提交 04b0318b 编写于 作者: W WenmuZhou

Delete unused files

上级 af3ce2cd
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import string
import re
from .check import check_config_params
import sys
class CharacterOps(object):
""" Convert between text-label and text-index """
def __init__(self, config):
self.character_type = config['character_type']
self.loss_type = config['loss_type']
self.max_text_len = config['max_text_length']
if self.character_type == "en":
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
elif self.character_type == "ch":
character_dict_path = config['character_dict_path']
add_space = False
if 'use_space_char' in config:
add_space = config['use_space_char']
self.character_str = ""
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
line = line.decode('utf-8').strip("\n").strip("\r\n")
self.character_str += line
if add_space:
self.character_str += " "
dict_character = list(self.character_str)
elif self.character_type == "en_sensitive":
# same with ASTER setting (use 94 char).
self.character_str = string.printable[:-6]
dict_character = list(self.character_str)
else:
self.character_str = None
assert self.character_str is not None, \
"Nonsupport type of the character: {}".format(self.character_str)
self.beg_str = "sos"
self.end_str = "eos"
if self.loss_type == "attention":
dict_character = [self.beg_str, self.end_str] + dict_character
elif self.loss_type == "srn":
dict_character = dict_character + [self.beg_str, self.end_str]
self.dict = {}
for i, char in enumerate(dict_character):
self.dict[char] = i
self.character = dict_character
def encode(self, text):
"""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]
"""
if self.character_type == "en":
text = text.lower()
text_list = []
for char in text:
if char not in self.dict:
continue
text_list.append(self.dict[char])
text = np.array(text_list)
return text
def decode(self, text_index, is_remove_duplicate=False):
""" convert text-index into text-label. """
char_list = []
char_num = self.get_char_num()
if self.loss_type == "attention":
beg_idx = self.get_beg_end_flag_idx("beg")
end_idx = self.get_beg_end_flag_idx("end")
ignored_tokens = [beg_idx, end_idx]
else:
ignored_tokens = [char_num]
for idx in range(len(text_index)):
if text_index[idx] in ignored_tokens:
continue
if is_remove_duplicate:
if idx > 0 and text_index[idx - 1] == text_index[idx]:
continue
char_list.append(self.character[int(text_index[idx])])
text = ''.join(char_list)
return text
def get_char_num(self):
return len(self.character)
def get_beg_end_flag_idx(self, beg_or_end):
if self.loss_type == "attention":
if beg_or_end == "beg":
idx = np.array(self.dict[self.beg_str])
elif beg_or_end == "end":
idx = np.array(self.dict[self.end_str])
else:
assert False, "Unsupport type %s in get_beg_end_flag_idx"\
% beg_or_end
return idx
else:
err = "error in get_beg_end_flag_idx when using the loss %s"\
% (self.loss_type)
assert False, err
def cal_predicts_accuracy(char_ops,
preds,
preds_lod,
labels,
labels_lod,
is_remove_duplicate=False):
acc_num = 0
img_num = 0
for ino in range(len(labels_lod) - 1):
beg_no = preds_lod[ino]
end_no = preds_lod[ino + 1]
preds_text = preds[beg_no:end_no].reshape(-1)
preds_text = char_ops.decode(preds_text, is_remove_duplicate)
beg_no = labels_lod[ino]
end_no = labels_lod[ino + 1]
labels_text = labels[beg_no:end_no].reshape(-1)
labels_text = char_ops.decode(labels_text, is_remove_duplicate)
img_num += 1
if preds_text == labels_text:
acc_num += 1
acc = acc_num * 1.0 / img_num
return acc, acc_num, img_num
def cal_predicts_accuracy_srn(char_ops,
preds,
labels,
max_text_len,
is_debug=False):
acc_num = 0
img_num = 0
char_num = char_ops.get_char_num()
total_len = preds.shape[0]
img_num = int(total_len / max_text_len)
for i in range(img_num):
cur_label = []
cur_pred = []
for j in range(max_text_len):
if labels[j + i * max_text_len] != int(char_num-1): #0
cur_label.append(labels[j + i * max_text_len][0])
else:
break
for j in range(max_text_len + 1):
if j < len(cur_label) and preds[j + i * max_text_len][
0] != cur_label[j]:
break
elif j == len(cur_label) and j == max_text_len:
acc_num += 1
break
elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1):
acc_num += 1
break
acc = acc_num * 1.0 / img_num
return acc, acc_num, img_num
def convert_rec_attention_infer_res(preds):
img_num = preds.shape[0]
target_lod = [0]
convert_ids = []
for ino in range(img_num):
end_pos = np.where(preds[ino, :] == 1)[0]
if len(end_pos) <= 1:
text_list = preds[ino, 1:]
else:
text_list = preds[ino, 1:end_pos[1]]
target_lod.append(target_lod[ino] + len(text_list))
convert_ids = convert_ids + list(text_list)
convert_ids = np.array(convert_ids)
convert_ids = convert_ids.reshape((-1, 1))
return convert_ids, target_lod
def convert_rec_label_to_lod(ori_labels):
img_num = len(ori_labels)
target_lod = [0]
convert_ids = []
for ino in range(img_num):
target_lod.append(target_lod[ino] + len(ori_labels[ino]))
convert_ids = convert_ids + list(ori_labels[ino])
convert_ids = np.array(convert_ids)
convert_ids = convert_ids.reshape((-1, 1))
return convert_ids, target_lod
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import sys
import logging
logger = logging.getLogger(__name__)
def check_config_params(config, config_name, params):
for param in params:
if param not in config:
err = "param %s didn't find in %s!" % (param, config_name)
assert False, err
return
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