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69 lines
2.3 KiB
Python
Executable File
69 lines
2.3 KiB
Python
Executable File
# 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 "image_shape" in config[
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"Global"], "image_shape must be assigned for static training mode..."
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supported_list = ["DB", "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 algo in supported_list, f"algorithms that supports static training must in in {supported_list} but got {algo}"
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specs = [
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InputSpec(
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[None] + config["Global"]["image_shape"], dtype='float32')
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]
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if algo == "SVTR":
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specs.append([
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InputSpec(
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[None, config["Global"]["max_text_length"]],
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dtype='int64'), InputSpec(
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[None, config["Global"]["max_text_length"]], dtype='int64'),
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InputSpec(
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[None], dtype='int64'), InputSpec(
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[None], dtype='float64')
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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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