def compute_metrics(self,
ind_x,
str_y,
metric_names=list(),
from_line=False):
if from_line:
str_x = list()
for lines_token in ind_x:
str_x.append(" ".join([
LM_ind_to_str(self.dataset.charset,
self.ctc_remove_successives_identical_ind(p),
oov_symbol="") if p is not None else ""
for p in lines_token
]).strip(" "))
else:
str_x = [
LM_ind_to_str(self.dataset.charset,
self.ctc_remove_successives_identical_ind(p),
oov_symbol="") if p is not None else ""
for p in ind_x
]
metrics = dict()
for metric_name in metric_names:
if metric_name == "cer":
metrics[metric_name] = [
editdistance.eval(u, v) for u, v in zip(str_y, str_x)
]
metrics["nb_chars"] = nb_chars_from_list(str_y)
elif metric_name == "wer":
metrics[metric_name] = edit_wer_from_list(str_y, str_x)
metrics["nb_words"] = nb_words_from_list(str_y)
metrics["nb_samples"] = len(str_x)
return metrics
def compute_metrics(self,
x,
y,
x_len,
y_len,
loss=None,
metric_names=list()):
batch_size = y.shape[0]
ind_x = [x[i][:x_len[i]] for i in range(batch_size)]
ind_y = [y[i][:y_len[i]] for i in range(batch_size)]
ind_x = [self.ctc_remove_successives_identical_ind(t) for t in ind_x]
str_x = [
LM_ind_to_str(self.dataset.charset, t, oov_symbol="")
for t in ind_x
]
str_y = [LM_ind_to_str(self.dataset.charset, t) for t in ind_y]
metrics = dict()
for metric_name in metric_names:
if metric_name == "cer":
metrics[metric_name] = [
editdistance.eval(u, v) for u, v in zip(str_y, str_x)
]
metrics["nb_chars"] = nb_chars_from_list(str_y)
elif metric_name == "wer":
metrics[metric_name] = edit_wer_from_list(str_y, str_x)
metrics["nb_words"] = nb_words_from_list(str_y)
elif metric_name == "pred":
metrics["pred"] = [
str_x,
]
if "loss_ctc" in metric_names:
metrics["loss_ctc"] = loss / metrics["nb_chars"]
metrics["nb_samples"] = len(x)
return metrics
def compute_metrics(self, ind_x, str_y, loss=None, metric_names=list()):
ind_x = [self.ctc_remove_successives_identical_ind(t) for t in ind_x]
str_x = [
LM_ind_to_str(self.dataset.charset, t, oov_symbol="")
for t in ind_x
]
metrics = dict()
for metric_name in metric_names:
if metric_name == "cer":
metrics[metric_name] = [
editdistance.eval(u, v) for u, v in zip(str_y, str_x)
]
metrics["nb_chars"] = nb_chars_from_list(str_y)
elif metric_name == "wer":
metrics[metric_name] = edit_wer_from_list(str_y, str_x)
metrics["nb_words"] = nb_words_from_list(str_y)
elif metric_name == "pred":
metrics["pred"] = [
str_x,
]
if "loss_ctc" in metric_names:
metrics["loss_ctc"] = loss
metrics["nb_samples"] = len(str_y)
return metrics