Better config for Feature Extractor

config.yaml

@@ -1,6 +1,8 @@
 name: EXAMPLE
 
 feature_extractor:
+  input_size: 1953 # Input size for the Feature Extractor (length of snippets). (=0.1s)
+  transforms:
     - type: 'identity'  # Pass the last n samples of the input data directly.
       length: 8  # Number of last samples to pass directly. Use full input size if set to null.
     - type: 'fourier'  # Apply Fourier transform to the input data.
@@ -35,7 +37,6 @@ feature_extractor:
 
 latent_projector:
   type: 'fc'  # Type of latent projector: 'fc', 'rnn', 'fourier'
-  input_size: 1953  # Input size for the Latent Projector (length of snippets). (=0.1s)
   latent_size: 4  # Size of the latent representation before message passing.
   layer_shapes: [32, 8]  # List of layer sizes for the latent projector if type is 'fc' or 'fourier'.
   activations: ['ReLU', 'ReLU']  # Activation functions for the latent projector layers if type is 'fc' or 'fourier'.
@@ -146,6 +147,11 @@ middle_out:
 name: FC
 import: $
 
+feature_extractor:
+  input size: 10
+  transforms:
+    - type: 'identity'
+
 latent_projector:
   type: fc
   input_size: 1953

models.py

@@ -24,7 +24,7 @@ class FeatureExtractor(nn.Module):
         transforms = []
         for item in config:
             transform_type = item['type']
-            length = item.get('length', self.input_size)
+            length = item.get('length', None)
             if length in [None, -1]:
                 length = self.input_size