# 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
import numpy as np
import os
import sys
import json
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list
import tools.program as program
def main():
global_config = config['Global']
# build post process
post_process_class = build_post_process(config['PostProcess'],
global_config)
# build model
if hasattr(post_process_class, 'character'):
char_num = len(getattr(post_process_class, 'character'))
if config['Architecture']["algorithm"] in ["Distillation",
]: # distillation model
for key in config['Architecture']["Models"]:
if config['Architecture']['Models'][key]['Head'][
'name'] == 'MultiHead': # for multi head
out_channels_list = {}
if config['PostProcess'][
'name'] == 'DistillationSARLabelDecode':
char_num = char_num - 2
out_channels_list['CTCLabelDecode'] = char_num
out_channels_list['SARLabelDecode'] = char_num + 2
config['Architecture']['Models'][key]['Head'][
'out_channels_list'] = out_channels_list
else:
config['Architecture']["Models"][key]["Head"][
'out_channels'] = char_num
elif config['Architecture']['Head'][
'name'] == 'MultiHead': # for multi head loss
out_channels_list = {}
if config['PostProcess']['name'] == 'SARLabelDecode':
char_num = char_num - 2
out_channels_list['CTCLabelDecode'] = char_num
out_channels_list['SARLabelDecode'] = char_num + 2
config['Architecture']['Head'][
'out_channels_list'] = out_channels_list
else: # base rec model
config['Architecture']["Head"]['out_channels'] = char_num
model = build_model(config['Architecture'])
load_model(config, model)
# create data ops
transforms = []
for op in config['Eval']['dataset']['transforms']:
op_name = list(op)[0]
if 'Label' in op_name:
continue
elif op_name in ['RecResizeImg']:
op[op_name]['infer_mode'] = True
elif op_name == 'KeepKeys':
if config['Architecture']['algorithm'] == "SRN":
op[op_name]['keep_keys'] = [
'image', 'encoder_word_pos', 'gsrm_word_pos',
'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
]
elif config['Architecture']['algorithm'] == "SAR":
op[op_name]['keep_keys'] = ['image', 'valid_ratio']
elif config['Architecture']['algorithm'] == "RobustScanner":
op[op_name][
'keep_keys'] = ['image', 'valid_ratio', 'word_positons']
else:
op[op_name]['keep_keys'] = ['image']
transforms.append(op)
global_config['infer_mode'] = True
ops = create_operators(transforms, global_config)
save_res_path = config['Global'].get('save_res_path',
"./output/rec/predicts_rec.txt")
if not os.path.exists(os.path.dirname(save_res_path)):
os.makedirs(os.path.dirname(save_res_path))
model.eval()
with open(save_res_path, "w") as fout:
for file in get_image_file_list(config['Global']['infer_img']):
logger.info("infer_img: {}".format(file))
with open(file, 'rb') as f:
img = f.read()
data = {'image': img}
batch = transform(data, ops)
if config['Architecture']['algorithm'] == "SRN":
encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
others = [
paddle.to_tensor(encoder_word_pos_list),
paddle.to_tensor(gsrm_word_pos_list),
paddle.to_tensor(gsrm_slf_attn_bias1_list),
paddle.to_tensor(gsrm_slf_attn_bias2_list)
]
if config['Architecture']['algorithm'] == "SAR":
valid_ratio = np.expand_dims(batch[-1], axis=0)
img_metas = [paddle.to_tensor(valid_ratio)]
if config['Architecture']['algorithm'] == "RobustScanner":
valid_ratio = np.expand_dims(batch[1], axis=0)
word_positons = np.expand_dims(batch[2], axis=0)
img_metas = [
paddle.to_tensor(valid_ratio),
paddle.to_tensor(word_positons),
]
if config['Architecture']['algorithm'] == "CAN":
image_mask = paddle.ones(
(np.expand_dims(
batch[0], axis=0).shape), dtype='float32')
label = paddle.ones((1, 36), dtype='int64')
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_tensor(images)
if config['Architecture']['algorithm'] == "SRN":
preds = model(images, others)
elif config['Architecture']['algorithm'] == "SAR":
preds = model(images, img_metas)
elif config['Architecture']['algorithm'] == "RobustScanner":
preds = model(images, img_metas)
elif config['Architecture']['algorithm'] == "CAN":
preds = model([images, image_mask, label])
else:
preds = model(images)
post_result = post_process_class(preds)
info = None
if isinstance(post_result, dict):
rec_info = dict()
for key in post_result:
if len(post_result[key][0]) >= 2:
rec_info[key] = {
"label": post_result[key][0][0],
"score": float(post_result[key][0][1]),
}
info = json.dumps(rec_info, ensure_ascii=False)
elif isinstance(post_result, list) and isinstance(post_result[0],
int):
# for RFLearning CNT branch
info = str(post_result[0])
else:
if len(post_result[0]) >= 2:
info = post_result[0][0] + "\t" + str(post_result[0][1])
if info is not None:
logger.info("\t result: {}".format(info))
fout.write(file + "\t" + info + "\n")
logger.info("success!")
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main()