https://github.com/ccinc/3d-ml

A versatile framework for 3D machine learning built on Pytorch Lightning and Hydra [looking for contributors!]
https://github.com/ccinc/3d-ml

3d 3d-deep-learning deep-learning point-cloud pytorch s3dis segmentation

Last synced: 2 months ago
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A versatile framework for 3D machine learning built on Pytorch Lightning and Hydra [looking for contributors!]

• Host: GitHub
• URL: https://github.com/ccinc/3d-ml
• Owner: CCInc
• Created: 2022-11-10T16:27:12.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-05T21:13:18.000Z (5 months ago)
• Last Synced: 2024-08-06T00:14:35.991Z (5 months ago)
• Topics: 3d, 3d-deep-learning, deep-learning, point-cloud, pytorch, s3dis, segmentation
• Language: Python
• Homepage:
• Size: 109 KB
• Stars: 15
• Watchers: 6
• Forks: 3
• Open Issues: 24
• Metadata Files:
  • Readme: README.md

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README

        


# 3D-ML












**A versatile framework for 3D machine learning built on Pytorch Lightning and Hydra.**

Please see the [Project Guidelines](#project-guidelines) section before diving into the code!

## 💻 Conda Installation (recommended)

```bash

# clone project

git clone https://github.com/CCInc/3d-ml.git --recurse-submodules && cd 3d-ml

# Create a fresh conda environment

conda activate base

conda create -n 3dml -y python=3.9

conda activate 3dml

# For GPU support see https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html for CUDA toolkit installation instructions

nvcc --version # To make sure you have the CUDA toolkit installed. Your PyTorch CUDA version must match your nvcc version.

# Install pytorch

# Pick a compatible combination of pytorch and cuda version from https://pytorch.org/

# Below is an example for CUDA 11.3

conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch -y

# Install pyg

conda install pyg pytorch-scatter -c pyg -y

# install other requirements

pip install -r requirements.txt

# install openpoints

./install_openpoints.sh

```

## 💻 Pip Installation

```bash

# clone project with submodules

git clone https://github.com/CCInc/3d-ml.git --recurse-submodules && cd 3d-ml

# (optional) create virtual environment with virtualenv

python -m virtualenv env_3d

source env_3d/bin/activate

# Install pytorch

# Pick a compatible combination of pytorch and cuda version from https://pytorch.org/

# Below is an example for CUDA 11.6

pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116

# Install pytorch geo with pip

# Below is an example for CUDA 11.6 and torch 1.13

pip install pyg-lib torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.13.0+cu116.html

pip install torch-geometric

# Install additional requirements

pip install -r requirements.txt

# If using virtual environments remove the "--user" argument for the last two setup.py commands

# in the file install_openpoints.sh.

./install_openpoints.sh

```

## 🚀 Quickstart

Train with specific model, and dataset

```bash

python src/train.py model=cls_pointnet++ data=cls_modelnet2048

```

Train model with chosen experiment configuration from [configs/experiment/](configs/experiment/)

```bash

python src/train.py experiment=cls_modelnet_pointnet++

```

## ⚡ Your Superpowers

Override any config parameter from command line

```bash

python train.py trainer.max_epochs=20 model.optimizer.lr=1e-4

```

> **Note**: You can also add new parameters with `+` sign.

```bash

python train.py +model.new_param="owo"

```

Train on CPU, GPU, multi-GPU and TPU

```bash

# train on CPU

python train.py trainer=cpu

# train on 1 GPU

python train.py trainer=gpu

# train on TPU

python train.py +trainer.tpu_cores=8

# train with DDP (Distributed Data Parallel) (4 GPUs)

python train.py trainer=ddp trainer.devices=4

# train with DDP (Distributed Data Parallel) (8 GPUs, 2 nodes)

python train.py trainer=ddp trainer.devices=4 trainer.num_nodes=2

# simulate DDP on CPU processes

python train.py trainer=ddp_sim trainer.devices=2

# accelerate training on mac

python train.py trainer=mps

```

> **Warning**: Currently there are problems with DDP mode, read [this issue](https://github.com/ashleve/lightning-hydra-template/issues/393) to learn more.

Train with mixed precision

```bash

# train with pytorch native automatic mixed precision (AMP)

python train.py trainer=gpu +trainer.precision=16

```

Train model with any logger available in PyTorch Lightning, like W&B or Tensorboard

```yaml

# set project and entity names in `configs/logger/wandb`

wandb:

  project: "your_project_name"

  entity: "your_wandb_team_name"

```

```bash

# train model with Weights&Biases (link to wandb dashboard should appear in the terminal)

python train.py logger=wandb

```

> **Note**: Lightning provides convenient integrations with most popular logging frameworks. Learn more [here](#experiment-tracking).

> **Note**: Using wandb requires you to [setup account](https://www.wandb.com/) first. After that just complete the config as below.

> **Note**: Click [here](https://wandb.ai/hobglob/template-dashboard/) to see example wandb dashboard generated with this template.

Train model with chosen experiment config

```bash

python train.py experiment=example

```

> **Note**: Experiment configs are placed in [configs/experiment/](configs/experiment/).

Attach some callbacks to run

```bash

python train.py callbacks=default

```

> **Note**: Callbacks can be used for things such as as model checkpointing, early stopping and [many more](https://pytorch-lightning.readthedocs.io/en/latest/extensions/callbacks.html#built-in-callbacks).

> **Note**: Callbacks configs are placed in [configs/callbacks/](configs/callbacks/).

Use different tricks available in Pytorch Lightning

```yaml

# gradient clipping may be enabled to avoid exploding gradients

python train.py +trainer.gradient_clip_val=0.5

# run validation loop 4 times during a training epoch

python train.py +trainer.val_check_interval=0.25

# accumulate gradients

python train.py +trainer.accumulate_grad_batches=10

# terminate training after 12 hours

python train.py +trainer.max_time="00:12:00:00"

```

> **Note**: PyTorch Lightning provides about [40+ useful trainer flags](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags).

Easily debug

```bash

# runs 1 epoch in default debugging mode

# changes logging directory to `logs/debugs/...`

# sets level of all command line loggers to 'DEBUG'

# enforces debug-friendly configuration

python train.py debug=default

# run 1 train, val and test loop, using only 1 batch

python train.py debug=fdr

# print execution time profiling

python train.py debug=profiler

# try overfitting to 1 batch

python train.py debug=overfit

# raise exception if there are any numerical anomalies in tensors, like NaN or +/-inf

python train.py +trainer.detect_anomaly=true

# log second gradient norm of the model

python train.py +trainer.track_grad_norm=2

# use only 20% of the data

python train.py +trainer.limit_train_batches=0.2 \

+trainer.limit_val_batches=0.2 +trainer.limit_test_batches=0.2

```

> **Note**: Visit [configs/debug/](configs/debug/) for different debugging configs.

Resume training from checkpoint

```yaml

python train.py ckpt_path="/path/to/ckpt/name.ckpt"

```

> **Note**: Checkpoint can be either path or URL.

> **Note**: Currently loading ckpt doesn't resume logger experiment, but it will be supported in future Lightning release.

Evaluate checkpoint on test dataset

```yaml

python eval.py ckpt_path="/path/to/ckpt/name.ckpt"

```

> **Note**: Checkpoint can be either path or URL.

Create a sweep over hyperparameters

```bash

# this will run 6 experiments one after the other,

# each with different combination of batch_size and learning rate

python train.py -m datamodule.batch_size=32,64,128 model.lr=0.001,0.0005

```

> **Note**: Hydra composes configs lazily at job launch time. If you change code or configs after launching a job/sweep, the final composed configs might be impacted.

Create a sweep over hyperparameters with Optuna

```bash

# this will run hyperparameter search defined in `configs/hparams_search/mnist_optuna.yaml`

# over chosen experiment config

python train.py -m hparams_search=mnist_optuna experiment=example

```

> **Note**: Using [Optuna Sweeper](https://hydra.cc/docs/next/plugins/optuna_sweeper) doesn't require you to add any boilerplate to your code, everything is defined in a [single config file](configs/hparams_search/mnist_optuna.yaml).

> **Warning**: Optuna sweeps are not failure-resistant (if one job crashes then the whole sweep crashes).

Execute all experiments from folder

```bash

python train.py -m 'experiment=glob(*)'

```

> **Note**: Hydra provides special syntax for controlling behavior of multiruns. Learn more [here](https://hydra.cc/docs/next/tutorials/basic/running_your_app/multi-run). The command above executes all experiments from [configs/experiment/](configs/experiment/).

Execute run for multiple different seeds

```bash

python train.py -m seed=1,2,3,4,5 trainer.deterministic=True logger=csv tags=["benchmark"]

```

> **Note**: `trainer.deterministic=True` makes pytorch more deterministic but impacts the performance.

Execute sweep on a remote AWS cluster

> **Note**: This should be achievable with simple config using [Ray AWS launcher for Hydra](https://hydra.cc/docs/next/plugins/ray_launcher). Example is not implemented in this template.

Use Hydra tab completion

> **Note**: Hydra allows you to autocomplete config argument overrides in shell as you write them, by pressing `tab` key. Read the [docs](https://hydra.cc/docs/tutorials/basic/running_your_app/tab_completion).

Apply pre-commit hooks

```bash

pre-commit run -a

```

> **Note**: Apply pre-commit hooks to do things like auto-formatting code and configs, performing code analysis or removing output from jupyter notebooks. See [# Best Practices](#best-practices) for more.

Run tests

```bash

# run all tests

pytest

# run tests from specific file

pytest tests/test_train.py

# run all tests except the ones marked as slow

pytest -k "not slow"

```

Use tags

Each experiment should be tagged in order to easily filter them across files or in logger UI:

```bash

python train.py tags=["mnist", "experiment_X"]

```

If no tags are provided, you will be asked to input them from command line:

```bash

>>> python train.py tags=[]

[2022-07-11 15:40:09,358][src.utils.utils][INFO] - Enforcing tags! 

[2022-07-11 15:40:09,359][src.utils.rich_utils][WARNING] - No tags provided in config. Prompting user to input tags...

Enter a list of comma separated tags (dev):

```

If no tags are provided for multirun, an error will be raised:

```bash

>>> python train.py -m +x=1,2,3 tags=[]

ValueError: Specify tags before launching a multirun!

```

> **Note**: Appending lists from command line is currently not supported in hydra :(




## Project Guidelines

This project is based on a plugin architecture and is to remain as lightweight as possible, serving merely as a connector between different downstream libraries. The main functions this framework is to serve is as follows:

- Provide experiment configuration control with Hydra config

- Provide consistent data augmentation and preprocessing steps across multiple datasets and models

- Provide reference implementations of commonly used 3D datasets using PyL

- Provide experiment tracking across a wide array of tools, e.g. Wandb and Tensorboard

- Provide PyL wrappers for downstream libraries which provide model implementations and backends

### Configuration

Refer to the [Hydra docs](https://hydra.cc/docs/tutorials/intro/) for overall best practices when using Hydra configs.

Relevant configuration parameters should be provided for every model/dataset to ensure easy reproducibility (e.g. batch sizes, learning rate, schedulers, optimizers).

Configuration and code should be completely decoupled. Parameters passed to code from a Hydra config should be in the form of primitives or dataclasses, not DictConfigs or generic key/value pairs. See, for example, the usage of the `LrScheduler` dataclass to duct-type the configuration parameters in [src/models/common.py](src/models/common.py). As as result, **all code should be able to be run independently of Hydra**. Configuration files should use [hydra instantiation](https://hydra.cc/docs/advanced/instantiate_objects/overview/) whenever possible to support this goal.

### Datasets

Dataset preprocessing should be as minimal as possible so that the user can have maximum flexbility in how to prepare the data for their specific model or usecase. For example, rather than hardcoding data augmentations within the dataset code, data augmentations should be specified in the configuration file. Datasets should be agnostic to models, and any specific model preparation should be left to the configuration.

All datasets should be implemented within `LightningDataModule`s and inherit from `Base3dDataModule`. This data module code should handle train/test/validation splits, downloading and storing the dataset, and initializing the underlying dataset classes.

Datasets should inherit from `torch_geometric.data.Dataset`, which allows the trainer to grab the number of classes and the number of features directly from the dataset object.

The `__getitem__` function of the dataset should return a `torch_geometric.data.Data` object, with at least the keys `pos` and `y`. For proper collation, keys must be Tensor objects or primitives. For all tasks, the key `pos` will be a floating point tensor of size `(n, 3)`. For classification tasks, the key `y` will be a single integer value. For segmentation tasks, the key `y` will be stored as a tensor of integers of size `(n)`.

Features such as RGB should be stored into dedicated keys in the `Data` object. Then, these features can be added to the `x` vector dynamically by using the `AddFeatsByKey` transform.

### Models

Metrics should be implemented using the [TorchMetrics](https://torchmetrics.readthedocs.io/en/stable/) library whenever possible. If a metric is not available, first try to add an implementation to the TorchMetrics repo.

Models should be implemented as much as possible via backend plugins, such as TorchSparse or OpenPoints. Then, construct a `LightningModule` which inherits from `BaseSegmentationModel` or `BaseClassificationModel` to interface with that backend. The backend model should be constructed in the module's initializer, and the `step` and `forward` functions should be used to transform the data into the appropriate format to pass to the backend model, apply the criterion, and return the predictions and loss.

## ❤️ Contributions

Have a question? Found a bug? Missing a specific feature? Feel free to file a new issue, discussion or PR with respective title and description.

Before making an issue, please verify that:

- The problem still exists on the current `main` branch.

- Your python dependencies are updated to recent versions.

Suggestions for improvements are always welcome!

Before contributing, make sure you set up [pre-commit](https://pre-commit.com/):

```bash

pip install pre-commit

pre-commit install

pre-commit run -a

```

## Experiment Config

Location: [configs/experiment](configs/experiment)


Experiment configs allow you to overwrite parameters from main config.


For example, you can use them to version control best hyperparameters for each combination of model and dataset.

Show example experiment config

```yaml

# @package _global_

# to execute this experiment run:

# python train.py experiment=example

defaults:

  - override /datamodule: mnist.yaml

  - override /model: mnist.yaml

  - override /callbacks: default.yaml

  - override /trainer: default.yaml

# all parameters below will be merged with parameters from default configurations set above

# this allows you to overwrite only specified parameters

tags: ["mnist", "simple_dense_net"]

seed: 12345

trainer:

  min_epochs: 10

  max_epochs: 10

  gradient_clip_val: 0.5

model:

  optimizer:

    lr: 0.002

  net:

    lin1_size: 128

    lin2_size: 256

    lin3_size: 64

datamodule:

  batch_size: 64

logger:

  wandb:

    tags: ${tags}

    group: "mnist"

```




**Experiment design**

_Say you want to execute many runs to plot how accuracy changes in respect to batch size._

1. Execute the runs with some config parameter that allows you to identify them easily, like tags:

   ```bash

   python train.py -m logger=csv datamodule.batch_size=16,32,64,128 tags=["batch_size_exp"]

   ```

2. Write a script or notebook that searches over the `logs/` folder and retrieves csv logs from runs containing given tags in config. Plot the results.




## Logs

Hydra creates new output directory for every executed run.

Default logging structure:

```

├── logs

│   ├── task_name

│   │   ├── runs                        # Logs generated by single runs

│   │   │   ├── YYYY-MM-DD_HH-MM-SS       # Datetime of the run

│   │   │   │   ├── .hydra                  # Hydra logs

│   │   │   │   ├── csv                     # Csv logs

│   │   │   │   ├── wandb                   # Weights&Biases logs

│   │   │   │   ├── checkpoints             # Training checkpoints

│   │   │   │   └── ...                     # Any other thing saved during training

│   │   │   └── ...

│   │   │

│   │   └── multiruns                   # Logs generated by multiruns

│   │       ├── YYYY-MM-DD_HH-MM-SS       # Datetime of the multirun

│   │       │   ├──1                        # Multirun job number

│   │       │   ├──2

│   │       │   └── ...

│   │       └── ...

│   │

│   └── debugs                          # Logs generated when debugging config is attached

│       └── ...

```

You can change this structure by modifying paths in [hydra configuration](configs/hydra).




## Experiment Tracking

PyTorch Lightning supports many popular logging frameworks: [Weights&Biases](https://www.wandb.com/), [Neptune](https://neptune.ai/), [Comet](https://www.comet.ml/), [MLFlow](https://mlflow.org), [Tensorboard](https://www.tensorflow.org/tensorboard/).

These tools help you keep track of hyperparameters and output metrics and allow you to compare and visualize results. To use one of them simply complete its configuration in [configs/logger](configs/logger) and run:

```bash

python train.py logger=logger_name

```

You can use many of them at once (see [configs/logger/many_loggers.yaml](configs/logger/many_loggers.yaml) for example).

You can also write your own logger.

Lightning provides convenient method for logging custom metrics from inside LightningModule. Read the [docs](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html#automatic-logging) or take a look at [MNIST example](src/models/mnist_module.py).




## Tests

Template comes with generic tests implemented with `pytest`.

```bash

# run all tests

pytest

# run tests from specific file

pytest tests/test_train.py

# run all tests except the ones marked as slow

pytest -k "not slow"

```

Most of the implemented tests don't check for any specific output - they exist to simply verify that executing some commands doesn't end up in throwing exceptions. You can execute them once in a while to speed up the development.

Currently, the tests cover cases like:

- running 1 train, val and test step

- running 1 epoch on 1% of data, saving ckpt and resuming for the second epoch

- running 2 epochs on 1% of data, with DDP simulated on CPU

And many others. You should be able to modify them easily for your use case.

There is also `@RunIf` decorator implemented, that allows you to run tests only if certain conditions are met, e.g. GPU is available or system is not windows. See the [examples](tests/test_train.py).




## Hyperparameter Search

You can define hyperparameter search by adding new config file to [configs/hparams_search](configs/hparams_search).

Show example hyperparameter search config

```yaml

# @package _global_

defaults:

  - override /hydra/sweeper: optuna

# choose metric which will be optimized by Optuna

# make sure this is the correct name of some metric logged in lightning module!

optimized_metric: "val/acc_best"

# here we define Optuna hyperparameter search

# it optimizes for value returned from function with @hydra.main decorator

hydra:

  sweeper:

    _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper

    # 'minimize' or 'maximize' the objective

    direction: maximize

    # total number of runs that will be executed

    n_trials: 20

    # choose Optuna hyperparameter sampler

    # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html

    sampler:

      _target_: optuna.samplers.TPESampler

      seed: 1234

      n_startup_trials: 10 # number of random sampling runs before optimization starts

    # define hyperparameter search space

    params:

      model.optimizer.lr: interval(0.0001, 0.1)

      datamodule.batch_size: choice(32, 64, 128, 256)

      model.net.lin1_size: choice(64, 128, 256)

      model.net.lin2_size: choice(64, 128, 256)

      model.net.lin3_size: choice(32, 64, 128, 256)

```

Next, execute it with: `python train.py -m hparams_search=mnist_optuna`

Using this approach doesn't require adding any boilerplate to code, everything is defined in a single config file. The only necessary thing is to return the optimized metric value from the launch file.

You can use different optimization frameworks integrated with Hydra, like [Optuna, Ax or Nevergrad](https://hydra.cc/docs/plugins/optuna_sweeper/).

The `optimization_results.yaml` will be available under `logs/task_name/multirun` folder.

This approach doesn't support advanced techniques like prunning - for more sophisticated search, you should probably write a dedicated optimization task (without multirun feature).