def main(params):
# Create vocabulary and author index
saved_model = torch.load(params['model'])
if 'misc' in saved_model:
misc = saved_model['misc']
char_to_ix = misc['char_to_ix']
auth_to_ix = misc['auth_to_ix']
ix_to_char = misc['ix_to_char']
ix_to_auth = misc['ix_to_auth']
else:
char_to_ix = saved_model['char_to_ix']
auth_to_ix = saved_model['auth_to_ix']
ix_to_char = saved_model['ix_to_char']
ix_to_auth = saved_model['ix_to_auth']
cp_params = saved_model['arch']
if params['softmax_scale']:
cp_params['softmax_scale'] = params['softmax_scale']
dp = DataProvider(cp_params)
if params['m_type'] == 'generative':
model = CharLstm(cp_params)
else:
model = CharTranslator(cp_params)
# set to train mode, this activates dropout
model.eval()
auth_colors = ['red', 'blue']
startc = dp.data['configs']['start']
endc = dp.data['configs']['end']
append_tensor = np.zeros((1, 1), dtype=np.int)
append_tensor[0, 0] = char_to_ix[startc]
append_tensor = torch.LongTensor(append_tensor).cuda()
# Restore saved checkpoint
model.load_state_dict(saved_model['state_dict'])
hidden = model.init_hidden(1)
jc = '' if cp_params.get('atoms','char') == 'char' else ' '
for i in xrange(params['num_samples']):
c_aid = np.random.choice(auth_to_ix.values())
if params['m_type'] == 'generative':
batch = dp.get_random_string(slen = params['seed_length'], split=params['split'])
else:
batch = dp.get_sentence_batch(1,split=params['split'], atoms=cp_params.get('atoms','char'), aid=ix_to_auth[c_aid])
inps, targs, auths, lens = dp.prepare_data(batch, char_to_ix, auth_to_ix, maxlen=cp_params['max_seq_len'])
auths_inp = 1 - auths if params['flip'] else auths
outs = adv_forward_pass(model, inps, lens, end_c=char_to_ix[endc], maxlen=cp_params['max_seq_len'], auths=auths_inp,
cycle_compute=params['show_rev'], append_symb=append_tensor)
#char_outs = model.forward_gen(inps, hidden, auths_inp, n_max = cp_params['max_len'],end_c=char_to_ix['.'])
print '--------------------------------------------'
#print 'Translate from %s to %s'%(batch[0]['author'], ix_to_auth[auths_inp[0]])
print colored('Inp %6s: '%(ix_to_auth[auths[0]]),'green') + colored('%s'%(jc.join([ix_to_char[c[0]] for c in inps[1:]])),auth_colors[auths[0]])
print colored('Out %6s: '%(ix_to_auth[auths_inp[0]]),'grey')+ colored('%s'%(jc.join([ix_to_char[c.data.cpu()[0]] for c in outs[0] if c.data.cpu()[0] in ix_to_char])),auth_colors[auths_inp[0]])
if params['show_rev']:
print colored('Rev %6s: '%(ix_to_auth[auths[0]]),'green')+ colored('%s'%(jc.join([ix_to_char[c.data.cpu()[0]] for c in outs[-1] if c.data.cpu()[0] in ix_to_char])),auth_colors[auths[0]])
def main(params):
# Create vocabulary and author index
saved_model = torch.load(params['model'])
if 'misc' in saved_model:
misc = saved_model['misc']
char_to_ix = misc['char_to_ix']
auth_to_ix = misc['auth_to_ix']
ix_to_char = misc['ix_to_char']
ix_to_auth = misc['ix_to_auth']
else:
char_to_ix = saved_model['char_to_ix']
auth_to_ix = saved_model['auth_to_ix']
ix_to_char = saved_model['ix_to_char']
ix_to_auth = saved_model['ix_to_auth']
cp_params = saved_model['arch']
if params['softmax_scale']:
cp_params['softmax_scale'] = params['softmax_scale']
dp = DataProvider(cp_params)
if params['m_type'] == 'generative':
model = CharLstm(cp_params)
else:
model = CharTranslator(cp_params)
# set to train mode, this activates dropout
model.eval()
auth_colors = ['red', 'blue']
startc = dp.data['configs']['start']
endc = dp.data['configs']['end']
append_tensor = np.zeros((1, 1), dtype=np.int)
append_tensor[0, 0] = char_to_ix[startc]
append_tensor = torch.LongTensor(append_tensor).cuda()
# Restore saved checkpoint
model.load_state_dict(saved_model['state_dict'])
hidden = model.init_hidden(1)
jc = '' if cp_params.get('atoms', 'char') == 'char' else ' '
for i in range(params['num_samples']):
c_aid = np.random.choice(list(auth_to_ix.values()))
if params['m_type'] == 'generative':
batch = dp.get_random_string(slen=params['seed_length'],
split=params['split'])
else:
batch = dp.get_sentence_batch(1,
split=params['split'],
atoms=cp_params.get('atoms', 'char'),
aid=ix_to_auth[c_aid])
inps, targs, auths, lens = dp.prepare_data(
batch, char_to_ix, auth_to_ix, maxlen=cp_params['max_seq_len'])
auths_inp = 1 - auths if params['flip'] else auths
forward, backward = adv_forward_pass(model,
inps,
lens,
end_c=char_to_ix[endc],
maxlen=cp_params['max_seq_len'],
auths=auths_inp,
cycle_compute=params['show_rev'],
append_symb=append_tensor)
# char_outs = model.forward_gen(inps, hidden, auths_inp, n_max = cp_params['max_len'],end_c=char_to_ix['.'])
print('--------------------------------------------')
print('Translate from %s to %s' %
(batch[0]['author'], ix_to_auth[auths_inp.item()]))
# General helper functions
# Clears whitespace but retains character for re.sub
def strip_match(match):
return match.group(0).strip()
# Joins together decimals
def fix_decimals(match):
match = match.group(0)
return re.sub('\s', '', match)
# Cleans text by removing unnecessary whitespace and substituting back in some symbols
def clean_text(text):
text = re.sub('-lrb-', '(', text)
text = re.sub('-rrb-', ')', text)
text = re.sub('-lsb-', '[', text)
text = re.sub('-rsb-', ']', text)
text = re.sub('-lcb-', '{', text)
text = re.sub('-rcb-', '}', text)
text = re.sub('\'\'', '\"', text)
text = re.sub('\si\s', ' I ', text)
text = re.sub('^i\s', 'I ', text)
text = re.sub('\sna\s', 'na ', text)
text = re.sub('\$\s', strip_match, text)
text = re.sub('[-#]\s|\s([-.!,\':;?]|n\'t)', strip_match, text)
text = re.sub('\d+. \d+', fix_decimals, text)
return text
# Get original sentence and clean it up a bit
input_list = [ix_to_char[c.item()] for c in inps[1:]]
input_string = jc.join(input_list)
input_string = clean_text(input_string)
# Get translated sentence and clean it up a bit
output_list = [
ix_to_char[c.item()] for c in forward if c.item() in ix_to_char
]
if output_list[-1] == 'END':
output_list = output_list[:-1]
output_string = jc.join(output_list)
output_string = clean_text(output_string)
print(
colored('Inp %6s: ' % (ix_to_auth[auths.item()]), 'green') +
colored('%s' % input_string, auth_colors[auths.item()]))
print(
colored('Out %6s: ' % (ix_to_auth[auths_inp.item()]), 'grey') +
colored('%s' % output_string, auth_colors[auths_inp.item()]))
if params['show_rev']:
print(
colored('Rev %6s: ' % (ix_to_auth[auths.item()]), 'green') +
colored(
'%s' % (jc.join([
ix_to_char[c.item()]
for c in backward if c.item() in ix_to_char
and ix_to_char[c.item()] != 'END'
])), auth_colors[auths.item()]))