3432 make vit support torchscript

monai/networks/blocks/patchembedding.py

@@ -80,7 +80,7 @@ def __init__(
             if self.pos_embed == "perceptron" and m % p != 0:
                 raise ValueError("patch_size should be divisible by img_size for perceptron.")
         self.n_patches = np.prod([im_d // p_d for im_d, p_d in zip(img_size, patch_size)])
-        self.patch_dim = in_channels * np.prod(patch_size)
+        self.patch_dim = int(in_channels * np.prod(patch_size))
 
         self.patch_embeddings: nn.Module
         if self.pos_embed == "conv":

monai/networks/blocks/selfattention.py

@@ -14,7 +14,7 @@
 
 from monai.utils import optional_import
 
-einops, _ = optional_import("einops")
+Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
 
 
 class SABlock(nn.Module):
@@ -43,17 +43,20 @@ def __init__(self, hidden_size: int, num_heads: int, dropout_rate: float = 0.0)
         self.num_heads = num_heads
         self.out_proj = nn.Linear(hidden_size, hidden_size)
         self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=False)
+        self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads)
+        self.out_rearrange = Rearrange("b h l d -> b l (h d)")
         self.drop_output = nn.Dropout(dropout_rate)
         self.drop_weights = nn.Dropout(dropout_rate)
         self.head_dim = hidden_size // num_heads
         self.scale = self.head_dim**-0.5
 
     def forward(self, x):
-        q, k, v = einops.rearrange(self.qkv(x), "b h (qkv l d) -> qkv b l h d", qkv=3, l=self.num_heads)
+        output = self.input_rearrange(self.qkv(x))
+        q, k, v = output[0], output[1], output[2]
         att_mat = (torch.einsum("blxd,blyd->blxy", q, k) * self.scale).softmax(dim=-1)
         att_mat = self.drop_weights(att_mat)
         x = torch.einsum("bhxy,bhyd->bhxd", att_mat, v)
-        x = einops.rearrange(x, "b h l d -> b l (h d)")
+        x = self.out_rearrange(x)
         x = self.out_proj(x)
         x = self.drop_output(x)
         return x

monai/networks/nets/vit.py

@@ -25,6 +25,8 @@ class ViT(nn.Module):
     """
     Vision Transformer (ViT), based on: "Dosovitskiy et al.,
     An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
+
+    ViT supports Torchscript but only works for Pytorch after 1.8.
     """
 
     def __init__(
@@ -99,14 +101,14 @@ def __init__(
 
     def forward(self, x):
         x = self.patch_embedding(x)
-        if self.classification:
+        if hasattr(self, "cls_token"):
             cls_token = self.cls_token.expand(x.shape[0], -1, -1)
             x = torch.cat((cls_token, x), dim=1)
         hidden_states_out = []
         for blk in self.blocks:
             x = blk(x)
             hidden_states_out.append(x)
         x = self.norm(x)
-        if self.classification:
+        if hasattr(self, "classification_head"):
             x = self.classification_head(x[:, 0])
         return x, hidden_states_out

tests/test_vit.py

@@ -16,6 +16,7 @@
 
 from monai.networks import eval_mode
 from monai.networks.nets.vit import ViT
+from tests.utils import SkipIfBeforePyTorchVersion, test_script_save
 
 TEST_CASE_Vit = []
 for dropout_rate in [0.6]:
@@ -27,7 +28,7 @@
                         for mlp_dim in [3072]:
                             for num_layers in [4]:
                                 for num_classes in [8]:
-                                    for pos_embed in ["conv"]:
+                                    for pos_embed in ["conv", "perceptron"]:
                                         for classification in [False, True]:
                                             for nd in (2, 3):
                                                 test_case = [
@@ -133,6 +134,17 @@ def test_ill_arg(self):
                 dropout_rate=0.3,
             )
 
+    @parameterized.expand(TEST_CASE_Vit)
+    @SkipIfBeforePyTorchVersion((1, 9))
+    def test_script(self, input_param, input_shape, _):
+        net = ViT(**(input_param))
+        net.eval()
+        with torch.no_grad():
+            torch.jit.script(net)
+
+        test_data = torch.randn(input_shape)
+        test_script_save(net, test_data)
+
 
 if __name__ == "__main__":
     unittest.main()