stitchfix / fauxtograph Goto Github PK

I don't know where this error is coming from. I have a tar.gz file here of a few images that I was hoping to train with your program: https://www.dropbox.com/s/zfekwp1cspfj9r2/images.tar.gz?dl=0

$ fauxtograph train ./images ./models/model_name
Loading Image Files...
Image Files Loaded!

 Training for 200 epochs.

epoch: 1
  0%|                                          | 0/1 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/Users/jonathan/anaconda/bin/fauxtograph", line 11, in <module>
    sys.exit(fauxtograph())
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/click/core.py", line 700, in __call__
    return self.main(*args, **kwargs)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/click/core.py", line 680, in main
    rv = self.invoke(ctx)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/click/core.py", line 1027, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/click/core.py", line 873, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/click/core.py", line 508, in invoke
    return callback(*args, **kwargs)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/fauxtograph/fauxto.py", line 116, in train
    iae.fit(batch_size=batch, n_epochs=epoch)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/fauxtograph/fauxtograph.py", line 269, in fit
    r_loss, kl_div, out = self._forward(x_batch)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/fauxtograph/fauxtograph.py", line 176, in _forward
    encoded = self._encode(batch)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/fauxtograph/fauxtograph.py", line 162, in _encode
    batch = F.relu(getattr(self.model, 'encode_%i' % i)(batch))
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/chainer/function.py", line 164, in __call__
    self._check_data_type_forward(in_data)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/chainer/function.py", line 191, in _check_data_type_forward
    self.check_type_forward(in_type)
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/chainer/functions/linear.py", line 105, in check_type_forward
    type_check.Variable(self.W.shape[1], 'W.shape[1]')),
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/chainer/utils/type_check.py", line 457, in expect
    expr.expect()
  File "/Users/jonathan/anaconda/lib/python2.7/site-packages/chainer/utils/type_check.py", line 428, in expect
    '{0} {1} {2}'.format(left, self.inv, right))
chainer.utils.type_check.InvalidType: Expect: prod(in_types[0].shape[1:]) == W.shape[1]
Actual: 30000 != 22500```

I'm on ubuntu
pip install fauxtograph appears to work fine, however
fauxtograph download images/
says fauxtograph is undefined?
I tried git cloning the code, then cding in and executing this
python fauxtograph.py download ./images
it pauses, but does nothing?

I´m testing out the --shape param, attempting to train/generate higher resolution images.
Seems like it does not like square formats, unfortunately resulting in black images.
Getting a bunch of these errors to: "RuntimeWarning: overflow encountered in exp self.y = 1 / (1 + numpy.exp(-x[0]))"

Any tips here?

Looks like the BeautifulSoup dependency does not work on py3. Is there a reason for this specific BS version as opposed to bs4?

pip install fauxtograph
Collecting fauxtograph
  Downloading fauxtograph-1.0.3.tar.gz
Collecting chainer==1.6.0 (from fauxtograph)
  Downloading chainer-1.6.0.tar.gz (904kB)
    100% |████████████████████████████████| 905kB 273kB/s
Requirement already satisfied (use --upgrade to upgrade): pillow in ./anaconda/envs/py34/lib/python3.4/site-packages (from fauxtograph)
Collecting joblib (from fauxtograph)
  Downloading joblib-0.9.4-py2.py3-none-any.whl (112kB)
    100% |████████████████████████████████| 114kB 1.7MB/s
Collecting tqdm (from fauxtograph)
  Downloading tqdm-3.8.0-py2.py3-none-any.whl
Collecting BeautifulSoup (from fauxtograph)
  Downloading BeautifulSoup-3.2.1.tar.gz
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 20, in <module>
      File "/private/var/folders/b1/jbv4n2f56mz8m3hxkdtc51cr0000gn/T/pip-build-9v2hhigl/BeautifulSoup/setup.py", line 22
        print "Unit tests have failed!"
                                      ^
    SyntaxError: Missing parentheses in call to 'print'

Hi

Can you possibly add the script to train/generate face images with VAE/GAN?
As you suggested, the hyper-parameters seem to be critical. Thanks!

VAE and VAEGAN code is currently using mean squared error as the reconstruction loss function. In most papers / implementations, I'm more used to seeing binary cross entropy with numbers reported in nats.

Curious what we think would be best here. I did do a quick look for this in the chainer docs but didn't see binary cross entropy listed as one of the built in loss functions.

Hi,
I have a question confused about the latent variable z

According to the code in class VAEGAN,

    def _forward(self, batch, test=False):

        encoded, means, ln_vars = self._encode(batch, test=test)
        rec = self._decode(encoded, test=test)
        normer = reduce(lambda x, y: x*y, means.data.shape)
        kl_loss = F.gaussian_kl_divergence(means, ln_vars)/normer

        samp_p = np.random.standard_normal(means.data.shape).astype('float32')
        z_p = chainer.Variable(samp_p)

It means that this z is first sampled according to VAE from:

encoded, means, ln_vars = self._encode(batch, test=test)

    def encode(self, data, test=False):
        x = self.enc(data, test=test)
        mean, ln_var = F.split_axis(x, 2, 1)
        samp = np.random.standard_normal(mean.data.shape).astype('float32')
        samp = Variable(samp)
        if self.flag_gpu:
            samp.to_gpu()
        z = samp * F.exp(0.5*ln_var) + mean

which means that the sampler z is s.t:

then, the sampled z is input to the decoder network to generate images, according to

rec = self._decode(encoded, test=test)

then, another z is sampled by:

        samp_p = np.random.standard_normal(means.data.shape).astype('float32')
        z_p = chainer.Variable(samp_p)

which means it is s.t.

and the generated images is created by:

rec_p = self._decode(z_p)

My question is since the two types of z are sampled from different distributions,
why they are input into the same decoder network?
And which output are the generated images from decoder, since they both created by sampling z according to a distribution ?

This is just my opinion.
Please feel free if I make mistakes.
Thank you.

Followed steps outline in the blog. Got an error during training:

fauxtograph train images/ models/model_out
Loading Image Files...
Image Files Loaded!

Training for 200 epochs.

epoch: 1
0%| | 0/12 [00:00<?, ?it/s]Traceback (most recent call last):
File "/usr/local/bin/fauxtograph", line 8, in
load_entry_point('fauxtograph==0.1.8', 'console_scripts', 'fauxtograph')()
File "/Library/Python/2.7/site-packages/click/core.py", line 716, in call
return self.main(_args, *_kwargs)
File "/Library/Python/2.7/site-packages/click/core.py", line 696, in main
rv = self.invoke(ctx)
File "/Library/Python/2.7/site-packages/click/core.py", line 1060, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Library/Python/2.7/site-packages/click/core.py", line 889, in invoke
return ctx.invoke(self.callback, *_ctx.params)
File "/Library/Python/2.7/site-packages/click/core.py", line 534, in invoke
return callback(_args, **kwargs)
File "/Library/Python/2.7/site-packages/fauxtograph/fauxto.py", line 116, in train
iae.fit(batch_size=batch, n_epochs=epoch)
File "/Library/Python/2.7/site-packages/fauxtograph/fauxtograph.py", line 271, in fit
loss.backward()
File "/Library/Python/2.7/site-packages/chainer/variable.py", line 171, in backward
self.grad = numpy.ones_like(self.data)
File "/Library/Python/2.7/site-packages/chainer/variable.py", line 110, in grad
% (type(self.data), type(g), error_msg))
TypeError: Type of data and grad mismatch: <type 'numpy.ndarray'> != <type 'numpy.float32'>
This error is occured in two cases. The first case is when the user manually
sets the Variable.grad incorrectly. The second case is when some Function
implementation has a bug.

The call to self.save in line #347 of fauxtograph.py always sends save_meta as False.

It appears that you should increase save_counter only when epoch % save_freq == 0.

The same issue is present in GAN and VAEGAN.

Hi, @jwhitmire @billeisenhauer @bjallen

When I tried the .ipynb on my dataset. I got the following error:

`
nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
nvcc fatal : Could not open output file '/tmp/tmpxft_00004d81_00000000'

Traceback (most recent call last):
File "see.py", line 17, in
vg.fit(x_all, save_freq=2, pic_freq=30, n_epochs=4, model_path = m_path, img_path=im_path) #, mirroring=True)#, flag_gpu=False)
File "/usr/local/lib/python2.7/dist-packages/fauxtograph/fauxtograph.py", line 1342, in fit
kl_loss, dif_l, disc_rec, disc_batch, disc_samp = self._forward(x_batch)
File "/usr/local/lib/python2.7/dist-packages/fauxtograph/fauxtograph.py", line 1099, in _forward
encoded, means, ln_vars = self._encode(batch, test=test)
File "/usr/local/lib/python2.7/dist-packages/fauxtograph/fauxtograph.py", line 1082, in _encode
x = self.enc(data, test=test)
File "/usr/local/lib/python2.7/dist-packages/fauxtograph/vaegan.py", line 127, in call
batch = self.bn1(batch, test=test)
File "/usr/local/lib/python2.7/dist-packages/chainer/links/normalization/batch_normalization.py", line 93, in call
ret = func(x, self.gamma, self.beta)
File "/usr/local/lib/python2.7/dist-packages/chainer/function.py", line 105, in call
outputs = self.forward(in_data)
File "/usr/local/lib/python2.7/dist-packages/chainer/functions/normalization/batch_normalization.py", line 53, in forward
mean = x.mean(axis=axis)
File "cupy/core/core.pyx", line 679, in cupy.core.core.ndarray.mean (cupy/core/core.cpp:13034)
File "cupy/core/core.pyx", line 687, in cupy.core.core.ndarray.mean (cupy/core/core.cpp:12899)
File "cupy/core/reduction.pxi", line 252, in cupy.core.core.simple_reduction_function.call (cupy/core/core.cpp:44135)
File "cupy/util.pyx", line 36, in cupy.util.memoize.decorator.ret (cupy/util.cpp:1194)
File "cupy/core/reduction.pxi", line 181, in cupy.core.core._get_simple_reduction_function (cupy/core/core.cpp:42698)
File "cupy/core/reduction.pxi", line 108, in cupy.core.core._get_simple_reduction_kernel (cupy/core/core.cpp:40479)
File "cupy/core/carray.pxi", line 87, in cupy.core.core.compile_with_cache (cupy/core/core.cpp:26123)
File "/usr/local/lib/python2.7/dist-packages/cupy/cuda/compiler.py", line 112, in compile_with_cache
base = _empty_file_preprocess_cache[env] = preprocess('', options)
File "/usr/local/lib/python2.7/dist-packages/cupy/cuda/compiler.py", line 75, in preprocess
pp_src = _run_nvcc(cmd, root_dir)
File "/usr/local/lib/python2.7/dist-packages/cupy/cuda/compiler.py", line 37, in _run_nvcc
return subprocess.check_output(cmd, cwd=cwd)
File "/usr/lib/python2.7/subprocess.py", line 573, in check_output
raise CalledProcessError(retcode, cmd, output=output)
subprocess.CalledProcessError: Command '['nvcc', '--preprocess', '/var/tmp/tmphEoIKW/kern.cu']' returned non-zero exit status 1

`

Please help..

While training on my own images, I'm getting this error,

chainer.utils.type_check.InvalidType:
Invalid operation is performed in: LinearFunction (Forward)

Expect: prod(in_types[0].shape[1:]) == in_types[1].shape[1]
Actual: 259656 != 27648

Saw your Deep Style presentation and the generated images based on trained model on Hubble dataset. I was wondering what parameter set you used to train over Hubble dataset. I used the default parameter set and only got black images generated at the end! Also played with image_multiplier argument and got a bunch of similar black images with a gray (lighter) circle in the center.
Any tips here?

Running on Mac for Hubble dataset:
$ fauxtograph train ./images ./models/model_name
Traceback (most recent call last):
File "/usr/local/bin/fauxtograph", line 9, in
load_entry_point('fauxtograph==1.0.2', 'console_scripts', 'fauxtograph')()
File "/Library/Python/2.7/site-packages/click/core.py", line 716, in call
return self.main(_args, *_kwargs)
File "/Library/Python/2.7/site-packages/click/core.py", line 696, in main
rv = self.invoke(ctx)
File "/Library/Python/2.7/site-packages/click/core.py", line 1060, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Library/Python/2.7/site-packages/click/core.py", line 889, in invoke
return ctx.invoke(self.callback, *_ctx.params)
File "/Library/Python/2.7/site-packages/click/core.py", line 534, in invoke
return callback(_args, **kwargs)
File "/Library/Python/2.7/site-packages/fauxtograph/fauxto.py", line 122, in train
x_all = vae.load_images(file_paths)
File "/Library/Python/2.7/site-packages/fauxtograph/fauxtograph.py", line 239, in load_images
x_all = np.array([read(fname) for fname in tqdm.tqdm(filepaths)])
ValueError: could not broadcast input array from shape (300,300,3) into shape (300)