adapt base model for already dense model

braindecode/models/base.py

@@ -15,6 +15,9 @@ from braindecode.datautil.signal_target import SignalAndTarget
 from braindecode.models.util import to_dense_prediction_model
 from braindecode.torch_ext.schedulers import CosineAnnealing, ScheduledOptimizer
 from braindecode.torch_ext.util import np_to_var, var_to_np
+import logging
+
+log = logging.getLogger(__name__)
 
 
 def find_optimizer(optimizer_name):
@@ -79,7 +82,14 @@ class BaseModel(object):
         self.loss = loss
         self._ensure_network_exists()
         if cropped:
-            to_dense_prediction_model(self.network)
+            model_already_dense = np.any([
+                hasattr(m, 'dilation') and (m.dilation != 1) and
+                (m.dilation) != (1, 1) for m in self.network.modules()
+            ])
+            if not model_already_dense:
+                to_dense_prediction_model(self.network)
+            else:
+                log.info("Seems model was already converted to dense model...")
         if not hasattr(optimizer, 'step'):
             optimizer_class = find_optimizer(optimizer)
             optimizer = optimizer_class(self.network.parameters())
@@ -141,7 +151,9 @@ class BaseModel(object):
                              "the network in a single pass.")
         if self.cropped:
             self.network.eval()
-            test_input = np_to_var(train_X[0:1], dtype=np.float32)
+            test_input = np_to_var(np.ones(
+                (1, train_X.shape[1], input_time_length,) + train_X.shape[3:],
+                dtype=np.float32))
             while len(test_input.size()) < 4:
                 test_input = test_input.unsqueeze(-1)
             if self.cuda: