eval.py

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__, '..')))

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
from onnxruntime import InferenceSession


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

    if args.use_onnx:
        pretrained_model = global_config.get('pretrained_model')
        print("pretrained_model:", pretrained_model)
        model = InferenceSession(pretrained_model, providers=[('ROCMExecutionProvider', {'device_id': '4'}),'CPUExecutionProvider'])
        # build metric
        eval_class = build_metric(config['Metric'])
        # start eval
        metric = program.eval_onnx(model, valid_dataloader, post_process_class,
                            eval_class)
        logger.info('metric eval ***************')
        for k, v in metric.items():
            logger.info('{}:{}'.format(k, v))
    else:
        model = build_model(config['Architecture'])
        extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR"]
        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():
            model_type = config['Architecture']['model_type']
        else:
            model_type = 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))

        # build metric
        eval_class = build_metric(config['Metric'])
        # start eval
        metric = program.eval(model, valid_dataloader, post_process_class,
                            eval_class, model_type, extra_input)
        logger.info('metric eval ***************')
        for k, v in metric.items():
            logger.info('{}:{}'.format(k, v))


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