def main(args):
assert args.model.endswith('.xml')
# load config
cfg = mmcv.Config.fromfile(args.config)
if args.update_config is not None:
cfg.merge_from_dict(args.update_config)
cfg = update_config(cfg, args)
cfg = propagate_root_dir(cfg, args.data_dir)
# Load eval_config from cfg
eval_config = cfg.get('eval_config', {})
# Overwrite eval_config from args.eval
eval_config = merge_configs(eval_config, dict(metrics=args.eval))
# Add options from args.option
eval_config = merge_configs(eval_config, args.options)
assert eval_config, 'Please specify at eval operation with the argument "--eval"'
# build the dataset
dataset = build_dataset(cfg.data, 'test', dict(test_mode=True))
assert dataset.num_datasets == 1
if cfg.get('classes'):
dataset = dataset.filter(cfg.classes)
print(f'Test datasets:\n{str(dataset)}')
# build the dataloader
data_loader = build_dataloader(
dataset,
videos_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False
)
# build class mapping between model.classes and dataset.classes
assert cfg.get('model_classes') is not None
model_classes = {k: v for k, v in enumerate(cfg.model_classes)}
class_map = build_class_map(dataset.class_maps[0], model_classes)
# load model
ie_core = load_ie_core()
model = ActionRecognizer(args.model, ie_core, class_map)
# collect results
outputs = collect_results(model, data_loader)
# get metrics
if eval_config:
eval_res = dataset.evaluate(outputs, **eval_config)
print('\nFinal metrics:')
for name, val in eval_res.items():
print(f'{name}: {val:.04f}')
def main():
# parse arguments
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
if args.update_config is not None:
cfg.merge_from_dict(args.update_config)
cfg = update_config(cfg, args)
cfg = propagate_root_dir(cfg, args.data_dir)
# init distributed env first, since logger depends on the dist info.
distributed = args.launcher != 'none'
if distributed:
init_dist(args.launcher, **cfg.dist_params)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# 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}')
if cfg.get('nncf_config'):
check_nncf_is_enabled()
logger.info('NNCF config: {}'.format(cfg.nncf_config))
meta.update(get_nncf_metadata())
# set random seeds
cfg.seed = args.seed
meta['seed'] = args.seed
if cfg.get('seed'):
logger.info(f'Set random seed to {cfg.seed}, deterministic: {args.deterministic}')
set_random_seed(cfg.seed, deterministic=args.deterministic)
# build datasets
datasets = [build_dataset(cfg.data, 'train', dict(logger=logger))]
logger.info(f'Train datasets:\n{str(datasets[0])}')
if len(cfg.workflow) == 2:
if not args.no_validate:
warnings.warn('val workflow is duplicated with `--validate`, '
'it is recommended to use `--validate`. see '
'https://github.com/open-mmlab/mmaction2/pull/123')
datasets.append(build_dataset(copy.deepcopy(cfg.data), 'val', dict(logger=logger)))
logger.info(f'Val datasets:\n{str(datasets[-1])}')
# filter dataset labels
if cfg.get('classes'):
datasets = [dataset.filter(cfg.classes) for dataset in datasets]
# build model
model = build_model(
cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg,
class_sizes=datasets[0].class_sizes,
class_maps=datasets[0].class_maps
)
# define ignore layers
ignore_prefixes = []
if hasattr(cfg, 'reset_layer_prefixes') and isinstance(cfg.reset_layer_prefixes, (list, tuple)):
ignore_prefixes += cfg.reset_layer_prefixes
ignore_suffixes = ['num_batches_tracked']
if hasattr(cfg, 'reset_layer_suffixes') and isinstance(cfg.reset_layer_suffixes, (list, tuple)):
ignore_suffixes += cfg.reset_layer_suffixes
# train model
train_model(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta,
ignore_prefixes=tuple(ignore_prefixes),
ignore_suffixes=tuple(ignore_suffixes)
)
def main():
parser = ArgumentParser()
parser.add_argument('--config',
type=str,
required=True,
help='Test config file path')
parser.add_argument('--checkpoint',
type=str,
required=True,
help='Checkpoint file')
parser.add_argument('--data_dir',
type=str,
required=True,
help='The dir with dataset')
parser.add_argument('--out_dir',
type=str,
required=True,
help='Output directory')
parser.add_argument('--dataset',
type=str,
required=True,
help='Dataset name')
parser.add_argument('--gpus',
default=1,
type=int,
help='GPU number used for annotating')
parser.add_argument('--proc_per_gpu',
default=2,
type=int,
help='Number of processes per GPU')
parser.add_argument('--mode',
choices=['train', 'val', 'test'],
default='train')
args = parser.parse_args()
assert exists(args.config)
assert exists(args.checkpoint)
assert exists(args.data_dir)
cfg = Config.fromfile(args.config)
cfg = update_config(cfg, args, trg_name=args.dataset)
cfg = propagate_root_dir(cfg, args.data_dir)
dataset = build_dataset(cfg.data, args.mode, dict(test_mode=True))
data_pipeline = Compose(dataset.pipeline.transforms[1:])
print('{} dataset:\n'.format(args.mode) + str(dataset))
tasks = prepare_tasks(dataset, cfg.input_clip_length)
print('Prepared tasks: {}'.format(sum([len(v) for v in tasks.values()])))
if not exists(args.out_dir):
makedirs(args.out_dir)
model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint, strict=False)
batch_size = 4 * cfg.data.videos_per_gpu
if args.gpus == 1:
model = MMDataParallel(model, device_ids=[0])
model.eval()
process_tasks(tasks, dataset, model, args.out_dir, batch_size,
cfg.input_clip_length, data_pipeline)
else:
raise NotImplementedError
def main():
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
if args.update_config is not None:
cfg.merge_from_dict(args.update_config)
cfg = update_config(cfg, args)
cfg = propagate_root_dir(cfg, args.data_dir)
# Load output_config from cfg
output_config = cfg.get('output_config', {})
# Overwrite output_config from args.out
output_config = merge_configs(output_config, dict(out=args.out))
# Load eval_config from cfg
eval_config = cfg.get('eval_config', {})
# Overwrite eval_config from args.eval
eval_config = merge_configs(eval_config, dict(metrics=args.eval))
# Add options from args.option
eval_config = merge_configs(eval_config, args.options)
assert output_config or eval_config, \
('Please specify at least one operation (save or eval the '
'results) with the argument "--out" or "--eval"')
# init distributed env first, since logger depends on the dist info.
distributed = args.launcher != 'none'
if distributed:
init_dist(args.launcher, **cfg.dist_params)
# get rank
rank, _ = get_dist_info()
if cfg.get('seed'):
print(f'Set random seed to {cfg.seed}')
set_random_seed(cfg.seed)
# build the dataset
dataset = build_dataset(cfg.data, 'test', dict(test_mode=True))
if cfg.get('classes'):
dataset = dataset.filter(cfg.classes)
if rank == 0:
print(f'Test datasets:\n{str(dataset)}')
# build the dataloader
data_loader = build_dataloader(dataset,
videos_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_model(cfg.model,
train_cfg=None,
test_cfg=cfg.test_cfg,
class_sizes=dataset.class_sizes,
class_maps=dataset.class_maps)
# nncf model wrapper
if is_checkpoint_nncf(args.checkpoint) and not cfg.get('nncf_config'):
# reading NNCF config from checkpoint
nncf_part = get_nncf_config_from_meta(args.checkpoint)
for k, v in nncf_part.items():
cfg[k] = v
if cfg.get('nncf_config'):
check_nncf_is_enabled()
if not is_checkpoint_nncf(args.checkpoint):
raise RuntimeError(
'Trying to make testing with NNCF compression a model snapshot that was NOT trained with NNCF'
)
cfg.load_from = args.checkpoint
cfg.resume_from = None
if torch.cuda.is_available():
model = model.cuda()
_, model = wrap_nncf_model(model, cfg, None, get_fake_input)
else:
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
# load model weights
load_checkpoint(model,
args.checkpoint,
map_location='cpu',
force_matching=True)
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)
if rank == 0:
if output_config:
out = output_config['out']
print(f'\nwriting results to {out}')
dataset.dump_results(outputs, **output_config)
if eval_config:
eval_res = dataset.evaluate(outputs, **eval_config)
print('\nFinal metrics:')
for name, val in eval_res.items():
if 'invalid_info' in name:
continue
if isinstance(val, float):
print(f'{name}: {val:.04f}')
elif isinstance(val, str):
print(f'{name}:\n{val}')
else:
print(f'{name}: {val}')
invalid_info = {
name: val
for name, val in eval_res.items() if 'invalid_info' in name
}
if len(invalid_info) > 0:
assert args.out_invalid is not None and args.out_invalid != ''
if os.path.exists(args.out_invalid):
shutil.rmtree(args.out_invalid)
if not os.path.exists(args.out_invalid):
os.makedirs(args.out_invalid)
for name, invalid_record in invalid_info.items():
out_invalid_dir = os.path.join(args.out_invalid, name)
item_gen = zip(invalid_record['ids'],
invalid_record['conf'],
invalid_record['pred'])
for invalid_idx, pred_conf, pred_label in item_gen:
record_info = dataset.get_info(invalid_idx)
gt_label = record_info['label']
if 'filename' in record_info:
src_data_path = record_info['filename']
in_record_name, record_extension = os.path.basename(
src_data_path).split('.')
out_record_name = f'{in_record_name}_gt{gt_label}_pred{pred_label}_conf{pred_conf:.3f}'
trg_data_path = os.path.join(
out_invalid_dir,
f'{out_record_name}.{record_extension}')
shutil.copyfile(src_data_path, trg_data_path)
else:
src_data_path = record_info['frame_dir']
in_record_name = os.path.basename(src_data_path)
out_record_name = f'{in_record_name}_gt{gt_label}_pred{pred_label}_conf{pred_conf:.3f}'
trg_data_path = os.path.join(
out_invalid_dir, out_record_name)
os.makedirs(trg_data_path)
start_frame_id = record_info[
'clip_start'] + dataset.start_index
end_frame_id = record_info[
'clip_end'] + dataset.start_index
for frame_id in range(start_frame_id,
end_frame_id):
img_name = f'{frame_id:05}.jpg'
shutil.copyfile(
os.path.join(src_data_path, img_name),
os.path.join(trg_data_path, img_name))
def main():
parser = ArgumentParser()
parser.add_argument('--config', '-c', type=str, required=True)
parser.add_argument('--checkpoint', '-w', type=str, required=True)
parser.add_argument('--dataset_name', '-n', type=str, required=True)
parser.add_argument('--data_dir', '-d', type=str, required=True)
parser.add_argument('--predictions', '-p', type=str, required=True)
parser.add_argument('--movements', '-m', type=str, required=True)
parser.add_argument('--keypoints', '-k', type=str, required=True)
parser.add_argument('--out_annotation', '-o', type=str, required=True)
args = parser.parse_args()
assert exists(args.config)
assert exists(args.weights)
assert exists(args.data_dir)
assert exists(args.predictions)
assert exists(args.movements)
assert exists(args.keypoints)
assert args.dataset_name is not None and args.dataset_name != ''
assert args.out_annotation is not None and args.out_annotation != ''
cfg = Config.fromfile(args.config)
cfg = update_config(cfg, args, trg_name=args.dataset_name)
cfg = propagate_root_dir(cfg, args.data_dir)
dataset = build_dataset(cfg.data, 'train', dict(test_mode=True))
data_pipeline = Compose(dataset.pipeline.transforms[1:])
print('{} dataset:\n'.format(args.mode) + str(dataset))
model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint, strict=False)
model = MMDataParallel(model, device_ids=[0])
model.eval()
annotation_path = join(args.data_dir, cfg.data.train.sources[0],
cfg.data.train.ann_file)
records = load_annotation(annotation_path)
predictions = load_distributed_data(args.predictions,
parse_predictions_file, 'txt')
movements = load_distributed_data(args.movements, parse_movements_file,
'txt')
hand_kpts = load_distributed_data(args.keypoints, parse_kpts_file, 'json')
print('Loaded records: {}'.format(len(records)))
invalid_stat = dict()
all_candidates = []
ignore_candidates = get_ignore_candidates(records, IGNORE_LABELS)
all_candidates += ignore_candidates
static_candidates, static_invalids = get_regular_candidates(
records,
predictions,
movements,
hand_kpts,
cfg.data.output.length,
False,
STATIC_LABELS,
NEGATIVE_LABEL,
NO_MOTION_LABEL,
min_score=0.9,
min_length=4,
max_distance=1)
all_candidates += static_candidates
invalid_stat = update_stat(invalid_stat, static_invalids)
print('Static candidates: {}'.format(len(static_candidates)))
if len(invalid_stat) > 0:
print('Ignored records after static analysis:')
for ignore_label, ignore_values in invalid_stat.items():
print(' - {}: {}'.format(ignore_label.replace('_', ' '),
len(ignore_values)))
dynamic_candidates, dynamic_invalids = get_regular_candidates(
records,
predictions,
movements,
hand_kpts,
cfg.data.output.length,
True,
DYNAMIC_LABELS,
NEGATIVE_LABEL,
NO_MOTION_LABEL,
min_score=0.9,
min_length=4,
max_distance=1)
all_candidates += dynamic_candidates
invalid_stat = update_stat(invalid_stat, dynamic_invalids)
print('Dynamic candidates: {}'.format(len(dynamic_candidates)))
if len(invalid_stat) > 0:
print('Ignored records after dynamic analysis:')
for ignore_label, ignore_values in invalid_stat.items():
print(' - {}: {}'.format(ignore_label.replace('_', ' '),
len(ignore_values)))
fixed_records, fix_stat = find_best_match(all_candidates, model, dataset,
NEGATIVE_LABEL)
invalid_stat = update_stat(invalid_stat, fix_stat)
print('Final records: {}'.format(len(fixed_records)))
if len(invalid_stat) > 0:
print('Final ignored records:')
for ignore_label, ignore_values in invalid_stat.items():
print(' - {}: {}'.format(ignore_label.replace('_', ' '),
len(ignore_values)))
for ignored_record in ignore_values:
print(' - {}'.format(ignored_record.path))
dump_records(fixed_records, args.out_annotation)
print('Fixed annotation has been stored at: {}'.format(
args.out_annotation))
def main():
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
if args.update_config is not None:
cfg.merge_from_dict(args.update_config)
cfg = update_config(cfg, args)
cfg = propagate_root_dir(cfg, args.data_dir)
# Load output_config from cfg
output_config = cfg.get('output_config', {})
# Overwrite output_config from args.out
output_config = merge_configs(output_config, dict(out=args.out))
# Load eval_config from cfg
eval_config = cfg.get('eval_config', {})
# Overwrite eval_config from args.eval
eval_config = merge_configs(eval_config, dict(metrics=args.eval))
# Add options from args.option
eval_config = merge_configs(eval_config, args.options)
assert output_config or eval_config, \
('Please specify at least one operation (save or eval the '
'results) with the argument "--out" or "--eval"')
# init distributed env first, since logger depends on the dist info.
distributed = args.launcher != 'none'
if distributed:
init_dist(args.launcher, **cfg.dist_params)
# get rank
rank, _ = get_dist_info()
if cfg.get('seed'):
print(f'Set random seed to {cfg.seed}')
set_random_seed(cfg.seed)
# build the dataset
dataset = build_dataset(cfg.data, 'test', dict(test_mode=True))
if cfg.get('classes'):
dataset = dataset.filter(cfg.classes)
if rank == 0:
print(f'Test datasets:\n{str(dataset)}')
# build the dataloader
data_loader = build_dataloader(
dataset,
videos_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False
)
# build the model and load checkpoint
model = build_model(
cfg.model,
train_cfg=None,
test_cfg=cfg.test_cfg,
class_sizes=dataset.class_sizes,
class_maps=dataset.class_maps
)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
# load model weights
load_checkpoint(model, args.checkpoint, map_location='cpu', force_matching=True)
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)
if rank == 0:
if output_config:
out = output_config['out']
print(f'\nwriting results to {out}')
dataset.dump_results(outputs, **output_config)
if eval_config:
eval_res = dataset.evaluate(outputs, **eval_config)
print('\nFinal metrics:')
for name, val in eval_res.items():
print(f'{name}: {val:.04f}')