batch_ctxs = batch_data["X"].tolist()
batch_floors = batch_data["X_floor"].tolist()
for idx in range(len(batch_ctxs)):
ctx_seq = batch_ctxs[idx]
ctx_floors = batch_floors[idx]
ctx_lst = []
for uttr_idx in range(len(ctx_seq)):
ctx = ctx_seq[uttr_idx]
if ctx[0] == tokenizer.pad_token_id:
continue
ctx = tokenizer.convert_ids_to_tokens(ids=ctx,
trim_bos=True,
trim_from_eos=True,
trim_pad=True)
ctx = tokenizer.convert_tokens_to_string(ctx)
ctx_floor = ctx_floors[uttr_idx]
ctx_floor = "A" if ctx_floor == 1 else "B"
ctx_lst.append((ctx, ctx_floor))
ctxs.append(ctx_lst)
batch_refs = batch_data["Y"].tolist()
batch_floors = batch_data["Y_floor"].tolist()
for idx in range(len(batch_refs)):
ref = batch_refs[idx]
ref = tokenizer.convert_ids_to_tokens(ids=ref,
trim_bos=True,
trim_from_eos=True,
trim_pad=True)
ref = tokenizer.convert_tokens_to_string(ref)
ref_floor = "A" if batch_floors[idx] == 1 else "B"
batch_data = test_sample_data_source.next(1)
log_s = "context:\n"
context = batch_data["X"].tolist()[0]
context_floors = batch_data["X_floor"].tolist()[0]
for uttr, floor in zip(context, context_floors):
if uttr[0] == tokenizer.pad_token_id:
continue
uttr = tokenizer.convert_ids_to_tokens(
ids=uttr,
trim_bos=True,
trim_from_eos=True,
trim_pad=True)
floor = "A" if floor == 1 else "B"
log_s += " {}: {}\n".format(
floor, tokenizer.convert_tokens_to_string(uttr))
mlog(log_s)
log_s = "ref text:\n"
floor = batch_data["Y_floor"][0].item()
floor = "A" if floor == 1 else "B"
uttr = batch_data["Y"][0].tolist()
uttr = tokenizer.convert_ids_to_tokens(ids=uttr,
trim_bos=True,
trim_from_eos=True,
trim_pad=True)
log_s += " {}: {}\n".format(
floor, tokenizer.convert_tokens_to_string(uttr))
mlog(log_s)
log_s = "hyp text:\n"
log_s = "<Test> - Samples:"
mlog(log_s)
for sample_idx in range(5):
ret_data, ret_stat = model.sample_step(batch_size=1)
log_s = "hyp text:\n"
hyp = ret_data["symbols"][0].tolist()
hyp = tokenizer.convert_ids_to_tokens(
ids=hyp,
trim_bos=True,
trim_from_eos=True,
trim_pad=True,
)
log_s += " {}\n".format(
tokenizer.convert_tokens_to_string(hyp)
)
log_s += "="*30
mlog(log_s)
# Evaluation on dev dataset
if n_step > 0 and n_step % config.validate_after_n_step == 0:
model.eval()
log_s = f"<Dev> learning rate: {lr}\n"
mlog(log_s)
dev_data_source.epoch_init(shuffle=False)
while True:
batch_data = dev_data_source.next(config.eval_batch_size)
if batch_data is None:
log_s = "context:\n"
context = batch_data["X"].tolist()[0]
context_floors = batch_data["X_floor"].tolist()[0]
for uttr, floor in zip(context, context_floors):
if uttr[0] == tokenizer.pad_token_id:
continue
uttr = tokenizer.convert_ids_to_tokens(
ids=uttr,
trim_bos=True,
trim_from_eos=True,
trim_pad=True,
)
floor = "A" if floor == 1 else "B"
log_s += " {}: {}\n".format(
floor, tokenizer.convert_tokens_to_string(uttr))
mlog(log_s)
log_s = "response:\n"
floor = batch_data["Y_floor"][0].item()
floor = "A" if floor == 1 else "B"
uttr = batch_data["Y"][0].tolist()
uttr = tokenizer.convert_ids_to_tokens(
ids=uttr,
trim_bos=True,
trim_from_eos=True,
trim_pad=True,
)
log_s += " {}: {}\n".format(
floor, tokenizer.convert_tokens_to_string(uttr))
mlog(log_s)