wt_ocr_and_docker/tools/eval.py

# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, __dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))

import paddle
from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import load_model
import tools.program as program


def main():
    global_config = config['Global']
    # build dataloader
    valid_dataloader = build_dataloader(config, 'Eval', device, logger)

    # build post process
    post_process_class = build_post_process(config['PostProcess'],
                                            global_config)

    # build model
    # for rec algorithm
    if hasattr(post_process_class, 'character'):
        char_num = len(getattr(post_process_class, 'character'))
        if config['Architecture']["algorithm"] in ["Distillation",
                                                   ]:  # distillation model
            for key in config['Architecture']["Models"]:
                if config['Architecture']['Models'][key]['Head'][
                        'name'] == 'MultiHead':  # for multi head
                    out_channels_list = {}
                    if config['PostProcess'][
                            'name'] == 'DistillationSARLabelDecode':
                        char_num = char_num - 2
                    out_channels_list['CTCLabelDecode'] = char_num
                    out_channels_list['SARLabelDecode'] = char_num + 2
                    config['Architecture']['Models'][key]['Head'][
                        'out_channels_list'] = out_channels_list
                else:
                    config['Architecture']["Models"][key]["Head"][
                        'out_channels'] = char_num
        elif config['Architecture']['Head'][
                'name'] == 'MultiHead':  # for multi head
            out_channels_list = {}
            if config['PostProcess']['name'] == 'SARLabelDecode':
                char_num = char_num - 2
            out_channels_list['CTCLabelDecode'] = char_num
            out_channels_list['SARLabelDecode'] = char_num + 2
            config['Architecture']['Head'][
                'out_channels_list'] = out_channels_list
        else:  # base rec model
            config['Architecture']["Head"]['out_channels'] = char_num

    model = build_model(config['Architecture'])
    extra_input_models = [
        "SRN", "NRTR", "SAR", "SEED", "SVTR", "VisionLAN", "RobustScanner"
    ]
    extra_input = False
    if config['Architecture']['algorithm'] == 'Distillation':
        for key in config['Architecture']["Models"]:
            extra_input = extra_input or config['Architecture']['Models'][key][
                'algorithm'] in extra_input_models
    else:
        extra_input = config['Architecture']['algorithm'] in extra_input_models
    if "model_type" in config['Architecture'].keys():
        if config['Architecture']['algorithm'] == 'CAN':
            model_type = 'can'
        else:
            model_type = config['Architecture']['model_type']
    else:
        model_type = None

    # build metric
    eval_class = build_metric(config['Metric'])
    # amp
    use_amp = config["Global"].get("use_amp", False)
    amp_level = config["Global"].get("amp_level", 'O2')
    amp_custom_black_list = config['Global'].get('amp_custom_black_list', [])
    if use_amp:
        AMP_RELATED_FLAGS_SETTING = {
            'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
            'FLAGS_max_inplace_grad_add': 8,
        }
        paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
        scale_loss = config["Global"].get("scale_loss", 1.0)
        use_dynamic_loss_scaling = config["Global"].get(
            "use_dynamic_loss_scaling", False)
        scaler = paddle.amp.GradScaler(
            init_loss_scaling=scale_loss,
            use_dynamic_loss_scaling=use_dynamic_loss_scaling)
        if amp_level == "O2":
            model = paddle.amp.decorate(
                models=model, level=amp_level, master_weight=True)
    else:
        scaler = None

    best_model_dict = load_model(
        config, model, model_type=config['Architecture']["model_type"])
    if len(best_model_dict):
        logger.info('metric in ckpt ***************')
        for k, v in best_model_dict.items():
            logger.info('{}:{}'.format(k, v))

    # start eval
    metric = program.eval(model, valid_dataloader, post_process_class,
                          eval_class, model_type, extra_input, scaler,
                          amp_level, amp_custom_black_list)
    logger.info('metric eval ***************')
    for k, v in metric.items():
        logger.info('{}:{}'.format(k, v))


if __name__ == '__main__':
    config, device, logger, vdl_writer = program.preprocess()
    main()