def evaluate(self,
results,
metrics=('mAP', ),
metric_options=None,
logger=None):
# need to create a temp result file
assert len(metrics) == 1 and metrics[0] == 'mAP', (
'For evaluation on AVADataset, you need to use metrics "mAP" '
'See https://github.com/open-mmlab/mmaction2/pull/567 '
'for more info.')
time_now = datetime.now().strftime('%Y%m%d_%H%M%S')
temp_file = f'AVA_{time_now}_result.csv'
results2csv(self, results, temp_file)
ret = {}
for metric in metrics:
msg = f'Evaluating {metric} ...'
if logger is None:
msg = '\n' + msg
print_log(msg, logger=logger)
eval_result = ava_eval(temp_file, metric, self.label_file,
self.ann_file, self.exclude_file)
log_msg = []
for k, v in eval_result.items():
log_msg.append(f'\n{k}\t{v: .4f}')
log_msg = ''.join(log_msg)
print_log(log_msg, logger=logger)
ret.update(eval_result)
os.remove(temp_file)
return ret
def evaluate(self,
results,
metrics=('proposal', 'bbox'),
metric_options=None,
logger=None):
# need to create a temp result file
time_now = datetime.now().strftime('%Y%m%d_%H%M%S')
temp_file = f'AVA_{time_now}_result.csv'
results2csv(self, results, temp_file)
ret = {}
for metric in metrics:
msg = f'Evaluating {metric} ...'
if logger is None:
msg = '\n' + msg
print_log(msg, logger=logger)
eval_result = ava_eval(temp_file, metric, self.label_file,
self.ann_file, self.exclude_file)
log_msg = []
for k, v in eval_result.items():
log_msg.append(f'\n{k}\t{v: .4f}')
log_msg = ''.join(log_msg)
print_log(log_msg, logger=logger)
ret.update(eval_result)
os.remove(temp_file)
return ret
def main():
global args
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.data.test.test_mode = True
dataset = obj_from_dict(cfg.data.test, datasets, dict(test_mode=True))
if args.out is None or not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
if osp.exists(args.out):
outputs = mmcv.load(args.out)
else:
if args.launcher == 'none':
raise NotImplementedError(
"By default, we use distributed testing, so that launcher should be pytorch"
)
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
model = build_detector(cfg.model,
train_cfg=None,
test_cfg=cfg.test_cfg)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=1,
dist=distributed,
shuffle=False)
load_checkpoint(model, args.checkpoint, map_location='cpu')
find_unused_parameters = cfg.get('find_unused_parameters', False)
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
outputs = multiple_test(model, data_loader)
rank, _ = get_dist_info()
if rank == 0:
print('writing results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_type = args.eval
if eval_type:
print('Starting evaluate {}'.format(eval_type))
result_file = osp.join(args.out + '.csv')
results2csv(dataset, outputs, result_file)
ava_eval(result_file, eval_type, args.label_file, args.ann_file,
args.exclude_file)
def main():
args = parse_args()
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.data.test.test_mode = True
dataset = obj_from_dict(cfg.data.test, datasets, dict(test_mode=True))
if args.gpus == 1:
model = build_detector(cfg.model,
train_cfg=None,
test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint, strict=True)
model = MMDataParallel(model, device_ids=[0])
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
num_gpus=1,
dist=False,
shuffle=False)
outputs = single_test(model, data_loader)
else:
model_args = cfg.model.copy()
model_args.update(train_cfg=None, test_cfg=cfg.test_cfg)
model_type = getattr(detectors, model_args.pop('type'))
outputs = parallel_test(model_type,
model_args,
args.checkpoint,
dataset,
_data_func,
range(args.gpus),
workers_per_gpu=args.proc_per_gpu)
if args.out:
print('writing results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_type = args.eval
if eval_type:
print('Starting evaluate {}'.format(eval_type))
result_file = osp.join(args.out + '.csv')
results2csv(dataset, outputs, result_file)
ava_eval(result_file, eval_type, args.label_file, args.ann_file,
args.exclude_file)
def dump_results(self, results, out):
assert out.endswith('csv')
results2csv(self, results, out)