def dump_latent(model, data_feed, config, log_dir):
model.eval()
de_tknize = utils.get_dekenize()
data_feed.epoch_init(config, verbose=False, shuffle=False)
logger.info("Dumping: {} batches".format(data_feed.num_batch))
all_zs = []
all_metas = []
while True:
batch = data_feed.next_batch()
if batch is None:
break
results = model(batch, mode=TEACH_FORCE, return_latent=True)
labels = batch.outputs
domains = batch.domains
acts = batch.get('output_actions')
latent_acts = results.latent_actions
if type(latent_acts) is tuple:
latent_acts = list(latent_acts[0].cpu().data.numpy())
else:
latent_acts = list(latent_acts.cpu().data.numpy())
for b_id in range(labels.shape[0]):
true_str, _ = get_sent(model, de_tknize, labels, b_id)
act_str, _ = get_sent(model, de_tknize, acts, b_id)
all_metas.append({'utt': true_str, 'domain': domains[b_id], 'acts':act_str})
all_zs.extend(latent_acts)
pickle.dump({'z': all_zs, "metas": all_metas}, open(os.path.join(log_dir,
"latent-{}.p".format(utils.get_time())), 'wb'))
logger.info("Dumping Done")
def generate(model, data_feed, config, evaluator, num_batch=1, dest_f=None):
model.eval()
de_tknize = utils.get_dekenize()
def write(msg):
if msg is None or msg == '':
return
if dest_f is None:
logger.info(msg)
else:
dest_f.write((msg + '\n').encode('utf-8'))
data_feed.epoch_init(config, shuffle=num_batch is not None, verbose=False)
evaluator.initialize()
logger.info("Generation: {} batches".format(data_feed.num_batch
if num_batch is None
else num_batch))
batch_cnt = 0
while True:
batch_cnt += 1
batch = data_feed.next_batch()
if batch is None or (num_batch is not None
and data_feed.ptr > num_batch):
break
outputs, labels = model(batch, mode=GEN, gen_type=config.gen_type)
# move from GPU to CPU
labels = labels.cpu()
pred_labels = [t.cpu().data.numpy() for t in
outputs[DecoderRNN.KEY_SEQUENCE]]
pred_labels = np.array(pred_labels, dtype=int).squeeze(-1).swapaxes(0,1)
true_labels = labels.data.numpy()
# get attention if possible
if config.use_attn or config.use_ptr:
pred_attns = [t.cpu().data.numpy() for t in outputs[DecoderRNN.KEY_ATTN_SCORE]]
pred_attns = np.array(pred_attns, dtype=float).squeeze(2).swapaxes(0,1)
else:
pred_attns = None
# get last 1 context
ctx = batch.get('contexts')
ctx_len = batch.get('context_lens')
domains = batch.domains
attn_ctx = outputs.get(DecoderPointerGen.KEY_PTR_CTX)
sel_acts = outputs.get(DecoderPointerGen.KEY_POLICY)
if sel_acts is not None:
sel_acts = sel_acts.cpu().data.numpy()
if attn_ctx is not None:
attn_ctx = attn_ctx.cpu().data.numpy()
attn_ctx = attn_ctx.reshape(attn_ctx.shape[0], -1)
# logger.info the batch in String.
for b_id in range(pred_labels.shape[0]):
pred_str, attn = get_sent(model, de_tknize, pred_labels, b_id, attn=pred_attns, attn_ctx=attn_ctx)
true_str, _ = get_sent(model, de_tknize, true_labels, b_id)
prev_ctx = ""
act_str = ""
if ctx is not None:
ctx_str = []
for t_id in range(ctx_len[b_id]):
temp_str, _ = get_sent(model, de_tknize, ctx[:, t_id, :], b_id, stop_eos=False)
ctx_str.append(temp_str)
ctx_str = '|'.join(ctx_str)[-200::]
prev_ctx = "Source: {}".format(ctx_str)
if sel_acts is not None:
act_str, _ = get_sent(model, de_tknize, sel_acts, b_id, stop_eos=False, stop_pad=False)
act_str = "Acts: {}".format(act_str)
domain = domains[b_id]
evaluator.add_example(true_str, pred_str, domain)
if num_batch is None or batch_cnt < 2:
write(prev_ctx)
write(act_str)
write("{}:: True: {} ||| Pred: {}".format(domain, true_str, pred_str))
if attn:
write("[[{}]]".format(attn))
write("-")
write(evaluator.get_report(include_error=dest_f is not None))
logger.info("Generation Done")