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device = "cuda:0" |
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is_half = True |
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n_cpu = 0 |
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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, unknown = parser.parse_known_args() |
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python_cmd = cmd_opts.pycmd |
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listen_port = cmd_opts.port |
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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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import sys |
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import torch |
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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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if device not in ["cpu", "mps"]: |
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gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1])) |
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if "16" in gpu_name or "MX" in gpu_name: |
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print("16系显卡/MX系显卡强制单精度") |
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is_half = False |
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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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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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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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