|
|
import numpy as np |
|
|
import torch |
|
|
import torch.nn.functional as F |
|
|
|
|
|
from .ctc_postprocess import BaseRecLabelDecode |
|
|
|
|
|
|
|
|
class VisionLANLabelDecode(BaseRecLabelDecode): |
|
|
"""Convert between text-label and text-index.""" |
|
|
|
|
|
def __init__(self, |
|
|
character_dict_path=None, |
|
|
use_space_char=False, |
|
|
**kwargs): |
|
|
super(VisionLANLabelDecode, self).__init__(character_dict_path, |
|
|
use_space_char) |
|
|
self.max_text_length = kwargs.get('max_text_length', 25) |
|
|
self.nclass = len(self.character) + 1 |
|
|
|
|
|
def decode(self, text_index, text_prob=None, is_remove_duplicate=False): |
|
|
"""convert text-index into text-label.""" |
|
|
result_list = [] |
|
|
ignored_tokens = self.get_ignored_tokens() |
|
|
batch_size = len(text_index) |
|
|
for batch_idx in range(batch_size): |
|
|
selection = np.ones(len(text_index[batch_idx]), dtype=bool) |
|
|
if is_remove_duplicate: |
|
|
selection[1:] = text_index[batch_idx][1:] != text_index[ |
|
|
batch_idx][:-1] |
|
|
for ignored_token in ignored_tokens: |
|
|
selection &= text_index[batch_idx] != ignored_token |
|
|
|
|
|
char_list = [ |
|
|
self.character[text_id - 1] |
|
|
for text_id in text_index[batch_idx][selection] |
|
|
] |
|
|
if text_prob is not None: |
|
|
conf_list = text_prob[batch_idx][selection] |
|
|
else: |
|
|
conf_list = [1] * len(selection) |
|
|
if len(conf_list) == 0: |
|
|
conf_list = [0] |
|
|
|
|
|
text = ''.join(char_list) |
|
|
result_list.append((text, np.mean(conf_list).tolist())) |
|
|
return result_list |
|
|
|
|
|
def __call__(self, preds, batch=None, *args, **kwargs): |
|
|
if len(preds) == 2: |
|
|
net_out, length = preds |
|
|
if batch is not None: |
|
|
label = batch[1] |
|
|
|
|
|
else: |
|
|
net_out = preds[0] |
|
|
label, length = batch[1], batch[5] |
|
|
net_out = torch.cat([t[:l] for t, l in zip(net_out, length)], |
|
|
dim=0) |
|
|
text = [] |
|
|
if not isinstance(net_out, torch.Tensor): |
|
|
net_out = torch.tensor(net_out, dtype=torch.float32) |
|
|
net_out = F.softmax(net_out, dim=1) |
|
|
for i in range(0, length.shape[0]): |
|
|
preds_idx = (net_out[int(length[:i].sum()):int(length[:i].sum() + |
|
|
length[i])].topk(1) |
|
|
[1][:, 0].tolist()) |
|
|
preds_text = ''.join([ |
|
|
self.character[idx - 1] |
|
|
if idx > 0 and idx <= len(self.character) else '' |
|
|
for idx in preds_idx |
|
|
]) |
|
|
preds_prob = net_out[int(length[:i].sum()):int(length[:i].sum() + |
|
|
length[i])].topk( |
|
|
1)[0][:, 0] |
|
|
preds_prob = torch.exp( |
|
|
torch.log(preds_prob).sum() / (preds_prob.shape[0] + 1e-6)) |
|
|
text.append((preds_text, float(preds_prob))) |
|
|
if batch is None: |
|
|
return text |
|
|
label = self.decode(label) |
|
|
return text, label |
|
|
|
