QualityScaler
torchinfo
| QualityScaler | torchinfo | |
|---|---|---|
| 69 | 3 | |
| 1,738 | 2,294 | |
| - | - | |
| 7.9 | 6.9 | |
| 15 days ago | 2 days ago | |
| Python | Python | |
| MIT License | MIT License |
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QualityScaler
torchinfo
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[D] PyTorch and Tensorflow Performance Different on the same model, dataset and hyperparameters
It may be a good idea to compare the implementation of the models using Keras's Model.summary and PyTorch's torchinfo.
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after adding a nn.Dropout() layer, the number of parameters won't change?
No they wont change. If you are interested in viewing parameters check out Torchinfo https://github.com/TylerYep/torchinfo
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zero_grad() is supposed to be invoked every time one data point passed? How does a scalar.backward() from a loss function affect another model parameters?
You can use torchinfo, it has a # params column and it's a nice way to log and debug a NN architecture to make sure all the layers you expect are connected.
What are some alternatives?
cupscale - Image Upscaling GUI based on ESRGAN
MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
BSRGAN - Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021) (PyTorch) - We released the training code!
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
NiceScaler - Images & Video DeepLearning Upscaler
deepo - Setup and customize deep learning environment in seconds.
zipslicer - A library for incremental loading of large PyTorch checkpoints
torchSR - Super Resolution datasets and models in Pytorch
halutmatmul - Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
image-background-remove-tool - ✂️ Automated high-quality background removal framework for an image using neural networks. ✂️
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.