support learnable pqmf

diffsptk/core/ipqmf.py

@@ -39,12 +39,15 @@ class InversePseudoQuadratureMirrorFilterBanks(nn.Module):
     alpha : float > 0 [scalar]
         Stopband attenuation in dB.
 
+    learnable : bool [scalar]
+        Whether to make filter-bank coefficients learnable.
+
     **kwargs : additional keyword arguments
         Parameters to find optimal filter-bank coefficients.
 
     """
 
-    def __init__(self, n_band, filter_order, alpha=100, **kwargs):
+    def __init__(self, n_band, filter_order, alpha=100, learnable=False, **kwargs):
         super(InversePseudoQuadratureMirrorFilterBanks, self).__init__()
 
         assert 1 <= n_band
@@ -59,7 +62,11 @@ def __init__(self, n_band, filter_order, alpha=100, **kwargs):
             warnings.warn("Failed to find PQMF coefficients")
         filters = np.expand_dims(filters, 0)
         filters = np.flip(filters, 2).copy()
-        self.register_buffer("filters", numpy_to_torch(filters))
+        filters = numpy_to_torch(filters)
+        if learnable:
+            self.filters = nn.Parameter(filters)
+        else:
+            self.register_buffer("filters", filters)
 
         # Make padding module.
         if filter_order % 2 == 0:

diffsptk/core/pqmf.py

@@ -154,12 +154,15 @@ class PseudoQuadratureMirrorFilterBanks(nn.Module):
     alpha : float > 0 [scalar]
         Stopband attenuation in dB.
 
+    learnable : bool [scalar]
+        Whether to make filter-bank coefficients learnable.
+
     **kwargs : additional keyword arguments
         Parameters to find optimal filter-bank coefficients.
 
     """
 
-    def __init__(self, n_band, filter_order, alpha=100, **kwargs):
+    def __init__(self, n_band, filter_order, alpha=100, learnable=False, **kwargs):
         super(PseudoQuadratureMirrorFilterBanks, self).__init__()
 
         assert 1 <= n_band
@@ -174,7 +177,11 @@ def __init__(self, n_band, filter_order, alpha=100, **kwargs):
             warnings.warn("Failed to find PQMF coefficients")
         filters = np.expand_dims(filters, 1)
         filters = np.flip(filters, 2).copy()
-        self.register_buffer("filters", numpy_to_torch(filters))
+        filters = numpy_to_torch(filters)
+        if learnable:
+            self.filters = nn.Parameter(filters)
+        else:
+            self.register_buffer("filters", filters)
 
         # Make padding module.
         if filter_order % 2 == 0:

tests/test_ipqmf.py

@@ -37,3 +37,8 @@ def test_compatibility(device, a, M, tau=0.01, eps=0.01, K=4, T=20):
     )
 
     U.check_differentiable(device, ipqmf, [K, T], opt={"keepdim": False})
+
+
+def test_learnable(K=4, M=10, T=20):
+    ipqmf = diffsptk.IPQMF(K, M, learnable=True)
+    U.check_learnable(ipqmf, (K, T))

tests/test_pqmf.py

@@ -43,3 +43,8 @@ def test_compatibility(device, a, M, tau=0.01, eps=0.01, K=4, T=20):
 def test_various_shape(K=4, M=10, T=20):
     pqmf = diffsptk.PQMF(K, M)
     U.check_various_shape(pqmf, [(T,), (1, T), (1, 1, T)])
+
+
+def test_learnable(K=4, M=10, T=20):
+    pqmf = diffsptk.PQMF(K, M, learnable=True)
+    U.check_learnable(pqmf, (T,))

tests/utils.py

@@ -184,3 +184,24 @@ def check_various_shape(module, shapes):
             target = y
         else:
             assert torch.allclose(y, target)
+
+
+def check_learnable(module, shape):
+    params_before = []
+    for p in module.parameters():
+        params_before.append(p.clone())
+
+    optimizer = torch.optim.SGD(module.parameters(), lr=0.01)
+    x = torch.randn(*shape)
+    y = module(x)
+    optimizer.zero_grad()
+    loss = y.mean()
+    loss.backward()
+    optimizer.step()
+
+    params_after = []
+    for p in module.parameters():
+        params_after.append(p.clone())
+
+    for pb, pa in zip(params_before, params_after):
+        assert not torch.allclose(pb, pa)