eval.py

import argparse
import os
import time

import uuid

from backbone.base import Base as BackboneBase
from config.eval_config import EvalConfig as Config
from dataset.base import Base as DatasetBase
from evaluator import Evaluator
from logger import Logger as Log
from model import Model
from roi.pooler import Pooler


def _eval(path_to_checkpoint: str, dataset_name: str, backbone_name: str, path_to_data_dir: str, path_to_results_dir: str):
    dataset = DatasetBase.from_name(dataset_name)(path_to_data_dir, DatasetBase.Mode.EVAL, Config.IMAGE_MIN_SIDE, Config.IMAGE_MAX_SIDE)
    evaluator = Evaluator(dataset, path_to_data_dir, path_to_results_dir)

    Log.i('Found {:d} samples'.format(len(dataset)))

    backbone = BackboneBase.from_name(backbone_name)(pretrained=False)
    model = Model(backbone, dataset.num_classes(), pooler_mode=Config.POOLER_MODE,
                  anchor_ratios=Config.ANCHOR_RATIOS, anchor_sizes=Config.ANCHOR_SIZES,
                  rpn_pre_nms_top_n=Config.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=Config.RPN_POST_NMS_TOP_N).cuda()
    model.load(path_to_checkpoint)

    Log.i('Start evaluating with 1 GPU (1 batch per GPU)')
    mean_ap, detail = evaluator.evaluate(model)
    Log.i('Done')

    Log.i('mean AP = {:.4f}'.format(mean_ap))
    Log.i('\n' + detail)


if __name__ == '__main__':
    def main():
        parser = argparse.ArgumentParser()
        parser.add_argument('-s', '--dataset', type=str, choices=DatasetBase.OPTIONS, required=True, help='name of dataset')
        parser.add_argument('-b', '--backbone', type=str, choices=BackboneBase.OPTIONS, required=True, help='name of backbone model')
        parser.add_argument('-d', '--data_dir', type=str, default='./data', help='path to data directory')
        parser.add_argument('--image_min_side', type=float, help='default: {:g}'.format(Config.IMAGE_MIN_SIDE))
        parser.add_argument('--image_max_side', type=float, help='default: {:g}'.format(Config.IMAGE_MAX_SIDE))
        parser.add_argument('--anchor_ratios', type=str, help='default: "{!s}"'.format(Config.ANCHOR_RATIOS))
        parser.add_argument('--anchor_sizes', type=str, help='default: "{!s}"'.format(Config.ANCHOR_SIZES))
        parser.add_argument('--pooler_mode', type=str, choices=Pooler.OPTIONS, help='default: {.value:s}'.format(Config.POOLER_MODE))
        parser.add_argument('--rpn_pre_nms_top_n', type=int, help='default: {:d}'.format(Config.RPN_PRE_NMS_TOP_N))
        parser.add_argument('--rpn_post_nms_top_n', type=int, help='default: {:d}'.format(Config.RPN_POST_NMS_TOP_N))
        parser.add_argument('--checkpoint', type=str, help='path to evaluating checkpoint')
        args = parser.parse_args()

        path_to_checkpoint = args.checkpoint
        dataset_name = args.dataset
        backbone_name = args.backbone
        path_to_data_dir = args.data_dir

        path_to_results_dir = os.path.join(os.path.dirname(path_to_checkpoint), 'results-{:s}-{:s}-{:s}'.format(
            time.strftime('%Y%m%d%H%M%S'), path_to_checkpoint.split(os.path.sep)[-1].split(os.path.curdir)[0],
            str(uuid.uuid4()).split('-')[0]))
        os.makedirs(path_to_results_dir)

        Config.setup(image_min_side=args.image_min_side, image_max_side=args.image_max_side,
                     anchor_ratios=args.anchor_ratios, anchor_sizes=args.anchor_sizes, pooler_mode=args.pooler_mode,
                     rpn_pre_nms_top_n=args.rpn_pre_nms_top_n, rpn_post_nms_top_n=args.rpn_post_nms_top_n)

        Log.initialize(os.path.join(path_to_results_dir, 'eval.log'))
        Log.i('Arguments:')
        for k, v in vars(args).items():
            Log.i(f'\t{k} = {v}')
        Log.i(Config.describe())

        _eval(path_to_checkpoint, dataset_name, backbone_name, path_to_data_dir, path_to_results_dir)

    main()