torch.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None) precision是每一个元素的输出精度,默认是八位;threshold是输出时的阈值,当tensor中元素的个数大于该值时,进行缩略输出,默认时1000;edgeitems是输出的维度,默认是3;linewidth字面意思,每一行输出的长度;profile=...
计算F1、准确率(Accuracy)、召回率(Recall)、精确率(Precision)、敏感性(Sensitivity)、特异性(Specificity)需要用到的包(PS:还有一些如AUC等后面再加上用法。) fromsklearn.metricsimportprecision_recall_curve,average_precision_score,roc_curve,auc,precision_score,recall_score,f1_score,confusion_matrix,accuracy_...
torch.masked_select(input, mask, out=None) → Tensor/masked_select(mask) 注意所有mask都为torch.ByteTensor,同时需要注意mask的shape不一定要和tensor相同数量也不一定要相同,shape中必须有一个轴要和tensor的轴对应,此时按此轴索引 a = torch.Tensor([[1,2,3],[4,5,6]]) mask = torch.Tensor([[1...
torch.set_default_ dtype(d) 对torch.tensor() 设置默认的浮点类型 torch.set_printoptions(precision= None, threshold=None, edgeitems = None, linewidth=None, profile=None) 设置printing的打印参数 1.4 Tensor与numpy的相互转换 torch.from_ numpy(ndarry) a.numpy() import torch import numpy as np a ...
precision是每一个元素的输出精度,默认是八位; threshold是输出时的阈值,当tensor中元素的个数大于该值时,进行缩略输出,默认时1000; edgeitems是输出的维度,默认是3; linewidth字面意思,每一行输出的长度; profile– pretty打印的完全默认值。 可以覆盖上述所有选项 (默认为short, full)©...
图像进行预处理,加载后统一RGB通道,tensor(3, H, W) 缩放到统一尺寸,中心裁剪,将[0,255]像素值进行归一化处理 '''foriinrange(4): img_path ='./data/img%d.JPG'%i img = Image.open(img_path)print(img) preprocess = transforms.Compose([ ...
import torchimport torchmetricsclass MyAccuracy(Metric): def __init__(self, delta): super().__init__() # to count the correct predictions self.add_state('corrects', default=torch.tensor(0)) # to count the total predictions self.add_state('total', default=torch.tensor...
state.mixed_precision == "bf16" dtype = torch.bfloat16 if use_bf16 else torch.float32 loss = torch.tensor(0, dtype=dtype).to(xm.xla_device()) return (loss, None, None) @@ -466,7 +468,6 @@ def _save_xla(self, output_dir: Optional[str] = None): if is_main_worker(): ...
self.add_state('total', default=torch.tensor(0)) def update(self, preds, target): # update correct predictions count self.correct += torch.sum(preds == target) # update total count, numel() returns the total number of elements
ndim) # 1 print(t.T) # tensor([[8, 8], # [2, 7], # [7, 3]], device='cuda:0') print(fix_t.T) # tensor([]) xutongye added a commit to xutongye/PySyft that referenced this issue Nov 4, 2020 Add .ndim and .T to FixPrecisionTensor and PointerTensor (OpenMined#4617 ...