Errors when run pytest tests

[lh12565]

Hi, @mattragoza I installed LiGAN successfully. But when I run pytest tests, there is a lot of errors occurred:

.............
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <torch.nn.utils.spectral_norm.SpectralNorm object at 0x7f2c7c6d6070>
module = Conv3d(18, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
do_power_iteration = True

    def compute_weight(self, module: Module, do_power_iteration: bool) -> torch.Tensor:
        # NB: If `do_power_iteration` is set, the `u` and `v` vectors are
        #     updated in power iteration **in-place**. This is very important
        #     because in `DataParallel` forward, the vectors (being buffers) are
        #     broadcast from the parallelized module to each module replica,
        #     which is a new module object created on the fly. And each replica
        #     runs its own spectral norm power iteration. So simply assigning
        #     the updated vectors to the module this function runs on will cause
        #     the update to be lost forever. And the next time the parallelized
        #     module is replicated, the same randomly initialized vectors are
        #     broadcast and used!
        #
        #     Therefore, to make the change propagate back, we rely on two
        #     important behaviors (also enforced via tests):
        #       1. `DataParallel` doesn't clone storage if the broadcast tensor
        #          is already on correct device; and it makes sure that the
        #          parallelized module is already on `device[0]`.
        #       2. If the out tensor in `out=` kwarg has correct shape, it will
        #          just fill in the values.
        #     Therefore, since the same power iteration is performed on all
        #     devices, simply updating the tensors in-place will make sure that
        #     the module replica on `device[0]` will update the _u vector on the
        #     parallized module (by shared storage).
        #
        #    However, after we update `u` and `v` in-place, we need to **clone**
        #    them before using them to normalize the weight. This is to support
        #    backproping through two forward passes, e.g., the common pattern in
        #    GAN training: loss = D(real) - D(fake). Otherwise, engine will
        #    complain that variables needed to do backward for the first forward
        #    (i.e., the `u` and `v` vectors) are changed in the second forward.
        weight = getattr(module, self.name + '_orig')
        u = getattr(module, self.name + '_u')
        v = getattr(module, self.name + '_v')
        weight_mat = self.reshape_weight_to_matrix(weight)

        if do_power_iteration:
            with torch.no_grad():
                for _ in range(self.n_power_iterations):
                    # Spectral norm of weight equals to `u^T W v`, where `u` and `v`
                    # are the first left and right singular vectors.
                    # This power iteration produces approximations of `u` and `v`.
>                   v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=self.eps, out=v)
E                   RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemv(handle, op, m, n, &alpha, a, lda, x, incx, &beta, y, incy)`

../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/utils/spectral_norm.py:84: RuntimeError
----------------------------- Captured stdout setup ------------------------------
Loading data
Initializing generative model and optimizer
Initializing prior model and optimizer
Initializing atom fitter and bond adder
------------------------------ Captured stdout call ------------------------------
Saving generative model state to tests/output/TEST_VAE2_iter_0.gen_model_state
Saving generative solver state to tests/output/TEST_VAE2_iter_0.gen_solver_state
Saving prior model state to tests/output/TEST_VAE2_iter_0.prior_model_state
Saving prior solver state to tests/output/TEST_VAE2_iter_0.prior_solver_state
Writing training metrics to tests/output/TEST_VAE2.train_metrics
Saving generative model state to tests/output/TEST_VAE2_iter_0.gen_model_state
Saving generative solver state to tests/output/TEST_VAE2_iter_0.gen_solver_state
Saving prior model state to tests/output/TEST_VAE2_iter_0.prior_model_state
Saving prior solver state to tests/output/TEST_VAE2_iter_0.prior_solver_state
------------------------------ Captured stderr call ------------------------------
==============================
*** Open Babel Warning  in PerceiveBondOrders
  Failed to kekulize aromatic bonds in OBMol::PerceiveBondOrders (title is data/crossdock2020/1A02_HUMAN_25_199_pep_0/1eez_A_rec.pdb)

_____________ TestGenerativeSolver.test_solver_train_and_test[CVAE2] _____________

self = <test_training.TestGenerativeSolver object at 0x7f2d6bd5b970>
solver = CVAE2Solver(
  (gen_model): CVAE2(
    (input_encoder): GridEncoder(
      (level0): Conv3DBlock(
        (0): Conv3DR...tures=96, out_features=128, bias=True)
      (3): LeakyReLU(negative_slope=0.1)
    )
  )
  (loss_fn): LossFunction()
)
train_params = {'fit_interval': 0, 'max_iter': 10, 'n_test_batches': 1, 'norm_interval': 10, ...}

    def test_solver_train_and_test(self, solver, train_params):

        max_iter = train_params['max_iter']
        test_interval = train_params['test_interval']
        n_test_batches = train_params['n_test_batches']

        t0 = time.time()
>       solver.train_and_test(**train_params)

tests/test_training.py:534: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
ligan/training.py:37: in wrapper
    return method(self, *args, **kwargs)
ligan/training.py:1163: in train_and_test
    self.test_models(n_batches=n_test_batches, fit_atoms=fit_atoms)
ligan/training.py:1056: in test_models
    self.test_model(
ligan/training.py:1023: in test_model
    loss, metrics = self.gen_forward(
ligan/training.py:625: in gen_forward
    self.gen_model(
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1130: in _call_impl
    return forward_call(*input, **kwargs)
ligan/models.py:1130: in forward
    (means, log_stds), _ = self.input_encoder(inputs)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1130: in _call_impl
    return forward_call(*input, **kwargs)
ligan/models.py:682: in forward
    outputs = f(inputs)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1130: in _call_impl
    return forward_call(*input, **kwargs)
ligan/models.py:364: in forward
    outputs = f(inputs)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1130: in _call_impl
    return forward_call(*input, **kwargs)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/container.py:139: in forward
    input = module(input)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1137: in _call_impl
    result = hook(self, input)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/utils/spectral_norm.py:105: in __call__
    setattr(module, self.name, self.compute_weight(module, do_power_iteration=module.training))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <torch.nn.utils.spectral_norm.SpectralNorm object at 0x7f2c7c958760>
module = Conv3d(36, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
do_power_iteration = True

    def compute_weight(self, module: Module, do_power_iteration: bool) -> torch.Tensor:
        # NB: If `do_power_iteration` is set, the `u` and `v` vectors are
        #     updated in power iteration **in-place**. This is very important
        #     because in `DataParallel` forward, the vectors (being buffers) are
        #     broadcast from the parallelized module to each module replica,
        #     which is a new module object created on the fly. And each replica
        #     runs its own spectral norm power iteration. So simply assigning
        #     the updated vectors to the module this function runs on will cause
        #     the update to be lost forever. And the next time the parallelized
        #     module is replicated, the same randomly initialized vectors are
        #     broadcast and used!
        #
        #     Therefore, to make the change propagate back, we rely on two
        #     important behaviors (also enforced via tests):
        #       1. `DataParallel` doesn't clone storage if the broadcast tensor
        #          is already on correct device; and it makes sure that the
        #          parallelized module is already on `device[0]`.
        #       2. If the out tensor in `out=` kwarg has correct shape, it will
        #          just fill in the values.
        #     Therefore, since the same power iteration is performed on all
        #     devices, simply updating the tensors in-place will make sure that
        #     the module replica on `device[0]` will update the _u vector on the
        #     parallized module (by shared storage).
        #
        #    However, after we update `u` and `v` in-place, we need to **clone**
        #    them before using them to normalize the weight. This is to support
        #    backproping through two forward passes, e.g., the common pattern in
        #    GAN training: loss = D(real) - D(fake). Otherwise, engine will
        #    complain that variables needed to do backward for the first forward
        #    (i.e., the `u` and `v` vectors) are changed in the second forward.
        weight = getattr(module, self.name + '_orig')
        u = getattr(module, self.name + '_u')
        v = getattr(module, self.name + '_v')
        weight_mat = self.reshape_weight_to_matrix(weight)

        if do_power_iteration:
            with torch.no_grad():
                for _ in range(self.n_power_iterations):
                    # Spectral norm of weight equals to `u^T W v`, where `u` and `v`
                    # are the first left and right singular vectors.
                    # This power iteration produces approximations of `u` and `v`.
>                   v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=self.eps, out=v)
E                   RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemv(handle, op, m, n, &alpha, a, lda, x, incx, &beta, y, incy)`

../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/utils/spectral_norm.py:84: RuntimeError
----------------------------- Captured stdout setup ------------------------------
Loading data
Initializing generative model and optimizer
Initializing prior model and optimizer
Initializing atom fitter and bond adder
------------------------------ Captured stdout call ------------------------------
Saving generative model state to tests/output/TEST_CVAE2_iter_0.gen_model_state
Saving generative solver state to tests/output/TEST_CVAE2_iter_0.gen_solver_state
Saving prior model state to tests/output/TEST_CVAE2_iter_0.prior_model_state
Saving prior solver state to tests/output/TEST_CVAE2_iter_0.prior_solver_state
Writing training metrics to tests/output/TEST_CVAE2.train_metrics
Saving generative model state to tests/output/TEST_CVAE2_iter_0.gen_model_state
Saving generative solver state to tests/output/TEST_CVAE2_iter_0.gen_solver_state
Saving prior model state to tests/output/TEST_CVAE2_iter_0.prior_model_state
Saving prior solver state to tests/output/TEST_CVAE2_iter_0.prior_solver_state
------------------------------ Captured stderr call ------------------------------
==============================
*** Open Babel Warning  in PerceiveBondOrders
  Failed to kekulize aromatic bonds in OBMol::PerceiveBondOrders (title is data/crossdock2020/1A02_HUMAN_25_199_pep_0/1eez_A_rec.pdb)

================================ warnings summary ================================
tests/test_atom_fitting.py: 21 warnings
tests/test_training.py: 112 warnings
  /NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/cuda/memory.py:278: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.
    warnings.warn(

tests/test_atom_grids.py::TestAtomGrid::test_get_coords
  /NAS/lh/software/liGAN/new/LiGAN/./ligan/atom_grids.py:163: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
    idx = idx // dim

tests/test_models.py::test_interpolate
  /NAS/lh/software/liGAN/new/LiGAN/tests/test_models.py:50: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
    start_idx = (interp_step + batch_idxs) // n_samples

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================ short test summary info =============================
FAILED tests/test_bond_adding.py::TestBondAdding::test_make_ob_mol[oadc-tests/input/benzene.sdf]
FAILED tests/test_bond_adding.py::TestBondAdding::test_make_ob_mol[oadc-tests/input/neopentane.sdf]
FAILED tests/test_bond_adding.py::TestBondAdding::test_make_ob_mol[oadc-tests/input/ATP.sdf]
FAILED tests/test_bond_adding.py::TestBondAdding::test_make_ob_mol[oadc-tests/input/4fic_C_0UL.sdf]
FAILED tests/test_data.py::TestMolDataset::test_benchmark[False] - ValueError: ...
FAILED tests/test_data.py::TestMolDataset::test_benchmark[True] - AssertionErro...
FAILED tests/test_models.py::TestConv3DReLU::test_forward_cuda[Conv3DReLU] - Ru...
FAILED tests/test_models.py::TestConv3DReLU::test_forward_cuda[TConv3DReLU] - R...
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-c-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-c-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-c-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-r-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-r-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-r-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-d-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-d-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-d-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-c-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-c-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-c-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-r-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-r-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-r-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-d-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-d-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-d-4]
FAILED tests/test_models.py::TestGridEncoder::test_enc1_forward - RuntimeError:...
FAILED tests/test_models.py::TestGridEncoder::test_enc1_backward0 - RuntimeErro...
FAILED tests/test_models.py::TestGridEncoder::test_enc1_backward1 - RuntimeErro...
FAILED tests/test_models.py::TestGridEncoder::test_enc2_forward - RuntimeError:...
FAILED tests/test_models.py::TestGridEncoder::test_enc2_backward0 - RuntimeErro...
FAILED tests/test_models.py::TestGridEncoder::test_enc2_backward1 - RuntimeErro...
FAILED tests/test_models.py::TestGridDecoder::test_forward - RuntimeError: CUDA...
FAILED tests/test_models.py::TestGridDecoder::test_backward0 - RuntimeError: CU...
FAILED tests/test_models.py::TestGridDecoder::test_backward1 - RuntimeError: CU...
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[AE-0-c-0] - ...
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[AE-1-c-0] - ...
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CE-0-c-0] - ...
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CE-1-c-0] - ...
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CVAE2-1-c-0]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster2[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster2[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior2[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior2[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_real[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_real[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_real[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_real[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster2[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster2[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior2[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_real[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_real[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_real[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_real[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_real[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_real[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_real[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_real[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[AE] - Ru...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CE] - Ru...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[VAE] - R...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CVAE] - ...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[GAN] - R...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CGAN] - ...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[VAE2] - ...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_disc[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_disc[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_disc[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_disc[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_disc[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_disc[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_disc[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_disc[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CVAE2]
ERROR tests/test_data.py::TestAtomGridData::test_data_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_find_real_mol[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_find_real_mol[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_split[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_split[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_no_transform - ValueError...
ERROR tests/test_data.py::TestAtomGridData::test_data_rand_rotate - ValueError:...
ERROR tests/test_data.py::TestAtomGridData::test_data_rand_translate - ValueErr...
ERROR tests/test_data.py::TestAtomGridData::test_data_diff_cond_transform - Val...
ERROR tests/test_data.py::TestAtomGridData::test_data_consecutive - ValueError:...
ERROR tests/test_data.py::TestAtomGridData::test_data_benchmark[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_benchmark[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_generator_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_generator_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_gen_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_gen_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_gen_forward2[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_gen_forward2[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_generate[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_generate[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
====== 316 failed, 599 passed, 135 warnings, 23 errors in 210.03s (0:03:30) ======

I don't know if it's a cuda issue. Thanks!

[lh12565]

Hi, @mattragoza I run the code: "python3 generate.py config/generate.config". There is also an error:

Setting random seed to 0
Loading data
Initializing generative model
Loading generative model state
Initializing atom fitter
Initializing bond adder
Initializing output writer
Starting to generate grids
Calling generator (prior=0, stage2=False)
Getting next batch of data
==============================
*** Open Babel Warning  in PerceiveBondOrders
  Failed to kekulize aromatic bonds in OBMol::PerceiveBondOrders (title is data/crossdock2020/B4GT1_HUMAN_125_398_0/2ah9_B_rec.pdb)

Traceback (most recent call last):
  File "/NAS/lh/software/liGAN/new/LiGAN/generate.py", line 47, in <module>
    main(sys.argv[1:])
  File "/NAS/lh/software/liGAN/new/LiGAN/generate.py", line 42, in main
    generator.generate(**config['generate'])
  File "/NAS/lh/software/liGAN/new/LiGAN/ligan/generating.py", line 302, in generate
    ) = self.forward(
  File "/NAS/lh/software/liGAN/new/LiGAN/ligan/generating.py", line 226, in forward
    lig_gen_grids, latents, _, _ = self.gen_model(
  File "/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/NAS/lh/software/liGAN/new/LiGAN/ligan/models.py", line 1130, in forward
    (means, log_stds), _ = self.input_encoder(inputs)
  File "/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/NAS/lh/software/liGAN/new/LiGAN/ligan/models.py", line 682, in forward
    outputs = f(inputs)
  File "/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/NAS/lh/software/liGAN/new/LiGAN/ligan/models.py", line 361, in forward
    identity = self.skip_conv(inputs) if i == 0 else inputs
  File "/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/container.py", line 139, in forward
    input = module(input)
  File "/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1137, in _call_impl
    result = hook(self, input)
  File "/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/utils/spectral_norm.py", line 105, in __call__
    setattr(module, self.name, self.compute_weight(module, do_power_iteration=module.training))
  File "/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/utils/spectral_norm.py", line 84, in compute_weight
    v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=self.eps, out=v)
RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemv(handle, op, m, n, &alpha, a, lda, x, incx, &beta, y, incy)`

This seems to be related to mismatch of nn.linear. But I don't know how to solve it. Thanks!

[mattragoza]

Hm, I have not seen this error. This post on the pytorch forum suggests that it might be resolved by reducing the batch size. I don't think it's a dimension mismatch as these tests should all be passing if you are up to date with the master branch.

[mattragoza]

How much GPU memory does your card have?

[lh12565]

How much GPU memory does your card have?

I tried to reduce the batch size to 1, but the error is still there. My GPU memory is 32G.

[mattragoza]

If you run the code on the CPU, it may give a more informative error message. I just pushed a commit that lets you control this by setting the config option device: cpu.

Can you try that and post the error message?

[lh12565]

If you run the code on the CPU, it may give a more informative error message. I just pushed a commit that lets you control this by setting the config option device: cpu.

Can you try that and post the error message?

Did you mean add device: cpu to the config/generate.config? like this:

--- # new_atom_typing/generate/gen0_CVAE-1.6_1zyu_A_rec_0_1.0_1.0/generate.config
out_prefix: DEMO
model_type: CVAE
random_seed: 0
verbose: True
device: cpu

It still reported the same error.

[mattragoza]

Did you mean add device: cpu to the config/generate.config? like this:

Yes.

It still reported the same error.

Did you git pull?

[lh12565]

Did you mean add device: cpu to the config/generate.config? like this:

Yes.

It still reported the same error.

Did you git pull?

When I git pull via 'git pull --rebase', it works. But there is still some warnings. Part of the output is below:

Setting random seed to 0
Loading data
Initializing generative model
Loading generative model state
Initializing atom fitter
Initializing bond adder
Initializing output writer
Starting to generate grids
Calling generator (prior=0, stage2=False)
Getting next batch of data
==============================
*** Open Babel Warning  in PerceiveBondOrders
  Failed to kekulize aromatic bonds in OBMol::PerceiveBondOrders (title is /NAS/lh/project/sclc/pharm/data/3htb/analy/rec.pdb)

Getting real input molecule from data root
.
Minimizing real molecule with gnina
              _             
             (_)            
   __ _ _ __  _ _ __   __ _ 
  / _` | '_ \| | '_ \ / _` |
 | (_| | | | | | | | | (_| |
  \__, |_| |_|_|_| |_|\__,_|
   __/ |                    
  |___/                     

gnina v1.0 HEAD:6381355   Built Mar  6 2021.
gnina is based on smina and AutoDock Vina.
Please cite appropriately.

Commandline: gnina --minimize -r /NAS/lh/project/sclc/pharm/data/3htb/analy/rec.pdb -l /tmp/tmpznldvd27.sdf.gz --autobox_ligand /tmp/tmpznldvd27.sdf.gz -o /tmp/tmpx7l0p6ao.sdf.gz
Affinity: 0.00000  0.00000 (kcal/mol)
RMSD: 0.00000
CNNscore: 0.84549 
CNNaffinity: 1.62269
CNNvariance: 0.03050
GNINA STDERR
==============================
*** Open Babel Warning  in Init
  Cannot initialize database 'space-groups.txt' which may cause further errors.
==============================
*** Open Babel Warning  in PerceiveBondOrders
  Failed to kekulize aromatic bonds in OBMol::PerceiveBondOrders

END GNINA STDERR
/NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/cuda/memory.py:278: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.
  warnings.warn(
[example_idx=0 sample_idx=0 grid_type=rec] norm=23.7528 gpu=0.0000
Writing molecules/DEMO_0_rec_src.sdf.gz sample 0
[example_idx=0 sample_idx=0 grid_type=lig] norm=5.2755 gpu=0.0000
Writing molecules/DEMO_0_lig_src.sdf.gz sample 0
Writing molecules/DEMO_0_lig_src_pkt.sdf.gz sample 0
Writing molecules/DEMO_0_lig_src_uff.sdf.gz sample 0
Writing molecules/DEMO_0_lig_src_gna.sdf.gz sample 0
[example_idx=0 sample_idx=0 grid_type=lig_gen] norm=2.5592 gpu=0.0000
Writing latents/DEMO_0_lig_gen_0.latent
Fitting atoms to generated grid
Adding bonds to atoms from generated grid
Minimizing molecule from generated grid with UFFMinimizing molecule from generated grid with gnina
Writing molecules/DEMO_0_lig_gen_fit_add.sdf.gz sample 0
Writing molecules/DEMO_0_lig_gen_fit_add_pkt.sdf.gz sample 0
Writing molecules/DEMO_0_lig_gen_fit_add_uff.sdf.gz sample 0
Writing molecules/DEMO_0_lig_gen_fit_add_gna.sdf.gz sample 0
Computing metrics for example 0 sample 0
reading NP model ...
model in
sample_idx                                   0
lig_grid_norm                         5.275463
lig_grid_elem_norm                    2.359259
lig_grid_prop_norm                    4.718518
lig_rec_prod                               0.0
lig_rec_elem_prod                          0.0
lig_rec_prop_prod                          0.0
lig_n_atoms                                3.0
lig_radius                                 0.0
lig_n_frags                                1.0
lig_valid                                 True
lig_reason                      Valid molecule
lig_MW                                  18.015
lig_logP                               -0.8247
lig_QED                               0.327748
lig_SAS                                7.50673
lig_NPS                                    0.0
lig_SMILES                             [H]O[H]
lig_UFF_init                          0.440598
lig_UFF_min                                0.0
lig_UFF_rmsd                          0.700301
lig_UFF_error                              NaN
lig_UFF_time                           0.00251
lig_vina_aff                               0.0
lig_vina_rmsd                              0.0
lig_cnn_pose                          0.845489
lig_cnn_aff                           1.622691
lig_gnina_error                            NaN
lig_gen_grid_norm                     2.559225
lig_gen_grid_elem_norm                1.275613
lig_gen_grid_prop_norm                2.218825
lig_gen_L2_loss                       7.230287
lig_gen_elem_L2_loss                  1.449093
lig_gen_prop_L2_loss                  5.781195
lig_gen_shape_sim                     0.045793
lig_gen_rec_prod                           0.0
lig_gen_rec_elem_prod                      0.0
lig_gen_rec_prop_prod                      0.0
lig_latent_norm                      10.682964
lig_latent_variance                        NaN
lig_gen_fit_grid_norm                      0.0
lig_gen_fit_grid_elem_norm                 0.0
lig_gen_fit_grid_prop_norm                 0.0
lig_gen_fit_L2_loss                    3.27683
lig_gen_fit_elem_L2_loss               0.81407
lig_gen_fit_prop_L2_loss               2.46276
lig_gen_fit_shape_sim                      0.0
lig_gen_fit_rec_prod                       0.0
lig_gen_fit_rec_elem_prod                  0.0
lig_gen_fit_rec_prop_prod                  0.0
lig_gen_fit_n_atoms                        0.0
lig_gen_fit_radius                         NaN
lig_gen_fit_n_atoms_diff                   1.0
lig_gen_fit_type_diff                      5.0
lig_gen_fit_elem_diff                      1.0
lig_gen_fit_prop_diff                      4.0
lig_gen_fit_RMSD                           NaN
lig_gen_fit_time                      2.773758
lig_gen_fit_n_visited                      3.0
lig_gen_est_type_diff                      NaN
lig_gen_est_exact_types                  False
lig_gen_fit_add_n_atoms                    0.0
lig_gen_fit_add_n_frags                    0.0
lig_gen_fit_add_valid                    False
lig_gen_fit_add_reason                No atoms
lig_gen_fit_add_MW                         0.0
lig_gen_fit_add_logP                       0.0
lig_gen_fit_add_QED                   0.339424
lig_gen_fit_add_SAS                        NaN
lig_gen_fit_add_NPS                        NaN
lig_gen_fit_add_SMILES                        
lig_gen_fit_add_n_atoms_diff               0.0
lig_gen_fit_add_SMILES_match             False
lig_gen_fit_add_ob_sim                     NaN
lig_gen_fit_add_rdkit_sim                  NaN
lig_gen_fit_add_morgan_sim                 NaN
lig_gen_fit_add_maccs_sim                  NaN
lig_gen_fit_add_UFF_init                   NaN
lig_gen_fit_add_UFF_min                    NaN
lig_gen_fit_add_UFF_rmsd                   NaN
lig_gen_fit_add_UFF_error             No atoms
lig_gen_fit_add_UFF_time              0.000749
lig_gen_fit_add_UFF_init_diff              NaN
lig_gen_fit_add_UFF_min_diff               NaN
lig_gen_fit_add_UFF_rmsd_diff              NaN
lig_gen_fit_add_vina_aff                   NaN
lig_gen_fit_add_vina_rmsd                  NaN
lig_gen_fit_add_cnn_pose                   NaN
lig_gen_fit_add_cnn_aff                    NaN
lig_gen_fit_add_gnina_error           No atoms
lig_gen_fit_add_vina_aff_diff              NaN
lig_gen_fit_add_vina_rmsd_diff             NaN
lig_gen_fit_add_cnn_pose_diff              NaN
lig_gen_fit_add_cnn_aff_diff               NaN
lig_gen_fit_add_radius                     NaN
lig_gen_fit_add_type_diff                  0.0
lig_gen_fit_add_elem_diff                  0.0
lig_gen_fit_add_prop_diff                  0.0
lig_gen_fit_add_RMSD                       NaN
Writing DEMO.gen_metrics
Writing DEMO.pymol
Freeing memory

I use a protein with no ligand. As said in the #44 , you said I can use any ligand (use tests/input/O_0_0_0.sdf) in prior sampling. And I also don't know where the binding pocket is. I just only use the protein and O_0_0_0.sdf in the file, like this:

0 0 0 rec.pdb O_0_0_0.sdf

The output is all H2O:

0
     RDKit          3D

  3  2  0  0  0  0  0  0  0  0999 V2000
    0.0000    0.0000    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0
    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0
  1  2  1  0
  1  3  1  0
M  END
$$$$

I am a beginner, so there are some issues that need to be solved: (1) Are these warnings ignored? Can I continue to use CUDA? (2) Change which parameters can distinguish the prior sampling and the posterior? (3) When there is no ligand and the binding pocket is unknown, can I directly provide the following files for drug design?

0 0 0 rec.pdb O_0_0_0.sdf

(4) If (3) yes, why all my results are the same: H2O? (5) What do the output files represent? (lig_gen_fit_add_gna.sdf.gz lig_gen_fit_add_pkt.sdf.gz lig_gen_fit_add.sdf.gz lig_gen_fit_add_uff.sdf.gz lig_src_gna.sdf.gz lig_src_pkt.sdf.gz lig_src.sdf.gz lig_src_uff.sdf.gz *rec_src.sdf.gz) Thanks!

[mattragoza]

Are these warnings ignored?

You can ignore OpenBabel warnings.

Can I continue to use CUDA?

If the code works when you use device: cpu, it should work with device: cuda as well.

Change which parameters can distinguish the prior sampling and the posterior?

The configuration setting generate: prior: False/True controls whether you sample from the prior or posterior.

When there is no ligand and the binding pocket is unknown, can I directly provide the following files for drug design?

Yes, you should be able to do prior sampling with an empty/dummy input ligand. However, note that the receptor and ligand grids are centered on the input ligand file, and generated ligands will be centered there accordingly. So in this case, the grid will be centered at the origin, which might result in the receptor grid containing an arbitrary location on the receptor structure. It might not contain any receptor structure at all, depending on the receptor structure coordinates.

why all my results are the same: H2O?

This is probably because the grid is not centered correctly and contains very little of the receptor structure, so the model is generated a ligand based on mostly empty space. It's ok if you do not have an input ligand, but you at least need to decide where the grid will be centered, by changing the coordinates on the dummy atom in the file O_0_0_0.sdf.

We have not tested this generative model on receptors with unknown binding pockets, so I can't say what the result will be or what the best approach is for discovering potential binding pockets. But at the very least you need to consider the fact that the entire receptor structure is unlikely to fit into the grid, so you need to decide where to center the grid. You could possible try scanning the entire receptor structure by generating many dummy ligand files centered on random locations or a uniform grid spanning the coordinate domain of the receptor structure, then generating ligands at each location and assessing their gnina score. I don't know how effective this would be but it's one possible approach!

What do the output files represent?

*_rec_src.sdf.gz        The input receptor structure

*_lig_src.sdf.gz        The input ligand structure
*_lig_src_uff.sdf.gz        The input ligand minimized with UFF (internal energy)
*_lig_src_gna.sdf.gz        The input ligand minimized with gnina (wrt recptor structure)
*_lig_src_pkt.sdf.gz        The receptor pocket used for gnina minimization

*_lig_gen_fit_add.sdf.gz    The generated molecule after bond adding
*_lig_gen_fit_add_uff.sdf.gz    The generated molecule after UFF minimization (internal energy)
*_lig_gen_fit_add_gna.sdf.gz    The generated molecule after gnina minimization (wrt receptor structure)
*_lig_gen_fit_add_pkt.sdf.gz    The receptor pocket used for gnina minimization

[lh12565]

Hi @mattragoza I guess I can use dock model, such as autodock, to determine ligand coordinates and the grids. right? Thanks!

[mattragoza]

Yes, that would work too.

[lh12565]

Hi @mattragoza I still have some issues to solve it. （1）I try to run the two models (using prior: 0): protein with a known ligand and pocket, protein with the docked O_0_0_0.sdf. I found the result of the first model has the very similar output molecules as the known ligand, such as only change H to OH, change single bond to double bond, and there's no extension of molecules in the pocket. The second model has the same issue as above: all result are H2O but in a different part of the pocket. Are these normal results, or does it need to change parameters or something else? （2）When I change the device to cuda, there is still the same error as before:

RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemv(handle, op, m, n, &alpha, a, lda, x, incx, &beta, y, incy)`

（3）Before run the LiGAN, did I need to do something else with the protein or the ligand, such as adding hydrogens and charge? Thanks!