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rec_utils.py
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rec_utils.py
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import torch
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class CTCLabelConverter(object):
""" Convert between text-label and text-index """
def __init__(self, character):
# character (str): set of the possible characters.
dict_character = list(character)
self.dict = {}
for i, char in enumerate(dict_character):
# NOTE: 0 is reserved for 'CTCblank' token required by CTCLoss
self.dict[char] = i + 1
self.character = ['[CTCblank]'] + dict_character # dummy '[CTCblank]' token for CTCLoss (index 0)
def encode(self, text, batch_max_length=25):
"""convert text-label into text-index.
input:
text: text labels of each image. [batch_size]
batch_max_length: max length of text label in the batch. 25 by default
output:
text: text index for CTCLoss. [batch_size, batch_max_length]
length: length of each text. [batch_size]
"""
length = [len(s) for s in text]
# The index used for padding (=0) would not affect the CTC loss calculation.
batch_text = torch.LongTensor(len(text), batch_max_length).fill_(0)
for i, t in enumerate(text):
text = list(t)
text = [self.dict[char] for char in text]
batch_text[i][:len(text)] = torch.LongTensor(text)
return (batch_text.to(device), torch.IntTensor(length).to(device))
def decode(self, text_index, length):
""" convert text-index into text-label. """
texts = []
for index, l in enumerate(length):
t = text_index[index, :]
char_list = []
for i in range(l):
if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])): # removing repeated characters and blank.
char_list.append(self.character[t[i]])
text = ''.join(char_list)
texts.append(text)
return texts
class CTCLabelConverterForBaiduWarpctc(object):
""" Convert between text-label and text-index for baidu warpctc """
def __init__(self, character):
# character (str): set of the possible characters.
dict_character = list(character)
self.dict = {}
for i, char in enumerate(dict_character):
# NOTE: 0 is reserved for 'CTCblank' token required by CTCLoss
self.dict[char] = i + 1
self.character = ['[CTCblank]'] + dict_character # dummy '[CTCblank]' token for CTCLoss (index 0)
def encode(self, text, batch_max_length=25):
"""convert text-label into text-index.
input:
text: text labels of each image. [batch_size]
output:
text: concatenated text index for CTCLoss.
[sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
length: length of each text. [batch_size]
"""
length = [len(s) for s in text]
text = ''.join(text)
text = [self.dict[char] for char in text]
return (torch.IntTensor(text), torch.IntTensor(length))
def decode(self, text_index, length):
""" convert text-index into text-label. """
texts = []
index = 0
for l in length:
t = text_index[index:index + l]
char_list = []
for i in range(l):
if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])): # removing repeated characters and blank.
char_list.append(self.character[t[i]])
text = ''.join(char_list)
texts.append(text)
index += l
return texts
class AttnLabelConverter(object):
""" Convert between text-label and text-index """
def __init__(self, character):
# character (str): set of the possible characters.
# [GO] for the start token of the attention decoder. [s] for end-of-sentence token.
list_token = ['[GO]', '[s]'] # ['[s]','[UNK]','[PAD]','[GO]']
list_character = list(character)
self.character = list_token + list_character
self.dict = {}
for i, char in enumerate(self.character):
# print(i, char)
self.dict[char] = i
def encode(self, text, batch_max_length=25):
""" convert text-label into text-index.
input:
text: text labels of each image. [batch_size]
batch_max_length: max length of text label in the batch. 25 by default
output:
text : the input of attention decoder. [batch_size x (max_length+2)] +1 for [GO] token and +1 for [s] token.
text[:, 0] is [GO] token and text is padded with [GO] token after [s] token.
length : the length of output of attention decoder, which count [s] token also. [3, 7, ....] [batch_size]
"""
length = [len(s) + 1 for s in text] # +1 for [s] at end of sentence.
# batch_max_length = max(length) # this is not allowed for multi-gpu setting
batch_max_length += 1
# additional +1 for [GO] at first step. batch_text is padded with [GO] token after [s] token.
batch_text = torch.LongTensor(len(text), batch_max_length + 1).fill_(0)
for i, t in enumerate(text):
text = list(t)
text.append('[s]')
text = [self.dict[char] for char in text]
try:
batch_text[i][1:1 + len(text)] = torch.LongTensor(text) # batch_text[:, 0] = [GO] token
except:
print(text)
print(len(text))
print(len(batch_text[i]))
# return (batch_text.to(device), torch.IntTensor(length).to(device))
return (batch_text, torch.IntTensor(length))
def decode(self, text_index, length):
""" convert text-index into text-label. """
texts = []
for index, l in enumerate(length):
text = ''.join([self.character[i] for i in text_index[index, :]])
texts.append(text)
return texts
class Averager(object):
"""Compute average for torch.Tensor, used for loss average."""
def __init__(self):
self.reset()
def add(self, v):
count = v.data.numel()
v = v.data.sum()
self.n_count += count
self.sum += v
def reset(self):
self.n_count = 0
self.sum = 0
def val(self):
res = 0
if self.n_count != 0:
res = self.sum / float(self.n_count)
return res