switch to instance norm

models.py

@@ -72,26 +72,27 @@ frontend = {
   'Mel': lambda sr, nfft, sampleDur, n_mel : nn.Sequential(
     STFT(nfft, int((sampleDur*sr - nfft)/128)),
     MelFilter(sr, nfft, n_mel, sr//nfft, sr//2),
-    nn.BatchNorm2d(1, affine=False),
+    nn.InstanceNorm2d(1),
     u.Croper2D(n_mel, 128)
   ),
   'logMel': lambda sr, nfft, sampleDur, n_mel : nn.Sequential(
     STFT(nfft, int((sampleDur*sr - nfft)/128)),
     MelFilter(sr, nfft, n_mel, 0, sr//2),
     Log1p(7, trainable=False),
-    nn.BatchNorm2d(1, affine=False),
+    nn.Instancenorm2d(1),
     u.Croper2D(n_mel, 128)
   ),
   'logSTFT': lambda sr, nfft, sampleDur, n_mel : nn.Sequential(
     STFT(nfft, int((sampleDur*sr - nfft)/128)),
     Log1p(7, trainable=False),
-    nn.BatchNorm2d(1, affine=False),
+    nn.InstanceNorm2d(1),
     u.Croper2D(n_mel, 128)
   ),
   'pcenMel': lambda sr, nfft, sampleDur, n_mel : nn.Sequential(
     STFT(nfft, int((sampleDur*sr - nfft)/128)),
     MelFilter(sr, nfft, n_mel, sr//nfft, sr//2),
     PCENLayer(n_mel),
+    nn.InstanceNorm2d(1),
     u.Croper2D(n_mel, 128)
   )
 }

train_AE.py

@@ -79,7 +79,7 @@ for epoch in range(100_000//len(loader)):
             scheduler.step()
 
             # Actual test
-            model.eval()
+            model[1:].eval()
             with torch.no_grad():
                 encodings, idxs = [], []
                 for x, idx in tqdm(loader, desc='test '+str(step), leave=False):
@@ -142,5 +142,5 @@ for epoch in range(100_000//len(loader)):
             writer.add_histogram('K-Means Recalls ', np.array(recs), step)
             df.drop('cluster', axis=1, inplace=True)
             print('\r', end='')
-            model.train()
+            model[1:].train()
         step += 1