def train_detector(model,
dataset,
cfg,
validate=False,
timestamp=None,
meta=None):
logger = Logging.getLogger()
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [build_dataloader(ds, data=cfg.data) for ds in dataset]
if torch.cuda.is_available():
model = model.cuda(cfg.gpu_ids[0])
model.device = cfg.gpu_ids[0]
if torch.cuda.device_count() > 1:
model = DataParallel(model, device_ids=cfg.gpu_ids)
else:
model.device = 'cpu'
# build runner
optimizer = cfg.optimizer
if 'ema' in cfg:
ema = cfg.ema
else:
ema = None
runner = Runner(model,
batch_processor,
optimizer,
cfg.work_dir,
logger=logger,
meta=meta,
ema=ema)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# register eval hooks 需要放在日志前面，不然打印不出日志。
if validate:
cfg.data.val.train = False
val_dataset = build_from_dict(cfg.data.val, DATASET)
val_dataloader = build_dataloader(val_dataset,
shuffle=False,
data=cfg.data)
eval_cfg = cfg.get('evaluation', {})
from yolodet.models.hooks.eval_hook import EvalHook
runner.register_hook(EvalHook(val_dataloader, **eval_cfg))
# register hooks
# runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,cfg.checkpoint_config)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
cx, cy, _w, _h = bbox
x, y = cx - _w / 2, cy - _h / 2
# x1,y1,x2,y2 = int(x1*w),int(y1*h),int(x2*w),int(y2*h)
x1,y1,x2,y2 = x,y, x+_w,y+_h
x1, y1, x2, y2 = int(x1 * w), int(y1 * h), int(x2 * w), int(y2 * h)
# img = cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 1)
img = cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 1)
return img
file = '/disk2/project/pytorch-YOLOv4/cfg/dataset_test.py'
cfg = Config.fromfile(file)
dataset = build_from_dict(cfg.data.train,DATASET)
dataloader = build_dataloader(dataset,data=cfg.data)
for i, data_batch in enumerate(dataloader):
if i>30:
break
for idx,data in enumerate(data_batch['img']):
gt = data_batch['gt_bboxes'][idx]
gt_xywh = xyxy2xywh(gt) # x,y ,w, h
n_gt = (gt.sum(dim=-1) > 0).sum(dim=-1)
n = int(n_gt)
if n == 0:
continue
gt = gt[:n].cpu().numpy()
gt_xywh = gt_xywh[:n].cpu().numpy()
data = data.cpu().numpy()*255
data = data.transpose(1, 2, 0)
help='report mAP by class')
parser.add_argument('--half',
action='store_true',
help='fp16 half precision')
opt = parser.parse_args()
print(opt)
# config = '/disk2/project/mmdetection/mount/pytorch-YOLOv4/cfg/yolov5_coco_gpu.py'
# checkpoint = '/disk2/project/pytorch-YOLOv4/work_dirs/yolov5-l_epoch_24.pth'
cfg = Config.fromfile(opt.config)
cfg.data.val.train = False
val_dataset = build_from_dict(cfg.data.val, DATASET)
val_dataloader = build_dataloader(val_dataset,
data=cfg.data,
shuffle=False)
device = select_device(opt.device)
# model = init_detector(opt.config, checkpoint=opt.checkpoint, device=device)
model = init_detector(opt.config, checkpoint=opt.checkpoint, device=device)
result = single_gpu_test(model,
val_dataloader,
half=opt.half,
conf_thres=opt.conf_thres,
iou_thres=opt.iou_thres,
merge=opt.merge,
save_json=opt.save_json,
augment=opt.augment,
verbose=opt.verbose,
coco_val_path=opt.coco_val_path)