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116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import importlib
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from paddle.jit import to_static
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from paddle.static import InputSpec
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from .base_model import BaseModel
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from .distillation_model import DistillationModel
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__all__ = ["build_model", "apply_to_static"]
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def build_model(config):
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config = copy.deepcopy(config)
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if not "name" in config:
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arch = BaseModel(config)
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else:
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name = config.pop("name")
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mod = importlib.import_module(__name__)
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arch = getattr(mod, name)(config)
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return arch
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def apply_to_static(model, config, logger):
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if config["Global"].get("to_static", False) is not True:
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return model
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assert (
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"d2s_train_image_shape" in config["Global"]
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), "d2s_train_image_shape must be assigned for static training mode..."
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supported_list = ["DB", "SVTR_LCNet", "TableMaster", "LayoutXLM", "SLANet", "SVTR"]
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if config["Architecture"]["algorithm"] in ["Distillation"]:
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algo = list(config["Architecture"]["Models"].values())[0]["algorithm"]
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else:
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algo = config["Architecture"]["algorithm"]
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assert (
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algo in supported_list
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), f"algorithms that supports static training must in in {supported_list} but got {algo}"
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specs = [
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InputSpec([None] + config["Global"]["d2s_train_image_shape"], dtype="float32")
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]
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if algo == "SVTR_LCNet":
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specs.append(
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[
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InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
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InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
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InputSpec([None], dtype="int64"),
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InputSpec([None], dtype="float64"),
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]
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)
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elif algo == "TableMaster":
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specs.append(
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[
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InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
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InputSpec(
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[None, config["Global"]["max_text_length"], 4], dtype="float32"
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),
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InputSpec(
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[None, config["Global"]["max_text_length"], 1], dtype="float32"
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),
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InputSpec([None, 6], dtype="float32"),
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]
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)
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elif algo == "LayoutXLM":
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specs = [
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[
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InputSpec(shape=[None, 512], dtype="int64"), # input_ids
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InputSpec(shape=[None, 512, 4], dtype="int64"), # bbox
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InputSpec(shape=[None, 512], dtype="int64"), # attention_mask
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InputSpec(shape=[None, 512], dtype="int64"), # token_type_ids
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InputSpec(shape=[None, 3, 224, 224], dtype="float32"), # image
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InputSpec(shape=[None, 512], dtype="int64"), # label
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]
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]
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elif algo == "SLANet":
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specs.append(
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[
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InputSpec(
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[None, config["Global"]["max_text_length"] + 2], dtype="int64"
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),
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InputSpec(
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[None, config["Global"]["max_text_length"] + 2, 4], dtype="float32"
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),
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InputSpec(
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[None, config["Global"]["max_text_length"] + 2, 1], dtype="float32"
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),
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InputSpec([None], dtype="int64"),
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InputSpec([None, 6], dtype="float64"),
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]
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)
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elif algo == "SVTR":
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specs.append(
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[
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InputSpec([None, config["Global"]["max_text_length"]], dtype="int64"),
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InputSpec([None], dtype="int64"),
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]
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)
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model = to_static(model, input_spec=specs)
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logger.info("Successfully to apply @to_static with specs: {}".format(specs))
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return model
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