Error in pytest #87
Description
Hi, when I installed the molgrid, I run the pytest .. as you said in https://github.com/mattragoza/conda-molgrid. there are errors as follows:
pytest ..
.....
==============================
*** Open Babel Warning in ReadMolecule
WARNING: Problem interpreting the valence field of an atom
The valence field specifies a valence 3 that is
less than the observed explicit valence 4.
WARNING: Problem interpreting the valence field of an atom
The valence field specifies a valence 1 that is
less than the observed explicit valence 2.
WARNING: Problem interpreting the valence field of an atom
The valence field specifies a valence 3 that is
less than the observed explicit valence 5.
WARNING: Problem interpreting the valence field of an atom
The valence field specifies a valence 1 that is
less than the observed explicit valence 2.
WARNING: Problem interpreting the valence field of an atom
The valence field specifies a valence 1 that is
less than the observed explicit valence 2.
WARNING: Problem interpreting the valence field of an atom
The valence field specifies a valence 3 that is
less than the observed explicit valence 4.
.............. [ 32%]
../test/test_grid.py . [ 33%]
../test/test_gridinterp.py ...... [ 43%]
../test/test_gridio.py . [ 44%]
../test/test_gridmaker.py .......... [ 60%]
../test/test_numpy.py .... [ 66%]
../test/test_torch.py ........ [ 78%]
../test/test_torch_cnn.py F [ 80%]
../test/test_transform.py ... [ 84%]
../test/test_typing.py .......... [100%]
==================================== FAILURES =====================================
_____________________________ test_coordset_from_mol ______________________________
def test_coordset_from_mol():
import psutil,os,gc
import pytest
import molgrid
from openbabel import pybel #3.0
m = pybel.readstring('smi','c1ccccc1CO')
m.addh()
m.make3D()
gc.collect()
before = psutil.Process(os.getpid()).memory_info().rss / 1024
for i in range(100):
m = pybel.readstring('smi','c1ccccc1CO')
m.addh()
m.make3D()
c = molgrid.CoordinateSet(m)
gc.collect()
after = psutil.Process(os.getpid()).memory_info().rss / 1024
> assert before == after
E assert 263424.0 == 263636.0
../test/test_coordinateset.py:49: AssertionError
______________________________ test_train_torch_cnn _______________________________
@pytest.mark.slow()
def test_train_torch_cnn():
batch_size = 50
datadir = os.path.dirname(__file__)+'/data'
fname = datadir+"/small.types"
molgrid.set_random_seed(0)
torch.manual_seed(0)
np.random.seed(0)
class Net(nn.Module):
def __init__(self, dims):
super(Net, self).__init__()
self.pool0 = nn.MaxPool3d(2)
self.conv1 = nn.Conv3d(dims[0], 32, kernel_size=3, padding=1)
self.pool1 = nn.MaxPool3d(2)
self.conv2 = nn.Conv3d(32, 64, kernel_size=3, padding=1)
self.pool2 = nn.MaxPool3d(2)
self.conv3 = nn.Conv3d(64, 128, kernel_size=3, padding=1)
self.last_layer_size = dims[1]//8 * dims[2]//8 * dims[3]//8 * 128
self.fc1 = nn.Linear(self.last_layer_size, 2)
def forward(self, x):
x = self.pool0(x)
x = F.relu(self.conv1(x))
x = self.pool1(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = F.relu(self.conv3(x))
x = x.view(-1, self.last_layer_size)
x = self.fc1(x)
return x
def weights_init(m):
if isinstance(m, nn.Conv3d) or isinstance(m, nn.Linear):
init.xavier_uniform_(m.weight.data)
init.constant_(m.bias.data, 0)
batch_size = 50
e = molgrid.ExampleProvider(data_root=datadir+"/structs",balanced=True,shuffle=True)
e.populate(fname)
gmaker = molgrid.GridMaker()
dims = gmaker.grid_dimensions(e.num_types())
tensor_shape = (batch_size,)+dims
model = Net(dims).to('cuda')
model.apply(weights_init)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
input_tensor = torch.zeros(tensor_shape, dtype=torch.float32, device='cuda')
float_labels = torch.zeros(batch_size, dtype=torch.float32)
losses = []
for iteration in range(100):
#load data
batch = e.next_batch(batch_size)
gmaker.forward(batch, input_tensor, 0, random_rotation=False) #not rotating since convergence is faster this way
batch.extract_label(0, float_labels)
labels = float_labels.long().to('cuda')
optimizer.zero_grad()
> output = model(input_tensor)
../test/test_torch_cnn.py:75:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../../miniconda3/envs/molgrid/lib/python3.9/site-packages/torch/nn/modules/module.py:1110: in _call_impl
return forward_call(*input, **kwargs)
../test/test_torch_cnn.py:42: in forward
x = self.fc1(x)
../../../miniconda3/envs/molgrid/lib/python3.9/site-packages/torch/nn/modules/module.py:1110: in _call_impl
return forward_call(*input, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = Linear(in_features=27648, out_features=2, bias=True)
input = tensor([[0.0071, 0.0033, 0.0000, ..., 0.0270, 0.0000, 0.0000],
[0.0336, 0.0000, 0.0000, ..., 0.0000, 0.0059,...393],
[0.0537, 0.0000, 0.0070, ..., 0.0000, 0.0000, 0.0000]],
device='cuda:1', grad_fn=<ViewBackward0>)
def forward(self, input: Tensor) -> Tensor:
> return F.linear(input, self.weight, self.bias)
E RuntimeError: CUDA error: CUBLAS_STATUS_NOT_SUPPORTED when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`
../../../miniconda3/envs/molgrid/lib/python3.9/site-packages/torch/nn/modules/linear.py:103: RuntimeError
================================ warnings summary =================================
test/test_numpy.py::test_numpy
/NAS/luohao/software/molgrid/libmolgrid/test/test_numpy.py:12: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
a3d = np.arange(27).astype(np.float).reshape(3,3,-1)
test/test_numpy.py::test_numpy_conv
/NAS/luohao/software/molgrid/libmolgrid/test/test_numpy.py:40: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
a3d = np.arange(27).astype(np.float).reshape(3,3,-1)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================= short test summary info =============================
FAILED ../test/test_coordinateset.py::test_coordset_from_mol - assert 263424.0 =...
FAILED ../test/test_torch_cnn.py::test_train_torch_cnn - RuntimeError: CUDA erro...
============== 2 failed, 63 passed, 2 warnings in 220.38s (0:03:40) ===============I don't know why the before !=after (os.getpid() returns the same value). CUDA also seems to have an error.
Thanks!