106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
########################硬件参数########################
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# 填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
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device = "cuda:0"
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# 9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
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is_half = True
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# 默认0用上所有线程，写数字限制CPU资源使用
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n_cpu = 0
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########################硬件参数########################
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##################下为参数处理逻辑，勿动##################
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########################命令行参数########################
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--port", type=int, default=7865, help="Listen port")
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parser.add_argument("--pycmd", type=str, default="python", help="Python command")
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parser.add_argument("--colab", action="store_true", help="Launch in colab")
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parser.add_argument(
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"--noparallel", action="store_true", help="Disable parallel processing"
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)
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parser.add_argument(
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"--noautoopen", action="store_true", help="Do not open in browser automatically"
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)
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cmd_opts = parser.parse_args()
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python_cmd = cmd_opts.pycmd
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listen_port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
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iscolab = cmd_opts.colab
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noparallel = cmd_opts.noparallel
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noautoopen = cmd_opts.noautoopen
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########################命令行参数########################
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import sys
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import torch
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# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
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# check `getattr` and try it for compatibility
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def has_mps() -> bool:
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if sys.platform != "darwin":
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return False
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else:
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if not getattr(torch, "has_mps", False):
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return False
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try:
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torch.zeros(1).to(torch.device("mps"))
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return True
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except Exception:
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return False
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if not torch.cuda.is_available():
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if has_mps():
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print("没有发现支持的N卡, 使用MPS进行推理")
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device = "mps"
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else:
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print("没有发现支持的N卡, 使用CPU进行推理")
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device = "cpu"
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is_half = False
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gpu_mem=None
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if device not in ["cpu", "mps"]:
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i_device=int(device.split(":")[-1])
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gpu_name = torch.cuda.get_device_name(i_device)
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if "16" in gpu_name or "P40"in gpu_name.upper() or "1070"in gpu_name or "1080"in gpu_name:
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print("16系显卡强制单精度")
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is_half = False
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with open("configs/32k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/32k.json","w")as f:f.write(strr)
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with open("configs/40k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/40k.json","w")as f:f.write(strr)
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with open("configs/48k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/48k.json","w")as f:f.write(strr)
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with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0")
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with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr)
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gpu_mem=int(torch.cuda.get_device_properties(i_device).total_memory/1024/1024/1024+0.4)
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if(gpu_mem<=4):
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with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0")
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with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr)
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from multiprocessing import cpu_count
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if n_cpu == 0:
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n_cpu = cpu_count()
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if is_half:
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# 6G显存配置
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x_pad = 3
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x_query = 10
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x_center = 60
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x_max = 65
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else:
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# 5G显存配置
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x_pad = 1
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x_query = 6
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x_center = 38
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x_max = 41
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if(gpu_mem!=None and gpu_mem<=4):
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x_pad = 1
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x_query = 5
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x_center = 30
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x_max = 32 |