def plot_constructability_over_time(logger):
tower_keys = ['2block', '3block', '4block', '5block']
tallest_stable_over_time = np.zeros((logger.args.max_acquisitions, 5))
tp = TowerPlanner(stability_mode='contains')
for tx in range(logger.args.max_acquisitions):
acquired_data, _ = logger.load_acquisition_data(tx)
# For each tower, figure out when it fell over.
for kx, k in enumerate(tower_keys):
towers = acquired_data[k]['towers']
for ix in range(0, towers.shape[0]):
height = 1
for top_id in range(1, towers.shape[1]):
block_tower = [Object.from_vector(towers[ix, bx, :]) for bx in range(0, top_id+1)]
if not tp.tower_is_constructable(block_tower):
break
height += 1
tallest_stable_over_time[tx, height-1] += 1
max_x = 40 + 10*logger.args.max_acquisitions
xs = np.arange(40, max_x, 10)
w = 10
plt.figure(figsize=(20, 10))
plt.bar(xs, tallest_stable_over_time[:, 0], width=w, label=1)
for kx in range(1, 5):
plt.bar(xs, tallest_stable_over_time[:, kx], bottom=np.sum(tallest_stable_over_time[:, :kx], axis=1), width=w, label=kx+1)
plt.xlabel('Acquisition Step')
plt.ylabel('Height of tallest stable subtower')
plt.legend()
plt.savefig(logger.get_figure_path('tallest_breakdown.png'))
def is_robust(orig_tower, n_attempts=10, noise=0.001):
""" Perturb each block in the tower by 1mm multiple times and make sure the label does not change. """
tp = TowerPlanner(stability_mode='contains')
robust = True
tower_vec = np.array(
[orig_tower[bx].vectorize() for bx in range(0, len(orig_tower))])
label = tp.tower_is_constructable(orig_tower)
for _ in range(n_attempts):
tower = tower_vec.copy()
tower[:, 7:9] += np.random.randn(2 * tower.shape[0]).reshape(
tower.shape[0], 2) * noise
block_tower = [
Object.from_vector(tower[kx, :]) for kx in range(tower.shape[0])
]
if tp.tower_is_constructable(block_tower) != label:
robust = False
return robust
def inspect_validation_set(fname):
with open(fname, 'rb') as handle:
val_towers = pickle.load(handle)
tp = TowerPlanner(stability_mode='contains')
towers = val_towers['5block']['towers']
labels = val_towers['5block']['labels']
# Check how may towers fall over at a lower block.
for tower_vec, label in zip(towers, labels):
if label == 0:
tower = [Object.from_vector(tower_vec[bx, :]) for bx in range(0, tower_vec.shape[0])]
print(tp.tower_is_constructable(tower[:1]))
def check_validation_robustness(noise=0.001, n_attempts=10):
"""
Try adding noise to the placement of each block in the validation set
to see how many of the towers are robust to placement noise.
"""
with open('learning/data/validation_towers_robust.pkl', 'rb') as handle:
#with open('learning/data/random_blocks_(x2000)_5blocks_uniform_mass.pkl', 'rb') as handle:
val_towers = pickle.load(handle)
robust = {k: 0 for k in val_towers.keys()}
tp = TowerPlanner(stability_mode='contains')
stable_towers = copy.deepcopy(val_towers)
unstable_towers = copy.deepcopy(val_towers)
for k in robust.keys():
stable_indices = []
unstable_indices = []
for ix in range(0, val_towers[k]['towers'].shape[0]):
stable = True
if val_towers[k]['labels'][ix] == 0:
continue
for _ in range(n_attempts):
tower = val_towers[k]['towers'][ix, :, :].copy()
label = val_towers[k]['labels'][ix]
tower[:, 7:9] += np.random.randn(2*tower.shape[0]).reshape(tower.shape[0], 2)*noise
block_tower = [Object.from_vector(tower[kx, :]) for kx in range(tower.shape[0])]
if tp.tower_is_constructable(block_tower) != label:
stable = False
if stable:
robust[k] += 1
stable_indices.append(ix)
else:
unstable_indices.append(ix)
stable_towers[k]['towers'] = stable_towers[k]['towers'][stable_indices,...]
stable_towers[k]['labels'] = stable_towers[k]['labels'][stable_indices]
unstable_towers[k]['towers'] = unstable_towers[k]['towers'][unstable_indices,...]
unstable_towers[k]['labels'] = unstable_towers[k]['labels'][unstable_indices]
# with open('learning/data/stable_val.pkl', 'wb') as handle:
# pickle.dump(stable_towers, handle)
# with open('learning/data/unstable_val.pkl', 'wb') as handle:
# pickle.dump(unstable_towers, handle)
print(k, ':', robust[k], '/', val_towers[k]['towers'].shape[0] )
def check_stable_bases(logger):
tower_keys = ['2block', '3block', '4block', '5block']
tp = TowerPlanner(stability_mode='contains')
for tx in range(0, 80):
print(tx)
towers, _ = logger.load_acquisition_data(tx)
for k in tower_keys:
print(k)
for tower_vec in towers[k]['towers']:
tower = [Object.from_vector(tower_vec[bx, :]) for bx in range(0, tower_vec.shape[0])]
if tp.tower_is_constructable(tower[:-1]):
print('Stable Base')
else:
print('Unstable Base')
def get_labels(samples,
exec_mode,
agent,
logger,
xy_noise,
save_tower=False,
label_subtowers=False):
""" Takes as input a dictionary from the get_subset function.
Augment it with stability labels.
:param samples:
:param exec_mode: str in ['simple-model', 'noisy-model', 'sim', 'real']
:param agent: PandaAgent or None (if exec_mode == 'simple-model' or 'noisy-model')
:return:
"""
labeled_samples = {'%dblock' % k: {} for k in [2, 3, 4, 5]}
for k in labeled_samples:
labeled_samples[k]['towers'] = []
labeled_samples[k]['block_ids'] = []
labeled_samples[k]['labels'] = []
block_placements = 0
tp = TowerPlanner(stability_mode='contains')
for k in samples.keys():
n_towers, n_blocks, _ = samples[k]['towers'].shape
labels = np.ones((n_towers, ))
for ix in range(0, n_towers):
print(f'Collecting tower {ix+1}/{n_towers} for {k} towers...')
# Add noise to blocks and convert tower to Block representation.
block_tower = []
for jx in range(n_blocks):
vec_block = deepcopy(samples[k]['towers'][ix, jx, :])
if exec_mode == 'noisy-model':
vec_block[7:9] += np.random.randn(2) * xy_noise
block = Object.from_vector(
vec_block) # block is already rotated
if 'block_ids' in samples[k].keys():
block.name = 'obj_' + str(samples[k]['block_ids'][ix, jx])
block_tower.append(block)
# Use tp to check for stability.
if exec_mode == 'simple-model' or exec_mode == 'noisy-model':
# iterate through each subtower until it falls (is not constructable)
subtowers = [
block_tower[:k_sub]
for k_sub in list(range(2,
len(block_tower) + 1))
]
for k_sub, subtower in enumerate(subtowers, 2):
if tp.tower_is_constructable(subtower):
label = 1.0
else:
label = 0.0
# add to labeled samples
labeled_samples['%dblock' % k_sub]['towers'].append(
samples[k]['towers'][ix, :k_sub, :])
if 'block_ids' in labeled_samples['%dblock' % k_sub]:
labeled_samples['%dblock' % k_sub]['block_ids'].append(
samples[k]['block_ids'][ix, :k_sub])
labeled_samples['%dblock' % k_sub]['labels'].append(label)
# save tower file
if save_tower:
if 'block_ids' in samples[k].keys():
logger.save_towers_data(
samples[k]['towers'][ix, :k_sub, :],
samples[k]['block_ids'][ix, :k_sub], label)
else:
logger.save_towers_data(
samples[k]['towers'][ix, :k_sub, :], None,
label)
# stop when tower falls
if label == 0.0:
block_placements += k_sub
labels[ix] = 0.0
break
else:
vis = True
success = False
real = (exec_mode == 'real')
# if planning fails, reset and try again
while not success:
success, label = agent.simulate_tower(block_tower,
vis,
real=real)
print(f"Received success: {success}, label: {label}")
if not success:
if real:
input(
'Resolve conflict causing planning to fail, then press \
enter to try again.')
if isinstance(agent, PandaClientAgent):
agent.restart_services()
else: # in sim
input(
'Should reset sim. Not yet handled. Exit and restart training.'
)
labels[ix] = label
if 'block_ids' in samples[k].keys():
logger.save_towers_data(samples[k]['towers'][ix, :, :],
samples[k]['block_ids'][ix, :],
labels[ix])
else:
logger.save_towers_data(samples[k]['towers'][ix, :, :],
None, labels[ix])
samples[k]['labels'] = labels
if save_tower:
# save block placement data
logger.save_block_placement_data(block_placements)
if label_subtowers:
# vectorize labeled samples and return
for ki, k in enumerate(labeled_samples, 2):
if labeled_samples[k]['towers'] == []:
labeled_samples[k]['towers'] = np.zeros((0, ki, 21))
labeled_samples[k]['block_ids'] = np.zeros((0, ki))
labeled_samples[k]['labels'] = np.zeros(0)
labeled_samples[k]['towers'] = np.array(
labeled_samples[k]['towers'])
labeled_samples[k]['block_ids'] = np.array(
labeled_samples[k]['block_ids'])
labeled_samples[k]['labels'] = np.array(
labeled_samples[k]['labels'])
return labeled_samples
else:
return samples
def evaluate_planner(logger, blocks, reward_fn, fname, args, save_imgs=False, img_prefix=''):
tower_keys = [str(ts)+'block' for ts in args.tower_sizes]
tp = TowerPlanner(stability_mode='contains')
ep = EnsemblePlanner(logger)
# Store regret for towers of each size.
regrets = {k: [] for k in tower_keys}
rewards = {k: [] for k in tower_keys}
if args.max_acquisitions is not None:
eval_range = range(0, args.max_acquisitions, 10)
elif args.acquisition_step is not None:
eval_range = [args.acquisition_step]
for tx in eval_range:
print('Acquisition step:', tx)
ensemble = logger.get_ensemble(tx)
if torch.cuda.is_available():
ensemble = ensemble.cuda()
for k, size in zip(tower_keys, args.tower_sizes):
print('Tower size', k)
num_failures, num_pw_failures = 0, 0
curr_regrets = []
curr_rewards = []
for t in range(0, args.n_towers):
print('Tower number', t)
if blocks is not None:
plan_blocks = np.random.choice(blocks, size, replace=False)
plan_blocks = copy.deepcopy(plan_blocks)
else:
plan_blocks = [Object.random() for _ in range(size)]
tower, reward, max_reward, tower_block_ids = ep.plan(plan_blocks,
ensemble,
reward_fn,
args,
num_blocks=size,
n_tower=t)
block_tower = []
for vec_block, block_id in zip(tower, tower_block_ids):
block = Object.from_vector(vec_block)
block.name = 'obj_%d' % block_id
block_tower.append(block)
# save tower info to /evaluation_towers
if args.exec_mode is None:
if args.planning_model == 'noisy-model':
logger.save_evaluation_tower(block_tower, reward, max_reward, tx, args.planning_model, args.problem, noise=args.plan_xy_noise)
else:
logger.save_evaluation_tower(block_tower, reward, max_reward, tx, args.planning_model, args.problem)
# perturb tower if evaluating with noisy model
if args.exec_mode == 'noisy-model':
block_tower = []
for vec_block, block_id in zip(tower, tower_block_ids):
vec_block[7:9] += np.random.randn(2)*args.exec_xy_noise
block = Object.from_vector(vec_block)
block.name = 'obj_%d' % block_id
block_tower.append(block)
# build found tower
if args.exec_mode == 'noisy-model' or args.exec_mode == 'simple-model':
if not tp.tower_is_constructable(block_tower):
reward = 0
num_failures += 1
if tp.tower_is_pairwise_stable(block_tower):
num_pw_failures += 1
else:
pairs = []
dists = []
for i in range(len(tower) - 1):
# check that each pair of blocks is stably individually
top = block_tower[i+1]
bottom = block_tower[i]
if not tp.pair_is_stable(bottom, top):
pairs.append(False)
else:
pairs.append(True)
top_rel_pos = np.array(top.pose.pos) - np.array(bottom.pose.pos)
top_rel_com = top_rel_pos + top.com
dists.append((np.abs(top_rel_com)*2 - bottom.dimensions)[:2])
#print('Pairs:', pairs, dists)
#print('PW Stable:', tp.tower_is_pairwise_stable(block_tower))
#print('Global Stable:', tp.tower_is_stable(block_tower))
if False and reward != 0:
print(reward, max_reward)
w = World(block_tower)
env = Environment([w], vis_sim=True, vis_frames=True)
input()
for tx in range(240):
env.step(vis_frames=True)
time.sleep(1/240.)
env.disconnect()
# Note that in general max reward may not be the best possible due to sampling.
#ground_truth = np.sum([np.max(b.dimensions) for b in blocks])
#print(max_reward, ground_truth)
# Compare heights and calculate regret.
regret = (max_reward - reward)/max_reward
#print(reward, max_reward)
#print(regret)
curr_regrets.append(regret)
curr_rewards.append(reward)
if args.exec_mode == 'noisy-model' or args.exec_mode == 'simple-model':
regrets[k].append(curr_regrets)
rewards[k].append(curr_rewards)
if args.max_acquisitions is not None:
if args.exec_mode == 'noisy-model' or args.exec_mode == 'simple-model':
with open(logger.get_figure_path(fname+'_regrets.pkl'), 'wb') as handle:
pickle.dump(regrets, handle)
with open(logger.get_figure_path(fname+'_rewards.pkl'), 'wb') as handle:
pickle.dump(rewards, handle)
# if just ran for one acquisition step, output final regret and reward
if args.acquisition_step is not None:
if args.exec_mode == 'noisy-model' or args.exec_mode == 'simple-model':
final_median_regret = np.median(regrets[k][0])
final_upper75_regret = np.quantile(regrets[k][0], 0.75)
final_lower25_regret = np.quantile(regrets[k][0][0], 0.25)
final_median_reward = np.median(rewards[k][0])
final_upper75_reward = np.quantile(rewards[k][0], 0.75)
final_lower25_reward = np.quantile(rewards[k][0], 0.25)
final_average_regret = np.average(regrets[k][0])
final_std_regret = np.std(regrets[k][0])
final_average_reward = np.average(rewards[k][0])
final_std_reward = np.std(rewards[k][0])
print('Final Median Regret: (%f) %f (%f)' % (final_lower25_regret, final_median_regret, final_upper75_regret))
print('Final Median Reward: (%f) %f (%f)' % (final_lower25_reward, final_median_reward, final_upper75_reward))
print('Final Average Regret: %f +/- %f' % (final_average_regret, final_std_regret))
print('Final Average Reward: %f +/- %f' % (final_average_reward, final_std_reward))
def inspect_2block_towers(logger):
"""
In the full dataset, show the distribution of features.
"""
tower_keys = ['2block', '3block', '4block', '5block']
# dataset = logger.load_dataset(logger.args.max_acquisitions - 1)
# print(dataset.tower_tensors['2block'].shape)
# plt.hist(dataset.tower_tensors['2block'][:,1,8], bins=10)
# plt.show()
#ensemble = logger.get_ensemble(10)
ensemble = logger.get_ensemble(logger.args.max_acquisitions - 1)
unlabeled = sample_unlabeled_data(10000)
preds = get_predictions(unlabeled, ensemble)
bald_scores = bald(preds).numpy()
print('Best BALD')
ixs = np.argsort(bald_scores)[::-1][:10]
print(bald_scores[ixs])
input()
tp = TowerPlanner(stability_mode='contains')
preds2 = preds[:unlabeled['2block']['towers'].shape[0], :]
bald_scores2 = bald_scores[:unlabeled['2block']['towers'].shape[0]]
acquire_indices = np.argsort(bald_scores2)[::-1][:50]
# for ix in range(preds2.shape[0]):
# print(np.around(preds2[ix,:].numpy(), 2), np.around(bald_scores2[ix], 3))
print('-----')
for ix in acquire_indices:
d = decision_distance(unlabeled['2block']['towers'][ix,:,:])
tower = unlabeled['2block']['towers'][ix,:,:]
l = tp.tower_is_constructable([Object.from_vector(tower[bx, :]) for bx in range(tower.shape[0])])
print(np.around(preds2[ix,:].numpy(), 4), np.around(bald_scores2[ix], 3), d, l)
for ix in acquire_indices:
unlabeled['2block']['towers'][ix,1,7:8] += 0.0
new_preds = get_predictions(unlabeled, ensemble)
print('-----')
for ix in acquire_indices:
d = decision_distance(unlabeled['2block']['towers'][ix,:,:])
print(np.around(new_preds[ix,:].numpy(), 2))
plt.hist(unlabeled['2block']['towers'][acquire_indices,1,0])
plt.show()
print('-----')
start = 0
for k in tower_keys:
end = start + unlabeled[k]['towers'].shape[0]
p, b = preds[start:end, :], bald_scores[start:end]
informative = b[b > 0.3]
print(p.shape, informative.shape)
accs = {k: [] for k in tower_keys}
with open('learning/data/random_blocks_(x2000)_5blocks_uniform_mass.pkl', 'rb') as handle:
val_towers = pickle.load(handle)
preds = get_predictions(val_towers, ensemble).mean(1).numpy()
dists = []
for ix in range(0, val_towers['2block']['towers'].shape[0]):
d = decision_distance(val_towers['2block']['towers'][ix,:,:])
dists.append(d)
print(len(dists))
plt.hist(dists, bins=100)
plt.show()
start = 0
for k in tower_keys:
end = start + val_towers[k]['towers'].shape[0]
acc = ((preds[start:end]>0.5) == val_towers[k]['labels']).mean()
accs[k].append(acc)
start = end
print(accs)
class EnsemblePlanner:
def __init__(self, logger, n_samples=None):
self.tower_keys = ['2block', '3block', '4block', '5block']
self.n_samples = {
2: 5000,
3: 10000,
4: 20000,
5: 100000,
6: 250000,
7: 500000
}
self.tp = TowerPlanner(stability_mode='contains')
self.logger = logger
self.using_cache = False
def get_cached_towers(self, args, num_blocks, blocks, n_tower):
key = '%dblock' % num_blocks
if not self.using_cache:
if args.block_set_fname == '':
cache_name = 'random_set.pkl'
else:
cache_name = os.path.basename(args.block_set_fname)
# see if cache for this block set exists
try:
with open(
os.path.join('learning/evaluate/cached_towers',
cache_name), 'rb') as handle:
block_set_towers = pickle.load(handle)
except:
return None, None
# if it cached file exists, save it
self.cached_towers = block_set_towers
if (key in block_set_towers) and \
(n_tower in block_set_towers[key]) and \
(len(block_set_towers[key][n_tower][0]) >= self.n_samples[num_blocks]):
print('Using random planning towers from cache for tower %d' %
n_tower)
self.using_cache = True
return self.cached_towers[key][n_tower][:self.
n_samples[num_blocks]]
else:
return None, None
# if already loaded cache, get relevant towers
else:
try:
towers, block_ids = self.cached_towers[key][
n_tower][:self.n_samples[num_blocks]]
except:
return None, None
print(
'Using saved random planning towers from cache for tower %d' %
n_tower)
return towers, block_ids
# NOTE: this will not cache towers if some towers have already previously been found in the cache
def cache_towers(self, args, towers, tower_block_ids, n_tower):
if not self.using_cache:
if args.block_set_fname == '':
cache_name = 'random_set.pkl'
else:
cache_name = os.path.basename(args.block_set_fname)
num_blocks = len(towers[0])
# see if cache exists
try:
with open(
os.path.join('learning/evaluate/cached_towers',
cache_name), 'rb') as handle:
block_set_towers = pickle.load(handle)
except:
block_set_towers = {}
if '%dblock' % num_blocks not in block_set_towers:
block_set_towers['%dblock' % num_blocks] = {}
print('Saving randomly generated towers for tower %d' % n_tower)
block_set_towers['%dblock' %
num_blocks][n_tower] = (towers, tower_block_ids)
with open(
os.path.join('learning/evaluate/cached_towers',
cache_name), 'wb') as handle:
pickle.dump(block_set_towers, handle)
def generate_candidate_towers(self,
blocks,
args,
num_blocks=None,
n_tower=None):
if num_blocks is None:
num_blocks = len(blocks)
#tower_vectors, tower_block_ids = self.get_cached_towers(args, num_blocks, blocks, n_tower)
tower_vectors = None
if tower_vectors is None:
tower_vectors = []
tower_block_ids = []
for _ in range(0, self.n_samples[num_blocks]):
tower, rotated_tower = sample_random_tower(blocks, num_blocks=num_blocks, \
ret_rotated=True, discrete=args.discrete)
tower_vectors.append([b.vectorize() for b in rotated_tower])
tower_block_ids.append([b.get_id() for b in rotated_tower])
#self.cache_towers(args, tower_vectors, tower_block_ids, n_tower)
return tower_vectors, tower_block_ids
def plan(self,
blocks,
ensemble,
reward_fn,
args,
num_blocks=None,
n_tower=None):
#n = len(blocks)
#max_height = 0
#max_tower = []
# Step (1): Build dataset of potential towers.
tower_vectors, tower_block_ids = self.generate_candidate_towers(
blocks, args, num_blocks, n_tower)
# Step (2): Get predictions for each tower.
towers = np.array(tower_vectors)
block_ids = np.array(tower_block_ids)
if args.planning_model == 'learned':
# Since we are only planning for towers of a single size,
# always use the '2block' key for simplicity. The rest currently
# need at least some data for the code to work.
labels = np.zeros((towers.shape[0], ))
tower_dict = {}
for k in self.tower_keys:
tower_dict[k] = {}
if k == '2block':
tower_dict[k]['towers'] = towers
tower_dict[k]['labels'] = labels
tower_dict[k]['block_ids'] = block_ids
else:
tower_dict[k]['towers'] = towers[:5, ...]
tower_dict[k]['labels'] = labels[:5]
tower_dict[k]['block_ids'] = block_ids[:5, ...]
tower_dataset = TowerDataset(tower_dict, augment=False)
tower_sampler = TowerSampler(dataset=tower_dataset,
batch_size=64,
shuffle=False)
tower_loader = DataLoader(dataset=tower_dataset,
batch_sampler=tower_sampler)
preds = []
if hasattr(self.logger.args,
'sampler') and self.logger.args.sampler == 'sequential':
for tensor, _ in tower_loader:
sub_tower_preds = []
for n_blocks in range(2, tensor.shape[1] + 1):
if torch.cuda.is_available():
tensor = tensor.cuda()
with torch.no_grad():
sub_tower_preds.append(
ensemble.forward(tensor[:, :n_blocks, :]))
sub_tower_preds = torch.stack(sub_tower_preds, dim=0)
preds.append(sub_tower_preds.prod(dim=0))
else:
for tensor, _ in tower_loader:
if torch.cuda.is_available():
tensor = tensor.cuda()
with torch.no_grad():
preds.append(ensemble.forward(tensor))
p_stables = torch.cat(preds, dim=0).mean(dim=1)
elif args.planning_model == 'noisy-model':
n_estimate = 10
p_stables = np.zeros(len(tower_vectors))
for ti, tower_vec in enumerate(tower_vectors):
# estimate prob of constructability
results = np.ones(n_estimate)
all_stable = 1.
for n in range(n_estimate):
noisy_tower = []
for block_vec in tower_vec:
noisy_block = deepcopy(block_vec)
noisy_block[7:9] += np.random.randn(
2) * args.plan_xy_noise
noisy_tower.append(noisy_block)
block_tower = [
Object.from_vector(block) for block in noisy_tower
]
if not self.tp.tower_is_constructable(block_tower):
results[n] = 0.
all_stable = 0.
break
p_stables[ti] = all_stable # np.mean(results)
elif args.planning_model == 'simple-model':
p_stables = np.zeros(len(tower_vectors))
for ti, tower_vec in enumerate(tower_vectors):
block_tower = [
Object.from_vector(block) for block in tower_vec
]
if self.tp.tower_is_constructable(block_tower):
p_stables[ti] = 1.
# Step (3): Find the tallest tower of a given height.
max_reward, max_exp_reward, max_tower, max_stable = -100, -100, None, 0
ground_truth = -100
max_reward_block_ids = None
for ix, (p, tower, tower_block_ids) in enumerate(
zip(p_stables, towers, block_ids)):
reward = reward_fn(tower)
exp_reward = p * reward
if exp_reward >= max_exp_reward: # and p > 0.5:
if exp_reward > max_exp_reward or (exp_reward == max_exp_reward
and p > max_stable):
max_tower = tower_vectors[ix]
max_reward = reward
max_exp_reward = exp_reward
max_stable = p
max_reward_block_ids = tower_block_ids
# Check ground truth stability to find maximum reward.
if self.tp.tower_is_constructable([Object.from_vector(tower[ix,:]) for ix in range(tower.shape[0])]) \
and reward > ground_truth:
ground_truth = reward
if max_tower is None:
print('None Found')
max_tower = tower_vectors[0]
max_reward = reward_fn(towers[0])
return max_tower, max_reward, ground_truth, max_reward_block_ids
class Tallest(Problem):
def __init__(self, max_height):
self.samples_per_block = 5
self.max_height = max_height
self.tp = TowerPlanner(stability_mode='contains')
def sample_actions(self, parent_node, model, discrete=False):
new_towers = []
new_blocks_remaining = []
if len(parent_node['tower']) == 0:
# first action: place each possible block at (0,0) at a random orientation
for block in parent_node['blocks_remaining']:
for _ in range(self.samples_per_block):
tower = random_placement(block, [], discrete=discrete)
tower[0].pose = Pose(ZERO_POS, tower[0].pose.orn)
blocks_remaining = copy(parent_node['blocks_remaining'])
blocks_remaining.remove(block)
new_towers.append(tower)
new_blocks_remaining.append(blocks_remaining)
else:
# randomly sample a placement of a random block
for block in parent_node['blocks_remaining']:
blocks_remaining = copy(parent_node['blocks_remaining'])
blocks_remaining.remove(block)
for _ in range(self.samples_per_block):
tower = random_placement(block,
parent_node['tower'],
discrete=discrete)
new_towers.append(tower)
new_blocks_remaining.append(blocks_remaining)
all_rewards = self.reward_fn(new_towers, model)
terms = [False] * len(new_towers)
for i, tower in enumerate(new_towers):
# rewards of 0 are unstable --> terminal nodes
# once max height is reached --> terminal nodes
if all_rewards['exp_reward'][i] == 0 or len(
tower) == self.max_height:
terms[i] = True
new_nodes = []
for i, (tower, blocks_remaining,
term) in enumerate(zip(new_towers, new_blocks_remaining,
terms)):
new_node = {
'parent': None,
'children': [],
'term': term,
'leaf': True,
'exp_reward': all_rewards['exp_reward'][i],
'value': 0,
'count': 0,
'tower': tower,
'blocks_remaining': blocks_remaining,
'tower_height': all_rewards['reward'][i],
'ground_truth': all_rewards['ground_truth'][i]
}
new_nodes.append(new_node)
return new_nodes
'''
def sample_action(self, parent_node, model, discrete=False):
block = np.random.choice(parent_node['blocks_remaining'])
if len(parent_node['tower']) == 0:
# first action: place block at (0,0)
block.pose = Pose(ZERO_POS, block.pose.orn)
tower = [block]
else:
# randomly sample a placement of a random block
tower = random_placement(block, parent_node['tower'], discrete=discrete)
blocks_remaining = copy(parent_node['blocks_remaining'])
blocks_remaining.remove(block)
all_rewards = self.reward_fn([tower], model)
# rewards of 0 are unstable --> terminal nodes
# once max height is reached --> terminal nodes
term = False
if all_rewards['exp_reward'][0] == 0 or len(tower) == self.max_height:
term = True
new_node = {'parent': None,
'children': [],
'term': term,
'leaf': True,
'exp_reward': all_rewards['exp_reward'][0],
'value': 0,
'count': 0,
'tower': tower,
'blocks_remaining': blocks_remaining,
'tower_height': all_rewards['reward'][0],
'ground_truth': all_rewards['ground_truth'][0]}
return new_node
'''
def reward_fn(self, towers, model):
all_rewards = {'exp_reward': [], 'reward': [], 'ground_truth': []}
if len(towers[0]) == 1:
# only single block towers, always stable
reward = [tower[0].dimensions[2] for tower in towers]
all_rewards['exp_reward'] = reward
all_rewards['reward'] = reward
all_rewards['ground_truth'] = reward
else:
tower_loader = make_tower_dataset(towers)
# this assumes there is only one batch
for tensor, _ in tower_loader:
with torch.no_grad():
preds = model.forward(tensor)
p_stables = preds.mean(dim=1) # average ensemble output
exp_rewards = []
rewards = []
ground_truths = []
for ix, (p, tower) in enumerate(zip(p_stables, towers)):
tower_height = np.sum([block.dimensions[2] for block in tower])
rewards += [tower_height]
if p > 0.5: # stable
exp_rewards += [float(p * tower_height)]
else:
exp_rewards += [0]
ground_truths += [
self.tp.tower_is_constructable(tower) * tower_height
]
all_rewards['exp_reward'] = exp_rewards
all_rewards['reward'] = rewards
all_rewards['ground_truth'] = ground_truths
return all_rewards
from block_utils import Object
from learning.domains.towers.generate_tower_training_data import sample_random_tower
from learning.domains.towers.tower_data import TowerDataset, TowerSampler
from tower_planner import TowerPlanner
import pickle
import copy
if __name__ == '__main__':
with open('learning/domains/towers/eval_block_set_12.pkl', 'rb') as handle:
blocks = pickle.load(handle)
tp = TowerPlanner(stability_mode='contains')
towers = []
n_stable = 0
for _ in range(0, 10000):
copy_blocks = copy.deepcopy(blocks)
tower, rotated_tower = sample_random_tower(copy_blocks, num_blocks=5, \
ret_rotated=True, discrete=False)
stable = tp.tower_is_constructable(rotated_tower)
n_stable += stable
print(f"n_stable: {n_stable}")