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omar-Fouad / MachineLearning-AI

This repository contains all the work that I regularly did and studied from Medium blogs, various research papers, and other Repos.

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100 days of Artificial Intelligence

This is the 100 days of Machine Learning, Deep Learning, AI, and Optimization mini-projects that I picked up at the start of January 2022. I have used various environments and Google Colab for this work as it required various libraries and datasets to be downloaded. The following are the problems that I tackled:

Classification for Cat (GradCAM-based Explainability) Classification for Dog (GradCAM-based Explainability)

Computer Vision domains CAM methods used Detected Images CAM-based images
Semantic Segmentation GradCAM
Object Detection EigenCAM
Object Detection AblationCAM
3D Point Clouds Meshes Used Sampled Meshes
Beds
Chair TBA
  1. Segmentation

  1. Implementing GNNs on YouChoose-Click dataset
  2. Implementing GNNs on YouChoose-Buy dataset
Dataset Loss Curve Accuracy Curve
YouChoose-Click
YouChoose-Buy
SN Training and Validation Metrices
1
2
Loss Metrices

Explore Difference between Ant Colony Optimization and Genetic Algorithms for Travelling Salesman Problem.

Methods Used Geo-locaion graph
Ant Colony Optimization
Genetic Algorithm
  1. Tug-Of-War Optimization (Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492.)
  2. Nuclear Reaction Optimization (Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access.)
    + So many equations and loops - take time to run on larger dimension 
    + General O (g * n * d) 
    + Good convergence curse because the used of gaussian-distribution and levy-flight trajectory
    + Use the variant of Differential Evolution
  1. Henry Gas Solubility Optimization (Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667.)
    + Too much constants and variables
    + Still have some unclear point in Eq. 9 and Algorithm. 1
    + Can improve this algorithm by opposition-based and levy-flight
    + A wrong logic code in line 91 "j = id % self.n_elements" => to "j = id % self.n_clusters" can make algorithm converge faster. I don't know why?
    + Good results come from CEC 2014
  1. Queuing Search Algorithm (Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490.)

  • Day 16 (01/16/2022): Evolutionary Optimization algorithms Explored the contents of Human Activity-based optimization techniques such as: Genetic Algorithms (Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73) Differential Evolution (Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359) Coral Reefs Optimization Algorithm (Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014)

  • Day 17 (01/17/2022): Swarm-based Optimization algorithms Explored the contents of Swarm-based optimization techniques such as:

  1. Particle Swarm Optimization (Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). IEEE)
  2. Cat Swarm Optimization (Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg)
  3. Whale Optimization (Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67)
  4. Bacterial Foraging Optimization (Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67)
  5. Adaptive Bacterial Foraging Optimization (Yan, X., Zhu, Y., Zhang, H., Chen, H., & Niu, B. (2012). An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discrete Dynamics in Nature and Society, 2012)
  6. Artificial Bee Colony (Karaboga, D., & Basturk, B. (2007, June). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In International fuzzy systems association world congress (pp. 789-798). Springer, Berlin, Heidelberg)
  7. Pathfinder Algorithm (Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568)
  8. Harris Hawks Optimization (Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872)
  9. Sailfish Optimizer (Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34)

Credits (from Day 14--17): Learnt a lot due to Nguyen Van Thieu and his repository that deals with metaheuristic algorithms. Plan to use these algorithms in the problems enountered later onwards.

CMAES without bounds CMAES with bounds

Refered from: Nikolaus Hansen, Dirk Arnold, Anne Auger. Evolution Strategies. Janusz Kacprzyk; Witold Pedrycz. Handbook of Computational Intelligence, Springer, 2015, 978-3-622-43504-5. ffhal-01155533f

S. No Forged Images Forgery Detection in Images
1
2
3
Contour Approximation Method Retrieval Method Actual Image Contours Detected
CHAIN_APPROX_NONE RETR_TREE
CHAIN_APPROX_SIMPLE RETR_TREE
CHAIN_APPROX_SIMPLE RETR_CCOMP
CHAIN_APPROX_SIMPLE RETR_LIST
CHAIN_APPROX_SIMPLE RETR_EXTERNAL
CHAIN_APPROX_SIMPLE RETR_TREE

Referenced from here

File used Actual File Estimated Background
Video 1
Methods used t-SNE Representation
Using PCA
Using Autoencoders
Methods used Representation
Using PCA
Using Variational Autoencoders
Methods used Representation
Using Generative Adversarial Networks
Library Used Actual Image Facial Detection Facial Landmarks Head Pose Estimation
Haar Cascades (To be done) (To be done)
Haar Cascades (To be done) (To be done)
Mult-task Cascaded Convolutional Neural Networks
Mult-task Cascaded Convolutional Neural Networks
OpenCV's Deep Neural Network
OpenCV's Deep Neural Network

(Yet to use Dlib for facial detection.)

Model Used Actual Image Monocular Depth Estimation Depth Map
MiDaS model for Depth Estimation
MiDaS model for Depth Estimation

Ref: (The model used was Large Model ONNX file)

  • Day 32 - 37 (02/01/2022 - 02/06/2022): [Exploring Latent Spaces in Depth]
Model Used Paper Link Pictures
Auxiliary Classifier GAN Paper
Bicycle GAN Paper
Conditional GAN Paper
Cluster GAN Paper
Context Conditional GAN Paper
Context Encoder Paper
Cycle GAN Paper
Deep Convolutional GAN Paper
DiscoGANs Paper
Enhanced SuperRes GAN Paper
InfoGAN Paper
MUNIT Paper
Pix2Pix Paper
PixelDA Paper
StarGAN Paper
SuperRes GAN Paper
WGAN DIV Paper
WGAN GP Paper

video

Paper accepted to the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021

Figure 1: We estimate the 6DoF rigid transformation of a 3D face (rendered in silver), aligning it with even the tiniest faces, without face detection or facial landmark localization. Our estimated 3D face locations are rendered by descending distances from the camera, for coherent visualization.

Summary: This repository provides a novel method for six degrees of fredoom (6DoF) detection on multiple faces without the need of prior face detection. After prediction, one can visualize the detections (as show in the figure above), customize projected bounding boxes, or crop and align each face for further processing. See details below.

Paper details

Vítor Albiero, Xingyu Chen, Xi Yin, Guan Pang, Tal Hassner, "img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation," CVPR, 2021, arXiv:2012.07791

Abstract

We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation without face detection or landmark localization. We observe that estimating the 6DoF rigid transformation of a face is a simpler problem than facial landmark detection, often used for 3D face alignment. In addition, 6DoF offers more information than face bounding box labels. We leverage these observations to make multiple contributions: (a) We describe an easily trained, efficient, Faster R-CNN--based model which regresses 6DoF pose for all faces in the photo, without preliminary face detection. (b) We explain how pose is converted and kept consistent between the input photo and arbitrary crops created while training and evaluating our model. (c) Finally, we show how face poses can replace detection bounding box training labels. Tests on AFLW2000-3D and BIWI show that our method runs at real-time and outperforms state of the art (SotA) face pose estimators. Remarkably, our method also surpasses SotA models of comparable complexity on the WIDER FACE detection benchmark, despite not been optimized on bounding box labels.

Video Spotlight CVPR 2021 Spotlight

Installation

Install dependecies with Python 3.

pip install -r requirements.txt

Install the renderer, which is used to visualize predictions. The renderer implementation is forked from here.

cd Sim3DR
sh build_sim3dr.sh

Training Prepare WIDER FACE dataset First, download our annotations as instructed in Annotations.

Download WIDER FACE dataset and extract to datasets/WIDER_Face.

Then, to create the train and validation files (LMDB), run the following scripts.

python3 convert_json_list_to_lmdb.py \
--json_list ./annotations/WIDER_train_annotations.txt \
--dataset_path ./datasets/WIDER_Face/WIDER_train/images/ \
--dest ./datasets/lmdb/ \
-—train

This first script will generate a LMDB dataset, which contains the training images along with annotations. It will also output a pose mean and std deviation files, which will be used for training and testing.

python3 convert_json_list_to_lmdb.py  \
--json_list ./annotations/WIDER_val_annotations.txt  \
--dataset_path ./datasets/WIDER_Face/WIDER_val/images/  \
--dest ./datasets/lmdb

This second script will create a LMDB containing the validation images along with annotations.

Train Once the LMDB train/val files are created, to start training simple run the script below.

CUDA_VISIBLE_DEVICES=0 python3 train.py \
--pose_mean ./datasets/lmdb/WIDER_train_annotations_pose_mean.npy \
--pose_stddev ./datasets/lmdb/WIDER_train_annotations_pose_stddev.npy \
--workspace ./workspace/ \
--train_source ./datasets/lmdb/WIDER_train_annotations.lmdb \
--val_source ./datasets/lmdb/WIDER_val_annotations.lmdb \
--prefix trial_1 \
--batch_size 2 \
--lr_plateau \
--early_stop \
--random_flip \
--random_crop \
--max_size 1400

To train with multiple GPUs (in the example below 4 GPUs), use the script below.

python3 -m torch.distributed.launch --nproc_per_node=4 --use_env train.py \
--pose_mean ./datasets/lmdb/WIDER_train_annotations_pose_mean.npy \
--pose_stddev ./datasets/lmdb/WIDER_train_annotations_pose_stddev.npy \
--workspace ./workspace/ \
--train_source ./datasets/lmdb/WIDER_train_annotations.lmdb \
--val_source ./datasets/lmdb/WIDER_val_annotations.lmdb \
--prefix trial_1 \
--batch_size 2 \
--lr_plateau \
--early_stop \
--random_flip \
--random_crop \
--max_size 1400 \
--distributed

Training on your own dataset If your dataset has facial landmarks and bounding boxes already annotated, store them into JSON files following the same format as in the WIDER FACE annotations.

If not, run the script below to annotate your dataset. You will need a detector and import it inside the script.

python3 utils/annotate_dataset.py 
--image_list list_of_images.txt 
--output_path ./annotations/dataset_name

After the dataset is annotated, create a list pointing to the JSON files there were saved. Then, follow the steps in Prepare WIDER FACE dataset replacing the WIDER annotations with your own dataset annotations. Once the LMDB and pose files are created, follow the steps in Train replacing the WIDER LMDB and pose files with your dataset own files.

Testing To evaluate with the pretrained model, download the model from Model Zoo, and extract it to the main folder. It will create a folder called models, which contains the model weights and the pose mean and std dev that was used for training.

If evaluating with own trained model, change the pose mean and standard deviation to the ones trained with.

Visualizing trained model To visualize a trained model on the WIDER FACE validation set run the notebook visualize_trained_model_predictions.

WIDER FACE dataset evaluation If you haven't done already, download the WIDER FACE dataset and extract to datasets/WIDER_Face.

Download the pre-trained model.

python3 evaluation/evaluate_wider.py \
--dataset_path datasets/WIDER_Face/WIDER_val/images/ \
--dataset_list datasets/WIDER_Face/wider_face_split/wider_face_val_bbx_gt.txt \
--pose_mean models/WIDER_train_pose_mean_v1.npy \
--pose_stddev models/WIDER_train_pose_stddev_v1.npy \
--pretrained_path models/img2pose_v1.pth \
--output_path results/WIDER_FACE/Val/

To check mAP and plot curves, download the eval tools and point to results/WIDER_FACE/Val.

AFLW2000-3D dataset evaluation Download the AFLW2000-3D dataset and unzip to datasets/AFLW2000.

Download the fine-tuned model.

Run the notebook aflw_2000_3d_evaluation.

BIWI dataset evaluation Download the BIWI dataset and unzip to datasets/BIWI.

Download the fine-tuned model.

Run the notebook biwi_evaluation.

Testing on your own images

Run the notebook test_own_images.

Output customization

For every face detected, the model outputs by default:

  • Pose: rx, ry, rz, tx, ty, tz
  • Projected bounding boxes: left, top, right, bottom
  • Face scores: 0 to 1

Since the projected bounding box without expansion ends at the start of the forehead, we provide a way of expanding the forehead invidually, along with default x and y expansion.

To customize the size of the projected bounding boxes, when creating the model change any of the bounding box expansion variables as shown below (a complete example can be seen at visualize_trained_model_predictions).

# how much to expand in width
bbox_x_factor = 1.1
# how much to expand in height
bbox_y_factor = 1.1
# how much to expand in the forehead
expand_forehead = 0.3
img2pose_model = img2poseModel(
    ...,    
    bbox_x_factor=bbox_x_factor,
    bbox_y_factor=bbox_y_factor,
    expand_forehead=expand_forehead,
)

Align faces To detect and align faces, simply run the command below, passing the path to the images you want to detect and align and the path to save them.

python3 run_face_alignment.py \
--pose_mean models/WIDER_train_pose_mean_v1.npy \
--pose_stddev models/WIDER_train_pose_stddev_v1.npy \
--pretrained_path models/img2pose_v1.pth \
--images_path image_path_or_list \
--output_path path_to_save_aligned_faces

Resources

  1. Model Zoo

  2. Annotations

  3. Data Zoo

Referred from here directly.

Segmentation of differenct components of a scene using deep learning & Computer Vision. Making uses of multiple modalities of a same scene ( eg: RGB image, Depth Image, NIR etc) gave better results compared to individual modalities.

We used Keras for implementation of Fully convolutional Network (FCN-32s) trained to predict semantically segmented images of forest like images with rgb & nir_color input images. (check out the presentation @ https://docs.google.com/presentation/d/1z8-GeTXvSuVbcez8R6HOG1Tw_F3A-WETahQdTV38_uc/edit?usp=sharing)

Note:

Do the following steps after you download the dataset before you proceed and train your models.

  1. run preprocess/process.sh (renames images)
  2. run preprocess/text_file_gen.py (generates txt files for train,val,test used in data generator)
  3. run preprocess/aug_gen.py (generates augmented image files beforehand the training, dynamic augmentation in runtime is slow an often hangs the training process)

The Following list describes the files :

Improved Architecture with Augmentation & Dropout

  1. late_fusion_improveed.py (late_fusion FCN TRAINING FILE, Augmentation= Yes, Dropout= Yes)
  2. late_fusion_improved_predict.py (predict with improved architecture)
  3. late_fusion_improved_saved_model.hdf5 (Architecture & weights of improved model)

Old Architecture without Augmentation & Dropout

  1. late_fusion_old.py (late_fusion FCN TRAINING FILE, Augmentation= No, Dropout= No)
  2. late_fusion_old_predict.py() (predict with old architecture)
  3. late_fusion_improved_saved_model.hdf5 (Architecture & weights of old model)

Architecture: Alt text Architecture Reference (first two models in this link): http://deepscene.cs.uni-freiburg.de/index.html

Dataset: Alt text Dataset Reference (Freiburg forest multimodal/spectral annotated): http://deepscene.cs.uni-freiburg.de/index.html#datasets

Note:Since the dataset is too small the training will overfit. To overcome this and train a generalized classifier image augmentation is done. Images are transformed geometrically with a combination of transsformations and added to the dataset before training. Alt text

Training: Loss : Categorical Cross Entropy

Optimizer : Stochastic gradient descent with lr = 0.008, momentum = 0.9, decay=1e-6

Results: Alt text

NOTE: This following files in the repository ::

1.Deepscene/nir_rgb_segmentation_arc_1.py :: ("CHANNEL-STACKING MODEL") 2.Deepscene/nir_rgb_segmentation_arc_2.py :: ("LATE-FUSION MODEL") 3.Deepscene/nir_rgb_segmentation_arc_3.py :: ("Convoluted Mixture of Deep Experts (CMoDE) Model")

are the exact replicas of the architectures described in Deepscene website.

This contains the code for Generating Diverse and Meaningful Captions: Unsupervised Specificity Optimization for Image Captioning (Lindh et al., 2018) to appear in Artificial Neural Networks and Machine Learning - ICANN 2018.

A detailed description of the work, including test results, can be found in our paper: [publisher version] [author version]

Please consider citing if you use the code:

@inproceedings{lindh_generating_2018,
series = {Lecture {Notes} in {Computer} {Science}},
title = {Generating {Diverse} and {Meaningful} {Captions}},
isbn = {978-3-030-01418-6},
doi = {10.1007/978-3-030-01418-6_18},
language = {en},
booktitle = {Artificial {Neural} {Networks} and {Machine} {Learning} – {ICANN} 2018},
publisher = {Springer International Publishing},
author = {Lindh, Annika and Ross, Robert J. and Mahalunkar, Abhijit and Salton, Giancarlo and Kelleher, John D.},
editor = {Kůrková, Věra and Manolopoulos, Yannis and Hammer, Barbara and Iliadis, Lazaros and Maglogiannis, Ilias},
year = {2018},
keywords = {Computer Vision, Contrastive Learning, Deep Learning, Diversity, Image Captioning, Image Retrieval, Machine Learning, MS COCO, Multimodal Training, Natural Language Generation, Natural Language Processing, Neural Networks, Specificity},
pages = {176--187}
}

The code in this repository builds on the code from the following two repositories: https://github.com/ruotianluo/ImageCaptioning.pytorch
https://github.com/facebookresearch/SentEval/
A note is included at the top of each file that has been changed from its original state. We make these changes (and our own original files) available under Attribution-NonCommercial 4.0 International where applicable (see LICENSE.txt in the root of this repository).
The code from the two repos listed above retain their original licenses. Please see visit their repositories for further details. The SentEval folder in our repo contains the LICENSE document for SentEval at the time of our fork.

Requirements
Python 2.7 (built with the tk-dev package installed)
PyTorch 0.3.1 and torchvision
h5py 2.7.1
sklearn 0.19.1
scipy 1.0.1
scikit-image (skimage) 0.13.1
ijson
Tensorflow is needed if you want to generate learning curve graphs (recommended!)

Setup for the Image Captioning side
For ImageCaptioning.pytorch (previously known as neuraltalk2.pytorch) you need the pretrained resnet model found here, which should be placed under combined_model/neuraltalk2_pytorch/data/imagenet_weights.
You will also need the cocotalk_label.h5 and cocotalk.json from here and the pretrained Image Captioning model from the topdown directory.
To run the prepro scripts for the Image Captioning model, first download the coco images from link. You should put the train2014/ and val2014/ in the same directory, denoted as $IMAGE_ROOT during preprocessing.

There’s some problems with the official COCO images. See this issue about manually replacing one image in the dataset. You should also run the script under utilities/check_file_types.py that will help you find one or two PNG images that are incorrectly marked as JPG images. I had to manually convert these to JPG files and replace them.

Next, download the preprocessed coco captions from link from Karpathy's homepage. Extract dataset_coco.json from the zip file and copy it in to data/. This file provides preprocessed captions and the train-val-test splits.
Once we have these, we can now invoke the prepro_*.py script, which will read all of this in and create a dataset (two feature folders, a hdf5 label file and a json file):

$ python scripts/prepro_labels.py --input_json data/dataset_coco.json --output_json data/cocotalk.json --output_h5 data/cocotalk
$ python scripts/prepro_feats.py --input_json data/dataset_coco.json --output_dir data/cocotalk --images_root $IMAGE_ROOT

See https://github.com/ruotianluo/ImageCaptioning.pytorch for more info on the scripts if needed.

Setup for the Image Retrieval side
You will need to train a SentEval model according to the instructions here using their pretrained InferSent embedder. IMPORTANT: Because of a change in SentEval, you will need to pull commit c7c7b3a instead of the latest version.
You also need the GloVe embeddings you used for this when you’re training the full combined model.

Setup for the combined model
You will need the official coco-caption evaluation code which you can find here:
https://github.com/tylin/coco-caption
This should go in a folder called coco_caption under src/combined_model/neuraltalk2_pytorch

Run the training

$ cd src/combined_model
$ python SentEval/examples/launch_training.py --id <your_model_id> --checkpoint_path <path_to_save_model> --start_from <directory_pretrained_captioning_model> --learning_rate 0.0000001 --max_epochs 10 --best_model_condition mean_rank --loss_function pairwise_cosine --losses_log_every 10000 --save_checkpoint_every 10000 --batch_size 2 --caption_model topdown --input_json neuraltalk2_pytorch/data/cocotalk.json --input_fc_dir neuraltalk2_pytorch/data/cocotalk_fc --input_att_dir neuraltalk2_pytorch/data/cocotalk_att --input_label_h5 neuraltalk2_pytorch/data/cocotalk_label.h5 --learning_rate_decay_start 0 --senteval_model <your_trained_senteval_model> --language_eval 1 --split val

The --loss_function options used for the models in the paper:
Cos = cosine_similarity
DP = direct_similarity
CCos = pairwise_cosine
CDP = pairwise_similarity

See combined_model/neuraltalk2_pytorch/opts.py for a list of the available parameters.

Run the test

$ cd src/combined_model
$ python SentEval/examples/launch_test.py --id <your_model_id> --checkpoint_path <path_to_model> --start_from <path_to_model> --load_best_model 1 --loss_function pairwise_cosine  --batch_size 2 --caption_model topdown --input_json neuraltalk2_pytorch/data/cocotalk.json --input_fc_dir neuraltalk2_pytorch/data/cocotalk_fc --input_att_dir neuraltalk2_pytorch/data/cocotalk_att --input_label_h5 neuraltalk2_pytorch/data/cocotalk_label.h5 --learning_rate_decay_start 0 --senteval_model <your_trained_senteval_model> --language_eval 1 --split test

To test the baseline or the latest version of a model (instead of the one marked with 'best' in the name) use:
--load_best_model 0
The --loss_function option will only decide which internal loss function to report the result for. No extra training will be carried out, and the other results won't be affected by this choice.

knowledge-distillation-pytorch

  • Exploring knowledge distillation of DNNs for efficient hardware solutions
  • Author Credits: Haitong Li
  • Dataset: CIFAR-10

Features

  • A framework for exploring "shallow" and "deep" knowledge distillation (KD) experiments
  • Hyperparameters defined by "params.json" universally (avoiding long argparser commands)
  • Hyperparameter searching and result synthesizing (as a table)
  • Progress bar, tensorboard support, and checkpoint saving/loading (utils.py)
  • Pretrained teacher models available for download

Install

  • Install the dependencies (including Pytorch) 
    pip install -r requirements.txt

Organizatoin:

  • ./train.py: main entrance for train/eval with or without KD on CIFAR-10
  • ./experiments/: json files for each experiment; dir for hypersearch
  • ./model/: teacher and student DNNs, knowledge distillation (KD) loss defination, dataloader

Key notes about usage for your experiments:

  • Download the zip file for pretrained teacher model checkpoints from this Box folder
  • Simply move the unzipped subfolders into 'knowledge-distillation-pytorch/experiments/' (replacing the existing ones if necessary; follow the default path naming)
  • Call train.py to start training 5-layer CNN with ResNet-18's dark knowledge, or training ResNet-18 with state-of-the-art deeper models distilled
  • Use search_hyperparams.py for hypersearch
  • Hyperparameters are defined in params.json files universally. Refer to the header of search_hyperparams.py for details

Train (dataset: CIFAR-10)

Note: all the hyperparameters can be found and modified in 'params.json' under 'model_dir'

-- Train a 5-layer CNN with knowledge distilled from a pre-trained ResNet-18 model

python train.py --model_dir experiments/cnn_distill

-- Train a ResNet-18 model with knowledge distilled from a pre-trained ResNext-29 teacher

python train.py --model_dir experiments/resnet18_distill/resnext_teacher

-- Hyperparameter search for a specified experiment ('parent_dir/params.json')

python search_hyperparams.py --parent_dir experiments/cnn_distill_alpha_temp

--Synthesize results of the recent hypersearch experiments

python synthesize_results.py --parent_dir experiments/cnn_distill_alpha_temp

Results: "Shallow" and "Deep" Distillation

Quick takeaways (more details to be added):

  • Knowledge distillation provides regularization for both shallow DNNs and state-of-the-art DNNs
  • Having unlabeled or partial dataset can benefit from dark knowledge of teacher models

-Knowledge distillation from ResNet-18 to 5-layer CNN

Model Dropout = 0.5 No Dropout
5-layer CNN 83.51% 84.74%
5-layer CNN w/ ResNet18 84.49% 85.69%

-Knowledge distillation from deeper models to ResNet-18

Model Test Accuracy
Baseline ResNet-18 94.175%
+ KD WideResNet-28-10 94.333%
+ KD PreResNet-110 94.531%
+ KD DenseNet-100 94.729%
+ KD ResNext-29-8 94.788%

References

H. Li, "Exploring knowledge distillation of Deep neural nets for efficient hardware solutions," CS230 Report, 2018

Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531 (2015).

Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2014). Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550.

https://github.com/cs230-stanford/cs230-stanford.github.io

https://github.com/bearpaw/pytorch-classification

  • Day 57 (02/26/2022): 3D Morphable Face Models Intuition They are using Mobilenet to regress sparse 3D Morphable face models (by default (by using only 40 best shape parameters, 10 best shape base parameters (using PCA), 12 parameters for rotational and translation in the equation)) (that seem like landmarks) and then these are then optimized using 2 cost functions (the WPDC and VDC) through an adaptive k-step lookahead (which is the meta-joint optimization). Here, we can see the differences between different Morphable Face Models.

  • Day 58 (02/27/2022): Federated Learning in Pytorch

Implementation of the vanilla federated learning paper : Communication-Efficient Learning of Deep Networks from Decentralized Data. Reference github respository here.

Experiments are produced on MNIST, Fashion MNIST and CIFAR10 (both IID and non-IID). In case of non-IID, the data amongst the users can be split equally or unequally.

Since the purpose of these experiments are to illustrate the effectiveness of the federated learning paradigm, only simple models such as MLP and CNN are used.

Requirments Install all the packages from requirments.txt

pip install -r requirements.txt

Data

  • Download train and test datasets manually or they will be automatically downloaded from torchvision datasets.
  • Experiments are run on Mnist, Fashion Mnist and Cifar.
  • To use your own dataset: Move your dataset to data directory and write a wrapper on pytorch dataset class.

Results on MNIST Baseline Experiment: The experiment involves training a single model in the conventional way.

Parameters:

  • Optimizer: : SGD
  • Learning Rate: 0.01

Table 1: Test accuracy after training for 10 epochs:

Model Test Acc
MLP 92.71%
CNN 98.42%

Federated Experiment: The experiment involves training a global model in the federated setting. Federated parameters (default values):

  • Fraction of users (C): 0.1
  • Local Batch size (B): 10
  • Local Epochs (E): 10
  • Optimizer : SGD
  • Learning Rate : 0.01
    Table 2: Test accuracy after training for 10 global epochs with: | Model | IID | Non-IID (equal)| | ----- | ----- |---- | | MLP | 88.38% | 73.49% | | CNN | 97.28% | 75.94% | Further Readings Find the papers and reading that I had done for understanding this topic more in depth here.
Novel View Synthesis Scene Editting + No NVS + GT Depth

RL-Atari-gym

Reinforcement Learning on Atari Games and Control Entrance of program:

  • Breakout.py

How to run

(1). Check DDQN_params.json, make sure that every parameter is set right.

GAME_NAME # Set the game's name . This will help you create a new dir to save your result.
MODEL_NAME # Set the algorithms and model you are using. This is only used for rename your result file, so you still need
to change the model isntanace manually.
MAX_ITERATION # In original paper, this is set to 25,000,000. But here we set it to 5,000,000 for Breakout.(2,500,000 for Pong will suffice.) 
num_episodes # Max number of episodes. We set it to a huge number in default so normally this stop condition 
usually won't be satisfied.
# the program will stop when one of the above condition is met.

(2). Select the model and game environment instance manually. Currently, we are mainly focusing on DQN_CNN_2015 and Dueling_DQN_2016_Modified.

(3). Run and prey :)

NOTE: When the program is running, wait for a couple of minutes and take a look at the estimated time printed in the console. Stop early and decrease the MAX_ITERATION if you cannot wait for such a long time. (Recommendation: typically, 24h could be a reasonable running time for your first training process. Since you can continue training your model, take a rest for both you and computer and check the saved figures to see if your model has a promising future. Hope so ~ )

How to continue training the model

The breakout.py will automatically save the mid point state and variables for you if the program exit w/o exception.

  1. set the middle_point_json file path.

  2. check DDQN_params.json, make sure that every parameter is set right. Typically, you need to set a new MAX_ITERATION or num_episodes .

  3. Run and prey :)

How to evaluate the Model

evaluation.py helps you evaluate the model. First, please modified param_json_fname and model_list_fname to your directory. Second, change the game environment instance and the model instance. Then run.

Results Structure

The program will automatically create the the directory like this:

├── GIF_Reuslts
│   └── ModelName:2015_CNN_DQN-GameName:Breakout-Time:03-28-2020-18-20-28
│       ├── Iterations:100000-Reward:0.69-Time:03-28-2020-18-20-27-EvalReward:0.0.gif
│       ├── Iterations:200000-Reward:0.69-Time:03-28-2020-18-20-27-EvalReward:1.0.gif
├── Results
│   ├── ModelName:2015_CNN_DQN-GameName:Breakout-Time:03-28-2020-18-20-28-Eval.pkl
│   └── ModelName:2015_CNN_DQN-GameName:Breakout-Time:03-28-2020-18-20-28.pkl
├── DDQN_params.json

Please zip these three files/folders and upload it to our shared google drive. Rename it, e.g. ModelName:2015_CNN_DQN-GameName:Breakout-Time:03-28-2020-18-20-28.

PS:

GIF_Reuslts record the game process

Results contains the history of training and eval process, which can be used to visualize later.

DDQN_params.json contains your algorithm settings, which should match your Results and GIF_Reuslts.

Referred from: Drew Wilimitis

It has been recently established that many real-world networks have a latent geometric structure that resembles negatively curved hyperbolic spaces. Therefore, complex networks, and particularly the hierarchical relationships often found within, can often be more accurately represented by embedding graphs in hyperbolic geometry, rather than flat Euclidean space.

The goal of this project is to provide Python implementations for a few recently published algorithms that leverage hyperbolic geometry for machine learning and network analysis. Several examples are given with real-world datasets, however; the time complexity is far from optimized and this repository is primarily for research purposes - specifically investigating how to integrate downstream supervised learning methods with hyperbolic embeddings.

IllinformedHalfAnemone-size_restricted

Contents

Models

  • Poincaré Embeddings:

    • Mostly an exploration of the hyperbolic embedding approach used in [1].
    • Available implementation in the gensim library and a PyTorch version released by the authors here.

  • Hyperbolic Multidimensional Scaling: nbviewer

    • Finds embedding in Poincaré disk with hyperbolic distances that preserve input dissimilarities [2].

  • K-Means Clustering in the Hyperboloid Model: nbviewer

    • Optimization approach using Frechet means to define a centroid/center of mass in hyperbolic space [3, 4].

    mammals_kmeans

  • Hyperbolic Support Vector Machine - nbviewer

    • Linear hyperbolic SVC based on the max-margin optimization problem in hyperbolic geometry [5].
    • Uses projected gradient descent to define decision boundary and predict classifications.

hsvm_decision_boundaries

  • Hyperbolic Gaussian Mixture Models - nbviewer

    • Iterative Expectation-Maximization (EM) algorithm used for clustering [6].
    • Wrapped normal distribution based on using parallel transport to map to hyperboloid

hyper_gaussian

  • Embedding Graphs in Lorentzian Spacetime - nbviewer

    • An algorithm based on notions of causality in the Minkowski spacetime formulation of special relativity [7].
    • Used to embed directed acyclic graphs where nodes are represented by space-like and time-like coordinates.

hep-th_citation_network

  • Application: fMRI Schizophrenia Classification - nbviewer

    • Deriving hyperbolic features from functional network connectomes and predicting schizophrenia.
    • Analyzing discriminating factors from coalescent embeddings and hyperbolic kmeans clustering

fmri_image

Datasets

  • Zachary Karate Club Network
  • WordNet
  • Enron Email Corpus
  • Polbooks Network
  • arXiv Citation Network
  • Synthetic generated data (sklearn.make_datasets, networkx.generators, etc.)

Dependencies

  • Models are designed based on the sklearn estimator API (sklearn generally used only in rare, non-essential cases)
  • Networkx is used to generate & display graphs

References

[1] Nickel, Kiela. "Poincaré embeddings for learning hierarchical representations" (2017). arXiv.

[2] A. Cvetkovski and M. Crovella. Multidimensional scaling in the Poincaré disk. arXiv:1105.5332, 2011.

[3] "Learning graph-structured data using Poincaré embeddings and Riemannian K-means algorithms". Hatem Hajri, Hadi Zaatiti, Georges Hebrail (2019) arXiv.

[4] Wilson, Benjamin R. and Matthias Leimeister. “Gradient descent in hyperbolic space.” (2018).

[5] "Large-margin classification in hyperbolic space". Cho, H., Demeo, B., Peng, J., Berger, B. CoRR abs/1806.00437 (2018).

[6] Nagano, Yoshihiro et al. “A Differentiable Gaussian-like Distribution on Hyperbolic Space for Gradient-Based Learning.” ArXiv abs/1902.02992 (2019)

[7] Clough JR, Evans TS (2017) Embedding graphs in Lorentzian spacetime. PLoS ONE 12(11):e0187301. https://doi.org/10.1371/journal.pone.0187301.



Animation

Requirements

CLD-SGM is built in Python 3.8.0 using PyTorch 1.8.1 and CUDA 11.1. Please use the following command to install the requirements:

pip install --upgrade pip
pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html -f https://storage.googleapis.com/jax-releases/jax_releases.html

Optionally, you may also install NVIDIA Apex. The Adam optimizer from this library is faster than PyTorch's native Adam.

Preparations

CIFAR-10 does not require any data preparation as the data will be downloaded directly. To download CelebA-HQ-256 and prepare the dataset for training models, please run the following lines:

mkdir -p data/celeba/
wget -P data/celeba/ https://openaipublic.azureedge.net/glow-demo/data/celeba-tfr.tar
tar -xvf data/celeba/celeba-tfr.tar -C data/celeba/
python util/convert_tfrecord_to_lmdb.py --dataset=celeba --tfr_path=data/celeba/celeba-tfr --lmdb_path=data/celeba/celeba-lmdb --split=train
python util/convert_tfrecord_to_lmdb.py --dataset=celeba --tfr_path=data/celeba/celeba-tfr --lmdb_path=data/celeba/celeba-lmdb --split=validation

For multi-node training, the following environment variables need to be specified: $IP_ADDR is the IP address of the machine that will host the process with rank 0 during training (see here). $NODE_RANK is the index of each node among all the nodes.

Checkpoints

We provide pre-trained CLD-SGM checkpoints for CIFAR-10 and CelebA-HQ-256 here.

Training and evaluation

CIFAR-10
  • Training our CIFAR-10 model on a single node with one GPU and batch size 64:
python main.py -cc configs/default_cifar10.txt -sc configs/specific_cifar10.txt --root $ROOT --mode train --workdir work_dir/cifar10 --n_gpus_per_node 1 --training_batch_size 64 --testing_batch_size 64 --sampling_batch_size 64

Hidden flags can be found in the config files: configs/default_cifar10.txt and configs/specific_cifar10.txt. The flag --sampling_batch_size indicates the batch size per GPU, whereas --training_batch_size and --eval_batch_size indicate the total batch size of all GPUs combined. The script will update a running checkpoint every --snapshot_freq iterations (saved, in this case, at work_dir/cifar10/checkpoints/checkpoint.pth), starting from --snapshot_threshold. In configs/specific_cifar10.txt, these values are set to 10000 and 1, respectively.

  • Training our CIFAR-10 model on two nodes with 8 GPUs each and batch size 128:
mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_cifar10.txt -sc configs/specific_cifar10.txt --root $ROOT --mode train --workdir work_dir/cifar10 --n_gpus_per_node 8 --training_batch_size 8 --testing_batch_size 8 --sampling_batch_size 128 --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'
  • To resume training, we simply change the mode from train to continue (two nodes of 8 GPUs):
mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_cifar10.txt -sc configs/specific_cifar10.txt --root $ROOT --mode continue --workdir work_dir/cifar10 --n_gpus_per_node 8 --training_batch_size 8 --testing_batch_size 8 --sampling_batch_size 128 --cont_nbr 1 --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

Any file within work_dir/cifar10/checkpoints/ can be used to resume training by setting --checkpoint to the particular file name. If --checkpoint is unspecified, the script automatically uses the last snapshot checkpoint (checkpoint.pth) to continue training. The flag --cont_nbr makes sure that a new random seed is used for training continuation; for additional continuation runs --cont_nbr may be incremented by one.

  • The following command can be used to evaluate the negative ELBO as well as the FID score (two nodes of 8 GPUs):
mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_cifar10.txt -sc configs/specific_cifar10.txt --root $ROOT --mode eval --workdir work_dir/cifar10 --n_gpus_per_node 8 --training_batch_size 8 --testing_batch_size 8 --sampling_batch_size 128 --eval_folder eval_elbo_and_fid --ckpt_file checkpoint_file --eval_likelihood --eval_fid --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

Before running this you need to download the FID stats file from here and place it into $ROOT/assets/stats/).

To evaluate our provided CIFAR-10 model download the checkpoint here, create a directory work_dir/cifar10_pretrained_seed_0/checkpoints, place the checkpoint in it, and set --ckpt_file checkpoint_800000.pth as well as --workdir cifar10_pretrained.

CelebA-HQ-256
  • Training the CelebA-HQ-256 model from our paper (two nodes of 8 GPUs and batch size 64):
mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_celeba_paper.txt -sc configs/specific_celeba_paper.txt --root $ROOT --mode train --workdir work_dir/celeba_paper --n_gpus_per_node 8 --training_batch_size 4 --testing_batch_size 4 --sampling_batch_size 64 --data_location data/celeba/celeba-lmdb/ --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

We found that training of the above model can potentially be unstable. Some modifications that we found post-publication lead to better numerical stability as well as improved performance:

mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_celeba_post_paper.txt -sc configs/specific_celeba_post_paper.txt --root $ROOT --mode train --workdir work_dir/celeba_post_paper --n_gpus_per_node 8 --training_batch_size 4 --testing_batch_size 4 --sampling_batch_size 64 --data_location data/celeba/celeba-lmdb/ --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

In contrast to the model reported in our paper, we make use of a non-constant time reparameterization function β(t). For more details, please check the config files.

Toy data
  • Training on the multimodal Swiss Roll dataset using a single node with one GPU and batch size 512:
python main.py -cc configs/default_toy_data.txt --root $ROOT --mode train --workdir work_dir/multi_swiss_roll --n_gpus_per_node 1 --training_batch_size 512 --testing_batch_size 512 --sampling_batch_size 512 --dataset multimodal_swissroll

Additional toy datasets can be implemented in util/toy_data.py.

Monitoring the training process

We use Tensorboard to monitor the progress of training. For example, monitoring the CIFAR-10 process can be done as follows:

tensorboard --logdir work_dir/cifar10_seed_0/tensorboard

Demonstration

Load our pretrained checkpoints and play with sampling and likelihood computation:

Link Description
Open In Colab CIFAR-10
Open In Colab CelebA-HQ-256

Citation If you find the code useful for your research, please consider citing our ICLR paper:

@inproceedings{dockhorn2022score,
  title={Score-Based Generative Modeling with Critically-Damped Langevin Diffusion},
  author={Tim Dockhorn and Arash Vahdat and Karsten Kreis},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2022}
}

DSS: Differentiable Surface Splatting

Paper PDF Project page

bunny

code for paper Differentiable Surface Splatting for Point-based Geometry Processing

+ Mar 2021: major updates tag 2.0.
+ > Now supports simultaneous normal and point position updates.
+ > Unified learning rate using Adam optimizer.
+ > Highly optimized cuda operations
+ > Shares pytorch3d structure
  1. install prequisitories. Our code uses python3.8, pytorch 1.6.1, pytorch3d. the installation instruction requires the latest anaconda.
# install cuda, cudnn, nccl from nvidia
# we tested with cuda 10.2 and pytorch 1.6.0
# update conda
conda update -n base -c defaults conda
# install requirements
conda create -n pytorch3d python=3.8
conda config --add channels pytorch
conda config --add channels conda-forge
conda activate pytorch3d
conda install -c pytorch pytorch=1.6.0 torchvision cudatoolkit=10.2
conda install -c conda-forge -c fvcore -c iopath fvcore iopath
conda install -c bottler nvidiacub
conda install pytorch3d -c pytorch3d
conda install --file requirements.txt
pip install "git+https://github.com/mmolero/pypoisson.git"
  1. clone and compile
git clone --recursive https://github.com/yifita/DSS.git
cd dss
# compile external dependencies
cd external/prefix
python setup.py install
cd ../FRNN
python setup.py install
cd ../torch-batch-svd
python setup.py install
# compile library
cd ../..
python setup.py develop

**Demos inverse rendering - shape deformation

# create mvr images using intrinsics defined in the script
python scripts/create_mvr_data_from_mesh.py --points example_data/mesh/yoga6.ply --output example_data/images --num_cameras 128 --image-size 512 --tri_color_light --point_lights --has_specular

python train_mvr.py --config configs/dss.yml

Check the optimization process in tensorboard.

tensorboard --logdir=exp/dss_proj

denoise_1noise

video accompanying video

cite Please cite us if you find the code useful!

@article{Yifan:DSS:2019,
author = {Yifan, Wang and
          Serena, Felice and
          Wu, Shihao and
          {\"{O}}ztireli, Cengiz and
         Sorkine{-}Hornung, Olga},
title = {Differentiable Surface Splatting for Point-based Geometry Processing},
journal = {ACM Transactions on Graphics (proceedings of ACM SIGGRAPH ASIA)},
volume = {38},
number = {6},
year = {2019},
}

Acknowledgement

We would like to thank Federico Danieli for the insightful discussion, Phillipp Herholz for the timely feedack, Romann Weber for the video voice-over and Derek Liu for the help during the rebuttal. This work was supported in part by gifts from Adobe, Facebook and Snap, Inc.

About

This repository contains all the work that I regularly did and studied from Medium blogs, various research papers, and other Repos.