nn.Parameter(torch.empty(out_features, **factory_kwargs)) else: self.register_parameter(...
这些分布函数都有一个下划线结尾的同名操作符,表示in-place运算的版本。什么叫in-place运算,刚才已经讲过了哦 :smile: 7.9.1 均匀分布:uniform_ # 方法一: # 服从 [5, 10] 的均匀分布,从中生成维度为 2,3 的tensor torch.empty(2,3).uniform_(5,10).type(torch.FloatTensor) # 方法二: from torch.di...
# 需要导入模块: import torch [as 别名]# 或者: from torch importempty[as 别名]def__init__(self, c_in, c_out, k_size, stride=1, pad=0, bias=True):""" constructor for the class """fromtorch.nn.modules.utilsimport_pairfromnumpyimportsqrt, prod super().__init__()# define the w...
bias=True)(1):MaxPool2d(kernel_size=2,stride=2,padding=0,dilation=1,ceil_mode=False)(2):Conv2d(1,1,kernel_size=(3,3),stride=(1,1)))module_list[0]#Linear(in_features=30, out_features=10, bias=True
def__init__(self,in_features,hidden_features=None,out_features=None, act_layer=nn.GELU,drop=0.,linear=False): super(Mlp,self).__init__() # 根据输入通道数进行隐层、输出的通道数 out_features=out_featuresorin_features hidden_features=hidden_featuresorin_features ...
🐛 Describe the bug Trying to compile a autograd.Function that calls out to a user-defined triton kernel. Traced the functions apply method with aot_eager backend as a sanity check and it compiles. However, when switching to the _inductor...
, linear=False): super(Mlp, self).__init__() # 根据输入通道数进行隐层、输出的通道数 out_features = out_features or in_features hidden_features = hidden_features or in_features self.FC1 = nn.Conv2d(in_features, hidden_features, 1) self.DW_Conv = DepthWiseConv(in_features) self.ACT...
z1=torch.empty(3) #tensor加法 z1=torch.add(x,y,out=z1) print(z1) #也可以这样做加法 z2=torch.add(x,y) print(z2) #更常用的加法操作是这个 z=x+y print(z) tensor([10., 10., 10.]) tensor([10, 10, 10]) tensor([10, 10, 10]) ...
If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtypetorch.float164) V100 GPU is used, 5) input data is not inPackedSequenceformat persistent algorithm can be selected to improve performance. ...
(self, data: Self) -> Self: r"""Concatenates :obj:`self` with another :obj:`data` object. All values needs to have matching shapes at non-concat dimensions. """ out = copy.copy(self) for store, other_store in zip(out.stores, data.stores): store.concat(other_store) return out ...