Transformer based SAR image despeckling

Related tags

Deep Learning sar_transformer
Overview

Transformer based SAR image despeckling

Using the code:

The code is stable while using Python 3.6.13, CUDA >=10.1

  • Clone this repository:
git clone https://github.com/malshaV/sar_transformer
cd sar_transformer

To install all the dependencies using conda:

conda env create -f environment.yml
conda activate sar

If you prefer pip, install following versions:

timm==0.3.2
mmcv-full==1.2.7
torch==1.7.1
torchvision==0.8.2
opencv-python==4.5.1.48

Creating synthetic data:

This network was trained synthetic SAR images generated using BSD500. To create the synthetic data use create_synthetic_data.py file.

To train the network:

python train.py --batch_size 1 --epoch 400 --modelname "TransSARV2" --learning_rate 0.0002 --train_dataset "path_to_training_data" --val_dataset "path_to _validation_data" --direc "path_to_save_results" --crop 256

To test the network:

python test.py --loadmodel "./pretrained_models/model.pth" --save_path "./test_images/" --model "TransSARV2"
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Comments
  • Error in test.py

    Error in test.py

    When using test.py I get an error:

    model = TransSARV2()
---->model.load_state_dict(torch.load("./pretrained_models/model.pth"))
      model.eval()

[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in load_state_dict(self, state_dict, strict)
   1603         if len(error_msgs) > 0:
   1604             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
-> 1605                                self.__class__.__name__, "\n\t".join(error_msgs)))
   1606         return _IncompatibleKeys(missing_keys, unexpected_keys)
   1607 

RuntimeError: Error(s) in loading state_dict for TransSARV2:
	Missing key(s) in state_dict: "Tenc.patch_embed1.proj.weight", "Tenc.patch_embed1.proj.bias", "Tenc.patch_embed1.norm.weight", "Tenc.patch_embed1.norm.bias", "Tenc.patch_embed2.proj.weight", "Tenc.patch_embed2.proj.bias", "Tenc.patch_embed2.norm.weight", "Tenc.patch_embed2.norm.bias", "Tenc.patch_embed3.proj.weight", "Tenc.patch_embed3.proj.bias", "Tenc.patch_embed3.norm.weight", "Tenc.patch_embed3.norm.bias", "Tenc.patch_embed4.proj.weight", "Tenc.patch_embed4.proj.bias", "Tenc.patch_embed4.norm.weight", "Tenc.patch_embed4.norm.bias", "Tenc.patch_embed5.proj.weight", "Tenc.patch_embed5.proj.bias", "Tenc.patch_embed5.norm.weight", "Tenc.patch_embed5.norm.bias", "Tenc.block1.0.norm1.weight", "Tenc.block1.0.norm1.bias", "Tenc.block1.0.attn.q.weight", "Tenc.block1.0.attn.kv.weight", "Tenc.block1.0.attn.proj.weight", "Tenc.block1.0.attn.proj.bias", "Tenc.block1.0.attn.sr.weight", "Tenc.block1.0.attn.sr.bias", "Tenc.block1.0.attn.norm.weight", "Tenc.block1.0.attn.norm.bias", "Tenc.block1.0.norm2.weight", "Tenc.block1.0.norm2.bias", "Tenc.block1.0.mlp.fc1.weight", "Tenc.block1.0.mlp.fc1.bias", "Tenc.block1.0.mlp.dwconv.dwconv.weight", "Tenc.block1.0.mlp.dwconv.dwconv.bias", "Tenc.block1.0.mlp.fc2.weight", "Tenc.block1.0.mlp.fc2.bias", "Tenc.block1.1.norm1.weight", "Tenc.block1.1.norm1.bias", "Tenc.block1.1.attn.q.weight", "Tenc.block1.1.attn.kv.weight", "Tenc.block1.1.attn.proj.weight", "Tenc.block1.1.attn.proj.bias", "Tenc.block1.1.attn.sr.weight", "Tenc.block1.1.attn.sr.bias"...
	Unexpected key(s) in state_dict: "module.Tenc.patch_embed1.proj.weight", "module.Tenc.patch_embed1.proj.bias", "module.Tenc.patch_embed1.norm.weight", "module.Tenc.patch_embed1.norm.bias", "module.Tenc.patch_embed2.proj.weight", "module.Tenc.patch_embed2.proj.bias", "module.Tenc.patch_embed2.norm.weight", "module.Tenc.patch_embed2.norm.bias", "module.Tenc.patch_embed3.proj.weight", "module.Tenc.patch_embed3.proj.bias", "module.Tenc.patch_embed3.norm.weight", "module.Tenc.patch_embed3.norm.bias", "module.Tenc.patch_embed4.proj.weight", "module.Tenc.patch_embed4.proj.bias", "module.Tenc.patch_embed4.norm.weight", "module.Tenc.patch_embed4.norm.bias", "module.Tenc.patch_embed5.proj.weight", "module.Tenc.patch_embed5.proj.bias", "module.Tenc.patch_embed5.norm.weight", "module.Tenc.patch_embed5.norm.bias", "module.Tenc.block1.0.norm1.weight", "module.Tenc.block1.0.norm1.bias", "module.Tenc.block1.0.attn.q.weight", "module.Tenc.block1.0.attn.kv.weight", "module.Tenc.block1.0.attn.proj.weight", "module.Tenc.block1.0.attn.proj.bias", "module.Tenc.block1.0.attn.sr.weight", "module.Tenc.block1.0.attn.sr.bias", "module.Tenc.block1.0.attn.norm.weight", "module.Tenc.block1.0.attn.norm.bias", "module.Tenc.block1.0.norm2.weight", "module.Tenc.block1.0.norm2.bias", "module.Tenc.block1.0.mlp.fc1.weight", "module.Tenc.block1.0.mlp.fc1.bias", "module.Tenc.block1.0.mlp.dwconv.dwconv.weight", "module.Tenc.block1.0.mlp.dwconv.dwconv.bias", "module.Tenc.block1.0.mlp.fc2.weight", "module.Tenc.bl...

    So the weights file is either corrupted, or has incorrect weights.

    opened by azhanmohammed 2
  • About train and test on real SAR image

    About train and test on real SAR image

    Thank you for your nice work!

    I am a bit confused about the training setup on real SAR data. You mentioned real SAR image data does not have clean ground truth data. In this case, did you train your model on synthetically generated speckled images, and then directly test your model on real SAR image data? Thank you very much!

    opened by longbai1006 2
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