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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 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, 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
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