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# 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 os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "..")))
import argparse
import paddle
from paddle.jit import to_static
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.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser
def export_single_model(model,
arch_config,
save_path,
logger,
input_shape=None,
quanter=None):
if arch_config["algorithm"] == "SRN":
max_text_length = arch_config["Head"]["max_text_length"]
other_shape = [
paddle.static.InputSpec(
shape=[None, 1, 64, 256], dtype="float32"), [
paddle.static.InputSpec(
shape=[None, 256, 1],
dtype="int64"), paddle.static.InputSpec(
shape=[None, max_text_length, 1], dtype="int64"),
paddle.static.InputSpec(
shape=[None, 8, max_text_length, max_text_length],
dtype="int64"), paddle.static.InputSpec(
shape=[None, 8, max_text_length, max_text_length],
dtype="int64")
]
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "SAR":
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 48, 160], dtype="float32"),
[paddle.static.InputSpec(
shape=[None], dtype="float32")]
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "SVTR":
if arch_config["Head"]["name"] == 'MultiHead':
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 48, -1], dtype="float32"),
]
else:
other_shape = [
paddle.static.InputSpec(
shape=[None] + input_shape, dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "PREN":
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 64, 256], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
elif arch_config["model_type"] == "sr":
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 16, 64], dtype="float32")
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "ViTSTR":
other_shape = [
paddle.static.InputSpec(
shape=[None, 1, 224, 224], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "ABINet":
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 32, 128], dtype="float32"),
]
# print([None, 3, 32, 128])
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] in ["NRTR", "SPIN", 'RFL']:
other_shape = [
paddle.static.InputSpec(
shape=[None, 1, 32, 100], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "VisionLAN":
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 64, 256], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "RobustScanner":
max_text_length = arch_config["Head"]["max_text_length"]
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 48, 160], dtype="float32"), [
paddle.static.InputSpec(
shape=[None, ], dtype="float32"),
paddle.static.InputSpec(
shape=[None, max_text_length], dtype="int64")
]
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "CAN":
other_shape = [[
paddle.static.InputSpec(
shape=[None, 1, None, None],
dtype="float32"), paddle.static.InputSpec(
shape=[None, 1, None, None], dtype="float32"),
paddle.static.InputSpec(
shape=[None, arch_config['Head']['max_text_length']],
dtype="int64")
]]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]:
input_spec = [
paddle.static.InputSpec(
shape=[None, 512], dtype="int64"), # input_ids
paddle.static.InputSpec(
shape=[None, 512, 4], dtype="int64"), # bbox
paddle.static.InputSpec(
shape=[None, 512], dtype="int64"), # attention_mask
paddle.static.InputSpec(
shape=[None, 512], dtype="int64"), # token_type_ids
paddle.static.InputSpec(
shape=[None, 3, 224, 224], dtype="int64"), # image
]
if 'Re' in arch_config['Backbone']['name']:
input_spec.extend([
paddle.static.InputSpec(
shape=[None, 512, 3], dtype="int64"), # entities
paddle.static.InputSpec(
shape=[None, None, 2], dtype="int64"), # relations
])
if model.backbone.use_visual_backbone is False:
input_spec.pop(4)
model = to_static(model, input_spec=[input_spec])
else:
infer_shape = [3, -1, -1]
if arch_config["model_type"] == "rec":
infer_shape = [3, 32, -1] # for rec model, H must be 32
if "Transform" in arch_config and arch_config[
"Transform"] is not None and arch_config["Transform"][
"name"] == "TPS":
logger.info(
"When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training"
)
infer_shape[-1] = 100
elif arch_config["model_type"] == "table":
infer_shape = [3, 488, 488]
if arch_config["algorithm"] == "TableMaster":
infer_shape = [3, 480, 480]
if arch_config["algorithm"] == "SLANet":
infer_shape = [3, -1, -1]
model = to_static(
model,
input_spec=[
paddle.static.InputSpec(
shape=[None] + infer_shape, dtype="float32")
])
if quanter is None:
paddle.jit.save(model, save_path)
else:
quanter.save_quantized_model(model, save_path)
logger.info("inference model is saved to {}".format(save_path))
return
def main():
FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config)
config = merge_config(config, FLAGS.opt)
logger = get_logger()
# build post process
post_process_class = build_post_process(config["PostProcess"],
config["Global"])
# build model
# for rec algorithm
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
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
# just one final tensor needs to exported for inference
config["Architecture"]["Models"][key][
"return_all_feats"] = False
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
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
# for sr algorithm
if config["Architecture"]["model_type"] == "sr":
config['Architecture']["Transform"]['infer_mode'] = True
model = build_model(config["Architecture"])
load_model(config, model, model_type=config['Architecture']["model_type"])
model.eval()
save_path = config["Global"]["save_inference_dir"]
arch_config = config["Architecture"]
if arch_config["algorithm"] == "SVTR" and arch_config["Head"][
"name"] != 'MultiHead':
input_shape = config["Eval"]["dataset"]["transforms"][-2][
'SVTRRecResizeImg']['image_shape']
else:
input_shape = None
if arch_config["algorithm"] in ["Distillation", ]: # distillation model
archs = list(arch_config["Models"].values())
for idx, name in enumerate(model.model_name_list):
sub_model_save_path = os.path.join(save_path, name, "inference")
export_single_model(model.model_list[idx], archs[idx],
sub_model_save_path, logger)
else:
save_path = os.path.join(save_path, "inference")
export_single_model(
model, arch_config, save_path, logger, input_shape=input_shape)
if __name__ == "__main__":
main()
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