「解析」Pytorch 自动选择设备训练

在使用 Pytorch 进行训练模型的时候，常常需要配置 CPU、GPU训练，甚至多卡用户还需要管理GPU。除此之外，用户虽然配置好了显卡配置，但是还需要检测下配置，以防报错，因此需要还是需要检测下

在此，通过 select_device 函数进行判断，首先获取 时间信息 和 torch的版本信息，然后首先判断是否是 cpu设备，然后再判断 GPU设备

如果是多卡用户，记得 batch_size 与 cuda数量匹配
assert batch_size % n == 0, f’batch-size {batch_size} not multiple of GPU count {n}’

import os
import torch
import datetime


def date_modified(path=__file__):
    # return human-readable file modification date, i.e. '2021-3-26'
    t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)
    return f'{
      t.year}-{
      t.month}-{
      t.day}'


def select_device(device='', batch_size=None):
    # device = 'cpu' or '0' or '0,1,2,3'
    s = f'YOLOv5  {
      date_modified()} torch {
      torch.__version__} '  # string
    cpu = device.lower() == 'cpu'
    if cpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
    elif device:  # non-cpu device requested
        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {
      device} requested'  # check availability

    cuda = not cpu and torch.cuda.is_available()
    if cuda:
        n = torch.cuda.device_count()
        if n > 1 and batch_size:  # check that batch_size is compatible with device_count
            assert batch_size % n == 0, f'batch-size {
      batch_size} not multiple of GPU count {
      n}'
        space = ' ' * len(s)
        for i, d in enumerate(device.split(',') if device else range(n)):
            p = torch.cuda.get_device_properties(i)
            s += f"{
      '' if i == 0 else space}CUDA:{
      d} ({
      p.name}, {
      p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
    else:
        s += 'CPU\n'

    # logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe
    print(s)
    return torch.device('cuda:0' if cuda else 'cpu')


# Print 结果
# Out[1]: 'YOLOv5  2021-7-2 torch 1.13+cu116 CUDA:0 (NVIDIA GeForce RTX 4090, 24563.5MB)\n'