oskopek / mvae Goto Github PK

Describe the bug
Despite setting the --fixed_curvature=True flag I can see that curvature is being changed during training.

To Reproduce
Steps to reproduce the behavior:

1. Set up your environment like described in README
2. Run experiment with python -m mt.examples.run --dataset="mnist" --model="s10" --fixed_curvature=True
3. Observe comp_000_s10/curvature is being changed

Expected behavior
Curvature should be constant and set to the default value, which is 1.

Desktop (please complete the following information):

• OS: Linux

Thanks for the package!

When running (Mac Hierra): python -m mt.examples.run --dataset="mnist" --model="h2,s2,e2" --fixed_curvature=False

I got an error after 100 epochs: FileNotFoundError: [Errno 2] No such file or directory: './chkpt/vae-mnist-e2,h2,s2-2020-06-14T17:45:14.337416/None.chkpt'

Thanks in advance!

Hi, thank you for open-sourcing the repo! Is there a particular reason why the exponential map of the Euclidean manifold is written as x+v/2 instead of x+v?

Dear authors,

Thanks for the great code! I am wondering do you also have the pre-trained embeddings of those four datasets included in the repository? Because I want to do some visualization for the embeddings. Seems like they should be in mt/data/, but only find four scripts there.

Also when I want to use the mt/examples/run.py to generate the embeddings myself, I find that with the following parameters:
--dataset="mnist" --model="h2,s2,e2" --fixed_curvature=True --epochs=50 --warmup=10 --lookahead=5 --show_embeddings=10
only the embeddings of the last epoch 49 will be recorded in chkpt/ folder, instead of recording embeddings every 10 epochs. I looked into the code a little bit, finding that show_embeddings is only used in _test_epoch, which is only called once in train_stopping. Maybe I miss something here, but seems like it will only save the last embeddings.

Thanks for your help in advance!

Hi, thanks for your paper, and I really enjoy it.
I found the code about calculating the logdet is different from that in the paper.
Specifically, the torch.log(radius) might be redundant in https://github.com/oskopek/mvae/blob/master/mt/mvae/ops/hyperbolics.py#L63,
since log(R) is contained by torch.log(r).
Also, the logdet in sphere might have the same problem.
Could you please help me check the codes? Thanks a lot!

Describe the bug
Running the example from README gives the following error:

Traceback (most recent call last):
  File "/home/maciej/.conda/envs/pt/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/maciej/.conda/envs/pt/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/maciej/Documents/mvae/mt/examples/run.py", line 22, in <module>
    from ..data import create_dataset
  File "/home/maciej/Documents/mvae/mt/data/__init__.py", line 18, in <module>
    from .image_reconstruction import CifarVaeDataset, MnistVaeDataset, OmniglotVaeDataset
  File "/home/maciej/Documents/mvae/mt/data/image_reconstruction.py", line 24, in <module>
    from ..mvae.distributions import EuclideanUniform
  File "/home/maciej/Documents/mvae/mt/mvae/distributions/__init__.py", line 16, in <module>
    from .hyperspherical_uniform import HypersphericalUniform
  File "/home/maciej/Documents/mvae/mt/mvae/distributions/hyperspherical_uniform.py", line 24, in <module>
    from ..ops.common import ln_2, ln_pi
  File "/home/maciej/Documents/mvae/mt/mvae/ops/__init__.py", line 18, in <module>
    from .poincare import PoincareBall
  File "/home/maciej/Documents/mvae/mt/mvae/ops/poincare.py", line 18, in <module>
    import geoopt.manifolds.poincare.math as pm
  File "/home/maciej/.local/lib/python3.7/site-packages/geoopt/__init__.py", line 1, in <module>
    from . import manifolds
  File "/home/maciej/.local/lib/python3.7/site-packages/geoopt/manifolds/__init__.py", line 6, in <module>
    from .birkhoff_polytope import BirkhoffPolytope
  File "/home/maciej/.local/lib/python3.7/site-packages/geoopt/manifolds/birkhoff_polytope.py", line 203, in <module>
    x, max_iter: int = 300, eps: float = 1e-5, tol: float = 1e-5
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/__init__.py", line 823, in script
    ast = get_jit_def(obj)
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 158, in get_jit_def
    return build_def(ctx, py_ast.body[0], type_line, self_name)
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 198, in build_def
    build_stmts(ctx, body))
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 122, in build_stmts
    stmts = [build_stmt(ctx, s) for s in stmts]
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 122, in <listcomp>
    stmts = [build_stmt(ctx, s) for s in stmts]
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 174, in __call__
    return method(ctx, node)
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 319, in build_While
    build_stmts(ctx, stmt.body))
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 122, in build_stmts
    stmts = [build_stmt(ctx, s) for s in stmts]
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 122, in <listcomp>
    stmts = [build_stmt(ctx, s) for s in stmts]
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 174, in __call__
    return method(ctx, node)
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 332, in build_If
    build_stmts(ctx, stmt.body),
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 122, in build_stmts
    stmts = [build_stmt(ctx, s) for s in stmts]
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 122, in <listcomp>
    stmts = [build_stmt(ctx, s) for s in stmts]
  File "/home/maciej/.conda/envs/pt/lib/python3.7/site-packages/torch/jit/frontend.py", line 173, in __call__
    raise UnsupportedNodeError(ctx, node)
torch.jit.frontend.UnsupportedNodeError: break statements aren't supported
    x, max_iter: int = 300, eps: float = 1e-5, tol: float = 1e-5
):
    iter = 0
    c = 1.0 / (x.sum(dim=-2, keepdim=True) + eps)
    r = 1.0 / ((x @ c.transpose(-1, -2)) + eps)
    while iter < max_iter:
        iter += 1
        cinv = torch.matmul(r.transpose(-1, -2), x)
        if torch.max(torch.abs(cinv * c - 1)) <= tol:
            break
            ~~~~~ <--- HERE
        c = 1.0 / (cinv + eps)
        r = 1.0 / ((x @ c.transpose(-1, -2)) + eps)
    return x * (r @ c)

To Reproduce
Steps to reproduce the behavior:

1. Set up your environment as described in README
2. Run python -m mt.examples.run --dataset="mnist" --model="h2,s2,e2" --fixed_curvature=False

Desktop (please complete the following information):

• OS: Linux (no CUDA)

Hello!

Great code, thank you!

However, I have a small question: usually, when VAE is in the eval mode we only generate samples using the mean (so we don't sample from the latent distribution and simply forward encoder(x) to decoder). Is it possible to enforce such a behavior in your models?

Thanks in advance!

Describe the bug
The following RuntimeError appears when I am trying to run run.py on cifar dataset.

To Reproduce
Steps to reproduce the behavior:

1. Set up your environment: conda activate pt
2. Run experiment with python -m mt.examples.run --dataset="cifar" --model="e2,h2,s2" --fixed_curvature=False --epochs=200
3. See error: Running on: cuda
   VAE Model: e2,h2,s2; Epochs: 200; Time: 2021-01-26T16:19:41.074145; Fixed curvature: False; Dataset: cifar
   TrainEpoch 0: Traceback (most recent call last):
   File "/home/chaopan2/miniconda3/envs/pt/lib/python3.7/runpy.py", line 193, in _run_module_as_main
   "main", mod_spec)
   File "/home/chaopan2/miniconda3/envs/pt/lib/python3.7/runpy.py", line 85, in _run_code
   exec(code, run_globals)
   File "/home/chaopan2/simulations/Py/p3/mvae/mt/examples/run.py", line 169, in
   main()
   File "/home/chaopan2/simulations/Py/p3/mvae/mt/examples/run.py", line 162, in main
   max_epochs=args.epochs)
   File "/home/chaopan2/simulations/Py/p3/mvae/mt/mvae/models/train.py", line 118, in train_stopping
   train_results[self.epoch] = self._train_epoch(optimizer, train_data, beta=beta)
   File "/home/chaopan2/simulations/Py/p3/mvae/mt/mvae/models/train.py", line 188, in _train_epoch
   stats, (reparametrized, _, ) = self.model.train_step(optimizer, x_mb, beta=beta)
   File "/home/chaopan2/simulations/Py/p3/mvae/mt/mvae/models/vae.py", line 154, in train_step
   reparametrized, concat_z, x_mb = self(x_mb)
   File "/home/chaopan2/miniconda3/envs/pt/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call
   result = self.forward(*input, **kwargs)
   File "/home/chaopan2/simulations/Py/p3/mvae/mt/mvae/models/vae.py", line 74, in forward
   q_z, p_z, _ = component(x_encoded)
   File "/home/chaopan2/miniconda3/envs/pt/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call
   result = self.forward(*input, **kwargs)
   File "/home/chaopan2/simulations/Py/p3/mvae/mt/mvae/components/component.py", line 33, in forward
   z_params = self.encode(x)
   File "/home/chaopan2/simulations/Py/p3/mvae/mt/mvae/components/component.py", line 64, in encode
   z_mean = self.fc_mean(x)
   File "/home/chaopan2/miniconda3/envs/pt/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call
   result = self.forward(*input, **kwargs)
   File "/home/chaopan2/miniconda3/envs/pt/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 92, in forward
   return F.linear(input, self.weight, self.bias)
   File "/home/chaopan2/miniconda3/envs/pt/lib/python3.7/site-packages/torch/nn/functional.py", line 1406, in linear
   ret = torch.addmm(bias, input, weight.t())
   RuntimeError: size mismatch, m1: [100 x 8192], m2: [400 x 2] at /opt/conda/conda-bld/pytorch_1556653215914/work/aten/src/THC/generic/THCTensorMathBlas.cu:268

Desktop (please complete the following information):

• OS: CentOS 7.7.1908
• PyTorch version 1.1.0
• Python version 3.7.3
• other additional Python packages installed by requirements