def test_hybridnet(input_sizes):
model = HybridNet(
input_sizes['n_channels'],
input_sizes['n_classes'],
input_sizes['n_in_times'],
)
check_forward_pass(model, input_sizes, only_check_until_dim=2)
def test_hybridnet():
rng = np.random.RandomState(42)
n_channels = 18
n_in_times = 600
n_classes = 2
n_samples = 7
X = rng.randn(n_samples, n_channels, n_in_times, 1)
X = torch.Tensor(X.astype(np.float32))
model = HybridNet(n_channels, n_classes, n_in_times)
y_pred = model(X)
assert y_pred.shape[:2] == (n_samples, n_classes)