id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
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|---|---|---|
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_246_0.txt |
# torch.Tensor.q_zero_point ¶
Tensor. q_zero_point ( ) → [ int
](https://docs.python.org/3/library/functions.html#int "\(in Python v3.12\)")
¶
Given a Tensor quantized by linear(affine) quantization, returns the
zero_point of the underlying quantizer().
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_124_0.txt |
# torch.lu ¶
torch. lu ( _ * args _ , _ ** kwargs _ ) ¶
Computes the LU factorization of a matrix or batches of matrices ` A ` .
Returns a tuple containing the LU factorization and pivots of ` A ` .
Pivoting is done if ` pivot ` is set to ` True ` .
Warning
` torch.lu() ` is deprecated in favo... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_14.txt | f set to ` True ` , will do this operation in-place. Default: ` False `
### dropout3d ¶
` torch.nn.functional. ` ` dropout3d ` ( _input_ , _p=0.5_ , _training=True_
, _inplace=False_ ) [ [source]
](_modules/torch/nn/functional.html#dropout3d) ¶
Randomly zero out entire channels (a channel is a 3D feature... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_131_0.txt |
# torch.frac ¶
torch. frac ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the fractional portion of each element in ` input ` .
out i = input i − ⌊ ∣ input i ∣ ⌋ ∗ sgn ( input i )
\text{out}_{i} = \text{input}_{i} - ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_45_2.txt | )
* [ Get Started ](https://pytorch.org/get-started)
* [ Features ](https://pytorch.org/features)
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* [ Tutorials ](https://pytorch.org/tu... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_22.txt | ` sign ` ¶
Returns the sign of the determinant of the Jacobian, if applicable. In general
this only makes sense for bijective transforms.
` log_abs_det_jacobian ` ( _x_ , _y_ ) [ [source]
](_modules/torch/distributions/transforms.html#Transform.log_abs_det_jacobian)
¶
Computes the log det jacobian lo... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_49.txt | SoftmaxTransform)
* [ Softmin (class in torch.nn) ](generated/torch.nn.Softmin.html#torch.nn.Softmin)
* [ softmin() (in module torch.nn.functional) ](nn.functional.html#torch.nn.functional.softmin)
* [ Softplus (class in torch.nn) ](generated/torch.nn.Softplus.html#torch.nn.Softplus)
* [ softplus() (in module t... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_93_0.txt |
# torch.Tensor.index_copy ¶
Tensor. index_copy ( _ dim _ , _ index _ , _ tensor2 _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Out-of-place version of [ ` torch.Tensor.index_copy_() `
](torch.Tensor.index_copy_.html#torch.Tensor.index_copy_
"torch.Tensor.index_copy_") .
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_12_2.txt | s, require changing the creator of all inputs to the ` Function ` representing this operation. This can be tricky, especially if there are many Tensors that reference the same storage (e.g. created by indexing or transposing), and in-place functions will actually raise an error if the storage of modified inputs is ref... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_19_0.txt | [ ](https://pytorch.org)
* [ Get Started ](/get-started)
* Ecosystem
[ PyTorch Conference - 2024 September 18-19 in San Francisco
](https://events.linuxfoundation.org/pytorch-conference/) [ Tools Learn about
the tools and frameworks in the PyTorch Ecosystem ](/ecosystem)
* Edge
[ About PyTorch Edge ](/e... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_63_1.txt | orch.Tensor "torch.Tensor") . PyTorch has
twelve different data types:
Data type | dtype | Legacy Constructors
---|---|---
32-bit floating point | ` torch.float32 ` or ` torch.float ` | ` torch.*.FloatTensor `
64-bit floating point | ` torch.float64 ` or ` torch.double ` | ` torch.*.DoubleTensor ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_48.txt | g.set_sharing_strategy)
* [ set_state() (torch.Generator method) ](generated/torch.Generator.html#torch.Generator.set_state)
* [ set_video_backend() (in module torchvision) ](torchvision/index.html#torchvision.set_video_backend)
* [ SGD (class in torch.optim) ](optim.html#torch.optim.SGD)
* [ shape_as_tensor() ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_27.txt | inear
, bilinear , and trilinear ) don’t proportionally align the output and
input pixels, and thus the output values can depend on the input size. This
was the default behavior for these modes up to version 0.3.1. Since then, the
default behavior is ` align_corners = False ` . See [ ` Upsample `
](generated/to... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_24.txt | g size by which to pad some dimensions of ` input ` are described
starting from the last dimension and moving forward. ⌊ len(pad) 2 ⌋
\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor ⌊ 2 len(pad) ⌋
dimensions of ` input ` will be padded. For example, to pad only the last
dimension of the input tensor, th... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_49_25.txt | or ¶
In-place version of ` sub() `
` sum ` ( _dim=None_ , _keepdim=False_ , _dtype=None_ ) → Tensor ¶
See [ ` torch.sum() ` ](generated/torch.sum.html#torch.sum "torch.sum")
` sum_to_size ` ( _*size_ ) → Tensor ¶
Sum ` this ` tensor to ` size ` . ` size ` must be broadcastable to `... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_25_16.txt | del on a given dataloader ` loader ` at the end of
training:
>>> torch.optim.swa_utils.update_bn(loader, swa_model)
` update_bn() ` applies the ` swa_model ` to every element in the dataloader
and computes the activation statistics for each batch normalization layer in
the model.
Warning
` upd... | |
pytorch_torch_tensor_functions/Solve_CUDA_out_of_memory_2_2.txt | size=(1, 256, 256), requires_grad=True)
# Forward pass with checkpointing
output = checkpoint(model, input_data)
# Compute loss
loss = loss_fn(output, target)
# Backward pass
loss.backward()
* **Parameter Swapping to/from CPU during Training** : If some parameters are ... | |
pytorch_torch_tensor_functions/Solve_CUDA_out_of_memory_4_0.txt | ## Conclusion
Dealing with the ‘CUDA out of memory’ error in PyTorch is not uncommon when
handling large datasets or complex models. While it can be a hindrance,
understanding its core causes paves the way for efficient solutions. Adjusting
the batch size, choosing smaller or more efficient model architectures,
lever... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_159_0.txt |
# torch.Tensor.to_sparse_csc ¶
Tensor. to_sparse_csc ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Convert a tensor to compressed column storage (CSC) format. Except for strided
tensors, only works with 2D tensors. If the ` self ` is strided, then the
number of dense dimensions could... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_185_0.txt |
# torch.nn.functional.hardshrink ¶
torch.nn.functional. hardshrink ( _ input _ , _ lambd = 0.5 _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Applies the hard shrinkage function element-wise
| |
pytorch_torch_tensor_functions/Solve_CUDA_out_of_memory_0_0.txt | [  ](/)
* __ Why Saturn Cloud
* [ __ For Data Scientists ](/why-sc/data-scientists/)
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pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_303_0.txt |
# torch.Tensor.baddbmm ¶
Tensor. baddbmm ( _ batch1 _ , _ batch2 _ , _ * _ , _ beta = 1 _ , _
alpha = 1 _ ) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor")
¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_105_0.txt |
# torch.greater ¶
torch. greater ( _ input _ , _ other _ , _ * _ , _ out = None _ ) →
[ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.gt() ` ](torch.gt.html#torch.gt "torch.gt") .
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_69_0.txt |
# torch.Tensor.greater_equal ¶
Tensor. greater_equal ( _ other _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_44.txt | r method) ](tensors.html#torch.Tensor.reshape)
* [ reshape_as() (torch.Tensor method) ](tensors.html#torch.Tensor.reshape_as)
* [ Resize (class in torchvision.transforms) ](torchvision/transforms.html#torchvision.transforms.Resize)
* [ resize() (in module torchvision.transforms.functional) ](torchvision/transform... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_32_2.txt | ones(4))
__main__:1: UserWarning: self and other do not have the same shape, but are broadcastable, and have the same number of elements.
Changing behavior in a backwards incompatible manner to broadcasting rather than viewing as 1-dimensional.
[ Next 
](cpu_... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_36_0.txt | [ ](https://pytorch.org/)
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pytorch_torch_tensor_functions/Pytorch_Documentations_46_8.txt | ble controls: [ Cookies Policy
](https://www.facebook.com/policies/cookies/) .
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pytorch_torch_tensor_functions/Pytorch_Documentations_62_12.txt | torch.Tensor "torch.Tensor") _]_ ) – List of tensors(on different GPUs) to be broadcast from current process. Note that ` len(input_tensor_list) ` needs to be the same for all the distributed processes calling this function.
* **group** ( _ProcessGroup_ _,_ _optional_ ) – The process group to work on
* **async... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_463_0.txt |
# torch.Tensor.nextafter ¶
Tensor. nextafter ( _ other _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_499_0.txt |
# torch.mul ¶
torch. mul ( _ input _ , _ other _ , _ * _ , _ out = None _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Multiplies ` input ` by ` other ` .
out i = input i × other i \text{out}_i = \text{input}_i \times
\text{other}_i out i = input i × other... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_194_0.txt |
# torch.median ¶
torch. median ( _ input _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Returns the median of the values in ` input ` .
Note
The median is not unique for ` input ` tensors with an even number of
elements. In this case the lower of the two medians is returned. To co... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_12_1.txt | write more efficient,
cleaner programs, and can aid you in debugging.
## Excluding subgraphs from backward ¶
Every Tensor has a flag: ` requires_grad ` that allows for fine grained
exclusion of subgraphs from gradient computation and can increase efficiency.
### ` requires_grad ` ¶
If there’s a single input t... | |
pytorch_torch_tensor_functions/Visualizing_PyTorch_memory_0_0.txt | [ Zach's Blog ](/)
[ About ](/about/)
# Visualizing PyTorch memory usage over time
Dec 9, 2022
[  ](/assets/trace.html)
[ Memory snapshots ](https://zdevito.github.io/2022/08/16/memory-
snapshots.html) are a way to dump and visualize the state of CUDA memory
allocation in PyTorch. They... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_21_0.txt | [ ](https://pytorch.org/)
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pytorch_torch_tensor_functions/Pytorch_Documentations_25_6.txt | rmsprop.html#RMSprop) ¶
Implements RMSprop algorithm.
Proposed by G. Hinton in his [ course
](https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf) .
The centered version first appears in [ Generating Sequences With Recurrent
Neural Networks ](https://arxiv.org/pdf/1308.0850v5.pdf) .
The i... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_25_0.txt | [ ](https://pytorch.org/)
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* Ecosystem
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](https://pytorch.org/hub) [ Tools & Libraries Explore the ecosystem of tools
and libraries ](https://pytorch.org/ecosystem)
* [ Mobile ](https://pytorch.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_433_0.txt |
# torch.Tensor.digamma ¶
Tensor. digamma ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_55_11.txt | #torch.BFloat16Storage)
* [ ` ComplexDoubleStorage ` ](storage.html#torch.ComplexDoubleStorage)
* [ ` ComplexFloatStorage ` ](storage.html#torch.ComplexFloatStorage)
* [ ` QUInt8Storage ` ](storage.html#torch.QUInt8Storage)
* [ ` QInt8Storage ` ](storage.html#torch.QInt8Storage)
* [ ` QInt32Stor... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_60_0.txt | [ ](https://pytorch.org/)
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[ Models (Beta) Discover, publish, and reuse pre-trained models
](https://pytorch.org/hub) [ Tools & Libraries Explore the ecosystem of tools
and libraries ](https://pytorch.org/ecosystem)
* [ Mobile ](https://pytorch.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_34_0.txt |
# torch.negative ¶
torch. negative ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.neg() ` ](torch.neg.html#torch.neg "torch.neg")
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_268_0.txt |
# torch.logical_not ¶
torch. logical_not ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the element-wise logical NOT of the given input tensor. If not
specified, the output tensor will have the bool dtype. If the input tensor is
not a boo... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_27_14.txt | h _GLIBCXX_USE_CXX11_ABI=1
---|---
[ ` result_type ` ](generated/torch.result_type.html#torch.result_type "torch.result_type") | Returns the [ ` torch.dtype ` ](tensor_attributes.html#torch.torch.dtype "torch.torch.dtype") that would result from performing an arithmetic operation on the provided input tensors. ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_31.txt | ## Resources
Find development resources and get your questions answered
[ View Resources ](https://pytorch.org/resources)
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pytorch_torch_tensor_functions/Memory Management_5_0.txt | # Advantages and benefits of using PYTORCH_CUDA_ALLOC_CONF:
1. **Improved performance** : PYTORCH_CUDA_ALLOC_CONF offers various memory allocation strategies to significantly enhance performance in deep learning tasks. By optimizing memory usage, it reduces memory fragmentation and improves overall memory managemen... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_62_6.txt | _Tensor_ ](tensors.html#torch.Tensor "torch.Tensor") ) – Tensor to send.
* **dst** ( [ _int_ ](https://docs.python.org/3/library/functions.html#int "\(in Python v3.8\)") ) – Destination rank.
* **group** ( _ProcessGroup_ _,_ _optional_ ) – The process group to work on
* **tag** ( [ _int_ ](https://docs.pyth... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_24_5.txt | CUDA semantics
](notes/cuda.html#cuda-semantics) for details.
Parameters
* **device** ( [ _torch.device_ ](tensor_attributes.html#torch.torch.device "torch.torch.device") _or_ [ _int_ ](https://docs.python.org/3/library/functions.html#int "\(in Python v3.8\)") _,_ _optional_ ) – a device on which to allocate... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_448_0.txt |
# torch.gcd ¶
torch. gcd ( _ input _ , _ other _ , _ * _ , _ out = None _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the element-wise greatest common divisor (GCD) of ` input ` and `
other ` .
Both ` input ` and ` other ` must have integer types.
Note
This defin... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_101_0.txt |
# torch.Tensor.addcdiv ¶
Tensor. addcdiv ( _ tensor1 _ , _ tensor2 _ , _ * _ , _ value = 1 _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_22_0.txt |
# torch.Tensor.is_quantized ¶
Tensor. is_quantized ¶
Is ` True ` if the Tensor is quantized, ` False ` otherwise.
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_46_1.txt | a directory for visualization within the TensorBoard UI.
Scalars, images, histograms, graphs, and embedding visualizations are all
supported for PyTorch models and tensors as well as Caffe2 nets and blobs.
The SummaryWriter class is your main entry to log data for consumption and
visualization by TensorBoard. For exa... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_318_0.txt |
# torch.Tensor.arctanh ¶
Tensor. arctanh ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_66_0.txt | [ ](https://pytorch.org/)
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* Ecosystem
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](https://pytorch.org/hub) [ Tools & Libraries Explore the ecosystem of tools
and libraries ](https://pytorch.org/ecosystem)
* [ Mobile ](https://pytorch.... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_22_3.txt | ry header includes, as well as the (pybind11) binding code. More
precisely, strings passed to ` cpp_sources ` are first concatenated into a
single ` .cpp ` file. This file is then prepended with ` #include
<torch/extension.h> ` .
Furthermore, if the ` functions ` argument is supplied, bindings will be
automatically... | |
pytorch_torch_tensor_functions/Memory Management_3_0.txt | # How does PYTORCH_CUDA_ALLOC_CONF work?
As discussed earlier, PYTORCH_CUDA_ALLOC_CONF is a PyTorch environment
variable that allows us to configure memory allocation behavior for CUDA
tensors. It controls memory allocation strategies, enabling users to optimize
memory usage and improve performance in deep learning t... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_52_7.txt | ` ](generated/torch.nn.SoftMarginLoss.html#torch.nn.SoftMarginLoss "torch.nn.SoftMarginLoss") | Creates a criterion that optimizes a two-class classification logistic loss between input tensor x x x and target tensor y y y (containing 1 or -1).
[ ` nn.MultiLabelSoftMarginLoss ` ](generated/torch.nn.MultiLa... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_25_1.txt | er ¶
To use ` torch.optim ` you have to construct an optimizer object, that will
hold the current state and will update the parameters based on the computed
gradients.
### Constructing it ¶
To construct an ` Optimizer ` you have to give it an iterable containing the
parameters (all should be ` Variable ` s) ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_264_0.txt |
# torch.topk ¶
torch. topk ( _ input _ , _ k _ , _ dim = None _ , _ largest = True
_ , _ sorted = True _ , _ * _ , _ out = None _ ) ¶
Returns the ` k ` largest elements of the given ` input ` tensor along a
given dimension.
If ` dim ` is not given, the last dimension of the input is c... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_389_0.txt |
# torch.Tensor.sparse_mask ¶
Tensor. sparse_mask ( _ mask _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a new [ sparse tensor ](../sparse.html#sparse-docs) with values from
a strided tensor ` self ` filtered by the indices of the sparse tensor ` mask
` . The values of ` mask... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_49_20.txt | generated/numpy.tile.html) . For
the operator similar to numpy.repeat , see [ ` torch.repeat_interleave() `
](generated/torch.repeat_interleave.html#torch.repeat_interleave
"torch.repeat_interleave") .
Parameters
**sizes** ( _torch.Size_ _or_ _int..._ ) – The number of times to repeat this
tensor along each ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_420_0.txt |
# torch.Tensor.cauchy_ ¶
Tensor. cauchy_ ( _ median = 0 _ , _ sigma = 1 _ , _ * _ , _
generator = None _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Fills the tensor with numbers drawn from the Cauchy distribution:
f ( x ) = 1 π σ ( x − median ) 2 \+ σ 2 f(... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_20.txt | ` ` max_pool2d ` ( _input_ , _kernel_size_ ,
_stride=None_ , _padding=0_ , _dilation=1_ , _ceil_mode=False_ ,
_return_indices=False_ ) [ [source]
](_modules/torch/nn/quantized/functional.html#max_pool2d) ¶
Applies a 2D max pooling over a quantized input signal composed of several
quantized input planes.
Note... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_373_0.txt |
# torch.Tensor.gcd ¶
Tensor. gcd ( _ other _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_119_0.txt |
# torch.unflatten ¶
torch. unflatten ( _ input _ , _ dim _ , _ sizes _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Expands a dimension of the input tensor over multiple dimensions.
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_60_4.txt | ml#torch.futures.Future
"torch.futures.Future") object that can be waited on. When completed, the
return value of ` func ` on ` args ` and ` kwargs ` can be retrieved from
the [ ` Future ` ](futures.html#torch.futures.Future "torch.futures.Future")
object.
Warning
Using GPU tensors as arguments or return values o... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_65_4.txt | pytorch.org/mobile)
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* [ Tutorials ](https://pytorch.org/tutorials)
* [ Docs ](https://pytorch.org/docs/stable/index.html)
* [ Resources ](https://pytorch.org/resources)
* [ Github ](https://github.com/pytorch/pytorch)
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pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_510_0.txt |
# torch.Tensor.new_full ¶
Tensor. new_full ( _ size _ , _ fill_value _ , _ * _ , _ dtype = None
_ , _ device = None _ , _ requires_grad = False _ , _ layout =
torch.strided _ , _ pin_memory = False _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a Tensor of size [... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_59.txt | (class in torch.nn) ](generated/torch.nn.ZeroPad2d.html#torch.nn.ZeroPad2d)
* [ zeros() (in module torch) ](generated/torch.zeros.html#torch.zeros)
* [ zeros_() (in module torch.nn.init) ](nn.init.html#torch.nn.init.zeros_)
* [ zeros_like() (in module torch) ](generated/torch.zeros_like.html#torch.zeros_like)
... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_49_10.txt | diagflat() ` ](generated/torch.diagflat.html#torch.diagflat
"torch.diagflat")
` diagonal ` ( _offset=0_ , _dim1=0_ , _dim2=1_ ) → Tensor ¶
See [ ` torch.diagonal() ` ](generated/torch.diagonal.html#torch.diagonal
"torch.diagonal")
` fill_diagonal_ ` ( _fill_value_ , _wrap=False_ ) → Tensor ¶
Fil... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_68_0.txt |
# torch.trace ¶
torch. trace ( _ input _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Returns the sum of the elements of the diagonal of the input 2-D matrix.
Example:
>>> x = torch.arange(1., 10.).view(3, 3)
>>> x
tensor([[ 1., 2., 3.],
[ 4., 5.... | |
pytorch_torch_tensor_functions/Solve_CUDA_out_of_memory_1_0.txt | ## What Causes ‘CUDA out of memory’ in PyTorch?
You might encounter the ‘CUDA out of memory’ error in PyTorch for several
reasons. Some of the most common causes include:
1. **Large batch sizes** : One of the most common causes of this error is trying to train your model with a batch size that’s too large. When yo... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_53_0.txt |
# torch.igamma ¶
torch. igamma ( _ input _ , _ other _ , _ * _ , _ out = None _ ) →
[ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.special.gammainc() `
](../special.html#torch.special.gammainc "torch.special.gammainc") .
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_174_0.txt |
# torch.Tensor.storage ¶
Tensor. storage ( ) → [ torch.TypedStorage
](../storage.html#torch.TypedStorage "torch.TypedStorage") [ [source]
](../_modules/torch/_tensor.html#Tensor.storage) ¶
Returns the underlying [ ` TypedStorage `
](../storage.html#torch.TypedStorage "torch.TypedStorage") .
Warning
[... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_27_3.txt | her") | Gathers values along an axis specified by dim .
[ ` index_select ` ](generated/torch.index_select.html#torch.index_select "torch.index_select") | Returns a new tensor which indexes the ` input ` tensor along dimension ` dim ` using the entries in ` index ` which is a LongTensor .
[ ` masked_sele... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_384_0.txt |
# torch.Tensor.lerp ¶
Tensor. lerp ( _ end _ , _ weight _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_212_0.txt |
# torch.Tensor.view_as ¶
Tensor. view_as ( _ other _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
View this tensor as the same size as ` other ` . ` self.view_as(other) ` is
equivalent to ` self.view(other.size()) ` .
Please see [ ` view() ` ](torch.Tensor.view.html#torch.Tensor.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_210_0.txt |
# torch.divide ¶
torch. divide ( _ input _ , _ other _ , _ * _ , _ rounding_mode = None
_ , _ out = None _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Alias for [ ` torch.div() ` ](torch.div.html#torch.div "torch.div") .
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_516_0.txt |
# torch.true_divide ¶
torch. true_divide ( _ dividend _ , _ divisor _ , _ * _ , _ out _ ) →
[ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.div() ` ](torch.div.html#torch.div "torch.div") with `
rounding_mode=None ` .
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_62_9.txt | list = output
>>> for i in range(world_size):
>>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
>>> input
tensor([0, 1, 2, 3, 4, 5]) # Rank 0
tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1
... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_29_1.txt | y in.
Parameters
* **devices** ( _iterable of CUDA IDs_ ) – CUDA devices for which to fork the RNG. CPU RNG state is always forked. By default, ` fork_rng() ` operates on all devices, but will emit a warning if your machine has a lot of devices, since this function will run very slowly in that case. If you ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_243_0.txt |
# torch.take_along_dim ¶
torch. take_along_dim ( _ input _ , _ indices _ , _ dim = None _ , _ *
_ , _ out = None _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Selects values from ` input ` at the 1-dimensional indices from ` indices `
along the given ` dim ` .
If ` dim ` ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_63_0.txt |
# torch.logical_xor ¶
torch. logical_xor ( _ input _ , _ other _ , _ * _ , _ out = None _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the element-wise logical XOR of the given input tensors. Zeros are
treated as ` False ` and nonzeros are treated as ` True ` .
Paramet... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_3.txt | f quantization parameters and all integer elements are the same
* [ ` int_repr() ` ](tensors.html#torch.Tensor.int_repr "torch.Tensor.int_repr") — Prints the underlying integer representation of the quantized tensor
* [ ` max() ` ](tensors.html#torch.Tensor.max "torch.Tensor.max") — Returns the maximum value ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_140_0.txt |
# torch.positive ¶
torch. positive ( _ input _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Returns ` input ` . Throws a runtime error if ` input ` is a bool tensor.
Parameters
**input** ( [ _Tensor_ ](../tensors.html#torch.Tensor "torch.Tensor") ) – the
input tensor.
Examp... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_346_0.txt |
# torch.Tensor.matrix_power ¶
Tensor. matrix_power ( _ n _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Note
` matrix_power() ` is deprecated, use [ ` torch.linalg.matrix_power() `
](torch.linalg.matrix_power.html#torch.linalg.matrix_power
"torch.linalg.matrix_power") instead.
Alia... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_44_3.txt | fan_out' ` preserves the magnitudes in the backwards pass.
* **nonlinearity** – the non-linear function ( nn.functional name), recommended to use only with ` 'relu' ` or ` 'leaky_relu' ` (default).
Examples
>>> w = torch.empty(3, 5)
>>> nn.init.kaiming_normal_(w, mode='fan_out', nonlineari... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_360_0.txt |
# torch.qr ¶
torch. qr ( _ input _ , _ some = True _ , _ * _ , _ out = None _ )
¶
Computes the QR decomposition of a matrix or a batch of matrices ` input ` ,
and returns a namedtuple (Q, R) of tensors such that input = Q R
\text{input} = Q R input = QR with Q Q Q being an orthogonal ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_26_0.txt |
# torch.Tensor.is_meta ¶
Tensor. is_meta ¶
Is ` True ` if the Tensor is a meta tensor, ` False ` otherwise. Meta
tensors are like normal tensors, but they carry no data.
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_375_0.txt |
# torch.flip ¶
torch. flip ( _ input _ , _ dims _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Reverse the order of an n-D tensor along given axis in dims.
Note
torch.flip makes a copy of ` input ` ’s data. This is different from NumPy’s
np.flip , which returns a view in constant... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_164_0.txt |
# torch.atan2 ¶
torch. atan2 ( _ input _ , _ other _ , _ * _ , _ out = None _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Element-wise arctangent of input i / other i \text{input}_{i} /
\text{other}_{i} input i / other i with consideration of the
quadrant. Retu... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_48_9.txt | X file
itself. These external binary files are stored in the same location as the
ONNX file. Argument ‘f’ must be a string specifying the location of the model.
model = torchvision.models.mobilenet_v2(pretrained=True)
input = torch.randn(2, 3, 224, 224, requires_grad=True)
torch.onnx.export(model... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_49_26.txt |
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')
>>> other = torch.randn((), dtype=torch.float64, device=cuda0)
>>> tensor.to(other, non_blocking=True)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_117_0.txt |
# torch.isnan ¶
torch. isnan ( _ input _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Returns a new tensor with boolean elements representing if each element of `
input ` is NaN or not. Complex values are considered NaN when either their
real and/or imaginary part is NaN.
Parameters... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_55_12.txt | load_url() ` ](model_zoo.html#torch.utils.model_zoo.load_url)
* [ torch.utils.tensorboard ](tensorboard.html)
* [ ` SummaryWriter ` ](tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter)
* [ Type Info ](type_info.html)
* [ torch.finfo ](type_info.html#torch-finfo)
* [ torch.iinfo ](type_inf... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_135_0.txt |
# torch.Tensor.is_signed ¶
Tensor. is_signed ( ) → [ bool
](https://docs.python.org/3/library/functions.html#bool "\(in Python v3.12\)")
¶
Returns True if the data type of ` self ` is a signed data type.
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_5.txt | ute) ](distributions.html#torch.distributions.half_normal.HalfNormal.arg_constraints)
* [ (torch.distributions.independent.Independent attribute) ](distributions.html#torch.distributions.independent.Independent.arg_constraints)
* [ (torch.distributions.laplace.Laplace attribute) ](distributions.html#torch.distr... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_66_2.txt | : ` OMP_NUM_THREADS ` and ` MKL_NUM_THREADS `
For the intra-op parallelism settings, ` at::set_num_threads ` , `
torch.set_num_threads ` always take precedence over environment variables, `
MKL_NUM_THREADS ` variable takes precedence over ` OMP_NUM_THREADS ` .
## Tuning the number of threads ¶
The follow... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_365_0.txt |
# torch.arctanh ¶
torch. arctanh ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.atanh() ` ](torch.atanh.html#torch.atanh "torch.atanh") .
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_37_4.txt | _inputs_ , _v=None_ ,
_create_graph=False_ , _strict=False_ ) [ [source]
](_modules/torch/autograd/functional.html#vjp) ¶
Function that computes the dot product between a vector ` v ` and the
Jacobian of the given function at the point given by the inputs.
Parameters
* **func** ( _function_ ) – a Py... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_331_0.txt |
# torch.sinh ¶
torch. sinh ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a new tensor with the hyperbolic sine of the elements of ` input ` .
out i = sinh ( input i ) \text{out}_{i} = \sinh(\text{input}_{i})
out i = sin... |
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