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SwinTransformer / Swin-Transformer-Object-Detection

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
https://arxiv.org/abs/2103.14030
Apache License 2.0
1.79k stars 378 forks source link

missing keys in source state_dict #22

Closed jiabeiwangTJU closed 2 years ago

jiabeiwangTJU commented 3 years ago

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 commented 3 years ago

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 commented 3 years ago

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

wzr0108 commented 2 years ago

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

alpinechipmunk commented 2 years ago

Keep focusing on this problem

weiyx16 commented 2 years ago

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.