def main(opt):
# Build dictionary from task data
if 'pretrained_model' in opt:
dictionary = None
else:
dictionary = build_dict(opt)
# Build document reader
doc_reader = DocReaderAgent(opt, word_dict=dictionary)
# Log params
logger.info('[ Created with options: ] %s' %
''.join(['\n{}\t{}'.format(k, v)
for k, v in doc_reader.opt.items()]))
# Build training world once
opt['datatype'] = 'train'
train_world = create_task(opt, doc_reader)
train_time = Timer()
# Keep track of best model + how long since the last improvement
best_valid = 0
impatience = 0
logger.info("[ Ok, let's go... ]")
iteration = 0
while impatience < opt['patience']:
# Train...
logger.info('[ Training for %d iters... ]' % opt['train_interval'])
train_time.reset()
for _ in range(opt['train_interval']):
train_world.parley()
logger.info('[ Done. Time = %.2f (s) ]' % train_time.time())
# ...validate!
valid_metric = validate(opt, doc_reader, iteration)
if valid_metric > best_valid:
logger.info(
'[ Best eval %d: %s = %.4f (old = %.4f) ]' %
(iteration, opt['valid_metric'], valid_metric, best_valid)
)
best_valid = valid_metric
impatience = 0
if 'model_file' in opt:
doc_reader.save(opt['model_file'])
if valid_metric == 1:
logger.info('[ Task solved! Stopping. ]')
break
else:
impatience += 1
iteration += 1
def main(opt):
# Check options
assert ('pretrained_model' in opt)
assert (opt['datatype'] in {'test', 'valid'})
# Load document reader
doc_reader = DocReaderAgent(opt)
# Log params
logger.info(
'[ Created with options: ] %s' %
''.join(['\n{}\t{}'.format(k, v) for k, v in doc_reader.opt.items()]))
logger.info('[ Running validation... ]')
valid_world = create_task(opt, doc_reader)
valid_time = Timer()
for _ in valid_world:
valid_world.parley()
metrics = valid_world.report()
if 'tasks' in metrics:
for task, t_metrics in metrics['tasks'].items():
logger.info('task = %s | EM = %.4f | F1 = %.4f | exs = %d | ' %
(task, t_metrics['accuracy'], t_metrics['f1'],
t_metrics['total']))
logger.info('Overall EM = %.4f | exs = %d' %
(metrics['accuracy'], metrics['total']))
else:
logger.info('EM = %.4f | F1 = %.4f | exs = %d' %
(metrics['accuracy'], metrics['f1'], metrics['total']))
logger.info('[ Done. Time = %.2f (s) ]' % valid_time.time())
def main(opt):
# Load document reader
assert ('pretrained_model' in opt)
doc_reader = DocReaderAgent(opt)
# Log params
logger.info(
'[ Created with options: ] %s' %
''.join(['\n{}\t{}'.format(k, v) for k, v in doc_reader.opt.items()]))
while True:
context = input('Context: ')
question = input('Question: ')
observation = {
'text': '\n'.join([context, question]),
'episode_done': True
}
doc_reader.observe(observation)
reply = doc_reader.act()
print('Reply: %s' % reply['text'])
logger.info('[ Running validation... ]')
valid_world = create_task(opt, doc_reader)
valid_time = Timer()
for _ in valid_world:
valid_world.parley()
metrics = valid_world.report()
logger.info('EM = %.2f | F1 = %.2f | exs = %d' %
(metrics['accuracy'], metrics['f1'], metrics['total']))
logger.info('[ Done. Time = %.2f (s) ]' % valid_time.time())
if __name__ == '__main__':
# Get command line arguments
argparser = ParlaiParser()
DocReaderAgent.add_cmdline_args(argparser)
opt = argparser.parse_args()
# Set logging (only stderr)
logger = logging.getLogger('DrQA')
logger.setLevel(logging.INFO)
fmt = logging.Formatter('%(asctime)s: %(message)s', '%m/%d/%Y %I:%M:%S %p')
console = logging.StreamHandler()
console.setFormatter(fmt)
logger.addHandler(console)
# Set cuda
opt['cuda'] = not opt['no_cuda'] and torch.cuda.is_available()
if opt['cuda']:
logger.info('[ Using CUDA (GPU %d) ]' % opt['gpu'])
torch.cuda.set_device(opt['gpu'])
def main(opt):
# Check options
assert ('pretrained_model' in opt)
assert (opt['datatype'] in {'test', 'valid'})
# Calculate TDNN embedding dim (after applying TDNN to char tensor)
opt['kernels'] = ''.join(opt['kernels'])
if isinstance(opt['kernels'], str):
opt['kernels'] = eval(
opt['kernels']) # convert string list of tuple --> list of tuple
if opt['add_char2word']:
opt['NULLWORD_Idx_in_char'] = opt['vocab_size_char'] - 1
opt['embedding_dim_TDNN'] = 0
for i, n in enumerate(opt['kernels']):
opt['embedding_dim_TDNN'] += n[1]
logger.info('TDNN embedding dim = %d' % (opt['embedding_dim_TDNN']))
#Write prediction file
f_predict = open((str(opt['model_file']) + '.prediction'), "w")
f_predict.write("{")
f_analysis = open((str(opt['model_file']) + '.analysis'), "w")
# Load document reader
doc_reader = DocReaderAgent(opt)
# Log params
logger.info(
'[ Created with options: ] %s' %
''.join(['\n{}\t{}'.format(k, v) for k, v in doc_reader.opt.items()]))
logger.info('[ Running validation... ]')
valid_world = create_task(opt, doc_reader)
valid_time = Timer()
# Sent prediction
#pdb.set_trace()
valid_world.agents[1].opt['ans_sent_predict'] = False
valid_world.agents[1].model.network.opt[
'ans_sent_predict'] = False # disable sentence predicction by default
if opt['ans_sent_predict']:
valid_world.agents[1].model.input_idx_bdy -= 1
nExample = 0
f1_avg_prev = 0
acc_avg_prev = 0
for _ in valid_world:
pdb.set_trace()
valid_world.parley()
nExample += 1
#pdb.set_trace()
f_predict.write('"' + valid_world.acts[0]['reward'] + '" ')
f_predict.write('"' + valid_world.acts[1]['text'] + '", ')
f_analysis.write('Paragraph & Question = ' +
valid_world.acts[0]['text'] + '\n')
f_analysis.write('Prediction = ' + valid_world.acts[1]['text'] + '\n')
f_analysis.write('Answer = ' + valid_world.agents[0].lastY_prev[0] +
'\n')
f1_avg_cur = valid_world.agents[0].report()['f1']
f1_cur = nExample * f1_avg_cur - (nExample - 1) * f1_avg_prev
f_analysis.write('F1 = ' + str(f1_cur) + '\n')
f1_avg_prev = f1_avg_cur
#pdb.set_trace()
metrics = valid_world.report()
if 'tasks' in metrics:
for task, t_metrics in metrics['tasks'].items():
logger.info('task = %s | EM = %.4f | F1 = %.4f | exs = %d | ' %
(task, t_metrics['accuracy'], t_metrics['f1'],
t_metrics['total']))
logger.info('Overall EM = %.4f | exs = %d' %
(metrics['accuracy'], metrics['total']))
else:
logger.info('EM = %.4f | F1 = %.4f | exs = %d' %
(metrics['accuracy'], metrics['f1'], metrics['total']))
logger.info('[ Done. Time = %.2f (s) ]' % valid_time.time())
# Close prediction file
f_predict.write("}")
f_predict.close()
f_analysis.close()
def main(opt):
# Check options
assert ('pretrained_model' in opt)
assert (opt['datatype'] in {'test', 'valid'})
# Calculate TDNN embedding dim (after applying TDNN to char tensor)
opt['kernels'] = ''.join(opt['kernels'])
if isinstance(opt['kernels'], str):
opt['kernels'] = eval(
opt['kernels']) # convert string list of tuple --> list of tuple
if opt['add_char2word']:
opt['embedding_dim_TDNN'] = 0
for i, n in enumerate(opt['kernels']):
opt['embedding_dim_TDNN'] += n[1]
logger.info('TDNN embedding dim = %d' % (opt['embedding_dim_TDNN']))
#Write prediction file
f_predict = open(("exp-squad/" + str(opt['expnum']) + '.prediction'), "w")
f_predict.write("{")
# Load document reader
doc_reader = DocReaderAgent(opt)
# Log params
logger.info(
'[ Created with options: ] %s' %
''.join(['\n{}\t{}'.format(k, v) for k, v in doc_reader.opt.items()]))
logger.info('[ Running validation... ]')
valid_world = create_task(opt, doc_reader)
valid_time = Timer()
# Sent prediction
valid_world.agents[1].opt['ans_sent_predict'] = False
valid_world.agents[1].model.network.opt[
'ans_sent_predict'] = False # disable sentence predicction by default
if opt['ans_sent_predict']:
valid_world.agents[1].model.input_idx_bdy -= 1
for _ in valid_world:
valid_world.parley()
#pdb.set_trace()
#f_predict.write('"' + valid_world.acts[0]['reward'] + '" ')
#f_predict.write('"' + valid_world.acts[1]['text'] + '", ')
#pdb.set_trace()
metrics = valid_world.report()
if 'tasks' in metrics:
for task, t_metrics in metrics['tasks'].items():
logger.info('task = %s | EM = %.4f | F1 = %.4f | exs = %d | ' %
(task, t_metrics['accuracy'], t_metrics['f1'],
t_metrics['total']))
logger.info('Overall EM = %.4f | exs = %d' %
(metrics['accuracy'], metrics['total']))
else:
logger.info('EM = %.4f | F1 = %.4f | exs = %d' %
(metrics['accuracy'], metrics['f1'], metrics['total']))
logger.info('[ Done. Time = %.2f (s) ]' % valid_time.time())
# Close prediction file
f_predict.write("}")
f_predict.close()
def main(opt):
#iter_global = 0
#pdb.set_trace()
# Cudnn
# Build word dictionary from task data
if os.path.isfile(("data/MSmarco/dict.word." + str(opt['vocab_size']) + ".pkl")):
dictionary = pickle.load( open( ("data/MSmarco/dict.word." + str(opt['vocab_size']) + ".pkl"), "rb") ) # word dictionary
logger.info('successfully load word dictionary')
else:
if 'pretrained_model' in opt:
dictionary = None
else:
dictionary = build_dict(opt)
pickle.dump( dictionary , open( ("data/MSmarco/dict.word." + str(opt['vocab_size']) + ".pkl"), "wb") )
# Generator dictionary
if os.path.isfile(("data/MSmarco/dict.word." + str(opt['vocab_size_generator']) + ".pkl")):
dictionary_gen = pickle.load( open( ("data/MSmarco/dict.word." + str(opt['vocab_size_generator']) + ".pkl"), "rb") ) # word dictionary
logger.info('successfully load word dictionary')
else:
dictionary_gen = build_dict(opt)
pickle.dump( dictionary , open( ("data/MSmarco/dict.word." + str(opt['vocab_size_gen']) + ".pkl"), "wb") )
# Character dictionary
dictionary_char=None
if opt['add_char2word']:
opt['NULLWORD_Idx_in_char'] = opt['vocab_size_char']-1
if os.path.isfile(("data/MSmarco/dict.char." + str(opt['vocab_size_char']) + ".pkl")):
dictionary_char = pickle.load( open( ("data/MSmarco/dict.char." + str(opt['vocab_size_char']) + ".pkl"), "rb") ) # char dictionary
logger.info('successfully load char dictionary')
else:
# Build char dictionary from task data
dictionary_char = build_dict_char(opt)
pickle.dump( dictionary_char , open( ("data/MSmarco/dict.char." + str(opt['vocab_size_char']) + ".pkl"), "wb") )
# Figure out max word len
# figuring out max_word_len from word dictionary is not valid choice ==> which is 25
#opt['max_word_len'] = -100 # initialize
#for i in range(len(dictionary)):
# cur_word_len = len(dictionary[i])
# if opt['max_word_len'] < cur_word_len:
# opt['max_word_len'] = cur_word_len
# just set as hyperparameter in config.py
logger.info('maximum word len = %d' % (opt['max_word_len']))
# Calculate TDNN embedding dim (after applying TDNN to char tensor)
opt['kernels'] = ''.join(opt['kernels'])
if isinstance(opt['kernels'], str):
opt['kernels'] = eval(opt['kernels']) # convert string list of tuple --> list of tuple
opt['embedding_dim_TDNN']=0
for i, n in enumerate(opt['kernels']):
opt['embedding_dim_TDNN'] += n[1]
logger.info('TDNN embedding dim = %d' % (opt['embedding_dim_TDNN']))
#pdb.set_trace()
# Build document reader
doc_reader = DocReaderAgent(opt, word_dict=dictionary, gen_dict = dictionary_gen, char_dict=dictionary_char)
# Log params
logger.info('[ Created with options: ] %s' %
''.join(['\n{}\t{}'.format(k, v)
for k, v in doc_reader.opt.items()]))
# Build training world once
opt['datatype'] = 'train'
train_world = create_task(opt, doc_reader)
train_time = Timer()
# Keep track of best model + how long since the last improvement
best_valid = 0
impatience = 0
lrate_decay = 0
logger.info("[ Ok, let's go... ]")
iteration = 0
while impatience < opt['patience']:
# Train...
logger.info('[ Training for %d iters... ]' % opt['train_interval'])
train_time.reset()
iter = 0
for _ in range(opt['train_interval']):
iter += 1
#pdb.set_trace()
train_world.parley()
#if iter % opt['collect_garbage_every'] == 0:
if iter % 100 == 0:
gc.collect()
# ...validate!
print('start validation')
valid_metric = validate(opt, doc_reader, iteration)
if valid_metric > best_valid:
#if False:
logger.info(
'[ Best eval %d: %s = %.4f (old = %.4f) ]' %
(iteration, opt['valid_metric'], valid_metric, best_valid)
)
best_valid = valid_metric
impatience = 0
if 'model_file' in opt:
doc_reader.save(opt['model_file'])
if valid_metric == 1:
logger.info('[ Task solved! Stopping. ]')
break
else:
if opt['lrate_decay']:
# doc_reader.model.opt['learning_rate'] *= 0.5
opt['learning_rate'] *= opt['lrate_decay_factor']
doc_reader.model.set_lrate(opt['learning_rate'])
logger.info('[ Decrease learning_rate %.2e]' % opt['learning_rate'] )
lrate_decay +=1
if lrate_decay > 10:
break
else:
impatience += 1
logger.info('[ Increase impatience %d ]' % impatience)
iteration += 1
logger.info('[ >> Best eval : %s = %.4f ]' % (opt['valid_metric'], best_valid))
def main(opt):
# Check options
assert('pretrained_model' in opt)
assert(opt['datatype'] in {'test', 'valid'})
# Calculate TDNN embedding dim (after applying TDNN to char tensor)
opt['kernels'] = ''.join(opt['kernels'])
if isinstance(opt['kernels'], str):
opt['kernels'] = eval(opt['kernels']) # convert string list of tuple --> list of tuple
if opt['add_char2word']:
opt['NULLWORD_Idx_in_char'] = opt['vocab_size_char']-1
opt['embedding_dim_TDNN']=0
for i, n in enumerate(opt['kernels']):
opt['embedding_dim_TDNN'] += n[1]
logger.info('TDNN embedding dim = %d' % (opt['embedding_dim_TDNN']))
# Load document reader
doc_reader = DocReaderAgent(opt)
logger.info('[ Running validation... ]')
valid_world = create_task(opt, doc_reader)
valid_time = Timer()
nExample = 0
for _ in valid_world:
valid_world.parley()
nExample+=1
if nExample % 10 == 0:
text = valid_world.acts[0]['text']
p = NLP.tokenizer(text.split('\n')[0])
q = NLP.tokenizer(text.split('\n')[1])
## ATTENTION weights
qemb = valid_world.agents[1].model.network.qemb_match.get_alpha().squeeze()
qp_attention = valid_world.agents[1].model.network.qp_match.get_alpha().squeeze()
#qp_gate = valid_world.agents[1].model.network.qp_match.get_gate().squeeze()
pp_attention = valid_world.agents[1].model.network.pp_match.get_alpha().squeeze()
# pp_gate = valid_world.agents[1].model.network.pp_match.get_gate().squeeze()
s_att = valid_world.agents[1].model.network.start_attn.get_alpha()
e_att = valid_world.agents[1].model.network.end_attn.get_alpha()
q_merge = valid_world.agents[1].model.network.self_attn.get_alpha()
#pdb.set_trace()
fig = plt.figure(1, figsize=(30,30))
i=4
subplot(qemb, plt, fig, 1, i, 1, p, q, 'q_emb')
subplot(qp_attention, plt, fig, 1, i, 2, p, q, 'qp-att')
#subplot(torch.cat([qp_gate, pp_gate],0).t(), plt, fig, 1, 5, 3, p, ['qp-gate', 'pp-gate'], '')
#subplot(pp_gate.t(), plt, fig, 1, 5, 4, p, [''], 'pp-gate')
subplot(torch.cat([s_att, e_att],0).t(), plt, fig, 1, i, 3, p, ['start', 'end'], valid_world.agents[0].lastY_prev[0])
subplot(q_merge.t(), plt, fig, 1, i, 4, q, [' '], 'q-merging')
fig.savefig((str(opt['model_file']) + '_'+ str(nExample) +'.png'), transparent=True)
plt.close()
fig = plt.figure(2, figsize=(30,30))
subplot(pp_attention, plt, fig, 1, 1, 1, p, p, 'pp-att')
fig.savefig((str(opt['model_file']) + '_'+ str(nExample) +'pp.png'), transparent=True)
plt.close()
"""
subplot(qemb.t(), plt, fig, 4, 1, 1, q, p)
subplot(qp_attention.t(), plt, fig, 4, 1, 2, q, p
subplot(pp_attention.t(), plt, fig, 4, 1, 3, p, p)
subplot(torch.cat([s_att, e_att],0), plt, fig, 4, 4, ['start', 'end'], p)
"""
if nExample == 100:
break
