# 需要导入模块: import torch [as 别名]# 或者: from torch importargmax[as 别名]defbatch_intersection_union(output, target, nclass):"""mIoU"""# inputs are NDarray, output 4D, target 3D# the category -1 is ignored class, typically for background / boundarymini =1maxi = nclass nbins =...
action = torch.zeros([model.number_of_actions], dtype=torch.float32)iftorch.cuda.is_available():# put on GPU if CUDA is availableaction = action.cuda()# get actionaction_index = torch.argmax(output)iftorch.cuda.is_available():# put on GPU if CUDA is availableaction_index = action_i...
import torch x_train, y_train, x_valid, y_valid = map( torch.tensor, (x_train, y_train, x_valid, y_valid) ) n, c = x_train.shape print(x_train, y_train) print(x_train.shape) print(y_train.min(), y_train.max()) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. tensor([[0...
通过torch.max返回的索引获取最大值的方法是使用torch.max函数的第二个返回值。torch.max函数返回输入张量的最大值和最大值的索引。可以通过将torch.max函数的返回值赋值给两个变量,然后使用第二个变量来获取最大值的索引。 具体代码如下: 代码语言:txt ...
argmax(output) if torch.cuda.is_available(): # put on GPU if CUDA is available action_index = action_index.cuda() action[action_index] = 1 # get next state image_data_1, reward, terminal = game_state.frame_step(action) image_data_1 = resize_and_bgr2gray(image_data_1) image_...
defaccuracy(output, target): preds = output.argmax(dim=1) correct = preds.eq(target).sum().item() total = target.size(0) returncorrect / total Cross-Entropy Loss: 在分类问题中,交叉熵损失也是一个常用的评价指标。它计算的是模型预测的类别分布与真实类别分布之间的差距。 python复制代码 criterion...
(x_true).argmax(axis=1)total+=len(y_true)right+=(y_true==y_pred).sum().item()returnright/totalclassEvaluator(Callback):"""评估与保存,这里定义仅在epoch结束后调用"""def__init__(self):self.best_val_acc=0.defon_epoch_end(self,global_step,epoch,logs=None):val_acc=evaluate(valid_...
y_hat = out.argmax(1).item() predicted_idx = str(y_hat) inferences.append(self.mapping[predicted_idx]) # Handling inference for question_answering. elif self.setup_config["mode"] == "question_answering": # the output should be only answer_start and answer_end ...
argmax(output, 1).item() index = np.ones((output.size()[0], 1)) * target_label_idx index = torch.tensor(index, dtype=torch.int64) if cuda: index = index.cuda() output = output.gather(1, index) # clear grad model.zero_grad() output.backward() gradient = input.grad.detach()...
output = model(data) preds = torch.argmax(output, 1) gt_labels.append(label.cpu().data.numpy()) # 将GPU中的tensor转化为numpy.array pred_labels.append(preds.cpu().data.numpy()) loss = criterion(output, label) val_loss += loss.item()*data.size(0) ...