images = Variable(images).cuda()
时间: 2023-11-27 16:02:41 浏览: 38
This line of code is converting the tensor variable "images" to a CUDA tensor variable. This is useful if you have a CUDA-enabled GPU, as it allows you to perform computations on the GPU instead of the CPU, which can be much faster for certain operations.
In other words, this line of code is sending the "images" variable to the GPU memory so that it can be used for GPU computations. The ".cuda()" function is a method of the PyTorch library that enables this transfer.
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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
```
def FGSM(self, x, y_true, y_target=None, eps=0.03, alpha=2/255, iteration=1): self.set_mode('eval') x = Variable(cuda(x, self.cuda), requires_grad=True) y_true = Variable(cuda(y_true, self.cuda), requires_grad=False) if y_target is not None: targeted = True y_target = Variable(cuda(y_target, self.cuda), requires_grad=False) else: targeted = False h = self.net(x) prediction = h.max(1)[1] accuracy = torch.eq(prediction, y_true).float().mean() cost = F.cross_entropy(h, y_true) if iteration == 1: if targeted: x_adv, h_adv, h = self.attack.fgsm(x, y_target, True, eps) else: x_adv, h_adv, h = self.attack.fgsm(x, y_true, False, eps) else: if targeted: x_adv, h_adv, h = self.attack.i_fgsm(x, y_target, True, eps, alpha, iteration) else: x_adv, h_adv, h = self.attack.i_fgsm(x, y_true, False, eps, alpha, iteration) prediction_adv = h_adv.max(1)[1] accuracy_adv = torch.eq(prediction_adv, y_true).float().mean() cost_adv = F.cross_entropy(h_adv, y_true) # make indication of perturbed images that changed predictions of the classifier if targeted: changed = torch.eq(y_target, prediction_adv) else: changed = torch.eq(prediction, prediction_adv) changed = torch.eq(changed, 0) changed = changed.float().view(-1, 1, 1, 1).repeat(1, 3, 28, 28) changed[:, 0, :, :] = where(changed[:, 0, :, :] == 1, 252, 91) changed[:, 1, :, :] = where(changed[:, 1, :, :] == 1, 39, 252) changed[:, 2, :, :] = where(changed[:, 2, :, :] == 1, 25, 25) changed = self.scale(changed/255) changed[:, :, 3:-2, 3:-2] = x_adv.repeat(1, 3, 1, 1)[:, :, 3:-2, 3:-2] self.set_mode('train') return x_adv.data, changed.data,\ (accuracy.item(), cost.item(), accuracy_adv.item(), cost_adv.item())
这段代码是一个实现了FGSM(Fast Gradient Sign Method)攻击的函数。FGSM是一种用于生成对抗样本的方法,通过在输入样本上添加一小步扰动来欺骗分类器,使其产生错误的分类结果。
该函数的输入参数包括原始输入样本x、真实标签y_true、目标标签y_target(可选)、扰动范围eps、扰动步长alpha和迭代次数iteration。
函数首先将模型设置为评估模式,然后将输入样本转化为可求导变量,并计算原始样本在模型上的预测结果和准确率。然后计算原始样本在模型上的交叉熵损失。
接下来根据迭代次数选择使用FGSM攻击还是I-FGSM(Iterative FGSM)攻击。如果目标标签y_target存在,则使用目标攻击,否则使用非目标攻击。攻击过程中,对输入样本添加扰动,并计算扰动后的样本在模型上的预测结果和准确率。
最后,将产生的对抗样本、扰动图像、原始样本在模型上的准确率和损失作为输出返回。
值得注意的是,代码中还包含了一个用于生成扰动图像的部分。首先根据模型预测结果确定哪些样本的分类结果发生了变化,然后将这些样本的对应像素位置修改为特定的颜色值,以突出显示扰动部分。最后将扰动图像与对抗样本叠加,形成最终的扰动图像。
请问我还能为您解答其他问题吗?