You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
8 months ago
|
# 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 copy
|
||
|
|
import importlib
|
||
|
|
|
||
|
|
from paddle.jit import to_static
|
||
|
|
from paddle.static import InputSpec
|
||
|
|
|
||
|
|
from .base_model import BaseModel
|
||
|
|
from .distillation_model import DistillationModel
|
||
|
|
|
||
|
|
__all__ = ["build_model", "apply_to_static"]
|
||
|
|
|
||
|
|
|
||
|
|
def build_model(config):
|
||
|
|
config = copy.deepcopy(config)
|
||
|
|
if not "name" in config:
|
||
|
|
arch = BaseModel(config)
|
||
|
|
else:
|
||
|
|
name = config.pop("name")
|
||
|
|
mod = importlib.import_module(__name__)
|
||
|
|
arch = getattr(mod, name)(config)
|
||
|
|
return arch
|
||
|
|
|
||
|
|
|
||
|
|
def apply_to_static(model, config, logger):
|
||
|
|
if config["Global"].get("to_static", False) is not True:
|
||
|
|
return model
|
||
|
|
assert (
|
||
|
|
"d2s_train_image_shape" in config["Global"]
|
||
|
|
), "d2s_train_image_shape must be assigned for static training mode..."
|
||
|
|
supported_list = ["DB", "SVTR_LCNet", "TableMaster", "LayoutXLM", "SLANet", "SVTR"]
|
||
|
|
if config["Architecture"]["algorithm"] in ["Distillation"]:
|
||
|
|
algo = list(config["Architecture"]["Models"].values())[0]["algorithm"]
|
||
|
|
else:
|
||
|
|
algo = config["Architecture"]["algorithm"]
|
||
|
|
assert (
|
||
|
|
algo in supported_list
|
||
|
|
), f"algorithms that supports static training must in in {supported_list} but got {algo}"
|
||
|
|
|
||
|
|
specs = [
|
||
|
|
InputSpec([None] + config["Global"]["d2s_train_image_shape"], dtype="float32")
|
||
|
|
]
|
||
|
|
|
||
|
|
if algo == "SVTR_LCNet":
|
||
|
|
specs.append(
|
||
|
|
[
|
||
|
|
InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
|
||
|
|
InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
|
||
|
|
InputSpec([None], dtype="int64"),
|
||
|
|
InputSpec([None], dtype="float64"),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
elif algo == "TableMaster":
|
||
|
|
specs.append(
|
||
|
|
[
|
||
|
|
InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
|
||
|
|
InputSpec(
|
||
|
|
[None, config["Global"]["max_text_length"], 4], dtype="float32"
|
||
|
|
),
|
||
|
|
InputSpec(
|
||
|
|
[None, config["Global"]["max_text_length"], 1], dtype="float32"
|
||
|
|
),
|
||
|
|
InputSpec([None, 6], dtype="float32"),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
elif algo == "LayoutXLM":
|
||
|
|
specs = [
|
||
|
|
[
|
||
|
|
InputSpec(shape=[None, 512], dtype="int64"), # input_ids
|
||
|
|
InputSpec(shape=[None, 512, 4], dtype="int64"), # bbox
|
||
|
|
InputSpec(shape=[None, 512], dtype="int64"), # attention_mask
|
||
|
|
InputSpec(shape=[None, 512], dtype="int64"), # token_type_ids
|
||
|
|
InputSpec(shape=[None, 3, 224, 224], dtype="float32"), # image
|
||
|
|
InputSpec(shape=[None, 512], dtype="int64"), # label
|
||
|
|
]
|
||
|
|
]
|
||
|
|
elif algo == "SLANet":
|
||
|
|
specs.append(
|
||
|
|
[
|
||
|
|
InputSpec(
|
||
|
|
[None, config["Global"]["max_text_length"] + 2], dtype="int64"
|
||
|
|
),
|
||
|
|
InputSpec(
|
||
|
|
[None, config["Global"]["max_text_length"] + 2, 4], dtype="float32"
|
||
|
|
),
|
||
|
|
InputSpec(
|
||
|
|
[None, config["Global"]["max_text_length"] + 2, 1], dtype="float32"
|
||
|
|
),
|
||
|
|
InputSpec([None], dtype="int64"),
|
||
|
|
InputSpec([None, 6], dtype="float64"),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
elif algo == "SVTR":
|
||
|
|
specs.append(
|
||
|
|
[
|
||
|
|
InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
|
||
|
|
InputSpec([None], dtype="int64"),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
model = to_static(model, input_spec=specs)
|
||
|
|
logger.info("Successfully to apply @to_static with specs: {}".format(specs))
|
||
|
|
return model
|