Merge pull request #3 from sliwy/test_fix

restored previous tests behavior

test/unit_tests/test_eegneuralnet.py

@@ -27,7 +27,7 @@ def __getitem__(self, item):
         return torch.rand(3, 10), item % 4
 
 
-class MockModule(EEGModuleMixin, torch.nn.Module):
+class MockModule1(EEGModuleMixin, torch.nn.Module):
     def __init__(
             self,
             preds,
@@ -46,7 +46,30 @@ def __init__(
             input_window_seconds=input_window_seconds,
             sfreq=sfreq,
         )
-        # self.preds = to_tensor(preds, device='cpu')
+        self.preds = to_tensor(preds, device='cpu')
+        self.final_layer = torch.nn.Conv1d(self.n_chans, self.n_outputs, self.n_times)
+
+    def forward(self, x):
+        return self.preds
+
+class MockModule2(EEGModuleMixin, torch.nn.Module):
+    def __init__(
+            self,
+            n_outputs=None,
+            n_chans=None,
+            chs_info=None,
+            n_times=None,
+            input_window_seconds=None,
+            sfreq=None,
+    ):
+        super().__init__(
+            n_outputs=n_outputs,
+            n_chans=n_chans,
+            chs_info=chs_info,
+            n_times=n_times,
+            input_window_seconds=input_window_seconds,
+            sfreq=sfreq,
+        )
         self.final_layer = torch.nn.Conv1d(self.n_chans, self.n_outputs, self.n_times)
 
     def forward(self, x):
@@ -138,15 +161,16 @@ def test_trialwise_predict_and_predict_proba(eegneuralnet_cls):
             [0.125, 0.875],
             [1., 0.],
             [0.8, 0.2],
+            [0.8, 0.2],
             [0.9, 0.1],
         ]
     )
     eegneuralnet = eegneuralnet_cls(
-        MockModule,
+        MockModule1,
         module__preds=preds,
         module__n_outputs=2,
         module__n_chans=3,
-        module__n_times=3,
+        module__n_times=10,
         optimizer=optim.Adam,
         batch_size=32
     )
@@ -158,7 +182,7 @@ def test_trialwise_predict_and_predict_proba(eegneuralnet_cls):
 
 def test_cropped_predict_and_predict_proba(eegneuralnet_cls, preds):
     eegneuralnet = eegneuralnet_cls(
-        MockModule,
+        MockModule1,
         module__preds=preds,
         module__n_outputs=4,
         module__n_chans=3,
@@ -180,7 +204,7 @@ def test_cropped_predict_and_predict_proba(eegneuralnet_cls, preds):
 
 def test_cropped_predict_and_predict_proba_not_aggregate_predictions(eegneuralnet_cls, preds):
     eegneuralnet = eegneuralnet_cls(
-        MockModule,
+        MockModule1,
         module__preds=preds,
         module__n_outputs=4,
         module__n_chans=3,
@@ -200,7 +224,7 @@ def test_cropped_predict_and_predict_proba_not_aggregate_predictions(eegneuralne
 
 def test_predict_trials(eegneuralnet_cls, preds):
     eegneuralnet = eegneuralnet_cls(
-        MockModule,
+        MockModule1,
         module__preds=preds,
         module__n_outputs=4,
         module__n_chans=3,
@@ -219,7 +243,7 @@ def test_predict_trials(eegneuralnet_cls, preds):
 
 def test_clonable(eegneuralnet_cls, preds):
     eegneuralnet = eegneuralnet_cls(
-        MockModule,
+        MockModule1,
         module__preds=preds,
         module__n_outputs=4,
         module__n_chans=3,
@@ -241,8 +265,7 @@ def test_clonable(eegneuralnet_cls, preds):
 def test_set_signal_params_numpy(eegneuralnet_cls, preds, Xy):
     X, y = Xy
     net = eegneuralnet_cls(
-        MockModule,
-        module__preds=preds,
+        MockModule2,
         cropped=False,
         optimizer=optim.Adam,
         batch_size=32,
@@ -262,9 +285,8 @@ def test_set_signal_params_epochs(eegneuralnet_cls, preds):
 def test_set_signal_params_torch_ds(eegneuralnet_cls, preds):
     n_outputs = (1 if eegneuralnet_cls == EEGRegressor else 4)
     net = eegneuralnet_cls(
-        MockModule,
+        MockModule2,
         module__n_outputs=n_outputs,
-        module__preds=preds,
         cropped=False,
         optimizer=optim.Adam,
         batch_size=32,
@@ -280,8 +302,7 @@ def test_set_signal_params_torch_ds(eegneuralnet_cls, preds):
 def test_set_signal_params_windows_ds_metadata(eegneuralnet_cls, preds, windows_dataset_metadata):
     n_outputs = (1 if eegneuralnet_cls == EEGRegressor else 4)
     net = eegneuralnet_cls(
-        MockModule,
-        module__preds=preds,
+        MockModule2,
         cropped=False,
         optimizer=optim.Adam,
         batch_size=32,
@@ -297,8 +318,7 @@ def test_set_signal_params_windows_ds_metadata(eegneuralnet_cls, preds, windows_
 def test_set_signal_params_windows_ds_channels(eegneuralnet_cls, preds, windows_dataset_channels):
     n_outputs = (1 if eegneuralnet_cls == EEGRegressor else 4)
     net = eegneuralnet_cls(
-        MockModule,
-        module__preds=preds,
+        MockModule2,
         module__n_outputs=n_outputs,
         cropped=False,
         optimizer=optim.Adam,
@@ -315,8 +335,7 @@ def test_set_signal_params_windows_ds_channels(eegneuralnet_cls, preds, windows_
 def test_set_signal_params_concat_ds_metadata(eegneuralnet_cls, preds, concat_dataset_metadata):
     n_outputs = (1 if eegneuralnet_cls == EEGRegressor else 4)
     net = eegneuralnet_cls(
-        MockModule,
-        module__preds=preds,
+        MockModule2,
         cropped=False,
         optimizer=optim.Adam,
         batch_size=32,
@@ -332,8 +351,7 @@ def test_set_signal_params_concat_ds_metadata(eegneuralnet_cls, preds, concat_da
 def test_set_signal_params_concat_ds_channels(eegneuralnet_cls, preds, concat_dataset_channels):
     n_outputs = (1 if eegneuralnet_cls == EEGRegressor else 4)
     net = eegneuralnet_cls(
-        MockModule,
-        module__preds=preds,
+        MockModule2,
         module__n_outputs=n_outputs,
         cropped=False,
         optimizer=optim.Adam,