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):
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 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:
# You can set average_clips during testing, it will override the
# original settting
if args.average_clips is not None:
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)
# 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}')
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)
# 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"')
# set cudnn benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.data.test.test_mode = True
if cfg.test_cfg is None:
cfg.test_cfg = dict(average_clips=args.average_clips)
else:
# You can set average_clips during testing, it will override the
# original settting
if args.average_clips is not None:
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)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# 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', 2),
workers_per_gpu=cfg.data.get(
'workers_per_gpu', 0),
dist=distributed,
shuffle=False)
dataloader_setting = dict(dataloader_setting,
**cfg.data.get('test_dataloader', {}))
data_loader = build_dataloader(dataset, **dataloader_setting)
# build the model and load checkpoint
model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
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:
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)
for name, val in eval_res.items():
print(f'{name}: {val:.04f}')
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():
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
# 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"')
# set cudnn benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.data.test.test_mode = True
if cfg.test_cfg is None:
cfg.test_cfg = dict(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)
# build the dataloader
dataset = build_dataset(cfg.data.test, dict(test_mode=True))
data_loader = build_dataloader(dataset,
videos_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# map lable from txt to csv file
df = pd.read_csv('/data2/phap/datasets/dataset3_test.txt', header=None)
df.columns = ['full_name']
df['file_name'] = df['full_name'].apply(lambda x: x.rsplit(' ')[0])
df['true_label'] = df['full_name'].apply(lambda x: x.rsplit(' ')[-1])
# build the model and load checkpoint
model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint, map_location='cpu')
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)
# convert softmax output to one hot
pred_arr = []
for i in outputs:
pred = np.argmax(i)
pred_arr.append(pred)
# import output into csv
df['pred_label_orig'] = outputs
df['pred_label'] = pred_arr
# save csv file
df.to_csv('dataset3_test_pred_w_rwf_model.csv')
print('\nSuccess, csv file saved')
rank, _ = get_dist_info()
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)
for name, val in eval_res.items():
print(f'{name}: {val:.04f}')
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}')