z = nmt.compute_prior_states(input)
# z = torch.zeros((1, 6, OPTS.latentdim))
mask = torch.ones((1, z.shape[1]))
if torch.cuda.is_available():
mask = mask.cuda()
z = z.cuda()
init_z = z.clone()
for _ in range(10):
z, tokens = nmt.refine(z,
mask,
n_steps=1,
step_size=0.5,
return_tokens=True)
# z[:, 0] = init_z[:, 0]
# z[:, -1] = init_z[:, -1]
line = tgt_vocab.decode(tokens[0])
print(" ".join(line))
raise SystemExit
result_path = OPTS.result_path
# Read data
lines = open(test_tgt_corpus).readlines()
trains_stop_stdout_monitor()
with open(OPTS.result_path, "w") as outf:
for i, line in enumerate(lines):
# Make a batch
tokens = tgt_vocab.encode("<s> {} </s>".format(
line.strip()).split())
x = torch.tensor([tokens])
if torch.cuda.is_available():
x = x.cuda()
# Predict latent and target words from prior
if OPTS.scorenet:
targets = scorenet.translate(x,
n_iter=OPTS.Trefine_steps,
step_size=1.0)
else:
targets = nmt.translate(x, refine_steps=OPTS.Trefine_steps)
target_tokens = targets[0].cpu()[0].numpy().tolist()
if targets is None:
target_tokens = [2, 2, 2]
# Record decoding time
end_time = time.time()
decode_times.append((end_time - start_time) * 1000.)
# Convert token IDs back to words
target_tokens = [t for t in target_tokens if t > 2]
target_words = tgt_vocab.decode(target_tokens)
target_sent = " ".join(target_words)
outf.write(target_sent + "\n")
sys.stdout.write("\rtranslating: {:.1f}% ".format(
float(i) * 100 / len(lines)))
sys.stdout.flush()
sys.stdout.write("\n")
trains_restore_stdout_monitor()
print("Average decoding time: {:.0f}ms, std: {:.0f}".format(
np.mean(decode_times), np.std(decode_times)))
# Translate multiple sentences in batch
if OPTS.batch_test:
# Translate using only one GPU
if not is_root_node():
sys.exit()