def simple_display(opt, world, turn):
if opt['batchsize'] > 1:
raise RuntimeError('Simple view only support batchsize=1')
act = world.get_acts()[0]
if turn == 0:
text = "- - - NEW EPISODE: " + act.get('id', "[no agent id]") + " - - -"
print(colorize(text, 'highlight'))
text = act.get('text', '[no text field]')
print(colorize(text, 'text'))
labels = act.get('labels', act.get('eval_labels', ['[no labels field]']))
labels = '|'.join(labels)
print(' ' + colorize(labels, 'labels'))
def simple_display(opt, world, turn):
if opt['batchsize'] > 1:
raise RuntimeError('Simple view only support batchsize=1')
teacher, response = world.get_acts()
if turn == 0:
text = "- - - NEW EPISODE: " + teacher.get('id', "[no agent id]") + "- - -"
print(colorize(text, 'highlight'))
text = teacher.get('text', '[no text field]')
print(colorize(text, 'text'))
response_text = response.get('text', 'No response')
labels = teacher.get('labels', teacher.get('eval_labels', ['[no labels field]']))
labels = '|'.join(labels)
print(colorize(' labels: ' + labels, 'labels'))
print(colorize(' model: ' + response_text, 'text2'))
def act(self):
reply = Message()
reply['id'] = self.getID()
try:
reply_text = input(colorize("Enter Your Message:", 'text') + ' ')
# f = open("my_log.txt","a+")
# f.write(reply_text+"\n")
# f.close()
except EOFError:
self.finished = True
return {'episode_done': True}
reply_text = reply_text.replace('\\n', '\n')
reply['episode_done'] = False
if self.opt.get('single_turn', False):
reply.force_set('episode_done', True)
reply['label_candidates'] = self.fixedCands_txt
if '[DONE]' in reply_text:
# let interactive know we're resetting
raise StopIteration
reply['text'] = reply_text
if '[EXIT]' in reply_text:
self.finished = True
raise StopIteration
return reply
def __init__(self, opt, shared=None):
super().__init__(opt)
self.id = 'localHuman'
self.episodeDone = False
self.finished = False
self.fixedCands_txt = load_cands(
self.opt.get('local_human_candidates_file'))
print(
colorize(
"Enter [DONE] if you want to end the episode, [EXIT] to quit.",
'highlight',
))
def simple_display_chateval(opt, world, turn):
if opt['batchsize'] > 1:
raise RuntimeError('Simple view only support batchsize=1')
teacher, response = world.get_acts()
if turn == 0:
text = "- - - NEW EPISODE: " + teacher.get('id',
"[no agent id]") + "- - -"
print(colorize(text, 'highlight'))
text = teacher.get('text', '[no text field]')
print(colorize(text, 'text'))
response_text = response.get('text', 'No response')
labels = teacher.get('labels',
teacher.get('eval_labels', ['[no labels field]']))
labels = '|'.join(labels)
print(colorize(' labels: ' + labels, 'labels'))
# print(colorize(' model: ' + response_text, 'text2'))
# for the chateval
timestr = time.strftime("%Y%m%d-%H%M%S")
# script_input_path = str(opt.get('script_input_path'))
# script_file = open(script_input_path, 'r', encoding='utf-8')
# file_name = script_input_path.split('/')[-1].split('.')[0]
if opt.get('chateval_multi') == True:
print(colorize(' model: ' + response_text, 'text2'))
script_response = open(
opt.get('script_out_path') + '/' + 'test' + '_' +
opt.get('model_file') + '_' + timestr + '.txt', 'w')
print(colorize(' model: ' + response_text, 'text2'))
script_response.write("%s\n" % (response_text))
# if opt.get('chateval_multi_num') == 2:
# script_response.write("%s\n" % (response_text))
# elif opt.get('chateval_multi_num') == 3:
# pass
# else:
# print("We only consider to 2 and 3 turns")
else:
print("Chateval multiturn script something wrong!")
def __init__(self, opt, shared=None, query_txt=None):
super().__init__(opt)
self.id = 'localHuman'
self.episodeDone = False
self.finished = False
self.fixedCands_txt = load_cands(
self.opt.get('local_human_candidates_file'))
# utilizing given query text
self.query_txt = self.make_txt_readable(query_txt)
self.line_no = 0
print(
colorize(
"Enter [DONE] if you want to end the episode, [EXIT] to quit.",
'highlight',
))
def act(self):
reply = Message()
reply['id'] = self.getID()
reply_text = input(colorize("Enter Your Message:", 'field') + ' ')
reply_text = reply_text.replace('\\n', '\n')
if self.opt.get('single_turn', False):
reply_text += '[DONE]'
reply['episode_done'] = False
reply['label_candidates'] = self.fixedCands_txt
if '[DONE]' in reply_text:
reply.force_set('episode_done', True)
self.episodeDone = True
reply_text = reply_text.replace('[DONE]', '')
reply['text'] = reply_text
if '[EXIT]' in reply_text:
self.finished = True
return reply
def _pretty_lines(indent_space, field, value, style):
line = '{}{} {}'.format(
indent_space, colorize('[' + field + ']:', 'field'), colorize(value, style)
)
return line
def display_messages(
msgs: List[Dict[str, Any]],
prettify: bool = False,
ignore_agent_reply: bool = False,
add_fields: str = '',
max_len: int = 1000,
verbose: bool = False,
) -> Optional[str]:
"""
Return a string describing the set of messages provided.
If prettify is true, candidates are displayed using prettytable. add_fields provides
a list of fields in the msgs which should be displayed if verbose is off.
"""
def _token_losses_line(
msg: Dict[str, Any], fields_to_show: List[str], space: str
) -> Optional[str]:
"""
Displays the loss associated with each token. Can be used for debugging
generative models.
See TorchGeneratorAgent._construct_token_losses for an example implementation.
"""
key = 'token_losses'
token_losses = msg.get(key, None)
if key not in fields_to_show or not token_losses:
return None
# Reduce losses to 4 significant figures
formatted_tl = ' | '.join(
[f"{tl[0]} {float('{:.4g}'.format(tl[1]))}" for tl in token_losses]
)
return _pretty_lines(space, key, formatted_tl, 'text2')
def _pretty_lines(indent_space, field, value, style):
line = '{}{} {}'.format(
indent_space, colorize('[' + field + ']:', 'field'), colorize(value, style)
)
return line
lines = []
episode_done = False
extra_add_fields_ = add_fields.split(',')
for index, msg in enumerate(msgs):
if msg is None or (index == 1 and ignore_agent_reply):
# We only display the first agent (typically the teacher) if we
# are ignoring the agent reply.
continue
if msg.get('episode_done'):
episode_done = True
# Possibly indent the text (for the second speaker, if two).
space = ''
if len(msgs) == 2 and index == 1:
space = ' '
agent_id = msg.get('id', '[no id field]')
if verbose:
line = _pretty_lines(
indent_space=space, field='id', value=agent_id, style='id'
)
lines.append(line)
# Only display rewards !=0 as they are confusing in non-RL tasks.
if msg.get('reward', 0) != 0:
lines.append(space + '[reward: {r}]'.format(r=msg['reward']))
fields_to_show = []
if verbose:
fields_to_show = [field for field in msg]
else:
fields_to_show = [
field
for field in msg
if field in list(MUST_SHOW_MESSAGE_FIELDS) + extra_add_fields_
]
fields_to_show.sort()
# Display fields without special format
for field in fields_to_show:
if field not in SPECIAL_FORMATED_DISPLAY_MESSAGE_FIELDS:
if type(msg[field]) is list:
value = _ellipse(msg[field], sep='\n ')
else:
value = clip_text(str(msg.get(field)), max_len)
line = _pretty_lines(
indent_space=space, field=field, value=value, style='text2'
)
lines.append(line)
# Display fields WITH special format requirements
# Display Image
if type(msg.get('image')) in [str, torch.Tensor]:
lines.append(f'[ image ]: {msg["image"]}')
# Display Text
if msg.get('text', ''):
value = clip_text(msg['text'], max_len)
style = 'bold_text' if index == 0 else 'labels'
field = 'text' if verbose else agent_id
line = _pretty_lines(
indent_space=space, field=field, value=value, style=style
)
lines.append(line)
# Display Label Fields
for field in {'labels', 'eval_labels', 'label_candidates', 'text_candidates'}:
if msg.get(field) and field in fields_to_show:
line = _pretty_lines(
indent_space=space,
field=field,
value=_ellipse(msg[field]),
style=field,
)
lines.append(line)
if msg.get('metrics') and verbose:
lines.append(
_pretty_lines(
indent_space=space,
field='metrics',
value="\n" + nice_report(msg['metrics']),
style='text',
)
)
# Handling this separately since we need to clean up the raw output before displaying.
token_loss_line = _token_losses_line(msg, fields_to_show, space)
if token_loss_line:
lines.append(token_loss_line)
if episode_done:
lines.append(
colorize('- - - - - - - END OF EPISODE - - - - - - - - - -', 'highlight')
)
return '\n'.join(lines)
def display_messages(
msgs: List[Dict[str, Any]],
prettify: bool = False,
ignore_fields: str = '',
max_len: int = 1000,
verbose: bool = False,
) -> Optional[str]:
"""
Return a string describing the set of messages provided.
If prettify is true, candidates are displayed using prettytable. ignore_fields
provides a list of fields in the msgs which should not be displayed.
"""
def _token_losses_line(msg: Dict[str, Any], ignore_fields: List[str],
space: str) -> Optional[str]:
"""
Displays the loss associated with each token. Can be used for debugging
generative models.
See TorchGeneratorAgent._construct_token_losses for an example implementation.
"""
key = 'token_losses'
token_losses = msg.get(key, None)
if key in ignore_fields or not token_losses:
return None
# Reduce losses to 4 significant figures
formatted_tl = ' | '.join([
f"{tl[0]} {float('{:.4g}'.format(tl[1]))}" for tl in token_losses
])
return f'{space}[{key}]: {formatted_tl}'
lines = []
episode_done = False
ignore_fields_ = ignore_fields.split(',')
for index, msg in enumerate(msgs):
if msg is None or (index == 1 and 'agent_reply' in ignore_fields_):
# We only display the first agent (typically the teacher) if we
# are ignoring the agent reply.
continue
agent_id = msg.get('id', '[no id field]')
if verbose:
lines.append(
colorize('[id]:', 'field') + ' ' + colorize(agent_id, 'id'))
if msg.get('episode_done'):
episode_done = True
# Possibly indent the text (for the second speaker, if two).
space = ''
if len(msgs) == 2 and index == 1:
space = ' '
# Only display rewards !=0 as they are confusing in non-RL tasks.
if msg.get('reward', 0) != 0:
lines.append(space + '[reward: {r}]'.format(r=msg['reward']))
for key in msg:
if key not in DISPLAY_MESSAGE_DEFAULT_FIELDS and key not in ignore_fields_:
field = colorize('[' + key + ']:', 'field')
if type(msg[key]) is list:
value = _ellipse(msg[key], sep='\n ')
else:
value = clip_text(str(msg.get(key)), max_len)
line = field + ' ' + colorize(value, 'text2')
lines.append(space + line)
if type(msg.get('image')) in [str, torch.Tensor]:
lines.append(f'[ image ]: {msg["image"]}')
if msg.get('text', ''):
text = clip_text(msg['text'], max_len)
if index == 0:
style = 'bold_text'
else:
style = 'labels'
if verbose:
lines.append(space + colorize('[text]:', 'field') + ' ' +
colorize(text, style))
else:
lines.append(space + colorize("[" + agent_id + "]:", 'field') +
' ' + colorize(text, style))
for field in {
'labels', 'eval_labels', 'label_candidates', 'text_candidates'
}:
if msg.get(field) and field not in ignore_fields_:
string = '{}{} {}'.format(
space,
colorize('[' + field + ']:', 'field'),
colorize(_ellipse(msg[field]), field),
)
lines.append(string)
# Handling this separately since we need to clean up the raw output before displaying.
token_loss_line = _token_losses_line(msg, ignore_fields_, space)
if token_loss_line:
lines.append(token_loss_line)
if episode_done:
lines.append(
colorize('- - - - - - - END OF EPISODE - - - - - - - - - -',
'highlight'))
return '\n'.join(lines)
def get_reply(self):
reply_text = input(colorize('Enter Your Message:', 'field') + ' ')
reply_text = reply_text.replace('\\n', '\n')
return reply_text
def __init__(self, *args, **kwargs):
from parlai.utils.strings import colorize
logo = ""
logo += colorize(' _', 'red') + "\n"
logo += colorize(' /', 'red') + colorize('"', 'brightblack')
logo += colorize(")", "yellow") + "\n"
logo += colorize(' //', 'red') + colorize(')', 'yellow') + '\n'
logo += colorize(' ==', 'green')
logo += colorize("/", 'blue') + colorize('/', 'red') + colorize(
"'", 'yellow')
logo += colorize("===", 'green') + " ParlAI\n"
logo += colorize(" /", 'blue')
kwargs['description'] = logo
return super().__init__(*args, **kwargs)
def __init__(self, *args, **kwargs):
# used to target help messages more correctly, see GH #3182
self._help_subparser = None
from parlai.utils.strings import colorize
logo = ""
logo += colorize(' _', 'red') + "\n"
logo += colorize(' /', 'red') + colorize('"', 'brightblack')
logo += colorize(")", "yellow") + "\n"
logo += colorize(' //', 'red') + colorize(')', 'yellow') + '\n'
logo += colorize(' ==', 'green')
logo += colorize("/", 'blue') + colorize('/', 'red') + colorize(
"'", 'yellow')
logo += colorize("===", 'green') + " ParlAI\n"
logo += colorize(" /", 'blue')
kwargs['description'] = logo
return super().__init__(*args, **kwargs)
def display_image():
if os.environ.get('PARLAI_DISPLAY_LOGO') == 'OFF':
return
logo = colorize('ParlAI - Dialogue Research Platform', 'labels')
print(logo)
