Pyramid Pooling Transformer for Scene Understanding

Related tags

Deep LearningP2T
Overview

Pyramid Pooling Transformer for Scene Understanding

Requirements:

  • torch 1.6+
  • torchvision 0.7.0
  • timm==0.3.2
  • Validated on torch 1.6.0, torchvision 0.7.0

Models Pretrained on ImageNet1K

Variants Input Size [email protected] [email protected] #Params (M) Pretrained Models
P2T-Tiny 224 x 224 78.1 94.1 11.1 Google Drive
P2T-Small 224 x 224 82.1 95.9 23.0 Google Drive
P2T-Base 224 x 224 83.0 96.2 36.2 Google Drive

Pretrained Models for Downstream tasks

To be updated.

Something Else

Note: we have prepared a stronger version of P2T. Since P2T is still in peer review, we will release the stronger P2T after the acceptance.

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Comments
  • How to load ImageNet1K pretrained weight to semantic segmentation model?

    How to load ImageNet1K pretrained weight to semantic segmentation model?

    Hello, thanks for open source!

    I use mmseg, and load weight from image classification result, it warns: WARNING - The model and loaded state dict do not match exactly missing keys in source state_dict: backbone.head.weight, backbone.head.bias unexpected key in source state_dict: cls_token, ln1.bias, ln1.weight, layers.0.ln1.bias, layers.0.ln1.weight, layers.0.ln2.bias, layers.0.ln2.weight, layers.0.ffn.layers.0.0.bias, layers.0.ffn.layers.0.0.weight, layers.0.ffn.layers.1.bias, layers.0.ffn.layers.1.weight, layers.0.attn.attn.out_proj.bias, layers.0.attn.attn.out_proj.weight, layers.0.attn.attn.in_proj_bias, layers.0.attn.attn.in_proj_weight, layers.1.ln1.bias, layers.1.ln1.weight, layers.1.ln2.bias, layers.1.ln2.weight, layers.1.ffn.layers.0.0.bias, layers.1.ffn.layers.0.0.weight, layers.1.ffn.layers.1.bias, layers.1.ffn.layers.1.weight, layers.1.attn.attn.out_proj.bias, layers.1.attn.attn.out_proj.weight ...... And the experimental results are terrible as the experiments initialize weight with random.

    So I load weight from ADE20K result, it work and warns: WARNING - The model and loaded state dict do not match exactly missing keys in source state_dict: backbone.head.weight, backbone.head.bias And the result is similar to the result you offer.

    Which weight should I load? ImageNet1K or ADE20K? Or should I modify the keys of weight in ImageNet1K to adapt the key in segmentation?

    opened by asd123pwj 8
  • Questions about your ablation studies

    Questions about your ablation studies

    Hello,

    I have some questions about your ablation studies of pyramid pooling. Could you detail about your baseline version in Table 9? First, you say that you replace P-MHSA with an MHSA with a single pooling operation, what is the detail about single pooling operation? Ex: Pooling Ratios? Second, do you compared your method with original MHSA?

    opened by pp00704831 3
  • P2T replaces PVT trunk bug

    P2T replaces PVT trunk bug

    When I replaced the PVT trunk with P2T in my code, I encountered an error ：
    RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [16, 512, 3, 3]], which is output 0 of AdaptiveAvgPool2DBackward, is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

    opened by liu-tianxiang 2
  • P2T on ImageNet-22K?

    P2T on ImageNet-22K?

    Hi @yuhuan-wu , thank you for share the code of this excellent work! Have you trained P2T on ImageNet-22K dataset or any further plan to do it? If so, could you please share the pretrained model on ImageNet-22k?

    Thank you.

    opened by fyaft2012 1
Owner
Yu-Huan Wu
Ph.D. student at Nankai University
Yu-Huan Wu
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