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December 6, 2024 - In PyTorch, the .dstack() function stacks a sequence of tensors depthwise, i.e., along the third axis (axis=2), creating a new tensor.

November 5, 2024 - The 1st argument with torch is tensors(Required-Type:tuple or list of tensor of int, float, complex or bool).

torch.dstack · Shortcuts · torch.dstack(tensors, *, out=None) → Tensor¶ · Stack tensors in sequence depthwise (along third axis). This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d().

torch_dstack.Rd · Dstack · torch_dstack(tensors) tensors · (sequence of Tensors) sequence of tensors to concatenate · Stack tensors in sequence depthwise (along third axis). This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by ...

torch.dstack is a function in PyTorch that stacks tensors along the third dimension. The name "dstack" stands for "depth stack." It's a handy tool for combining a sequence of tensors that have the same number of dimensions.

August 21, 2025 - / torch_dstack: Dstack · View source: R/gen-namespace.R · Dstack · torch_dstack(tensors) Stack tensors in sequence depthwise (along third axis). This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch_atleast_3d().

torch.dstack · Shortcuts · torch.dstack(tensors, *, out=None) → 张量¶ · 按顺序深度堆叠张量（沿第三轴）。 · 这相当于在1-D和2-D张量被torch.atleast_3d()重塑后沿第轴进行连接。 · Parameters · 张量 (序列 的 张量) – 要连接的张量序列 ·

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torch.dstack ============ .. currentmodule:: torch ..

December 7, 2020 - When we work with data dstack is a very useful function · torch.hstack(tensors, *, out=None) Like dstack function hstack function stack tensors in sequence but horizontally. This is equivalent to concatenation along the first axis for 1D tensors, and along the second axis for all other tensors.

January 26, 2023 - In compile mode, the third value is nondeterministic and always not equal to 1. It's worth noting that if we directly pass a tensor with shape [1, 1, 3] to torch.reciprocal, the result is correct. Also, if we only pass input twice in dstack, i.e., v1 = torch.dstack([input, input]), the program also works fine.

February 28, 2022 - PyTorch torch.stack() method joins (concatenates) a sequence of tensors (two or more tensors) along a new dimension. It inserts new dimension and concatenates the tensors along that dimension.

November 5, 2024 - My post explains vstack() and dstack(). My post explains cat(). stack() can get the 1D or more D stacked tensor of zero or more elements from the one or more 0D or more D tensors of zero or more elements as shown below: ... The 1st argument with torch is tensors(Required-Type:tuple or list of tensor of int, float, complex or bool).

November 5, 2024 - My post explains vstack() and dstack(). My post explains cat(). hstack() can get the 1D or more D horizontally(column-wisely) stacked tensor of zero or more elements from the one or more 0D or more D tensors of zero or more elements as shown below: ... The 1st argument with torch is tensors(Required-Type:tuple or list of tensor of int, float, complex or bool).

March 4, 2025 - import torch def f(): sym_0 = 964 sym_1 = 806 sym_2 = -9063443332548498471 sym_3 = 2 sym_4 = 1 sym_5 = 0 sym_6 = False sym_7 = False sym_8 = -1 sym_9 = (776984,) var_161 = torch.triu_indices(row=sym_0, col=sym_1, offset=sym_2) var_315 = torch.randperm(n=sym_3) var_46 = torch.ops.aten.embedding_backward(var_161, var_315, sym_4, sym_5, sym_6, sym_7) var_336 = var_46.unflatten(dim=sym_8, sizes=sym_9) tup_0 = (var_336,) return torch.dstack(tup_0) print('eager', f()) print('inductor', torch.compile(f)()) running result: eager tensor([[[0], [0], [0], ..., [0], [0], [0]]]) Traceback (most recent call

def find_max_triples(p1, p2, topN=5, prob_thd=None): """ Find a list of (k1, k2) where k1 >= k2 with the maximum values of p1[k1] * p2[k2] Args: p1 (torch.CudaTensor): (N, L) batched start_idx probabilities p2 (torch.CudaTensor): (N, L) batched end_idx probabilities topN (int): return topN pairs with highest values prob_thd (float): Returns: batched_sorted_triple: N * [(st_idx, ed_idx, confidence), ...] """ product = torch.bmm(p1.unsqueeze(2), p2.unsqueeze(1)) # (N, L, L), end_idx >= start_idx upper_product = torch.stack([torch.triu(p) for p in product] ).data.cpu().numpy() # (N, L, L) the lower part becomes zeros batched_sorted_triple = [] for idx, e in enumerate(upper_product): sorted_triple = topN_array_2d(e, topN=topN) if prob_thd is not None: sorted_triple = [t for t in sorted_triple if t[2] >= prob_thd] batched_sorted_triple.append(sorted_triple) return batched_sorted_triple

August 11, 2020 - Tensors and Dynamic neural networks in Python with strong GPU acceleration - Implement hstack, vstack, dstack by muthuArivoli · Pull Request #42799 · pytorch/pytorch

One of the many useful functions it provides is `torch.stack`. This function plays a crucial role in tensor manipulation, allowing users to combine a sequence of tensors along a new dimension.