3194 update vitautoenc for 2d

monai/networks/nets/vitautoenc.py

@@ -18,6 +18,7 @@
 
 from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
 from monai.networks.blocks.transformerblock import TransformerBlock
+from monai.networks.layers import Conv
 
 __all__ = ["ViTAutoEnc"]
 
@@ -35,6 +36,8 @@ def __init__(
         in_channels: int,
         img_size: Union[Sequence[int], int],
         patch_size: Union[Sequence[int], int],
+        out_channels: int = 1,
+        deconv_chns: int = 16,
         hidden_size: int = 768,
         mlp_dim: int = 3072,
         num_layers: int = 12,
@@ -49,6 +52,8 @@ def __init__(
             img_size: dimension of input image.
             patch_size: dimension of patch size.
             hidden_size: dimension of hidden layer.
+            out_channels: number of output channels.
+            deconv_chns: number of channels for the deconvolution layers.
             mlp_dim: dimension of feedforward layer.
             num_layers: number of transformer blocks.
             num_heads: number of attention heads.
@@ -69,14 +74,7 @@ def __init__(
 
         super().__init__()
 
-        if not (0 <= dropout_rate <= 1):
-            raise ValueError("dropout_rate should be between 0 and 1.")
-
-        if hidden_size % num_heads != 0:
-            raise ValueError("hidden_size should be divisible by num_heads.")
-
-        if spatial_dims == 2:
-            raise ValueError("Not implemented for 2 dimensions, please try 3")
+        self.spatial_dims = spatial_dims
 
         self.patch_embedding = PatchEmbeddingBlock(
             in_channels=in_channels,
@@ -86,17 +84,18 @@ def __init__(
             num_heads=num_heads,
             pos_embed=pos_embed,
             dropout_rate=dropout_rate,
-            spatial_dims=spatial_dims,
+            spatial_dims=self.spatial_dims,
         )
         self.blocks = nn.ModuleList(
             [TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate) for i in range(num_layers)]
         )
         self.norm = nn.LayerNorm(hidden_size)
 
-        new_patch_size = (4, 4, 4)
-        self.conv3d_transpose = nn.ConvTranspose3d(hidden_size, 16, kernel_size=new_patch_size, stride=new_patch_size)
-        self.conv3d_transpose_1 = nn.ConvTranspose3d(
-            in_channels=16, out_channels=1, kernel_size=new_patch_size, stride=new_patch_size
+        new_patch_size = [4] * self.spatial_dims
+        conv_trans = Conv[Conv.CONVTRANS, self.spatial_dims]
+        self.conv_transpose = conv_trans(hidden_size, deconv_chns, kernel_size=new_patch_size, stride=new_patch_size)
+        self.conv_transpose_1 = conv_trans(
+            in_channels=deconv_chns, out_channels=out_channels, kernel_size=new_patch_size, stride=new_patch_size
         )
 
     def forward(self, x):
@@ -107,9 +106,8 @@ def forward(self, x):
             hidden_states_out.append(x)
         x = self.norm(x)
         x = x.transpose(1, 2)
-        cuberoot = round(math.pow(x.size()[2], 1 / 3))
-        x_shape = x.size()
-        x = torch.reshape(x, [x_shape[0], x_shape[1], cuberoot, cuberoot, cuberoot])
-        x = self.conv3d_transpose(x)
-        x = self.conv3d_transpose_1(x)
+        d = [round(math.pow(x.shape[2], 1 / self.spatial_dims))] * self.spatial_dims
+        x = torch.reshape(x, [x.shape[0], x.shape[1], *d])
+        x = self.conv_transpose(x)
+        x = self.conv_transpose_1(x)
         return x, hidden_states_out

tests/test_vitautoenc.py

@@ -21,7 +21,7 @@
     for img_size in [64, 96, 128]:
         for patch_size in [16]:
             for pos_embed in ["conv", "perceptron"]:
-                for nd in [3]:
+                for nd in [2, 3]:
                     test_case = [
                         {
                             "in_channels": in_channels,