Clean deeponet.py (#26)

* Clean deeponet.py

pina/model/deeponet.py

@@ -18,8 +18,7 @@ class DeepONet(torch.nn.Module):
         <https://doi.org/10.1038/s42256-021-00302-5>`_
 
     """
-    def __init__(self, branch_net, trunk_net, output_variables, inner_size=10,
+    def __init__(self, branch_net, trunk_net, output_variables, inner_size=10):
-                 features=None, features_net=None):
         """
         :param torch.nn.Module branch_net: the neural network to use as branch
             model. It has to take as input a :class:`LabelTensor`. The number
@@ -30,6 +29,8 @@ class DeepONet(torch.nn.Module):
         :param list(str) output_variables: the list containing the labels
             corresponding to the components of the output computed by the
             model.
+        :param int inner_size: the output dimension of the branch and trunk
+            networks. Default is 10.
 
         :Example:
             >>> branch = FFN(input_variables=['a', 'c'], output_variables=20)
@@ -74,22 +75,6 @@ class DeepONet(torch.nn.Module):
         self.trunk_net = trunk_net
         self.branch_net = branch_net
 
-        # if features:
-            # if len(features) != features_net.layers[0].in_features:
-            #     raise ValueError('Incompatible features')
-            # if trunk_out_dim != features_net.layers[-1].out_features:
-            #     raise ValueError('Incompatible features')
-
-            # self.features = features
-            # self.features_net = nn.Sequential(
-            #     nn.Linear(len(features), 10), nn.Softplus(),
-            #     # nn.Linear(10, 10), nn.Softplus(),
-            #     nn.Linear(10, trunk_out_dim)
-            # )
-            # self.features_net = nn.Sequential(
-            #     nn.Linear(len(features), trunk_out_dim)
-            # )
-
         self.reduction = nn.Linear(trunk_out_dim, self.output_dimension)
 
     @property
@@ -105,41 +90,16 @@ class DeepONet(torch.nn.Module):
         :return: the output computed by the model.
         :rtype: LabelTensor
         """
-        # print(x.shape)
-        #input_feature = []
-        #for feature in self.features:
-        #    #print(feature)
-        #    input_feature.append(feature(x))
-        #input_feature = torch.cat(input_feature, dim=1)
 
         branch_output = self.branch_net(
            x.extract(self.branch_net.input_variables))
-        # print(branch_output.shape)
+
         trunk_output = self.trunk_net(
            x.extract(self.trunk_net.input_variables))
-        # print(trunk_output.shape)
-        #feat_output = self.features_net(input_feature)
-        # print(feat_output.shape)
-        # inner_input = torch.cat([
-        #     branch_output * trunk_output,
-        #     branch_output,
-        #     trunk_output,
-        #     feat_output], dim=1)
-        # print(inner_input.shape)
 
-        # output_ = self.reduction(inner_input)
-        # print(output_.shape)
         output_ = self.reduction(trunk_output * branch_output)
-        # output_ = LabelTensor(output_, self.output_variables)
+
         output_ = output_.as_subclass(LabelTensor)
         output_.labels = self.output_variables
-        # local_size = int(trunk_output.shape[1]/self.output_dimension)
+
-        # for i, var in enumerate(self.output_variables):
-        #     start = i*local_size
-        #     stop = (i+1)*local_size
-        #     local_output = LabelTensor(torch.sum(branch_output[:, start:stop] * trunk_output[:, start:stop], dim=1).reshape(-1, 1), var)
-        #     if i==0:
-        #         output_ = local_output
-        #     else:
-        #         output_ = output_.append(local_output)
         return output_