Fixing a few more bugs

jloveric · May 5, 2024 · 87e2c83 · 87e2c83
1 parent 7b1ad16
commit 87e2c83
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Showing 3 changed files with 7 additions and 4 deletions.
diff --git a/examples/high_order_interpolation.py b/examples/high_order_interpolation.py
@@ -56,7 +56,9 @@ def run_language_interpolation(cfg: DictConfig):
             if cfg.net.model_type in [
                 "high_order_transformer",
                 "high_order_input_transformer",
+                "dual_convolution"
             ]:
+                print('Using transformer dataloader')
                 # dataset_generator is only one type so using the default
                 datamodule = TransformerDataModule(
                     characters_per_feature=cfg.data.characters_per_feature,

diff --git a/language_interpolation/dual_convolutional_network.py b/language_interpolation/dual_convolutional_network.py
@@ -23,6 +23,7 @@ def __init__(
         super().__init__()
 
         self._out_width = out_width
+        self.device = device
 
         self.input_layer = HighOrderMLP(
             layer_type="continuous",
@@ -68,7 +69,7 @@ def forward(self, x: Tensor):
             if val.shape[1] % 2 == 1:
                 # Add padding to the end, hope this doesn't bust anything
                 val = torch.cat(
-                    [val, torch.zeros(val.shape[0], 1, val.shape[2])], dim=1
+                    [val, torch.zeros(val.shape[0], 1, val.shape[2])], dim=1, device=self.device
                 )
 
             valshape = val.shape
@@ -91,12 +92,12 @@ def __init__(
         segments: int = None,
         device: str = "cpu",
     ):
-
+        super().__init__()
         self.dual_layer = DualConvolutionalLayer(
             n=n,
             in_width=in_width,
             out_width=embedding_dimension,
-            hidden_layer=hidden_layers,
+            hidden_layers=hidden_layers,
             hidden_width=hidden_width,
             in_segments=in_segments,
             segments=segments,

diff --git a/language_interpolation/networks.py b/language_interpolation/networks.py
@@ -846,7 +846,7 @@ def forward(self, x) :
             max_context=cfg.data.max_features,
             non_linearity=torch.nn.ReLU(),
         )
-    elif cfg.net.model_type == "dual_convolutional_network":
+    elif cfg.net.model_type == "dual_convolution":
         model = DualConvolutionNetwork(
             n=cfg.net.n,
             in_width=cfg.net.in_width,