Delete unused files
This commit is contained in:
WenmuZhou
parent
af3ce2cd92
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04b0318b56
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import string
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import re
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from .check import check_config_params
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import sys
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class CharacterOps(object):
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""" Convert between text-label and text-index """
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def __init__(self, config):
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self.character_type = config['character_type']
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self.loss_type = config['loss_type']
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self.max_text_len = config['max_text_length']
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if self.character_type == "en":
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self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
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dict_character = list(self.character_str)
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elif self.character_type == "ch":
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character_dict_path = config['character_dict_path']
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add_space = False
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if 'use_space_char' in config:
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add_space = config['use_space_char']
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self.character_str = ""
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with open(character_dict_path, "rb") as fin:
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lines = fin.readlines()
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for line in lines:
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line = line.decode('utf-8').strip("\n").strip("\r\n")
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self.character_str += line
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if add_space:
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self.character_str += " "
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dict_character = list(self.character_str)
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elif self.character_type == "en_sensitive":
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# same with ASTER setting (use 94 char).
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self.character_str = string.printable[:-6]
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dict_character = list(self.character_str)
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else:
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self.character_str = None
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assert self.character_str is not None, \
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"Nonsupport type of the character: {}".format(self.character_str)
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self.beg_str = "sos"
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self.end_str = "eos"
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if self.loss_type == "attention":
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dict_character = [self.beg_str, self.end_str] + dict_character
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elif self.loss_type == "srn":
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dict_character = dict_character + [self.beg_str, self.end_str]
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self.dict = {}
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for i, char in enumerate(dict_character):
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self.dict[char] = i
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self.character = dict_character
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def encode(self, text):
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"""convert text-label into text-index.
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input:
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text: text labels of each image. [batch_size]
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output:
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text: concatenated text index for CTCLoss.
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[sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
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length: length of each text. [batch_size]
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"""
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if self.character_type == "en":
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text = text.lower()
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text_list = []
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for char in text:
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if char not in self.dict:
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continue
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text_list.append(self.dict[char])
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text = np.array(text_list)
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return text
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def decode(self, text_index, is_remove_duplicate=False):
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""" convert text-index into text-label. """
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char_list = []
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char_num = self.get_char_num()
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if self.loss_type == "attention":
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beg_idx = self.get_beg_end_flag_idx("beg")
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end_idx = self.get_beg_end_flag_idx("end")
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ignored_tokens = [beg_idx, end_idx]
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else:
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ignored_tokens = [char_num]
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for idx in range(len(text_index)):
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if text_index[idx] in ignored_tokens:
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continue
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if is_remove_duplicate:
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if idx > 0 and text_index[idx - 1] == text_index[idx]:
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continue
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char_list.append(self.character[int(text_index[idx])])
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text = ''.join(char_list)
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return text
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def get_char_num(self):
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return len(self.character)
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def get_beg_end_flag_idx(self, beg_or_end):
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if self.loss_type == "attention":
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if beg_or_end == "beg":
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idx = np.array(self.dict[self.beg_str])
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elif beg_or_end == "end":
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idx = np.array(self.dict[self.end_str])
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else:
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assert False, "Unsupport type %s in get_beg_end_flag_idx"\
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% beg_or_end
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return idx
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else:
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err = "error in get_beg_end_flag_idx when using the loss %s"\
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% (self.loss_type)
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assert False, err
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def cal_predicts_accuracy(char_ops,
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preds,
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preds_lod,
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labels,
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labels_lod,
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is_remove_duplicate=False):
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acc_num = 0
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img_num = 0
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for ino in range(len(labels_lod) - 1):
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beg_no = preds_lod[ino]
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end_no = preds_lod[ino + 1]
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preds_text = preds[beg_no:end_no].reshape(-1)
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preds_text = char_ops.decode(preds_text, is_remove_duplicate)
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beg_no = labels_lod[ino]
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end_no = labels_lod[ino + 1]
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labels_text = labels[beg_no:end_no].reshape(-1)
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labels_text = char_ops.decode(labels_text, is_remove_duplicate)
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img_num += 1
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if preds_text == labels_text:
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acc_num += 1
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acc = acc_num * 1.0 / img_num
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return acc, acc_num, img_num
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def cal_predicts_accuracy_srn(char_ops,
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preds,
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labels,
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max_text_len,
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is_debug=False):
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acc_num = 0
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img_num = 0
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char_num = char_ops.get_char_num()
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total_len = preds.shape[0]
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img_num = int(total_len / max_text_len)
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for i in range(img_num):
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cur_label = []
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cur_pred = []
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for j in range(max_text_len):
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if labels[j + i * max_text_len] != int(char_num-1): #0
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cur_label.append(labels[j + i * max_text_len][0])
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else:
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break
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for j in range(max_text_len + 1):
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if j < len(cur_label) and preds[j + i * max_text_len][
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0] != cur_label[j]:
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break
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elif j == len(cur_label) and j == max_text_len:
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acc_num += 1
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break
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elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1):
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acc_num += 1
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break
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acc = acc_num * 1.0 / img_num
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return acc, acc_num, img_num
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def convert_rec_attention_infer_res(preds):
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img_num = preds.shape[0]
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target_lod = [0]
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convert_ids = []
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for ino in range(img_num):
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end_pos = np.where(preds[ino, :] == 1)[0]
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if len(end_pos) <= 1:
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text_list = preds[ino, 1:]
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else:
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text_list = preds[ino, 1:end_pos[1]]
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target_lod.append(target_lod[ino] + len(text_list))
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convert_ids = convert_ids + list(text_list)
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convert_ids = np.array(convert_ids)
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convert_ids = convert_ids.reshape((-1, 1))
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return convert_ids, target_lod
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def convert_rec_label_to_lod(ori_labels):
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img_num = len(ori_labels)
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target_lod = [0]
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convert_ids = []
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for ino in range(img_num):
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target_lod.append(target_lod[ino] + len(ori_labels[ino]))
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convert_ids = convert_ids + list(ori_labels[ino])
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convert_ids = np.array(convert_ids)
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convert_ids = convert_ids.reshape((-1, 1))
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return convert_ids, target_lod
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@ -1,31 +0,0 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import sys
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import logging
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logger = logging.getLogger(__name__)
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def check_config_params(config, config_name, params):
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for param in params:
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if param not in config:
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err = "param %s didn't find in %s!" % (param, config_name)
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assert False, err
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return
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