Fix 1D data error in VarAutoEncoder (#5236)

Signed-off-by: KumoLiu <yunl@nvidia.com>

Fixes #5225 .

### Description
1. fix 1d data error in `VarAutoEncoder`
2. add `use_sigmoid` flag

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [x] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [x] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: KumoLiu <yunl@nvidia.com>
Co-authored-by: Wenqi Li <831580+wyli@users.noreply.github.com>

Author: KumoLiu

monai/networks/layers/convutils.py

@@ -74,7 +74,7 @@ def calculate_out_shape(
     out_shape_np = ((in_shape_np - kernel_size_np + padding_np + padding_np) // stride_np) + 1
     out_shape = tuple(int(s) for s in out_shape_np)
 
-    return out_shape if len(out_shape) > 1 else out_shape[0]
+    return out_shape
 
 
 def gaussian_1d(

monai/networks/nets/varautoencoder.py

@@ -48,6 +48,7 @@ class VarAutoEncoder(AutoEncoder):
         bias: whether to have a bias term in convolution blocks. Defaults to True.
             According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,
             if a conv layer is directly followed by a batch norm layer, bias should be False.
+        use_sigmoid: whether to use the sigmoid function on final output. Defaults to True.
 
     Examples::
 
@@ -86,9 +87,11 @@ def __init__(
         norm: Union[Tuple, str] = Norm.INSTANCE,
         dropout: Optional[Union[Tuple, str, float]] = None,
         bias: bool = True,
+        use_sigmoid: bool = True,
     ) -> None:
 
         self.in_channels, *self.in_shape = in_shape
+        self.use_sigmoid = use_sigmoid
 
         self.latent_size = latent_size
         self.final_size = np.asarray(self.in_shape, dtype=int)
@@ -148,4 +151,4 @@ def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor
     def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         mu, logvar = self.encode_forward(x)
         z = self.reparameterize(mu, logvar)
-        return self.decode_forward(z), mu, logvar, z
+        return self.decode_forward(z, self.use_sigmoid), mu, logvar, z

tests/test_varautoencoder.py

@@ -75,7 +75,34 @@
     (1, 3, 128, 128, 128),
 ]
 
-CASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]
+TEST_CASE_4 = [  # 4-channel 1D, batch 4
+    {
+        "spatial_dims": 1,
+        "in_shape": (4, 128),
+        "out_channels": 3,
+        "latent_size": 2,
+        "channels": (4, 8, 16),
+        "strides": (2, 2, 2),
+    },
+    (1, 4, 128),
+    (1, 3, 128),
+]
+
+TEST_CASE_5 = [  # 4-channel 1D, batch 4, use_sigmoid = False
+    {
+        "spatial_dims": 1,
+        "in_shape": (4, 128),
+        "out_channels": 3,
+        "latent_size": 2,
+        "channels": (4, 8, 16),
+        "strides": (2, 2, 2),
+        "use_sigmoid": False,
+    },
+    (1, 4, 128),
+    (1, 3, 128),
+]
+
+CASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]
 
 
 class TestVarAutoEncoder(unittest.TestCase):