This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.
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Updated
Jun 7, 2020 - Jupyter Notebook
This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.
PyTorch implementation of Super SloMo by Jiang et al.
I add a function(according the code in the tutorial4) to calculate the bleu score, but i get the vey low score(0.09), could you tell me why?
This is code to calculate bleu:
def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):
model.eval()
if isinstance(sentence, str):
nlp = spacy.load('de')
tokens = [token.teDeep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels (CVPR, 2019) (PyTorch)
Need help for retraining and cross validation and see if the ROUGE score matches exactly (or better) with the numbers reported in the paper.
I just train for 500k iteration (with batch size 8) with pointer generation enabled + coverage loss disabled and next 100k iteration (with batch size 8) with pointer generation enabled + coverage loss enabled.
It would be great if someone can help re-r
Bilinear attention networks for visual question answering
I have trained almost 80thousand examples within 2000 labels,valid acc almost 92%,but test result all example prob is blew 0.01.
I have tried tranning examples to predict.
The implementation of StyleGAN on PyTorch 1.0.1
Implementation of various human pose estimation models in pytorch on multiple datasets (MPII & COCO) along with pretrained models
The PyTorch Implementation of SummaRuNNer
PyTorch implementation of soft actor critic
A pytorch reproduction of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
PyTorch Implementation of Focal Loss and Lovasz-Softmax Loss
Another pytorch implementation of FCN (Fully Convolutional Networks)
Character-level Convolutional Neural Networks for text classification in PyTorch
Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.
PyTorch Implementation of Fully Convolutional Networks (a very simple and easy demo).
A pytorch implemention of MoCoGAN
This is the pytorch implementation of Hindsight Experience Replay (HER) - Experiment on all fetch robotic environments.
Unofficially Pytorch implementation of High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection
Fact Extraction and VERification baseline published in NAACL2018
LSTM and GRU in PyTorch
At the outset, this is a great implementation of StyleGAN in PyTorch. I really like the way the modules are structured.
This is more of a suggestion from my side:
Seems like you are not sanitizing your gradients in the code. Please check this from the official StyleGAN implementation.
I am currently
A Pytorch tutorial for implementation of Dynamic memory Network Plus
Pytorch implementation of GCN architecture for semantic segmentation
Pytorch implementation of Unsupervised Attention-guided Image-to-Image Translation.
Pytorch implementation of "An intriguing failing of convolutional neural networks and the CoordConv solution" - https://arxiv.org/abs/1807.03247
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Thanks for your tutorial from scratch. It helps me a lot.
In article part 3, there are some codes you wrote. I copy that codes but some error for me.
TypeError: forward() missing 1 required positional argument: 'CUDA'
and I want to ask you that training module is the only left work