def load_annotations(self, ann_file):
"""Load annotation from COCO style annotation file.
Args:
ann_file (str): Path of annotation file.
Returns:
list[dict]: Annotation info from COCO api.
"""
if self.ann_file_backend == 'disk':
self.coco = COCO(ann_file)
else:
mmcv_version = digit_version(mmcv.__version__)
if mmcv_version < digit_version('1.3.16'):
raise Exception('Please update mmcv to 1.3.16 or higher '
'to enable "get_local_path" of "FileClient".')
file_client = mmcv.FileClient(backend=self.ann_file_backend)
with file_client.get_local_path(ann_file) as local_path:
self.coco = COCO(local_path)
self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
self.img_ids = self.coco.get_img_ids()
data_infos = []
count = 0
for i in self.img_ids:
info = self.coco.load_imgs([i])[0]
info['filename'] = info['file_name']
data_infos.append(info)
count = count + 1
if count > self.select_first_k and self.select_first_k > 0:
break
return data_infos
def train_detector(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
logger = get_root_logger(cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
# step 1: give default values and override (if exist) from cfg.data
loader_cfg = {
**dict(seed=cfg.get('seed'),
drop_last=False,
dist=distributed,
num_gpus=len(cfg.gpu_ids)),
**({} if torch.__version__ != 'parrots' else dict(
prefetch_num=2,
pin_memory=False,
)),
**dict((k, cfg.data[k]) for k in [
'samples_per_gpu',
'workers_per_gpu',
'shuffle',
'seed',
'drop_last',
'prefetch_num',
'pin_memory',
'persistent_workers',
] if k in cfg.data)
}
# step 2: cfg.data.train_dataloader has highest priority
train_loader_cfg = dict(loader_cfg, **cfg.data.get('train_dataloader', {}))
data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
if not torch.cuda.is_available():
assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
'Please use MMCV >= 1.4.4 for CPU training!'
model = MMDataParallel(model, device_ids=cfg.gpu_ids)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
if 'runner' not in cfg:
cfg.runner = {
'type': 'EpochBasedRunner',
'max_epochs': cfg.total_epochs
}
warnings.warn(
'config is now expected to have a `runner` section, '
'please set `runner` in your config.', UserWarning)
else:
if 'total_epochs' in cfg:
assert cfg.total_epochs == cfg.runner.max_epochs
runner = build_runner(cfg.runner,
default_args=dict(model=model,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta))
# an ugly workaround to make .log and .log.json filenames the same
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg,
distributed=distributed)
elif distributed and 'type' not in cfg.optimizer_config:
optimizer_config = OptimizerHook(**cfg.optimizer_config)
else:
optimizer_config = cfg.optimizer_config
# register hooks
runner.register_training_hooks(cfg.lr_config,
optimizer_config,
cfg.checkpoint_config,
cfg.log_config,
cfg.get('momentum_config', None),
custom_hooks_config=cfg.get(
'custom_hooks', None))
if distributed:
if isinstance(runner, EpochBasedRunner):
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
val_samples_per_gpu = (cfg.data.get('val_dataloader', {})).get(
'samples_per_gpu', cfg.data.get('samples_per_gpu', 1))
if val_samples_per_gpu > 1:
# Support batch_size > 1 in test for text recognition
# by disable MultiRotateAugOCR since it is useless for most case
cfg = disable_text_recog_aug_test(cfg)
cfg = replace_image_to_tensor(cfg)
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_loader_cfg = {
**loader_cfg,
**dict(shuffle=False, drop_last=False),
**cfg.data.get('val_dataloader', {}),
**dict(samples_per_gpu=val_samples_per_gpu)
}
val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow)
def test_digit_version():
assert digit_version('0.2.16') == (0, 2, 16, 0, 0, 0)
assert digit_version('1.2.3') == (1, 2, 3, 0, 0, 0)
assert digit_version('1.2.3rc0') == (1, 2, 3, 0, -1, 0)
assert digit_version('1.2.3rc1') == (1, 2, 3, 0, -1, 1)
assert digit_version('1.0rc0') == (1, 0, 0, 0, -1, 0)
assert digit_version('1.0') == digit_version('1.0.0')
assert digit_version('1.5.0+cuda90_cudnn7.6.3_lms') == digit_version('1.5')
assert digit_version('1.0.0dev') < digit_version('1.0.0a')
assert digit_version('1.0.0a') < digit_version('1.0.0a1')
assert digit_version('1.0.0a') < digit_version('1.0.0b')
assert digit_version('1.0.0b') < digit_version('1.0.0rc')
assert digit_version('1.0.0rc1') < digit_version('1.0.0')
assert digit_version('1.0.0') < digit_version('1.0.0post')
assert digit_version('1.0.0post') < digit_version('1.0.0post1')
assert digit_version('v1') == (1, 0, 0, 0, 0, 0)
assert digit_version('v1.1.5') == (1, 1, 5, 0, 0, 0)