test.py

# ------------------------------------------------------------------------------------------------
# Modified from mmsegmentation (https://github.com/open-mmlab/mmsegmentation) 
# Apache-2.0 License
# Copyright (c) Open-MMLab. 
# ------------------------------------------------------------------------------------------------

import argparse
import os

import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmcv.utils import DictAction

from mmseg.apis import multi_gpu_test, single_gpu_test
from modified_mmseg.datasets import build_dataset
from mmseg.datasets import build_dataloader
from builder import build_segmentor
from models import *



def parse_args():
    parser = argparse.ArgumentParser(
        description='test/eval a model')
    parser.add_argument('--config', type=str, help='test config file path')
    parser.add_argument('--checkpoint', help='checkpoint file')
    parser.add_argument('--aug-test', action='store_true', help='Use Flip and Multi scale aug')
    parser.add_argument('--out', help='output result file in pickle format')
    parser.add_argument('--format-only', action='store_true', help='Format the output results without perform evaluation. It is'
                                                                        'useful when you want to format the result to a specific format and '
                                                                        'submit it to the test server')
    parser.add_argument('--eval', type=str, nargs='+', help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
                                                                ' for generic datasets, and "cityscapes" for Cityscapes')
    parser.add_argument('--show', action='store_true', help='show results')
    parser.add_argument('--show-dir', help='directory where painted images will be saved')
    parser.add_argument('--gpu-collect', action='store_true', help='whether to use gpu to collect results.')
    parser.add_argument('--tmpdir', help='tmp directory used for collecting results from multiple '
                                            'workers, available when gpu_collect is not specified')
    parser.add_argument('--options', nargs='+', action=DictAction, help='custom options')
    parser.add_argument('--eval-options', nargs='+', action=DictAction, help='custom options for evaluation')
    parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm', 'mpi'], default='none', help='job launcher')
    parser.add_argument('--local_rank', type=int, default=0)
    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)
    return args


def main():
    args = parse_args()

    assert args.out or args.eval or args.format_only or args.show \
        or args.show_dir, \
        ('Please specify at least one operation (save/eval/format/show the '
         'results / save the results) with the argument "--out", "--eval"'
         ', "--format-only", "--show" or "--show-dir"')

    if args.eval and args.format_only:
        raise ValueError('--eval and --format_only cannot be both specified')

    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
        raise ValueError('The output file must be a pkl file.')

    cfg = mmcv.Config.fromfile(args.config)
    if args.options is not None:
        cfg.merge_from_dict(args.options)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    if args.aug_test:
        # hard code index
        cfg.data.test.pipeline[1].img_ratios = [
            0.5, 0.75, 1.0, 1.25, 1.5, 1.75
        ]
        cfg.data.test.pipeline[1].flip = True
    # cfg.model.pretrained = None
    cfg.data.test.test_mode = True


    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # build the dataloader
    # TODO: support multiple images per gpu (only minor changes are needed)
    dataset = build_dataset(cfg.data.test)
    
    data_loader = build_dataloader(
        dataset,
        samples_per_gpu=1,
        workers_per_gpu=cfg.data.workers_per_gpu,
        dist=distributed,
        shuffle=False)
    # build the model and load checkpoint
    model = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
   
    # later will be replaced
    checkpoint = torch.load(args.checkpoint, map_location="cpu")
    state_dict = checkpoint['state_dict']
    
    model.load_state_dict(state_dict)

    model.CLASSES = dataset.CLASSES
    model.PALETTE = dataset.PALETTE

    if not distributed:
        model = MMDataParallel(model, device_ids=[0])
        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir)
    else:
        model = MMDistributedDataParallel(
            model.cuda(),
            device_ids=[torch.cuda.current_device()],
            broadcast_buffers=False)
        outputs = multi_gpu_test(model, data_loader, args.tmpdir,
                                 args.gpu_collect)

    rank, _ = get_dist_info()
    if rank == 0:
        if args.out:
            print(f'\nwriting results to {args.out}')
            mmcv.dump(outputs, args.out)
        kwargs = {} if args.eval_options is None else args.eval_options
        if args.format_only:
            dataset.format_results(outputs, **kwargs)
        if args.eval:
            dataset.evaluate(outputs, args.eval, **kwargs)


if __name__ == '__main__':
    main()