def main():
"""runs script functionality"""
configure_usage('text_reconstruction.py')
opt = cfg.opt
model = cfg.model
text = cfg.train
n_batch = cfg.n_batch
valid = cfg.valid
nv_batch = cfg.nv_batch
test = cfg.test
nt_batch = cfg.nt_batch
TIMESTEPS = cfg.seq_length
lr_scheduler = cfg.lr
outer_loop = cfg.outer_loop
saver = cfg.saver
save_iters = opt.save_iters
save_epochs = opt.save_epochs
histories = []
val_histories = []
test_histories = []
if opt.benchmark:
torch.backends.cudnn.benchmark = True
logger = cfg.logger
train_fn = make_data_fn(True, TIMESTEPS)
eval_fn = make_data_fn(False, TIMESTEPS)
for e in outer_loop:
loss_avg = 0.
history = []
try:
if text is not None:
_, history = run_epoch(model, e, text, train_fn, n_batch, is_training=not opt.should_test,
inner_lr=cfg.inner_lr, save_every=save_iters,
saver=lambda ext, x: saver('e'+str(e)+ext, x))
if save_epochs > 0 and e % save_epochs == 0:
saver('e'+str(e)+'.pt',history)
else:
logger.log_pkl(histories, 'histories', 'e%s.pkl' % (e,), 'wb')
if valid is not None:
_, val_history = run_epoch(model, e, valid, eval_fn, nv_batch, is_training=False)
val_histories.append(val_history)
logger.log_pkl(val_histories, 'val_history', 'e%s.pkl' % (e,), 'wb')
if test is not None:
_, test_history = run_epoch(model, e, test, eval_fn, nt_batch, is_training=False)
test_histories.append(test_history)
logger.log_pkl(test_histories, 'test_history', 'e%s.pkl' % (e,), 'wb')
if opt.lr_scheduler == 'linear' or opt.no_loss:
pass
lr_scheduler.step()
e += 1
except Exception as ex:
saver('e'+str(e)+'.pt', history)
print('Exiting from training early')
raise ex
exit()
overwrite *= -1
t_g, v_g = generate_text(o, s, embed, rnn, neuron, last,
seq_length - len(t), temperature, overwrite)
out_text += t_g
out_values += v_g
print(''.join(out_text).encode('utf-8'))
save_str = ''
if logger is not None:
save_str = logger.get_log_dir('heatmaps')
save_str = os.path.join(save_str, ''.join(out_text[:50]) + '.png')
plot_neuron_heatmap(out_text, out_values, save_str, negate)
if __name__ == '__main__':
configure_usage('visualize.py')
opt = cfg.opt
embed = cfg.model.module.embedder
rnn = cfg.model.module.rnn
states = cfg.model.init_state
logger = cfg.logger
visualize(text=opt.text,
embed=embed,
rnn=rnn,
init_state=states,
seq_length=opt.seq_length,
temperature=opt.temperature,
overwrite=opt.overwrite,
neuron=opt.neuron,
logger=logger,
def main():
"""runs script functionality"""
configure_usage('text_reconstruction.py')
opt = cfg.opt
model = cfg.model
text = cfg.train
n_batch = cfg.n_batch
valid = cfg.valid
nv_batch = cfg.nv_batch
test = cfg.test
nt_batch = cfg.nt_batch
TIMESTEPS = cfg.seq_length
lr_scheduler = cfg.lr
outer_loop = cfg.outer_loop
saver = cfg.saver
histories = []
val_histories = []
test_histories = []
if opt.benchmark:
torch.backends.cudnn.benchmark = True
logger = cfg.logger
train_fn = make_data_fn(True, TIMESTEPS)
eval_fn = make_data_fn(False, TIMESTEPS)
for e in outer_loop:
loss_avg = 0.
history = []
try:
if opt.train != 'None':
_, history = run_epoch(
model,
e,
text,
train_fn,
n_batch,
is_training=not opt.should_test,
inner_lr=cfg.inner_lr,
saver=lambda ext, x: saver('e' + str(e) + ext, x))
histories.append(history)
pkl.dump(histories, open(loss_file, 'wb'))
if opt.valid != 'None':
_, val_history = run_epoch(model,
e,
valid,
eval_fn,
nv_batch,
is_training=False)
logger.log_pkl(str(np.mean(val_histories)), 'val_history',
'e%s.pkl' % (e, ), 'wb')
# pkl.dump(str(np.mean(val_history)), open(os.path.basename(save_file)+'.'+'val_loss.pkl', 'wb'))
val_histories.append(val_history)
if opt.test != 'None':
_, test_history = run_epoch(model,
e,
test,
eval_fn,
nt_batch,
is_training=False)
logger.log_pkl(str(np.mean(test_histories)), 'test_history',
'e%s.pkl' % (e, ), 'wb')
# pkl.dump(str(np.mean(test_history)), open(os.path.basename(save_file)+'.'+'test_loss.pkl', 'wb'))
test_histories.append(test_history)
#save progress
saver('e' + str(e), history)
if opt.lr_scheduler == 'linear' or opt.no_loss:
pass
lr_scheduler.step()
e += 1
except Exception as ex:
saver('e' + str(e), history)
print('Exiting from training early')
raise ex
exit()
for i, _ in enumerate(top_logits):
row = []
row.append(all_proba[i][1])
row.append(masked_proba[i][1])
if use_five and five_proba is not None:
row.append(five_proba[i][1])
if use_five and five_logits is not None:
row.extend(list(five_logits[i].squeeze()))
else:
row.extend(list(top_logits[i].squeeze()))
yield row
if __name__ == '__main__':
configure_usage('sentiment_transfer.py')
opt = cfg.opt
model = cfg.model
batch_size = cfg.batch_size
trX = cfg.train
trY = None
vaX = cfg.valid
vaY = None
teX = cfg.test
teY = None
logger = cfg.logger
# format output logging
dsname = os.path.basename(os.path.split(opt.train)[0])
formatted_name = format_task_prefix(opt.load_model, dsname)