219 lines
9.0 KiB
Python
219 lines
9.0 KiB
Python
# 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"
|
|
): # multi head
|
|
out_channels_list = {}
|
|
if config["PostProcess"]["name"] == "DistillationSARLabelDecode":
|
|
char_num = char_num - 2
|
|
if config["PostProcess"]["name"] == "DistillationNRTRLabelDecode":
|
|
char_num = char_num - 3
|
|
out_channels_list["CTCLabelDecode"] = char_num
|
|
out_channels_list["SARLabelDecode"] = char_num + 2
|
|
out_channels_list["NRTRLabelDecode"] = char_num + 3
|
|
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": # multi head
|
|
out_channels_list = {}
|
|
char_num = len(getattr(post_process_class, "character"))
|
|
if config["PostProcess"]["name"] == "SARLabelDecode":
|
|
char_num = char_num - 2
|
|
if config["PostProcess"]["name"] == "NRTRLabelDecode":
|
|
char_num = char_num - 3
|
|
out_channels_list["CTCLabelDecode"] = char_num
|
|
out_channels_list["SARLabelDecode"] = char_num + 2
|
|
out_channels_list["NRTRLabelDecode"] = char_num + 3
|
|
config["Architecture"]["Head"]["out_channels_list"] = out_channels_list
|
|
else: # base rec model
|
|
config["Architecture"]["Head"]["out_channels"] = char_num
|
|
|
|
if config["Architecture"].get("algorithm") in ["LaTeXOCR"]:
|
|
config["Architecture"]["Backbone"]["is_predict"] = True
|
|
config["Architecture"]["Backbone"]["is_export"] = True
|
|
config["Architecture"]["Head"]["is_export"] = True
|
|
|
|
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()
|
|
|
|
infer_imgs = config["Global"]["infer_img"]
|
|
infer_list = config["Global"].get("infer_list", None)
|
|
with open(save_res_path, "w") as fout:
|
|
for file in get_image_file_list(infer_imgs, infer_list=infer_list):
|
|
logger.info("infer_img: {}".format(file))
|
|
with open(file, "rb") as f:
|
|
img = f.read()
|
|
if config["Architecture"]["algorithm"] in [
|
|
"UniMERNet",
|
|
"PP-FormulaNet-S",
|
|
"PP-FormulaNet-L",
|
|
]:
|
|
data = {"image": img, "filename": file}
|
|
else:
|
|
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])
|
|
elif config["Architecture"]["algorithm"] in [
|
|
"LaTeXOCR",
|
|
"UniMERNet",
|
|
"PP-FormulaNet-S",
|
|
"PP-FormulaNet-L",
|
|
]:
|
|
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()
|