def load_listener(curr_opts, trainpop_files):
model = SpeakerListener(args).to(device=args.device)
model.beholder = Beholder(curr_opts).to(device=args.device)
model.listener = Listener(curr_opts, model.beholder).to(device=args.device)
model_dict = torch.load(os.path.join(trainpop_files, f'pop{args.seed}.pt'),
map_location=args.device)
model.listener.load_state_dict(model_dict)
return model
def disable_get_listener_name(update: Update, context: CallbackContext):
listener_id = update.callback_query.data
listener = Listener.get(Listener.id == listener_id)
listener.enable = False
listener.save()
msg = f'Disable listener {listener.description}'
update.effective_message.edit_text(msg)
return ConversationHandler.END
def post(self):
data = ping_parser.parse_args()
dt = datetime.datetime.utcnow()
listener = Listener.get(Listener.key == data["key"])
if listener.enable:
Message.create(listener=listener, data="PING", timestamp=dt)
return {}, 200
return {}, 403
def post(self):
data = update_parser.parse_args()
dt = datetime.datetime.utcnow()
listener = Listener.get(Listener.key == data["key"])
if listener.enable:
Message.create(listener=listener, data=data["data"], timestamp=dt)
msg = f"{listener.description} listener new message:\n{data['data']}"
self.notify(listener.chat_id, msg)
return {}, 200
return {}, 403
def create_listener_get_description(update: Update, context: CallbackContext):
description = update.message.text
chat_id: int = update.message.chat_id
listener = Listener.create(chat_id=chat_id,
description=description,
key=str(uuid.uuid4()))
update.message.reply_text(
text=f"Create listener with key = {listener.key}",
reply_markup=ReplyKeyboardRemove())
return ConversationHandler.END
def __init__(self,
target_size,
vocab_size,
bias,
test_data,
seed,
episodes=1000):
self.data = test_data
self.vocab_size = vocab_size
self.target_size = target_size
self.speaker_bias = 1.0 - bias
self.listener_bias = bias
self.speaker = Speaker(512, self.target_size, self.vocab_size,
self.speaker_bias, seed)
self.listener = Listener(512, self.vocab_size, self.target_size,
self.listener_bias, seed)
self.episode_rewards = []
self.accuracy_history = deque(maxlen=100)
self.symbols_used = {}
self.synonym_array = np.zeros((target_size, vocab_size))
self.accuracy_record = []
self.episodes = 1000
def __init__(self,target_size,vocab_size,bias,test_data,seed,episodes=1000):
self.data = test_data
self.vocab_size = vocab_size
self.target_size = target_size
self.speaker_bias = 1.0 - bias
self.listener_bias = bias
self.speaker = Speaker(512,self.target_size,self.vocab_size,self.speaker_bias,seed)
self.listener = Listener(512,self.vocab_size,self.target_size,self.listener_bias,seed)
self.episode_rewards = []
self.accuracy_history = deque(maxlen=100)
self.symbols_used = {}
self.synonym_array = np.zeros((target_size,vocab_size))
self.accuracy_record = []
self.episodes = 1000
def listener_index():
if request.method=='POST':
listener_name=request.form['name']
profile_pic=request.files['profile_pic']
filename=images.save(profile_pic)
newuser=Listener(name=listener_name,profile_pic=filename)
try:
db.session.add(newuser)
db.session.commit()
return redirect('/listeners')
except:
return 'There was an issue adding listener'
else:
listeners=Listener.query.order_by('name').all()
return render_template('listener_index.html', listeners=listeners)
class Game_Master():
def __init__(self,
target_size,
vocab_size,
bias,
test_data,
seed,
episodes=1000):
self.data = test_data
self.vocab_size = vocab_size
self.target_size = target_size
self.speaker_bias = 1.0 - bias
self.listener_bias = bias
self.speaker = Speaker(512, self.target_size, self.vocab_size,
self.speaker_bias, seed)
self.listener = Listener(512, self.vocab_size, self.target_size,
self.listener_bias, seed)
self.episode_rewards = []
self.accuracy_history = deque(maxlen=100)
self.symbols_used = {}
self.synonym_array = np.zeros((target_size, vocab_size))
self.accuracy_record = []
self.episodes = 1000
def create_labels(self, num_labels, num_repeats):
arr = np.array([])
for i in range(num_labels):
n = np.repeat(i, num_repeats)
arr = np.concatenate((arr, n))
np.random.shuffle(arr)
return arr
def sample(self, label, test_or_train="X_train"):
train = self.data[test_or_train]
size = train[0].shape[0]
target_one_hot = to_categorical(label, num_classes=self.target_size)
target_image = 0
image_list = []
for i in range(self.target_size):
sampled_image = np.expand_dims(train[i][np.random.choice(size), :],
0)
if i == label:
target_image = sampled_image
image_list.append(sampled_image)
return target_image, image_list, target_one_hot
def cross_entropy(self, predictions, targets, epsilon=1e-12):
predictions = np.clip(predictions, epsilon, 1. - epsilon)
N = predictions.shape[0]
ce = -np.sum(np.sum(targets * np.log(predictions + 1e-9))) / N
return ce
def test(self):
y = self.create_labels(10, 1)
one_hot = to_categorical(y)
it = 0
while it < y.shape[0]:
label = y[it]
target_image, image_list, target_one_hot = self.sample(
label, "X_test")
speaker_action, speaker_probs = self.speaker.act(target_image)
speaker_action_class = np.expand_dims(
to_categorical(speaker_action, num_classes=self.vocab_size), 0)
image_list.append(speaker_action_class)
listener_action, listener_probs = self.listener.act(image_list)
if listener_action == label:
speaker_reward = 1.0
listener_reward = 1.0
else:
speaker_reward = 0.0
listener_reward = 0.0
self.synonym_array[int(label)][int(speaker_action)] += 1
it += 1
def play(self):
y = self.create_labels(10, 100)
one_hot = to_categorical(y)
bar = tqdm(np.arange(self.episodes))
for i in bar:
it = 0
acc = np.zeros(self.target_size)
right = 0
while it < y.shape[0]:
label = y[it]
target_image, image_list, target_one_hot = self.sample(label)
speaker_action, speaker_probs = self.speaker.act(target_image)
speaker_action_class = np.expand_dims(
to_categorical(speaker_action,
num_classes=self.vocab_size), 0)
image_list.append(speaker_action_class)
listener_action, listener_probs = self.listener.act(image_list)
if listener_action == label:
speaker_reward = 1.0
listener_reward = 1.0
right += 1
else:
speaker_reward = 0.0
listener_reward = 0.0
acc = np.vstack((acc, listener_probs))
self.speaker.save(target_image, speaker_action, speaker_probs,
speaker_reward)
self.listener.save(image_list, speaker_action_class,
listener_action, listener_probs,
listener_reward)
it += 1
accuracy = right / y.shape[0]
total = np.sum(self.speaker.params["rewards"])
cross = self.cross_entropy(acc[1:, :], one_hot)
self.episode_rewards.append(total)
self.accuracy_history.append(accuracy)
self.accuracy_record.append(accuracy)
bar.set_description("Cross Entropy: " + str(cross) +
", Rolling Acc 100: " +
str(np.mean(self.accuracy_history)) +
", Accuracy: " + str(accuracy))
self.speaker.train()
self.listener.train()
self.test()
dt = datetime.datetime.utcnow()
listener = Listener.get(Listener.key == data["key"])
if listener.enable:
Message.create(listener=listener, data=data["data"], timestamp=dt)
msg = f"{listener.description} listener new message:\n"
self.notify(listener.chat_id, data['data'], msg)
return {}, 200
return {}, 403
api.add_resource(Update, '/update')
api.add_resource(UpdateImage, '/update_image')
admin = Admin(app,
url='/listener_bot_api/admin',
name='ListenerBotAdmin',
template_mode='bootstrap3')
admin.add_view(ListenerAdmin(Listener))
admin.add_view(MessageAdmin(Message))
if __name__ == '__main__':
try:
Listener.create_table()
Message.create_table()
except:
pass
app.run()
class Game_Master():
def __init__(self,target_size,vocab_size,bias,test_data,seed,episodes=1000):
self.data = test_data
self.vocab_size = vocab_size
self.target_size = target_size
self.speaker_bias = 1.0 - bias
self.listener_bias = bias
self.speaker = Speaker(512,self.target_size,self.vocab_size,self.speaker_bias,seed)
self.listener = Listener(512,self.vocab_size,self.target_size,self.listener_bias,seed)
self.episode_rewards = []
self.accuracy_history = deque(maxlen=100)
self.symbols_used = {}
self.synonym_array = np.zeros((target_size,vocab_size))
self.accuracy_record = []
self.episodes = 1000
def create_labels(self,num_labels,num_repeats):
arr = np.array([])
for i in range(num_labels):
n = np.repeat(i,num_repeats)
arr = np.concatenate((arr,n))
np.random.shuffle(arr)
return arr
def sample(self,label,test_or_train="X_train"):
train = self.data[test_or_train]
size = train[0].shape[0]
target_one_hot = to_categorical(label,num_classes=self.target_size)
target_image = 0
image_list = []
for i in range(self.target_size):
sampled_image = np.expand_dims(train[i][np.random.choice(size),:],0)
if i == label:
target_image = sampled_image
image_list.append(sampled_image)
return target_image, image_list, target_one_hot
def cross_entropy(self,predictions, targets, epsilon=1e-12):
predictions = np.clip(predictions, epsilon, 1. - epsilon)
N = predictions.shape[0]
ce = -np.sum(np.sum(targets*np.log(predictions+1e-9)))/N
return ce
def test(self):
y = self.create_labels(10,1)
one_hot = to_categorical(y)
it = 0
while it < y.shape[0]:
label = y[it]
target_image, image_list, target_one_hot = self.sample(label,"X_test")
speaker_action, speaker_probs = self.speaker.act(target_image)
speaker_action_class = np.expand_dims(to_categorical(speaker_action,num_classes=self.vocab_size),0)
image_list.append(speaker_action_class)
listener_action, listener_probs = self.listener.act(image_list)
if listener_action == label:
speaker_reward = 1.0
listener_reward = 1.0
else:
speaker_reward = 0.0
listener_reward = 0.0
self.synonym_array[int(label)][int(speaker_action)] += 1
it += 1
def play(self):
y = self.create_labels(10,100)
one_hot = to_categorical(y)
bar = tqdm(np.arange(self.episodes))
for i in bar:
it = 0
acc = np.zeros(self.target_size)
right = 0
while it < y.shape[0]:
label = y[it]
target_image, image_list, target_one_hot = self.sample(label)
speaker_action, speaker_probs = self.speaker.act(target_image)
speaker_action_class = np.expand_dims(to_categorical(speaker_action,num_classes=self.vocab_size),0)
image_list.append(speaker_action_class)
listener_action, listener_probs = self.listener.act(image_list)
if listener_action == label:
speaker_reward = 1.0
listener_reward = 1.0
right += 1
else:
speaker_reward = 0.0
listener_reward = 0.0
acc = np.vstack((acc,listener_probs))
self.speaker.save(target_image,speaker_action,speaker_probs,speaker_reward)
self.listener.save(image_list,speaker_action_class,listener_action,listener_probs,listener_reward)
it += 1
accuracy = right/y.shape[0]
total = np.sum(self.speaker.params["rewards"])
cross = self.cross_entropy(acc[1:,:],one_hot)
self.episode_rewards.append(total)
self.accuracy_history.append(accuracy)
self.accuracy_record.append(accuracy)
bar.set_description("Cross Entropy: " + str(cross) + ", Rolling Acc 100: " + str(np.mean(self.accuracy_history)) + ", Accuracy: " + str(accuracy))
self.speaker.train()
self.listener.train()
self.test()
def get_current_listeners(chat_id):
return Listener.select().where(Listener.chat_id == chat_id,
Listener.enable)