motion2prog_release Hierarchical Motion Understanding via Motion Programs (CVPR 2021)This repository contains the official implementation of: Hierarchical Motion Understanding via Motion Programs Running motion2prog 0. We start with video file and first prepare the input data $ ffmpeg -i ${video_dir}/video.mp4 ${video_dir}/frames/%05d.jpg $ python AlphaPose/scripts/demo_inference.py –cfg AlphaPose/pretrained_models/256x192_res50_lr1e-3_1x.yaml –checkpoint AlphaPose/pretrained_models/halpe26_fast_res50_256x192.pth –indir ${video_dir}/frames –outdir ${video_dir}/pose_mpii_track –pose_track –showbox –flip –qsize 256 $ mv ${video_dir}/pose_mpii_track/alphapose-results.json ${video_dir}/alphapose-results-halpe26-posetrack.json We packaged a demo video with necessary inputs for quickly testing our code $ wget https://sumith1896.github.io/motion2prog/static/demo.zip $ mv demo.zip data/ […]

Soft DTW Loss Function for PyTorch in CUDA This is a Pytorch Implementation of Soft-DTW: a Differentiable Loss Function for Time-Series which is batch supported computation, CUDA-friendly, and feasible to use as a final loss. I can confirm that you can train a (sequential) model with this as a final loss! The following image shows training logs of a TTS model using the Soft-DTW Loss Function. There are some previous implementations: mblondel’s soft-dtw lyprince’s sdtw_pytorch Maghoumi’s pytorch-softdtw-cuda But they are […]

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch List of Algorithms Covered 📌 Day 1 – Linear Regression 📌 Day 2 – Logistic Regression 📌 Day 3 – Decision Tree 📌 Day 4 – KMeans Clustering 📌 Day 5 – Naive Bayes 📌 Day 6 – K Nearest Neighbour (KNN) 📌 Day 7 – Support Vector Machine 📌 Day 8 – Tf-Idf Model 📌 […]

CloudAAE This is an tensorflow implementation of “CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds” Files log: directory to store log files during training. losses: loss functions for training. models: a python file defining model structure. object_model_tfrecord: full object models for data synthesizing and visualization purpose. tf_ops: tensorflow implementation of sampling operations (credit: Haoqiang Fan, Charles R. Qi). trained_network: a trained network. utils: utility files for defining model structure. ycb_video_data_tfRecords: synthetic training data and real […]

Patch-NetVLAD This repository contains code for the CVPR2021 paper “Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition” The article can be found on arXiv and the official proceedings. Installation We recommend using conda (or better: mamba) to install all dependencies. If you have not yet installed conda/mamba, please download and install mambaforge. conda create -n patchnetvlad python=3.8 numpy pytorch-gpu torchvision natsort tqdm opencv pillow scikit-learn faiss matplotlib-base -c conda-forge conda activate patchnetvlad We provide several pre-trained models and configuration […]

BRepNet A topological message passing system for solid models This repository contains the an implementation of BRepNet: A topological message passing system for solid models. About BRepNet BRepNet is a neural network specifically designed to operate on solid models. It uses additional topological information present in the boundary representation (B-Rep) data structure to perform convolutions in a way which is not possible for arbitrary graphs. As B-Reps describe manifolds, they contain additional topological information which includes the ordering of edges […]

DLCF-DCA codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification. submitted to 《Neurocomputing》. Requirement Python >= 3.6 PyTorch >= 1.0 pytorch-transformers == 1.2.0 SpaCy >= 2.2 To use our models, you need download en_core_web_sm by python -m spacy download en_core_web_sm Training python train.py –model dlcf_dca Model Architecture Note Some important scripts to note: datasets/semeval14/*.seg: Preprocessed training and testing sentences in SemEval2014. datasets/semeval15/*.seg: Preprocessed training and testing sentences in SemEval2015. datasets/semeval16/*.seg: Preprocessed training and […]