Feature request

We currently have 2 monocular depth estimation models in the library, namely DPT and GLPN.

It would be great to have a pipeline for this task, with the following API:

from transformers import pipeline

pipe = pipeline("depth-estimation")
pipe("ca

What could be better?
依赖项的 requ…….txt 应该被更新，而且 pytorch 更要描述具体版本和安装方式

本人配环境一个下午，勉强配出来 Tag0.0.1 pytorch 1.9.0 OnlyCpu 的版本。

@babysor @oceanarium

This is a nice first issue:

Add types/docments to the Learner.get_preds function. This function is essential to any fastai user and has almost no documentation. Add types and text to the variables as we have in many places now.

What happened + What you expected to happen

The current default autoscaler error message color is painfully difficult to read on the default background.

Versions / Dependencies

Master

Reproduction script

Launch a 4 CPU head node with availa

🚀 Typing coverage

Let's improve typing coverage of PyTorch Lightning together!

I'm creating a new issue in order to increase visibility. There are three older issues (#7037, #5023, #4698) which became stale over time.

Plan

Currently, there are 55 files which are excluded from mypy checks so that our CI does not fail. These files vastly differ in difficulty in order to make the t

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

🚀 The feature, motivation and pitch

The overall goal of this roadmap is to ensure a tighter connection between PyG core and the GraphGym configuration manager. Furthermore, an additional goal is to not re-invent the wheel in GraphGym and make use of popular open-source frameworks whenever applicable, e.g., for configuration managament, training, logging, and autoML.

As such, this roadm

Adding a Dataset

• Name: Stanford dog dataset
• Description: The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/
• Paper: http://vision.stanford.edu/aditya86/ImageNetDogs/
• Data: *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/Ima

Feature Request

System information

ONNX version (you are using): latest main branch

What is the problem that this feature solves?

Making the checker behavior consistent can prevent confusion. And checker should not modify the model in place.

Describe the alternatives you have considered

Keep the same behavior as before.

Describe the feature

Current behavior:
ch

Bug with GPU Model

Currently, while using pruning methods like TaylorFOWeight Pruner, If I use a model on GPU for getting the metrics (as calculated for getting masks), it fails on line while creating masks. The reason why it fails i