Remove asserts

generative/networks/nets/diffusion_model_unet.py

@@ -468,7 +468,8 @@ def get_timestep_embedding(timesteps: torch.Tensor, embedding_dim: int, max_peri
         embedding_dim: the dimension of the output.
         max_period: controls the minimum frequency of the embeddings.
     """
-    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+    if timesteps.ndim != 1:
+        raise ValueError("Timesteps should be a 1d-array")
 
     half_dim = embedding_dim // 2
     exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device)
@@ -491,7 +492,8 @@ class Downsample(nn.Module):
     Args:
         spatial_dims: number of spatial dimensions.
         num_channels: number of input channels.
-        use_conv: if True uses Convolution instead of Pool average to perform downsampling.
+        use_conv: if True uses Convolution instead of Pool average to perform downsampling. In case that use_conv is
+            False, the number of output channels must be the same as the number of input channels.
         out_channels: number of output channels.
         padding: controls the amount of implicit zero-paddings on both sides for padding number of points
             for each dimension.
@@ -515,12 +517,17 @@ def __init__(
                 conv_only=True,
             )
         else:
-            assert self.num_channels == self.out_channels
+            if self.num_channels != self.out_channels:
+                raise ValueError("num_channels and out_channels must be equal when use_conv=False")
             self.op = Pool[Pool.AVG, spatial_dims](kernel_size=2, stride=2)
 
     def forward(self, x: torch.Tensor, emb: torch.Tensor | None = None) -> torch.Tensor:
         del emb
-        assert x.shape[1] == self.num_channels
+        if x.shape[1] != self.num_channels:
+            raise ValueError(
+                f"Input number of channels ({x.shape[1]}) is not equal to expected number of channels "
+                f"({self.num_channels})"
+            )
         return self.op(x)
 
 
@@ -559,7 +566,8 @@ def __init__(
 
     def forward(self, x: torch.Tensor, emb: torch.Tensor | None = None) -> torch.Tensor:
         del emb
-        assert x.shape[1] == self.num_channels
+        if x.shape[1] != self.num_channels:
+            raise ValueError("Input channels should be equal to num_channels")
 
         # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
         # https://github.com/pytorch/pytorch/issues/86679

tests/test_diffusion_model_unet.py

@@ -240,6 +240,20 @@ def test_shape_unconditioned_models(self, input_param):
             result = net.forward(torch.rand((1, 1, 16, 16)), torch.randint(0, 1000, (1,)).long())
             self.assertEqual(result.shape, (1, 1, 16, 16))
 
+    def test_timestep_with_wrong_shape(self):
+        net = DiffusionModelUNet(
+            spatial_dims=2,
+            in_channels=1,
+            out_channels=1,
+            num_res_blocks=1,
+            num_channels=(8, 8, 8),
+            attention_levels=(False, False, False),
+            norm_num_groups=8,
+        )
+        with self.assertRaises(ValueError):
+            with eval_mode(net):
+                net.forward(torch.rand((1, 1, 16, 16)), torch.randint(0, 1000, (1, 1)).long())
+
     def test_shape_with_different_in_channel_out_channel(self):
         in_channels = 6
         out_channels = 3