missing keys in source state_dict

[jiabeiwangTJU]

patch_embed.proj.weight, patch_embed.proj.bias, patch_embed.norm.weight, patch_embed.norm.bias, layers.0.blocks.0.norm1.weight, layers.0.blocks.0.norm1.bias, layers.0.blocks.0.attn.relative_position_bias_table, layers.0.blocks.0.attn.relative_position_index, layers.0.blocks.0.attn.qkv.weight, layers.0.blocks.0.attn.qkv.bias, layers.0.blocks.0.attn.proj.weight, layers.0.blocks.0.attn.proj.bias, layers.0.blocks.0.norm2.weight, layers.0.blocks.0.norm2.bias, layers.0.blocks.0.mlp.fc1.weight, layers.0.blocks.0.mlp.fc1.bias, layers.0.blocks.0.mlp.fc2.weight, layers.0.blocks.0.mlp.fc2.bias, layers.0.blocks.1.norm1.weight, layers.0.blocks.1.norm1.bias, layers.0.blocks.1.attn.relative_position_bias_table, layers.0.blocks.1.attn.relative_position_index, layers.0.blocks.1.attn.qkv.weight, layers.0.blocks.1.attn.qkv.bias, layers.0.blocks.1.attn.proj.weight, layers.0.blocks.1.attn.proj.bias, layers.0.blocks.1.norm2.weight, layers.0.blocks.1.norm2.bias, layers.0.blocks.1.mlp.fc1.weight, layers.0.blocks.1.mlp.fc1.bias, layers.0.blocks.1.mlp.fc2.weight, layers.0.blocks.1.mlp.fc2.bias, layers.0.downsample.reduction.weight, layers.0.downsample.norm.weight, layers.0.downsample.norm.bias, layers.1.blocks.0.norm1.weight, layers.1.blocks.0.norm1.bias, layers.1.blocks.0.attn.relative_position_bias_table, layers.1.blocks.0.attn.relative_position_index, layers.1.blocks.0.attn.qkv.weight, layers.1.blocks.0.attn.qkv.bias, layers.1.blocks.0.attn.proj.weight, layers.1.blocks.0.attn.proj.bias, layers.1.blocks.0.norm2.weight, layers.1.blocks.0.norm2.bias, layers.1.blocks.0.mlp.fc1.weight, layers.1.blocks.0.mlp.fc1.bias, layers.1.blocks.0.mlp.fc2.weight, layers.1.blocks.0.mlp.fc2.bias, layers.1.blocks.1.norm1.weight, layers.1.blocks.1.norm1.bias, layers.1.blocks.1.attn.relative_position_bias_table, layers.1.blocks.1.attn.relative_position_index, layers.1.blocks.1.attn.qkv.weight, layers.1.blocks.1.attn.qkv.bias, layers.1.blocks.1.attn.proj.weight, layers.1.blocks.1.attn.proj.bias, layers.1.blocks.1.norm2.weight, layers.1.blocks.1.norm2.bias, layers.1.blocks.1.mlp.fc1.weight, layers.1.blocks.1.mlp.fc1.bias, layers.1.blocks.1.mlp.fc2.weight, layers.1.blocks.1.mlp.fc2.bias, layers.1.downsample.reduction.weight, layers.1.downsample.norm.weight, layers.1.downsample.norm.bias, layers.2.blocks.0.norm1.weight, layers.2.blocks.0.norm1.bias, layers.2.blocks.0.attn.relative_position_bias_table, layers.2.blocks.0.attn.relative_position_index, layers.2.blocks.0.attn.qkv.weight, layers.2.blocks.0.attn.qkv.bias, layers.2.blocks.0.attn.proj.weight, layers.2.blocks.0.attn.proj.bias, layers.2.blocks.0.norm2.weight, layers.2.blocks.0.norm2.bias, layers.2.blocks.0.mlp.fc1.weight, layers.2.blocks.0.mlp.fc1.bias, layers.2.blocks.0.mlp.fc2.weight, layers.2.blocks.0.mlp.fc2.bias, layers.2.blocks.1.norm1.weight, layers.2.blocks.1.norm1.bias, layers.2.blocks.1.attn.relative_position_bias_table, layers.2.blocks.1.attn.relative_position_index, layers.2.blocks.1.attn.qkv.weight, layers.2.blocks.1.attn.qkv.bias, layers.2.blocks.1.attn.proj.weight, layers.2.blocks.1.attn.proj.bias, layers.2.blocks.1.norm2.weight, layers.2.blocks.1.norm2.bias, layers.2.blocks.1.mlp.fc1.weight, layers.2.blocks.1.mlp.fc1.bias, layers.2.blocks.1.mlp.fc2.weight, layers.2.blocks.1.mlp.fc2.bias, layers.2.blocks.2.norm1.weight, layers.2.blocks.2.norm1.bias, layers.2.blocks.2.attn.relative_position_bias_table, layers.2.blocks.2.attn.relative_position_index, layers.2.blocks.2.attn.qkv.weight, layers.2.blocks.2.attn.qkv.bias, layers.2.blocks.2.attn.proj.weight, layers.2.blocks.2.attn.proj.bias, layers.2.blocks.2.norm2.weight, layers.2.blocks.2.norm2.bias, layers.2.blocks.2.mlp.fc1.weight, layers.2.blocks.2.mlp.fc1.bias, layers.2.blocks.2.mlp.fc2.weight, layers.2.blocks.2.mlp.fc2.bias, layers.2.blocks.3.norm1.weight, layers.2.blocks.3.norm1.bias, layers.2.blocks.3.attn.relative_position_bias_table, layers.2.blocks.3.attn.relative_position_index, layers.2.blocks.3.attn.qkv.weight, layers.2.blocks.3.attn.qkv.bias, layers.2.blocks.3.attn.proj.weight, layers.2.blocks.3.attn.proj.bias, layers.2.blocks.3.norm2.weight, layers.2.blocks.3.norm2.bias, layers.2.blocks.3.mlp.fc1.weight, layers.2.blocks.3.mlp.fc1.bias, layers.2.blocks.3.mlp.fc2.weight, layers.2.blocks.3.mlp.fc2.bias, layers.2.blocks.4.norm1.weight, layers.2.blocks.4.norm1.bias, layers.2.blocks.4.attn.relative_position_bias_table, layers.2.blocks.4.attn.relative_position_index, layers.2.blocks.4.attn.qkv.weight, layers.2.blocks.4.attn.qkv.bias, layers.2.blocks.4.attn.proj.weight, layers.2.blocks.4.attn.proj.bias, layers.2.blocks.4.norm2.weight, layers.2.blocks.4.norm2.bias, layers.2.blocks.4.mlp.fc1.weight, layers.2.blocks.4.mlp.fc1.bias, layers.2.blocks.4.mlp.fc2.weight, layers.2.blocks.4.mlp.fc2.bias, layers.2.blocks.5.norm1.weight, layers.2.blocks.5.norm1.bias, layers.2.blocks.5.attn.relative_position_bias_table, layers.2.blocks.5.attn.relative_position_index, layers.2.blocks.5.attn.qkv.weight, layers.2.blocks.5.attn.qkv.bias, layers.2.blocks.5.attn.proj.weight, layers.2.blocks.5.attn.proj.bias, layers.2.blocks.5.norm2.weight, layers.2.blocks.5.norm2.bias, layers.2.blocks.5.mlp.fc1.weight, layers.2.blocks.5.mlp.fc1.bias, layers.2.blocks.5.mlp.fc2.weight, layers.2.blocks.5.mlp.fc2.bias, layers.2.downsample.reduction.weight, layers.2.downsample.norm.weight, layers.2.downsample.norm.bias, layers.3.blocks.0.norm1.weight, layers.3.blocks.0.norm1.bias, layers.3.blocks.0.attn.relative_position_bias_table, layers.3.blocks.0.attn.relative_position_index, layers.3.blocks.0.attn.qkv.weight, layers.3.blocks.0.attn.qkv.bias, layers.3.blocks.0.attn.proj.weight, layers.3.blocks.0.attn.proj.bias, layers.3.blocks.0.norm2.weight, layers.3.blocks.0.norm2.bias, layers.3.blocks.0.mlp.fc1.weight, layers.3.blocks.0.mlp.fc1.bias, layers.3.blocks.0.mlp.fc2.weight, layers.3.blocks.0.mlp.fc2.bias, layers.3.blocks.1.norm1.weight, layers.3.blocks.1.norm1.bias, layers.3.blocks.1.attn.relative_position_bias_table, layers.3.blocks.1.attn.relative_position_index, layers.3.blocks.1.attn.qkv.weight, layers.3.blocks.1.attn.qkv.bias, layers.3.blocks.1.attn.proj.weight, layers.3.blocks.1.attn.proj.bias, layers.3.blocks.1.norm2.weight, layers.3.blocks.1.norm2.bias, layers.3.blocks.1.mlp.fc1.weight, layers.3.blocks.1.mlp.fc1.bias, layers.3.blocks.1.mlp.fc2.weight, layers.3.blocks.1.mlp.fc2.bias, norm0.weight, norm0.bias, norm1.weight, norm1.bias, norm2.weight, norm2.bias, norm3.weight, norm3.bias

model = dict(
pretrained='/storage/wjb/AlignPS/pretrained/swin_tiny_patch4_window7_224.pth',
backbone=dict(
type='SwinTransformer',
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
ape=False,
patch_norm=True,
out_indices=(0, 1, 2, 3),
use_checkpoint=False),

[NEUdeep]

yes ,i also error like this:
Swin-Transformer-Object-Detection/checkpoints/swin_small_patch4_window7_224.pth
2021-05-31 23:22:46,178 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: norm.weight, norm.bias, head.weight, head.bias, layers.0.blocks.1.attn_mask, layers.1.blocks.1.attn_mask, layers.2.blocks.1.attn_mask, layers.2.blocks.3.attn_mask, layers.2.blocks.5.attn_mask, layers.2.blocks.7.attn_mask, layers.2.blocks.9.attn_mask, layers.2.blocks.11.attn_mask, layers.2.blocks.13.attn_mask, layers.2.blocks.15.attn_mask, layers.2.blocks.17.attn_mask

missing keys in source state_dict: norm0.weight, norm0.bias, norm1.weight, norm1.bias, norm2.weight, norm2.bias, norm3.weight, norm3.bias

loading annotations into memory...

[hukaixuan19970627]

@NEUdeep @jiabeiwangTJU I have the same problem as you. Did you solve it?

[wzr0108]

I have the same problem as you. Did you solve it?

[alpinechipmunk]

Keep focusing on this problem

[weiyx16]

yes ,i also error like this: Swin-Transformer-Object-Detection/checkpoints/swin_small_patch4_window7_224.pth 2021-05-31 23:22:46,178 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: norm.weight, norm.bias, head.weight, head.bias, layers.0.blocks.1.attn_mask, layers.1.blocks.1.attn_mask, layers.2.blocks.1.attn_mask, layers.2.blocks.3.attn_mask, layers.2.blocks.5.attn_mask, layers.2.blocks.7.attn_mask, layers.2.blocks.9.attn_mask, layers.2.blocks.11.attn_mask, layers.2.blocks.13.attn_mask, layers.2.blocks.15.attn_mask, layers.2.blocks.17.attn_mask

missing keys in source state_dict: norm0.weight, norm0.bias, norm1.weight, norm1.bias, norm2.weight, norm2.bias, norm3.weight, norm3.bias

loading annotations into memory...

It's ok, since we need to re-create three normalization functions for FPN, and the attn_mask is not needed.