test.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import time
import warnings

import mmcv
import mmcv_custom  # noqa: F401,F403
import mmdet_custom  # noqa: F401,F403
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                         wrap_fp16_model)
from mmdet.apis import multi_gpu_test, single_gpu_test
from mmdet.datasets import (build_dataloader, build_dataset,
                            replace_ImageToTensor)
from mmdet.models import build_detector


def parse_args():
    parser = argparse.ArgumentParser(
        description='MMDet test (and eval) a model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument(
        '--work-dir',
        help='the directory to save the file containing evaluation metrics')
    parser.add_argument('--out', help='output result file in pickle format')
    parser.add_argument(
        '--fuse-conv-bn',
        action='store_true',
        help='Whether to fuse conv and bn, this will slightly increase'
        'the inference speed')
    parser.add_argument('--gpu-ids',
                        type=int,
                        nargs='+',
                        help='ids of gpus to use '
                        '(only applicable to non-distributed testing)')
    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., "bbox",'
        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
    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('--show-score-thr',
                        type=float,
                        default=0.3,
                        help='score threshold (default: 0.3)')
    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(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--options',
        nargs='+',
        action=DictAction,
        help='custom options for evaluation, the key-value pair in xxx=yyy '
        'format will be kwargs for dataset.evaluate() function (deprecate), '
        'change to --eval-options instead.')
    parser.add_argument(
        '--eval-options',
        nargs='+',
        action=DictAction,
        help='custom options for evaluation, the key-value pair in xxx=yyy '
        'format will be kwargs for dataset.evaluate() function')
    parser.add_argument('--launcher',
                        choices=['none', 'pytorch', 'slurm', 'mpi'],
                        default='none',
                        help='job launcher')
    parser.add_argument('--local_rank', type=int, default=0)
    parser.add_argument('--plot-distribution', action='store_true', default=False)
    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)

    if args.options and args.eval_options:
        raise ValueError(
            '--options and --eval-options cannot be both '
            'specified, --options is deprecated in favor of --eval-options')
    if args.options:
        warnings.warn('--options is deprecated in favor of --eval-options')
        args.eval_options = args.options
    return args


def main():
    args = parse_args()

    if args.plot_distribution:
        from global_storage.global_storage import __global_storage__

    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 = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True

    cfg.model.pretrained = None
    if cfg.model.get('neck'):
        if isinstance(cfg.model.neck, list):
            for neck_cfg in cfg.model.neck:
                if neck_cfg.get('rfp_backbone'):
                    if neck_cfg.rfp_backbone.get('pretrained'):
                        neck_cfg.rfp_backbone.pretrained = None
        elif cfg.model.neck.get('rfp_backbone'):
            if cfg.model.neck.rfp_backbone.get('pretrained'):
                cfg.model.neck.rfp_backbone.pretrained = None

    # in case the test dataset is concatenated
    samples_per_gpu = 1
    if isinstance(cfg.data.test, dict):
        cfg.data.test.test_mode = True
        samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
        if samples_per_gpu > 1:
            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
            cfg.data.test.pipeline = replace_ImageToTensor(
                cfg.data.test.pipeline)
    elif isinstance(cfg.data.test, list):
        for ds_cfg in cfg.data.test:
            ds_cfg.test_mode = True
        samples_per_gpu = max(
            [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
        if samples_per_gpu > 1:
            for ds_cfg in cfg.data.test:
                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)

    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids
    else:
        cfg.gpu_ids = range(1)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
        if len(cfg.gpu_ids) > 1:
            warnings.warn(
                f'We treat {cfg.gpu_ids} as gpu-ids, and reset to '
                f'{cfg.gpu_ids[0:1]} as gpu-ids to avoid potential error in '
                'non-distribute testing time.')
            cfg.gpu_ids = cfg.gpu_ids[0:1]
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    rank, _ = get_dist_info()
    # allows not to create
    if args.work_dir is not None and rank == 0:
        mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
        timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
        json_file = osp.join(args.work_dir, f'eval_{timestamp}.json')

    # build the dataloader
    dataset = build_dataset(cfg.data.test)
    print(f'num_workers: {cfg.data.workers_per_gpu}')
    data_loader = build_dataloader(dataset,
                                   samples_per_gpu=samples_per_gpu,
                                   workers_per_gpu=cfg.data.workers_per_gpu,
                                   dist=distributed,
                                   shuffle=False)

    # build the model and load checkpoint
    cfg.model.train_cfg = None
    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
    if args.fuse_conv_bn:
        model = fuse_conv_bn(model)
    # old versions did not save class info in checkpoints, this walkaround is
    # for backward compatibility
    if 'CLASSES' in checkpoint.get('meta', {}):
        model.CLASSES = checkpoint['meta']['CLASSES']
    else:
        model.CLASSES = dataset.CLASSES

    if not distributed:
        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
                                  args.show_score_thr)
    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:
            eval_kwargs = cfg.get('evaluation', {}).copy()
            # hard-code way to remove EvalHook args
            for key in [
                    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
                    'rule', 'dynamic_intervals'
            ]:
                eval_kwargs.pop(key, None)
            eval_kwargs.update(dict(metric=args.eval, **kwargs))
            metric = dataset.evaluate(outputs, **eval_kwargs)
            print(metric)
            metric_dict = dict(config=args.config, metric=metric)
            if args.work_dir is not None and rank == 0:
                mmcv.dump(metric_dict, json_file)

    if args.plot_distribution:
        # -------------Plot Distribution---------------
        import torch
        import matplotlib.pyplot as plt
        raw_selectors = [x.squeeze(0) for x in __global_storage__]  # List of 5000, each has shape (12,xx1, xx2)
        # ------------
        #            |
        interpo_selectors = [torch.nn.functional.interpolate(x.unsqueeze(0), size=(50, 50), mode='bilinear') for x in
                             raw_selectors]
        depth_map = sum(interpo_selectors).squeeze(0).sum(0) / len(interpo_selectors)
        plt.imshow(depth_map.cpu(), cmap='hot', vmin=4, vmax=11)
        plt.colorbar()
        plt.show()
        #            |
        # ------------
        use_next = [torch.zeros_like(x) for x in raw_selectors]  # List of 5000, each has shape (12,xx1, xx2)
        use_later = [torch.zeros_like(x) for x in raw_selectors]  # List of 5000, each has shape (12,xx1, xx2)
        for i in range(len(raw_selectors)):  # 0-4999 samples
            for j in range(len(raw_selectors[0]) - 1):  # 0-11 layers
                use_next[i][j] = (1 - raw_selectors[i][j]) * raw_selectors[i][j + 1]
                use_later[i][j] = torch.logical_and(~raw_selectors[i][j].bool(),
                                                    raw_selectors[i][j + 1:].sum(0) >= 1).float()
        drop = [torch.logical_and(~raw_selectors[i].bool(), ~use_later[i].bool()).float() for i in
                range(len(raw_selectors))]

        ls_n_tokens1 = [x.view(x.shape[0], -1).shape[-1] for x in raw_selectors]
        ls_n_tokens2 = [x.view(x.shape[0], -1).shape[-1] for x in use_next]
        ls_n_tokens3 = [x.view(x.shape[0], -1).shape[-1] for x in use_later]
        ls_n_tokens4 = [x.view(x.shape[0], -1).shape[-1] for x in drop]
        assert ls_n_tokens1 == ls_n_tokens2 == ls_n_tokens3 == ls_n_tokens4
        n_tokens = sum(ls_n_tokens1)

        ratio_use_next = (sum([x.view(x.shape[0], -1).sum(-1) for x in use_next]) / n_tokens).cpu()
        ratio_use_later = (sum([x.view(x.shape[0], -1).sum(-1) for x in use_later]) / n_tokens).cpu()
        ratio_drop = (sum([x.view(x.shape[0], -1).sum(-1) for x in drop]) / n_tokens).cpu()
        X = ['4', '5', '6', '7', '8', '9', '10', '11', '12']
        X_axis = torch.arange(len(X))
        width = 0.25
        Black = [0, 0, 0, 1]
        Amber = [1, 0.75, 0, 1]
        Aqua = [0, 1, 1, 1]
        plt.bar(X_axis - width, ratio_drop[3:], width, label='tokens skipped & not used later', color=Black)
        plt.bar(X_axis, ratio_use_later[3:], width, label='tokens skipped & used later', color=Aqua)
        plt.bar(X_axis + width, ratio_use_next[3:], width, label='tokens skipped & used in the next layer', color=Amber)
        plt.xticks(X_axis, X)
        plt.ylim(0, 0.7)
        plt.legend()
        plt.xlabel('Layers')
        plt.ylabel('Ratio of tokens')
        plt.show()


if __name__ == '__main__':
    main()