Comments (9)
I print out the shape as the following in load_data_new
:
print(features[base].shape, features[novel].shape, features[neg].shape)
and it comes out as the following:
torch.Size([6519899, 512]) torch.Size([0, 512]) torch.Size([0, 512])
from detpro.
I print out the shape as the following in
load_data_new
:print(features[base].shape, features[novel].shape, features[neg].shape)
and it comes out as the following:
torch.Size([6519899, 512]) torch.Size([0, 512]) torch.Size([0, 512])
Hi, do you use the latest version?
from detpro.
I print out the shape as the following in
load_data_new
:print(features[base].shape, features[novel].shape, features[neg].shape)
and it comes out as the following:
torch.Size([6519899, 512]) torch.Size([0, 512]) torch.Size([0, 512])
Hi, do you use the latest version?
Do I need to rerun prepare.sh
?
from detpro.
When I am running the latest code, I meet the following model load warning. Is this normal?
[04/12 18:25:16] mmdet INFO: load checkpoint from current_mmdetection_Head.pth
2022-04-12 18:25:16,430 - mmdet - WARNING - The model and loaded state dict do not match exactly
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[04/12 18:25:16] mmdet WARNING: The model and loaded state dict do not match exactly
from detpro.
When I am running the latest code, I meet the following model load warning. Is this normal?
[04/12 18:25:16] mmdet INFO: load checkpoint from current_mmdetection_Head.pth 2022-04-12 18:25:16,430 - mmdet - WARNING - The model and loaded state dict do not match exactly missing keys in source state_dict: rpn_head.rpn_conv.weight, rpn_head.rpn_conv.bias, rpn_head.rpn_cls.weight, rpn_head.rpn_cls.bias, rpn_head.rpn_reg.weight, rpn_head.rpn_reg.bias, roi_head.bbox_head.fc_reg.weight, roi_head.bbox_head.fc_reg.bias, roi_head.mask_head.convs.0.conv.weight, roi_head.mask_head.convs.0.conv.bias, roi_head.mask_head.convs.1.conv.weight, roi_head.mask_head.convs.1.conv.bias, roi_head.mask_head.convs.2.conv.weight, roi_head.mask_head.convs.2.conv.bias, roi_head.mask_head.convs.3.conv.weight, roi_head.mask_head.convs.3.conv.bias, roi_head.mask_head.upsample.weight, roi_head.mask_head.upsample.bias, roi_head.mask_head.conv_logits.weight, roi_head.mask_head.conv_logits.bias, roi_head.clip_model.positional_embedding, roi_head.clip_model.text_projection, roi_head.clip_model.logit_scale, 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roi_head.clip_model.transformer.resblocks.8.mlp.c_proj.weight, roi_head.cl ip_model.transformer.resblocks.8.mlp.c_proj.bias, roi_head.clip_model.transformer.resblocks.8.ln_2.weight, roi_head.clip_model.transformer.resblocks.8.ln_2.bias, roi_head.clip_mo del.transformer.resblocks.9.attn.in_proj_weight, roi_head.clip_model.transformer.resblocks.9.attn.in_proj_bias, roi_head.clip_model.transformer.resblocks.9.attn.out_proj.weight, roi_head.clip_model.transformer.resblocks.9.attn.out_proj.bias, roi_head.clip_model.transformer.resblocks.9.ln_1.weight, roi_head.clip_model.transformer.resblocks.9.ln_1.bias, ro i_head.clip_model.transformer.resblocks.9.mlp.c_fc.weight, roi_head.clip_model.transformer.resblocks.9.mlp.c_fc.bias, roi_head.clip_model.transformer.resblocks.9.mlp.c_proj.weigh t, roi_head.clip_model.transformer.resblocks.9.mlp.c_proj.bias, roi_head.clip_model.transformer.resblocks.9.ln_2.weight, roi_head.clip_model.transformer.resblocks.9.ln_2.bias, ro i_head.clip_model.transformer.resblocks.10.attn.in_proj_weight, roi_head.clip_model.transformer.resblocks.10.attn.in_proj_bias, roi_head.clip_model.transformer.resblocks.10.attn. out_proj.weight, roi_head.clip_model.transformer.resblocks.10.attn.out_proj.bias, roi_head.clip_model.transformer.resblocks.10.ln_1.weight, roi_head.clip_model.transformer.resblo cks.10.ln_1.bias, roi_head.clip_model.transformer.resblocks.10.mlp.c_fc.weight, roi_head.clip_model.transformer.resblocks.10.mlp.c_fc.bias, roi_head.clip_model.transformer.resblo cks.10.mlp.c_proj.weight, roi_head.clip_model.transformer.resblocks.10.mlp.c_proj.bias, roi_head.clip_model.transformer.resblocks.10.ln_2.weight, roi_head.clip_model.transformer. resblocks.10.ln_2.bias, roi_head.clip_model.transformer.resblocks.11.attn.in_proj_weight, roi_head.clip_model.transformer.resblocks.11.attn.in_proj_bias, roi_head.clip_model.tran sformer.resblocks.11.attn.out_proj.weight, roi_head.clip_model.transformer.resblocks.11.attn.out_proj.bias, roi_head.clip_model.transformer.resblocks.11.ln_1.weight, roi_head.cli p_model.transformer.resblocks.11.ln_1.bias, roi_head.clip_model.transformer.resblocks.11.mlp.c_fc.weight, roi_head.clip_model.transformer.resblocks.11.mlp.c_fc.bias, roi_head.cli p_model.transformer.resblocks.11.mlp.c_proj.weight, roi_head.clip_model.transformer.resblocks.11.mlp.c_proj.bias, roi_head.clip_model.transformer.resblocks.11.ln_2.weight, roi_he ad.clip_model.transformer.resblocks.11.ln_2.bias, roi_head.clip_model.token_embedding.weight, roi_head.clip_model.ln_final.weight, roi_head.clip_model.ln_final.bias, roi_head.bg_ embedding.weight, roi_head.bg_embedding.bias, roi_head.projection.weight, roi_head.projection.bias [04/12 18:25:16] mmdet WARNING: The model and loaded state dict do not match exactly
Yes, it is normal.
from detpro.
Dear author,
I am using the latest code, and when I running prepare.sh
, meeting an error:
AttributeError: module 'mmdet.models.roi_heads' has no attribute 'StandardRoIHeadColReuseStandardRoIHeadTEXTPrompt'
from detpro.
Dear author,
I am using the latest code, and when I running
prepare.sh
, meeting an error:AttributeError: module 'mmdet.models.roi_heads' has no attribute 'StandardRoIHeadColReuseStandardRoIHeadTEXTPrompt'
Thank you for pointing out, I have fixed that.
from detpro.
Again, I meet another error:
max_coordinate = boxes.max()
RuntimeError: operation does not have an identity.
Could you help me sort out?
from detpro.
Again, I meet another error:
max_coordinate = boxes.max() RuntimeError: operation does not have an identity.
Could you help me sort out?
Could you please provide more error info?
from detpro.
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