fastai | The fastai deep learning library | Machine Learning library

by fastai Jupyter Notebook Version: 2.7.15 License: Apache-2.0

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kandi X-RAY | fastai Summary

fastai is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. fastai has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes:. fastai is organized around two main design goals: to be approachable and rapidly productive, while also being deeply hackable and configurable. It is built on top of a hierarchy of lower-level APIs which provide composable building blocks. This way, a user wanting to rewrite part of the high-level API or add particular behavior to suit their needs does not have to learn how to use the lowest level.

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fastai has a medium active ecosystem.

It has 23959 star(s) with 7393 fork(s). There are 613 watchers for this library.

There were 2 major release(s) in the last 12 months.

There are 145 open issues and 1575 have been closed. On average issues are closed in 87 days. There are 11 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of fastai is 2.7.15

Quality

fastai has 0 bugs and 0 code smells.

Security

fastai has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

fastai code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

fastai is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

fastai releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

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Community Discussions

Trending Discussions on fastai

Loading a pretrained model in PyTorch, error:object not callable

ToPILImage : TypeError: Input type int64 is not supported

ModuleNotFoundError: No module named 'fastai.structured'

Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas

How to change size of confusion matrix in fastai?

multiclass sequence classifiaction with fastai and huggingface

no implementation found for 'torch.nn.functional.smooth_l1_loss'

Using densenet with fastai

mamba fails to create env

Why does Pytorch autograd need a scalar?

QUESTION

Loading a pretrained model in PyTorch, error:object not callable

Asked 2022-Feb-07 at 21:09

I am trying to load the Efficientnet-b6 weights using PyTorch and Fastai:

...

ANSWER

Answered 2022-Feb-07 at 21:09

Given the restrained context, I suspect that the problem resides in model, probably containing an OrderedDict of the EfficientNet model state dict, while the EARUnet expects the EfficientNet nn.Module.

You should instead, try something like:

Source https://stackoverflow.com/questions/71023630

QUESTION

ToPILImage : TypeError: Input type int64 is not supported

Asked 2021-Dec-11 at 17:40

I'm trying to develop a GAN using FastAi. When converting the Tensor to an Image I get this error.

...

ANSWER

Answered 2021-Dec-11 at 17:40

I suggest for you to use this code to convert the output of your model from a tensor to a PIL image:

Source https://stackoverflow.com/questions/70316929

QUESTION

ModuleNotFoundError: No module named 'fastai.structured'

Asked 2021-Oct-28 at 11:10

I was trying to create some features from date column using 'add_datepart' function from 'fastai.structured' module in 'fastai' library. I got this error:

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ANSWER

Answered 2021-Oct-28 at 11:10

Finally I found the solution to this problem. 'structured' module in 'fastai' has been replaced with 'core' module inside 'tabular' folder in 'fastai' library. So instead of importing 'add_datepart' from 'structured' module import it from 'core'. In short we need to make following changes in our code:

Replace this code-

Source https://stackoverflow.com/questions/69745680

QUESTION

Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas

Asked 2021-Oct-11 at 14:51

data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data

I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.

...

ANSWER

Answered 2021-Oct-11 at 14:21

geopandas 0.10.1

have noted that your data is on kaggle, so start by sourcing it
there really is only one issue shapely.geometry.MultiPoint() constructor does not work with a filtered series. Pass it a numpy array instead and it works.
full code below, have randomly selected a point to serve as gpdPoint

Source https://stackoverflow.com/questions/69521034

QUESTION

How to change size of confusion matrix in fastai?

Asked 2021-Sep-08 at 11:48

I am drawing a Confusion Matrix in fastai with following code:

...

ANSWER

Answered 2021-Aug-20 at 02:24

If you check the code of the function ClassificationInterpretation.plot_confusion_matrix (in file fastai / interpret.py), this is what you see:

Source https://stackoverflow.com/questions/68861402

QUESTION

multiclass sequence classifiaction with fastai and huggingface

Asked 2021-Aug-25 at 19:09

I am looking to implement DistilBERT via fastai and huggingface for a mutliclass sequence classification problem. I found a useful tutorial that gave a good example on how to do this with binary classification. The code is below:

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ANSWER

Answered 2021-Aug-25 at 19:09

You need to define num_labels=9 when loading the model:

Source https://stackoverflow.com/questions/68928299

QUESTION

no implementation found for 'torch.nn.functional.smooth_l1_loss'

Asked 2021-Aug-25 at 05:36

I am trying to use the fastai library to build a machine learning model for object detection. I'm not an expert in machine learning, so I searched online and found this tutorial at object detection tutorial but when I used Google colab to run it, I got error saying: TypeError: no implementation found for 'torch.nn.functional.smooth_l1_loss' on types that implement torch_function: [, ] And I've also tried a 2018 version of the same tutorial before but ran into the same problem. Does anyone know what's going on?

...

ANSWER

Answered 2021-Aug-25 at 05:36

The repository you referred to in your question is outdated a/c to their README here.

You can find instructions to go to the latest versions of notebooks there.

Source https://stackoverflow.com/questions/68916544

QUESTION

Using densenet with fastai

Asked 2021-Aug-10 at 07:17

I am trying to train a densenet model using the fast.ai library. I checked the documentation and I managed to make it work for resnet50. However, for densenet, it seems to be unable to find the module.

I tried to use arch=models.dn121 as stated by this forum. But I get the same error.

Can anyone please help?

Here is the code:

...

ANSWER

Answered 2021-Aug-10 at 07:17

According to this post on the fast.ai forum, this is the solution to use densenet with fast.ai:

Source https://stackoverflow.com/questions/68722235

QUESTION

mamba fails to create env

Asked 2021-Aug-04 at 05:11

I had just installed Anaconda from anaconda.com. The installation proceeded smoothly. After that, I was trying to create a new environment from this environment.yml file. (nbdev.yml)

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ANSWER

Answered 2021-Aug-04 at 05:11

After a lot of research, I stumbled on to Mamba doesn't find a solution when mixing conda forge defaults and not specifying Python explicitly 1102. So I just edited nbdev.yml from

Source https://stackoverflow.com/questions/68645697

QUESTION

Why does Pytorch autograd need a scalar?

Asked 2021-Jul-26 at 21:46

I am working through "Deep Learning for Coders with fastai & Pytorch". Chapter 4 introduces the autograd function from the PyTorch library on a trivial example.

...

ANSWER

Answered 2021-Jul-26 at 21:46

TLDR; the derivative of a sum of functions is the sum of their derivatives

Let x be your input vector made of x_i (where i in [0,n]), y = x**2 and L = sum(y_i). You are looking to compute dL/dx, a vector of the same size as x whose components are the dL/dx_j (where j in [0,n]).

For j in [0,n], dL/dx_j is simply dy_j/dx_j (derivative of the sum is the sum of derivates and only one of them is different to zero), which is d(x_j**2)/dx_j, i.e. 2*x_j. Therefore, dL/dx = [2*x_j where j in [0,n]].

This is the result you get in x.grad when either computing the gradient of x as:

Source https://stackoverflow.com/questions/68536392

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Install fastai

You can download it from GitHub.

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When installing with mamba or conda replace -c fastchan in the installation with -c pytorch -c nvidia -c fastai, since fastchan is not currently supported on Windows. Due to python multiprocessing issues on Jupyter and Windows, num_workers of Dataloader is reset to 0 automatically to avoid Jupyter hanging. This makes tasks such as computer vision in Jupyter on Windows many times slower than on Linux. This limitation doesn't exist if you use fastai from a script. See this example to fully leverage the fastai API on Windows.

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