ipclab / gatedconvolution_pytorch

A modified reimplemented in pytorch of inpainting model in Free-Form Image Inpainting with Gated Convolution [http://jiahuiyu.com/deepfill2/]

GatedConvolution_pytorch

A modified reimplemented in pytorch of inpainting model in Free-Form Image Inpainting with Gated Convolution [http://jiahuiyu.com/deepfill2/] This repo is transfered from the https://github.com/avalonstrel/GatedConvolution and https://github.com/JiahuiYu/generative_inpainting.

It is a model for image inpainting task. I implement the network structure and gated convolution in Free-Form Image Inpainting with Gated Convolution, but a little difference about the original structure described in Free-Form Image Inpainting with Gated Convolution.

• In refine network, I do not employ the contextual attention but a self-attention layer instead.
• I add batch norm to each layer.

Some results

BenchMark data and Mask data can be found in Google Drive

How to test images by pre-trained model?

I provide a pre-trained Baidu, Google model on Places2 256x256 dataset, (but unfortunately only the coarse network can be loaded since I change the network structure after the pre-train process, in fact the coarse network also work).

Run bash scripts/test_inpaint.sh

You should provide a file containing file paths you want to test following the form of

test1.png

test2.png

... ...

Change the parameters in config/test_places2_sagan.yml About the image

places2:

[

  'flist_file_for_train',
  'flist_file_for_test'

 ]

About the mask

val:

[

  'mask_flist_file_for_train',
  
  'mask_flist_file_for_test'
  
]

The mask file should be a pkl file containing a numpy.array.

The MODEL_RESTORE should be set to the path of the pre-trained model. After successfully running, you can find the results in result_logs/MODEL_RESTORE

How to train your own model?

To train your own model with some other dataset you can

Run bash scripts/run_inpaint_sa.sh

By providing the

places2:

[

  'flist_file_for_train',
  'flist_file_for_test'

 ]

About the mask

val:

[

  'mask_flist_file_for_train',
  
  'mask_flist_file_for_test'
  
]

And in training you can use random free-form mask or random rectangular mask. I use random free-form mask. If you want use random rectangular mask you need to change the process in train_sagan.py(line 163) and set MASK_TYPES: ['random_bbox'].

Some detials about the training parameters is easy to understand as shown in config file.

Tensorboard

Run tensorboard --logdir model_logs --port 6006 to view training progress.

Some tips about mask generation?

We provide two random mask generation function.

• random free form masks

  The parameters about this function are

  RANDOM_FF_SETTING:

  img_shape: [256,256]
  
  mv: 5
  
  ma: 4.0
  
  ml: 40
  
  mbw: 10
  

  Following the meaning in http://jiahuiyu.com/deepfill2/.

• random rectangular masks

  RANDOM_BBOX_SHAPE: [32, 32]

  RANDOM_BBOX_MARGIN: [64, 64]

  means the shape of the random bbox and the margin between the boarder. (The number of rectangulars can be set in inpaint_dataset.py random_bbox_number=5)

Acknowledgments

My project acknowledge the official code DeepFillv1 and SNGAN. Especially, thanks for the authors of this amazing algorithm.