Use torch.unsqueeze(input, dim, out=None):
>>> import torch
>>> a = torch.Tensor([1, 2, 3, 4, 5])
>>> a
1
2
3
4
5
[torch.FloatTensor of size 5]
>>> a = a.unsqueeze(0)
>>> a
1 2 3 4 5
[torch.FloatTensor of size 1x5]
PyTorch
docs.pytorch.org › intro › introduction to pytorch - youtube series › introduction to pytorch tensors
Introduction to PyTorch Tensors — PyTorch Tutorials 2.11.0+cu130 documentation
July 20, 2022 - For more information, see the PyTorch documentation on reproducibility. Often, when you’re performing operations on two or more tensors, they will need to be of the same shape - that is, having the same number of dimensions and the same number of cells in each dimension. For that, we have the torch.*_like() methods: x = torch.empty(2, 2, 3) print(x.shape) print(x) empty_like_x = torch.empty_like(x) print(empty_like_x.shape) print(empty_like_x) zeros_like_x = torch.zeros_like(x) print(zeros_like_x.shape) print(zeros_like_x) ones_like_x = torch.ones_like(x) print(ones_like_x.shape) print(ones_like_x) rand_like_x = torch.rand_like(x) print(rand_like_x.shape) print(rand_like_x)
PyTorch Forums
discuss.pytorch.org › jit
`x.shape` or `x.size()` return torch.Size with elements of type Tensor? - jit - PyTorch Forums
June 1, 2022 - Hi PyTorch community, TL;DR Sometimes torch.Size contains elements of type torch.Tensor when using shape or size() . Why? # x:torch.Tensor [1, 40, 32, 32] tsize = x.shape[2:] print(tsize) > torch.Size([32, 32]) pri…
Videos
04:24
PyTorch Beginner Tutorial - Part 6 (Examine the Shape of the Data) ...
What's New for Dynamic Shapes in PyTorch 2.1
11:14
PyTorch 2.0: Dynamic Shapes Support - YouTube
39:43
PyTorch Dimensions Explained – The Only Guide You Need! - YouTube
02:13
PyTorch Tensor Shape: Get the PyTorch Tensor size - PyTorch Tutorial ...
Top answer 1 of 11
56
Use torch.unsqueeze(input, dim, out=None):
>>> import torch
>>> a = torch.Tensor([1, 2, 3, 4, 5])
>>> a
1
2
3
4
5
[torch.FloatTensor of size 5]
>>> a = a.unsqueeze(0)
>>> a
1 2 3 4 5
[torch.FloatTensor of size 1x5]
2 of 11
23
you might use
a.view(1,5)
Out:
1 2 3 4 5
[torch.FloatTensor of size 1x5]
Stanford University
web.stanford.edu › class › cs224n › materials › CS224N_PyTorch_Tutorial.html
CS224N: PyTorch Tutorial (Winter '21)
The shape property tells us the shape of our tensor. This can help us identify how many dimensional our tensor is as well as how many elements exist in each dimension. ... # Print out the number of elements in a particular dimension # 0th dimension corresponds to the rows x.shape[0]
Johnwlambert
johnwlambert.github.io › pytorch-tutorial
PyTorch Tutorial | John Lambert
PyTorch’s fundamental data structure is the torch.Tensor, an n-dimensional array. You may be more familiar with matrices, which are 2-dimensional tensors, or vectors, which are 1-dimensional tensors. import numpy as np import torch x = torch.Tensor([1., 2., 3.]) print(x) # Prints "tensor([1., 2., 3.])" print(x.shape) # Prints "torch.Size([3])" print(torch.ones(2,1)) # Prints "tensor([[1.],[1.]])" print(torch.zeros_like(x)) # Prints "tensor([0., 0., 0.])" # Alternatively, create a tensor by bringing it in from Numpy y = np.array([0,1,2,3]) print(y) # Prints "[0 1 2 3]" x = torch.from_numpy(y) print(x) # Prints "tensor([0, 1, 2, 3])"
Sparrow Computing
sparrow.dev › home › blog › pytorch add dimension: expanding a tensor with a dummy axis
PyTorch Add Dimension: Expanding a Tensor with a Dummy Axis
October 21, 2021 - Although the actual PyTorch function is called unsqueeze(), you can think of this as the PyTorch “add dimension” operation. Let’s look at two ways to do it. The easiest way to expand tensors with dummy dimensions is by inserting None into the axis you want to add. For example, say you have a feature vector with 16 elements. To add a dummy batch dimension, you should index the 0th axis with None: import torch x = torch.randn(16) x = x[None, :] x.shape # Expected result # torch.Size([1, 16])
PyTorch
docs.pytorch.org › reference api › torch.export › dynamic shapes
Dynamic Shapes — PyTorch 2.9 documentation
Because TorchDynamo must know upfront if a compiled trace is valid (we do not support bailouts, like some JIT compilers), we must be able to reduce z.size(0) as an expression in terms of the inputs, x.size(0) + y.size(0). This is done by writing meta functions for all operators in PyTorch which can propagate size information to the output of a tensor without actually performing computation on the node. ... When we start compiling a frame in Dynamo, we allocate a ShapeEnv (attached to FakeTensorMode) which keeps track of symbolic shapes state.
GitHub
github.com › pytorch › pytorch › issues › 46826
x.shape return tuple instead of torch.Size · Issue #46826 · pytorch/pytorch
October 25, 2020 - 🐛 Bug Pytorch 1.8.0.dev20201025 here. Calling x.shape on Tensor x should return a torch.Size object but it returns a tuple instead. To Reproduce from torch import Tensor import torch class MyTensor(Tensor): pass a = MyTensor([1,2,3]) b =...
Author jamarju
PyTorch
docs.pytorch.org › user guide › torch.export › exportdb › torch.dynamic-shape
torch.dynamic-shape — PyTorch 2.10 documentation
January 1, 2023 - """ def __init__(self) -> None: super().__init__() def forward(self, x): return x.shape[1] + 1 example_args = (x,) tags = {"torch.dynamic-shape"} dynamic_shapes = {"x": {1: dim1_x}} model = ScalarOutput() torch.export.export(model, example_args, dynamic_shapes=dynamic_shapes) Result: ExportedProgram: class GraphModule(torch.nn.Module): def forward(self, x: "f32[3, s11]"): # No stacktrace found for following nodes sym_size_int_1: "Sym(s11)" = torch.ops.aten.sym_size.int(x, 1); x = None add: "Sym(s11 + 1)" = sym_size_int_1 + 1; sym_size_int_1 = None return (add,) Graph signature: # inputs x: USER_INPUT # outputs add: USER_OUTPUT Range constraints: {s11: VR[0, int_oo]} On this page · Show Source · PyTorch Libraries ·
PyTorch
docs.pytorch.org › reference api › torch.tensor › torch.tensor.shape
torch.Tensor.shape — PyTorch 2.11 documentation
January 1, 2023 - X · GitHub · PyTorch Forum · PyPi · Rate this Page · ★ ★ ★ ★ ★ · Tensor.shape# Returns the size of the self tensor. Alias for size. See also Tensor.size(). Example: >>> t = torch.empty(3, 4, 5) >>> t.size() torch.Size([3, 4, ...
Top answer 1 of 4
114
For PyTorch v1.0 and possibly above:
>>> import torch
>>> var = torch.tensor([[1,0], [0,1]])
# Using .size function, returns a torch.Size object.
>>> var.size()
torch.Size([2, 2])
>>> type(var.size())
<class 'torch.Size'>
# Similarly, using .shape
>>> var.shape
torch.Size([2, 2])
>>> type(var.shape)
<class 'torch.Size'>
You can cast any torch.Size object to a native Python list:
>>> list(var.size())
[2, 2]
>>> type(list(var.size()))
<class 'list'>
In PyTorch v0.3 and 0.4:
Simply list(var.size()), e.g.:
>>> import torch
>>> from torch.autograd import Variable
>>> from torch import IntTensor
>>> var = Variable(IntTensor([[1,0],[0,1]]))
>>> var
Variable containing:
1 0
0 1
[torch.IntTensor of size 2x2]
>>> var.size()
torch.Size([2, 2])
>>> list(var.size())
[2, 2]
2 of 4
14
If you're a fan of NumPyish syntax, then there's tensor.shape.
In [3]: ar = torch.rand(3, 3)
In [4]: ar.shape
Out[4]: torch.Size([3, 3])
# method-1
In [7]: list(ar.shape)
Out[7]: [3, 3]
# method-2
In [8]: [*ar.shape]
Out[8]: [3, 3]
# method-3
In [9]: [*ar.size()]
Out[9]: [3, 3]
P.S.: Note that tensor.shape is an alias to tensor.size(), though tensor.shape is an attribute of the tensor in question whereas tensor.size() is a function.
PyTorch
docs.pytorch.org › recipes › reasoning about shapes in pytorch
Reasoning about Shapes in PyTorch — PyTorch Tutorials 2.10.0+cu130 documentation
March 27, 2023 - import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = torch.flatten(x, 1) # flatten all dimensions except batch x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x · We can view the intermediate shapes within an entire network by registering a forward hook to each layer that prints the shape of the output.
GitHub
github.com › ofnote › tsalib
GitHub - ofnote/tsalib: Tensor Shape Annotation Library (numpy, tensorflow, pytorch, ...)
Shape assertions (assert x.shape === (B,T,D)) enable catching inadvertent shape bugs at runtime. Pick either or both to work with. ... A proposal for designing a tensor library with named dimensions from ground-up. tsalib takes care of some use cases, without requiring any change in the tensor libraries. Pytorch ...
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Codefinity
codefinity.com › courses › v2 › 1dd2b0f6-6ec0-40e6-a570-ed0ac2209666 › 8a329f24-2e0c-4e15-bc32-802e817f6856 › 6f054f4c-f699-45fb-a1d4-0d252768f49c
Learn Shapes and Dimensions in PyTorch | PyTorch Introduction
Similarly to NumPy arrays, a tensor's shape determines its dimensions.
GitHub
github.com › pytorch › pytorch › issues › 5544
.size() vs .shape, which one should be used? · Issue #5544 · pytorch/pytorch
March 3, 2018 - Hi, This is not an issue per se, but more of a question of best practices. I notice that there a shape attribute and a size function that both return the size/shape of a tensor. I also notice that .shape is not documented, but according ...
Author freud14
Dive into Deep Learning
d2l.ai › chapter_preliminaries › ndarray.html
2.1. Data Manipulation — Dive into Deep Learning 1.0.3 documentation
In our case, instead of calling x.reshape(3, 4), we could have equivalently called x.reshape(-1, 4) or x.reshape(3, -1). Practitioners often need to work with tensors initialized to contain all 0s or 1s. We can construct a tensor with all elements set to 0 and a shape of (2, 3, 4) via the zeros function. pytorchmxnetjaxtensorflow ·
PyTorch
docs.pytorch.org › TensorRT › user_guide › dynamic_shapes.html
Dynamic shapes with Torch-TensorRT — Torch-TensorRT v2.12.0.dev0+66e7b2a documentation
import torch import torch_tensorrt model = MyModel().eval().cuda() # Define Input with dynamic shapes once inputs = [ torch_tensorrt.Input( min_shape=(1, 3, 224, 224), opt_shape=(8, 3, 224, 224), max_shape=(32, 3, 224, 224), dtype=torch.float32, name="x" # Optional: provides better dimension naming ) ] # Compile with dynamic shapes trt_model = torch_tensorrt.compile(model, ir="dynamo", inputs=inputs) # Save - dynamic shapes inferred automatically!
Habana
docs.habana.ai › en › latest › PyTorch › Model_Optimization_PyTorch › Dynamic_Shapes.html
Handling Dynamic Shapes — Gaudi Documentation 1.23.0 documentation
To bypass this, rewrite the computation as x + torch.arange(0, 5). To detect dynamic ops, perform a simple string search in the repo for ops or run the script with GRAPH_VISUALIZATION=1 to create a .graph_dumps folder and monitor it to see if the number of graphs dumped keeps increasing. If dynamic shapes are present, multiple recompilations cause the number of dumps in the .graph_dumps folder to increase.
Uoit
csgrad.science.uoit.ca › courses › csci5550g-f18 › code › pytorch › lesson-1 › pytorch-basics.html
pytorch-basics
# First, the numpy way x = np.random.rand(3,4) print('Before', x.shape) x = x[None,:,:] print('After', x.shape) # Next, the torch way t = torch.rand(3,4) print('Before:', t.shape) t1 = t.unsqueeze(0) print('After:', t1.shape) # Say we get another 3x4 matrix (say a grayscale image or a frame) t2 = torch.rand(3,4) # Say we want to combine t1 and t2, such that the first dimension # iterates over the frames t.unsqueeze_(0) # inplace unsqueeze, we just added a dimension t2.unsqueeze_(0) t_and_t2 = torch.cat((t1,t2),0) # The first dimension is the print(t_and_t2.shape)