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homointerpgan's Issues

Interpolation between dogs and cats

Hello! I am very interested in your work, especially the interpolation between dogs and cats. I saw this result at supplementary material. I want to interpolate without attribute, because there is no label or attribute in my dataset. So could you please tell me how to do?

weird results

I tried
python run.py attribute_manipulation -mp checkpoints/CelebA -sp checkpoints/CelebA/test/Smiling --filter_target_attr NOTSmiling -s 1 --branch_idx 0 --n_ref 5 -bs 8 --test_folder examples/aligned

and am getting weird results that lack identity preservation which is shown in the paper
Screen Shot 2019-06-11 at 3 42 51 PM

vs

Screen Shot 2019-06-11 at 3 45 12 PM

Why we need "Rigorous Training" for attribute classifier ?

First of all, congratulation for this outstanding work, but I still have two small questions:

  • Why we need "Rigorous Training" for attribute classifier ? Can you give a more exactly example?
  • Can I consider T^k in interpolator as a "fliter" used to get rid of variables which are not related to correspond attribute from "feature code" F ?

Thanks!

Is it possible to generate an image without reference image ?

Hi,

I would like to thank you first for your amazing work !

As the title said it, I would like to know if is there any way to generate an image without reference image at all ?
For example, generate a Woman face with only a Man face (without a Man face as reference).

test_selected_curve & attribute_manipulation need both as well a reference image to generate the interpolation (correct me if I''m wrong)

Thank you in advance !

Questions about the results in supplementary material

Thanks for sharing the nice work!

I have a few questions about the results of testing RaFD model on wild images:

  1. As the images in RaFD dataset are well aligned, I am wondering whether you take any alignment steps there? Or just simply crop the face -> transform the expression -> place it back to the original image?
  2. May I know whether you use all the images in RaFD to train the model or only the images under frontal camera (90 degree)?

Looking forward to your reply. Thanks.

TypeError: '>' not supported between instances of 'str' and 'int'

I encountered this error when I tried to execute the training script. The line of code that caused the exception is seen here url. I am looking forward to your help.

Below is the detailed error message:

/home/shhs/anaconda3/envs/torch_1_0_py3_6/bin/python /home.bak/shhs/soft/pycharm-2019.1.1/helpers/pydev/pydevd.py --multiproc --qt-support=auto --client 127.0.0.1 --port 46783 --file /media/shhs/Peterou2/user/code/HomoInterpGAN/run.py attribute_manipulation -bs 8 -gpu 0
pydev debugger: process 6843 is connecting

Connected to pydev debugger (build 191.6605.12)
loading default VGG

  • Total Images: 162770
    Traceback (most recent call last):
    File "/home.bak/shhs/soft/pycharm-2019.1.1/helpers/pydev/pydevd.py", line 1741, in
    main()
    File "/home.bak/shhs/soft/pycharm-2019.1.1/helpers/pydev/pydevd.py", line 1735, in main
    globals = debugger.run(setup['file'], None, None, is_module)
    File "/home.bak/shhs/soft/pycharm-2019.1.1/helpers/pydev/pydevd.py", line 1135, in run
    pydev_imports.execfile(file, globals, locals) # execute the script
    File "/home.bak/shhs/soft/pycharm-2019.1.1/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
    File "/media/shhs/Peterou2/user/code/HomoInterpGAN/run.py", line 205, in
    engine.run()
    File "/media/shhs/Peterou2/user/code/HomoInterpGAN/run.py", line 200, in run
    exec ('self.{}()'.format(self.args.command))
    File "", line 1, in
    File "/media/shhs/Peterou2/user/code/HomoInterpGAN/run.py", line 161, in attribute_manipulation
    _, test_dataset = self.load_dataset()
    File "/media/shhs/Peterou2/user/code/HomoInterpGAN/run.py", line 106, in load_dataset
    csv_path='info/celeba-with-orientation.csv')
    File "/media/shhs/Peterou2/user/code/HomoInterpGAN/data/attributeDataset.py", line 220, in init
    f3 = self.frame.iloc[:, 1:] > 0
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/ops.py", line 2108, in f
    res = self._combine_const(other, func)
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/frame.py", line 5120, in _combine_const
    return ops.dispatch_to_series(self, other, func)
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/ops.py", line 1157, in dispatch_to_series
    new_data = expressions.evaluate(column_op, str_rep, left, right)
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 208, in evaluate
    return _evaluate(op, op_str, a, b, **eval_kwargs)
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 68, in _evaluate_standard
    return op(a, b)
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/ops.py", line 1128, in column_op
    for i in range(len(a.columns))}
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/ops.py", line 1128, in
    for i in range(len(a.columns))}
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/ops.py", line 1766, in wrapper
    res = na_op(values, other)
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/ops.py", line 1625, in na_op
    result = _comp_method_OBJECT_ARRAY(op, x, y)
    File "/home/shhs/anaconda3/envs/torch_1_0_py3_6/lib/python3.6/site-packages/pandas/core/ops.py", line 1603, in _comp_method_OBJECT_ARRAY
    result = libops.scalar_compare(x, y, op)
    File "pandas/_libs/ops.pyx", line 97, in pandas._libs.ops.scalar_compare
    TypeError: '>' not supported between instances of 'str' and 'int'

TypeError: 'NoneType' object has no attribute 'getitem'

loading default VGG
/mnt/backup/project/ycchen/datasets/face/images/celeba_aligned/185954.jpg
Traceback (most recent call last):
File "run.py", line 192, in
engine.run()
File "run.py", line 187, in run
exec ('self.{}()'.format(self.args.command))
File "", line 1, in
File "run.py", line 156, in attribute_manipulation
img_out = [tmp for tmp in ref_loader]
File "/usr/local/lib/python2.7/dist-packages/torch/utils/data/dataloader.py", line 615, in next
batch = self.collate_fn([self.dataset[i] for i in indices])
File "/content/HomoInterpGAN/data/attributeDataset.py", line 163, in getitem
img = util.readRGB(self.files[index]).astype(np.float32)
File "/content/HomoInterpGAN/util/util.py", line 144, in readRGB
return img[:, :, [2, 1, 0]]
TypeError: 'NoneType' object has no attribute 'getitem'

at
python run.py attribute_manipulation -mp checkpoints/CelebA -sp checkpoints/CelebA/test/Smiling --filter_target_attr Smiling -s 1 --branch_idx 0 --n_ref 5 -bs 8 --test_folder examples/aligned

supplementary material

Very fancy idea!
When I was reading the paper, I could not find the supplementary material. Is it available?
Thanks~

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