id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pytorch_torch_tensor_functions/Pytorch_Documentations_55_7.txt | [ ` StorageReader ` ](distributed.checkpoint.html#torch.distributed.checkpoint.StorageReader)
* [ ` StorageWriter ` ](distributed.checkpoint.html#torch.distributed.checkpoint.StorageWriter)
* [ ` LoadPlanner ` ](distributed.checkpoint.html#torch.distributed.checkpoint.LoadPlanner)
* [ ` LoadPlan ` ](dis... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_320_0.txt |
# torch.allclose ¶
torch. allclose ( _ input _ , _ other _ , _ rtol = 1e-05 _ , _ atol =
1e-08 _ , _ equal_nan = False _ ) → [ bool
](https://docs.python.org/3/library/functions.html#bool "\(in Python v3.12\)")
¶
This function checks if ` input ` and ` other ` satisfy the condition:
∣ inpu... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_333_0.txt |
# torch.logical_or ¶
torch. logical_or ( _ input _ , _ other _ , _ * _ , _ out = None _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the element-wise logical OR of the given input tensors. Zeros are
treated as ` False ` and nonzeros are treated as ` True ` .
Parameters... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_78_0.txt |
# torch.sspaddmm ¶
torch. sspaddmm ( _ input _ , _ mat1 _ , _ mat2 _ , _ * _ , _ beta =
1 _ , _ alpha = 1 _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Matrix multiplies a sparse tensor ` mat1 ` with a dense tensor ` mat2 ` ,
then adds the sparse tensor `... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_149_0.txt |
# torch.Tensor.fill_diagonal_ ¶
Tensor. fill_diagonal_ ( _ fill_value _ , _ wrap = False _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Fill the main diagonal of a tensor that has at least 2-dimensions. When
dims>2, all dimensions of input must be of equal length. This function
mod... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_21_3.txt | test = gradcheck(linear, input, eps=1e-6, atol=1e-4)
print(test)
See [ Numerical gradient checking ](../autograd.html#grad-check) for more
details on finite-difference gradient comparisons. If your function is used in
higher order derivatives (differentiating the backward pass) you can use the `
gradgradch... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_226_0.txt |
# torch.histogram ¶
torch. histogram ( _ input _ , _ bins _ , _ * _ , _ range = None _ ,
_ weight = None _ , _ density = False _ , _ out = None _ ) ¶
Computes a histogram of the values in a tensor.
` bins ` can be an integer or a 1D tensor.
If ` bins ` is an int, it specifies the numbe... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_26.txt | ch.qint8
>>> qm = Quantize(scale, zero_point, dtype)
>>> quantized_input = qm(input)
>>> dqm = DeQuantize()
>>> dequantized = dqm(quantized_input)
>>> print(dequantized)
tensor([[ 1., -1.],
[ 1., -1.]], dtype=torch.float32)
### Linear ¶
_class_ ` torch.nn.quantized. ` ` Line... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_2.txt | er an input image composed of several input planes.
See [ ` Conv3d ` ](generated/torch.nn.Conv3d.html#torch.nn.Conv3d
"torch.nn.Conv3d") for details and output shape.
Note
In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_55_9.txt | ](linalg.html#misc)
* [ Experimental Functions ](linalg.html#experimental-functions)
* [ torch.monitor ](monitor.html)
* [ API Reference ](monitor.html#module-torch.monitor)
* [ torch.signal ](signal.html)
* [ torch.signal.windows ](signal.html#module-torch.signal.windows)
* [ torch.special ](special... | |
pytorch_torch_tensor_functions/Memory Management_2_0.txt | # Overview of PYTORCH_CUDA_ALLOC_CONF
**PYTORCH_CUDA_ALLOC_CONF** is a configuration option introduced in PyTorch to
enhance memory management and allocation for deep learning applications
utilizing CUDA. It is designed to optimize GPU memory allocation and improve
performance during training and inference processes.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_200_0.txt |
# torch.arctan ¶
torch. arctan ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.atan() ` ](torch.atan.html#torch.atan "torch.atan") .
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_470_0.txt |
# torch.Tensor.ndimension ¶
Tensor. ndimension ( ) → [ int
](https://docs.python.org/3/library/functions.html#int "\(in Python v3.12\)")
¶
Alias for [ ` dim() ` ](torch.Tensor.dim.html#torch.Tensor.dim
"torch.Tensor.dim")
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_31_1.txt | mon preprocessing and
integration tasks needed for incorporating ML in mobile applications.
## Save your model
torch.jit.script(model).save("my_mobile_model.pt")
## iOS prebuilt binary
pod ‘LibTorch’
## Android prebuilt binary
implementation 'org.pytorch:pytorch_andro... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_9_11.txt | . Edge  ](https://pytorch.org/executorch/stable/demo-apps-android.html)
#### [ Lowering a Model as a Delegate Learn to accelerate your program using
ExecuTorch by applying delegates through three methods: lowering the whole
module, composing it with another module, and p... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_2.txt | need to be able to represent
quantized data in Tensors. A Quantized Tensor allows for storing quantized
data (represented as int8/uint8/int32) along with quantization parameters like
scale and zero_point. Quantized Tensors allow for many useful operations
making quantized arithmetic easy, in addition to allowing for se... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_25_12.txt | _Optimizer_ ) – Wrapped optimizer.
* **base_lr** ( [ _float_ ](https://docs.python.org/3/library/functions.html#float "\(in Python v3.8\)") _or_ [ _list_ ](https://docs.python.org/3/library/stdtypes.html#list "\(in Python v3.8\)") ) – Initial learning rate which is the lower boundary in the cycle for each paramete... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_376_0.txt |
# torch.logaddexp2 ¶
torch. logaddexp2 ( _ input _ , _ other _ , _ * _ , _ out = None _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Logarithm of the sum of exponentiations of the inputs in base-2.
Calculates pointwise log 2 ( 2 x \+ 2 y ) \log_2\left(2^x +
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_170_0.txt |
# torch.max ¶
torch. max ( _ input _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Returns the maximum value of all elements in the ` input ` tensor.
Warning
This function produces deterministic (sub)gradients unlike ` max(dim=0) `
Parameters
**input** ( [ _Tensor_ ](../ten... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_437_0.txt |
# torch.tril ¶
torch. tril ( _ input _ , _ diagonal = 0 _ , _ * _ , _ out = None _
) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns the lower triangular part of the matrix (2-D tensor) or batch of
matrices ` input ` , the other elements of the result tensor ` out ` are se... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_137_0.txt |
# torch.Tensor.xlogy ¶
Tensor. xlogy ( _ other _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_13.txt | ler) ](optim.html#torch.optim.lr_scheduler.CyclicLR)
---|---
## D
* [ data() (torch.nn.utils.rnn.PackedSequence property) ](generated/torch.nn.utils.rnn.PackedSequence.html#torch.nn.utils.rnn.PackedSequence.data)
* [ data_parallel() (in module torch.nn.parallel) ](nn.functional.html#torch.nn.parallel.dat... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_263_0.txt |
# torch.broadcast_to ¶
torch. broadcast_to ( _ input _ , _ shape _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Broadcasts ` input ` to the shape ` shape ` . Equivalent to calling `
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_50_2.txt | torch binaries provided: one compiled with GCC pre-cxx11 ABI
and the other with GCC cxx11 ABI, and you should make the selection based on
the GCC ABI your system is using.
[ Next  ](torch.html "torch") [
 Previous ](notes/windows.h... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_15_2.txt | ytorch.org)
* [ Github Issues ](https://github.com/pytorch/pytorch/issues)
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pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_370_0.txt |
# torch.Tensor.to_sparse_bsr ¶
Tensor. to_sparse_bsr ( _ blocksize _ , _ dense_dim _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Convert a tensor to a block sparse row (BSR) storage format of given
blocksize. If the ` self ` is strided, then the number of dense dimensions
could be s... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_39_1.txt | ide additional automatic runtime correctness checks
2. propagate names from input tensors to output tensors
Below is a list of all operations that are supported with named tensors and
their associated name inference rules.
If you don’t see an operation listed here, but it would help your use case,
please [ searc... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_17.txt |
](_modules/torch/nn/qat/modules/conv.html#Conv2d) ¶
A Conv2d module attached with FakeQuantize modules for both output activation
and weight, used for quantization aware training.
We adopt the same interface as torch.nn.Conv2d , please see [
https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.C... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_460_0.txt |
# torch.ormqr ¶
torch. ormqr ( _ input _ , _ tau _ , _ other _ , _ left = True _ , _
transpose = False _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the matrix-matrix multiplication of a product of Householder matrices
with a general matri... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_514_0.txt |
# torch.floor_divide ¶
torch. floor_divide ( _ input _ , _ other _ , _ * _ , _ out = None _
) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Note
Before PyTorch 1.13 ` torch.floor_divide() ` incorrectly performed
truncation division. To restore the previous behavior use [ ` torch.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_447_0.txt |
# torch.sigmoid ¶
torch. sigmoid ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.special.expit() ` ](../special.html#torch.special.expit
"torch.special.expit") .
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_6_0.txt |
# torch.resolve_neg ¶
torch. resolve_neg ( _ input _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a new tensor with materialized negation if ` input ` ’s negative bit
is set to True , else returns ` input ` . The output tensor will always
have its negative bit set to False... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_9_1.txt | er_tutorial.html)
* [ Optimizing Vision Transformer Model for Deployment ](beginner/vt_tutorial.html)
* [ Whole Slide Image Classification Using PyTorch and TIAToolbox ](intermediate/tiatoolbox_tutorial.html)
Audio
* [ Audio I/O ](beginner/audio_io_tutorial.html)
* [ Audio Resampling ](beginner/audio_resampli... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_25_15.txt | x .
It has been proposed in [ SGDR: Stochastic Gradient Descent with Warm Restarts
](https://arxiv.org/abs/1608.03983) .
Parameters
* **optimizer** ( _Optimizer_ ) – Wrapped optimizer.
* **T_0** ( [ _int_ ](https://docs.python.org/3/library/functions.html#int "\(in Python v3.8\)") ) – Number of it... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_22.txt | .gradcheck)
* [ gradgradcheck() (in module torch.autograd) ](autograd.html#torch.autograd.gradgradcheck)
* [ GradScaler (class in torch.cuda.amp) ](amp.html#torch.cuda.amp.GradScaler)
* [ graph() (torch.jit.ScriptModule property) ](generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule.graph)
* [ Grayscal... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_3.txt | torch.Tensor method) ](tensors.html#torch.Tensor.addmv_)
* [ addr() (in module torch) ](generated/torch.addr.html#torch.addr)
* [ (torch.Tensor method) ](tensors.html#torch.Tensor.addr)
* [ addr_() (torch.Tensor method) ](tensors.html#torch.Tensor.addr_)
* [ adjust_brightness() (in module torchvision.transfor... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_327_0.txt |
# torch.deg2rad ¶
torch. deg2rad ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a new tensor with each of the elements of ` input ` converted from
angles in degrees to radians.
Parameters
**input** ( [ _Tensor_ ](../tensors.html#t... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_60_2.txt | utograd primitives, initialization must take
place. To initialize the RPC framework we need to use ` init_rpc() ` which
would initialize the RPC framework, RRef framework and distributed autograd.
` torch.distributed.rpc. ` ` init_rpc ` ( _name_ ,
_backend=BackendType.PROCESS_GROUP_ , _rank=-1_ , _world_size=None_ ... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_63_3.txt | >> torch.randn((2,3), device=1) # legacy
## torch.layout ¶
_class_ ` torch. ` ` layout ` ¶
A ` torch.layout ` is an object that represents the memory layout of a [ `
torch.Tensor ` ](tensors.html#torch.Tensor "torch.Tensor") . Currently, we
support ` torch.strided ` (dense Tensors) and have beta su... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_324_0.txt |
# torch.masked_select ¶
torch. masked_select ( _ input _ , _ mask _ , _ * _ , _ out = None _
) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a new 1-D tensor which indexes the ` input ` tensor according to the
boolean mask ` mask ` which is a BoolTensor .
The shapes of ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_319_0.txt |
# torch.all ¶
torch. all ( _ input _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Tests if all elements in ` input ` evaluate to True .
Note
This function matches the behaviour of NumPy in returning output of dtype
bool for all supported dtypes except uint8 . For uint8 the d... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_59_7.txt | ...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)
>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]
>>> # Mult-process loading with two worker processes
>>> #... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_199_0.txt |
# torch.Tensor.requires_grad ¶
Tensor. requires_grad ¶
Is ` True ` if gradients need to be computed for this Tensor, ` False `
otherwise.
Note
The fact that gradients need to be computed for a Tensor do not mean that the
[ ` grad ` ](torch.Tensor.grad.html#torch.Tensor.grad "torch.Tensor.grad")
attrib... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_192_0.txt |
# torch.lerp ¶
torch. lerp ( _ input _ , _ end _ , _ weight _ , _ * _ , _ out = None
_ ) ¶
Does a linear interpolation of two tensors ` start ` (given by ` input ` )
and ` end ` based on a scalar or tensor ` weight ` and returns the resulting
` out ` tensor.
out i = start i \+ weight i... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_190_0.txt |
# torch.Tensor.geometric_ ¶
Tensor. geometric_ ( _ p _ , _ * _ , _ generator = None _ ) → [
Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Fills ` self ` tensor with elements drawn from the geometric distribution:
P ( X = k ) = ( 1 − p ) k − 1 p , k = 1 , 2 , . .... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_51_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_47_4.txt | in_channels , groups out_channels , k T , k H , k W )
* **bias** – optional bias of shape ( out_channels ) (\text{out\\_channels}) ( out_channels ) . Default: None
* **stride** – the stride of the convolving kernel. Can be a single number or a tuple ` (sT, sH, sW) ` . Default: 1
... | |
pytorch_torch_tensor_functions/Solve_CUDA_out_of_memory_4_1.txt | aders/)
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##### Resource... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_136_0.txt |
# torch.sgn ¶
torch. sgn ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
This function is an extension of torch.sign() to complex tensors. It computes
a new tensor whose elements have the same angles as the corresponding elements
of ` input ` and ... | |
pytorch_torch_tensor_functions/Solve_CUDA_out_of_memory_2_0.txt | ## How to Solve ‘CUDA out of memory’ in PyTorch
### Solution #1: Reduce Batch Size or Use Gradient Accumulation
As we mentioned earlier, one of the most common causes of the ‘CUDA out of
memory’ error is using a batch size that’s too large. If you’re encountering
this error, try reducing your batch size and see if ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_316_0.txt |
# torch.Tensor.ndim ¶
Tensor. ndim ¶
Alias for [ ` dim() ` ](torch.Tensor.dim.html#torch.Tensor.dim
"torch.Tensor.dim")
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_5.txt | ne_ ) [ [source]
](_modules/torch/distributions/categorical.html#Categorical) ¶
Bases: ` torch.distributions.distribution.Distribution `
Creates a categorical distribution parameterized by either ` probs ` or `
logits ` (but not both).
Note
It is equivalent to the distribution that [ ` torch.multinomia... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_7.txt | vers to select the scale factor and bias
based on observed tensor data are provided, developers can provide their own
quantization functions. Quantization can be applied selectively to different
parts of the model or configured differently for different parts of the model.
We also provide support for per channel quant... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_501_0.txt |
# torch.Tensor.log2 ¶
Tensor. log2 ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_64_0.txt |
# torch.Tensor.index_copy_ ¶
Tensor. index_copy_ ( _ dim _ , _ index _ , _ tensor _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Copies the elements of [ ` tensor ` ](torch.tensor.html#torch.tensor
"torch.tensor") into the ` self ` tensor by selecting the indices in the
order given... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_267_0.txt |
# torch.erfinv ¶
torch. erfinv ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.special.erfinv() ` ](../special.html#torch.special.erfinv
"torch.special.erfinv") .
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_20.txt | .TransformerDecoderLayer method) ](generated/torch.nn.TransformerDecoderLayer.html#torch.nn.TransformerDecoderLayer.forward)
* [ (torch.nn.TransformerEncoder method) ](generated/torch.nn.TransformerEncoder.html#torch.nn.TransformerEncoder.forward)
* [ (torch.nn.TransformerEncoderLayer method) ](generated/torch.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_167_0.txt |
# torch.Tensor.storage_offset ¶
Tensor. storage_offset ( ) → [ int
](https://docs.python.org/3/library/functions.html#int "\(in Python v3.12\)")
¶
Returns ` self ` tensor’s offset in the underlying storage in terms of number
of storage elements (not bytes).
Example:
>>> x = torch.tensor... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_317_0.txt |
# torch.Tensor.indices ¶
Tensor. indices ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Return the indices tensor of a [ sparse COO tensor ](../sparse.html#sparse-
coo-docs) .
Warning
Throws an error if ` self ` is not a sparse COO tensor.
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_33_0.txt | [ ](https://pytorch.org/)
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* [ Mobile ](https://pytorch.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_430_0.txt |
# torch.Tensor.imag ¶
Tensor. imag ¶
Returns a new tensor containing imaginary values of the ` self ` tensor. The
returned tensor and ` self ` share the same underlying storage.
Warning
[ ` imag() ` ](torch.imag.html#torch.imag "torch.imag") is only supported for
tensors with complex dtypes.
Example:... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_5_0.txt |
# torch.argmin ¶
torch. argmin ( _ input _ , _ dim = None _ , _ keepdim = False _ )
→ LongTensor ¶
Returns the indices of the minimum value(s) of the flattened tensor or along a
dimension
This is the second value returned by [ ` torch.min() `
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_55_1.txt | cuda_memory.html#using-the-visualizer)
* [ Snapshot API Reference ](torch_cuda_memory.html#snapshot-api-reference)
* [ torch.mps ](mps.html)
* [ torch.xpu ](xpu.html)
* [ Meta device ](meta.html)
* [ torch.backends ](backends.html)
* [ torch.export ](export.html)
* [ torch.distributed ](distributed.html)
... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_496_0.txt |
# torch.permute ¶
torch. permute ( _ input _ , _ dims _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a view of the original tensor ` input ` with its dimensions permuted.
Parameters
* **input** ( [ _Tensor_ ](../tensors.html#torch.Tensor "torch.Tensor") ) – the inpu... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_116_0.txt |
# torch.Tensor.addr ¶
Tensor. addr ( _ vec1 _ , _ vec2 _ , _ * _ , _ beta = 1 _ , _ alpha
= 1 _ ) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_27.txt | ization scale of the output, type: double.
* **zero_point** \- quantization zero point of the output, type: long.
### InstanceNorm1d ¶
_class_ ` torch.nn.quantized. ` ` InstanceNorm1d ` ( _num_features_ ,
_weight_ , _bias_ , _scale_ , _zero_point_ , _eps=1e-05_ , _momentum=0.1_ ,
_affine=False_ , _track_runni... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_251_0.txt |
# torch.neg ¶
torch. neg ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a new tensor with the negative of the elements of ` input ` .
out = − 1 × input \text{out} = -1 \times \text{input} out = − 1 ×
input
Parameters
*... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_154_0.txt |
# torch.abs ¶
torch. abs ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Computes the absolute value of each element in ` input ` .
out i = ∣ input i ∣ \text{out}_{i} = |\text{input}_{i}| out i =
∣ input i ∣
Parameters
... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_283_1.txt | inalg.matrix_norm() `
](torch.linalg.matrix_norm.html#torch.linalg.matrix_norm
"torch.linalg.matrix_norm") with ` ord='fro' ` aligns with the mathematical
definition, since it can only be applied across exactly two dimensions.
Example:
>>> import torch
>>> a = torch.arange(9, dtype= torch.float) -... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_306_0.txt |
# torch.not_equal ¶
torch. not_equal ( _ input _ , _ other _ , _ * _ , _ out = None _ )
→ [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Alias for [ ` torch.ne() ` ](torch.ne.html#torch.ne "torch.ne") .
| |
pytorch_torch_tensor_functions/Pytorch_Documentations_25_9.txt | source]
](_modules/torch/optim/lr_scheduler.html#StepLR) ¶
Decays the learning rate of each parameter group by gamma every step_size
epochs. Notice that such decay can happen simultaneously with other changes to
the learning rate from outside this scheduler. When last_epoch=-1, sets
initial lr as lr.
Parameters... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_37_10.txt | 2
4
6
[torch.FloatTensor of size (3,)]
>>> h.remove() # removes the hook
` retain_grad ` ( ) [ [source]
](_modules/torch/tensor.html#Tensor.retain_grad) ¶
Enables .grad attribute for non-leaf Tensors.
## Function ¶
_class_ ` torch.autograd. ` ` Function ` [ [source]
](_module... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_62_4.txt | available) ¶
Checks if the NCCL backend is available.
* * *
Currently three initialization methods are supported:
### TCP initialization ¶
There are two ways to initialize using TCP, both requiring a network address
reachable from all processes and a desired ` world_size ` . The first way
requires specify... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_31_0.txt |
# torch.select_scatter ¶
torch. select_scatter ( _ input _ , _ src _ , _ dim _ , _ index _ ) →
[ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Embeds the values of the ` src ` tensor into ` input ` at the given index.
This function returns a tensor with fresh storage; it does not create ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_29_0.txt |
# torch.asin ¶
torch. asin ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a new tensor with the arcsine of the elements of ` input ` .
out i = sin − 1 ( input i ) \text{out}_{i} =
\sin^{-1}(\text{input}_{i}) out i = s... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_33_1.txt | t on tape-based autograd systems.
## The PyTorch Contribution Process ¶
The PyTorch organization is governed by [ PyTorch Governance
](governance.html) .
The PyTorch development process involves a healthy amount of open discussions
between the core development team and the community.
PyTorch operates similar to m... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_175_0.txt |
# torch.Tensor.igamma ¶
Tensor. igamma ( _ other _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_380_0.txt |
# torch.Tensor.unfold ¶
Tensor. unfold ( _ dimension _ , _ size _ , _ step _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns a view of the original tensor which contains all slices of size [ `
size ` ](torch.Tensor.size.html#torch.Tensor.size "torch.Tensor.size") from `
self `... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_49_1.txt | or ` torch.float ` | ` torch.FloatTensor ` | ` torch.cuda.FloatTensor `
64-bit floating point | ` torch.float64 ` or ` torch.double ` | ` torch.DoubleTensor ` | ` torch.cuda.DoubleTensor `
16-bit floating point 1 | ` torch.float16 ` or ` torch.half ` | ` torch.HalfTensor ` | ` torch.cuda.HalfT... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_7_5.txt | of the observed tensor inputs (per tensor variant)
* ` MovingAverageMinMaxObserver ` — Derives the quantization parameters from the running averages of the minimums and maximums of the observed tensor inputs (per tensor variant)
* ` PerChannelMinMaxObserver ` — Derives the quantization parameters from th... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_45.txt | ndOptions.rpc_timeout)
* [ RpcBackendOptions (class in torch.distributed.rpc) ](rpc.html#torch.distributed.rpc.RpcBackendOptions)
* [ Rprop (class in torch.optim) ](optim.html#torch.optim.Rprop)
* [ RRef (class in torch.distributed.rpc) ](rpc.html#torch.distributed.rpc.RRef)
* [ RReLU (class in torch.nn) ](gene... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_67_0.txt | [ ](https://pytorch.org/)
* [ Get Started ](https://pytorch.org/get-started)
* Ecosystem
[ 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_Documentations_19_1.txt | le
* 
* *
octoml-profile is a python library and cloud service designed to provide a
simple experience for assessing and optimizing the performance of PyTorch
models. ](https://github.com/octoml/octoml-profile)
[
* raster-vision
* 
... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_59_1.txt | batching ,
* single- and multi-process data loading ,
* automatic memory pinning .
These options are configured by the constructor arguments of a ` DataLoader
` , which has signature:
DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,
batch_sampler=None, num_worke... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_25.txt | s.html#torch.Tensor.index_copy)
* [ index_copy_() (torch.Tensor method) ](tensors.html#torch.Tensor.index_copy_)
* [ index_fill() (torch.Tensor method) ](tensors.html#torch.Tensor.index_fill)
* [ index_fill_() (torch.Tensor method) ](tensors.html#torch.Tensor.index_fill_)
* [ index_put() (torch.Tensor method) ]... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_59_6.txt | e.g., a lambda function. See [ Multiprocessing best
practices ](notes/multiprocessing.html#multiprocessing-best-practices) on
more details related to multiprocessing in PyTorch.
Warning
` len(dataloader) ` heuristic is based on the length of the sampler used.
When ` dataset ` is an ` IterableDataset ` , it inste... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_47_5.txt | in k H × k W kH \times kW k H ×
k W regions by step size s H × s W sH \times sW s H × s W steps.
The number of output features is equal to the number of input planes.
See [ ` AvgPool2d ` ](generated/torch.nn.AvgPool2d.html#torch.nn.AvgPool2d
"torch.nn.AvgPool2d") for details and output shape.
... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_33_0.txt |
# torch.Tensor.expm1 ¶
Tensor. expm1 ( ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
| |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_315_0.txt |
# torch.Tensor.scatter_add_ ¶
Tensor. scatter_add_ ( _ dim _ , _ index _ , _ src _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Adds all values from the tensor ` src ` into ` self ` at the indices
specified in the ` index ` tensor in a similar fashion as [ ` scatter_() `
](torch.... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_436_0.txt |
# torch.mean ¶
torch. mean ( _ input _ , _ * _ , _ dtype = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Returns the mean value of all elements in the ` input ` tensor.
Parameters
**input** ( [ _Tensor_ ](../tensors.html#torch.Tensor "torch.Tensor") ) – the
input ten... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_57.txt | s.html#torch.distributions.categorical.Categorical.variance)
* [ (torch.distributions.cauchy.Cauchy property) ](distributions.html#torch.distributions.cauchy.Cauchy.variance)
* [ (torch.distributions.continuous_bernoulli.ContinuousBernoulli property) ](distributions.html#torch.distributions.continuous_bernoulli... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_49_7.txt | ting point ` dtype ` , and the result will have the same ` dtype `
.
See [ ` torch.bernoulli() ` ](generated/torch.bernoulli.html#torch.bernoulli
"torch.bernoulli")
` bernoulli_ ` ( ) ¶
` bernoulli_ ` ( _p=0.5_ , _*_ , _generator=None_ ) → Tensor
Fills each location of ` self ` with an independe... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_339_0.txt |
# torch.Tensor.element_size ¶
Tensor. element_size ( ) → [ int
](https://docs.python.org/3/library/functions.html#int "\(in Python v3.12\)")
¶
Returns the size in bytes of an individual element.
Example:
>>> torch.tensor([]).element_size()
4
>>> torch.tensor([], dtype=torch.uint8... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_67_2.txt |
a list of available entrypoint names
Return type
entrypoints
Example
>>> entrypoints = torch.hub.list('pytorch/vision', force_reload=True)
` torch.hub. ` ` help ` ( _github_ , _model_ , _force_reload=False_ ) [
[source] ](_modules/torch/hub.html#help) ¶
Show the docstring ... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_277_0.txt |
# torch.adjoint ¶
torch. adjoint ( _ Tensor _ ) → [ Tensor ](../tensors.html#torch.Tensor
"torch.Tensor") ¶
Returns a view of the tensor conjugated and with the last two dimensions
transposed.
` x.adjoint() ` is equivalent to ` x.transpose(-2, -1).conj() ` for complex
tensors and to ` x.transpose(... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_9_6.txt | Use TorchRL to code a DDPG Loss Reinforcement-
Learning  ](advanced/coding_ddpg.html)
#### [ Writing your environment and transforms Use TorchRL to code a Pendulum
Reinforcement-Learning 
](advanced/pendulum.html)
#### [ Deploying PyTorch in Python via... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_58_4.txt | method) ](generated/torch.nn.utils.prune.LnStructured.html#torch.nn.utils.prune.LnStructured.apply)
* [ (torch.nn.utils.prune.PruningContainer class method) ](generated/torch.nn.utils.prune.PruningContainer.html#torch.nn.utils.prune.PruningContainer.apply)
* [ (torch.nn.utils.prune.RandomStructured class metho... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_347_0.txt |
# torch.as_strided ¶
torch. as_strided ( _ input _ , _ size _ , _ stride _ , _ storage_offset
= None _ ) → [ Tensor ](../tensors.html#torch.Tensor "torch.Tensor") ¶
Create a view of an existing torch.Tensor ` input ` with specified ` size
` , ` stride ` and ` storage_offset ` .
Warning
Pref... | |
pytorch_torch_tensor_functions/pytorch_torch_tensor_functions_46_0.txt |
# torch.isposinf ¶
torch. isposinf ( _ input _ , _ * _ , _ out = None _ ) → [ Tensor
](../tensors.html#torch.Tensor "torch.Tensor") ¶
Tests if each element of ` input ` is positive infinity or not.
Parameters
**input** ( [ _Tensor_ ](../tensors.html#torch.Tensor "torch.Tensor") ) – the
inp... | |
pytorch_torch_tensor_functions/Pytorch_Documentations_57_23.txt | C. However this acts mostly
coordinate-wise (except for the final normalization), and thus is appropriate
for coordinate-wise optimization algorithms.
_class_ ` torch.distributions.transforms. ` ` StickBreakingTransform ` (
_cache_size=0_ ) [ [source]
](_modules/torch/distributions/transforms.html#StickBreakingTransf... |
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