def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = 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)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
train_dataset = build_dataset(cfg.data.train)
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmdet_version=__version__,
config=cfg.text,
CLASSES=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES
train_detector(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
if args.out is not None and not args.out.endswith(('.json', '.pickle')):
raise ValueError('The output file must be a pkl file.')
for i in range(args.checkpoint_start, args.checkpoint_end):
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# 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
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(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)
if not args.mean_teacher:
while not osp.exists(args.checkpoint + str(i) + '.pth'):
time.sleep(5)
while i+1 != args.checkpoint_end and not osp.exists(args.checkpoint + str(i+1) + '.pth'):
time.sleep(5)
checkpoint = load_checkpoint(model, args.checkpoint + str(i) + '.pth', map_location='cpu')
else:
while not osp.exists(args.checkpoint + str(i) + '.pth.stu'):
time.sleep(5)
while i+1 != args.checkpoint_end and not osp.exists(args.checkpoint + str(i+1) + '.pth.stu'):
time.sleep(5)
checkpoint = load_checkpoint(model, args.checkpoint + str(i) + '.pth.stu', map_location='cpu')
checkpoint['meta'] = dict()
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show, args.save_img, args.save_img_dir)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
res = []
for id, boxes in enumerate(outputs):
boxes=boxes[0]
if type(boxes) == list:
boxes = boxes[0]
boxes[:, [2, 3]] -= boxes[:, [0, 1]]
if len(boxes) > 0:
for box in boxes:
# box[:4] = box[:4] / 0.6
temp = dict()
temp['image_id'] = id+1
temp['category_id'] = 1
temp['bbox'] = box[:4].tolist()
temp['score'] = float(box[4])
res.append(temp)
with open(args.out, 'w') as f:
json.dump(res, f)
MRs = validate('datasets/crowdhuman/validation.json', args.out)
print(MRs)
print('Checkpoint %d: [Reasonable: %.2f%%], [Bare: %.2f%%], [Partial: %.2f%%], [Heavy: %.2f%%]'
% (i, MRs[0] * 100, MRs[1] * 100, MRs[2] * 100, MRs[3] * 100))
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.model.pretrained = None
cfg.data.test.test_mode = True
# 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
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(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)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'meta' in checkpoint and 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.show, args.save_img,
args.save_img_dir)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json(dataset, outputs, args.out)
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file)
coco_eval(result_files, eval_types, dataset.coco)