ppocr/modeling/architectures/distillation_model.py

# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from paddle import nn
from ppocr.modeling.transforms import build_transform
from ppocr.modeling.backbones import build_backbone
from ppocr.modeling.necks import build_neck
from ppocr.modeling.heads import build_head
from .base_model import BaseModel
from ppocr.utils.save_load import load_pretrained_params

__all__ = ['DistillationModel']


class DistillationModel(nn.Layer):
    def __init__(self, config):
        """
        the module for OCR distillation.
        args:
            config (dict): the super parameters for module.
        """
        super().__init__()
        self.model_list = []
        self.model_name_list = []
        for key in config["Models"]:
            model_config = config["Models"][key]
            freeze_params = False
            pretrained = None
            if "freeze_params" in model_config:
                freeze_params = model_config.pop("freeze_params")
            if "pretrained" in model_config:
                pretrained = model_config.pop("pretrained")
            model = BaseModel(model_config)
            if pretrained is not None:
                load_pretrained_params(model, pretrained)
            if freeze_params:
                for param in model.parameters():
                    param.trainable = False
            self.model_list.append(self.add_sublayer(key, model))
            self.model_name_list.append(key)

    def forward(self, x, data=None):
        result_dict = dict()
        for idx, model_name in enumerate(self.model_name_list):
            result_dict[model_name] = self.model_list[idx](x, data)
        return result_dict