optimize(infer): move transforms into rvc

infer/lib/infer_pack/modules.py

@@ -12,7 +12,7 @@ from torch.nn.utils import remove_weight_norm, weight_norm
 
 from rvc import utils
 from rvc.utils import get_padding, call_weight_data_normal_if_Conv
-from infer.lib.infer_pack.transforms import piecewise_rational_quadratic_transform
+from rvc.transforms import piecewise_rational_quadratic_transform
 
 LRELU_SLOPE = 0.1
 
@@ -583,7 +583,7 @@ class ConvFlow(nn.Module):
         reverse=False,
     ):
         x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
+        h: torch.Tensor = self.pre(x0)
         h = self.convs(h, x_mask, g=g)
         h = self.proj(h) * x_mask
 

rvc/transforms.py

@@ -8,13 +8,13 @@ DEFAULT_MIN_DERIVATIVE = 1e-3
 
 
 def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
+    inputs: torch.Tensor,
+    unnormalized_widths: torch.Tensor,
+    unnormalized_heights: torch.Tensor,
+    unnormalized_derivatives: torch.Tensor,
+    inverse: bool = False,
+    tails: str | None = None,
+    tail_bound: float = 1.0,
     min_bin_width=DEFAULT_MIN_BIN_WIDTH,
     min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
     min_derivative=DEFAULT_MIN_DERIVATIVE,
@@ -46,13 +46,13 @@ def searchsorted(bin_locations, inputs, eps=1e-6):
 
 
 def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
+    inputs: torch.Tensor,
+    unnormalized_widths: torch.Tensor,
+    unnormalized_heights: torch.Tensor,
+    unnormalized_derivatives: torch.Tensor,
+    inverse: bool = False,
+    tails: str = "linear",
+    tail_bound: float = 1.0,
     min_bin_width=DEFAULT_MIN_BIN_WIDTH,
     min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
     min_derivative=DEFAULT_MIN_DERIVATIVE,
@@ -96,11 +96,11 @@ def unconstrained_rational_quadratic_spline(
 
 
 def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
+    inputs: torch.Tensor,
+    unnormalized_widths: torch.Tensor,
+    unnormalized_heights: torch.Tensor,
+    unnormalized_derivatives: torch.Tensor,
+    inverse: bool = False,
     left=0.0,
     right=1.0,
     bottom=0.0,