def test_to_tensor():
var_x = Variable(torch.Tensor([0.0]))
x = to_tensor(var_x)
assert torch.is_tensor(x)
var_x = (Variable(torch.Tensor([0.0])), Variable(torch.Tensor([0.0])))
x = to_tensor(var_x)
assert isinstance(x, list) and torch.is_tensor(x[0]) and torch.is_tensor(x[1])
var_x = {'a': Variable(torch.Tensor([0.0])), 'b': Variable(torch.Tensor([0.0]))}
x = to_tensor(var_x)
assert isinstance(x, dict) and torch.is_tensor(x['a']) and torch.is_tensor(x['b'])
with pytest.raises(TypeError):
to_tensor(12345)
def _inference(batch):
model.eval()
x, y = _prepare_batch(batch)
y_pred = model(x)
return to_tensor(y_pred, cpu=not cuda), to_tensor(y, cpu=not cuda)
def _inference(engine, batch):
model.eval()
# x, y = _prepare_batch(batch, cuda, volatile=True)
y = batch.label
y_pred, _ = model(batch)
return to_tensor(y_pred, cpu=not cuda), to_tensor(y, cpu=not cuda)