view_as_real()仅支持具有complex dtypes的张量。 例子: >>>x=torch.randn(4, dtype=torch.cfloat)>>>x tensor([(0.4737-0.3839j), (-0.2098-0.6699j), (0.3470-0.9451j), (-0.5174-1.3136j)])>>>torch.view_as_real(x) tensor([[0.4737,-0.3839], [-0.2098,-0.6699], [0.3470,-0.9451], [-0...
【摘要】 import torch a = torch.rand(3, 3, 4, 5)b = a.view(3, -1)c = b.view(3, 3, 4, 5) d = torch.rand(3, 12, 1, 5)print(torch.equal(c, a)) e=a.view_as(d)print(e) view_as reshape到跟d一样 &nbs... import torch a = torch.rand(3, 3, 4, 5) b = a...
1.view()函数 可以理解成先把原始tensor按维度顺序排成一个一维向量,然后根据view()函数的参数把一维向量调整为我们所希望的维度。 下面是一些例子,另外view()函数里的参数可以是列表。 注意,view()返回的tensor和传入的tensor共享内存,换句话说,修改其中一个,数据都会变。 还有一个view_as函数,其作用在于返回和...
self.view_as(other) RuntimeError: shape '[1, 1, 1, 1, 2]' is invalid for input of size 1 Test code (c++): #include<torch/torch.h>intmain() { torch::TensorOptions toptions =torch::TensorOptions(); toptions = toptions.dtype(torch::kComplexFloat); torch::Tensor self =torch::r...
view_as(other) → Tensor where(condition, y) → Tensor zero_() → Tensor class torch.BoolTensor all() all() → bool all(dim, keepdim=False, out=None) → Tensor any() → bool any(dim, keepdim=False, out=None) → Tensor
defforward(ctx,*args,**kwargs):returnargs[0].view_as(args[0])@staticmethod defbackward(ctx,*grad_outputs):returnGradDecay.alpha*grad_outputs[0] 在这里我通过对类提供一个公有的静态的属性 alpha 来代表乘上的那一个常数。通过 GradDecay.alpha = ... 的语句我们可以轻而易举地修改这个属性的值。
conv_weights = conv_weights.view_as(conv.weight.data) conv.weight.data.copy_(conv_weights) 测试网络 图像的大小处理 把图像缩放到网络要求的大小。 def letterbox_image(img, inp_dim): '''resize image with unchanged aspect ratio using padding''' ...
torch.view_as_real(tensor) if not isinstance(tensor, torch.nn.UninitializedParameter) and tensor.is_complex() else tensor )def _element_size(dtype): """ Returns the element size for a dtype, in bytes """ if not isinstance(dtype, torch.dtype): ...
在yolov3中,损失函数的计算代码如下: import torch.nn.functional as F class Criterion(object): def __init__(self, cfg, device, num_classes=80): self.cfg = cfg self.device = device self.num_classes = …
importtorchimportnumpy as np"""数据类型"""#常见的类型判断a = torch.randn(2,3)#正态分布print(a.type())#torch.FloatTensorprint(type(a))#<class 'torch.Tensor'>, python自带print(isinstance(a, torch.FloatTensor))#True#标量b = torch.tensor(2.)print(b)#tensor(2.)#获取形状print(b.shape)...