Tensor.repeat(*size):沿着特定的维度重复这个张量,和expand()不同的是,这个函数拷贝张量的数据。 Tensor.repeat(*sizes) → TensorRepeats this tensor along the specified dimensions.Unlike expand(), this function copies the tensor’s dat
pytorch里的数据统称为张量(tensor),张量表示由⼀个数值组成的数组,这个数组可能有多个维度。具有一个轴的张量对应数学上的向量(vector)。 具有两个轴的张量对应数学上的矩阵(matrix)。具有两个轴以上的张量没有特殊的数学名称。 优点:张量(tensor)⽀持⾃动微分和GPU并行加速运算。 1 Tensor数据类型 32位浮点...
addcdiv(self, tensor1, tensor2, *args, **kwargs) addcdiv_(self, tensor1, tensor2, *args, **kwargs) addcmul(self, tensor1, tensor2, *args, **kwargs) addcmul_(self, tensor1, tensor2, *args, **kwargs) addmm(self, mat1, mat2, *args, **kwargs) addmm_(self, mat1, mat2,...
shape of x.mean(axis=0,keepdim=False): torch.Size([3, 4]) tensor([[7., 8., 9., 10.], [11., 12., 13., 14.], [15., 16., 17., 18.]]) shape of x.mean(axis=1,keepdim=True): torch.Size([2, 1, 4]) tensor([[[5., 6., 7., 8.]], [[17., 18., 19., ...
shape of x.mean(axis=0,keepdim=False): torch.Size([3, 4]) tensor([[7., 8., 9., 10.], [11., 12., 13., 14.], [15., 16., 17., 18.]]) shape of x.mean(axis=1,keepdim=True): torch.Size([2, 1, 4]) tensor([[[5., 6., 7., 8.]], ...
The primitives are simple but powerful when used to express tensor distributions with both sharded and replicated parallelism strategies. PyTorch DTensor empowered PyTorch Tensor Parallelism along with other advanced parallelism explorations. In addition, it also offers a uniform way to save/load state_...
beta和gamma一定是为最后一维计算的,其shape一定是inputs.shape[begin_params_axis:],也就是被normalized的shape.就也就是说用mean和std归一化后,tensor中的每一个元素都要进行一个独立的变换(tensor层面称为仿射变换),而非整体的平移和旋转。 The shapes ofbetaandgammaareinputs.shape[begin_params_axis:], an...
Tensor: tab_slice = slice(0, self.tab_incoming_dim) text_slice = slice( self.tab_incoming_dim, self.tab_incoming_dim + self.text_incoming_dim ) image_slice = slice( self.tab_incoming_dim + self.text_incoming_dim, self.tab_incoming_dim + self.text_incoming_dim + self.image_incoming...
(input, dim, index, out=None) -> Tensor # Gathers values along an axis specified by dim # Gather的本质就是查表,input要查的表,dim维度上的index索引序列 prob = torch.randn(4, 10) idx = prob.topk(dim=1, k=3) idx = idx[1] print(idx) # tensor([[8, 0, 4], # [4, 8, 3]...
ToTensor: 就是字面意思,把np.ndarray转化成torch tensor类型 类初始化里面针对self.transforms_z和self.transforms_x数据增强方法中具体参数的设置可以参考issue#21,作者提到在train phase和test phase embedding size不一样没太大的影响,而且255-16可以模拟测试阶段目标的移动(个人感觉这里没有完全就按照论文上来,但也...