Malpolon

Main changes
- [x] Updated all examples to fix inference path issue. Previously, PyTorch Lightning overwrote checkpoint_paths with the values contained in the saved checkpoint files. However, those provided by the Malpolon team for pure inference purposes, contained absolute path incompatible with other people's machines. Now, only relative paths are stored.
- [x] Updated all examples to prevent downloading the model weights twice when running models in inference mode
- [x] Updated URL and md5 checksum signature to download `glc24_pre_extracted` pre-trained weights

Other changes
- Added Malpolon QR code in project resources

What's changed

Main changes

- [x] Updated link to glc24_pre_extracted model weights, addressing `pos_weight` loading issue following previous Malpolon updates.

What's changed

Main changes

- [x] Added possibility for users to choose their optimizer and scheduler via their config file:
- `malpolon.models.utils`: Changed behavior of `check_optimizer()` and added `check_scheduler()` to allow users to input one or several optimizers (and optionally 1 scheduler per optimizer, possibly with a lr_scheduler_config descriptor) via their config files.
- `malpolon.models.standard_prediction_systems`: changed instantiation of optimizer(s) and scheduler(s) in class `GenericPredictionSystem`. The class attributes are now lists of instantiated optimizers (respectively, of `lr_scheduler_config dictionaries`). Updated behavior of method `configure_optimizers()` to return a dictionary containing all the optimizers and schedulers (cf. https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.core.LightningModule.html#lightning.pytorch.core.LightningModule.configure_optimizers).
- Updated all examples and added all corresponding unit tests, testing both valid scenarios and edge cases of incorrect user inputs in the config file.

Others

- [x] In glc24_pre_extracted example: added habitat version dataset which consists of symbolic links to the species version. Running the habitat's main script will trigger the download of the data predictors (rasters, satellite, time-series).
- [x] Updated split_obs_per_species_frequency() to include more input arguments

What's Changed
Main changes
- [x] Added GLC24 pre_extracted habitat dataset and example (see PR 58 in the Links section)
- [x] Changed the way checkpoints are loaded from loading the `state_dict` of the model object to loading the `state_dict` of the LightningModule. This is a breaking change as examples needed to be updated by **removing** the replacement of "model." string in the loaded state_dict.
- [x] Added possibility to download model weights for any Malpolon model given a URL and a few file paths

- [x] Updated the way checkpoint_path is passed on to models. Added an attribute checkpoint_path for all Malpolon models
- Updated every examples consequently

- [x] Added Malpolon as (local) model provider.
- Created new module `malpolon.models.custom_models` which will host custom models proposed by Malpolon
- Split classes from `geolifeclef2024_multimodal_ensemble.py` to glc2024_multimodal_ensemble_model.py and glc2024_pre_extracted_prediction_system.py in custom_models to prevent circular import from malpolon.models.model_builder after adding Malpolon as (local) provider

Others
- [x] Updated `malpolon.data.data_module.export_predict_csv` to enable more flexibility when outputting the prediction CSV for a single data point.

- [x] Added GLC24 pre-extracted examples (habitat and species) using the MultiModalEnsemble (MME) model
- Automatic download of the dataset from Kaggle (depending on the value of boolean config parameter `data.download_data`)
- Automatic download of the model weights from Seafile if not already on disk, via a new `model.model_kwargs.pretrained` key in the config file. The weights enable users to directly run our MME model on our GLC24_pre_extracted Test set and reach ~30% micro F1-score with ~26% micro precision and ~36% micro Recall, as well as ~96% micro AuC.

- [x] Added and updated unit tests for GLC24 pre-extracted examples (habitat and species)

- [x] Added new content in online documentation and tutorial files

**Full Changelog**: https://github.com/plantnet/malpolon/compare/v1.3.0...v2.0.0

What's Changed
Main changes
- Created new module `malpolon.models.custom_models` which will host custom models proposed by Malpolon
- Split datamodule and model from `geolifeclef2024_multimodal_ensemble.py` to `glc2024_multimodal_ensemble_model.py` and `glc2024_pre_extracted_prediction_system.py` in `custom_models`.

- Added `malpolon` as model provider. Currently we only provide MultiModalEnsemble (MME) model which can be called for in config files "model_name" key as: **`glc24_multimodal_ensemble`** (see repository `examples/benchmark/geolifeclef/geolifeclef2024_pre_extracted/config/glc24_cnn_multimodal_ensemble.yaml`)

- Added possibility to download model weights for any Malpolon model given a URL and a few file paths via `malpolon.standard_prediction_system.download_weights`
- Added model weight download info for the MME model. The example experiment file of MME now automatically downloads the weights from Seafile if not already on disk, via `model.model_kwargs.pretrained` key in the config file

- Updated the way `checkpoint_path` is passed on to models. Added an attribute `checkpoint_path` for all Malpolon models
- Updated every examples consequently


Others
- MME: changed the way loss parameter `loss.pos_weight` is used in the model's `_step()` method so that its _state\_dict_ object stays the same before and after running the model in train mode.

- GLC22 examples in `benchmark` and `custom_train` have been updated to include an inference run option. This led to changing the return values of the class getter for the `test` dataset. The class now always return a `{data, label}` pair, with `label` of value `-1` for `test` dataset (inference run)
- Updated `malpolon/tests/test_examples.py` accordingly

Changes
- Fixed models import from `malpolon.models`

Other
- Purged poisoned PyPi package from unwanted `dev` files