x1,x2,x3 = torch.split(torch.index_select(verts,0, faces[:,0]) - torch.index_select(verts,0, faces[:,1]),1, dim =1) y1,y2,y3 = torch.split(torch.index_select(verts,0, faces[:,1]) - torch.index_select(verts,0, faces[:,2]),1, dim =1) a = (x2*y3 - x3*y2)**...
h = torch.index_select(self.weight, 4, self.copyNodes) # And now we add the zero padding if Nin < self.N: zeroPad = torch.zeros(B, F, self.N-Nin).type(x.dtype).to(x.device) x = torch.cat((x, zeroPad), dim = 2) # Compute the filter output u = NVGF(self.h, self....
torch.index_fill_ formula to support duplicate indices (#57101). derivative of torch.sinc around x=0 (#56763, #56986). torch.cdist backward formula to correctly support broadcasting (#56605) and empty inputs (#56606). view creation metadata for functions that return multiple views in no...
images.sum(dim=1) images.select(dim=1, index=0) # PyTorch 1.3之后 NCHW = [‘N’, ‘C’, ‘H’, ‘W’] images = torch.randn(32, 3, 56, 56, names=NCHW) images.sum('C') images.select('C', index=0) # 也可以这么设置 tensor = torch.rand(3,4,1,2,names=('C', 'N', ...
pack_sequence Recurrent layers RNN classtorch.nn.RNN(*args,**kwargs)[source] Applies a multi-layer Elman RNN with tanhtanhtanh or ReLUReLUReLU non-linearity to an input sequence. For each element in the input sequence, each layer computes the following function: ...
cutorch.reserveStreams(n [, nonblocking]): creates n user streams for use on every device. NOTE: stream indexson device 1 is a different cudaStream_t than streamson device 2. Takes an optional non-blocking flag; by default, this is assumed to be false. If true, then the stream is cr...
#在PyTorch 1.3之前,需要使用注释# Tensor[N, C, H, W]images = torch.randn(32, 3, 56, 56)images.sum(dim=1)images.select(dim=1, index=0) # PyTorch 1.3之后NCHW = [‘N’, ‘C’, ‘H’, ‘W’]images = torch.randn(32, 3, 56, 56, names=NCHW)images.sum('C')images.select('...
torch包包含多维张量的数据结构,并定义了这些结构的数学运算。另外,它提供了许多实用程序来有效地序列化张量和任意类型,以及其他有用的实用程序。 It has a CUDA counterpart, that enables you to run your tensor computations on an NVIDIA GPU with compute capability >= 3.0 ...
Containers Module Sequential ModuleList ModuleDict ParameterList ParameterDict Convolution layers Conv1d Conv2d Conv3d ConvTranspose1d ConvTranspose2d ConvTranspose3d Unfold Fold Pooling layers MaxPool1d MaxPool2d MaxPool3d MaxUnpool1d MaxUnpool2d
Feature:Durable;Function:Portable Daily Carry;Logo:Custom Logo Acceptable;Color Temperature(CCT):5000K (Daylight);Color Rendering Index(Ra):70;Support Dimmer:Yes;Lighting solutions service:Lighting and circuitry design;Lighting solutions service:Project