From ecc744d748a72913140176d6c26f159fe602c6b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?= <41315874+fumiama@users.noreply.github.com> Date: Tue, 11 Apr 2023 18:14:55 +0800 Subject: [PATCH] =?UTF-8?q?optimize:=20=E7=B2=BE=E7=AE=80=E6=9C=AA?= =?UTF-8?q?=E7=94=A8=E5=88=B0=E7=9A=84=E9=85=8D=E7=BD=AE=E9=A1=B9=E5=B9=B6?= =?UTF-8?q?=E5=9C=A8=E7=89=B9=E5=BE=81=E6=8F=90=E5=8F=96=E5=88=9D=E6=AD=A5?= =?UTF-8?q?=E5=BC=95=E5=85=A5mps=20(#32)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config.py | 77 ++++++++++++++++++--------- extract_feature_print.py | 22 ++++---- infer-web.py | 10 ++-- infer_pack/models.py | 2 +- infer_pack/models_onnx.py | 2 +- infer_uvr5.py | 2 +- slicer2.py | 2 +- trainset_preprocess_pipeline_print.py | 14 ++--- uvr5_pack/utils.py | 2 +- vc_infer_pipeline.py | 6 +-- 10 files changed, 82 insertions(+), 57 deletions(-) diff --git a/config.py b/config.py index 086f882..bffccf7 100644 --- a/config.py +++ b/config.py @@ -1,3 +1,20 @@ +########################硬件参数######################## + +#填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速 +device = "cuda:0" + +#9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速 +is_half = True + +#默认0用上所有线程，写数字限制CPU资源使用 +n_cpu = 0 + +########################硬件参数######################## + + +##################下为参数处理逻辑，勿动################## + +########################命令行参数######################## import argparse parser = argparse.ArgumentParser() parser.add_argument("--port", type=int, default=7865, help="Listen port") @@ -5,34 +22,48 @@ parser.add_argument("--pycmd", type=str, default="python", help="Python command" parser.add_argument("--colab", action='store_true', help="Launch in colab") parser.add_argument("--noparallel", action='store_true', help="Disable parallel processing") cmd_opts = parser.parse_args() -############离线VC参数 -inp_root=r"白鹭霜华长条"#对输入目录下所有音频进行转换，别放非音频文件 -opt_root=r"opt"#输出目录 -f0_up_key=0#升降调，整数，男转女12，女转男-12 -person=r"weights\洛天依v3.pt"#目前只有洛天依v3 -############硬件参数 -device = "cuda:0"#填写cuda:x或cpu，x指代第几张卡，只支持N卡加速 -is_half=True#9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速 -n_cpu=0#默认0用上所有线程，写数字限制CPU资源使用 -############python命令路径 + python_cmd=cmd_opts.pycmd listen_port=cmd_opts.port iscolab=cmd_opts.colab noparallel=cmd_opts.noparallel -############下头别动 +########################命令行参数######################## + +import sys import torch -if(torch.cuda.is_available()==False): - print("没有发现支持的N卡, 使用CPU进行推理") - device="cpu" - is_half=False -if(device!="cpu"): - gpu_name=torch.cuda.get_device_name(int(device.split(":")[-1])) - if("16"in gpu_name or "MX"in gpu_name): + +# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+. +# check `getattr` and try it for compatibility +def has_mps() -> bool: + if sys.platform != "darwin": + return False + else: + if not getattr(torch, 'has_mps', False): return False + try: + torch.zeros(1).to(torch.device("mps")) + return True + except Exception: + return False + + +if(not torch.cuda.is_available()): + if has_mps(): + print("没有发现支持的N卡, 使用MPS进行推理") + device = "mps" + else: + print("没有发现支持的N卡, 使用CPU进行推理") + device = "cpu" + is_half = False + +if(device not in ["cpu", "mps"]): + gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1])) + if("16" in gpu_name or "MX" in gpu_name): print("16系显卡/MX系显卡强制单精度") - is_half=False + is_half = False + from multiprocessing import cpu_count -if(n_cpu==0):n_cpu=cpu_count() -if(is_half==True): +if(n_cpu==0): n_cpu=cpu_count() +if(is_half): #6G显存配置 x_pad = 3 x_query = 10 @@ -41,10 +72,6 @@ if(is_half==True): else: #5G显存配置 x_pad = 1 - # x_query = 6 - # x_center = 30 - # x_max = 32 - #6G显存配置 x_query = 6 x_center = 38 x_max = 41 diff --git a/extract_feature_print.py b/extract_feature_print.py index 23f08e3..ef05bd4 100644 --- a/extract_feature_print.py +++ b/extract_feature_print.py @@ -1,13 +1,12 @@ import os,sys,traceback -if len(sys.argv) == 4: - n_part=int(sys.argv[1]) - i_part=int(sys.argv[2]) - exp_dir=sys.argv[3] -else: - n_part=int(sys.argv[1]) - i_part=int(sys.argv[2]) - i_gpu=sys.argv[3] +device=sys.argv[1] +n_part=int(sys.argv[2]) +i_part=int(sys.argv[3]) +if len(sys.argv) == 5: exp_dir=sys.argv[4] +else: + i_gpu=sys.argv[4] + exp_dir=sys.argv[5] os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) import torch @@ -15,7 +14,6 @@ import torch.nn.functional as F import soundfile as sf import numpy as np from fairseq import checkpoint_utils -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") f = open("%s/extract_f0_feature.log"%exp_dir, "a+") def printt(strr): @@ -50,8 +48,8 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( ) model = models[0] model = model.to(device) -if torch.cuda.is_available(): - model = model.half() +printt("move model to "+device) +if device != "cpu": model = model.half() model.eval() todo=sorted(list(os.listdir(wavPath)))[i_part::n_part] @@ -70,7 +68,7 @@ else: feats = readwave(wav_path, normalize=saved_cfg.task.normalize) padding_mask = torch.BoolTensor(feats.shape).fill_(False) inputs = { - "source": feats.half().to(device) if torch.cuda.is_available() else feats.to(device), + "source": feats.half().to(device) if device != "cpu" else feats.to(device), "padding_mask": padding_mask.to(device), "output_layer": 9, # layer 9 } diff --git a/infer-web.py b/infer-web.py index d2cd506..7ce8890 100644 --- a/infer-web.py +++ b/infer-web.py @@ -36,7 +36,7 @@ from fairseq import checkpoint_utils import gradio as gr import logging from vc_infer_pipeline import VC -from config import is_half,device,is_half,python_cmd,listen_port,iscolab,noparallel +from config import is_half,device,python_cmd,listen_port,iscolab,noparallel from infer_uvr5 import _audio_pre_ from my_utils import load_audio from train.process_ckpt import show_info,change_info,merge,extract_small_model @@ -53,7 +53,7 @@ class ToolButton(gr.Button, gr.components.FormComponent): hubert_model=None def load_hubert(): global hubert_model - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",) + models, _, _ = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",) hubert_model = models[0] hubert_model = hubert_model.to(device) if(is_half):hubert_model = hubert_model.half() @@ -79,7 +79,7 @@ def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_np if(hubert_model==None):load_hubert() if_f0 = cpt.get("f0", 1) audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file) - print("npy: ", times[0], "s, f0:", times[1], "s, infer: ", times[2], "s", sep='') + print("npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep='') return "Success", (tgt_sr, audio_opt) except: info=traceback.format_exc() @@ -267,7 +267,7 @@ def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir): leng=len(gpus) ps=[] for idx,n_g in enumerate(gpus): - cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir) + cmd=python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s"%(device,leng,idx,n_g,now_dir,exp_dir) print(cmd) p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir ps.append(p) @@ -382,7 +382,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth leng=len(gpus) ps=[] for idx,n_g in enumerate(gpus): - cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir1) + cmd=python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s"%(device,leng,idx,n_g,now_dir,exp_dir1) yield get_info_str(cmd) p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir ps.append(p) diff --git a/infer_pack/models.py b/infer_pack/models.py index c47213f..9eda8be 100644 --- a/infer_pack/models.py +++ b/infer_pack/models.py @@ -345,7 +345,7 @@ class SourceModuleHnNSF(torch.nn.Module): def forward(self, x,upp=None): sine_wavs, uv, _ = self.l_sin_gen(x,upp) - if(self.is_half==True):sine_wavs=sine_wavs.half() + if(self.is_half):sine_wavs=sine_wavs.half() sine_merge = self.l_tanh(self.l_linear(sine_wavs)) return sine_merge,None,None# noise, uv class GeneratorNSF(torch.nn.Module): diff --git a/infer_pack/models_onnx.py b/infer_pack/models_onnx.py index 3e36567..ea90d1c 100644 --- a/infer_pack/models_onnx.py +++ b/infer_pack/models_onnx.py @@ -345,7 +345,7 @@ class SourceModuleHnNSF(torch.nn.Module): def forward(self, x,upp=None): sine_wavs, uv, _ = self.l_sin_gen(x,upp) - if(self.is_half==True):sine_wavs=sine_wavs.half() + if(self.is_half):sine_wavs=sine_wavs.half() sine_merge = self.l_tanh(self.l_linear(sine_wavs)) return sine_merge,None,None# noise, uv class GeneratorNSF(torch.nn.Module): diff --git a/infer_uvr5.py b/infer_uvr5.py index b38dd73..209c44d 100644 --- a/infer_uvr5.py +++ b/infer_uvr5.py @@ -39,7 +39,7 @@ class _audio_pre_(): cpk = torch.load( model_path , map_location='cpu') model.load_state_dict(cpk) model.eval() - if(is_half==True):model = model.half().to(device) + if(is_half):model = model.half().to(device) else:model = model.to(device) self.mp = mp diff --git a/slicer2.py b/slicer2.py index a09f0de..78c08f0 100644 --- a/slicer2.py +++ b/slicer2.py @@ -182,4 +182,4 @@ def main(): if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/trainset_preprocess_pipeline_print.py b/trainset_preprocess_pipeline_print.py index a5af367..54e0d3e 100644 --- a/trainset_preprocess_pipeline_print.py +++ b/trainset_preprocess_pipeline_print.py @@ -40,7 +40,7 @@ class PreProcess(): os.makedirs(self.gt_wavs_dir,exist_ok=True) os.makedirs(self.wavs16k_dir,exist_ok=True) - def print(self, strr): + def println(self, strr): mutex.acquire() print(strr) self.f.write("%s\n" % strr) @@ -70,9 +70,9 @@ class PreProcess(): tmp_audio = audio[start:] break self.norm_write(tmp_audio, idx0, idx1) - self.print("%s->Suc."%path) + self.println("%s->Suc."%path) except: - self.print("%s->%s"%(path,traceback.format_exc())) + self.println("%s->%s"%(path,traceback.format_exc())) def pipeline_mp(self,infos): for path, idx0 in infos: @@ -91,14 +91,14 @@ class PreProcess(): ps.append(p) for p in ps:p.join() except: - self.print("Fail. %s"%traceback.format_exc()) + self.println("Fail. %s"%traceback.format_exc()) def preprocess_trainset(inp_root, sr, n_p, exp_dir): pp=PreProcess(sr,exp_dir) - pp.print("start preprocess") - pp.print(sys.argv) + pp.println("start preprocess") + pp.println(sys.argv) pp.pipeline_mp_inp_dir(inp_root,n_p) - pp.print("end preprocess") + pp.println("end preprocess") if __name__=='__main__': preprocess_trainset(inp_root, sr, n_p, exp_dir) diff --git a/uvr5_pack/utils.py b/uvr5_pack/utils.py index cfaffee..6d1e0e9 100644 --- a/uvr5_pack/utils.py +++ b/uvr5_pack/utils.py @@ -27,7 +27,7 @@ def inference(X_spec, device, model, aggressiveness,data): start = i * roi_size X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']] X_mag_window = torch.from_numpy(X_mag_window) - if(is_half==True):X_mag_window=X_mag_window.half() + if(is_half):X_mag_window=X_mag_window.half() X_mag_window=X_mag_window.to(device) pred = model.predict(X_mag_window, aggressiveness) diff --git a/vc_infer_pipeline.py b/vc_infer_pipeline.py index e05ef4c..9f75517 100644 --- a/vc_infer_pipeline.py +++ b/vc_infer_pipeline.py @@ -58,7 +58,7 @@ class VC(object): def vc(self,model,net_g,sid,audio0,pitch,pitchf,times,index,big_npy,index_rate):#,file_index,file_big_npy feats = torch.from_numpy(audio0) - if(self.is_half==True):feats=feats.half() + if(self.is_half):feats=feats.half() else:feats=feats.float() if feats.dim() == 2: # double channels feats = feats.mean(-1) @@ -78,10 +78,10 @@ class VC(object): if(isinstance(index,type(None))==False and isinstance(big_npy,type(None))==False and index_rate!=0): npy = feats[0].cpu().numpy() - if(self.is_half==True):npy=npy.astype("float32") + if(self.is_half):npy=npy.astype("float32") _, I = index.search(npy, 1) npy=big_npy[I.squeeze()] - if(self.is_half==True):npy=npy.astype("float16") + if(self.is_half):npy=npy.astype("float16") feats = torch.from_numpy(npy).unsqueeze(0).to(self.device)*index_rate + (1-index_rate)*feats feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)