torch clamp alternative

discuss.pytorch.org › t › alternative-to-clamp › 28006

Alternative to clamp - PyTorch Forums

October 25, 2018 - Good afternoon, I am using the function torch clamp to clip a tensor between its minimum and 1e-10, however it seems to be very slow, would it be an alternative to that? or a better way to do it ? I am doing : torch.clamp(dist,torch.min(dist),1e-10)

Is there a way to clip with a tensor? Mar 10, 2020

6y ago

Clipping a tensor between two corresponding tensor values Apr 18, 2019

7y ago

How can I do clamp using a tensor as threshold? Feb 4, 2018

8y ago

.clamp function has gradient clip effect? - autograd Jul 23, 2018

8y ago

How to clamp tensor to some range without doing an inplace operation? May 17, 2018

8y ago

More results from pytorch.org

stackoverflow.com › questions › 54738045 › column-dependent-bounds-in-torch-clamp

python - Column-dependent bounds in torch.clamp - Stack Overflow

1 of 2

Not as neat as np.clip, but you can use torch.max and torch.min:

In [1]: x
Out[1]:
tensor([[0.9752, 0.5587, 0.0972],
        [0.9534, 0.2731, 0.6953]])

Setting the lower and upper bound per column

l = torch.tensor([[0.2, 0.3, 0.]])
u = torch.tensor([[0.8, 1., 0.65]])

clipped_x = torch.max(torch.min(x, u), l)

Resulting with

tensor([[0.8000, 0.5587, 0.0972],
        [0.8000, 0.3000, 0.6500]])

2 of 2

For anyone, who is having the same problem like me a few minutes ago:

For about two years it is also possible to have column-dependent bounds in torch.clamp (see PR):

In: x = torch.randn(2, 3)
        print(x)

Out: tensor([[-0.2069, 1.4082, 0.2615],
             [0.6478, 0.0883, -0.7795]])

Setting a lower and upper bound:

lower = torch.Tensor([[-1., 0., 0.]])
upper = torch.Tensor([[0., 1., 1.]])

Now you can simply use torch.clamp as follows:

In: clamped_x = torch.clamp(x, min=lower, max=upper)
    print(clamped_x)

Out: tensor([[-0.2069, 1.0000, 0.2615],
             [0.0000, 0.0883, 0.0000]])

I hope that helps :)

Discussions

What is the difference between `pykeen.utils.clamp_norm` and `torch.clamp`?

I see that while developing pykeen.utils.clamp_norm, you have used a different method to clamp the value instead of using torch.clamp. More on github.com

github.com

August 25, 2021

A difference between np.clamp and torch.clamp, any possible workaround?

", line 1, in TypeError: clamp(): argument 'min' (position 2) must be Number, not list Is there any way I can use t... More on discuss.pytorch.org

discuss.pytorch.org

February 11, 2021

Exluding torch.clamp() from backpropagation (as tf.stop_gradient in tensorflow)

Hi, when using torch.clamp(), the derivative w.r.t. to its input is zero if the input is outside [min, max]. This results in all gradients for previous operations in the graph to become zero due to the chain rule: In tensorflow, one can use tf.stop_gradient (tf.stop_gradient | TensorFlow ... More on discuss.pytorch.org

discuss.pytorch.org

August 2, 2019

python - Pytorch Autograd gives different gradients when using .clamp instead of torch.relu - Stack Overflow

It's especially confusing as .clamp is used equivalently to relu in PyTorch tutorials, such as https://pytorch.org/tutorials/beginner/pytorch_with_examples.html#pytorch-nn. I found this when analysing the gradients of a simple fully connected net with one hidden layer and a relu activation (linear in the outputlayer). to my understanding the output of the following code should be just zeros. I hope someone can show me what I am missing. Copyimport torch ... More on stackoverflow.com

stackoverflow.com

discuss.pytorch.org › autograd

Is clamp on torch.exp is a good alternative to softmax - autograd - PyTorch Forums

October 4, 2022 - For example, x = torch.tensor([1., 2, 150]) F.softmax(x, dim = 0) tensor([0., 0., 1.]) I actually have to manually calculated the softmax where I can not directly use softmax function.

GitHub

github.com › pykeen › pykeen › discussions › 582

What is the difference between `pykeen.utils.clamp_norm` and `torch.clamp`? · pykeen/pykeen · Discussion #582

Author: pykeen

Hi @nari97 ,

torch.clamp does element-wise clamping, i.e., each element of the tensor is afterwards inside the given range. However, we want to ensure that a vector's norm is no greater than a given value.

It is somewhat similar to torch.nn.utils.clip_grad_norm_, but operates on a single tensor, instead of all gradients. and also is out-of-place, the latter being required for backprop.

discuss.pytorch.org › t › a-difference-between-np-clamp-and-torch-clamp-any-possible-workaround › 111605

A difference between np.clamp and torch.clamp, any possible workaround? - PyTorch Forums

February 11, 2021 - In numpy, while using np.clamp(x, min, max) we can pass an array of min/max values but pytorch only accepts an integer. >>> a = np.array([0.4, 10, 0.1]) >>> np.clip(a, [0.3, 0.4,0.5], [0.9, 0.99, 0.999]) array([0.4 , 0.…

discuss.pytorch.org › t › exluding-torch-clamp-from-backpropagation-as-tf-stop-gradient-in-tensorflow › 52404

Exluding torch.clamp() from backpropagation (as tf.stop_gradient in tensorflow) - PyTorch Forums

August 2, 2019 - Hi, when using torch.clamp(), the derivative w.r.t. to its input is zero if the input is outside [min, max]. This results in all gradients for previous operations in the graph to become zero due to the chain rule: In tensorflow, one can use tf.stop_gradient (tf.stop_gradient | TensorFlow v2.15.0.post1) to prevent this behavior.

GitHub

alband.github.io › doc_view › generated › torch.clip.html

torch.clip — PyTorch master documentation

Alias for torch.clamp().

stackoverflow.com › questions › 60618346 › pytorch-autograd-gives-different-gradients-when-using-clamp-instead-of-torch-re

python - Pytorch Autograd gives different gradients when using .clamp instead of torch.relu - Stack Overflow

The reason is that relu and clamp produce different gradients at 0. For a scalar tensor x = 0:

(relu(x) - 1.0).pow(2).backward() gives x.grad == 0
(x.clamp(min=0) - 1.0).pow(2).backward() gives x.grad == -2

This indicates that:

relu chooses x == 0 --> grad = 0
clamp chooses x == 0 --> grad = 1

Find elsewhere

Google Bing Mojeek

discuss.pytorch.org › t › solved-should-i-use-torch-clamp-after-torch-sigmoid › 46819

[SOLVED] Should I use torch.clamp after torch.sigmoid? - PyTorch Forums

June 12, 2019 - Hello, I’ve tried to write a custom loss function, a Weighted Binary Cross Entropy. As suggested by @miguelvr here: I’ve tried to use the following function (wrapped inside a class): def weighted_binary_cross_entropy(output, target, weights=None): if weights is not None: assert len(weights) == 2 loss = weights[1] * (target * torch.log(output)) + \ weights[0] * ((1 - target) * torch.log(1 - output)) else: loss = target * torch.lo...

stackoverflow.com › questions › 62002130 › do-relu1-in-pytorch

Do ReLU1 in PyTorch - Stack Overflow

github.com › pytorch › pytorch › issues › 7002

Without trying it, my guess is that clamp and hardtanh will have the same speed, and it will be hard to do this operation any faster if you optimize it in isolation. The arithmetic is trivial so this operation will be bottlenecked by GPU memory bandwidth. To run faster, you'd want to fuse this operation with the operation that produced x. If you don't want to write a custom kernel for the combined operation, you can try using TorchScript.

GitHub

torch.clamp kills gradients at the border · Issue #7002 · pytorch/pytorch

April 26, 2018 - I.e. clamping the value 0 to min=0, max=1 should have no effect on the gradient for that value, but it does--the gradient is being set to 0. a=Variable(torch.tensor([0.0]), requires_grad=True)

Author: pytorch

Medium

medium.com › @MarkAiCode › mastering-pytorch-clamp-method-932f8bf7a46a

Mastering PyTorch Clamp Method. Are you looking to level up your… | by Mark Ai Code | Medium

July 28, 2024 - Here’s how you can use clamp in a custom layer: import torch.nn as nn class ClampedLinear(nn.Linear): def __init__(self, in_features, out_features, min_value, max_value): super().__init__(in_features, out_features) self.min_value = min_value self.max_value = max_value def forward(self, input): return torch.clamp(super().forward(input), min=self.min_value, max=self.max_value) # Usage clamped_layer = ClampedLinear(10, 5, min_value=-1, max_value=1)

Medium

kenichinakanishi.medium.com › pytorch-vs-numpy-exploring-some-syntactical-and-behavioural-differences-8d8ef7b3a130

PyTorch vs Numpy — exploring some syntactical and behavioural differences | by Kenichi Nakanishi | Medium

June 17, 2020 - Like numpy.concatenate, torch.cat can be used whenever you want to join two data sets together. ... The PyTorch equivalent to numpy.clip, this function clamps (clips) all elements in the input tensor into a range min, max and returns a tensor where every element below the minimum has been set to the minimum, every element above the maximum has been set to the maximum, and every value inbetween remains as-is.

stackoverflow.com › questions › 62275778 › what-does-clamp-does-in-pytorch-and-how-to-change-it-to-the-tensorflow-2-0

what does clamp_ does in pytorch and how to change it to the tensorflow 2.0? - Stack Overflow

tutorialspoint.com › python-pytorch-clamp-method

clamp_(0, 1) Clamp all elements in prior_boxes into the range [ 0, 1].

Tensorflow:

tf.clip_by_value
https://www.tensorflow.org/api_docs/python/tf/clip_by_value

TutorialsPoint

Python – PyTorch clamp() method

January 20, 2022 - It returns a new tensor clamped all elements in input into the range [min, max]. Import the required library. In all the following examples, the required Python library is torch.