337 lines
14 KiB
Python
Executable File
337 lines
14 KiB
Python
Executable File
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 os
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import sys
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import math
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import random
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import numpy as np
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import cv2
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import string
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import lmdb
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from ppocr.utils.utility import initial_logger
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from ppocr.utils.utility import get_image_file_list
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logger = initial_logger()
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from .img_tools import process_image, process_image_srn, get_img_data
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class LMDBReader(object):
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def __init__(self, params):
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if params['mode'] != 'train':
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self.num_workers = 1
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else:
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self.num_workers = params['num_workers']
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self.lmdb_sets_dir = params['lmdb_sets_dir']
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self.char_ops = params['char_ops']
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self.image_shape = params['image_shape']
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self.loss_type = params['loss_type']
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self.max_text_length = params['max_text_length']
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self.mode = params['mode']
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self.drop_last = False
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self.use_tps = False
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self.num_heads = None
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if "num_heads" in params:
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self.num_heads = params['num_heads']
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if "tps" in params:
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self.ues_tps = True
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self.use_distort = False
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if "distort" in params:
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self.use_distort = params['distort'] and params['use_gpu']
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if not params['use_gpu']:
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logger.info(
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"Distort operation can only support in GPU. Distort will be set to False."
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)
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if params['mode'] == 'train':
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self.batch_size = params['train_batch_size_per_card']
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self.drop_last = True
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else:
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self.batch_size = params['test_batch_size_per_card']
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self.drop_last = False
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self.use_distort = False
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self.infer_img = params['infer_img']
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def load_hierarchical_lmdb_dataset(self):
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lmdb_sets = {}
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dataset_idx = 0
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for dirpath, dirnames, filenames in os.walk(self.lmdb_sets_dir + '/'):
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if not dirnames:
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env = lmdb.open(
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dirpath,
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max_readers=32,
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readonly=True,
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lock=False,
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readahead=False,
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meminit=False)
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txn = env.begin(write=False)
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num_samples = int(txn.get('num-samples'.encode()))
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lmdb_sets[dataset_idx] = {"dirpath":dirpath, "env":env, \
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"txn":txn, "num_samples":num_samples}
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dataset_idx += 1
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return lmdb_sets
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def print_lmdb_sets_info(self, lmdb_sets):
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lmdb_info_strs = []
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for dataset_idx in range(len(lmdb_sets)):
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tmp_str = " %s:%d," % (lmdb_sets[dataset_idx]['dirpath'],
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lmdb_sets[dataset_idx]['num_samples'])
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lmdb_info_strs.append(tmp_str)
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lmdb_info_strs = ''.join(lmdb_info_strs)
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logger.info("DataSummary:" + lmdb_info_strs)
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return
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def close_lmdb_dataset(self, lmdb_sets):
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for dataset_idx in lmdb_sets:
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lmdb_sets[dataset_idx]['env'].close()
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return
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def get_lmdb_sample_info(self, txn, index):
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label_key = 'label-%09d'.encode() % index
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label = txn.get(label_key)
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if label is None:
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return None
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label = label.decode('utf-8')
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img_key = 'image-%09d'.encode() % index
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imgbuf = txn.get(img_key)
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img = get_img_data(imgbuf)
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if img is None:
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return None
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return img, label
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def __call__(self, process_id):
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if self.mode != 'train':
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process_id = 0
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def sample_iter_reader():
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if self.mode != 'train' and self.infer_img is not None:
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image_file_list = get_image_file_list(self.infer_img)
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for single_img in image_file_list:
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img = cv2.imread(single_img)
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if self.loss_type == 'srn':
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norm_img = process_image_srn(
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img=img,
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image_shape=self.image_shape,
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num_heads=self.num_heads,
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char_ops=self.char_ops,
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max_text_length=self.max_text_length)
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else:
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norm_img = process_image(
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img=img,
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image_shape=self.image_shape,
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char_ops=self.char_ops,
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tps=self.use_tps,
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infer_mode=True)
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yield norm_img
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else:
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lmdb_sets = self.load_hierarchical_lmdb_dataset()
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if process_id == 0:
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self.print_lmdb_sets_info(lmdb_sets)
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cur_index_sets = [1 + process_id] * len(lmdb_sets)
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while True:
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finish_read_num = 0
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for dataset_idx in range(len(lmdb_sets)):
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cur_index = cur_index_sets[dataset_idx]
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if cur_index > lmdb_sets[dataset_idx]['num_samples']:
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finish_read_num += 1
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else:
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sample_info = self.get_lmdb_sample_info(
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lmdb_sets[dataset_idx]['txn'], cur_index)
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cur_index_sets[dataset_idx] += self.num_workers
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if sample_info is None:
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continue
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img, label = sample_info
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outs = []
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if self.loss_type == "srn":
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outs = process_image_srn(
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img=img,
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image_shape=self.image_shape,
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num_heads=self.num_heads,
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max_text_length=self.max_text_length,
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label=label,
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char_ops=self.char_ops,
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loss_type=self.loss_type)
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else:
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outs = process_image(
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img=img,
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image_shape=self.image_shape,
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label=label,
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char_ops=self.char_ops,
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loss_type=self.loss_type,
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max_text_length=self.max_text_length)
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if outs is None:
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continue
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yield outs
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if finish_read_num == len(lmdb_sets):
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break
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self.close_lmdb_dataset(lmdb_sets)
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def batch_iter_reader():
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batch_outs = []
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for outs in sample_iter_reader():
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batch_outs.append(outs)
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if len(batch_outs) == self.batch_size:
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yield batch_outs
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batch_outs = []
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if not self.drop_last:
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if len(batch_outs) != 0:
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yield batch_outs
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if self.infer_img is None:
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return batch_iter_reader
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return sample_iter_reader
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class SimpleReader(object):
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def __init__(self, params):
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if params['mode'] != 'train':
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self.num_workers = 1
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else:
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self.num_workers = params['num_workers']
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if params['mode'] != 'test':
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self.img_set_dir = params['img_set_dir']
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self.label_file_path = params['label_file_path']
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self.use_gpu = params['use_gpu']
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self.char_ops = params['char_ops']
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self.image_shape = params['image_shape']
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self.loss_type = params['loss_type']
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self.max_text_length = params['max_text_length']
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self.mode = params['mode']
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self.infer_img = params['infer_img']
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self.use_tps = False
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if "num_heads" in params:
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self.num_heads = params['num_heads']
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if "tps" in params:
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self.use_tps = True
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self.use_distort = False
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if "distort" in params:
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self.use_distort = params['distort'] and params['use_gpu']
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if not params['use_gpu']:
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logger.info(
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"Distort operation can only support in GPU.Distort will be set to False."
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)
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if params['mode'] == 'train':
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self.batch_size = params['train_batch_size_per_card']
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self.drop_last = True
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else:
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self.batch_size = params['test_batch_size_per_card']
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self.drop_last = False
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self.use_distort = False
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def __call__(self, process_id):
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if self.mode != 'train':
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process_id = 0
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def get_device_num():
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if self.use_gpu:
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gpus = os.environ.get("CUDA_VISIBLE_DEVICES", '1')
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gpu_num = len(gpus.split(','))
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return gpu_num
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else:
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cpu_num = os.environ.get("CPU_NUM", 1)
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return int(cpu_num)
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def sample_iter_reader():
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if self.mode != 'train' and self.infer_img is not None:
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image_file_list = get_image_file_list(self.infer_img)
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for single_img in image_file_list:
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img = cv2.imread(single_img)
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if self.loss_type == 'srn':
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norm_img = process_image_srn(
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img=img,
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image_shape=self.image_shape,
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char_ops=self.char_ops,
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num_heads=self.num_heads,
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max_text_length=self.max_text_length)
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else:
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norm_img = process_image(
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img=img,
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image_shape=self.image_shape,
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char_ops=self.char_ops,
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tps=self.use_tps,
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infer_mode=True)
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yield norm_img
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else:
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with open(self.label_file_path, "rb") as fin:
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label_infor_list = fin.readlines()
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img_num = len(label_infor_list)
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img_id_list = list(range(img_num))
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random.shuffle(img_id_list)
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if sys.platform == "win32" and self.num_workers != 1:
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print("multiprocess is not fully compatible with Windows."
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"num_workers will be 1.")
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self.num_workers = 1
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if self.batch_size * get_device_num(
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) * self.num_workers > img_num:
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raise Exception(
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"The number of the whole data ({}) is smaller than the batch_size * devices_num * num_workers ({})".
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format(img_num, self.batch_size * get_device_num() *
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self.num_workers))
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for img_id in range(process_id, img_num, self.num_workers):
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label_infor = label_infor_list[img_id_list[img_id]]
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substr = label_infor.decode('utf-8').strip("\n").split("\t")
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img_path = self.img_set_dir + "/" + substr[0]
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img = cv2.imread(img_path)
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if img is None:
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logger.info("{} does not exist!".format(img_path))
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continue
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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label = substr[1]
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if self.loss_type == "srn":
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outs = process_image_srn(
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img=img,
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image_shape=self.image_shape,
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num_heads=self.num_heads,
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max_text_length=self.max_text_length,
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label=label,
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char_ops=self.char_ops,
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loss_type=self.loss_type)
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else:
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outs = process_image(
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img=img,
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image_shape=self.image_shape,
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label=label,
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char_ops=self.char_ops,
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loss_type=self.loss_type,
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max_text_length=self.max_text_length,
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distort=self.use_distort)
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if outs is None:
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continue
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yield outs
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def batch_iter_reader():
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batch_outs = []
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for outs in sample_iter_reader():
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batch_outs.append(outs)
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if len(batch_outs) == self.batch_size:
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yield batch_outs
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batch_outs = []
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if not self.drop_last:
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if len(batch_outs) != 0:
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yield batch_outs
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if self.infer_img is None:
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return batch_iter_reader
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return sample_iter_reader
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