def test_single_gpu_test_kie_novisual(cfg_file):
curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
config_file = os.path.join(curr_dir, cfg_file)
cfg = Config.fromfile(config_file)
meta_keys = list(cfg.data.test.pipeline[-1]['meta_keys'])
must_keys = ['img_norm_cfg', 'ori_filename', 'img_shape']
for key in must_keys:
meta_keys.append(key)
cfg.data.test.pipeline[-1]['meta_keys'] = tuple(meta_keys)
with tempfile.TemporaryDirectory() as tmpdirname:
out_dir = osp.join(tmpdirname, 'tmp')
model, data_loader = gene_sdmgr_model_dataloader(cfg,
out_dir,
curr_dir,
empty_img=True)
results = single_gpu_test(model,
data_loader,
out_dir=out_dir,
is_kie=True)
assert check_argument.is_type_list(results, dict)
model, data_loader = gene_sdmgr_model_dataloader(
cfg, out_dir, curr_dir)
results = single_gpu_test(model,
data_loader,
out_dir=out_dir,
is_kie=True)
assert check_argument.is_type_list(results, dict)
def test_single_gpu_test_kie(cfg_file):
curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
config_file = os.path.join(curr_dir, cfg_file)
cfg = Config.fromfile(config_file)
with tempfile.TemporaryDirectory() as tmpdirname:
out_dir = osp.join(tmpdirname, 'tmp')
model, data_loader = gene_sdmgr_model_dataloader(
cfg, out_dir, curr_dir)
results = single_gpu_test(model,
data_loader,
out_dir=out_dir,
is_kie=True)
assert check_argument.is_type_list(results, dict)
def test_single_gpu_test_det(cfg_file):
curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
config_file = os.path.join(curr_dir, cfg_file)
cfg = Config.fromfile(config_file)
model = build_model(cfg)
img_prefix = 'data/toy_dataset/imgs'
ann_file = 'data/toy_dataset/instances_test.json'
data_loader = generate_sample_dataloader(cfg, curr_dir, img_prefix,
ann_file)
with tempfile.TemporaryDirectory() as tmpdirname:
out_dir = osp.join(tmpdirname, 'tmp')
results = single_gpu_test(model, data_loader, out_dir=out_dir)
assert check_argument.is_type_list(results, dict)
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 = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
setup_multi_processes(cfg)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if cfg.model.get('pretrained'):
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 = (cfg.data.get('test_dataloader', {})).get(
'samples_per_gpu', cfg.data.get('samples_per_gpu', 1))
if samples_per_gpu > 1:
cfg = disable_text_recog_aug_test(cfg)
cfg = replace_image_to_tensor(cfg)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
cfg.gpu_ids = [args.gpu_id]
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
dataset = build_dataset(cfg.data.test, dict(test_mode=True))
# step 1: give default values and override (if exist) from cfg.data
default_loader_cfg = {
**dict(seed=cfg.get('seed'), drop_last=False, dist=distributed),
**({} if torch.__version__ != 'parrots' else dict(
prefetch_num=2,
pin_memory=False,
))
}
default_loader_cfg.update({
k: v
for k, v in cfg.data.items() if k not in [
'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
'test_dataloader'
]
})
test_loader_cfg = {
**default_loader_cfg,
**dict(shuffle=False, drop_last=False),
**cfg.data.get('test_dataloader', {}),
**dict(samples_per_gpu=samples_per_gpu)
}
data_loader = build_dataloader(dataset, **test_loader_cfg)
# build the model and load checkpoint
cfg.model.train_cfg = None
model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
model = revert_sync_batchnorm(model)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
load_checkpoint(model, args.checkpoint, map_location='cpu')
if args.fuse_conv_bn:
model = fuse_conv_bn(model)
if not distributed:
model = MMDataParallel(model, device_ids=cfg.gpu_ids)
is_kie = cfg.model.type in ['SDMGR']
outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
is_kie, 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'
]:
eval_kwargs.pop(key, None)
eval_kwargs.update(dict(metric=args.eval, **kwargs))
print(dataset.evaluate(outputs, **eval_kwargs))