First full push after cleaning.

Author: Confusezius

Images/Detailed_Rel_Recall_Rho_CAR.pdf

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-## Deep Metric Learning Research in PyTorch
+# Deep Metric Learning Research in PyTorch
 
-This repository will contain all code and implementations used in:
+---
+## What can I find here?
+
+This repository contains all code and implementations used in:
 
-(1) https://arxiv.org/abs/2002.08473
 ```
 Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
 ```
 
-and
+**Link**: https://arxiv.org/abs/2002.08473
+
+The code is meant to serve as a research starting point in Deep Metric Learning.
+By implementing key baselines under a consistent setting and logging a vast set of metrics, it should be easier to ensure that method gains are not due to implementational variations, while better understanding driving factors.
+
+It is set up in a modular way to allow for fast and detailed prototyping, but with key elements written in a way that allows the code to be directly copied into other pipelines. In addition, multiple training and test metrics are logged in W&B to allow for easy and large-scale evaluation.
+
+Finally, please find a public W&B repo with key runs performed in (1) here: https://app.wandb.ai/confusezius/RevisitDML.
+
+**Contact**: Karsten Roth, [email protected]  
+
+*Suggestions are always welcome!*
+
+---
+## Some Notes:
+
+If you use this code in your research, please cite
+```
+@misc{roth2020revisiting,
+    title={Revisiting Training Strategies and Generalization Performance in Deep Metric Learning},
+    author={Karsten Roth and Timo Milbich and Samarth Sinha and Prateek Gupta and Björn Ommer and Joseph Paul Cohen},
+    year={2020},
+    eprint={2002.08473},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+This repository contains (in parts) code that has been adapted from:
+* https://github.com/idstcv/SoftTriple
+* https://github.com/bnu-wangxun/Deep_Metric
+* https://github.com/valerystrizh/pytorch-histogram-loss
+* https://github.com/Confusezius/Deep-Metric-Learning-Baselines
+
+Make sure to also check out the following repo with a great plug-and-play implementation of DML methods:
+* https://github.com/KevinMusgrave/pytorch-metric-learning
+
+---
+
+**[All implemented methods and metrics are listed at the bottom!]()**
+
+---
+
+## Paper-related Information
+
+#### Reproduce results from our paper **-> Revisiting Training Strategies and Generalization Performance in Deep Metric Learning <-**
+
+* *ALL* standardized Runs that were used are available in `Revisit_Runs.sh`.
+* These runs are also logged in this public W&B repo: https://app.wandb.ai/confusezius/RevisitDML.
+* All Runs and their respective metrics can be downloaded and evaluated to generate the plots in our paper by following `Result_Evaluations.py`. This also allows for potential introspection of other relations. It also converts results directly into Latex-table format with mean and standard deviations.
+* To utilize different batch-creation methods, simply set the flag `--data_sampler` to the method of choice. Allowed flags are listed in `datasampler/__init__.py`.
+* To use the proposed spectral regularization for tuple-based methods, set `--batch_mining rho_distance` with flip probability `--miner_rho_distance_cp e.g. 0.2`.
+* A script to run the toy experiments in the paper is provided in `toy_experiments`.
+
+**Note**: There may be small deviations in results based on the Hardware (e.g. between P100 and RTX GPUs) and Software (different PyTorch/Cuda versions) used to run these experiments, but they should be covered in the standard deviations reported in the paper.
+
+---
+
+## How to use this Repo
+
+### Requirements:
+
+* PyTorch 1.2.0+ & Faiss-Gpu
+* Python 3.6+
+* pretrainedmodels, torchvision 0.3.0+
+
+An exemplary setup of a virtual environment containing everything needed:
+```
+(1) wget  https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
+(2) bash Miniconda3-latest-Linux-x86_64.sh (say yes to append path to bashrc)
+(3) source .bashrc
+(4) conda create -n DL python=3.6
+(5) conda activate DL
+(6) conda install matplotlib scipy scikit-learn scikit-image tqdm pandas pillow
+(7) conda install pytorch torchvision faiss-gpu cudatoolkit=10.0 -c pytorch
+(8) pip install wandb pretrainedmodels
+(9) Run the scripts!
+```
+
+### Datasets:
+Data for
+* CUB200-2011 (http://www.vision.caltech.edu/visipedia/CUB-200.html)
+* CARS196 (https://ai.stanford.edu/~jkrause/cars/car_dataset.html)
+* Stanford Online Products (http://cvgl.stanford.edu/projects/lifted_struct/)
+
+can be downloaded either from the respective project sites or directly via Dropbox:
+
+* CUB200-2011 (1.08 GB): https://www.dropbox.com/s/tjhf7fbxw5f9u0q/cub200.tar?dl=0
+* CARS196 (1.86 GB): https://www.dropbox.com/s/zi2o92hzqekbmef/cars196.tar?dl=0
+* SOP (2.84 GB): https://www.dropbox.com/s/fu8dgxulf10hns9/online_products.tar?dl=0
+
+**The latter ensures that the folder structure is already consistent with this pipeline and the dataloaders**.   
+
+Otherwise, please make sure that the datasets have the following internal structure:
+
+* For CUB200-2011/CARS196:
+```
+cub200/cars196
+└───images
+|    └───001.Black_footed_Albatross
+|           │   Black_Footed_Albatross_0001_796111
+|           │   ...
+|    ...
+```
+
+* For Stanford Online Products:
+```
+online_products
+└───images
+|    └───bicycle_final
+|           │   111085122871_0.jpg
+|    ...
+|
+└───Info_Files
+|    │   bicycle.txt
+|    │   ...
+```
+
+Assuming your folder is placed in e.g. `<$datapath/cub200>`, pass `$datapath` as input to `--source`.
+
+### Training:
+Training is done by using `main.py` and setting the respective flags, all of which are listed and explained in `parameters.py`. A vast set of exemplary runs is provided in `Revisit_Runs.sh`.
+
+**[I.]** **A basic sample run using default parameters would like this**:
+
+```
+python main.py --loss margin --batch_mining distance --log_online \
+              --project DML_Project --group Margin_with_Distance --seed 0 \
+              --gpu 0 --bs 112 --data_sampler class_random --samples_per_class 2 \
+              --arch resnet50_frozen_normalize --source $datapath --n_epochs 150 \
+              --lr 0.00001 --embed_dim 128 --evaluate_on_gpu
+```
+
+The purpose of each flag explained:
+
+* `--loss <loss_name>`: Name of the training objective used. See folder `criteria` for implementations of these methods.
+* `--batch_mining <batchminer_name>`: Name of the batch-miner to use (for tuple-based ranking methods). See folder `batch_mining` for implementations of these methods.
+* `--log_online`: Log metrics online via either W&B (Default) or CometML. Regardless, plots, weights and parameters are all stored offline as well.
+*  `--project`, `--group`: Project name as well as name of the run. Different seeds will be logged into the same `--group` online. The group as well as the used seed also define the local savename.
+* `--seed`, `--gpu`, `--source`: Basic Parameters setting the training seed, the used GPU and the path to the parent folder containing the respective Datasets.
+* `--arch`: The utilized backbone, e.g. ResNet50. You can append `_frozen` and `_normalize` to the name to ensure that BatchNorm layers are frozen and embeddings are normalized, respectively.
+* `--data_sampler`, `--samples_per_class`: How to construct a batch. The default method, `class_random`, selects classes at random and places `<samples_per_class>` samples into the batch until the batch is filled.
+* `--lr`, `--n_epochs`, `--bs` ,`--embed_dim`: Learning rate, number of training epochs, the batchsize and the embedding dimensionality.  
+* `--evaluate_on_gpu`: If set, all metrics are computed using the gpu - requires Faiss-GPU and may need additional GPU memory.
+
+#### Some Notes:
+* During training, metrics listed in `--evaluation_metrics` will be logged for both training and validation/test set. If you do not care about detailed training metric logging, simply set the flag `--no_train_metrics`. A checkpoint is saved for improvements in metrics listed in `--storage_metrics` on training, validation or test sets.
+* If one wishes to use a training/validation split, simply set `--use_tv_split` and `--tv_split_perc <train/val split percentage>`.
+
+
+**[II.]** **Advanced Runs**:
 
-(2) https://arxiv.org/abs/2004.13458
 ```
-DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
+python main.py --loss margin --batch_mining distance --loss_margin_beta 0.6 --miner_distance_lower_cutoff 0.5 ... (basic parameters)
 ```
 
+* To use specific parameters that are loss, batchminer or e.g. datasampler-related, simply set the respective flag.
+* For structure and ease of use, parameters relating to a specifc loss function/batchminer etc. are marked as e.g. `--loss_<lossname>_<parameter_name>`, see `parameters.py`.
+* However, every parameter can be called from every class, as all parameters are stored in a shared namespace that is passed to all methods. This makes it easy to create novel fusion losses and the likes.
 
-It is set up in a modular way to allow for fast and detailed prototyping. In addition, multiple training and test metrics are logged in WandB to allow for easy and large-scale evaluation.
 
+### Evaluating Results with W&B
+Here some information on using W&B (highly encouraged!)
+
+* Create an account here (free): https://wandb.ai
+* After the account is set, make sure to include your API key in `parameters.py` under `--wandb_key`.
+* To make sure that W&B data can be stored, ensure to run `wandb on` in the folder pointed to by `--save_path`.
+* When data is logged online to W&B, one can use `Result_Evaluations.py` to download all data, create named metric and correlation plots and output a summary in the form of a latex-ready table with mean and standard deviations of all metrics. **This ensures that there are no errors between computed and reported results.**
+
+
+### Creating custom methods:
+
+1. **Create custom objectives**: Simply take a look at e.g. `criteria/margin.py`, and ensure that the used methods has the following properties:
+  * Inherit from `torch.nn.Module` and define a custom `forward()` function.
+  * When using trainable parameters, make sure to either provide a `self.lr` to set the learning rate of the loss-specific parameters, or set `self.optim_dict_list`, which is a list containing optimization dictionaries passed to the optimizer (see e.g `criteria/proxynca.py`). If both are set, `self.optim_dict_list` has priority.
+  * Depending on the loss, remember to set the variables `ALLOWED_MINING_OPS  = None or list of allowed mining operations`, `REQUIRES_BATCHMINER = False or True`, `REQUIRES_OPTIM = False or True` to denote if the method needs a batchminer or optimization of internal parameters.
+
+
+2. **Create custom batchminer**: Simply take a look at e.g. `batch_mining/distance.py` - The miner needs to be a class with a defined `__call__()`-function, taking in a batch and labels and returning e.g. a list of triplets.
+
+3. **Create custom datasamplers**:Simply take a look at e.g. `datasampler/class_random_sampler.py`. The sampler needs to inherit from `torch.utils.data.sampler.Sampler` and has to provide a `__iter__()` and a `__len__()` function. It has to yield a set of indices that are used to create the batch.
 
-*WandB-Results for key runs in (1) will be made available as well.*
 
 ---
 
-Contact: Karsten Roth, [email protected]
+## Implemented Methods
+
+For a detailed explanation of everything, please refer to the supplementary of our paper!
+
+### DML criteria
+
+* **Angular** [[Deep Metric Learning with Angular Loss](https://arxiv.org/pdf/1708.01682.pdf)]
+* **ArcFace** [[ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/pdf/1801.07698.pdf)]
+* **Contrastive** [[Dimensionality Reduction by Learning an Invariant Mapping](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf)]
+* **Generalized Lifted Structure** [[In Defense of the Triplet Loss for Person Re-Identification](https://arxiv.org/abs/1703.07737)]
+* **Histogram** [[Learning Deep Embeddings with Histogram Loss](https://arxiv.org/pdf/1611.00822.pdf)]
+* **Marginloss** [[Sampling Matters in Deep Embeddings Learning](https://arxiv.org/abs/1706.07567)]
+* **MultiSimilarity** [[Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://arxiv.org/abs/1904.06627)]
+* **N-Pair** [[Improved Deep Metric Learning with Multi-class N-pair Loss Objective](https://papers.nips.cc/paper/6200-improved-deep-metric-learning-with-multi-class-n-pair-loss-objective)]
+* **ProxyNCA** [[No Fuss Distance Metric Learning using Proxies](https://arxiv.org/pdf/1703.07464.pdf)]
+* **Quadruplet** [[Beyond triplet loss: a deep quadruplet network for person re-identification](https://arxiv.org/abs/1704.01719)]
+* **Signal-to-Noise Ratio (SNR)** [[Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning](https://arxiv.org/pdf/1904.02616.pdf)]
+* **SoftTriple** [[SoftTriple Loss: Deep Metric Learning Without Triplet Sampling](https://arxiv.org/abs/1909.05235)]
+* **Normalized Softmax** [[Classification is a Strong Baseline for Deep Metric Learning](https://arxiv.org/abs/1811.12649)]
+* **Triplet** [[Facenet: A unified embedding for face recognition and clustering](https://arxiv.org/abs/1503.03832)]
+
+### DML batchminer
+
+* **Random** [[Facenet: A unified embedding for face recognition and clustering](https://arxiv.org/abs/1503.03832)]
+* **Semihard** [[Facenet: A unified embedding for face recognition and clustering](https://arxiv.org/abs/1503.03832)]
+* **Softhard** [https://github.com/Confusezius/Deep-Metric-Learning-Baselines]
+* **Distance-based** [[Sampling Matters in Deep Embeddings Learning](https://arxiv.org/abs/1706.07567)]
+* **Rho-Distance** [[Revisiting Training Strategies and Generalization Performance in Deep Metric Learning](https://arxiv.org/abs/2002.08473)]
+* **Parametric** [[PADS: Policy-Adapted Sampling for Visual Similarity Learning](https://arxiv.org/abs/2003.11113)]
+
+### Architectures
+
+* **ResNet50** [[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)]
+* **Inception-BN** [[Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842)]
+* **GoogLeNet** (torchvision variant w/ BN) [[Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842)]
+
+### Datasets
+* **CUB200-2011** [[Caltech-UCSD Birds-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html)]
+* **CARS196** [[Cars Dataset](https://ai.stanford.edu/~jkrause/cars/car_dataset.html)]
+* **Stanford Online Products** [[Deep Metric Learning via Lifted Structured Feature Embedding](https://cvgl.stanford.edu/projects/lifted_struct/)]
+
+### Evaluation Metrics
+
+* **Recall@k**
+* **Normalized Mutual Information (NMI)**
+* **F1**
+* **mAP (class-averaged)**
+* **Spectral Variance**
+* **Mean Intraclass Distance**
+* **Mean Interclass Distance**
+* **Ratio Intra- to Interclass Distance**