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support export after save model #13844

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Sep 24, 2024
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support export after save model #13844

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381 changes: 381 additions & 0 deletions ppocr/utils/export_model.py
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# Copyright (c) 2021 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 yaml
import json
import copy
import paddle
import paddle.nn as nn
from paddle.jit import to_static

from collections import OrderedDict
from argparse import ArgumentParser, RawDescriptionHelpFormatter
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


def represent_dictionary_order(self, dict_data):
return self.represent_mapping("tag:yaml.org,2002:map", dict_data.items())


def setup_orderdict():
yaml.add_representer(OrderedDict, represent_dictionary_order)


def dump_infer_config(config, path, logger):
setup_orderdict()
infer_cfg = OrderedDict()
if config["Global"].get("hpi_config_path", None):
hpi_config = yaml.safe_load(open(config["Global"]["hpi_config_path"], "r"))
rec_resize_img_dict = next(
(
item
for item in config["Eval"]["dataset"]["transforms"]
if "RecResizeImg" in item
),
None,
)
if rec_resize_img_dict:
dynamic_shapes = [1] + rec_resize_img_dict["RecResizeImg"]["image_shape"]
if hpi_config["Hpi"]["backend_config"].get("paddle_tensorrt", None):
hpi_config["Hpi"]["backend_config"]["paddle_tensorrt"][
"dynamic_shapes"
]["x"] = [dynamic_shapes for i in range(3)]
hpi_config["Hpi"]["backend_config"]["paddle_tensorrt"][
"max_batch_size"
] = 1
if hpi_config["Hpi"]["backend_config"].get("tensorrt", None):
hpi_config["Hpi"]["backend_config"]["tensorrt"]["dynamic_shapes"][
"x"
] = [dynamic_shapes for i in range(3)]
hpi_config["Hpi"]["backend_config"]["tensorrt"]["max_batch_size"] = 1
else:
if hpi_config["Hpi"]["backend_config"].get("paddle_tensorrt", None):
hpi_config["Hpi"]["supported_backends"]["gpu"].remove("paddle_tensorrt")
del hpi_config["Hpi"]["backend_config"]["paddle_tensorrt"]
if hpi_config["Hpi"]["backend_config"].get("tensorrt", None):
hpi_config["Hpi"]["supported_backends"]["gpu"].remove("tensorrt")
del hpi_config["Hpi"]["backend_config"]["tensorrt"]
infer_cfg["Hpi"] = hpi_config["Hpi"]
if config["Global"].get("pdx_model_name", None):
infer_cfg["Global"] = {}
infer_cfg["Global"]["model_name"] = config["Global"]["pdx_model_name"]

infer_cfg["PreProcess"] = {"transform_ops": config["Eval"]["dataset"]["transforms"]}
postprocess = OrderedDict()
for k, v in config["PostProcess"].items():
postprocess[k] = v

if config["Architecture"].get("algorithm") in ["LaTeXOCR"]:
tokenizer_file = config["Global"].get("rec_char_dict_path")
if tokenizer_file is not None:
with open(tokenizer_file, encoding="utf-8") as tokenizer_config_handle:
character_dict = json.load(tokenizer_config_handle)
postprocess["character_dict"] = character_dict
else:
if config["Global"].get("character_dict_path") is not None:
with open(config["Global"]["character_dict_path"], encoding="utf-8") as f:
lines = f.readlines()
character_dict = [line.strip("\n") for line in lines]
postprocess["character_dict"] = character_dict

infer_cfg["PostProcess"] = postprocess

with open(path, "w") as f:
yaml.dump(
infer_cfg, f, default_flow_style=False, encoding="utf-8", allow_unicode=True
)
logger.info("Export inference config file to {}".format(os.path.join(path)))


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"] in ["SVTR_LCNet", "SVTR_HGNet"]:
other_shape = [
paddle.static.InputSpec(shape=[None, 3, 48, -1], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] in ["SVTR", "CPPD"]:
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":
if not input_shape:
input_shape = [3, 32, 128]
other_shape = [
paddle.static.InputSpec(shape=[None] + input_shape, dtype="float32"),
]
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"] in ["SATRN"]:
other_shape = [
paddle.static.InputSpec(shape=[None, 3, 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"] == "LaTeXOCR":
other_shape = [
paddle.static.InputSpec(shape=[None, 1, None, None], dtype="float32"),
]
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 (
arch_config["model_type"] != "sr"
and arch_config["Backbone"]["name"] == "PPLCNetV3"
):
# for rep lcnetv3
for layer in model.sublayers():
if hasattr(layer, "rep") and not getattr(layer, "is_repped"):
layer.rep()

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 export(config, base_model=None, save_path=None):
if paddle.distributed.get_rank() != 0:
return
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
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
# 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
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

# for sr algorithm
if config["Architecture"]["model_type"] == "sr":
config["Architecture"]["Transform"]["infer_mode"] = True

# for latexocr algorithm
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
if base_model is not None:
model = base_model
if isinstance(model, paddle.DataParallel):
model = copy.deepcopy(model._layers)
else:
model = copy.deepcopy(model)
else:
model = build_model(config["Architecture"])
load_model(config, model, model_type=config["Architecture"]["model_type"])
model.eval()

if not save_path:
save_path = config["Global"]["save_inference_dir"]
yaml_path = os.path.join(save_path, "inference.yml")

arch_config = config["Architecture"]

if (
arch_config["algorithm"] in ["SVTR", "CPPD"]
and arch_config["Head"]["name"] != "MultiHead"
):
input_shape = config["Eval"]["dataset"]["transforms"][-2]["SVTRRecResizeImg"][
"image_shape"
]
elif arch_config["algorithm"].lower() == "ABINet".lower():
rec_rs = [
c
for c in config["Eval"]["dataset"]["transforms"]
if "ABINetRecResizeImg" in c
]
input_shape = rec_rs[0]["ABINetRecResizeImg"]["image_shape"] if rec_rs else None
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
)
dump_infer_config(config, yaml_path, logger)
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