def step(self, player, home_away_race, upgrades, available_act_mask, minimap):
"""Sample actions and compute logp(a|s)"""
out = self.call(player, available_act_mask, minimap)
# Gumbel-max sampling
action_id = categorical_sample(out["action_id"], available_act_mask)
tf.assert_greater(available_act_mask[:, action_id.numpy().item()], 0.0)
# Fill out args based on sampled action type
arg_spatial = []
arg_nonspatial = []
logp_a = log_prob(action_id, out["action_id"])
for arg_type in self.action_spec.functions[action_id.numpy().item()].args:
if arg_type.name in ["screen", "screen2", "minimap"]:
location_id = categorical_sample(out["target_location"])
arg_spatial.append(location_id)
logp_a += log_prob(location_id, out["target_location"])
else:
# non-spatial args
sample = categorical_sample(out[arg_type.name])
arg_nonspatial.append(sample)
logp_a += log_prob(sample, out[arg_type.name])
# tf.debugging.check_numerics(logp_a, "Bad logp(a|s)")
return (
out["value"],
action_id,
arg_spatial,
arg_nonspatial,
logp_a,
)
def get_words(expn, parent, lmk=None, rel=None):
words = []
probs = []
entropy = []
for n in expn.split():
if n in NONTERMINALS:
if n == parent == 'LANDMARK-PHRASE':
# we need to move to the parent landmark
lmk = parent_landmark(lmk)
# we need to keep expanding
expansion, exp_prob, exp_ent = get_expansion(n, parent, lmk, rel)
w, w_prob, w_ent = get_words(expansion, n, lmk, rel)
words.append(w)
probs.append(exp_prob * w_prob)
entropy.append(exp_ent + w_ent)
else:
# get word for POS
w_db = Word.get_words(pos=n, lmk=lmk_id(lmk), rel=rel_type(rel))
counter = collections.Counter(w_db)
keys, counts = zip(*counter.items())
counts = np.array(counts)
counts /= counts.sum()
w, w_prob, w_entropy = categorical_sample(keys, counts)
words.append(w.word)
probs.append(w.prob)
entropy.append(w_entropy)
p, H = np.prod(probs), np.sum(entropy)
print 'expanding %s to %s (p: %f, H: %f)' % (expn, words, p, H)
return words, p, H
def get_words(terminals, landmarks, rel=None):
words = []
probs = []
entropy = []
for n,lmk in zip(terminals, landmarks):
# if we could not get an expansion for the LHS, we just pass down the unexpanded nonterminal symbol
# it gets the probability of 1 and entropy of 0
if n in NONTERMINALS:
words.append(n)
probs.append(1.0)
entropy.append(0.0)
continue
lmk_class = (lmk.object_class if lmk else None)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(rel, 'measurement') else None)
cp_db = CWord.get_word_counts(pos=n,
lmk_class=lmk_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=lmk_color,
rel=rel_class,
rel_dist_class=dist_class,
rel_deg_class=deg_class)
if cp_db.count() <= 0:
logger( 'Could not expand %s (lmk_class: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, lmk_class, lmk_color, rel_class, dist_class, deg_class) )
terminals.append( n )
continue
logger( 'Expanded %s (lmk_class: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, lmk_class, lmk_color, rel_class, dist_class, deg_class) )
ckeys, ccounts = zip(*[(cword.word,cword.count) for cword in cp_db.all()])
ccounter = {}
for cword in cp_db.all():
if cword.word in ccounter: ccounter[cword.word] += cword.count
else: ccounter[cword.word] = cword.count
ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
ccounts = np.array(ccounts, dtype=float)
ccounts /= ccounts.sum()
w, w_prob, w_entropy = categorical_sample(ckeys, ccounts)
words.append(w)
probs.append(w_prob)
entropy.append(w_entropy)
p, H = np.prod(probs), np.sum(entropy)
# print 'expanding %s to %s (p: %f, H: %f)' % (terminals, words, p, H)
return words, p, H
def sample_next(self, a):
transitions = self.successors[a]
i = categorical_sample([t[0] for t in transitions])
p, n_s, rew, done = transitions[i]
if n_s not in self.node_register:
n_s = SearchNode(n_s, self.env, self.gamma, done,
self.node_register)
else:
n_s = self.node_register[n_s]
return n_s
def choose_action(self, observations, influencer_action): #使用obs和influencer的动作作为输入
influencee_logist = []
influencee_onehot = []
for agent, obs, inf_act in zip(self.agents[self.influencer_num:],
observations[self.influencer_num:],
influencer_action[self.influencer_num:]):
prob, logist = agent.choose_action(obs, inf_act) #具体见network中A3CAgent
int_act, act = categorical_sample(prob)
influencee_logist.append(logist)
influencee_onehot.append(act)
return influencee_onehot, influencee_logist
def get_expansion(lhs, parent=None, lmk=None, rel=None):
p_db = Production.get_productions(lhs=lhs, parent=parent,
lmk=lmk_id(lmk), rel=rel_type(rel))
counter = collections.Counter(p_db)
keys, counts = zip(*counter.items())
counts = np.array(counts)
counts /= counts.sum()
prod, prod_prob, prod_entropy = categorical_sample(keys, counts)
print 'expanding:', prod, prod_prob, prod_entropy
return prod.rhs, prod_prob, prod_entropy
def choose_influencer_action(self, observations): #选择influencer的action， 直接放obs
influencer_act_logists = []
influencer_act_prob = []
influencer_act_int = []
influencer_act_onehot = []
for agent, obs in zip(self.agents[:self.influencer_num], observations[:self.influencer_num]):
prob, logist = agent.choose_action(obs)
int_act, act = categorical_sample(prob) #MAAC里的函数直接那过来的， 放入probability distribution, 返回int型动作和 onehot 动作
influencer_act_logists.append(logist)
influencer_act_prob.append(prob)
influencer_act_onehot.append(act)
influencer_act_int.append(int_act)
return influencer_act_onehot, influencer_act_prob, influencer_act_int, influencer_act_logists
def sample_landmark(self, landmarks, trajector, usebest=False):
''' Weight by inverse of distance to landmark center and choose probabilistically '''
lm_probabilities = self.all_landmark_probs(landmarks, trajector)
if usebest:
index = index_max(lm_probabilities)
else:
index = categorical_sample(lm_probabilities)
sampled_landmark = landmarks[index]
head_on = self.get_head_on_viewpoint(sampled_landmark)
self.set_orientations(sampled_landmark, head_on)
return sampled_landmark, lm_probabilities[index], self.get_entropy(lm_probabilities), head_on
def sample_landmark(self, landmarks, trajector, usebest=False):
''' Weight by inverse of distance to landmark center and choose probabilistically '''
lm_probabilities = self.all_landmark_probs(landmarks, trajector)
if usebest:
index = index_max(lm_probabilities)
else:
index = categorical_sample(lm_probabilities)
sampled_landmark = landmarks[index]
head_on = self.get_head_on_viewpoint(sampled_landmark)
self.set_orientations(sampled_landmark, head_on)
return sampled_landmark, lm_probabilities[index], self.get_entropy(
lm_probabilities), head_on
def sample_relation(self, trajector, bounding_box, perspective, landmark, step=0.02, usebest=False):
"""
Sample a relation given a trajector and landmark.
Evaluate each relation and probabilisticaly choose the one that is likely to
generate the trajector given a landmark.
"""
rel_probabilities, rel_classes = self.all_relation_probs(trajector, bounding_box, perspective, landmark, step)
if usebest:
index = index_max(rel_probabilities)
else:
index = categorical_sample(rel_probabilities)
index = rel_probabilities.cumsum().searchsorted( random.sample(1) )[0]
return rel_classes[index], rel_probabilities[index], self.get_entropy(rel_probabilities)
def sample_relation(self,
trajector,
bounding_box,
perspective,
landmark,
step=0.02,
usebest=False):
"""
Sample a relation given a trajector and landmark.
Evaluate each relation and probabilisticaly choose the one that is likely to
generate the trajector given a landmark.
"""
rel_probabilities, rel_classes = self.all_relation_probs(
trajector, bounding_box, perspective, landmark, step)
if usebest:
index = index_max(rel_probabilities)
else:
index = categorical_sample(rel_probabilities)
index = rel_probabilities.cumsum().searchsorted(random.sample(1))[0]
return rel_classes[index], rel_probabilities[index], self.get_entropy(
rel_probabilities)
def get_expansion(lhs, parent=None, lmk=None, rel=None):
lhs_rhs_parent_chain = []
prob_chain = []
entropy_chain = []
terminals = []
landmarks = []
for n in lhs.split():
if n in NONTERMINALS:
if n == parent == 'LANDMARK-PHRASE':
# we need to move to the parent landmark
lmk = parent_landmark(lmk)
lmk_class = (lmk.object_class if lmk else None)
lmk_ori_rels = get_lmk_ori_rels_str(lmk)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(rel, 'measurement') else None)
cp_db = CProduction.get_production_counts(lhs=n,
parent=parent,
lmk_class=lmk_class,
lmk_ori_rels=lmk_ori_rels,
lmk_color=lmk_color,
rel=rel_class,
dist_class=dist_class,
deg_class=deg_class)
if cp_db.count() <= 0:
logger('Could not expand %s (parent: %s, lmk_class: %s, lmk_ori_rels: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, parent, lmk_class, lmk_ori_rels, lmk_color, rel_class, dist_class, deg_class))
terminals.append( n )
continue
ckeys, ccounts = zip(*[(cprod.rhs,cprod.count) for cprod in cp_db.all()])
ccounter = {}
for cprod in cp_db.all():
if cprod.rhs in ccounter: ccounter[cprod.rhs] += cprod.count
else: ccounter[cprod.rhs] = cprod.count
ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
ccounts = np.array(ccounts, dtype=float)
ccounts /= ccounts.sum()
cprod, cprod_prob, cprod_entropy = categorical_sample(ckeys, ccounts)
# print cprod, cprod_prob, cprod_entropy
lhs_rhs_parent_chain.append( ( n,cprod,parent,lmk ) )
prob_chain.append( cprod_prob )
entropy_chain.append( cprod_entropy )
lrpc, pc, ec, t, ls = get_expansion( lhs=cprod, parent=n, lmk=lmk, rel=rel )
lhs_rhs_parent_chain.extend( lrpc )
prob_chain.extend( pc )
entropy_chain.extend( ec )
terminals.extend( t )
landmarks.extend( ls )
else:
terminals.append( n )
landmarks.append( lmk )
return lhs_rhs_parent_chain, prob_chain, entropy_chain, terminals, landmarks
def get_words(terminals, landmarks, rel=None, prevword=None):
words = []
probs = []
alphas = []
entropy = []
C = CWord.get_count
for n,lmk in zip(terminals, landmarks):
# if we could not get an expansion for the LHS, we just pass down the unexpanded nonterminal symbol
# it gets the probability of 1 and entropy of 0
if n in NONTERMINALS:
words.append(n)
probs.append(1.0)
entropy.append(0.0)
continue
lmk_class = (lmk.object_class if lmk else None)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(rel, 'measurement') else None)
meaning = dict(pos=n,
lmk_class=lmk_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=lmk_color,
rel=rel_class,
rel_dist_class=dist_class,
rel_deg_class=deg_class)
cp_db_uni = CWord.get_word_counts(**meaning)
ccounter = {}
for c in cp_db_uni:
ccounter[c.word] = ccounter.get(c.word, 0) + c.count
ckeys, ccounts_uni = zip(*ccounter.items())
ccounts_uni = np.array(ccounts_uni, dtype=float)
ccounts_uni /= ccounts_uni.sum()
prev_word = words[-1] if words else prevword
alpha = C(prev_word=prev_word, **meaning) / C(**meaning)
alphas.append(alpha)
if alpha:
cp_db_bi = CWord.get_word_counts(prev_word=prev_word, **meaning)
ccounter = {}
for c in cp_db_bi:
ccounter[c.word] = ccounter.get(c.word, 0) + c.count
ccounts_bi = np.array([ccounter.get(k,0) for k in ckeys], dtype=float)
ccounts_bi /= ccounts_bi.sum()
cprob = (alpha * ccounts_bi) + ((1-alpha) * ccounts_uni)
else:
cprob = ccounts_uni
# if cp_db.count() <= 0:
# logger( 'Could not expand %s (lmk_class: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, lmk_class, lmk_color, rel_class, dist_class, deg_class) )
# terminals.append( n )
# continue
# ckeys, ccounts = zip(*[(cword.word,cword.count) for cword in cp_db.all()])
# ccounter = {}
# for cword in cp_db.all():
# if cword.word in ccounter: ccounter[cword.word] += cword.count
# else: ccounter[cword.word] = cword.count
# ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
# ccounts = np.array(ccounts, dtype=float)
# ccounts /= ccounts.sum()
w, w_prob, w_entropy = categorical_sample(ckeys, cprob)
words.append(w)
probs.append(w_prob)
entropy.append(w_entropy)
p, H = np.prod(probs), np.sum(entropy)
# print 'expanding %s to %s (p: %f, H: %f)' % (terminals, words, p, H)
return words, p, H, alphas
def get_expansion(lhs, parent=None, lmk=None, rel=None):
lhs_rhs_parent_chain = []
prob_chain = []
entropy_chain = []
terminals = []
landmarks = []
for n in lhs.split():
if n in NONTERMINALS:
if n == parent == 'LANDMARK-PHRASE':
# we need to move to the parent landmark
lmk = parent_landmark(lmk)
lmk_class = (lmk.object_class if lmk else None)
lmk_ori_rels = get_lmk_ori_rels_str(lmk)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(
rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(
rel, 'measurement') else None)
cp_db = CProduction.get_production_counts(
lhs=n,
parent=parent,
lmk_class=lmk_class,
lmk_ori_rels=lmk_ori_rels,
lmk_color=lmk_color,
rel=rel_class,
dist_class=dist_class,
deg_class=deg_class)
if cp_db.count() <= 0:
logger(
'Could not expand %s (parent: %s, lmk_class: %s, lmk_ori_rels: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)'
% (n, parent, lmk_class, lmk_ori_rels, lmk_color,
rel_class, dist_class, deg_class))
terminals.append(n)
continue
ckeys, ccounts = zip(*[(cprod.rhs, cprod.count)
for cprod in cp_db.all()])
ccounter = {}
for cprod in cp_db.all():
if cprod.rhs in ccounter: ccounter[cprod.rhs] += cprod.count
else: ccounter[cprod.rhs] = cprod.count
ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
ccounts = np.array(ccounts, dtype=float)
ccounts /= ccounts.sum()
cprod, cprod_prob, cprod_entropy = categorical_sample(
ckeys, ccounts)
# print cprod, cprod_prob, cprod_entropy
lhs_rhs_parent_chain.append((n, cprod, parent, lmk))
prob_chain.append(cprod_prob)
entropy_chain.append(cprod_entropy)
lrpc, pc, ec, t, ls = get_expansion(lhs=cprod,
parent=n,
lmk=lmk,
rel=rel)
lhs_rhs_parent_chain.extend(lrpc)
prob_chain.extend(pc)
entropy_chain.extend(ec)
terminals.extend(t)
landmarks.extend(ls)
else:
terminals.append(n)
landmarks.append(lmk)
return lhs_rhs_parent_chain, prob_chain, entropy_chain, terminals, landmarks
def get_words(terminals, landmarks, rel=None, prevword=None):
words = []
probs = []
alphas = []
entropy = []
C = CWord.get_count
for n, lmk in zip(terminals, landmarks):
# if we could not get an expansion for the LHS, we just pass down the unexpanded nonterminal symbol
# it gets the probability of 1 and entropy of 0
if n in NONTERMINALS:
words.append(n)
probs.append(1.0)
entropy.append(0.0)
continue
lmk_class = (lmk.object_class if lmk else None)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(
rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(
rel, 'measurement') else None)
meaning = dict(pos=n,
lmk_class=lmk_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=lmk_color,
rel=rel_class,
rel_dist_class=dist_class,
rel_deg_class=deg_class)
cp_db_uni = CWord.get_word_counts(**meaning)
ccounter = {}
for c in cp_db_uni:
ccounter[c.word] = ccounter.get(c.word, 0) + c.count
ckeys, ccounts_uni = zip(*ccounter.items())
ccounts_uni = np.array(ccounts_uni, dtype=float)
ccounts_uni /= ccounts_uni.sum()
prev_word = words[-1] if words else prevword
alpha = C(prev_word=prev_word, **meaning) / C(**meaning)
alphas.append(alpha)
if alpha:
cp_db_bi = CWord.get_word_counts(prev_word=prev_word, **meaning)
ccounter = {}
for c in cp_db_bi:
ccounter[c.word] = ccounter.get(c.word, 0) + c.count
ccounts_bi = np.array([ccounter.get(k, 0) for k in ckeys],
dtype=float)
ccounts_bi /= ccounts_bi.sum()
cprob = (alpha * ccounts_bi) + ((1 - alpha) * ccounts_uni)
else:
cprob = ccounts_uni
# if cp_db.count() <= 0:
# logger( 'Could not expand %s (lmk_class: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, lmk_class, lmk_color, rel_class, dist_class, deg_class) )
# terminals.append( n )
# continue
# ckeys, ccounts = zip(*[(cword.word,cword.count) for cword in cp_db.all()])
# ccounter = {}
# for cword in cp_db.all():
# if cword.word in ccounter: ccounter[cword.word] += cword.count
# else: ccounter[cword.word] = cword.count
# ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
# ccounts = np.array(ccounts, dtype=float)
# ccounts /= ccounts.sum()
w, w_prob, w_entropy = categorical_sample(ckeys, cprob)
words.append(w)
probs.append(w_prob)
entropy.append(w_entropy)
p, H = np.prod(probs), np.sum(entropy)
# print 'expanding %s to %s (p: %f, H: %f)' % (terminals, words, p, H)
return words, p, H, alphas
def loop(data):
time.sleep(random())
if 'num_iterations' in data:
scene, speaker = construct_training_scene(True)
num_iterations = data['num_iterations']
else:
scene = data['scene']
speaker = data['speaker']
num_iterations = len(data['loc_descs'])
utils.scene.set_scene(scene,speaker)
scene_bb = scene.get_bounding_box()
scene_bb = scene_bb.inflate( Vec2(scene_bb.width*0.5,scene_bb.height*0.5) )
table = scene.landmarks['table'].representation.get_geometry()
# step = 0.04
loi = [lmk for lmk in scene.landmarks.values() if lmk.name != 'table']
all_heatmaps_tupless, xs, ys = speaker.generate_all_heatmaps(scene, step=step, loi=loi)
loi_infos = []
all_meanings = set()
for obj_lmk,all_heatmaps_tuples in zip(loi, all_heatmaps_tupless):
lmks, rels, heatmapss = zip(*all_heatmaps_tuples)
meanings = zip(lmks,rels)
# print meanings
all_meanings.update(meanings)
loi_infos.append( (obj_lmk, meanings, heatmapss) )
all_heatmaps_tupless, xs, ys = speaker.generate_all_heatmaps(scene, step=step)
all_heatmaps_tuples = all_heatmaps_tupless[0]
# x = np.array( [list(xs-step*0.5)]*len(ys) )
# y = np.array( [list(ys-step*0.5)]*len(xs) ).T
# for lamk, rel, (heatmap1,heatmap2) in all_heatmaps_tuples:
# logger( m2s(lamk,rel))
# if isinstance(rel, DistanceRelation):
# probabilities = heatmap2.reshape( (len(xs),len(ys)) ).T
# plt.pcolor(x, y, probabilities, cmap = 'jet', edgecolors='none', alpha=0.7)
# plt.colorbar()
# for lmk in scene.landmarks.values():
# if isinstance(lmk.representation, GroupLineRepresentation):
# xxs = [lmk.representation.line.start.x, lmk.representation.line.end.x]
# yys = [lmk.representation.line.start.y, lmk.representation.line.end.y]
# plt.fill(xxs,yys,facecolor='none',linewidth=2)
# elif isinstance(lmk.representation, RectangleRepresentation):
# rect = lmk.representation.rect
# xxs = [rect.min_point.x,rect.min_point.x,rect.max_point.x,rect.max_point.x]
# yys = [rect.min_point.y,rect.max_point.y,rect.max_point.y,rect.min_point.y]
# plt.fill(xxs,yys,facecolor='none',linewidth=2)
# plt.text(rect.min_point.x+0.01,rect.max_point.y+0.02,lmk.name)
# plt.title(m2s(lamk,rel))
# logger("Showing")
# plt.show()
# logger("End")
x = np.array( [list(xs-step*0.5)]*len(ys) )
y = np.array( [list(ys-step*0.5)]*len(xs) ).T
lmks, rels, heatmapss = zip(*all_heatmaps_tuples)
# graphmax1 = graphmax2 = 0
meanings = zip(lmks,rels)
landmarks = list(set(lmks))
# relations = list(set(rels))
epsilon = 0.0001
def heatmaps_for_sentences(sentences, all_meanings, loi_infos, xs, ys, scene, speaker, step=0.02):
printing=False
x = np.array( [list(xs-step*0.5)]*len(ys) )
y = np.array( [list(ys-step*0.5)]*len(xs) ).T
scene_bb = scene.get_bounding_box()
scene_bb = scene_bb.inflate( Vec2(scene_bb.width*0.5,scene_bb.height*0.5) )
# x = np.array( [list(xs-step*0.5)]*len(ys) )
# y = np.array( [list(ys-step*0.5)]*len(xs) ).T
combined_heatmaps = []
for obj_lmk, ms, heatmapss in loi_infos:
# for m,(h1,h2) in zip(ms, heatmapss):
# logger( h1.shape )
# logger( x.shape )
# logger( y.shape )
# logger( xs.shape )
# logger( ys.shape )
# plt.pcolor(x, y, h1.reshape((len(xs),len(ys))).T, cmap = 'jet', edgecolors='none', alpha=0.7)
# plt.colorbar()
# for lmk in scene.landmarks.values():
# if isinstance(lmk.representation, GroupLineRepresentation):
# xxs = [lmk.representation.line.start.x, lmk.representation.line.end.x]
# yys = [lmk.representation.line.start.y, lmk.representation.line.end.y]
# plt.fill(xxs,yys,facecolor='none',linewidth=2)
# elif isinstance(lmk.representation, RectangleRepresentation):
# rect = lmk.representation.rect
# xxs = [rect.min_point.x,rect.min_point.x,rect.max_point.x,rect.max_point.x]
# yys = [rect.min_point.y,rect.max_point.y,rect.max_point.y,rect.min_point.y]
# plt.fill(xxs,yys,facecolor='none',linewidth=2)
# plt.text(rect.min_point.x+0.01,rect.max_point.y+0.02,lmk.name)
# plt.title(m2s(*m))
# logger( m2s(*m))
# plt.axis('scaled')
# plt.show()
combined_heatmap = None
for sentence in sentences:
posteriors = get_all_sentence_posteriors(sentence, all_meanings, printing=printing)
big_heatmap1 = None
for m,(h1,h2) in zip(ms, heatmapss):
lmk,rel = m
p = posteriors[rel]*posteriors[lmk]
if big_heatmap1 is None:
big_heatmap1 = p*h1
else:
big_heatmap1 += p*h1
if combined_heatmap is None:
combined_heatmap = big_heatmap1
else:
combined_heatmap *= big_heatmap1
combined_heatmaps.append(combined_heatmap)
return combined_heatmaps
object_meaning_applicabilities = {}
for obj_lmk, ms, heatmapss in loi_infos:
for m,(h1,h2) in zip(ms, heatmapss):
ps = [p for (x,y),p in zip(list(product(xs,ys)),h1) if obj_lmk.representation.contains_point( Vec2(x,y) )]
if m not in object_meaning_applicabilities:
object_meaning_applicabilities[m] = {}
object_meaning_applicabilities[m][obj_lmk] = sum(ps)/len(ps)
k = len(loi)
for meaning_dict in object_meaning_applicabilities.values():
total = sum( meaning_dict.values() )
if total != 0:
for obj_lmk in meaning_dict.keys():
meaning_dict[obj_lmk] = meaning_dict[obj_lmk]/total - 1.0/k
total = sum( [value for value in meaning_dict.values() if value > 0] )
for obj_lmk in meaning_dict.keys():
meaning_dict[obj_lmk] = (2 if meaning_dict[obj_lmk] > 0 else 1)*meaning_dict[obj_lmk] - total
sorted_meaning_lists = {}
for m in object_meaning_applicabilities.keys():
for obj_lmk in object_meaning_applicabilities[m].keys():
if obj_lmk not in sorted_meaning_lists:
sorted_meaning_lists[obj_lmk] = []
sorted_meaning_lists[obj_lmk].append( (object_meaning_applicabilities[m][obj_lmk], m) )
for obj_lmk in sorted_meaning_lists.keys():
sorted_meaning_lists[obj_lmk].sort(reverse=True)
min_dists = []
lmk_priors = []
rel_priors = []
lmk_posts = []
rel_posts = []
golden_log_probs = []
golden_entropies = []
golden_ranks = []
rel_types = []
total_mass = []
student_probs = []
student_entropies = []
student_ranks = []
student_rel_types = []
object_answers = []
object_distributions = []
object_sentences =[]
epsilon = 1e-15
for iteration in range(num_iterations):
logger(('Iteration %d comprehension' % iteration),'okblue')
if 'loc_descs' in data:
trajector = data['lmks'][iteration]
logger( 'Teacher chooses: %s' % trajector )
sentences = data['loc_descs'][iteration]
probs, sorted_meanings = zip(*sorted_meaning_lists[trajector][:30])
probs = np.array(probs)# - min(probs)
probs /= probs.sum()
if sentences is None:
(sampled_landmark, sampled_relation) = categorical_sample( sorted_meanings, probs )[0]
logger( 'Teacher tries to say: %s' % m2s(sampled_landmark,sampled_relation) )
head_on = speaker.get_head_on_viewpoint(sampled_landmark)
sentences = [describe( head_on, trajector, sampled_landmark, sampled_relation )]
else:
# Teacher describe
trajector = choice(loi)
# sentence, sampled_relation, sampled_landmark = speaker.describe(trajector, scene, max_level=1)
logger( 'Teacher chooses: %s' % trajector )
# Choose from meanings
probs, sorted_meanings = zip(*sorted_meaning_lists[trajector][:30])
probs = np.array(probs)# - min(probs)
probs /= probs.sum()
(sampled_landmark, sampled_relation) = categorical_sample( sorted_meanings, probs )[0]
logger( 'Teacher tries to say: %s' % m2s(sampled_landmark,sampled_relation) )
# Generate sentence
# _, sentence = generate_sentence(None, False, scene, speaker, meaning=(sampled_landmark, sampled_relation), golden=True, printing=printing)
sentences = [describe( speaker.get_head_on_viewpoint(sampled_landmark), trajector, sampled_landmark, sampled_relation )]
object_sentences.append( ' '.join(sentences) )
logger( 'Teacher says: %s' % ' '.join(sentences))
for i,(p,sm) in enumerate(zip(probs[:15],sorted_meanings[:15])):
lm,re = sm
logger( '%i: %f %s' % (i,p,m2s(*sm)) )
# head_on = speaker.get_head_on_viewpoint(lm)
# speaker.visualize( scene, trajector, head_on, lm, re)
lmk_probs = []
try:
combined_heatmaps = heatmaps_for_sentences(sentences, all_meanings, loi_infos, xs, ys, scene, speaker, step=step)
for combined_heatmap,obj_lmk in zip(combined_heatmaps, loi):
# x = np.array( [list(xs-step*0.5)]*len(ys) )
# y = np.array( [list(ys-step*0.5)]*len(xs) ).T
# logger( combined_heatmap.shape )
# logger( x.shape )
# logger( y.shape )
# logger( xs.shape )
# logger( ys.shape )
# plt.pcolor(x, y, combined_heatmap.reshape((len(xs),len(ys))).T, cmap = 'jet', edgecolors='none', alpha=0.7)
# plt.colorbar()
# for lmk in scene.landmarks.values():
# if isinstance(lmk.representation, GroupLineRepresentation):
# xxs = [lmk.representation.line.start.x, lmk.representation.line.end.x]
# yys = [lmk.representation.line.start.y, lmk.representation.line.end.y]
# plt.fill(xxs,yys,facecolor='none',linewidth=2)
# elif isinstance(lmk.representation, RectangleRepresentation):
# rect = lmk.representation.rect
# xxs = [rect.min_point.x,rect.min_point.x,rect.max_point.x,rect.max_point.x]
# yys = [rect.min_point.y,rect.max_point.y,rect.max_point.y,rect.min_point.y]
# plt.fill(xxs,yys,facecolor='none',linewidth=2)
# plt.text(rect.min_point.x+0.01,rect.max_point.y+0.02,lmk.name)
# plt.axis('scaled')
# plt.axis([scene_bb.min_point.x, scene_bb.max_point.x, scene_bb.min_point.y, scene_bb.max_point.y])
# plt.show()
ps = [p for (x,y),p in zip(list(product(xs,ys)),combined_heatmap) if obj_lmk.representation.contains_point( Vec2(x,y) )]
# print ps, xs.shape, ys.shape, combined_heatmap.shape
lmk_probs.append( (sum(ps)/len(ps), obj_lmk) )
lmk_probs = sorted(lmk_probs, reverse=True)
top_p, top_lmk = lmk_probs[0]
lprobs, lmkss = zip(*lmk_probs)
answer, distribution = loi.index(trajector), [ (lprob, loi.index(lmk)) for lprob,lmk in lmk_probs ]
logger( sorted(zip(np.array(lprobs)/sum(lprobs), [(l.name, l.color, l.object_class) for l in lmkss]), reverse=True) )
logger( 'I bet %f you are talking about a %s %s %s' % (top_p/sum(lprobs), top_lmk.name, top_lmk.color, top_lmk.object_class) )
# objects.append(top_lmk)
except Exception as e:
logger( 'Unable to get object from sentence. %s' % e, 'fail' )
answer = None
top_lmk = None
distribution = [(0,False)]
object_answers.append( answer )
object_distributions.append( distribution )
# Present top_lmk to teacher
if top_lmk == trajector:
# Give morphine
pass
else:
updates, _ = zip(*sorted_meaning_lists[trajector][:30])
howmany=5
for sentence in sentences:
for _ in range(howmany):
meaning = categorical_sample( sorted_meanings, probs )[0]
update = updates[ sorted_meanings.index(meaning) ]
try:
accept_object_correction( meaning, sentence, update*scale, printing=printing)
except:
pass
for update, meaning in sorted_meaning_lists[trajector][-howmany:]:
try:
accept_object_correction( meaning, sentence, update*scale, printing=printing)
except:
pass
for _ in range(0):
logger(('Iteration %d production' % iteration),'okblue')
rand_p = Vec2(random()*table.width+table.min_point.x, random()*table.height+table.min_point.y)
trajector = Landmark( 'point', PointRepresentation(rand_p), None, Landmark.POINT )
meaning, sentence = generate_sentence(rand_p, False, scene, speaker, usebest=True, printing=printing)
logger( 'Generated sentence: %s' % sentence)
landmark = meaning.args[0]
# if ambiguous_pointing:
# pointing_point = landmark.representation.middle + Vec2(random()*0.1-0.05,random()*0.1-0.05)
#_, bestsentence = generate_sentence(rand_p, False, scene, speaker, usebest=True, printing=printing)
try:
golden_posteriors = get_all_sentence_posteriors(sentence, meanings, golden=True, printing=printing)
except ParseError as e:
logger( e )
prob = 0
rank = len(meanings)-1
entropy = 0
ed = len(sentence)
golden_log_probs.append( prob )
golden_entropies.append( entropy )
golden_ranks.append( rank )
min_dists.append( ed )
continue
epsilon = 1e-15
ps = [[golden_posteriors[lmk]*golden_posteriors[rel],(lmk,rel)] for lmk, rel in meanings if (lmk == landmark)]
all_lmk_probs = speaker.all_landmark_probs(landmarks, Landmark(None, PointRepresentation(rand_p), None))
all_lmk_probs = dict(zip(landmarks, all_lmk_probs))
temp = None
for i,(p,(lmk,rel)) in enumerate(ps):
# lmk,rel = meanings[i]
# logger( '%f, %s' % (p, m2s(lmk,rel)))
head_on = speaker.get_head_on_viewpoint(lmk)
ps[i][0] *= speaker.get_probabilities_points( np.array([rand_p]), rel, head_on, lmk)[0]
if lmk == meaning.args[0] and rel == meaning.args[3]:
temp = i
ps,_meanings = zip(*ps)
print ps
ps = np.array(ps)
ps += epsilon
ps = ps/ps.sum()
temp = ps[temp]
ps = sorted(zip(ps,_meanings),reverse=True)
logger( 'Attempted to say: %s' % m2s(meaning.args[0],meaning.args[3]) )
logger( 'Interpreted as: %s' % m2s(ps[0][1][0],ps[0][1][1]) )
logger( 'Attempted: %f vs Interpreted: %f' % (temp, ps[0][0]))
# logger( 'Golden entropy: %f, Max entropy %f' % (golden_entropy, max_entropy))
landmark, relation = ps[0][1]
head_on = speaker.get_head_on_viewpoint(landmark)
all_descs = speaker.get_all_meaning_descriptions(trajector, scene, landmark, relation, head_on, 1)
distances = []
for desc in all_descs:
distances.append([edit_distance( sentence, desc ), desc])
distances.sort()
print distances
correction = distances[0][1]
# if correction == sentence:
# correction = None
# logger( 'No correction!!!!!!!!!!!!!!!!!!', 'okgreen' )
accept_correction( meaning, correction, update_scale=scale, eval_lmk=False, multiply=False, printing=printing )
def probs_metric(inverse=False):
rand_p = Vec2(random()*table.width+table.min_point.x, random()*table.height+table.min_point.y)
try:
bestmeaning, bestsentence = generate_sentence(rand_p, False, scene, speaker, usebest=True, golden=inverse, printing=printing)
sampled_landmark, sampled_relation = bestmeaning.args[0], bestmeaning.args[3]
golden_posteriors = get_all_sentence_posteriors(bestsentence, meanings, golden=(not inverse), printing=printing)
# lmk_prior = speaker.get_landmark_probability(sampled_landmark, landmarks, PointRepresentation(rand_p))[0]
all_lmk_probs = speaker.all_landmark_probs(landmarks, Landmark(None, PointRepresentation(rand_p), None))
all_lmk_probs = dict(zip(landmarks, all_lmk_probs))
lmk_prior = all_lmk_probs[sampled_landmark]
head_on = speaker.get_head_on_viewpoint(sampled_landmark)
rel_prior = speaker.get_probabilities_points( np.array([rand_p]), sampled_relation, head_on, sampled_landmark)
lmk_post = golden_posteriors[sampled_landmark]
rel_post = golden_posteriors[sampled_relation]
ps = np.array([golden_posteriors[lmk]*golden_posteriors[rel] for lmk, rel in meanings])
rank = None
for i,p in enumerate(ps):
lmk,rel = meanings[i]
# logger( '%f, %s' % (p, m2s(lmk,rel)))
head_on = speaker.get_head_on_viewpoint(lmk)
# ps[i] *= speaker.get_landmark_probability(lmk, landmarks, PointRepresentation(rand_p))[0]
ps[i] *= all_lmk_probs[lmk]
ps[i] *= speaker.get_probabilities_points( np.array([rand_p]), rel, head_on, lmk)
if lmk == sampled_landmark and rel == sampled_relation:
idx = i
ps += epsilon
ps = ps/ps.sum()
prob = ps[idx]
rank = sorted(ps, reverse=True).index(prob)
entropy = entropy_of_probs(ps)
except (ParseError,RuntimeError) as e:
logger( e )
lmk_prior = 0
rel_prior = 0
lmk_post = 0
rel_post = 0
prob = 0
rank = len(meanings)-1
entropy = 0
distances = [[None]]
head_on = speaker.get_head_on_viewpoint(sampled_landmark)
all_descs = speaker.get_all_meaning_descriptions(trajector, scene, sampled_landmark, sampled_relation, head_on, 1)
distances = []
for desc in all_descs:
distances.append([edit_distance( bestsentence, desc ), desc])
distances.sort()
return lmk_prior,rel_prior,lmk_post,rel_post,\
prob,entropy,rank,distances[0][0],type(sampled_relation)
def db_mass():
total = CProduction.get_production_sum(None)
total += CWord.get_word_sum(None)
return total
def choosing_object_metric():
trajector = choice(loi)
sentence, sampled_relation, sampled_landmark = speaker.describe(trajector, scene, max_level=1)
lmk_probs = []
try:
combined_heatmaps = heatmaps_for_sentence(sentence, all_meanings, loi_infos, xs, ys, scene, speaker, step=step)
for combined_heatmap,obj_lmk in zip(combined_heatmaps, loi):
ps = [p for (x,y),p in zip(list(product(xs,ys)),combined_heatmap) if obj_lmk.representation.contains_point( Vec2(x,y) )]
# print ps, xs.shape, ys.shape, combined_heatmap.shape
lmk_probs.append( (sum(ps)/len(ps), obj_lmk) )
lmk_probs = sorted(lmk_probs, reverse=True)
top_p, top_lmk = lmk_probs[0]
lprobs, lmkss = zip(*lmk_probs)
logger( sorted(zip(np.array(lprobs)/sum(lprobs), [(l.name, l.color, l.object_class) for l in lmkss]), reverse=True) )
logger( 'I bet %f you are talking about a %s %s %s' % (top_p/sum(lprobs), top_lmk.name, top_lmk.color, top_lmk.object_class) )
# objects.append(top_lmk)
except Exception as e:
logger( 'Unable to get object from sentence. %s' % e, 'fail' )
print traceback.format_exc()
exit()
return loi.index(trajector), [ (lprob, loi.index(lmk)) for lprob,lmk in lmk_probs ]
if golden_metric:
lmk_prior,rel_prior,lmk_post,rel_post,prob,entropy,rank,ed,rel_type = probs_metric()
else:
lmk_prior,rel_prior,lmk_post,rel_post,prob,entropy,rank,ed,rel_type = [None]*9
lmk_priors.append( lmk_prior )
rel_priors.append( rel_prior )
lmk_posts.append( lmk_post )
rel_posts.append( rel_post )
golden_log_probs.append( prob )
golden_entropies.append( entropy )
golden_ranks.append( rank )
min_dists.append( ed )
rel_types.append( rel_type )
if mass_metric:
total_mass.append( db_mass() )
else:
total_mass.append( None )
if student_metric:
_,_,_,_,student_prob,student_entropy,student_rank,_,student_rel_type = probs_metric(inverse=True)
else:
_,_,_,_,student_prob,student_entropy,student_rank,_,student_rel_type = \
None, None, None, None, None, None, None, None, None
student_probs.append( student_prob )
student_entropies.append( student_entropy )
student_ranks.append( student_rank )
student_rel_types.append( student_rel_type )
# if choosing_metric:
# answer, distribution = choosing_object_metric()
# else:
# answer, distribution = None, None
# object_answers.append( answer )
# object_distributions.append( distribution )
return zip(lmk_priors, rel_priors, lmk_posts, rel_posts,
golden_log_probs, golden_entropies, golden_ranks,
min_dists, rel_types, total_mass, student_probs,
student_entropies, student_ranks, student_rel_types,
object_answers, object_distributions, object_sentences)
def loop(data):
time.sleep(data['delay'])
scene = data['scene']
speaker = data['speaker']
utils.scene.set_scene(scene,speaker)
num_iterations = len(data['loc_descs'])
all_meanings = data['all_meanings']
loi = data['loi']
loi_infos = data['loi_infos']
landmarks = data['landmarks']
sorted_meaning_lists = data['sorted_meaning_lists']
learn_objects = data['learn_objects']
def heatmaps_for_sentences(sentences, all_meanings, loi_infos, xs, ys, scene, speaker, step=0.02):
printing=False
x = np.array( [list(xs-step*0.5)]*len(ys) )
y = np.array( [list(ys-step*0.5)]*len(xs) ).T
scene_bb = scene.get_bounding_box()
scene_bb = scene_bb.inflate( Vec2(scene_bb.width*0.5,scene_bb.height*0.5) )
combined_heatmaps = []
for obj_lmk, ms, heatmapss in loi_infos:
combined_heatmap = None
for sentence in sentences:
posteriors = None
while not posteriors:
try:
posteriors = get_all_sentence_posteriors(sentence, all_meanings, printing=printing)
except ParseError as pe:
raise pe
except Exception as e:
print e
sleeptime = random()*0.5
logger('Sleeping for %f and retrying "%s"' % (sleeptime,sentence))
time.sleep(sleeptime)
continue
big_heatmap1 = None
for m,(h1,h2) in zip(ms, heatmapss):
lmk,rel = m
p = posteriors[rel]*posteriors[lmk]
if big_heatmap1 is None:
big_heatmap1 = p*h1
else:
big_heatmap1 += p*h1
if combined_heatmap is None:
combined_heatmap = big_heatmap1
else:
combined_heatmap *= big_heatmap1
combined_heatmaps.append(combined_heatmap)
return combined_heatmaps
object_answers = []
object_distributions = []
object_sentences =[]
object_ids = []
epsilon = 1e-15
for iteration in range(num_iterations):
logger(('Iteration %d comprehension' % iteration),'okblue')
trajector = data['lmks'][iteration]
if trajector is None:
trajector = choice(loi)
logger( 'Teacher chooses: %s' % trajector )
probs, sorted_meanings = zip(*sorted_meaning_lists[trajector][:30])
probs = np.array(probs)# - min(probs)
probs /= probs.sum()
sentences = data['loc_descs'][iteration]
if sentences is None:
(sampled_landmark, sampled_relation) = categorical_sample( sorted_meanings, probs )[0]
logger( 'Teacher tries to say: %s' % m2s(sampled_landmark,sampled_relation) )
head_on = speaker.get_head_on_viewpoint(sampled_landmark)
sentences = [describe( head_on, trajector, sampled_landmark, sampled_relation )]
object_sentences.append( ' '.join(sentences) )
object_ids.append( data['ids'][iteration] )
logger( 'Teacher says: %s' % ' '.join(sentences))
for i,(p,sm) in enumerate(zip(probs[:15],sorted_meanings[:15])):
lm,re = sm
logger( '%i: %f %s' % (i,p,m2s(*sm)) )
lmk_probs = []
try:
combined_heatmaps = heatmaps_for_sentences(sentences, all_meanings, loi_infos, xs, ys, scene, speaker, step=step)
except ParseError as e:
logger( 'Unable to get object from sentence. %s' % e, 'fail' )
top_lmk = None
distribution = [(0, False, False)]
else:
for combined_heatmap,obj_lmk in zip(combined_heatmaps, loi):
ps = [p for (x,y),p in zip(list(product(xs,ys)),combined_heatmap) if obj_lmk.representation.contains_point( Vec2(x,y) )]
# print ps, xs.shape, ys.shape, combined_heatmap.shape
lmk_probs.append( (sum(ps)/len(ps), obj_lmk) )
lmk_probs = sorted(lmk_probs, reverse=True)
top_p, top_lmk = lmk_probs[0]
lprobs, lmkss = zip(*lmk_probs)
distribution = [ (lprob, lmk.name, loi.index(lmk)) for lprob,lmk in lmk_probs ]
logger( sorted(zip(np.array(lprobs)/sum(lprobs), [(l.name, l.color, l.object_class) for l in lmkss]), reverse=True) )
logger( 'I bet %f you are talking about a %s %s %s' % (top_p/sum(lprobs), top_lmk.name, top_lmk.color, top_lmk.object_class) )
# objects.append(top_lmk)
answer = (trajector.name,loi.index(trajector))
object_answers.append( answer )
object_distributions.append( distribution )
# Present top_lmk to teacher
logger("top_lmk == trajector: %r, learn_objects: %r" % (top_lmk == trajector,learn_objects), 'okgreen')
if top_lmk == trajector or not learn_objects:
# Give morphine
logger("Ahhhhh, morphine...", 'okgreen')
pass
else:
logger("LEARNING!!!!!!!!!!!", 'okgreen')
updates, _ = zip(*sorted_meaning_lists[trajector][:30])
howmany=5
for sentence in sentences:
for _ in range(howmany):
meaning = categorical_sample( sorted_meanings, probs )[0]
update = updates[ sorted_meanings.index(meaning) ]
try:
accept_object_correction( meaning, sentence, update*scale, printing=printing)
except:
pass
for update, meaning in sorted_meaning_lists[trajector][-howmany:]:
try:
accept_object_correction( meaning, sentence, update*scale, printing=printing)
except:
pass
return zip(object_answers, object_distributions, object_sentences, object_ids)