evaluate.py

# Copyright (c) 2022 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.

import argparse
import os
import random

import paddle
import numpy as np

from paddle3d.apis.config import Config
from paddle3d.apis.trainer import Trainer
from paddle3d.utils.logger import logger


def parse_args():
    """
    """
    parser = argparse.ArgumentParser(description='Model evaluation')
    # params of training
    parser.add_argument(
        "--config", dest="cfg", help="The config file.", default=None, type=str)
    parser.add_argument(
        '--batch_size',
        dest='batch_size',
        help='Mini batch size of one gpu or cpu',
        type=int,
        default=None)
    parser.add_argument(
        '--model',
        dest='model',
        help='pretrained parameters of the model',
        type=str,
        default=None)
    parser.add_argument(
        '--num_workers',
        dest='num_workers',
        help='Num workers for data loader',
        type=int,
        default=0)

    return parser.parse_args()


def main(args):
    """
    """
    if args.cfg is None:
        raise RuntimeError

    if not os.path.exists(args.cfg):
        raise RuntimeError

    cfg = Config(path=args.cfg, batch_size=args.batch_size)

    if cfg.val_dataset is None:
        raise RuntimeError(
            'The validation dataset is not specified in the configuration file.'
        )
    elif len(cfg.val_dataset) == 0:
        raise ValueError(
            'The length of validation dataset is 0. Please check if your dataset is valid'
        )

    dic = cfg.to_dict()
    batch_size = dic.pop('batch_size')
    dic.update({
        'dataloader_fn': {
            'batch_size': batch_size,
            'num_workers': args.num_workers
        }
    })

    trainer = Trainer(**dic)
    trainer.model.set_state_dict(paddle.load(args.model))
    
    trainer.evaluate()


if __name__ == '__main__':
    args = parse_args()
    main(args)