Input size can not be dynamic?
lucasjinreal opened this issue · comments
I tried something like this:
python demo.py --resume weights/yolos_s_dWr.pth --data_file ../yolov7/images/COCO_val2014_000000001856.jpg --mid_pe_size 800 864 --init_pe_size 800 864
Not using distributed mode
Namespace(backbone_name='small_dWr', batch_size=2, bbox_loss_coef=5, clip_max_norm=0.1, coco_panoptic_path=None, coco_path=None, data_file='../yolo/images/COCO_val2014_000000001856.jpg', dataset_file='coco', decay_rate=0.1, det_token_num=100, device='cuda', dice_loss_coef=1, dist_url='env://', distributed=False, eos_coef=0.1, epochs=150, eval=False, eval_size=800, giou_loss_coef=2, init_pe_size=[800, 864], lr=0.0001, lr_backbone=1e-05, lr_drop=100, mid_pe_size=[800, 864], min_lr=1e-07, num_workers=2, output_dir='', pre_trained='', remove_difficult=False, resume='weights/yolos_s_dWr.pth', sched='warmupcos', seed=42, set_cost_bbox=5, set_cost_class=1, set_cost_giou=2, start_epoch=0, use_checkpoint=False, warmup_epochs=0, warmup_lr=1e-06, weight_decay=0.0001, world_size=1)
Got:
torch1.8/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1223, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Detector:
size mismatch for backbone.pos_embed: copying a param with shape torch.Size([1, 1829, 330]) from checkpoint, the shape in current model is torch.Size([1, 2801, 330]).
size mismatch for backbone.mid_pos_embed: copying a param with shape torch.Size([13, 1, 1829, 330]) from checkpoint, the shape in current model is torch.Size([13, 1, 2801, 330]).
The input sizes can be dynamic, we did't test demo.py, please directly try to train or inference YOLOS using the provided scripts.
@Yuxin-CV how? I occured above errors by specific different input size. demo.py is just copied from your coco_visualizexx.py, same args.
You should set --init_pe_size 512 864 & --mid_pe_size 512 864, note that these two params are irrelevant with input size, they are with pre-trained YOLOS weight.
@Yuxin-CV if I set input to 800x800, what will happen? Does it means it's miss aligned with training?
Of course it will be miss aligned with training, but the model can still process it, with degenerated accuracy.
I don't konw why you would like to change the aspect ratio, but if you prefer H : W = 1 : 1 inputs, I suggest you re-train the model with scale jittering.
