class Dialog(object):
def __init__(self, agents, args):
# For now we only suppport dialog of 2 agents
assert len(agents) == 2
self.agents = agents
self.args = args
self.domain = domain.get_domain(args.domain)
self.metrics = MetricsContainer()
self._register_metrics()
def _register_metrics(self):
self.metrics.register_average('dialog_len')
self.metrics.register_average('sent_len')
self.metrics.register_percentage('agree')
self.metrics.register_moving_percentage('moving_agree')
self.metrics.register_average('advantage')
self.metrics.register_moving_average('moving_advantage')
self.metrics.register_time('time')
self.metrics.register_average('comb_rew')
self.metrics.register_average('agree_comb_rew')
for agent in self.agents:
self.metrics.register_average('%s_rew' % agent.name)
self.metrics.register_moving_average('%s_moving_rew' % agent.name)
self.metrics.register_average('agree_%s_rew' % agent.name)
self.metrics.register_percentage('%s_sel' % agent.name)
self.metrics.register_uniqueness('%s_unique' % agent.name)
# text metrics
if self.args.ref_text:
ref_text = ' '.join(data.read_lines(self.args.ref_text))
self.metrics.register_ngram('full_match', text=ref_text)
def _is_selection(self, out):
"""if dialog end"""
return len(out) == 1 and (out[0] in ['<selection>', '<no_agreement>'])
def show_metrics(self):
return ' '.join(
['%s=%s' % (k, v) for k, v in self.metrics.dict().items()])
def run(self, ctxs, logger, max_words=5000):
assert len(self.agents) == len(ctxs)
for agent, ctx, partner_ctx in zip(self.agents, ctxs, reversed(ctxs)):
agent.feed_context(ctx)
agent.feed_partner_context(partner_ctx)
logger.dump_ctx(agent.name, ctx)
logger.dump('-' * 80)
# Choose who goes first by random
if np.random.rand() < 0.5:
writer, reader = self.agents
else:
reader, writer = self.agents
conv = []
self.metrics.reset()
#words_left = np.random.randint(50, 200)
words_left = max_words # max 5000 words
length = 0
expired = False
turn_num = 0
while True:
# print('dialog turn [{}]'.format(turn_num))
turn_num += 1
# print('\twrite')
out = writer.write(max_words=20) #words_left)
# print('\twrite done')
words_left -= len(out)
length += len(out)
self.metrics.record('sent_len', len(out))
if 'full_match' in self.metrics.metrics:
self.metrics.record('full_match', out)
self.metrics.record('%s_unique' % writer.name, out)
conv.append(out)
# print('\tread')
reader.read(out)
if not writer.human:
logger.dump_sent(writer.name, out)
if self._is_selection(out):
self.metrics.record('%s_sel' % writer.name, 1)
self.metrics.record('%s_sel' % reader.name, 0)
break
if words_left <= 1:
break
writer, reader = reader, writer
# print('turn_num:{}'.format(turn_num))
choices = []
for agent in self.agents:
choice = agent.choose()
choices.append(choice)
logger.dump_choice(agent.name,
choice[:self.domain.selection_length() // 2])
agree, rewards = self.domain.score_choices(choices, ctxs)
if expired:
agree = False
logger.dump('-' * 80)
logger.dump_agreement(agree)
for i, (agent, reward) in enumerate(zip(self.agents, rewards)):
logger.dump_reward(agent.name, agree, reward)
j = 1 if i == 0 else 0
agent.update(agree,
reward,
choice=choices[i],
partner_choice=choices[j],
partner_input=ctxs[j],
partner_reward=rewards[j])
if agree:
self.metrics.record('advantage', rewards[0] - rewards[1])
self.metrics.record('moving_advantage', rewards[0] - rewards[1])
self.metrics.record('agree_comb_rew', np.sum(rewards))
for agent, reward in zip(self.agents, rewards):
self.metrics.record('agree_%s_rew' % agent.name, reward)
self.metrics.record('time')
self.metrics.record('dialog_len', len(conv))
self.metrics.record('agree', int(agree))
self.metrics.record('moving_agree', int(agree))
self.metrics.record('comb_rew', np.sum(rewards) if agree else 0)
for agent, reward in zip(self.agents, rewards):
self.metrics.record('%s_rew' % agent.name, reward if agree else 0)
self.metrics.record('%s_moving_rew' % agent.name,
reward if agree else 0)
logger.dump('-' * 80)
logger.dump(self.show_metrics())
logger.dump('-' * 80)
for ctx, choice in zip(ctxs, choices):
logger.dump('debug: %s %s' % (' '.join(ctx), ' '.join(choice)))
return conv, agree, rewards
class Dialog(object):
def __init__(self, agents, args, markable_detector,
markable_detector_corpus):
# For now we only suppport dialog of 2 agents
assert len(agents) == 2
self.agents = agents
self.args = args
self.domain = domain.get_domain(args.domain)
self.metrics = MetricsContainer()
self._register_metrics()
self.markable_detector = markable_detector
self.markable_detector_corpus = markable_detector_corpus
self.selfplay_markables = {}
self.selfplay_referents = {}
def _register_metrics(self):
self.metrics.register_average('dialog_len')
self.metrics.register_average('sent_len')
self.metrics.register_percentage('agree')
self.metrics.register_moving_percentage('moving_agree')
self.metrics.register_average('advantage')
self.metrics.register_moving_average('moving_advantage')
self.metrics.register_time('time')
self.metrics.register_average('comb_rew')
self.metrics.register_average('agree_comb_rew')
for agent in self.agents:
self.metrics.register_average('%s_rew' % agent.name)
self.metrics.register_moving_average('%s_moving_rew' % agent.name)
self.metrics.register_average('agree_%s_rew' % agent.name)
self.metrics.register_percentage('%s_make_sel' % agent.name)
self.metrics.register_uniqueness('%s_unique' % agent.name)
if "plot_metrics" in self.args and self.args.plot_metrics:
self.metrics.register_select_frequency('%s_sel_bias' %
agent.name)
# text metrics
if self.args.ref_text:
ref_text = ' '.join(data.read_lines(self.args.ref_text))
self.metrics.register_ngram('full_match', text=ref_text)
def _is_selection(self, out):
return '<selection>' in out
def show_metrics(self):
return ' '.join(
['%s=%s' % (k, v) for k, v in self.metrics.dict().items()])
def plot_metrics(self):
self.metrics.plot()
def run(self, ctxs, logger, max_words=5000):
scenario_id = ctxs[0][0]
for agent, agent_id, ctx, real_ids in zip(self.agents, [0, 1], ctxs[1],
ctxs[2]):
agent.feed_context(ctx)
agent.real_ids = real_ids
agent.agent_id = agent_id
# Choose who goes first by random
if np.random.rand() < 0.5:
writer, reader = self.agents
else:
reader, writer = self.agents
conv = []
speaker = []
self.metrics.reset()
words_left = max_words
length = 0
expired = False
while True:
out = writer.write(max_words=words_left)
words_left -= len(out)
length += len(out)
self.metrics.record('sent_len', len(out))
if 'full_match' in self.metrics.metrics:
self.metrics.record('full_match', out)
self.metrics.record('%s_unique' % writer.name, out)
conv.append(out)
speaker.append(writer.agent_id)
reader.read(out)
if not writer.human:
logger.dump_sent(writer.name, out)
if logger.scenarios and self.args.log_attention:
attention = writer.get_attention()
if attention is not None:
logger.dump_attention(writer.name, writer.agent_id,
scenario_id, attention)
if self._is_selection(out):
self.metrics.record('%s_make_sel' % writer.name, 1)
self.metrics.record('%s_make_sel' % reader.name, 0)
break
if words_left <= 1:
break
writer, reader = reader, writer
choices = []
for agent in self.agents:
choice = agent.choose()
choices.append(choice)
if logger.scenarios:
logger.dump_choice(scenario_id, choices)
if "plot_metrics" in self.args and self.args.plot_metrics:
for agent in [0, 1]:
for obj in logger.scenarios[scenario_id]['kbs'][agent]:
if obj['id'] == choices[agent]:
self.metrics.record(
'%s_sel_bias' % writer.name, obj,
logger.scenarios[scenario_id]['kbs'][agent])
agree, rewards = self.domain.score_choices(choices, ctxs)
if expired:
agree = False
logger.dump('-' * 80)
logger.dump_agreement(agree)
for i, (agent, reward) in enumerate(zip(self.agents, rewards)):
j = 1 if i == 0 else 0
agent.update(agree, reward, choice=choices[i])
if agree:
self.metrics.record('advantage', rewards[0] - rewards[1])
self.metrics.record('moving_advantage', rewards[0] - rewards[1])
self.metrics.record('agree_comb_rew', np.sum(rewards))
for agent, reward in zip(self.agents, rewards):
self.metrics.record('agree_%s_rew' % agent.name, reward)
self.metrics.record('time')
self.metrics.record('dialog_len', len(conv))
self.metrics.record('agree', int(agree))
self.metrics.record('moving_agree', int(agree))
self.metrics.record('comb_rew', np.sum(rewards) if agree else 0)
for agent, reward in zip(self.agents, rewards):
self.metrics.record('%s_rew' % agent.name, reward if agree else 0)
self.metrics.record('%s_moving_rew' % agent.name,
reward if agree else 0)
if self.markable_detector is not None and self.markable_detector_corpus is not None:
markable_list = []
referents_dict = {}
markable_starts = []
for agent in [0, 1]:
dialog_tokens = []
dialog_text = ""
markables = []
for spkr, uttr in zip(speaker, conv):
if spkr == agent:
dialog_tokens.append("YOU:")
else:
dialog_tokens.append("THEM:")
dialog_tokens += uttr
dialog_text += str(spkr) + ": " + " ".join(
uttr[:-1]) + "\n"
words = self.markable_detector_corpus.word_dict.w2i(
dialog_tokens)
words = torch.Tensor(words).long().cuda()
score, tag_seq = self.markable_detector(words)
referent_inpt = []
markable_ids = []
my_utterance = None
current_text = ""
for i, word in enumerate(words):
if word.item(
) == self.markable_detector_corpus.word_dict.word2idx[
"YOU:"]:
my_utterance = True
current_speaker = agent
elif word.item(
) == self.markable_detector_corpus.word_dict.word2idx[
"THEM:"]:
my_utterance = False
current_speaker = 1 - agent
if my_utterance:
if tag_seq[i].item(
) == self.markable_detector_corpus.bio_dict["B"]:
start_idx = i
for j in range(i + 1, len(tag_seq)):
if tag_seq[j].item(
) != self.markable_detector_corpus.bio_dict[
"I"]:
end_idx = j - 1
break
for j in range(i + 1, len(tag_seq)):
if tag_seq[j].item(
) in self.markable_detector_corpus.word_dict.w2i(
["<eos>", "<selection>"]):
end_uttr = j
break
markable_start = len(current_text + " ")
if markable_start not in markable_starts:
referent_inpt.append(
[start_idx, end_idx, end_uttr])
markable_ids.append(len(markable_starts))
# add markable
markable = {}
markable["start"] = markable_start
markable["end"] = len(
current_text + " " + " ".join(
dialog_tokens[start_idx:end_idx +
1]))
#markable["start"] = len(str(spkr) + ": " + " ".join(dialog_tokens[1:start_idx]) + " ")
#markable["end"] = len(str(spkr) + ": " + " ".join(dialog_tokens[1:end_idx + 1]))
markable["markable_id"] = len(
markable_starts)
markable["speaker"] = current_speaker
markable["text"] = " ".join(
dialog_tokens[start_idx:end_idx + 1])
markable_starts.append(markable["start"])
markable_list.append(markable)
if word.item(
) == self.markable_detector_corpus.word_dict.word2idx[
"YOU:"]:
current_text += "{}:".format(current_speaker)
elif word.item(
) == self.markable_detector_corpus.word_dict.word2idx[
"THEM:"]:
current_text += "{}:".format(current_speaker)
elif word.item(
) in self.markable_detector_corpus.word_dict.w2i(
["<eos>", "<selection>"]):
current_text += "\n"
else:
current_text += " " + self.markable_detector_corpus.word_dict.idx2word[
word.item()]
assert len(current_text) == len(dialog_text)
ref_out = self.agents[agent].predict_referents(
referent_inpt)
if ref_out is not None:
for i, markable_id in enumerate(markable_ids):
ent_ids = [
ent["id"] for ent in
logger.scenarios[scenario_id]['kbs'][agent]
]
referents = []
for j, is_referent in enumerate(
(ref_out[i] > 0).tolist()):
if is_referent:
referents.append("agent_" + str(agent) +
"_" + ent_ids[j])
referents_dict[markable_id] = referents
#markable_starts = list(set(markable_starts))
# reindex markable ids
markable_id_and_start = [
(markable_id, markable_start)
for markable_id, markable_start in zip(
range(len(markable_starts)), markable_starts)
]
reindexed_markable_ids = [
markable_id for markable_id, _ in sorted(markable_id_and_start,
key=lambda x: x[1])
]
self.selfplay_markables[scenario_id] = {}
self.selfplay_referents[scenario_id] = {}
# add markables
self.selfplay_markables[scenario_id]["markables"] = []
for new_markable_id, old_markable_id in enumerate(
reindexed_markable_ids):
markable = markable_list[old_markable_id]
markable["markable_id"] = "M{}".format(new_markable_id + 1)
self.selfplay_markables[scenario_id]["markables"].append(
markable)
# add dialogue text
self.selfplay_markables[scenario_id]["text"] = dialog_text
# add final selections
self.selfplay_markables[scenario_id]["selections"] = choices
# add referents
for new_markable_id, old_markable_id in enumerate(
reindexed_markable_ids):
referents = referents_dict[old_markable_id]
self.selfplay_referents[scenario_id]["M{}".format(
new_markable_id + 1)] = referents
logger.dump('-' * 80)
logger.dump(self.show_metrics())
logger.dump('-' * 80)
#for ctx, choice in zip(ctxs, choices):
# logger.dump('debug: %s %s' % (' '.join(ctx), ' '.join(choice)))
return conv, agree, rewards