Source File: model.py From transfer-nlp with MIT License 5 votes def forward(self, x): """ x has shape [batch, seq length]""" padding_mask = (x == self.tokenizer.vocab['[PAD]']) x = x.transpose(0, 1).contiguous() positions = torch.arange(len(x), device=x.device)....
transpose(1, 2).contiguous() out = torch.nested.to_padded_tensor(out, padding=0) out = out.reshape(10, 512, 256) return out def main(): x = torch.randn((100, 512, 256), requires_grad=True).cuda() y = torch.randint(0, 5, size=(10,)).cuda() model = Model().cuda() ...
transpose(0, 1).reshape(*batch_shape, -1, num_cols) return res else: return torch.dsmm(sparse, dense) Example #22Source File: sparse.py From gpytorch with MIT License 4 votes def sparse_getitem(sparse, idxs): """ """ if not isinstance(idxs, tuple): idxs = (idxs,) if not...
custom_cast=True)res_eager_no_cast,_=scaled_dot_product_attention(query,key,value,is_causal=is_causal,custom_cast=False)res_softmax_0=torch.softmax(softmax_inp,dim=-1)res_softmax_1=torch.softmax(softmax_inp,dim=-1,dtype=torch.float32).to(torch.float16)print("---")absdiff=(...
t=torch.arange(6)# t: [0, 1, 2, 3, 4, 5]t1=t.view(2,3)# t1.shape: [2, 3]t2=t.view(3,2).transpose(0,1)# t2.shape: [2, 3]torch.eq(t1,t2)# tensor([[ True, False, False],# [False, False, True]])# 更进阶地:先view再transpose,和直接view到对应形状,结果为什么不...
points = (1- u)*xs + (u*(1-v))*ys + u*v*zsifret_choice:returnpoints, choiceselse:returnpoints 开发者ID:nv-tlabs,项目名称:DIB-R,代码行数:24,代码来源:check_chamfer.py 示例5: visualize ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importindex_select[as...
transpose(dim0,dim1)→Tensor# See torch.transpose()permute(*dims)→Tensor# dim(int). Returns a view of the original tensor with its dimensions permuted. 在CNN模型中,我们经常遇到交换维度的问题,举例:四个维度表示的 tensor:[batch, channel, h, w](nchw),如果想把channel放到最后去,形成[batch, ...
torch中的torch.view() torch.permute()和 torch.transpose()以及numpy中的numpy.random.permutation(),程序员大本营,技术文章内容聚合第一站。
(image):image=cv2.resize(image,(640,640))image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB).astype(np.float32)image/=255image=np.transpose(image,[2,0,1])image=np.expand_dims(image,axis=0)ort_inputs={ort_session.get_inputs()[0].name:to_numpy(image)}ort_outputs=ort_session.run(None,ort...
transpose(0, 1) support = torch.mm(input, self.weight) output = torch.sparse.mm(A, support) if self.bias is not None: return output + self.bias else: return output Example #8Source File: tensor.py From pytorch_sparse with MIT License 5 votes def to_torch_sparse_coo_tensor( self,...