100
])
ax[i // 5, i % 5].set_xlabel(t[:5])
plt.show()
def __init__(self, simulator, task_ids, nsteps, max_attempts_per_task):
self.simulator = simulator
self.task_ids = task_ids
self.evaluators = [phyre.Evaluator(task_ids) for _ in range(nsteps)]
self.max_attempts_per_task = max_attempts_per_task
def evaluate_agent(task_ids, tier, solved_actions_pdf):
cache = phyre.get_default_100k_cache(tier)
evaluator = phyre.Evaluator(task_ids)
simulator = phyre.initialize_simulator(task_ids, tier)
task_data_dict = phyre.loader.load_compiled_task_dict()
stride = 5
empty_action = phyre.simulator.scene_if.UserInput()
tasks_solved = 0
for task_index in tqdm(range(len(task_ids)), desc='Evaluate tasks'):
task_id = task_ids[task_index]
task_type = task_id.split(":")[0]
task_data = task_data_dict[task_id]
statuses = cache.load_simulation_states(task_id)
_, _, images, _ = phyre.simulator.magic_ponies(task_data,
empty_action,
need_images=True,
stride=stride)
evaluator.maybe_log_attempt(task_index,
phyre.simulation_cache.NOT_SOLVED)
seq_data = ImgToObj.getObjectAndGoalSequence(images)
goal_type = ImgToObj.Layer.dynamic_goal.value
if goal_type not in images[0]:
goal_type = ImgToObj.Layer.static_goal.value
tested_actions = np.array([[-1, -1, -1, 1]])
solved_task = False
while evaluator.get_attempts_for_task(
task_index) < phyre.MAX_TEST_ATTEMPTS and not solved_task:
random_action = np.random.random_sample((1, 4))
if task_type in solved_actions_pdf and np.random.random_sample(
) >= .25:
random_action[0, 0:3] = np.squeeze(
solved_actions_pdf[task_type].resample(size=1))
test_action_dist = np.linalg.norm(tested_actions[:, 0:3] -
random_action[:, 0:3],
axis=1)
if np.any(test_action_dist <= tested_actions[:, 3]
) and np.random.random_sample() >= .75:
continue
if ImgToObj.check_seq_action_intersect(
images[0], seq_data, stride, goal_type,
np.squeeze(random_action[0:3])):
eval_stride = 10
goal = 3.0 * 60.0 / eval_stride
sim_result = simulator.simulate_action(
task_index,
np.squeeze(random_action[:, 0:3]),
need_images=True,
stride=eval_stride)
evaluator.maybe_log_attempt(task_index, sim_result.status)
if not sim_result.status.is_invalid():
score = ImgToObj.objectTouchGoalSequence(sim_result.images)
eval_dist = .25 * (score == 0) + .1
random_action[0, 3] = eval_dist
tested_actions = np.concatenate(
(tested_actions, random_action), 0)
solved_task = sim_result.status.is_solved()
tasks_solved += solved_task
print(tasks_solved, "Tasks solved out of ", len(task_ids), "Total Tasks")
return (evaluator.get_aucess(), tasks_solved, len(task_ids))
def evaluate_agent(tasks, tier):
"""Evaluates the random agent on the given tasks/tier.
Args:
tasks: A list of task instances (strings) in the split to evaluate.
tier: A string of the action tier.
Returns:
A Evaluator object updated with the results of all the siulations.
"""
# Create a simulator for the task and tier.
simulator = phyre.initialize_simulator(tasks, tier)
evaluator = phyre.Evaluator(tasks)
task_data_dict = phyre.loader.load_compiled_task_dict()
empty_action = phyre.simulator.scene_if.UserInput()
tasks_solved = 0
for task_index in tqdm(range(len(tasks)), desc='Evaluate tasks'):
task_id = tasks[task_index]
task_data = task_data_dict[task_id]
_, _, images, _ = phyre.simulator.magic_ponies(task_data,
empty_action,
need_images=True,
stride=100)
evaluator.maybe_log_attempt(task_index,
phyre.simulation_cache.NOT_SOLVED)
seq_data = ImgToObj.getObjectAndGoalSequence(images)
goal_type = ImgToObj.Layer.dynamic_goal.value
if goal_type not in images[0]:
goal_type = ImgToObj.Layer.static_goal.value
simFunc = partial(evalAction,
initial_img=images[0],
seq_data=seq_data,
goal_type=goal_type,
simulator=simulator,
task_index=task_index,
evaluator=evaluator)
space = {
'x': hp.uniform('x', 0, 1),
'y': hp.uniform('y', 0, 1),
'r': hp.uniform('r', 0, 1),
}
trials = Trials()
max_evals = 0
solved_task = False
best_score = 0
while evaluator.get_attempts_for_task(
task_index) < phyre.MAX_TEST_ATTEMPTS and not solved_task:
max_evals += phyre.MAX_TEST_ATTEMPTS - evaluator.get_attempts_for_task(
task_index)
if best_score > -1.0:
best = fmin(simFunc,
space=space,
algo=hyperopt.rand.suggest,
max_evals=max_evals,
trials=trials,
rstate=random.seed(0),
show_progressbar=False)
else:
best = fmin(simFunc,
space=space,
algo=tpe.suggest,
max_evals=max_evals,
trials=trials,
rstate=random.seed(0),
show_progressbar=False)
counter = Counter(result['solved'] for result in trials.results)
solved_task = counter[True] > 0
tasks_solved += solved_task
best_score = trials.best_trial['result']['loss']
print(tasks_solved, "Tasks solved out of ", len(tasks), "Total Tasks")
return (evaluator.get_aucess(), tasks_solved, len(tasks))
t0 = time.time()
min_index = np.argmin(np.linalg.norm(cache.action_array - test_action,axis=1))
t1 = time.time()
print((t1-t0), "Search Time")
print(test_action[:,0:2])
print(cache.action_array[min_index,0:2])
print(np.linalg.norm(test_action[:,0:2] - cache.action_array[min_index,0:2]))
print(np.concatenate((cache.action_array[min_index,:][np.newaxis,:],test_action),0))
task_ids = list(cache.task_ids)[0:2]
print(task_ids)
evaluator = phyre.Evaluator(task_ids)
print(evaluator.task_ids)
'''
for i in range(len(task_ids)-1):
while evaluator.get_attempts_for_task(i) < phyre.MAX_TEST_ATTEMPTS:
evaluator.maybe_log_attempt(i, phyre.simulation_cache.SOLVED)
'''
for i in range(len(task_ids)):
while evaluator.get_attempts_for_task(i) < phyre.MAX_TEST_ATTEMPTS - 91:
evaluator.maybe_log_attempt(i, phyre.simulation_cache.NOT_SOLVED)
evaluator.maybe_log_attempt(i, phyre.simulation_cache.SOLVED)
print(evaluator.get_aucess())
k = np.zeros(100)
auccess = np.zeros(100)
def solve(tasks,
generator,
save_images=False,
force_collect=False,
static=256,
show=False):
# Collect Interaction Data
data_path = './data/cgan_solver'
if not os.path.exists(data_path + '/interactions.pickle') or force_collect:
os.makedirs(data_path, exist_ok=True)
wid = generator.width
print("Collecting Data")
collect_interactions(data_path,
tasks,
10,
stride=1,
size=(wid, wid),
static=static)
with open(data_path + '/interactions.pickle', 'rb') as fs:
X = T.tensor(pickle.load(fs), dtype=T.float)
with open(data_path + '/info.pickle', 'rb') as fs:
info = pickle.load(fs)
tasklist = info['tasks']
positions = info['pos']
orig_actions = info['action']
print('loaded dataset with shape:', X.shape)
#data_set = T.utils.data.TensorDataset(X)
#data_loader = T.utils.data.DataLoader(data_set, batch_size=BATCH_SIZE, shuffle=False)
# Sim SETUP
print('Succesfull collection for tasks:\n', tasklist)
eval_setup = 'ball_within_template'
sim = phyre.initialize_simulator(tasklist, 'ball')
eva = phyre.Evaluator(tasklist)
# Solve Loop
error = np.zeros((X.shape[0], 3))
generator.eval()
solved, tried = 0, 0
for i, task in enumerate(tasklist):
# generate 'fake'
noise = T.randn(1, generator.noise_dim)
with T.no_grad():
fake = generator((X[i, :generator.s_chan])[None], noise)[0, 0]
#action = np.array(pic_to_action_vector(fake, r_fac=1.8))
action = np.array(pic_to_action_vector(fake.numpy(), r_fac=1))
raw_action = action.copy()
# PROCESS ACTION
print(action, 'raw')
# shift by half to get relative position
action[:2] -= 0.5
# multiply by half because extracted scope is already half of the scene
action[:2] *= 0.5
# multiply by 4 because action value is always 4*diameter -> 8*radius, but scope is already halfed -> 8*0.5*radius
action[2] *= 4
# finetuning
action[2] *= 1.0
print(action, 'relativ')
pos = positions[i]
print(pos)
action[:2] += pos
print(action, 'added')
res = sim.simulate_action(i, action, need_featurized_objects=True)
# Noisy tries while invalid actions
t = 0
temp = 1
base_action = action
while res.status.is_invalid() and t < 200:
t += 1
action = base_action + (np.random.rand(3) - 0.5) * 0.01 * temp
res = sim.simulate_action(i, action, need_featurized_objects=False)
temp *= 1.01
print(action, 'final action')
# Check for and log Solves
if not res.status.is_invalid():
tried += 1
if res.status.is_solved():
solved += 1
print(orig_actions[i], 'orig action')
print(task, "solved", res.status.is_solved())
error[i] = orig_actions[i] - base_action
# Visualization
if show:
x, y, d = np.round(raw_action * fake.shape[0])
y = fake.shape[0] - y
print(x, y, d)
def generate_crosses(points):
xx = []
yy = []
for x, y in points:
xx.extend([x, x + 1, x - 1, x, x])
yy.extend([y, y, y, y + 1, y - 1])
return xx, yy
xx, yy = [
x, (x + d) if (x + d) < fake.shape[0] - 1 else 62, x - d, x, x
], [
y, y, y, (y + d) if (y + d) < fake.shape[0] - 1 else 62, y - d
]
xx, yy = generate_crosses(zip(xx, yy))
fake[yy, xx] = 0.5
os.makedirs(f'result/cgan_solver/vector_extractions',
exist_ok=True)
plt.imsave(f'result/cgan_solver/vector_extractions/{i}.png', fake)
if not res.status.is_invalid():
os.makedirs(f'result/cgan_solver/scenes', exist_ok=True)
plt.imsave(f'result/cgan_solver/scenes/{i}.png',
res.images[0, ::-1])
else:
print("invalid")
plt.imshow(
phyre.observations_to_float_rgb(sim.initial_scenes[i]))
plt.show()
print("solving percentage:", solved / tried, 'overall:', tried)
print("mean x error:", np.mean(error[:, 0]), 'mean x abs error:',
np.mean(np.abs(error[:, 0])))
print("mean y error:", np.mean(error[:, 1]), 'mean y abs error:',
np.mean(np.abs(error[:, 1])))
print("mean r error:", np.mean(error[:, 2]), 'mean r abs error:',
np.mean(np.abs(error[:, 2])))
def evaluate_agent(task_ids, tier, solved_actions_pdf):
cache = phyre.get_default_100k_cache(tier)
evaluator = phyre.Evaluator(task_ids)
simulator = phyre.initialize_simulator(task_ids, tier)
task_data_dict = phyre.loader.load_compiled_task_dict()
stride = 100
eval_stride = 2
goal = 3.0 * 60.0 / eval_stride
empty_action = phyre.simulator.scene_if.UserInput()
tasks_solved = 0
alpha = 1.0
N = 5
max_actions = 100
for task_index in tqdm(range(len(task_ids)), desc='Evaluate tasks'):
task_id = task_ids[task_index]
task_type = task_id.split(":")[0]
task_data = task_data_dict[task_id]
statuses = cache.load_simulation_states(task_id)
_, _, images, _ = phyre.simulator.magic_ponies(task_data,
empty_action,
need_images=True,
stride=stride)
evaluator.maybe_log_attempt(task_index,
phyre.simulation_cache.NOT_SOLVED)
seq_data = ImgToObj.getObjectAndGoalSequence(images)
goal_type = ImgToObj.Layer.dynamic_goal.value
if goal_type not in images[0]:
goal_type = ImgToObj.Layer.static_goal.value
tested_actions = np.array([[-1, -1, -1, 1, 0]])
solved_task = False
max_score = 0
while evaluator.get_attempts_for_task(
task_index
) < phyre.MAX_TEST_ATTEMPTS and not solved_task and max_score < 1.0:
random_action = np.random.random_sample((1, 5))
if task_type in solved_actions_pdf and np.random.random_sample(
) >= .25:
random_action[0, 0:3] = np.squeeze(
solved_actions_pdf[task_type].resample(size=1))
test_action_dist = np.linalg.norm(tested_actions[:, 0:3] -
random_action[:, 0:3],
axis=1)
if np.any(test_action_dist <= tested_actions[:, 3]
) and np.random.random_sample() >= .75:
continue
if ImgToObj.check_seq_action_intersect(
images[0], seq_data, stride, goal_type,
np.squeeze(random_action[0:3])):
sim_result = simulator.simulate_action(
task_index,
np.squeeze(random_action[:, 0:3]),
need_images=True,
stride=eval_stride)
evaluator.maybe_log_attempt(task_index, sim_result.status)
if not sim_result.status.is_invalid():
score = ImgToObj.objectTouchGoalSequence(sim_result.images)
eval_dist = .1
random_action[0, 3] = eval_dist
random_action[0, 4] = 1.0 - np.linalg.norm(
seq_data['object'][-1]['centroid'] -
seq_data['goal'][-1]['centroid']) / 256.0
random_action[0, 4] += ImgToObj.objectTouchGoalSequence(
sim_result.images) / goal
if random_action[0, 4] > max_score:
max_score = random_action[0, 4]
tested_actions = np.concatenate(
(tested_actions, random_action), 0)
solved_task = sim_result.status.is_solved()
tasks_solved += solved_task
if not solved_task and evaluator.get_attempts_for_task(
task_index) < phyre.MAX_TEST_ATTEMPTS:
tested_actions = np.delete(tested_actions, 0, 0)
theta = tested_actions[np.argmax(tested_actions[:, 4]), 0:3]
theta_score = tested_actions[np.argmax(tested_actions[:, 4]), 4]
while evaluator.get_attempts_for_task(
task_index
) + 2 * N + 1 < phyre.MAX_TEST_ATTEMPTS and not solved_task:
delta = np.random.normal(0, .2, (N, 3))
test_actions_pos = theta + delta
test_actions_neg = theta - delta
old_theta = np.copy(theta)
for i in range(N):
pos_score = 0
sim_result_pos = simulator.simulate_action(
task_index,
np.squeeze(test_actions_pos[i, :]),
need_images=True,
stride=eval_stride)
evaluator.maybe_log_attempt(task_index,
sim_result_pos.status)
if not sim_result_pos.status.is_invalid():
pos_result_seq_data = ImgToObj.getObjectAndGoalSequence(
sim_result_pos.images)
pos_score = 1.0 - np.linalg.norm(
pos_result_seq_data['object'][-1]['centroid'] -
pos_result_seq_data['goal'][-1]['centroid']) / 256.0
pos_score += ImgToObj.objectTouchGoalSequence(
sim_result_pos.images) / goal
solved_task = sim_result_pos.status.is_solved()
tasks_solved += solved_task
neg_score = 0
sim_result_neg = simulator.simulate_action(
task_index,
np.squeeze(test_actions_neg[i, :]),
need_images=True,
stride=eval_stride)
evaluator.maybe_log_attempt(task_index,
sim_result_neg.status)
if not sim_result_neg.status.is_invalid():
neg_result_seq_data = ImgToObj.getObjectAndGoalSequence(
sim_result_neg.images)
neg_score = 1.0 - np.linalg.norm(
neg_result_seq_data['object'][-1]['centroid'] -
neg_result_seq_data['goal'][-1]['centroid']) / 256.0
neg_score += ImgToObj.objectTouchGoalSequence(
sim_result_neg.images) / goal
solved_task = sim_result_neg.status.is_solved()
tasks_solved += solved_task
theta = theta + alpha / N * (pos_score -
neg_score) * delta[i, :]
sim_result = simulator.simulate_action(task_index,
np.squeeze(theta),
need_images=True,
stride=eval_stride)
evaluator.maybe_log_attempt(task_index, sim_result.status)
if not sim_result.status.is_invalid():
result_seq_data = ImgToObj.getObjectAndGoalSequence(
sim_result.images)
score = 1.0 - np.linalg.norm(
result_seq_data['object'][-1]['centroid'] -
result_seq_data['goal'][-1]['centroid']) / 256.0
score += ImgToObj.objectTouchGoalSequence(
sim_result.images) / goal
solved_task = sim_result.status.is_solved()
tasks_solved += solved_task
print(tasks_solved, "Tasks solved out of ", len(task_ids), "Total Tasks")
return (evaluator.get_aucess(), tasks_solved, len(task_ids))
def real_eval(cls, cache, model, actions, task_ids, tier,
max_attempts_per_task, eval_batch_size, finetune_iterations,
refine_iterations, refine_loss, refine_lr):
# TODO: move to a flag.
finetune_lr = 1e-4
model.cuda()
simulator = phyre.initialize_simulator(task_ids, tier)
observations = simulator.initial_scenes
# CUSTOM
if os.path.exists(cls.ACTION_PATH_DIR):
with open(cls.ACTION_PATH_DIR + '/channel_paths.pickle',
'rb') as fp:
action_path_dict = pickle.load(fp)
action_paths = torch.Tensor([
action_path_dict[task]
if task in action_path_dict else torch.zeros(256, 256)
for task in task_ids
])[:, None].cuda()
else:
print("can't find action_path_dict!")
exit(-1)
assert tuple(task_ids) == simulator.task_ids
logging.info('Ranking %d actions and simulating top %d', len(actions),
max_attempts_per_task)
if refine_iterations > 0:
logging.info(
'Will do refining for %d iterations with lr=%e and loss=%s',
refine_iterations, refine_lr, refine_loss)
evaluator = phyre.Evaluator(task_ids)
for task_index in tqdm.trange(len(task_ids)):
task_id = simulator.task_ids[task_index]
if refine_iterations > 0:
refined_actions = neural_agent.refine_actions(
model, actions, observations[task_index], refine_lr,
refine_iterations, eval_batch_size, refine_loss)
else:
refined_actions = actions
scores = neural_agent.eval_actions(
model,
refined_actions,
eval_batch_size,
observations[task_index],
action_path=action_paths[task_index])
# Order of descendig scores.
action_order = np.argsort(-scores)
if not refine_iterations > 0:
statuses = cache.load_simulation_states(task_id)
finetune_data = []
for action_id in action_order:
if evaluator.get_attempts_for_task(
task_index) >= max_attempts_per_task:
break
action = refined_actions[action_id]
if refine_iterations > 0:
status = simulator.simulate_action(
task_index,
action,
need_images=False,
need_scenes=False).status
else:
status = phyre.SimulationStatus(statuses[action_id])
finetune_data.append((task_index, status, action))
evaluator.maybe_log_attempt(task_index, status)
if evaluator.get_attempts_for_task(task_index) == 0:
logging.warning('Made 0 attempts for task %s', task_id)
if finetune_iterations > 0:
neural_agent.finetune(model, finetune_data, simulator,
finetune_lr, finetune_iterations)
return evaluator
