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2 changed files with 29 additions and 19 deletions
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8
infer_cli.py
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@ -234,16 +234,12 @@ def get_vc(model_path):
version = cpt.get("version", "v1") version = cpt.get("version", "v1")
if version == "v1": if version == "v1":
if if_f0 == 1: if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid( net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
*cpt["config"], is_half=is_half
)
else: else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2": elif version == "v2":
if if_f0 == 1: if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid( net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
*cpt["config"], is_half=is_half
)
else: else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g.enc_q del net_g.enc_q

40
tools/calc_rvc_model_similarity.py
View File

@ -1,10 +1,11 @@
# This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py # 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. # 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
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
def cal_cross_attn(to_q, to_k, to_v, rand_input): def cal_cross_attn(to_q, to_k, to_v, rand_input):
hidden_dim, embed_dim = to_q.shape hidden_dim, embed_dim = to_q.shape
attn_to_q = nn.Linear(hidden_dim, embed_dim, bias=False) 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( return torch.einsum(
"ik, jk -> ik", "ik, jk -> ik",
F.softmax(torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)), dim=-1), F.softmax(
attn_to_v(rand_input) 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): def model_hash(filename):
try: try:
with open(filename, "rb") as file: with open(filename, "rb") as file:
import hashlib import hashlib
m = hashlib.sha256() m = hashlib.sha256()
file.seek(0x100000) file.seek(0x100000)
m.update(file.read(0x10000)) m.update(file.read(0x10000))
return m.hexdigest()[0:8] return m.hexdigest()[0:8]
except FileNotFoundError: except FileNotFoundError:
return 'NOFILE' return "NOFILE"
def eval(model, n, input): def eval(model, n, input):
qk = f"enc_p.encoder.attn_layers.{n}.conv_q.weight" qk = f"enc_p.encoder.attn_layers.{n}.conv_q.weight"
uk = f"enc_p.encoder.attn_layers.{n}.conv_k.weight" uk = f"enc_p.encoder.attn_layers.{n}.conv_k.weight"
vk = f"enc_p.encoder.attn_layers.{n}.conv_v.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) attn = cal_cross_attn(atoq, atok, atov, input)
return attn return attn
def main(path,root):
def main(path, root):
torch.manual_seed(114514) torch.manual_seed(114514)
model_a = torch.load(path, map_location="cpu")["weight"] 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_attn_a = {}
map_rand_input = {} map_rand_input = {}
for n in range(6): 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]) rand_input = torch.randn([embed_dim, hidden_dim])
map_attn_a[n] = eval(model_a, n, rand_input) map_attn_a[n] = eval(model_a, n, rand_input)
@ -59,7 +69,7 @@ def main(path,root):
del model_a del model_a
for name in sorted(list(os.listdir(root))): 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"] model_b = torch.load(path, map_location="cpu")["weight"]
sims = [] sims = []
@ -70,9 +80,13 @@ def main(path,root):
sim = torch.mean(torch.cosine_similarity(attn_a, attn_b)) sim = torch.mean(torch.cosine_similarity(attn_a, attn_b))
sims.append(sim) 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__": if __name__ == "__main__":
query_path=r"weights\mi v3.pth" query_path = r"weights\mi v3.pth"
reference_root=r"weights" reference_root = r"weights"
main(query_path,reference_root) main(query_path, reference_root)