torch.cuda.max_memory_allocated() / MB
时间: 2024-03-09 07:14:23 浏览: 14
这段代码计算了已分配的CUDA内存的最大值,并将其转换为以兆字节(MB)为单位的值。
`torch.cuda.max_memory_allocated()`是PyTorch中的一个函数,用于获取当前进程中已分配的CUDA内存的最大值。它返回的结果是以字节为单位的整数。
通过除以`MB`,将字节转换为兆字节。这里的`MB`是一个自定义的变量,它应该是一个表示兆字节的整数值。
该代码片段的目的是获取已分配的CUDA内存的最大值,并将其以易读的方式(以兆字节为单位)呈现给用户。
如果还有其他问题,请随时提问。
相关问题
torch.save(model.state_dict(), r'./saved_model/' + str(args.arch) + '_' + str(args.batch_size) + '_' + str(args.dataset) + '_' + str(args.epoch) + '.pth') # 计算GFLOPs flops = 0 for name, module in model.named_modules(): if isinstance(module, torch.nn.Conv2d): flops += module.weight.numel() * 2 * module.in_channels * module.out_channels * module.kernel_size[ 0] * module.kernel_size[1] / module.stride[0] / module.stride[1] elif isinstance(module, torch.nn.Linear): flops += module.weight.numel() * 2 * module.in_features start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) start_event.record() with torch.no_grad(): output = UNet(args,3,1).to(device) end_event.record() torch.cuda.synchronize() elapsed_time_ms = start_event.elapsed_time(end_event) gflops = flops / (elapsed_time_ms * 10 ** 6) print("GFLOPs: {:.2f}".format(gflops)) return best_iou, aver_iou, aver_dice, aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, fps, parameters, gflops出现错误 best_iou,aver_iou,aver_dice,aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, FPS, parameters, gflops = val(model,best_iou,val_dataloader) File "D:/BaiduNetdiskDownload/0605_ghostv2unet _tunnelcrack/ghostunet++/UNET++/main.py", line 143, in val return best_iou, aver_iou, aver_dice, aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, fps, parameters, gflops UnboundLocalError: local variable 'gflops' referenced before assignment怎么修改
这个错误是因为在函数中定义了 `gflops` 变量,但是在函数返回时并没有为它赋值,导致出现了未赋值的情况。可以将 `gflops` 变量在函数一开始就赋一个初始值,比如设为0。代码修改如下:
```
def val(model, best_iou, val_dataloader, device):
model.eval()
aver_iou = 0
aver_dice = 0
aver_hd = 0
aver_accuracy = 0
aver_recall = 0
aver_precision = 0
aver_f1score = 0
aver_memory = 0
fps = 0
parameters = sum(param.numel() for param in model.parameters())
gflops = 0 # 在这里为 gflops 赋一个初始值
with torch.no_grad():
for step, (images, labels) in enumerate(val_dataloader):
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
iou, dice, hd, accuracy, recall, precision, f1score = eval_metrics(outputs, labels)
memory = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
aver_iou += iou
aver_dice += dice
aver_hd += hd
aver_accuracy += accuracy
aver_recall += recall
aver_precision += precision
aver_f1score += f1score
aver_memory += memory
aver_iou /= len(val_dataloader)
aver_dice /= len(val_dataloader)
aver_hd /= len(val_dataloader)
aver_accuracy /= len(val_dataloader)
aver_recall /= len(val_dataloader)
aver_precision /= len(val_dataloader)
aver_f1score /= len(val_dataloader)
aver_memory /= len(val_dataloader)
fps = len(val_dataloader.dataset) / (time.time() - start_time)
# 统计模型的GFLOPs
flops = 0
for name, module in model.named_modules():
if isinstance(module, torch.nn.Conv2d):
flops += module.weight.numel() * 2 * module.in_channels * module.out_channels * module.kernel_size[0] * module.kernel_size[1] / module.stride[0] / module.stride[1]
elif isinstance(module, torch.nn.Linear):
flops += module.weight.numel() * 2 * module.in_features
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
start_event.record()
with torch.no_grad():
output = UNet(args, 3, 1).to(device)
end_event.record()
torch.cuda.synchronize()
elapsed_time_ms = start_event.elapsed_time(end_event)
gflops = flops / (elapsed_time_ms * 10 ** 6)
print("GFLOPs: {:.2f}".format(gflops))
return best_iou, aver_iou, aver_dice, aver_hd, aver_accuracy, aver_recall, aver_precision, aver_f1score, aver_memory, fps, parameters, gflops
```
device = 'cuda' if torch.cuda.is_available() else 'cpu' model = Model().to(device) 报错CUDA out of memory. Tried to allocate 4.93 GiB (GPU 0; 6.00 GiB total capacity; 1.48 GiB already allocated; 3.16 GiB free; 1.49 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF 如何解决,生成相关代码
你可以尝试调整 PyTorch 的内存管理参数 max_split_size_mb 来避免内存碎片化,具体的代码实现如下:
```python
import torch
# 设置 PyTorch 内存管理参数
torch.backends.cuda.max_split_size = 256000000
torch.backends.cuda.cache_allocated = True
# 检查是否有可用的 GPU
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
# 把模型移动到 GPU 上
model = Model().to(device)
```
这里我将 max_split_size_mb 设置为 256000000,你可以根据需要进行调整。同时,我设置了 cache_allocated 参数为 True,这个参数可以在 GPU 内存不足时释放已经分配的内存,从而避免 out of memory 的错误。
希望这个代码片段能够帮助你解决问题。