def inference_pytorch(args, cfg, distributed, data_loader):
"""Get predictions by pytorch models."""
# remove redundant pretrain steps for testing
turn_off_pretrained(cfg.model)
# build the model and load checkpoint
model = build_model(cfg.model,
train_cfg=None,
test_cfg=cfg.get('test_cfg'))
if len(cfg.module_hooks) > 0:
register_module_hooks(model, cfg.module_hooks)
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=[0])
outputs = single_gpu_test(model, data_loader)
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)
return outputs
def test_register_module_hooks():
_module_hooks = [
dict(type='GPUNormalize',
hooked_module='backbone',
hook_pos='forward_pre',
input_format='NCHW',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375])
]
repo_dpath = osp.dirname(osp.dirname(osp.dirname(__file__)))
config_fpath = osp.join(repo_dpath, 'configs/_base_/models/tsm_r50.py')
config = mmcv.Config.fromfile(config_fpath)
config.model['backbone']['pretrained'] = None
# case 1
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hook_pos'] = 'forward_pre'
recognizer = build_recognizer(config.model)
handles = register_module_hooks(recognizer, module_hooks)
assert recognizer.backbone._forward_pre_hooks[
handles[0].id].__name__ == 'normalize_hook'
# case 2
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hook_pos'] = 'forward'
recognizer = build_recognizer(config.model)
handles = register_module_hooks(recognizer, module_hooks)
assert recognizer.backbone._forward_hooks[
handles[0].id].__name__ == 'normalize_hook'
# case 3
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hooked_module'] = 'cls_head'
module_hooks[0]['hook_pos'] = 'backward'
recognizer = build_recognizer(config.model)
handles = register_module_hooks(recognizer, module_hooks)
assert recognizer.cls_head._backward_hooks[
handles[0].id].__name__ == 'normalize_hook'
# case 4
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hook_pos'] = '_other_pos'
recognizer = build_recognizer(config.model)
with pytest.raises(ValueError):
handles = register_module_hooks(recognizer, module_hooks)
# case 5
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hooked_module'] = '_other_module'
recognizer = build_recognizer(config.model)
with pytest.raises(ValueError):
handles = register_module_hooks(recognizer, module_hooks)
def test_register_module_hooks():
_module_hooks = [
dict(type='GPUNormalize',
hook_pos='forward_pre',
input_format='NCHW',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375])
]
# case 1
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hook_pos'] = 'forward_pre'
resnet = models.resnet50()
handles = register_module_hooks(resnet, module_hooks)
assert resnet._forward_pre_hooks[
handles[0].id].__name__ == 'normalize_hook'
# case 2
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hook_pos'] = 'forward'
resnet = models.resnet50()
handles = register_module_hooks(resnet, module_hooks)
assert resnet._forward_hooks[handles[0].id].__name__ == 'normalize_hook'
# case 3
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hook_pos'] = 'backward'
resnet = models.resnet50()
handles = register_module_hooks(resnet, module_hooks)
assert resnet._backward_hooks[handles[0].id].__name__ == 'normalize_hook'
# case 4
module_hooks = copy.deepcopy(_module_hooks)
module_hooks[0]['hook_pos'] = '_other_pos'
resnet = models.resnet50()
with pytest.raises(ValueError):
handles = register_module_hooks(resnet, module_hooks)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
cfg.merge_from_dict(args.cfg_options)
# Load output_config from cfg
output_config = cfg.get('output_config', {})
if args.out:
# Overwrite output_config from args.out
output_config = Config._merge_a_into_b(dict(out=args.out),
output_config)
# Load eval_config from cfg
eval_config = cfg.get('eval_config', {})
if args.eval:
# Overwrite eval_config from args.eval
eval_config = Config._merge_a_into_b(dict(metrics=args.eval),
eval_config)
if args.eval_options:
# Add options from args.eval_options
eval_config = Config._merge_a_into_b(args.eval_options, eval_config)
assert output_config or eval_config, \
('Please specify at least one operation (save or eval the '
'results) with the argument "--out" or "--eval"')
dataset_type = cfg.data.test.type
if output_config.get('out', None):
if 'output_format' in output_config:
# ugly workround to make recognition and localization the same
warnings.warn(
'Skip checking `output_format` in localization task.')
else:
out = output_config['out']
# make sure the dirname of the output path exists
mmcv.mkdir_or_exist(osp.dirname(out))
_, suffix = osp.splitext(out)
if dataset_type == 'AVADataset':
assert suffix[1:] == 'csv', ('For AVADataset, the format of '
'the output file should be csv')
else:
assert suffix[1:] in file_handlers, (
'The format of the output '
'file should be json, pickle or yaml')
# set cudnn benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.data.test.test_mode = True
if args.average_clips is not None:
# You can set average_clips during testing, it will override the
# original setting
if cfg.model.get('test_cfg') is None and cfg.get('test_cfg') is None:
cfg.model.setdefault('test_cfg',
dict(average_clips=args.average_clips))
else:
if cfg.model.get('test_cfg') is not None:
cfg.model.test_cfg.average_clips = args.average_clips
else:
cfg.test_cfg.average_clips = args.average_clips
# 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)
# The flag is used to register module's hooks
cfg.setdefault('module_hooks', [])
# build the dataloader
dataset = build_dataset(cfg.data.test, dict(test_mode=True))
dataloader_setting = dict(videos_per_gpu=cfg.data.get('videos_per_gpu', 1),
workers_per_gpu=cfg.data.get(
'workers_per_gpu', 1),
dist=distributed,
shuffle=False)
dataloader_setting = dict(dataloader_setting,
**cfg.data.get('test_dataloader', {}))
data_loader = build_dataloader(dataset, **dataloader_setting)
# remove redundant pretrain steps for testing
turn_off_pretrained(cfg.model)
# build the model and load checkpoint
model = build_model(cfg.model,
train_cfg=None,
test_cfg=cfg.get('test_cfg'))
if len(cfg.module_hooks) > 0:
register_module_hooks(model, cfg.module_hooks)
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=[0])
outputs = single_gpu_test(model, data_loader)
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 output_config.get('out', None):
out = output_config['out']
print(f'\nwriting results to {out}')
#import pdb
#pdb.set_trace()
print(out[-4:])
if out[-4:] == 'json':
result_dict = {}
for result in outputs:
video_name = result['video_name']
result_dict[video_name] = result['proposal_list']
output_dict = {
'version': 'VERSION 1.3',
'results': result_dict,
'external_data': {}
}
mmcv.dump(output_dict, out)
else:
dataset.dump_results(outputs, **output_config)
if eval_config:
eval_res = dataset.evaluate(outputs, **eval_config)
for name, val in eval_res.items():
print(f'{name}: {val:.04f}')
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
cfg.merge_from_dict(args.cfg_options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority:
# CLI > config file > default (base filename)
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
# 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)
# The flag is used to determine whether it is omnisource training
cfg.setdefault('omnisource', False)
# The flag is used to register module's hooks
cfg.setdefault('module_hooks', [])
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config: {cfg.text}')
# set random seeds
if args.seed is not None:
logger.info(f'Set random seed to {args.seed}, '
f'deterministic: {args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
meta['config_name'] = osp.basename(args.config)
meta['work_dir'] = osp.basename(cfg.work_dir.rstrip('/\\'))
model = build_model(cfg.model,
train_cfg=cfg.get('train_cfg'),
test_cfg=cfg.get('test_cfg'))
register_module_hooks(model.backbone, cfg.module_hooks)
if cfg.omnisource:
# If omnisource flag is set, cfg.data.train should be a list
assert type(cfg.data.train) is list
datasets = [build_dataset(dataset) for dataset in cfg.data.train]
else:
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
# For simplicity, omnisource is not compatiable with val workflow,
# we recommend you to use `--validate`
assert not cfg.omnisource
if args.validate:
warnings.warn('val workflow is duplicated with `--validate`, '
'it is recommended to use `--validate`. see '
'https://github.com/open-mmlab/mmaction2/pull/123')
val_dataset = copy.deepcopy(cfg.data.val)
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmaction version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmaction_version=__version__ +
get_git_hash(digits=7),
config=cfg.text)
train_model(model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
timestamp=timestamp,
meta=meta)
