Format code (#932)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

infer_cli.py

@@ -234,16 +234,12 @@ def get_vc(model_path):
     version = cpt.get("version", "v1")
     if version == "v1":
         if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(
-                *cpt["config"], is_half=is_half
-            )
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
         else:
             net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
     elif version == "v2":
         if if_f0 == 1:
-            net_g = SynthesizerTrnMs768NSFsid(
-                *cpt["config"], is_half=is_half
-            )
+            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
         else:
             net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
     del net_g.enc_q

tools/calc_rvc_model_similarity.py

@@ -1,10 +1,11 @@
 # This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py
 # Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models.
-import sys,os
+import sys, os
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+
 def cal_cross_attn(to_q, to_k, to_v, rand_input):
     hidden_dim, embed_dim = to_q.shape
     attn_to_q = nn.Linear(hidden_dim, embed_dim, bias=False)
@@ -16,41 +17,50 @@ def cal_cross_attn(to_q, to_k, to_v, rand_input):
 
     return torch.einsum(
         "ik, jk -> ik",
-        F.softmax(torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)), dim=-1),
-        attn_to_v(rand_input)
+        F.softmax(
+            torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)),
+            dim=-1,
+        ),
+        attn_to_v(rand_input),
     )
 
+
 def model_hash(filename):
     try:
         with open(filename, "rb") as file:
             import hashlib
+
             m = hashlib.sha256()
 
             file.seek(0x100000)
             m.update(file.read(0x10000))
             return m.hexdigest()[0:8]
     except FileNotFoundError:
-        return 'NOFILE'
+        return "NOFILE"
+
 
 def eval(model, n, input):
     qk = f"enc_p.encoder.attn_layers.{n}.conv_q.weight"
     uk = f"enc_p.encoder.attn_layers.{n}.conv_k.weight"
     vk = f"enc_p.encoder.attn_layers.{n}.conv_v.weight"
-    atoq, atok, atov = model[qk][:,:,0], model[uk][:,:,0], model[vk][:,:,0]
+    atoq, atok, atov = model[qk][:, :, 0], model[uk][:, :, 0], model[vk][:, :, 0]
 
     attn = cal_cross_attn(atoq, atok, atov, input)
     return attn
 
-def main(path,root):
+
+def main(path, root):
     torch.manual_seed(114514)
     model_a = torch.load(path, map_location="cpu")["weight"]
 
-    print("query:\t\t%s\t%s"%(path,model_hash(path)))
+    print("query:\t\t%s\t%s" % (path, model_hash(path)))
 
     map_attn_a = {}
     map_rand_input = {}
     for n in range(6):
-        hidden_dim, embed_dim,_ = model_a[f"enc_p.encoder.attn_layers.{n}.conv_v.weight"].shape
+        hidden_dim, embed_dim, _ = model_a[
+            f"enc_p.encoder.attn_layers.{n}.conv_v.weight"
+        ].shape
         rand_input = torch.randn([embed_dim, hidden_dim])
 
         map_attn_a[n] = eval(model_a, n, rand_input)
@@ -59,7 +69,7 @@ def main(path,root):
     del model_a
 
     for name in sorted(list(os.listdir(root))):
-        path="%s/%s"%(root,name)
+        path = "%s/%s" % (root, name)
         model_b = torch.load(path, map_location="cpu")["weight"]
 
         sims = []
@@ -70,9 +80,13 @@ def main(path,root):
             sim = torch.mean(torch.cosine_similarity(attn_a, attn_b))
             sims.append(sim)
 
-        print("reference:\t%s\t%s\t%s"%(path,model_hash(path),f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%"))
+        print(
+            "reference:\t%s\t%s\t%s"
+            % (path, model_hash(path), f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%")
+        )
+
 
 if __name__ == "__main__":
-    query_path=r"weights\mi v3.pth"
-    reference_root=r"weights"
-    main(query_path,reference_root)
+    query_path = r"weights\mi v3.pth"
+    reference_root = r"weights"
+    main(query_path, reference_root)