move cutting of spec to baseline model since it's only necessary there

models/baseline.py

@@ -47,6 +47,7 @@ class CNN(pl.LightningModule):
     def forward(self, x):
         # x = x.unsqueeze(1)
 
+        x = x[:, :, :, :512]
         # init bn
         x = self.bn_init(x)
         # print(x.shape)
@@ -84,20 +85,6 @@ class CNN(pl.LightningModule):
     def my_loss(self, y_hat, y):
         return my_loss(y_hat, y)
 
-    def forward_full_song(self, x, y):
-        # print(x.shape)
-        #TODO full song???
-        return self.forward(x[:, :, :, :512])
-        # y_hat = torch.zeros((x.shape[0], 56), requires_grad=True).cuda()
-        # hop_size = 256
-        # i=0
-        # count = 0
-        # while i < x.shape[-1]:
-        #     y_hat += self.forward(x[:,:,:,i:i+512])
-        #     i += hop_size
-        #     count += 1
-        # return y_hat/count
-
     def training_step(self, data_batch, batch_nb):
         x, _, y = data_batch
         y_hat = self.forward_full_song(x, y)

models/shared_stuff.py

@@ -24,7 +24,7 @@ def validation_step(model, data_batch, batch_nb):
     x, _, y = data_batch
     # print("x", x)
     # print("y", y)
-    y_hat = model.forward(x[:, :, :, :512])  # TODO: why is this necessary?
+    y_hat = model.forward(x)
     y = y.float()
     y_hat = y_hat.float()
     #print("y", y)
@@ -77,7 +77,7 @@ def test_step(model, data_batch, batch_nb):
     x, _, y = data_batch
     # print("x", x)
     # print("y", y)
-    y_hat = model.forward(x[:, :, :, :512])  # TODO why is this necessary?
+    y_hat = model.forward(x)
     y = y.float()
     y_hat = y_hat.float()
     rocauc = metrics.roc_auc_score(y.t().cpu(), y_hat.t().cpu(), average='macro')