cudnn.benchmark = True device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

As an AI language model, I can tell you that this code seems to be setting the cudnn benchmark to True and checking if a CUDA-enabled device is available. If a CUDA-enabled device is available, it sets the device to be used as 'cuda:0', otherwise it sets the device to be used as 'cpu'.

The cudnn.benchmark setting is used to optimize the performance of the convolutional neural network (CNN) during training by finding the best algorithm to use for the given hardware. This can speed up training time, but may take longer to set up the first time the code is run.

Overall, this code seems to be preparing the environment for using a GPU (if available) to speed up training of a CNN.

相关问题

try: import thop except ImportError: thop = None logger = logging.getLogger(__name__) @contextmanager def torch_distributed_zero_first(local_rank: int): if local_rank not in [-1, 0]: torch.distributed.barrier() yield if local_rank == 0: torch.distributed.barrier() def init_torch_seeds(seed=0): torch.manual_seed(seed) if seed == 0: cudnn.benchmark, cudnn.deterministic = False, True else: cudnn.benchmark, cudnn.deterministic = True, False def select_device(device='', batch_size=None): s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' cpu = device.lower() == 'cpu' if cpu: os.environ['CUDA_VISIBLE_DEVICES'] = '-1' elif device: # non-cpu device requested os.environ['CUDA_VISIBLE_DEVICES'] = device assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' 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" s += 'CPU\n' logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe return torch.device('cuda:0' if cuda else 'cpu') def time_synchronized(): if torch.cuda.is_available(): torch.cuda.synchronize() return time.time()

这段代码是一个Python脚本，包含了一些用于PyTorch训练的实用函数。具体来说，它包含了以下函数：

- import thop：导入PyTorch的模块thop，用于计算模型的FLOPs和参数数量。
- torch_distributed_zero_first：用于在分布式训练中同步所有进程的执行顺序。
- init_torch_seeds：用于初始化PyTorch的随机数种子。
- select_device：用于选择PyTorch的设备，可以选择CPU或GPU。
- time_synchronized：用于在CPU和GPU之间同步时间。

这些函数都是用于方便PyTorch训练的实用函数，可以大大简化代码编写。

global args args = parser.parse_args() os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "0" device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Create save directory if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) model_dir = "./%s/%s_layer_%d_lr_%.4f_ratio_%.2f" % (args.save_dir, args.model, args.layer_num, args.lr, args.sensing_rate) log_file_name = "%s/%s_layer_%d_lr_%.4f_ratio_%d.txt" % (model_dir, args.model, args.layer_num, args.lr, args.sensing_rate) if not os.path.exists(model_dir): print("model_dir:", model_dir) os.mkdir(model_dir) torch.backends.cudnn.benchmark = True

这段代码是一个 PyTorch 训练脚本的一部分。首先，它通过 argparse 模块解析命令行参数。然后，它设置了 CUDA 环境变量，指定使用 GPU 进行训练。接着，它创建了一个保存模型的目录，如果目录不存在的话就会创建。其中，模型路径的命名规则为“save_dir/model_layer_层数_lr_学习率_ratio_感知率”。同时，它还创建了一个日志文件，用于保存训练过程中的指标和日志信息。如果模型目录不存在，它还会对其进行创建。最后，它启用了一个 PyTorch 的优化器加速库，以加速训练过程。