# convert_hf_to_gguf.py38.classLazyTorchTensor(gguf.LazyBase):39._tensor_type=torch.Tensor# to keep the type-checker happy40.dtype:torch.dtype41.shape:torch.Size# only used when converting a torch.Tensor to a np
outi=inputi+value×tensor1i×tensor2i\text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i outi=inputi+value×tensor1i×tensor2i The shapes of tensor, tensor1, and tensor2 must be broadcastable. For inputs of type FloatTe...
classM(torch.nn.Module):defforward(self,x):val=torch.neg(x)+torch.relu(x)returntorch.add(val,val)# 使用fx符号追踪获取模型结构traced=symbolic_trace(M())# 查看原计算图print(traced.graph)'''graph(x):%neg_1 : [#users=1] = call_function[target=torch.neg](args = (%x,), kwargs = ...
有些特别的操作要求contiguous,也就是按照空间顺序排列,`strides[i] = prod(shape[i+1:])`4.zeros, rand等新建tensor的函数5.`grad_central_difference`,`grad_check`使用数值方法简单计算梯度,但是有误差,`grad_check`一般选择10%的相对误差-`tensor_data`1.`index_to_position`,`to_index`,`broadcast_inde...
equal(st, ones_st)) self.assertEqual(st, ones_st, rtol=0, atol=0, exact_device=True) # no grad inplace_copy should work between two with different requires_grad st_with_grad = sharded_tensor.rand(spec, (12, 5), requires_grad=True) 8 changes: 4 additions & 4 deletions 8 test/...
../paddle/phi/kernels/funcs/gather.cu.h:67 Assertion `index_value >= 0 && index_value < input_dims[j]` failed. The index is out of bounds, please check whether the dimensions of index and input meet the requirements. It should be less than [2000] and greater than or equal to 0,...
specifying either of those two args will override :attr:`reduction`. Default: ``'mean'`` pos_weight (Tensor, optional): a weight of positive examples. Must be a vector with length equal to the number of classes. Examples:: >>> input = torch.randn(3, requires_grad=True) ...
set_default_tensor_type(torch.cuda.FloatTensor) hidden = torch.zeros((num_layers, self.b, self.h), dtype=typ) output, _ = rnn(packed_seq, hidden) self.assertEqual(output.data.type(), HALF) output.data.float().sum().backward() self.assertEqual(x.grad.dtype, x.dtype) ...
Image对象，* 而不是 * Tensor。因此，您不能使用torch.equal来比较两个PIL.Image对象。请尝试：...
为什么在使用torch.utils.data.ConcatDataset连接两个数据集时图像不对齐？您的代码中有一个小错误，它会导致所有问题:在内部循环中不增加new_idx。因此，您可以将直接从dataset中提取的元素与self.concat_datasets中相应数据集的第一个元素进行比较。