mirror of https://github.com/open-mmlab/mmocr.git
240 lines
9.3 KiB
Python
240 lines
9.3 KiB
Python
#!/usr/bin/env python
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import argparse
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import os
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import warnings
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import mmcv
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import torch
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from mmcv import Config, DictAction
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from mmcv.cnn import fuse_conv_bn
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
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wrap_fp16_model)
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from mmdet.apis import multi_gpu_test, single_gpu_test
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from mmdet.datasets import replace_ImageToTensor
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from mmocr.apis.inference import disable_text_recog_aug_test
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from mmocr.datasets import build_dataloader, build_dataset
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from mmocr.models import build_detector
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMOCR test (and eval) a model.')
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parser.add_argument('config', help='Test config file path.')
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parser.add_argument('checkpoint', help='Checkpoint file.')
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parser.add_argument('--out', help='Output result file in pickle format.')
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parser.add_argument(
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'--fuse-conv-bn',
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action='store_true',
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help='Whether to fuse conv and bn, this will slightly increase'
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'the inference speed.')
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parser.add_argument(
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'--format-only',
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action='store_true',
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help='Format the output results without performing evaluation. It is'
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'useful when you want to format the results to a specific format and '
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'submit them to the test server.')
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parser.add_argument(
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'--eval',
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type=str,
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nargs='+',
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help='The evaluation metrics, which depends on the dataset, e.g.,'
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'"bbox", "seg", "proposal" for COCO, and "mAP", "recall" for'
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'PASCAL VOC.')
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parser.add_argument('--show', action='store_true', help='Show results.')
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parser.add_argument(
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'--show-dir', help='Directory where the output images will be saved.')
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parser.add_argument(
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'--show-score-thr',
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type=float,
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default=0.3,
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help='Score threshold (default: 0.3).')
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parser.add_argument(
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'--gpu-collect',
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action='store_true',
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help='Whether to use gpu to collect results.')
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parser.add_argument(
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'--tmpdir',
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help='The tmp directory used for collecting results from multiple '
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'workers, available when gpu-collect is not specified.')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='Override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into the config file. If the value '
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'to be overwritten is a list, it should be of the form of either '
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'key="[a,b]" or key=a,b. The argument also allows nested list/tuple '
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'values, e.g. key="[(a,b),(c,d)]". Note that the quotation marks '
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'are necessary and that no white space is allowed.')
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parser.add_argument(
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'--options',
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nargs='+',
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action=DictAction,
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help='Custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be kwargs for dataset.evaluate() function (deprecate), '
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'change to --eval-options instead.')
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parser.add_argument(
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'--eval-options',
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nargs='+',
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action=DictAction,
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help='Custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be kwargs for dataset.evaluate() function.')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='Options for job launcher.')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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if args.options and args.eval_options:
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raise ValueError(
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'--options and --eval-options cannot be both '
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'specified, --options is deprecated in favor of --eval-options.')
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if args.options:
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warnings.warn('--options is deprecated in favor of --eval-options.')
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args.eval_options = args.options
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return args
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def main():
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args = parse_args()
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assert (
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args.out or args.eval or args.format_only or args.show
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or args.show_dir), (
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'Please specify at least one operation (save/eval/format/show the '
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'results / save the results) with the argument "--out", "--eval"'
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', "--format-only", "--show" or "--show-dir".')
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if args.eval and args.format_only:
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raise ValueError('--eval and --format_only cannot be both specified.')
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if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
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raise ValueError('The output file must be a pkl file.')
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cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# import modules from string list.
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if cfg.get('custom_imports', None):
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from mmcv.utils import import_modules_from_strings
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import_modules_from_strings(**cfg['custom_imports'])
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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if cfg.model.get('pretrained'):
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cfg.model.pretrained = None
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if cfg.model.get('neck'):
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if isinstance(cfg.model.neck, list):
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for neck_cfg in cfg.model.neck:
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if neck_cfg.get('rfp_backbone'):
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if neck_cfg.rfp_backbone.get('pretrained'):
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neck_cfg.rfp_backbone.pretrained = None
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elif cfg.model.neck.get('rfp_backbone'):
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if cfg.model.neck.rfp_backbone.get('pretrained'):
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cfg.model.neck.rfp_backbone.pretrained = None
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# in case the test dataset is concatenated
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samples_per_gpu = 1
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if isinstance(cfg.data.test, dict):
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samples_per_gpu = (cfg.data.get('test_dataloader', {})).get(
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'samples_per_gpu', cfg.data.get('samples_per_gpu', 1))
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if samples_per_gpu > 1:
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# Support batch_size > 1 in test for text recognition
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# by disable MultiRotateAugOCR since it is useless for most case
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cfg = disable_text_recog_aug_test(cfg)
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if cfg.data.test.get('pipeline', None) is not None:
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# Replace 'ImageToTensor' to 'DefaultFormatBundle'
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cfg.data.test.pipeline = replace_ImageToTensor(
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cfg.data.test.pipeline)
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elif isinstance(cfg.data.test, list):
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for ds_cfg in cfg.data.test:
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ds_cfg.test_mode = True
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samples_per_gpu = max(
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[ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
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if samples_per_gpu > 1:
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for ds_cfg in cfg.data.test:
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ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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init_dist(args.launcher, **cfg.dist_params)
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# build the dataloader
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dataset = build_dataset(cfg.data.test, dict(test_mode=True))
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# step 1: give default values and override (if exist) from cfg.data
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loader_cfg = {
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**dict(seed=cfg.get('seed'), drop_last=False, dist=distributed),
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**({} if torch.__version__ != 'parrots' else dict(
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prefetch_num=2,
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pin_memory=False,
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)),
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**dict((k, cfg.data[k]) for k in [
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'workers_per_gpu',
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'seed',
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'prefetch_num',
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'pin_memory',
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] if k in cfg.data)
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}
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test_loader_cfg = {
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**loader_cfg,
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**dict(shuffle=False, drop_last=False),
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**cfg.data.get('test_dataloader', {}),
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**dict(samples_per_gpu=samples_per_gpu)
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}
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data_loader = build_dataloader(dataset, **test_loader_cfg)
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# build the model and load checkpoint
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cfg.model.train_cfg = None
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model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
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fp16_cfg = cfg.get('fp16', None)
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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load_checkpoint(model, args.checkpoint, map_location='cpu')
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if args.fuse_conv_bn:
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model = fuse_conv_bn(model)
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if not distributed:
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model = MMDataParallel(model, device_ids=[0])
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outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
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args.show_score_thr)
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else:
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model = MMDistributedDataParallel(
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model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False)
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outputs = multi_gpu_test(model, data_loader, args.tmpdir,
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args.gpu_collect)
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rank, _ = get_dist_info()
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if rank == 0:
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if args.out:
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print(f'\nwriting results to {args.out}')
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mmcv.dump(outputs, args.out)
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kwargs = {} if args.eval_options is None else args.eval_options
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if args.format_only:
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dataset.format_results(outputs, **kwargs)
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if args.eval:
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eval_kwargs = cfg.get('evaluation', {}).copy()
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# hard-code way to remove EvalHook args
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for key in [
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'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
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'rule'
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]:
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eval_kwargs.pop(key, None)
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eval_kwargs.update(dict(metric=args.eval, **kwargs))
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print(dataset.evaluate(outputs, **eval_kwargs))
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if __name__ == '__main__':
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main()
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