Description
Hi, I came up with an issue when training on my dataset which has 102 classes. It seems like a size mismatch problem when using the pretrained model.
Here is my code:
PYTHONWARNINGS="ignore" cvnets-train
--common.config-file config/detection/ssd_coco/coco-ssd-mobilevitv2-1.75.yaml
--common.results-loc exp/exp1
--common.override-kwargs model.detection.pretrained="ckpt/coco-ssd-mobilevitv2-1.75.pt" model.detection.n-classes=103
and this is the error:
2022-12-12 21:09:24 - ERROR - Unable to load pretrained weights from ckpt/coco-ssd-mobilevitv2-1.75.pt. Error: Error(s) in loading state_dict for SingleShotMaskDetector:
size mismatch for ssd_heads.0.loc_cls_layer.pw_conv.block.conv.weight: copying a param with shape torch.Size([510, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([642, 512, 1, 1]).
size mismatch for ssd_heads.0.loc_cls_layer.pw_conv.block.conv.bias: copying a param with shape torch.Size([510]) from checkpoint, the shape in current model is torch.Size([642]).
size mismatch for ssd_heads.1.loc_cls_layer.pw_conv.block.conv.weight: copying a param with shape torch.Size([510, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([642, 256, 1, 1]).
size mismatch for ssd_heads.1.loc_cls_layer.pw_conv.block.conv.bias: copying a param with shape torch.Size([510]) from checkpoint, the shape in current model is torch.Size([642]).
size mismatch for ssd_heads.2.loc_cls_layer.pw_conv.block.conv.weight: copying a param with shape torch.Size([510, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([642, 256, 1, 1]).
size mismatch for ssd_heads.2.loc_cls_layer.pw_conv.block.conv.bias: copying a param with shape torch.Size([510]) from checkpoint, the shape in current model is torch.Size([642]).
size mismatch for ssd_heads.3.loc_cls_layer.pw_conv.block.conv.weight: copying a param with shape torch.Size([510, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([642, 128, 1, 1]).
size mismatch for ssd_heads.3.loc_cls_layer.pw_conv.block.conv.bias: copying a param with shape torch.Size([510]) from checkpoint, the shape in current model is torch.Size([642]).
size mismatch for ssd_heads.4.loc_cls_layer.pw_conv.block.conv.weight: copying a param with shape torch.Size([510, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([642, 128, 1, 1]).
size mismatch for ssd_heads.4.loc_cls_layer.pw_conv.block.conv.bias: copying a param with shape torch.Size([510]) from checkpoint, the shape in current model is torch.Size([642]).
size mismatch for ssd_heads.5.loc_cls_layer.block.conv.weight: copying a param with shape torch.Size([340, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([428, 64, 1, 1]).
size mismatch for ssd_heads.5.loc_cls_layer.block.conv.bias: copying a param with shape torch.Size([340]) from checkpoint, the shape in current model is torch.Size([428]).
How to use the pretrained model when i try to train on my own dataset with different num_classes with mscoco?