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load mvit pretrained model,some error occurred

See original GitHub issue

Running the code below:

from pytorchvideo.models.hub import mvit_base_16x4
model = mvit_base_16x4(pretrained=True)

the output is:

RuntimeError: Error(s) in loading state_dict for MultiscaleVisionTransformers:
	Missing key(s) in state_dict: "blocks.0.attn._attention_pool_k.pool.weight", "blocks.0.attn._attention_pool_k.norm.weight", "blocks.0.attn._attention_pool_k.norm.bias", "blocks.0.attn._attention_pool_v.pool.weight", "blocks.0.attn._attention_pool_v.norm.weight", "blocks.0.attn._attention_pool_v.norm.bias", "blocks.1.attn._attention_pool_q.pool.weight", "blocks.1.attn._attention_pool_q.norm.weight", "blocks.1.attn._attention_pool_q.norm.bias", "blocks.1.attn._attention_pool_k.pool.weight", "blocks.1.attn._attention_pool_k.norm.weight", "blocks.1.attn._attention_pool_k.norm.bias", "blocks.1.attn._attention_pool_v.pool.weight", "blocks.1.attn._attention_pool_v.norm.weight", "blocks.1.attn._attention_pool_v.norm.bias", "blocks.2.attn._attention_pool_k.pool.weight", "blocks.2.attn._attention_pool_k.norm.weight", "blocks.2.attn._attention_pool_k.norm.bias", "blocks.2.attn._attention_pool_v.pool.weight", "blocks.2.attn._attention_pool_v.norm.weight", "blocks.2.attn._attention_pool_v.norm.bias", "blocks.3.attn._attention_pool_q.pool.weight", "blocks.3.attn._attention_pool_q.norm.weight", "blocks.3.attn._attention_pool_q.norm.bias", "blocks.3.attn._attention_pool_k.pool.weight", "blocks.3.attn._attention_pool_k.norm.weight", "blocks.3.attn._attention_pool_k.norm.bias", "blocks.3.attn._attention_pool_v.pool.weight", "blocks.3.attn._attention_pool_v.norm.weight", "blocks.3.attn._attention_pool_v.norm.bias", "blocks.4.attn._attention_pool_k.pool.weight", "blocks.4.attn._attention_pool_k.norm.weight", "blocks.4.attn._attention_pool_k.norm.bias", "blocks.4.attn._attention_pool_v.pool.weight", "blocks.4.attn._attention_pool_v.norm.weight", "blocks.4.attn._attention_pool_v.norm.bias", "blocks.5.attn._attention_pool_k.pool.weight", "blocks.5.attn._attention_pool_k.norm.weight", "blocks.5.attn._attention_pool_k.norm.bias", "blocks.5.attn._attention_pool_v.pool.weight", "blocks.5.attn._attention_pool_v.norm.weight", "blocks.5.attn._attention_pool_v.norm.bias", "blocks.6.attn._attention_pool_k.pool.weight", "blocks.6.attn._attention_pool_k.norm.weight", "blocks.6.attn._attention_pool_k.norm.bias", "blocks.6.attn._attention_pool_v.pool.weight", "blocks.6.attn._attention_pool_v.norm.weight", "blocks.6.attn._attention_pool_v.norm.bias", "blocks.7.attn._attention_pool_k.pool.weight", "blocks.7.attn._attention_pool_k.norm.weight", "blocks.7.attn._attention_pool_k.norm.bias", "blocks.7.attn._attention_pool_v.pool.weight", "blocks.7.attn._attention_pool_v.norm.weight", "blocks.7.attn._attention_pool_v.norm.bias", "blocks.8.attn._attention_pool_k.pool.weight", "blocks.8.attn._attention_pool_k.norm.weight", "blocks.8.attn._attention_pool_k.norm.bias", "blocks.8.attn._attention_pool_v.pool.weight", "blocks.8.attn._attention_pool_v.norm.weight", "blocks.8.attn._attention_pool_v.norm.bias", "blocks.9.attn._attention_pool_k.pool.weight", "blocks.9.attn._attention_pool_k.norm.weight", "blocks.9.attn._attention_pool_k.norm.bias", "blocks.9.attn._attention_pool_v.pool.weight", "blocks.9.attn._attention_pool_v.norm.weight", "blocks.9.attn._attention_pool_v.norm.bias", "blocks.10.attn._attention_pool_k.pool.weight", "blocks.10.attn._attention_pool_k.norm.weight", "blocks.10.attn._attention_pool_k.norm.bias", "blocks.10.attn._attention_pool_v.pool.weight", "blocks.10.attn._attention_pool_v.norm.weight", "blocks.10.attn._attention_pool_v.norm.bias", "blocks.11.attn._attention_pool_k.pool.weight", "blocks.11.attn._attention_pool_k.norm.weight", "blocks.11.attn._attention_pool_k.norm.bias", "blocks.11.attn._attention_pool_v.pool.weight", "blocks.11.attn._attention_pool_v.norm.weight", "blocks.11.attn._attention_pool_v.norm.bias", "blocks.12.attn._attention_pool_k.pool.weight", "blocks.12.attn._attention_pool_k.norm.weight", "blocks.12.attn._attention_pool_k.norm.bias", "blocks.12.attn._attention_pool_v.pool.weight", "blocks.12.attn._attention_pool_v.norm.weight", "blocks.12.attn._attention_pool_v.norm.bias", "blocks.13.attn._attention_pool_k.pool.weight", "blocks.13.attn._attention_pool_k.norm.weight", "blocks.13.attn._attention_pool_k.norm.bias", "blocks.13.attn._attention_pool_v.pool.weight", "blocks.13.attn._attention_pool_v.norm.weight", "blocks.13.attn._attention_pool_v.norm.bias", "blocks.14.attn._attention_pool_q.pool.weight", "blocks.14.attn._attention_pool_q.norm.weight", "blocks.14.attn._attention_pool_q.norm.bias", "blocks.14.attn._attention_pool_k.pool.weight", "blocks.14.attn._attention_pool_k.norm.weight", "blocks.14.attn._attention_pool_k.norm.bias", "blocks.14.attn._attention_pool_v.pool.weight", "blocks.14.attn._attention_pool_v.norm.weight", "blocks.14.attn._attention_pool_v.norm.bias", "blocks.15.attn._attention_pool_k.pool.weight", "blocks.15.attn._attention_pool_k.norm.weight", "blocks.15.attn._attention_pool_k.norm.bias", "blocks.15.attn._attention_pool_v.pool.weight", "blocks.15.attn._attention_pool_v.norm.weight", "blocks.15.attn._attention_pool_v.norm.bias".

Issue Analytics

State:
Created a year ago
Comments:6 (3 by maintainers)

Top GitHub Comments

1reaction

datumboxcommented, Jun 21, 2022

@aiot-tech I just noticed that you are not loading the weights properly. The issue is the given checkpoint contains not only the model weights but also other training specific info. Try doing:

model.load_state_dict(torch.load(path)["model_state"], strict=False)

0reactions

aiot-techcommented, Jun 21, 2022

Brilliant!

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