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"code" + ] + }, + { + "login": "markus-kreft", + "name": "Markus Kreft", + "avatar_url": "https://avatars.githubusercontent.com/u/129367085?v=4", + "profile": "https://github.com/markus-kreft", + "contributions": [ + "code" + ] + }, + { + "login": "JackKelly", + "name": "Jack Kelly", + "avatar_url": "https://avatars.githubusercontent.com/u/460756?v=4", + "profile": "http://jack-kelly.com", + "contributions": [ + "ideas" + ] + } + ], + "contributorsPerLine": 7, + "skipCi": true, + "repoType": "github", + "repoHost": "https://github.com", + "projectName": "PVNet", + "projectOwner": "openclimatefix" +} diff --git a/.bumpversion.cfg b/.bumpversion.cfg new file mode 100644 index 0000000000000000000000000000000000000000..68625292f23bfc7ae58de4345b1c8bf0dae99f20 --- /dev/null +++ b/.bumpversion.cfg @@ -0,0 +1,9 @@ +[bumpversion] +commit = True +tag = True +current_version = 4.1.18 +message = Bump version: {current_version} → {new_version} [skip ci] + +[bumpversion:file:pvnet/__init__.py] +search = __version__ = "{current_version}" +replace = __version__ = "{new_version}" diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..514fded748794ab989011b98f39286acbd2cfc74 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,19 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +experiments/india/003_wind_plevels/MAE.png filter=lfs diff=lfs merge=lfs -text +experiments/india/003_wind_plevels/MAEvstimesteps.png filter=lfs diff=lfs merge=lfs -text +experiments/india/003_wind_plevels/p10.png filter=lfs diff=lfs merge=lfs -text +experiments/india/003_wind_plevels/p50.png filter=lfs diff=lfs merge=lfs -text +experiments/india/004_n_training_samples/mae_step.png filter=lfs diff=lfs merge=lfs -text +experiments/india/005_extra_nwp_variables/mae_steps.png filter=lfs diff=lfs merge=lfs -text +experiments/india/005_extra_nwp_variables/mae_steps_grouped.png filter=lfs diff=lfs merge=lfs -text +experiments/india/006_da_only/bad.png filter=lfs diff=lfs merge=lfs -text +experiments/india/006_da_only/good.png filter=lfs diff=lfs merge=lfs -text +experiments/india/006_da_only/mae_steps.png filter=lfs diff=lfs merge=lfs -text +experiments/india/007_different_seeds/mae_all_steps.png filter=lfs diff=lfs merge=lfs -text +experiments/india/007_different_seeds/mae_steps.png filter=lfs diff=lfs merge=lfs -text +experiments/india/008_coarse4/mae_step.png filter=lfs diff=lfs merge=lfs -text +experiments/india/008_coarse4/mae_step_smooth.png filter=lfs diff=lfs merge=lfs -text +experiments/uk/011[[:space:]]-[[:space:]]Extending[[:space:]]forecast[[:space:]]to[[:space:]]36[[:space:]]hours[[:space:]](updated[[:space:]]ECMWF[[:space:]]data)/PVNEt_national_XG_comparison.png filter=lfs diff=lfs merge=lfs -text +experiments/uk/011[[:space:]]-[[:space:]]Extending[[:space:]]forecast[[:space:]]to[[:space:]]36[[:space:]]hours[[:space:]](updated[[:space:]]ECMWF[[:space:]]data)/PVNets_comparison.png filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000000000000000000000000000000000000..d4c9b242fddab0ba58592776aeba0ba2ec25e614 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,17 @@ +name: Python Bump Version & release + +on: + push: + branches: + - main + paths-ignore: + - "configs.example/**" # ignores all files in configs.example + - "**/README.md" # ignores all README files + - "experiments/**" # ignores all files in experiments directory + +jobs: + release: + uses: openclimatefix/.github/.github/workflows/python-release.yml@main + secrets: + token: ${{ secrets.PYPI_API_TOKEN }} + PAT_TOKEN: ${{ secrets.PAT_TOKEN }} diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000000000000000000000000000000000000..53c4e1b50f02314092346f696f09dc2230073438 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,22 @@ +name: Python package tests + +on: + push: + pull_request: + types: [opened, reopened] + schedule: + - cron: "0 12 * * 1" +jobs: + call-run-python-tests: + uses: openclimatefix/.github/.github/workflows/python-test.yml@main + with: + # 0 means don't use pytest-xdist + pytest_numcpus: "4" + # pytest-cov looks at this folder + pytest_cov_dir: "pvnet" + # extra things to install + sudo_apt_install: "libgeos++-dev libproj-dev proj-data proj-bin" + # brew_install: "proj geos librttopo" + os_list: '["ubuntu-latest"]' + python-version: "['3.10', '3.11']" + extra_commands: "pip3 install -e '.[all]'" diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..6c1a2a5391e7b047b104cfe931490fe47e96f905 --- /dev/null +++ b/.gitignore @@ -0,0 +1,146 @@ +# Custom +config_tree.txt +configs/ +lightning_logs/ +logs/ +output/ +checkpoints* +csv/ +notebooks/ +*.html +*.csv +latest_logged_train_batch.png + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ +.DS_Store + +# vim +*swp diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0cfebf6de93f462d8cfe62ffd10cbbd00a976df7 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +default_language_version: + python: python3 + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + # list of supported hooks: https://pre-commit.com/hooks.html + - id: trailing-whitespace + - id: end-of-file-fixer + - id: debug-statements + - id: detect-private-key + + # python code formatting/linting + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: "v0.11.0" + hooks: + - id: ruff + args: [--fix] + # yaml formatting + - repo: https://github.com/pre-commit/mirrors-prettier + rev: v3.0.2 + hooks: + - id: prettier + types: [yaml] diff --git a/.prettierignore b/.prettierignore new file mode 100644 index 0000000000000000000000000000000000000000..b980d35f246ddd13f56483e713c231517f94751a --- /dev/null +++ b/.prettierignore @@ -0,0 +1 @@ +configs.example diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..beda6112722e2d37cb16ea69ac1c6225bea66ad7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Open Climate Fix Ltd + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000000000000000000000000000000000000..a6a831fd8fa607b61f7f9a47eb465e25609ecf0d --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,2 @@ +include *.txt +recursive-include pvnet/models/model_cards *.md diff --git a/README.md b/README.md index d0baefa77b4798188c4898f0480624f14925a4f3..b8df444a4bcc81b254bcad1c80c4e1fdc0afb05d 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,6 @@ Data is prepared with the `ocf_data_sampler/torch_datasets/datasets/pvnet_uk` Da ## Results The training logs for the current model can be found here: - - [https://wandb.ai/openclimatefix/NL-Solar/runs/hc2sv5fj](https://wandb.ai/openclimatefix/NL-Solar/runs/hc2sv5fj) The training logs for all model runs of PVNet2 can be found [here](https://wandb.ai/openclimatefix/pvnet2.1). @@ -53,5 +52,11 @@ Trained on a single NVIDIA Tesla T4 ### Software +This model was trained using the following Open Climate Fix packages: + - [1] https://github.com/openclimatefix/PVNet -- [2] https://github.com/openclimatefix/ocf-data-sampler \ No newline at end of file +- [2] https://github.com/openclimatefix/ocf-data-sampler + +The versions of these packages can be found below: + - pvnet==4.1.18 + - ocf-data-sampler==0.2.31 diff --git a/configs.example/callbacks/default.yaml b/configs.example/callbacks/default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..999a5bf82ed6f2db7b5be537d75f1ce2ab37b80c --- /dev/null +++ b/configs.example/callbacks/default.yaml @@ -0,0 +1,30 @@ +early_stopping: + _target_: pvnet.callbacks.MainEarlyStopping + # name of the logged metric which determines when model is improving + monitor: "${resolve_monitor_loss:${model.output_quantiles}}" + mode: "min" # can be "max" or "min" + patience: 10 # how many epochs (or val check periods) of not improving until training stops + min_delta: 0 # minimum change in the monitored metric needed to qualify as an improvement + +learning_rate_monitor: + _target_: lightning.pytorch.callbacks.LearningRateMonitor + logging_interval: "epoch" + +model_summary: + _target_: lightning.pytorch.callbacks.ModelSummary + max_depth: 3 + +model_checkpoint: + _target_: lightning.pytorch.callbacks.ModelCheckpoint + # name of the logged metric which determines when model is improving + monitor: "${resolve_monitor_loss:${model.output_quantiles}}" + mode: "min" # can be "max" or "min" + save_top_k: 1 # save k best models (determined by above metric) + save_last: True # additionaly always save model from last epoch + every_n_epochs: 1 + verbose: False + filename: "epoch={epoch}-step={step}" + # The path to where the model checkpoints will be stored + dirpath: "PLACEHOLDER/${model_name}" #${..model_name} + auto_insert_metric_name: False + save_on_train_epoch_end: False diff --git a/configs.example/callbacks/none.yaml b/configs.example/callbacks/none.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/configs.example/callbacks/wandb.yaml b/configs.example/callbacks/wandb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c6ae21d3aec18aa281a6d1ef5ea41199a4c04295 --- /dev/null +++ b/configs.example/callbacks/wandb.yaml @@ -0,0 +1,26 @@ +defaults: + - default.yaml + +watch_model: + _target_: src.callbacks.wandb_callbacks.WatchModel + log: "all" + log_freq: 100 + +upload_code_as_artifact: + _target_: src.callbacks.wandb_callbacks.UploadCodeAsArtifact + code_dir: ${work_dir}/src + +upload_ckpts_as_artifact: + _target_: src.callbacks.wandb_callbacks.UploadCheckpointsAsArtifact + ckpt_dir: "checkpoints/" + upload_best_only: True + +log_f1_precision_recall_heatmap: + _target_: src.callbacks.wandb_callbacks.LogF1PrecRecHeatmap + +log_confusion_matrix: + _target_: src.callbacks.wandb_callbacks.LogConfusionMatrix + +log_image_predictions: + _target_: src.callbacks.wandb_callbacks.LogImagePredictions + num_samples: 8 diff --git a/configs.example/config.yaml b/configs.example/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33c01dac3d51daebfb0773ef6a3b7aea046b1312 --- /dev/null +++ b/configs.example/config.yaml @@ -0,0 +1,45 @@ +# @package _global_ + +# specify here default training configuration +defaults: + - _self_ + - trainer: default.yaml + - model: multimodal.yaml + - datamodule: premade_samples.yaml + - callbacks: default.yaml # set this to null if you don't want to use callbacks + - logger: wandb.yaml # set logger here or use command line (e.g. `python run.py logger=wandb`) + - experiment: null + - hparams_search: null + - hydra: default.yaml + +renewable: "pv_uk" + +# enable color logging +# - override hydra/hydra_logging: colorlog +# - override hydra/job_logging: colorlog + +# path to original working directory +# hydra hijacks working directory by changing it to the current log directory, +# so it's useful to have this path as a special variable +# learn more here: https://hydra.cc/docs/next/tutorials/basic/running_your_app/working_directory +work_dir: ${hydra:runtime.cwd} + +model_name: "default" + +# use `python run.py debug=true` for easy debugging! +# this will run 1 train, val and test loop with only 1 batch +# equivalent to running `python run.py trainer.fast_dev_run=true` +# (this is placed here just for easier access from command line) +debug: False + +# pretty print config at the start of the run using Rich library +print_config: True + +# disable python warnings if they annoy you +ignore_warnings: True + +# check performance on test set, using the best model achieved during training +# lightning chooses best model based on metric specified in checkpoint callback +test_after_training: False + +seed: 2727831 diff --git a/configs.example/datamodule/configuration/example_configuration.yaml b/configs.example/datamodule/configuration/example_configuration.yaml new file mode 100644 index 0000000000000000000000000000000000000000..994ac4cc2af6828a08541f0de2829d8c19d572b3 --- /dev/null +++ b/configs.example/datamodule/configuration/example_configuration.yaml @@ -0,0 +1,288 @@ +general: + description: Example config for producing PVNet samples + name: example_config + +input_data: + + # Either use Site OR GSP configuration + site: + # Path to Site data in NetCDF format + file_path: PLACEHOLDER.nc + # Path to metadata in CSV format + metadata_file_path: PLACEHOLDER.csv + time_resolution_minutes: 15 + interval_start_minutes: -60 + # Specified for intraday currently + interval_end_minutes: 480 + dropout_timedeltas_minutes: [] + dropout_fraction: 0 # Fraction of samples with dropout + + gsp: + # Path to GSP data in zarr format + # e.g. gs://solar-pv-nowcasting-data/PV/GSP/v7/pv_gsp.zarr + zarr_path: PLACEHOLDER.zarr + interval_start_minutes: -60 + # Specified for intraday currently + interval_end_minutes: 480 + time_resolution_minutes: 30 + # Random value from the list below will be chosen as the delay when dropout is used + # If set to null no dropout is applied. Only values before t0 are dropped out for GSP. + # Values after t0 are assumed as targets and cannot be dropped. + dropout_timedeltas_minutes: [] + dropout_fraction: 0 # Fraction of samples with dropout + + nwp: + + ecmwf: + provider: ecmwf + # Path to ECMWF NWP data in zarr format + # n.b. It is not necessary to use multiple or any NWP data. These entries can be removed + zarr_path: PLACEHOLDER.zarr + interval_start_minutes: -60 + # Specified for intraday currently + interval_end_minutes: 480 + time_resolution_minutes: 60 + channels: + - t2m # 2-metre temperature + - dswrf # downwards short-wave radiation flux + - dlwrf # downwards long-wave radiation flux + - hcc # high cloud cover + - mcc # medium cloud cover + - lcc # low cloud cover + - tcc # total cloud cover + - sde # snow depth water equivalent + - sr # direct solar radiation + - duvrs # downwards UV radiation at surface + - prate # precipitation rate + - u10 # 10-metre U component of wind speed + - u100 # 100-metre U component of wind speed + - u200 # 200-metre U component of wind speed + - v10 # 10-metre V component of wind speed + - v100 # 100-metre V component of wind speed + - v200 # 200-metre V component of wind speed + # The following channels are accumulated and need to be diffed + accum_channels: + - dswrf # downwards short-wave radiation flux + - dlwrf # downwards long-wave radiation flux + - sr # direct solar radiation + - duvrs # downwards UV radiation at surface + image_size_pixels_height: 24 + image_size_pixels_width: 24 + dropout_timedeltas_minutes: [-360] + dropout_fraction: 1.0 # Fraction of samples with dropout + max_staleness_minutes: null + normalisation_constants: + t2m: + mean: 283.48333740234375 + std: 3.692270040512085 + dswrf: + mean: 11458988.0 + std: 13025427.0 + dlwrf: + mean: 27187026.0 + std: 15855867.0 + hcc: + mean: 0.3961029052734375 + std: 0.42244860529899597 + mcc: + mean: 0.3288780450820923 + std: 0.38039860129356384 + lcc: + mean: 0.44901806116104126 + std: 0.3791404366493225 + tcc: + mean: 0.7049227356910706 + std: 0.37487083673477173 + sde: + mean: 8.107526082312688e-05 + std: 0.000913831521756947 # Mapped from "sd" in the Python file + sr: + mean: 12905302.0 + std: 16294988.0 + duvrs: + mean: 1305651.25 + std: 1445635.25 + prate: + mean: 3.108070450252853e-05 + std: 9.81039775069803e-05 + u10: + mean: 1.7677178382873535 + std: 5.531515598297119 + u100: + mean: 2.393547296524048 + std: 7.2320556640625 + u200: + mean: 2.7963004112243652 + std: 8.049470901489258 + v10: + mean: 0.985887885093689 + std: 5.411230564117432 + v100: + mean: 1.4244288206100464 + std: 6.944501876831055 + v200: + mean: 1.6010299921035767 + std: 7.561611652374268 + # Added diff_ keys for the channels under accum_channels: + diff_dlwrf: + mean: 1136464.0 + std: 131942.03125 + diff_dswrf: + mean: 420584.6875 + std: 715366.3125 + diff_duvrs: + mean: 48265.4765625 + std: 81605.25 + diff_sr: + mean: 469169.5 + std: 818950.6875 + + ukv: + provider: ukv + # Path to UKV NWP data in zarr format + # e.g. gs://solar-pv-nowcasting-data/NWP/UK_Met_Office/UKV_intermediate_version_7.zarr + # n.b. It is not necessary to use multiple or any NWP data. These entries can be removed + zarr_path: PLACEHOLDER.zarr + interval_start_minutes: -60 + # Specified for intraday currently + interval_end_minutes: 480 + time_resolution_minutes: 60 + channels: + - t # 2-metre temperature + - dswrf # downwards short-wave radiation flux + - dlwrf # downwards long-wave radiation flux + - hcc # high cloud cover + - mcc # medium cloud cover + - lcc # low cloud cover + - sde # snow depth water equivalent + - r # relative humidty + - vis # visibility + - si10 # 10-metre wind speed + - wdir10 # 10-metre wind direction + - prate # precipitation rate + # These variables exist in CEDA training data but not in the live MetOffice live service + - hcct # height of convective cloud top, meters above surface. NaN if no clouds + - cdcb # height of lowest cloud base > 3 oktas + - dpt # dew point temperature + - prmsl # mean sea level pressure + - h # geometrical? (maybe geopotential?) height + image_size_pixels_height: 24 + image_size_pixels_width: 24 + dropout_timedeltas_minutes: [-360] + dropout_fraction: 1.0 # Fraction of samples with dropout + max_staleness_minutes: null + normalisation_constants: + t: + mean: 283.64913206 + std: 4.38818501 + dswrf: + mean: 111.28265039 + std: 190.47216887 + dlwrf: + mean: 325.03130139 + std: 39.45988077 + hcc: + mean: 29.11949682 + std: 38.07184418 + mcc: + mean: 40.88984494 + std: 41.91144559 + lcc: + mean: 50.08362643 + std: 39.33210726 + sde: + mean: 0.00289545 + std: 0.1029753 + r: + mean: 81.79229501 + std: 11.45012499 + vis: + mean: 32262.03285118 + std: 21578.97975625 + si10: + mean: 6.88348448 + std: 3.94718813 + wdir10: + mean: 199.41891636 + std: 94.08407495 + prate: + mean: 3.45793433e-05 + std: 0.00021497 + hcct: + mean: -18345.97478167 + std: 18382.63958991 + cdcb: + mean: 1412.26599062 + std: 2126.99350113 + dpt: + mean: 280.54379901 + std: 4.57250482 + prmsl: + mean: 101321.61574029 + std: 1252.71790539 + h: + mean: 2096.51991356 + std: 1075.77812282 + + satellite: + # Path to Satellite data (non-HRV) in zarr format + # e.g. gs://solar-pv-nowcasting-data/satellite/EUMETSAT/SEVIRI_RSS/v4/2020_nonhrv.zarr + zarr_path: PLACEHOLDER.zarr + interval_start_minutes: -30 + interval_end_minutes: 0 + time_resolution_minutes: 5 + channels: + - IR_016 # Surface, cloud phase + - IR_039 # Surface, clouds, wind fields + - IR_087 # Surface, clouds, atmospheric instability + - IR_097 # Ozone + - IR_108 # Surface, clouds, wind fields, atmospheric instability + - IR_120 # Surface, clouds, atmospheric instability + - IR_134 # Cirrus cloud height, atmospheric instability + - VIS006 # Surface, clouds, wind fields + - VIS008 # Surface, clouds, wind fields + - WV_062 # Water vapor, high level clouds, upper air analysis + - WV_073 # Water vapor, atmospheric instability, upper-level dynamics + image_size_pixels_height: 24 + image_size_pixels_width: 24 + dropout_timedeltas_minutes: [] + dropout_fraction: 0 # Fraction of samples with dropout + normalisation_constants: + IR_016: + mean: 0.17594202 + std: 0.21462157 + IR_039: + mean: 0.86167645 + std: 0.04618041 + IR_087: + mean: 0.7719318 + std: 0.06687243 + IR_097: + mean: 0.8014212 + std: 0.0468558 + IR_108: + mean: 0.71254843 + std: 0.17482725 + IR_120: + mean: 0.89058584 + std: 0.06115861 + IR_134: + mean: 0.944365 + std: 0.04492306 + VIS006: + mean: 0.09633306 + std: 0.12184761 + VIS008: + mean: 0.11426069 + std: 0.13090034 + WV_062: + mean: 0.7359355 + std: 0.16111417 + WV_073: + mean: 0.62479186 + std: 0.12924142 + + solar_position: + interval_start_minutes: -60 + interval_end_minutes: 480 + time_resolution_minutes: 30 diff --git a/configs.example/datamodule/premade_batches.yaml b/configs.example/datamodule/premade_batches.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b501525ed4d4bd89d853078a01ebcd07ab3fcd1e --- /dev/null +++ b/configs.example/datamodule/premade_batches.yaml @@ -0,0 +1,10 @@ +_target_: pvnet.data.DataModule +configuration: null + +# The sample_dir is the location batches were saved to using the save_batches.py script +# The sample_dir should contain train and val subdirectories with batches + +sample_dir: "PLACEHOLDER" +num_workers: 10 +prefetch_factor: 2 +batch_size: 8 diff --git a/configs.example/datamodule/streamed_batches.yaml b/configs.example/datamodule/streamed_batches.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ccddc149f54f4180d33fba80d9eef811bae7af14 --- /dev/null +++ b/configs.example/datamodule/streamed_batches.yaml @@ -0,0 +1,20 @@ +_target_: pvnet.data.DataModule +# Path to the data configuration yaml file. You can find examples in the configuration subdirectory +# in configs.example/datamodule/configuration +# Use the full local path such as: /FULL/PATH/PVNet/configs/datamodule/configuration/gcp_configuration.yaml" + +configuration: "PLACEHOLDER.yaml" +num_workers: 20 +prefetch_factor: 2 +batch_size: 8 + +sample_output_dir: "PLACEHOLDER" +num_train_samples: 2 +num_val_samples: 1 + +train_period: + - null + - "2022-05-07" +val_period: + - "2022-05-08" + - "2023-05-08" diff --git a/configs.example/experiment/baseline.yaml b/configs.example/experiment/baseline.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4eb408a65df6fb129ba7d85e39b78469ec7815ca --- /dev/null +++ b/configs.example/experiment/baseline.yaml @@ -0,0 +1,21 @@ +# @package _global_ + +# to execute this experiment run: +# python run.py experiment=example_simple.yaml + +defaults: + - override /trainer: default.yaml # choose trainer from 'configs/trainer/' + - override /model: baseline.yaml + - override /datamodule: premade_samples.yaml + - override /callbacks: default.yaml + - override /logger: neptune.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +seed: 518 +validate_only: "1" # by putting this key in the config file, the model does not get trained. + +trainer: + min_epochs: 1 + max_epochs: 1 diff --git a/configs.example/experiment/conv3d_sat_nwp.yaml b/configs.example/experiment/conv3d_sat_nwp.yaml new file mode 100644 index 0000000000000000000000000000000000000000..390ecc60e4e9e8d4b59f1fa8edcc313048d1276b --- /dev/null +++ b/configs.example/experiment/conv3d_sat_nwp.yaml @@ -0,0 +1,23 @@ +# @package _global_ + +# to execute this experiment run: +# python run.py experiment=example_simple.yaml + +defaults: + - override /trainer: default.yaml # choose trainer from 'configs/trainer/' + - override /model: conv3d_sat_nwp.yaml + - override /datamodule: premade_samples.yaml + - override /callbacks: default.yaml +# - override /logger: neptune.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +seed: 518 + +trainer: + min_epochs: 1 + max_epochs: 10 + +model: + conv3d_channels: 32 diff --git a/configs.example/experiment/example_simple.yaml b/configs.example/experiment/example_simple.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bc4cdec212f777e4456dd53807f7086a267fc98b --- /dev/null +++ b/configs.example/experiment/example_simple.yaml @@ -0,0 +1,27 @@ +# @package _global_ + +# to execute this experiment run: +# python run.py experiment=example_simple.yaml + +defaults: + - override /trainer: default.yaml # choose trainer from 'configs/trainer/' + - override /model: conv3d_sat_nwp.yaml + - override /datamodule: premade_samples.yaml + - override /callbacks: default.yaml + - override /logger: tensorboard.yaml + - override /hparams_search: null + - override /hydra: default.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +seed: 518 + +trainer: + min_epochs: 1 + max_epochs: 2 + +datamodule: + batch_size: 16 + +validate_only: "1" # by putting this key in the config file, the model does not get trained. diff --git a/configs.example/experiment/test.yaml b/configs.example/experiment/test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dd788ffdfe9240d4a21240e83d70c2df4b6fa4c4 --- /dev/null +++ b/configs.example/experiment/test.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +# to execute this experiment run: +# python run.py experiment=test.yaml + +defaults: + - override /trainer: default.yaml # choose trainer from 'configs/trainer/' + - override /model: test.yaml + - override /datamodule: premade_samples.yaml + - override /callbacks: default.yaml + +# all parameters below will be merged with parameters from default configurations set above +# this allows you to overwrite only specified parameters + +seed: 518 + +trainer: + min_epochs: 0 + max_epochs: 2 + reload_dataloaders_every_n_epochs: 0 + limit_train_batches: 2000 + limit_val_batches: 100 + limit_test_batches: 100 + val_check_interval: 100 + num_sanity_val_steps: 8 + accumulate_grad_batches: 4 + #fast_dev_run: 3 + +datamodule: + num_workers: 10 + prefetch_factor: 2 + batch_size: 8 +#validate_only: '1' # by putting this key in the config file, the model does not get trained. diff --git a/configs.example/hparams_search/conv3d_optuna.yaml b/configs.example/hparams_search/conv3d_optuna.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c7daea26529d7350d167c70e19fb809f47873ee3 --- /dev/null +++ b/configs.example/hparams_search/conv3d_optuna.yaml @@ -0,0 +1,49 @@ +# @package _global_ + +# example hyperparameter optimization of some experiment with Optuna: +# python run.py -m hparams_search=conv3d_optuna experiment=conv3d_sat_nwp + +defaults: + - override /hydra/sweeper: optuna + +# choose metric which will be optimized by Optuna +optimized_metric: "MSE/Validation_epoch" + +hydra: + # here we define Optuna hyperparameter search + # it optimizes for value returned from function with @hydra.main decorator + # learn more here: https://hydra.cc/docs/next/plugins/optuna_sweeper + sweeper: + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + storage: null + study_name: null + n_jobs: 1 + + # 'minimize' or 'maximize' the objective + direction: minimize + + # number of experiments that will be executed + n_trials: 20 + + # choose Optuna hyperparameter sampler + # learn more here: https://optuna.readthedocs.io/en/stable/reference/samplers.html + sampler: + _target_: optuna.samplers.TPESampler + seed: 12345 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + + # define range of hyperparameters + search_space: + model.include_pv_yield_history: + type: categorical + choices: [true, false] + model.include_future_satellite: + type: categorical + choices: [true, false] diff --git a/configs.example/hydra/default.yaml b/configs.example/hydra/default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fb8e779d045eede5127a77ddebce49b6f1b8c00b --- /dev/null +++ b/configs.example/hydra/default.yaml @@ -0,0 +1,14 @@ +# output paths for hydra logs +run: + # Local log directory for hydra + dir: PLACEHOLDER/runs/${now:%Y-%m-%d}/${now:%H-%M-%S} +sweep: + # Local log directory for hydra + dir: PLACEHOLDER/multiruns/${now:%Y-%m-%d_%H-%M-%S} + subdir: ${hydra.job.num} + +# you can set here environment variables that are universal for all users +# for system specific variables (like data paths) it's better to use .env file! +job: + env_set: + EXAMPLE_VAR: "example_value" diff --git a/configs.example/logger/csv.yaml b/configs.example/logger/csv.yaml new file mode 100644 index 0000000000000000000000000000000000000000..784883d245669b5bd8265895697ca08cf19ad847 --- /dev/null +++ b/configs.example/logger/csv.yaml @@ -0,0 +1,9 @@ +# csv logger built in lightning + +csv: + _target_: pytorch_lightning.loggers.csv_logs.CSVLogger + # local path to log training process + save_dir: "PLACEHOLDER" + name: "csv/" + version: null + prefix: "" diff --git a/configs.example/logger/many_loggers.yaml b/configs.example/logger/many_loggers.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5ab9ca8cf1bb43e79d30e023014e02946d7d0fe8 --- /dev/null +++ b/configs.example/logger/many_loggers.yaml @@ -0,0 +1,7 @@ +# train with many loggers at once + +defaults: + - csv.yaml + # - neptune.yaml + # - tensorboard.yaml + - wandb.yaml diff --git a/configs.example/logger/neptune.yaml b/configs.example/logger/neptune.yaml new file mode 100644 index 0000000000000000000000000000000000000000..abc78b31c01ac192a7ac45ef89a41ac4c24bc6d9 --- /dev/null +++ b/configs.example/logger/neptune.yaml @@ -0,0 +1,8 @@ +# https://neptune.ai + +neptune: + _target_: pytorch_lightning.loggers.NeptuneLogger + api_key: ${oc.env:NEPTUNE_API_TOKEN} # api key is loaded from environment variable + # Neptune project placeholder + project: PLACEHOLDER + prefix: "" diff --git a/configs.example/logger/tensorboard.yaml b/configs.example/logger/tensorboard.yaml new file mode 100644 index 0000000000000000000000000000000000000000..036c955916f02be9781de4b2374c42422e50ee72 --- /dev/null +++ b/configs.example/logger/tensorboard.yaml @@ -0,0 +1,11 @@ +# https://www.tensorflow.org/tensorboard/ + +tensorboard: + _target_: pytorch_lightning.loggers.tensorboard.TensorBoardLogger + # Path to use for tensorboard logs + save_dir: "PLACEHOLDER" + name: "default" + version: "${model_name}" + log_graph: False + default_hp_metric: False + prefix: "" diff --git a/configs.example/logger/wandb.yaml b/configs.example/logger/wandb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0fe80aa12be64694ea28a1918804092274240021 --- /dev/null +++ b/configs.example/logger/wandb.yaml @@ -0,0 +1,17 @@ +# https://wandb.ai + +wandb: + _target_: lightning.pytorch.loggers.wandb.WandbLogger + # wandb project to log to + project: "PLACEHOLDER" + name: "${model_name}" + # location to store the wandb local logs + save_dir: "PLACEHOLDER" + offline: False # set True to store all logs only locally + id: null # pass correct id to resume experiment! + # entity: "" # set to name of your wandb team or just remove it + log_model: False + prefix: "" + job_type: "train" + group: "" + tags: [] diff --git a/configs.example/model/baseline.yaml b/configs.example/model/baseline.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dc1667fd8f1a5540912623649f9732128b04c95f --- /dev/null +++ b/configs.example/model/baseline.yaml @@ -0,0 +1,4 @@ +_target_: pvnet.models.baseline.last_value.Model + +forecast_minutes: 120 +history_minutes: 30 diff --git a/configs.example/model/multimodal.yaml b/configs.example/model/multimodal.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d98f8cd9e690da0f3f4fa3a12197bed23114eaad --- /dev/null +++ b/configs.example/model/multimodal.yaml @@ -0,0 +1,115 @@ +_target_: pvnet.models.multimodal.multimodal.Model + +output_quantiles: [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98] + +#-------------------------------------------- +# NWP encoder +#-------------------------------------------- + +nwp_encoders_dict: + ukv: + _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet + _partial_: True + in_channels: 2 + out_features: 256 + number_of_conv3d_layers: 6 + conv3d_channels: 32 + image_size_pixels: 24 + ecmwf: + _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet + _partial_: True + in_channels: 12 + out_features: 256 + number_of_conv3d_layers: 4 + conv3d_channels: 32 + image_size_pixels: 12 + +#-------------------------------------------- +# Sat encoder settings +#-------------------------------------------- + +sat_encoder: + _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet + _partial_: True + in_channels: 11 + out_features: 256 + number_of_conv3d_layers: 6 + conv3d_channels: 32 + image_size_pixels: 24 + +add_image_embedding_channel: False + +#-------------------------------------------- +# PV encoder settings +#-------------------------------------------- + +pv_encoder: + _target_: pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork + _partial_: True + num_sites: 349 + out_features: 40 + num_heads: 4 + kdim: 40 + id_embed_dim: 20 + +#-------------------------------------------- +# Tabular network settings +#-------------------------------------------- + +output_network: + _target_: pvnet.models.multimodal.linear_networks.networks.ResFCNet2 + _partial_: True + fc_hidden_features: 128 + n_res_blocks: 6 + res_block_layers: 2 + dropout_frac: 0.0 + +embedding_dim: 16 +include_sun: True +include_gsp_yield_history: False +include_site_yield_history: False + +# The mapping between the location IDs and their embedding indices +location_id_mapping: + 1: 1 + 5: 2 + 110: 3 +# ... + +#-------------------------------------------- +# Times +#-------------------------------------------- + +# Foreast and time settings +forecast_minutes: 480 +history_minutes: 120 + +min_sat_delay_minutes: 60 + +# These must also be set even if identical to forecast_minutes and history_minutes +sat_history_minutes: 90 +pv_history_minutes: 180 + +# These must be set for each NWP encoder +nwp_history_minutes: + ukv: 120 + ecmwf: 120 +nwp_forecast_minutes: + ukv: 480 + ecmwf: 480 +# Optional; defaults to 60, so must be set for data with different time resolution +nwp_interval_minutes: + ukv: 60 + ecmwf: 60 + +# ---------------------------------------------- +# Optimizer +# ---------------------------------------------- +optimizer: + _target_: pvnet.optimizers.EmbAdamWReduceLROnPlateau + lr: 0.0001 + weight_decay: 0.01 + amsgrad: True + patience: 5 + factor: 0.1 + threshold: 0.002 diff --git a/configs.example/model/nwp_dwsrf_weighting.yaml b/configs.example/model/nwp_dwsrf_weighting.yaml new file mode 100644 index 0000000000000000000000000000000000000000..257b63cc3a3146ee23c1349bf5cafea19c48e18b --- /dev/null +++ b/configs.example/model/nwp_dwsrf_weighting.yaml @@ -0,0 +1,21 @@ +_target_: pvnet.models.multimodal.nwp_weighting.Model + +#-------------------------------------------- +# Network settings +#-------------------------------------------- + +# Foreast and time settings +forecast_minutes: 480 +history_minutes: 120 + +nwp_history_minutes: 120 +nwp_forecast_minutes: 480 + +nwp_image_size_pixels: 24 +dwsrf_channel: 1 + +# ---------------------------------------------- + +optimizer: + _target_: pvnet.optimizers.AdamW + lr: 0.0005 diff --git a/configs.example/model/test.yaml b/configs.example/model/test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..240b853cf2467a12538e92c9a6a6bf267ec5867a --- /dev/null +++ b/configs.example/model/test.yaml @@ -0,0 +1,4 @@ +_target_: pvnet.models.baseline.single_value.Model + +history_minutes: 120 +forecast_minutes: 360 diff --git a/configs.example/model/wind_multimodal.yaml b/configs.example/model/wind_multimodal.yaml new file mode 100644 index 0000000000000000000000000000000000000000..390372bf0d7ec9f959443521374dfebcdc855258 --- /dev/null +++ b/configs.example/model/wind_multimodal.yaml @@ -0,0 +1,83 @@ +_target_: pvnet.models.multimodal.multimodal.Model + +output_quantiles: [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98] + +#-------------------------------------------- +# NWP encoder +#-------------------------------------------- +nwp_encoders_dict: + ecmwf: + _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet + _partial_: True + in_channels: 14 + out_features: 256 + number_of_conv3d_layers: 6 + conv3d_channels: 32 + image_size_pixels: 16 + +#-------------------------------------------- +# Sensor encoder settings +#-------------------------------------------- + +wind_encoder: + _target_: pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork + _partial_: True + num_sites: 19 + out_features: 40 + num_heads: 4 + kdim: 40 + id_embed_dim: 20 + +#-------------------------------------------- +# Tabular network settings +#-------------------------------------------- + +output_network: + _target_: pvnet.models.multimodal.linear_networks.networks.ResFCNet2 + _partial_: True + fc_hidden_features: 128 + n_res_blocks: 6 + res_block_layers: 2 + dropout_frac: 0.0 + +embedding_dim: 16 +include_sun: False +include_gsp_yield_history: False + +# The mapping between the location IDs and their embedding indices +location_id_mapping: + 1: 1 + 5: 2 + 110: 3 +# ... + +#-------------------------------------------- +# Times +#-------------------------------------------- + +# Foreast and time settings +forecast_minutes: 480 +history_minutes: 120 + +min_sat_delay_minutes: 60 + +# --- set to null if same as history_minutes --- +sat_history_minutes: 90 +nwp_history_minutes: 60 +nwp_forecast_minutes: 2880 +pv_history_minutes: 180 +pv_interval_minutes: 15 +sat_interval_minutes: 15 + +target_key: "sensor" +# ---------------------------------------------- +# Optimizer +# ---------------------------------------------- +optimizer: + _target_: pvnet.optimizers.EmbAdamWReduceLROnPlateau + lr: 0.0001 + weight_decay: 0.01 + amsgrad: True + patience: 5 + factor: 0.1 + threshold: 0.002 diff --git a/configs.example/readme.md b/configs.example/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..831e5dd86a3067c0606156e9b0ce1caa0e188e4c --- /dev/null +++ b/configs.example/readme.md @@ -0,0 +1,5 @@ +This directory contains example configuration files for the PVNet project. Many paths will need to unique to each user. You can find these paths by searching for PLACEHOLDER within these logs. Not all of +the values with a placeholder need to be set. For example in the logger subdirectory there are many different loggers with PLACEHOLDERS. If only one logger is used, then only that placeholder needs to be set. + +run experiments by: +`python run.py experiment=example_simple ` diff --git a/configs.example/trainer/all_params.yaml b/configs.example/trainer/all_params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..64f5fdf5081d17ba7e5dc4e33ff56d8ea07d6752 --- /dev/null +++ b/configs.example/trainer/all_params.yaml @@ -0,0 +1,48 @@ +_target_: pytorch_lightning.Trainer + +# default values for all trainer parameters +checkpoint_callback: True +default_root_dir: null +gradient_clip_val: 0.0 +process_position: 0 +num_nodes: 1 +num_processes: 1 +gpus: null +auto_select_gpus: False +tpu_cores: null +log_gpu_memory: null +overfit_batches: 0.0 +track_grad_norm: -1 +check_val_every_n_epoch: 1 +fast_dev_run: False +accumulate_grad_batches: 1 +max_epochs: 1 +min_epochs: 1 +max_steps: null +min_steps: null +limit_train_batches: 1.0 +limit_val_batches: 1.0 +limit_test_batches: 1.0 +val_check_interval: 1.0 +flush_logs_every_n_steps: 100 +log_every_n_steps: 50 +accelerator: null +sync_batchnorm: False +precision: 32 +weights_save_path: null +num_sanity_val_steps: 2 +truncated_bptt_steps: null +resume_from_checkpoint: null +profiler: null +benchmark: False +deterministic: False +reload_dataloaders_every_epoch: False +auto_lr_find: False +replace_sampler_ddp: True +terminate_on_nan: False +auto_scale_batch_size: False +prepare_data_per_node: True +plugins: null +amp_backend: "native" +amp_level: "O2" +move_metrics_to_cpu: False diff --git a/configs.example/trainer/default.yaml b/configs.example/trainer/default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..965c274ca9f57283cab6e50d5ed36103b3edee0d --- /dev/null +++ b/configs.example/trainer/default.yaml @@ -0,0 +1,14 @@ +_target_: lightning.pytorch.trainer.trainer.Trainer + +# set `1` to train on GPU, `0` to train on CPU only +accelerator: auto +devices: auto + +min_epochs: null +max_epochs: null +reload_dataloaders_every_n_epochs: 0 +num_sanity_val_steps: 8 +fast_dev_run: false + +accumulate_grad_batches: 4 +log_every_n_steps: 50 diff --git a/data_config.yaml b/data_config.yaml index 835b8a0a5ebb9a3f182cc64997860d1db62ac367..9f4900ecb248623e4f0fd3d1f58c6a91d5d6d5f0 100644 --- a/data_config.yaml +++ b/data_config.yaml @@ -27,10 +27,9 @@ input_data: dropout_fraction: 1.0 dropout_timedeltas_minutes: - -180 - forecast_minutes: 3120.0 - image_size_pixels_height: 36 - image_size_pixels_width: 36 - interval_end_minutes: 2880 + image_size_pixels_height: 30 + image_size_pixels_width: 30 + interval_end_minutes: 2160 interval_start_minutes: -120 max_staleness_minutes: null normalisation_constants: @@ -124,11 +123,10 @@ input_data: - -20 - -25 - -30 - image_size_pixels_height: 144 - image_size_pixels_width: 144 - interval_end_minutes: 0 + image_size_pixels_height: 100 + image_size_pixels_width: 100 + interval_end_minutes: -60 interval_start_minutes: -90 - live_delay_minutes: 60 normalisation_constants: HRV: mean: 0.09298719 @@ -166,18 +164,16 @@ input_data: WV_073: mean: 0.62479186 std: 0.12924142 - satellite_image_size_pixels_height: 144 - satellite_image_size_pixels_width: 144 time_resolution_minutes: 5 zarr_path: PLACEHOLDER.zarr site: capacity_mode: variable file_path: /home/zak/projects/PVNet/nl_solar/pv_data/netherlands_pv_data_v2.nc - interval_end_minutes: 2880 + interval_end_minutes: 2160 interval_start_minutes: -2880 metadata_file_path: /home/zak/projects/PVNet/nl_solar/pv_data/netherlands_metadata.csv time_resolution_minutes: 15 solar_position: - interval_end_minutes: 2880 + interval_end_minutes: 2160 interval_start_minutes: -2880 time_resolution_minutes: 15 diff --git a/experiments/india/001_v1/india_pv_wind.md b/experiments/india/001_v1/india_pv_wind.md new file mode 100644 index 0000000000000000000000000000000000000000..aaa33101c4a97924f04dcd1463ece0efa667dcb5 --- /dev/null +++ b/experiments/india/001_v1/india_pv_wind.md @@ -0,0 +1,69 @@ +# PVNet for Wind and PV Sites in India + +## PVNet for sites + +### Data + +We use PV generation data for India from April 2019-Nov 2022 for training +and Dec 2022- Nov 2023 for validation. This is only with ECMWF data, and PV generation history. + +The forecast is every 15 minutes for 48 hours for PV generation. + +The input NWP data is hourly, and 32x32 pixels (corresponding to around 320kmx320km) around a central +point in NW-India. + +[WandB Link](https://wandb.ai/openclimatefix/pvnet_india2.1/runs/o4xpvzrc) + +### Results + +Overall MAE is 4.9% on the validation set, and forecasts look overall good. + +![batch_idx_1_all_892_2ca7e12db5de2cf2e244](https://github.com/openclimatefix/PVNet/assets/7170359/07e8199a-11b5-4400-9897-37b7738a4f39) + +![W B Chart 05_02_2024, 10_07_12_pvnet](https://github.com/openclimatefix/PVNet/assets/7170359/abaefdc1-dedd-4a12-8a26-afaf36d7786b) + +## WindNet + + +### April-29-2024 WindNet v1 Production Model + +[WandB Link](https://wandb.ai/openclimatefix/india/runs/5llq8iw6) + +Improvements: Larger input size (64x64), 7 hour delay for ECMWF NWP inputs, to match productions. +New, much more efficient encoder for NWP, allowing for more filters and layers, with less parameters. +The 64x64 input size corresponds to 6.4 degrees x 6.4 degrees, which is around 700km x 700km. This allows for the +model to see the wind over the wind generation sites, which seems to be the biggest reason for the improvement in the model. + + + +MAE is 7.6% with real improvements on the production side of things. + + +There were other experiments with slightly different numbers of filters, model parameters and the like, but generally no +improvements were seen. + + +## WindNet v1 Results + +### Data + +We use Wind generation data for India from April 2019-Nov 2022 for training +and Dec 2022- Nov 2023 for validation. This is only with ECMWF data, and Wind generation history. + +The forecast is every 15 minutes for 48 hours for Wind generation. + +The input NWP data is hourly, and 32x32 pixels (corresponding to around 320kmx320km) around a central +point in NW-India. Note: The majority of the wind generation is likely not covered in the 320kmx320km area. + + +[WandB Link](https://wandb.ai/openclimatefix/pvnet_india2.1/runs/otdx7axx) + +### Results + +![W B Chart 05_02_2024, 10_05_19](https://github.com/openclimatefix/PVNet/assets/7170359/6a8cd9c5-bdfe-41ab-996d-37fd1be2a07c) + +![W B Chart 05_02_2024, 10_06_51_windnet](https://github.com/openclimatefix/PVNet/assets/7170359/77554ef0-4411-4432-af95-8530aef4a701) + +![batch_idx_1_all_1730_379a9f881a7f01153f98](https://github.com/openclimatefix/PVNet/assets/7170359/243d9f3e-4cb9-405e-80c5-40c6c218c17f) + +MAE is around 10% overall, although it doesn't seem to do very well on the ramps up and down. diff --git a/experiments/india/002_wind_meteomatics/india_windnet_v2.md b/experiments/india/002_wind_meteomatics/india_windnet_v2.md new file mode 100644 index 0000000000000000000000000000000000000000..3e89af59c7bbb016c1162a0145d4dd5dcb1cd2bc --- /dev/null +++ b/experiments/india/002_wind_meteomatics/india_windnet_v2.md @@ -0,0 +1,46 @@ +### WindNet v2 Meteomatics + ECMWF Model + +[WandB Linl](https://wandb.ai/openclimatefix/india/runs/v3mja33d) + +This newest experiment uses Meteomatics data in addition to ECMWF data. The Meteomatics data is at specific locations corresponding +to the gneeration sites we know about. It is smartly downscaled ECMWF data, down to 15 minutes and at a few height levels we are +interested in, primarily 10m, 100m, and 200m. The Meteomatics data is a semi-reanalysis, with each block of 6 hours being from one forecast run. +For example, in one day, hours 00-06 are from the same, 00 forecast run, and hours 06-12 are from the 06 forecast run. This is important to note +as it is both not a real reanalysis, but we also can't have it exactly match the live data, as any forecast steps beyond 6 hours are thrown away. +This does mean that these results should be taken as a best case or better than best case scenario, as every 6 hour, observations from the future +are incorporated into the Meteomatics input data from the next NWP mode run. + +For the purposes of WindNet, Meteomatics data is treated as Sensor data that goes into the future. +The model encodes the sensor information the same way as for the historical PV, Wind, and GSP generation, and has +a simple, single attention head to encode the information. This is then concatenated along with the rest of the data, like in +previous experiments. + +This model also has an even larger input size of ECMWF data, 81x81 pixels, corresponding to around 810kmx810km. +![Screenshot_20240430_082855](https://github.com/openclimatefix/PVNet/assets/7170359/6981a088-8664-474b-bfea-c94c777fc119) + +MAE is 7.0% on the validation set, showing a slight improvement over the previous model. + +Comperison with the production model: + +| Timestep | Prod MAE % | No Meteomatics MAE % | Meteomatics MAE % | +| --- | --- | --- | --- | +| 0-0 minutes | 7.586 | 5.920 | 2.475 | +| 15-15 minutes | 8.021 | 5.809 | 2.968 | +| 30-45 minutes | 7.233 | 5.742 | 3.472 | +| 45-60 minutes | 7.187 | 5.698 | 3.804 | +| 60-120 minutes | 7.231 | 5.816 | 4.650 | +| 120-240 minutes | 7.287 | 6.080 | 6.028 | +| 240-360 minutes | 7.319 | 6.375 | 6.738 | +| 360-480 minutes | 7.285 | 6.638 | 6.964 | +| 480-720 minutes | 7.143 | 6.747 | 6.906 | +| 720-1440 minutes | 7.380 | 7.207 | 6.962 | +| 1440-2880 minutes | 7.904 | 7.507 | 7.507 | + +![mae_per_timestep](https://github.com/openclimatefix/PVNet/assets/7170359/e3c942e8-65c6-4b95-8c51-f25d43e7a082) + + + + +Example plot + +![Screenshot_20240430_082937](https://github.com/openclimatefix/PVNet/assets/7170359/88db342e-bf82-414e-8255-5ad4af659fb8) diff --git a/experiments/india/003_wind_plevels/MAE.png b/experiments/india/003_wind_plevels/MAE.png new file mode 100644 index 0000000000000000000000000000000000000000..a60bc0a497a06c7dfb1dab71eedf373e512a7602 --- /dev/null +++ b/experiments/india/003_wind_plevels/MAE.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b06d6f85c2ee708e9555969afd622353b950a744f604d6c31d3c32d9b1543c23 +size 174091 diff --git a/experiments/india/003_wind_plevels/MAEvstimesteps.png b/experiments/india/003_wind_plevels/MAEvstimesteps.png new file mode 100644 index 0000000000000000000000000000000000000000..d8d7583033df5f4304a613929f0fbef774543c0f --- /dev/null +++ b/experiments/india/003_wind_plevels/MAEvstimesteps.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3646fe682b4d13b2e00d68cf6d19dec9d00e6c56cc4d3995c3903920b35b8707 +size 219278 diff --git a/experiments/india/003_wind_plevels/p10.png b/experiments/india/003_wind_plevels/p10.png new file mode 100644 index 0000000000000000000000000000000000000000..184772526a22af2c27e7f269de3c1edbc1c32030 --- /dev/null +++ b/experiments/india/003_wind_plevels/p10.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cce6f27ce1bafc89e9b5cb75cc2dad7c1053bea931ea4f5dfa5a1ef404d1042b +size 149827 diff --git a/experiments/india/003_wind_plevels/p50.png b/experiments/india/003_wind_plevels/p50.png new file mode 100644 index 0000000000000000000000000000000000000000..4e4380e6e0f73275fcacd0f5834c563db108f37b --- /dev/null +++ b/experiments/india/003_wind_plevels/p50.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ceae23a3f91f6bc56cf688bdbcaf5172f1a54736e412c5f0e80d8c056f7d9754 +size 228694 diff --git a/experiments/india/003_wind_plevels/plevel.md b/experiments/india/003_wind_plevels/plevel.md new file mode 100644 index 0000000000000000000000000000000000000000..3e1fcfbc529c8495465d1d53d9d87b67938765cf --- /dev/null +++ b/experiments/india/003_wind_plevels/plevel.md @@ -0,0 +1,54 @@ +# Running WindNet for RUVNL for diferent Plevels + +https://wandb.ai/openclimatefix/india/runs/5llq8iw6 is the current production one +This has 7 plevels and a small patch size. + +## Experiments + +1. Only used plevel 50 (orange) +https://wandb.ai/openclimatefix/india/runs/ziudzweq/ + +2. Use plevels of [2, 10, 25, 50, 75, 90, 98]. This is what is already used. (green) +https://wandb.ai/openclimatefix/india/runs/xdlew7ib + +3. Use plevels of [1, 02, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80 (brown) +, 90, 98, 99] +https://wandb.ai/openclimatefix/india/runs/pcr2zsrc + + +## Training + +Each epoch took about ~4 hours, so the training runs took several days. + +TODO add number of samples + +## Results + +MAE results show that using the plevel of 50 only, gives better results +![](Mae.png "Mae") + +The p50 results are about the same +![](p50.png "p50") + +We can see that for p10 the results are not right, as they should converge to 0.1 +![](p10.png "p10") + +Interestingly the more plevels you have the better the results are for before 4 hours +but the less plevels you have the better the results for >= 8 hours. + +| Timestep | P50 only MAE % | 7 plevels MAE % | 15 plevel MAE % | 7 plevels small patch MAE % | +| --- | --- | --- | --- | --- | +| 0-0 minutes | 5.416 | 5.920 | 3.933 | 7.586 | +| 15-15 minutes | 5.458 | 5.809 | 4.003 | 8.021 | +| 30-45 minutes | 5.525 | 5.742 | 4.442 | 7.233 | +| 45-60 minutes | 5.595 | 5.698 | 4.772 | 7.187 | +| 60-120 minutes | 5.890 | 5.816 | 5.307 | 7.231 | +| 120-240 minutes | 6.423 | 6.080 | 6.275 | 7.287 | +| 240-360 minutes | 6.608 | 6.375 | 6.707 | 7.319 | +| 360-480 minutes | 6.728 | 6.638 | 6.904 | 7.285 | +| 480-720 minutes | 6.634 | 6.747 | 6.872 | 7.143 | +| 720-1440 minutes | 6.940 | 7.207 | 7.176 | 7.380 | +| 1440-2880 minutes | 7.446 | 7.507 | 7.735 | 7.904 | + + +![](MAEvstimesteps.png "MAEvstimesteps") diff --git a/experiments/india/004_n_training_samples/log-plot.py b/experiments/india/004_n_training_samples/log-plot.py new file mode 100644 index 0000000000000000000000000000000000000000..e876384a195c0a0e6023edb9c1f31cba3c061cf2 --- /dev/null +++ b/experiments/india/004_n_training_samples/log-plot.py @@ -0,0 +1,14 @@ +""" Small script to make MAE vs number of batches plot""" + +import pandas as df +import plotly.graph_objects as go + +data = [[100, 7.779], [300, 7.441], [1000, 7.181], [3000, 7.180], [6711, 7.151]] +df = df.DataFrame(data, columns=["n_samples", "MAE [%]"]) + +fig = go.Figure() +fig.add_trace(go.Scatter(x=df["n_samples"], y=df["MAE [%]"], mode="lines+markers")) +fig.update_layout(title="MAE % for N samples", xaxis_title="N Samples", yaxis_title="MAE %") +# change to log log +fig.update_xaxes(type="log") +fig.show(renderer="browser") diff --git a/experiments/india/004_n_training_samples/mae_samples.png b/experiments/india/004_n_training_samples/mae_samples.png new file mode 100644 index 0000000000000000000000000000000000000000..d2bbd907c3e7b21291b4099ceeb05cc95a87f3f0 Binary files /dev/null and b/experiments/india/004_n_training_samples/mae_samples.png differ diff --git a/experiments/india/004_n_training_samples/mae_step.png b/experiments/india/004_n_training_samples/mae_step.png new file mode 100644 index 0000000000000000000000000000000000000000..3bfb91469accc638c90a935ef0098ec66a82112c --- /dev/null +++ b/experiments/india/004_n_training_samples/mae_step.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a3180a382e4b2c1534524f92a633d488912475a1e8a4effb0b28caf44368834 +size 325416 diff --git a/experiments/india/004_n_training_samples/readme.md b/experiments/india/004_n_training_samples/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..0d2ce2ef6f6a592e0ff57acce4bd7ed0d49329a8 --- /dev/null +++ b/experiments/india/004_n_training_samples/readme.md @@ -0,0 +1,48 @@ +# N samples experiments + +Kicked off an experiment that uses N samples +This is done by adding `limit_train_batches` to the `trainer/default.yaml`. + +I checked that when limiting the batches, the same batches are shown to model for each epoch. + +## Experiments + +Original is 6711 batches + +- 100: 3p6scx2r +- 300: am46tno1 +- 1000: u04xlb6p +- 3000: p11lhreo + +## Results + +Overall + +| Experiment | MAE % | +|------------|-------| +| 100 | 7.779 | +| 300 | 7.441 | +| 1000 | 7.181 | +| 3000 | 7.180 | +| 6711 | 7.151 | + +Results by timestamps + + +| Timestep | 100 MAE % | 300 MAE % | 1000 MAE % | 3000 MAE % | 6711 MAE % | +| --- | --- | --- | --- | --- | --- | +| 0-0 minutes | 7.985 | 7.453 | 7.155 | 5.553 | 5.920 | +| 15-15 minutes | 7.953 | 7.055 | 6.923 | 5.453 | 5.809 | +| 30-45 minutes | 8.043 | 7.172 | 6.907 | 5.764 | 5.742 | +| 45-60 minutes | 7.850 | 7.070 | 6.790 | 5.815 | 5.698 | +| 60-120 minutes | 7.698 | 6.809 | 6.597 | 5.890 | 5.816 | +| 120-240 minutes | 7.355 | 6.629 | 6.495 | 6.221 | 6.080 | +| 240-360 minutes | 7.230 | 6.729 | 6.559 | 6.541 | 6.375 | +| 360-480 minutes | 7.415 | 6.997 | 6.770 | 6.855 | 6.638 | +| 480-720 minutes | 7.258 | 7.037 | 6.668 | 6.876 | 6.747 | +| 720-1440 minutes | 7.659 | 7.362 | 7.038 | 7.142 | 7.207 | +| 1440-2880 minutes | 8.027 | 7.745 | 7.518 | 7.535 | 7.507 | + +![](mae_step.png "mae_steps") + +![](mae_samples.png "mae_samples") diff --git a/experiments/india/005_extra_nwp_variables/mae_steps.png b/experiments/india/005_extra_nwp_variables/mae_steps.png new file mode 100644 index 0000000000000000000000000000000000000000..1ca8734a4e0dc25ca4cd7634be51a9033543772b --- /dev/null +++ b/experiments/india/005_extra_nwp_variables/mae_steps.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ef7f7af4dafe38aac5a5df6cc74acc606cb4f0a1a9fc78972b09d68dd7574ad +size 214884 diff --git a/experiments/india/005_extra_nwp_variables/mae_steps_grouped.png b/experiments/india/005_extra_nwp_variables/mae_steps_grouped.png new file mode 100644 index 0000000000000000000000000000000000000000..27a2f202225582392db3090358c201943978ceaf --- /dev/null +++ b/experiments/india/005_extra_nwp_variables/mae_steps_grouped.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:547d3aafbb1658602fe03ea1677589de4e208467756e9ce9cd1d8727f364dffa +size 133040 diff --git a/experiments/india/005_extra_nwp_variables/readmd.md b/experiments/india/005_extra_nwp_variables/readmd.md new file mode 100644 index 0000000000000000000000000000000000000000..7d40f1e28ddbfe2559912defae6cf8ad37340786 --- /dev/null +++ b/experiments/india/005_extra_nwp_variables/readmd.md @@ -0,0 +1,55 @@ +# Adding extra nwp variables + +I wanted to run Windnet but testing some new nwp variables from ecmwf + +General conclusion, although more experiments could be done. +The current nwp variables are about right. +If you add lots it makes it worse. +If you take some away, it makes it worse. + +## Bugs + +Ran into a problem where found that some xamples have +`d.__getitem__('nwp-ecmwf__init_time_utc').values` had size 50, where it should be just one values. I removed these examples. This might + +## Experiments + +The number of samples were 8000 when training. + +### 15 variablles +Run windnet with `'hcc', 'lcc', 'mcc', 'prate', 'sde', 'sr', 't2m', 'tcc', 'u10', + 'v10', 'u100', 'v100', 'u200', 'v200', 'dlwrf', 'dswrf'`. + +The experiment on wandb is [here](https://wandb.ai/openclimatefix/india/runs/k91rdffo) + +### 7 variables +Run windnet with the original 7 variables. +`t2m, u10, u100, u200, v10, v100, v200 ` + +The experiment on wandb is [here](https://wandb.ai/openclimatefix/india/runs/miszfep5) + +### 3 variables +Run windnet with only `t, u10, v100` + +The experiment on wandb is [here](https://wandb.ai/openclimatefix/india/runs/22v3a39g) + +## Results + +| Timestep | 15 MAE % | 7 MAE % | 3 MAE % | +| --- | --- | --- | --- | +| 0-0 minutes | 7.450 | 6.623 | 7.529 | +| 15-15 minutes | 7.348 | 6.441 | 7.408 | +| 30-45 minutes | 7.242 | 6.544 | 7.294 | +| 45-60 minutes | 7.134 | 6.567 | 7.185 | +| 60-120 minutes | 7.058 | 6.295 | 7.009 | +| 120-240 minutes | 6.965 | 6.290 | 6.800 | +| 240-360 minutes | 6.807 | 6.374 | 6.580 | +| 360-480 minutes | 6.749 | 6.482 | 6.548 | +| 480-720 minutes | 6.892 | 6.686 | 6.685 | +| 720-1440 minutes | 7.020 | 6.756 | 6.780 | +| 1440-2880 minutes | 7.445 | 7.095 | 7.214 | + +![](mae_steps_grouped.png "mae_steps") + +The raw data is here +![](mae_steps.png "mae_steps") diff --git a/experiments/india/006_da_only/bad.png b/experiments/india/006_da_only/bad.png new file mode 100644 index 0000000000000000000000000000000000000000..c547d17d206cceed224391a03165ece5cf849b82 --- /dev/null +++ b/experiments/india/006_da_only/bad.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37cbbf51e7fa7dceb8b2074419267b4bde8186ddcd40b4a49c085735fdf72e43 +size 357736 diff --git a/experiments/india/006_da_only/da_only.md b/experiments/india/006_da_only/da_only.md new file mode 100644 index 0000000000000000000000000000000000000000..ec25244a095e9975a37e9adcf957cb82c95b18e0 --- /dev/null +++ b/experiments/india/006_da_only/da_only.md @@ -0,0 +1,37 @@ +## DA forecasts only + +The idea was to create a forecast for DA (day-ahead) only for Windnet. +We hope this would bring down the DA MAE values. + +We do this by not forecasting the first X hours. + +Unfortunately, it doesnt not look like ignore X hours, make the DA forecast better. + +## Experiments + +1. Baseline - [here](https://wandb.ai/openclimatefix/india/runs/miszfep5) +2. Ignore first 6 hours - [here](https://wandb.ai/openclimatefix/india/runs/uosk0qug) +3. Ignore first 12 hours - [here](https://wandb.ai/openclimatefix/india/runs/s9cnn4ei) + +## Results + +| Timestep | all MAE % | 6 MAE % | 12 MAE % | +| --- | --- |---------|---------| +| 0-0 minutes | nan | nan | nan | +| 15-15 minutes | nan | nan | nan | +| 30-45 minutes | 0.065 | nan | nan | +| 45-60 minutes | 0.066 | nan | nan | +| 60-120 minutes | 0.063 | nan | nan | +| 120-240 minutes | 0.063 | nan | nan | +| 240-360 minutes | 0.064 | nan | nan | +| 360-480 minutes | 0.065 | 0.068 | nan | +| 480-720 minutes | 0.067 | 0.065 | nan | +| 720-1440 minutes | 0.068 | 0.065 | 0.065 | +| 1440-2880 minutes | 0.071 | 0.071 | 0.071 | + +![](mae_steps.png "mae_steps") + +Here's two examples from the 6 hour ignore model, one that forecated it well, one that didnt + +![](bad.png "bad") +![](good.png "good") diff --git a/experiments/india/006_da_only/good.png b/experiments/india/006_da_only/good.png new file mode 100644 index 0000000000000000000000000000000000000000..d075508d742e969598532e2123d2ade1126677c8 --- /dev/null +++ b/experiments/india/006_da_only/good.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f4b6a11ac1560dbea1214ce381602b9eab7334a74110052dda072f0f53c3de8 +size 423727 diff --git a/experiments/india/006_da_only/mae_steps.png b/experiments/india/006_da_only/mae_steps.png new file mode 100644 index 0000000000000000000000000000000000000000..36432e743230e02b213d0cb90f376d741c5d7b23 --- /dev/null +++ b/experiments/india/006_da_only/mae_steps.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ca49fbc24530c3d75d0ec5cd2ba6345082c1747a600143afc40faf7bade0cd6 +size 121876 diff --git a/experiments/india/007_different_seeds/mae_all_steps.png b/experiments/india/007_different_seeds/mae_all_steps.png new file mode 100644 index 0000000000000000000000000000000000000000..5d0738d6782bb56013532f3d3f5a499de5f3b2fe --- /dev/null +++ b/experiments/india/007_different_seeds/mae_all_steps.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b06eaa2f75d645185bea5b874d6020bae3bccd7de25ec519cf348cde511f27c6 +size 203335 diff --git a/experiments/india/007_different_seeds/mae_steps.png b/experiments/india/007_different_seeds/mae_steps.png new file mode 100644 index 0000000000000000000000000000000000000000..ac65e39e1ec881ae462350509d621731ac3a1ed3 --- /dev/null +++ b/experiments/india/007_different_seeds/mae_steps.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3adfaa5394e9f45c684812e47e385c25d1796a6c772d04f4e7a3cbcbeffafda3 +size 130071 diff --git a/experiments/india/007_different_seeds/readme.md b/experiments/india/007_different_seeds/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..7c3b77c68ce5a2214bc6df87b796cd7488ab042c --- /dev/null +++ b/experiments/india/007_different_seeds/readme.md @@ -0,0 +1,33 @@ +# Training models with different seeds + +Want to see the effect or training a model with different seeds. + +We can see that the results for different seeds can vary by 0.5%, +and some models being better at different time horizons than others + +## Experiments +- seed 1 - [miszfep5](https://wandb.ai/openclimatefix/india/runs/miszfep5) +- seed 2 - [cxshv2q4](https://wandb.ai/openclimatefix/india/runs/cxshv2q4) +- seed 3 - [m46wdrr7](https://wandb.ai/openclimatefix/india/runs/m46wdrr7) + +These were trained with 1000 batches, and 300 batches for validation + +## Results + +| Timestep | s1 MAE % | s2 MAE % | s3 MAE % | +| --- | --- | --- | --- | +| 0-0 minutes | 0.066 | 0.061 | 0.066 | +| 15-15 minutes | 0.064 | 0.058 | 0.064 | +| 30-45 minutes | 0.065 | 0.060 | 0.063 | +| 45-60 minutes | 0.066 | 0.060 | 0.063 | +| 60-120 minutes | 0.063 | 0.060 | 0.063 | +| 120-240 minutes | 0.063 | 0.063 | 0.065 | +| 240-360 minutes | 0.064 | 0.066 | 0.065 | +| 360-480 minutes | 0.065 | 0.066 | 0.066 | +| 480-720 minutes | 0.067 | 0.066 | 0.065 | +| 720-1440 minutes | 0.068 | 0.068 | 0.066 | +| 1440-2880 minutes | 0.071 | 0.072 | 0.071 | + +![](mae_steps.png "mae_steps") + +![](mae_all_steps.png "mae_steps") diff --git a/experiments/india/008_coarse4/mae_step.png b/experiments/india/008_coarse4/mae_step.png new file mode 100644 index 0000000000000000000000000000000000000000..d84adfad4511672d2633ca318d40bba292d4aac7 --- /dev/null +++ b/experiments/india/008_coarse4/mae_step.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52e85df6c2ed7865e0f6f412ae47e7e5f0a1b12550b72702ebe7e166dec53636 +size 178598 diff --git a/experiments/india/008_coarse4/mae_step_smooth.png b/experiments/india/008_coarse4/mae_step_smooth.png new file mode 100644 index 0000000000000000000000000000000000000000..e06c4e0e3b551548e062a24404aee8229401f93b --- /dev/null +++ b/experiments/india/008_coarse4/mae_step_smooth.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:38e2772ac0c28684a10f8fc98fc55afc0401a403d025ac6e3d97a9d328ab8624 +size 140153 diff --git a/experiments/india/008_coarse4/readme.md b/experiments/india/008_coarse4/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..6ba4f7fed25f5bae206355bf8caf5441d45886cc --- /dev/null +++ b/experiments/india/008_coarse4/readme.md @@ -0,0 +1,77 @@ +# Coarser data and more examples + +We downsampled the ECMWF data from 0.05 to 0.2. +In previous experiments we used a 0.1 resolution, as this is the same as the live ECMWF data. + +By reducing the resolution we can increase the number of samples we have to train on. +We used 41408 number of samples to train, and 10352 samples to validate +This is approximately 5 times more samples than the previous experiments. + +## Experiments + + +### b8_s1 +Batche size 8, with 0.2 degree NWP data. +https://wandb.ai/openclimatefix/india/runs/w85hftb6 + + +### b8_s2 +Batch size 8, different seed, with 0.2 degree NWP data. +https://wandb.ai/openclimatefix/india/runs/k4x1tunj + +### b32_s3 +Batch size 32, with 0.2 degree NWP data. Also kept the learning rate a bit higher +https://wandb.ai/openclimatefix/india/runs/ktale7pa + +### epochs +We set the early stopping epochs from 10 to 15. This should mean model will train a bit more +https://wandb.ai/openclimatefix/india/runs/8hfc83uv + +### small model +We made the model about 50% of the size by reduce the reducing the channels in the NWP encoder fomr 256 to 64 and reducing the hidden features in the output network fomr 1024 to 256 +https://wandb.ai/openclimatefix/india/runs/sk5ek3pk + + +### early stopping on MAE/val +Changing from quantile_loss to MAE/val to stop early on. This should mean the model does more training epochs, and the results we are interested int. +https://wandb.ai/openclimatefix/india/runs/a5nkkzj6 + + +### old +Old experiment with 0.1 degree NWP data. +https://wandb.ai/openclimatefix/india/runs/m46wdrr7. +Note the validation batches are different that the experiments above. + +Interesting the GPU memory did not increase much better experiments 2 and 3. +Need to check that 32 batches were being passed through. + +## Results + +The coarsening data does seem to improve the experiments results in the first 10 hours of the forecast. +DA forecast looks very similar. Note the 0 hour forecast has a large amount of variation. + + + +Still spike results in the individual runs. + +| Timestep | b8_s1 MAE % | b8_s2 MAE % | b32_s3 MAE % | epochs MAE % | small MAE % | mae/val MAE % | old MAE % | +| --- | --- | --- | --- | --- | --- | --- | --- | +| 0-0 minutes | 0.052 | 0.047 | 0.027 | 0.030 | 0.041 | 0.041 | 0.066 | +| 15-15 minutes | 0.052 | 0.049 | 0.031 | 0.033 | 0.041 | 0.041 | 0.064 | +| 30-45 minutes | 0.052 | 0.051 | 0.037 | 0.039 | 0.043 | 0.043 | 0.063 | +| 45-60 minutes | 0.053 | 0.052 | 0.040 | 0.043 | 0.044 | 0.044 | 0.063 | +| 60-120 minutes | 0.056 | 0.054 | 0.048 | 0.052 | 0.048 | 0.048 | 0.063 | +| 120-240 minutes | 0.061 | 0.060 | 0.060 | 0.064 | 0.057 | 0.057 | 0.065 | +| 240-360 minutes | 0.061 | 0.062 | 0.063 | 0.065 | 0.061 | 0.061 | 0.065 | +| 360-480 minutes | 0.062 | 0.062 | 0.062 | 0.063 | 0.063 | 0.063 | 0.066 | +| 480-720 minutes | 0.063 | 0.063 | 0.062 | 0.064 | 0.064 | 0.064 | 0.065 | +| 720-1440 minutes | 0.065 | 0.066 | 0.065 | 0.067 | 0.066 | 0.066 | 0.066 | +| 1440-2880 minutes | 0.069 | 0.070 | 0.071 | 0.071 | 0.071 | 0.071 | 0.071 | + + +![](mae_step.png "mae_steps") + +![](mae_step_smooth.png "mae_steps") + +I think its worth noting the model traing MAE is around `3`% and the validation MAE is about `7`%, so there is good reason to believe that the model is over fit to the trianing set. +It would be good to plot some of the trainin examples, to see if they are less spiky. diff --git a/experiments/mae_analysis.py b/experiments/mae_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..66f090249efb908f350102c25f5d03f767422dd3 --- /dev/null +++ b/experiments/mae_analysis.py @@ -0,0 +1,152 @@ +""" +Script to generate analysis of MAE values for multiple model forecasts + +Does this for 48 hour horizon forecasts with 15 minute granularity + +""" + +import argparse + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import wandb + +matplotlib.rcParams["axes.prop_cycle"] = matplotlib.cycler( + color=[ + "FFD053", # yellow + "7BCDF3", # blue + "63BCAF", # teal + "086788", # dark blue + "FF9736", # dark orange + "E4E4E4", # grey + "14120E", # black + "FFAC5F", # orange + "4C9A8E", # dark teal + ] +) + + +def main(project: str, runs: list[str], run_names: list[str]) -> None: + """ + Compare MAE values for multiple model forecasts for 48 hour horizon with 15 minute granularity + + Args: + project: name of W&B project + runs: W&B ids of runs + run_names: user specified names for runs + + """ + api = wandb.Api() + dfs = [] + epoch_num = [] + for run in runs: + run = api.run(f"openclimatefix/{project}/{run}") + + df = run.history(samples=run.lastHistoryStep + 1) + # Get the columns that are in the format 'MAE_horizon/step_/val` + mae_cols = [col for col in df.columns if "MAE_horizon/step_" in col and "val" in col] + # Sort them + mae_cols.sort() + df = df[mae_cols] + # Get last non-NaN value + # Drop all rows with all NaNs + df = df.dropna(how="all") + # Select the last row + # Get average across entire row, and get the IDX for the one with the smallest values + min_row_mean = np.inf + for idx, (row_idx, row) in enumerate(df.iterrows()): + if row.mean() < min_row_mean: + min_row_mean = row.mean() + min_row_idx = idx + df = df.iloc[min_row_idx] + # Calculate the timedelta for each group + # Get the step from the column name + column_timesteps = [int(col.split("_")[-1].split("/")[0]) * 15 for col in mae_cols] + dfs.append(df) + epoch_num.append(min_row_idx) + # Get the timedelta for each group + groupings = [ + [0, 0], + [15, 15], + [30, 45], + [45, 60], + [60, 120], + [120, 240], + [240, 360], + [360, 480], + [480, 720], + [720, 1440], + [1440, 2880], + ] + + groups_df = [] + grouping_starts = [grouping[0] for grouping in groupings] + header = "| Timestep |" + separator = "| --- |" + for run_name in run_names: + header += f" {run_name} MAE % |" + separator += " --- |" + print(header) + print(separator) + for grouping in groupings: + group_string = f"| {grouping[0]}-{grouping[1]} minutes |" + # Select indicies from column_timesteps that are within the grouping, inclusive + group_idx = [ + idx + for idx, timestep in enumerate(column_timesteps) + if timestep >= grouping[0] and timestep <= grouping[1] + ] + data_one_group = [] + for df in dfs: + mean_row = df.iloc[group_idx].mean() + group_string += f" {mean_row:0.3f} |" + data_one_group.append(mean_row) + print(group_string) + + groups_df.append(data_one_group) + + groups_df = pd.DataFrame(groups_df, columns=run_names, index=grouping_starts) + + for idx, df in enumerate(dfs): + print(f"{run_names[idx]}: {df.mean()*100:0.3f}") + + # Plot the error per timestep + plt.figure() + for idx, df in enumerate(dfs): + plt.plot( + column_timesteps, df, label=f"{run_names[idx]}, epoch: {epoch_num[idx]}", linestyle="-" + ) + plt.legend() + plt.xlabel("Timestep (minutes)") + plt.ylabel("MAE %") + plt.title("MAE % for each timestep") + plt.savefig("mae_per_timestep.png") + plt.show() + + # Plot the error per grouped timestep + plt.figure() + for idx, run_name in enumerate(run_names): + plt.plot( + groups_df[run_name], + label=f"{run_name}, epoch: {epoch_num[idx]}", + marker="o", + linestyle="-", + ) + plt.legend() + plt.xlabel("Timestep (minutes)") + plt.ylabel("MAE %") + plt.title("MAE % for each grouped timestep") + plt.savefig("mae_per_grouped_timestep.png") + plt.show() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--project", type=str, default="") + # Add arguments that is a list of strings + parser.add_argument("--list_of_runs", nargs="+") + parser.add_argument("--run_names", nargs="+") + args = parser.parse_args() + main(args.project, args.list_of_runs, args.run_names) diff --git a/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNEt_national_XG_comparison.png b/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNEt_national_XG_comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..17d65efe31928be5d6fdb8a336a6f0af1903ecbd --- /dev/null +++ b/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNEt_national_XG_comparison.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eab8cf00defbfb39a9d5b9cea319f1b78db8b05e2baec7ef80351dc37eb041c4 +size 168754 diff --git a/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNet_day_ahead.md b/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNet_day_ahead.md new file mode 100644 index 0000000000000000000000000000000000000000..5621399417f155facb0db4b816ce326a2db5e01c --- /dev/null +++ b/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNet_day_ahead.md @@ -0,0 +1,22 @@ +PVNet day ahead was retrained to produce a 36 hour forecast, it was given its [previous configuration](https://huggingface.co/openclimatefix/pvnet_uk_region/tree/main) and data except for being given ECMWF NWP data with a longer forecast horizon (max 85 hours but 37 hours given to the model). Longer horizon UKV NWP data was not available at time of training and will be a further addition in the future. + +**Results** \ +[The training run](https://wandb.ai/openclimatefix/pvnet_day_ahead_36_hours/runs/m4d3wlft/overview) had 3.15% normalised mean absolute error (NMAE) on validation data (100,000 samples from May 2022 to May 2023), [previous training of PVNet day ahead](https://wandb.ai/openclimatefix/pvnet2.1/runs/2ghzwbxg/overview?) had similar results of 3.19% NMAE. + + +![](PVNets_comparison.png "PVNets comparison") + +When comparing the two versions of PVNet day ahead (the new version in green) by forecast accuracy at each step on the validation dataset samples we see some small differences in the model up to 33 hours, such as first the first few steps and between steps 5 and 10, which could be explained by differences in samples seen and evaluated on between the two versions. + +However the larger difference is an improvement toward the end of the forecast horizon, from 33 hours onwards which is likely due to ECMWF data now being available for this period, when previously no NWP data was given past 33 hours due to the NWP forecast horizon of previous data and factoring in NWP initialization times and production delays. + +UKV NWP data used in the model is currently up to 30 hours, we would expect a further reduction in error from 30+ hours when training with longer horizon UKV data which would cover up to 36 hours. + + +A very rough comparison is also plotted between these two PVNet model versions and the National XG model which is currently used for day ahead predictions in production. + +![](PVNEt_national_XG_comparison.png "PVNets national XG comparison") + + + +This comparison is rough and should not be seen as a fair comparison as the national XG numbers are just an estimate derived from backtest data on different time periods. However, it can show roughly what relative improvement could be achieved from replacing the National XG Day ahead model with a PVNet Day Ahead model. diff --git a/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNets_comparison.png b/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNets_comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..1af7661f086ede8c7961308470354e10c0cdabee --- /dev/null +++ b/experiments/uk/011 - Extending forecast to 36 hours (updated ECMWF data)/PVNets_comparison.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e604d9b403293bbac688dc9c786cb4f0c70e1a9c6b78188a1e4f228ad0ae4b1b +size 160003 diff --git a/model_config.yaml b/model_config.yaml index ed540797672748dbf9296d5729449427e6a38ce0..3a09fe951c7175e40c688df1b4c26bf2a3667f00 100644 --- a/model_config.yaml +++ b/model_config.yaml @@ -15,7 +15,7 @@ nwp_encoders_dict: out_features: 64 number_of_conv3d_layers: 4 conv3d_channels: 32 - image_size_pixels: 36 + image_size_pixels: 30 sat_encoder: _target_: pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet _partial_: true @@ -23,7 +23,7 @@ sat_encoder: out_features: 256 number_of_conv3d_layers: 6 conv3d_channels: 32 - image_size_pixels: 144 + image_size_pixels: 100 add_image_embedding_channel: false pv_encoder: _target_: pvnet.models.multimodal.site_encoders.encoders.SingleAttentionNetwork @@ -44,7 +44,8 @@ output_network: embedding_dim: 16 include_sun: true include_gsp_yield_history: false -forecast_minutes: 2880 +adapt_batches: true +forecast_minutes: 2160 history_minutes: 2880 interval_minutes: 15 min_sat_delay_minutes: 60 @@ -54,7 +55,7 @@ pv_interval_minutes: 15 nwp_history_minutes: ecmwf: 120 nwp_forecast_minutes: - ecmwf: 2880 + ecmwf: 2160 nwp_interval_minutes: ecmwf: 60 optimizer: diff --git a/pvnet/__init__.py b/pvnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b09ec550ef004e9dc47181657a41789947d309 --- /dev/null +++ b/pvnet/__init__.py @@ -0,0 +1,2 @@ +"""PVNet""" +__version__ = "4.1.18" diff --git a/pvnet/callbacks.py b/pvnet/callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..0455d7741dac5923be52d144d458884e9c1945ca --- /dev/null +++ b/pvnet/callbacks.py @@ -0,0 +1,129 @@ +"""Custom callbacks +""" +from lightning.pytorch import Trainer +from lightning.pytorch.callbacks import BaseFinetuning, EarlyStopping, LearningRateFinder +from lightning.pytorch.trainer.states import TrainerFn + + +class PhaseEarlyStopping(EarlyStopping): + """Monitor a validation metric and stop training when it stops improving. + + Only functions in a specific phase of training. + """ + + training_phase = None + + def switch_phase(self, phase: str): + """Switch phase of callback""" + if phase == self.training_phase: + self.activate() + else: + self.deactivate() + + def deactivate(self): + """Deactivate callback""" + self.active = False + + def activate(self): + """Activate callback""" + self.active = True + + def _should_skip_check(self, trainer: Trainer) -> bool: + return ( + (trainer.state.fn != TrainerFn.FITTING) or (trainer.sanity_checking) or not self.active + ) + + +class PretrainEarlyStopping(EarlyStopping): + """Monitor a validation metric and stop training when it stops improving. + + Only functions in the 'pretrain' phase of training. + """ + + training_phase = "pretrain" + + +class MainEarlyStopping(EarlyStopping): + """Monitor a validation metric and stop training when it stops improving. + + Only functions in the 'main' phase of training. + """ + + training_phase = "main" + + +class PretrainFreeze(BaseFinetuning): + """Freeze the satellite and NWP encoders during pretraining""" + + training_phase = "pretrain" + + def __init__(self): + """Freeze the satellite and NWP encoders during pretraining""" + super().__init__() + + def freeze_before_training(self, pl_module): + """Freeze satellite and NWP encoders before training start""" + # freeze any module you want + modules = [] + if pl_module.include_sat: + modules += [pl_module.sat_encoder] + if pl_module.include_nwp: + modules += [pl_module.nwp_encoder] + self.freeze(modules) + + def finetune_function(self, pl_module, current_epoch, optimizer): + """Unfreeze satellite and NWP encoders""" + if not self.active: + modules = [] + if pl_module.include_sat: + modules += [pl_module.sat_encoder] + if pl_module.include_nwp: + modules += [pl_module.nwp_encoder] + self.unfreeze_and_add_param_group( + modules=modules, + optimizer=optimizer, + train_bn=True, + ) + + def switch_phase(self, phase: str): + """Switch phase of callback""" + if phase == self.training_phase: + self.activate() + else: + self.deactivate() + + def deactivate(self): + """Deactivate callback""" + self.active = False + + def activate(self): + """Activate callback""" + self.active = True + + +class PhasedLearningRateFinder(LearningRateFinder): + """Finds a learning rate at the start of each phase of learning""" + + active = True + + def on_fit_start(self, *args, **kwargs): + """Do nothing""" + return + + def on_train_epoch_start(self, trainer, pl_module): + """Run learning rate finder on epoch start and then deactivate""" + if self.active: + self.lr_find(trainer, pl_module) + self.deactivate() + + def switch_phase(self, phase: str): + """Switch training phase""" + self.activate() + + def deactivate(self): + """Deactivate callback""" + self.active = False + + def activate(self): + """Activate callback""" + self.active = True diff --git a/pvnet/data/__init__.py b/pvnet/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dac616d39154977c7548cef70224faafcf31e497 --- /dev/null +++ b/pvnet/data/__init__.py @@ -0,0 +1,3 @@ +"""Data parts""" +from .site_datamodule import SiteDataModule +from .uk_regional_datamodule import DataModule diff --git a/pvnet/data/base_datamodule.py b/pvnet/data/base_datamodule.py new file mode 100644 index 0000000000000000000000000000000000000000..eacaac17070ecc170774d6b32a6789488d41a459 --- /dev/null +++ b/pvnet/data/base_datamodule.py @@ -0,0 +1,118 @@ +""" Data module for pytorch lightning """ + +from glob import glob + +from lightning.pytorch import LightningDataModule +from ocf_data_sampler.numpy_sample.collate import stack_np_samples_into_batch +from ocf_data_sampler.torch_datasets.sample.base import ( + NumpyBatch, + SampleBase, + TensorBatch, + batch_to_tensor, +) +from torch.utils.data import DataLoader, Dataset + + +def collate_fn(samples: list[NumpyBatch]) -> TensorBatch: + """Convert a list of NumpySample samples to a tensor batch""" + return batch_to_tensor(stack_np_samples_into_batch(samples)) + + +class PremadeSamplesDataset(Dataset): + """Dataset to load samples from + + Args: + sample_dir: Path to the directory of pre-saved samples. + sample_class: sample class type to use for save/load/to_numpy + """ + + def __init__(self, sample_dir: str, sample_class: SampleBase): + """Initialise PremadeSamplesDataset""" + self.sample_paths = glob(f"{sample_dir}/*") + self.sample_class = sample_class + + def __len__(self): + return len(self.sample_paths) + + def __getitem__(self, idx): + sample = self.sample_class.load(self.sample_paths[idx]) + return sample.to_numpy() + + +class BaseDataModule(LightningDataModule): + """Base Datamodule for training pvnet and using pvnet pipeline in ocf-data-sampler.""" + + def __init__( + self, + configuration: str | None = None, + sample_dir: str | None = None, + batch_size: int = 16, + num_workers: int = 0, + prefetch_factor: int | None = None, + train_period: list[str | None] = [None, None], + val_period: list[str | None] = [None, None], + ): + """Base Datamodule for training pvnet architecture. + + Can also be used with pre-made batches if `sample_dir` is set. + + Args: + configuration: Path to ocf-data-sampler configuration file. + sample_dir: Path to the directory of pre-saved samples. Cannot be used together with + `configuration` or '[train/val]_period'. + batch_size: Batch size. + num_workers: Number of workers to use in multiprocess batch loading. + prefetch_factor: Number of data will be prefetched at the end of each worker process. + train_period: Date range filter for train dataloader. + val_period: Date range filter for val dataloader. + + """ + super().__init__() + + if not ((sample_dir is not None) ^ (configuration is not None)): + raise ValueError("Exactly one of `sample_dir` or `configuration` must be set.") + + if sample_dir is not None: + if any([period != [None, None] for period in [train_period, val_period]]): + raise ValueError("Cannot set `(train/val)_period` with presaved samples") + + self.configuration = configuration + self.sample_dir = sample_dir + self.train_period = train_period + self.val_period = val_period + + self._common_dataloader_kwargs = dict( + batch_size=batch_size, + sampler=None, + batch_sampler=None, + num_workers=num_workers, + collate_fn=collate_fn, + pin_memory=False, + drop_last=False, + timeout=0, + worker_init_fn=None, + prefetch_factor=prefetch_factor, + persistent_workers=False, + ) + + def _get_streamed_samples_dataset(self, start_time, end_time) -> Dataset: + raise NotImplementedError + + def _get_premade_samples_dataset(self, subdir) -> Dataset: + raise NotImplementedError + + def train_dataloader(self) -> DataLoader: + """Construct train dataloader""" + if self.sample_dir is not None: + dataset = self._get_premade_samples_dataset("train") + else: + dataset = self._get_streamed_samples_dataset(*self.train_period) + return DataLoader(dataset, shuffle=True, **self._common_dataloader_kwargs) + + def val_dataloader(self) -> DataLoader: + """Construct val dataloader""" + if self.sample_dir is not None: + dataset = self._get_premade_samples_dataset("val") + else: + dataset = self._get_streamed_samples_dataset(*self.val_period) + return DataLoader(dataset, shuffle=False, **self._common_dataloader_kwargs) diff --git a/pvnet/data/site_datamodule.py b/pvnet/data/site_datamodule.py new file mode 100644 index 0000000000000000000000000000000000000000..6301ef980fefb7d66979ef24155ff809178a3f07 --- /dev/null +++ b/pvnet/data/site_datamodule.py @@ -0,0 +1,53 @@ +""" Data module for pytorch lightning """ + +from ocf_data_sampler.torch_datasets.datasets.site import SitesDataset +from ocf_data_sampler.torch_datasets.sample.site import SiteSample +from torch.utils.data import Dataset + +from pvnet.data.base_datamodule import BaseDataModule, PremadeSamplesDataset + + +class SiteDataModule(BaseDataModule): + """Datamodule for training pvnet and using pvnet pipeline in `ocf-data-sampler`.""" + + def __init__( + self, + configuration: str | None = None, + sample_dir: str | None = None, + batch_size: int = 16, + num_workers: int = 0, + prefetch_factor: int | None = None, + train_period: list[str | None] = [None, None], + val_period: list[str | None] = [None, None], + ): + """Datamodule for training pvnet architecture. + + Can also be used with pre-made batches if `sample_dir` is set. + + Args: + configuration: Path to configuration file. + sample_dir: Path to the directory of pre-saved samples. Cannot be used together with + `configuration` or '[train/val]_period'. + batch_size: Batch size. + num_workers: Number of workers to use in multiprocess batch loading. + prefetch_factor: Number of data will be prefetched at the end of each worker process. + train_period: Date range filter for train dataloader. + val_period: Date range filter for val dataloader. + + """ + super().__init__( + configuration=configuration, + sample_dir=sample_dir, + batch_size=batch_size, + num_workers=num_workers, + prefetch_factor=prefetch_factor, + train_period=train_period, + val_period=val_period, + ) + + def _get_streamed_samples_dataset(self, start_time, end_time) -> Dataset: + return SitesDataset(self.configuration, start_time=start_time, end_time=end_time) + + def _get_premade_samples_dataset(self, subdir) -> Dataset: + split_dir = f"{self.sample_dir}/{subdir}" + return PremadeSamplesDataset(split_dir, SiteSample) diff --git a/pvnet/data/uk_regional_datamodule.py b/pvnet/data/uk_regional_datamodule.py new file mode 100644 index 0000000000000000000000000000000000000000..889c818e29fa29e184ed23bfd5e631ac814e05b9 --- /dev/null +++ b/pvnet/data/uk_regional_datamodule.py @@ -0,0 +1,54 @@ +""" Data module for pytorch lightning """ + +from ocf_data_sampler.torch_datasets.datasets.pvnet_uk import PVNetUKRegionalDataset +from ocf_data_sampler.torch_datasets.sample.uk_regional import UKRegionalSample +from torch.utils.data import Dataset + +from pvnet.data.base_datamodule import BaseDataModule, PremadeSamplesDataset + + +class DataModule(BaseDataModule): + """Datamodule for training pvnet and using pvnet pipeline in `ocf-data-sampler`.""" + + def __init__( + self, + configuration: str | None = None, + sample_dir: str | None = None, + batch_size: int = 16, + num_workers: int = 0, + prefetch_factor: int | None = None, + train_period: list[str | None] = [None, None], + val_period: list[str | None] = [None, None], + ): + """Datamodule for training pvnet architecture. + + Can also be used with pre-made batches if `sample_dir` is set. + + Args: + configuration: Path to configuration file. + sample_dir: Path to the directory of pre-saved samples. Cannot be used together with + `configuration` or '[train/val]_period'. + batch_size: Batch size. + num_workers: Number of workers to use in multiprocess batch loading. + prefetch_factor: Number of data will be prefetched at the end of each worker process. + train_period: Date range filter for train dataloader. + val_period: Date range filter for val dataloader. + + """ + super().__init__( + configuration=configuration, + sample_dir=sample_dir, + batch_size=batch_size, + num_workers=num_workers, + prefetch_factor=prefetch_factor, + train_period=train_period, + val_period=val_period, + ) + + def _get_streamed_samples_dataset(self, start_time, end_time) -> Dataset: + return PVNetUKRegionalDataset(self.configuration, start_time=start_time, end_time=end_time) + + def _get_premade_samples_dataset(self, subdir) -> Dataset: + split_dir = f"{self.sample_dir}/{subdir}" + # Returns a dict of np arrays + return PremadeSamplesDataset(split_dir, UKRegionalSample) diff --git a/pvnet/load_model.py b/pvnet/load_model.py new file mode 100644 index 0000000000000000000000000000000000000000..a7a188df3c97c711f9f85950633797622fd7500b --- /dev/null +++ b/pvnet/load_model.py @@ -0,0 +1,71 @@ +""" Load a model from its checkpoint directory """ +import glob +import os + +import hydra +import torch +from pyaml_env import parse_config + +from pvnet.models.ensemble import Ensemble +from pvnet.models.multimodal.unimodal_teacher import Model as UMTModel + + +def get_model_from_checkpoints( + checkpoint_dir_paths: list[str], + val_best: bool = True, +): + """Load a model from its checkpoint directory""" + is_ensemble = len(checkpoint_dir_paths) > 1 + + model_configs = [] + models = [] + data_configs = [] + + for path in checkpoint_dir_paths: + # Load the model + model_config = parse_config(f"{path}/model_config.yaml") + + model = hydra.utils.instantiate(model_config) + + if val_best: + # Only one epoch (best) saved per model + files = glob.glob(f"{path}/epoch*.ckpt") + if len(files) != 1: + raise ValueError( + f"Found {len(files)} checkpoints @ {path}/epoch*.ckpt. Expected one." + ) + # TODO: Loading with weights_only=False is not recommended + checkpoint = torch.load(files[0], map_location="cpu", weights_only=False) + else: + checkpoint = torch.load(f"{path}/last.ckpt", map_location="cpu", weights_only=False) + + model.load_state_dict(state_dict=checkpoint["state_dict"]) + + if isinstance(model, UMTModel): + model, model_config = model.convert_to_multimodal_model(model_config) + + # Check for data config + data_config = f"{path}/data_config.yaml" + + if os.path.isfile(data_config): + data_configs.append(data_config) + else: + data_configs.append(None) + + model_configs.append(model_config) + models.append(model) + + if is_ensemble: + model_config = { + "_target_": "pvnet.models.ensemble.Ensemble", + "model_list": model_configs, + } + model = Ensemble(model_list=models) + data_config = data_configs[0] + + else: + model_config = model_configs[0] + model = models[0] + data_config = data_configs[0] + + return model, model_config, data_config diff --git a/pvnet/models/__init__.py b/pvnet/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b0490dc4d2ddfe6db038a0f3c9d9b5c638559e46 --- /dev/null +++ b/pvnet/models/__init__.py @@ -0,0 +1 @@ +"""Models for PVNet""" diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py new file mode 100644 index 0000000000000000000000000000000000000000..6129833a89ef36e73b37d915c12dd2658e369b41 --- /dev/null +++ b/pvnet/models/base_model.py @@ -0,0 +1,973 @@ +"""Base model for all PVNet submodels""" +import copy +import logging +import os +import tempfile +import time +from pathlib import Path +from typing import Dict, Optional, Union + +import hydra +import lightning.pytorch as pl +import matplotlib.pyplot as plt +import pandas as pd +import pkg_resources +import torch +import torch.nn.functional as F +import wandb +import yaml +from huggingface_hub import ModelCard, ModelCardData, PyTorchModelHubMixin +from huggingface_hub.constants import PYTORCH_WEIGHTS_NAME +from huggingface_hub.file_download import hf_hub_download +from huggingface_hub.hf_api import HfApi +from ocf_data_sampler.torch_datasets.sample.base import copy_batch_to_device +from torchvision.transforms.functional import center_crop + +from pvnet.models.utils import ( + BatchAccumulator, + MetricAccumulator, + PredAccumulator, +) +from pvnet.optimizers import AbstractOptimizer +from pvnet.utils import plot_batch_forecasts + +DATA_CONFIG_NAME = "data_config.yaml" +MODEL_CONFIG_NAME = "model_config.yaml" + + +logger = logging.getLogger(__name__) + +activities = [torch.profiler.ProfilerActivity.CPU] +if torch.cuda.is_available(): + activities.append(torch.profiler.ProfilerActivity.CUDA) + + +def make_clean_data_config(input_path, output_path, placeholder="PLACEHOLDER"): + """Resave the data config and replace the filepaths with a placeholder. + + Args: + input_path: Path to input configuration file + output_path: Location to save the output configuration file + placeholder: String placeholder for data sources + """ + with open(input_path) as cfg: + config = yaml.load(cfg, Loader=yaml.FullLoader) + + config["general"]["description"] = "Config for training the saved PVNet model" + config["general"]["name"] = "PVNet current" + + for source in ["gsp", "satellite", "hrvsatellite"]: + if source in config["input_data"]: + # If not empty - i.e. if used + if config["input_data"][source]["zarr_path"] != "": + config["input_data"][source]["zarr_path"] = f"{placeholder}.zarr" + + if "nwp" in config["input_data"]: + for source in config["input_data"]["nwp"]: + if config["input_data"]["nwp"][source]["zarr_path"] != "": + config["input_data"]["nwp"][source]["zarr_path"] = f"{placeholder}.zarr" + + if "pv" in config["input_data"]: + for d in config["input_data"]["pv"]["pv_files_groups"]: + d["pv_filename"] = f"{placeholder}.netcdf" + d["pv_metadata_filename"] = f"{placeholder}.csv" + + if "sensor" in config["input_data"]: + # If not empty - i.e. if used + if config["input_data"][source][f"{source}_filename"] != "": + config["input_data"][source][f"{source}_filename"] = f"{placeholder}.nc" + + with open(output_path, "w") as outfile: + yaml.dump(config, outfile, default_flow_style=False) + + +def minimize_data_config(input_path, output_path, model): + """Strip out parts of the data config which aren't used by the model + + Args: + input_path: Path to input configuration file + output_path: Location to save the output configuration file + model: The PVNet model object + """ + with open(input_path) as cfg: + config = yaml.load(cfg, Loader=yaml.FullLoader) + + if "nwp" in config["input_data"]: + if not model.include_nwp: + del config["input_data"]["nwp"] + else: + for nwp_source in list(config["input_data"]["nwp"].keys()): + nwp_config = config["input_data"]["nwp"][nwp_source] + + if nwp_source not in model.nwp_encoders_dict: + # If not used, delete this source from the config + del config["input_data"]["nwp"][nwp_source] + else: + # Replace the image size + nwp_pixel_size = model.nwp_encoders_dict[nwp_source].image_size_pixels + nwp_config["image_size_pixels_height"] = nwp_pixel_size + nwp_config["image_size_pixels_width"] = nwp_pixel_size + + # Replace the interval_end_minutes minutes + nwp_config["interval_end_minutes"] = ( + nwp_config["interval_start_minutes"] + + (model.nwp_encoders_dict[nwp_source].sequence_length - 1) + * nwp_config["time_resolution_minutes"] + ) + + if "satellite" in config["input_data"]: + if not model.include_sat: + del config["input_data"]["satellite"] + else: + sat_config = config["input_data"]["satellite"] + + # Replace the image size + sat_pixel_size = model.sat_encoder.image_size_pixels + sat_config["image_size_pixels_height"] = sat_pixel_size + sat_config["image_size_pixels_width"] = sat_pixel_size + + # Replace the interval_end_minutes minutes + sat_config["interval_end_minutes"] = ( + sat_config["interval_start_minutes"] + + (model.sat_encoder.sequence_length - 1) + * sat_config["time_resolution_minutes"] + ) + + if "pv" in config["input_data"]: + if not model.include_pv: + del config["input_data"]["pv"] + + if "gsp" in config["input_data"]: + gsp_config = config["input_data"]["gsp"] + + # Replace the forecast minutes + gsp_config["interval_end_minutes"] = model.forecast_minutes + + if "solar_position" in config["input_data"]: + solar_config = config["input_data"]["solar_position"] + solar_config["interval_end_minutes"] = model.forecast_minutes + + with open(output_path, "w") as outfile: + yaml.dump(config, outfile, default_flow_style=False) + + +def download_hf_hub_with_retries( + repo_id, + filename, + revision, + cache_dir, + force_download, + proxies, + resume_download, + token, + local_files_only, + max_retries=5, + wait_time=10, +): + """ + Tries to download a file from HuggingFace up to max_retries times. + + Args: + repo_id (str): HuggingFace repo ID + filename (str): Name of the file to download + revision (str): Specific model revision + cache_dir (str): Cache directory + force_download (bool): Whether to force a new download + proxies (dict): Proxy settings + resume_download (bool): Resume interrupted downloads + token (str): HuggingFace auth token + local_files_only (bool): Use local files only + max_retries (int): Maximum number of retry attempts + wait_time (int): Wait time (in seconds) before retrying + + Returns: + str: The local file path of the downloaded file + """ + for attempt in range(1, max_retries + 1): + try: + return hf_hub_download( + repo_id=repo_id, + filename=filename, + revision=revision, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + token=token, + local_files_only=local_files_only, + ) + except Exception as e: + if attempt == max_retries: + raise Exception( + f"Failed to download {filename} from {repo_id} after {max_retries} attempts." + ) from e + logging.warning( + ( + f"Attempt {attempt}/{max_retries} failed to download {filename} " + f"from {repo_id}. Retrying in {wait_time} seconds..." + ) + ) + time.sleep(wait_time) + + +class PVNetModelHubMixin(PyTorchModelHubMixin): + """ + Implementation of [`PyTorchModelHubMixin`] to provide model Hub upload/download capabilities. + """ + + @classmethod + def from_pretrained( + cls, + *, + model_id: str, + revision: str, + cache_dir: Optional[Union[str, Path]] = None, + force_download: bool = False, + proxies: Optional[Dict] = None, + resume_download: Optional[bool] = None, + local_files_only: bool = False, + token: Union[str, bool, None] = None, + map_location: str = "cpu", + strict: bool = False, + ): + """Load Pytorch pretrained weights and return the loaded model.""" + + if os.path.isdir(model_id): + print("Loading weights from local directory") + model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME) + config_file = os.path.join(model_id, MODEL_CONFIG_NAME) + else: + # load model file + model_file = download_hf_hub_with_retries( + repo_id=model_id, + filename=PYTORCH_WEIGHTS_NAME, + revision=revision, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + token=token, + local_files_only=local_files_only, + max_retries=5, + wait_time=10, + ) + + # load config file + config_file = download_hf_hub_with_retries( + repo_id=model_id, + filename=MODEL_CONFIG_NAME, + revision=revision, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + token=token, + local_files_only=local_files_only, + max_retries=5, + wait_time=10, + ) + + with open(config_file, "r") as f: + config = yaml.safe_load(f) + + model = hydra.utils.instantiate(config) + + state_dict = torch.load(model_file, map_location=torch.device(map_location)) + model.load_state_dict(state_dict, strict=strict) # type: ignore + model.eval() # type: ignore + + return model + + @classmethod + def get_data_config( + cls, + model_id: str, + revision: str, + cache_dir: Optional[Union[str, Path]] = None, + force_download: bool = False, + proxies: Optional[Dict] = None, + resume_download: bool = False, + local_files_only: bool = False, + token: Optional[Union[str, bool]] = None, + ): + """Load data config file.""" + if os.path.isdir(model_id): + print("Loading data config from local directory") + data_config_file = os.path.join(model_id, DATA_CONFIG_NAME) + else: + data_config_file = download_hf_hub_with_retries( + repo_id=model_id, + filename=DATA_CONFIG_NAME, + revision=revision, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + token=token, + local_files_only=local_files_only, + max_retries=5, + wait_time=10, + ) + + return data_config_file + + def _save_pretrained(self, save_directory: Path) -> None: + """Save weights from a Pytorch model to a local directory.""" + model_to_save = self.module if hasattr(self, "module") else self # type: ignore + torch.save(model_to_save.state_dict(), save_directory / PYTORCH_WEIGHTS_NAME) + + def save_pretrained( + self, + save_directory: Union[str, Path], + config: dict, + data_config: Optional[Union[str, Path]], + repo_id: Optional[str] = None, + push_to_hub: bool = False, + wandb_repo: Optional[str] = None, + wandb_ids: Optional[Union[list[str], str]] = None, + card_template_path: Optional[Path] = None, + **kwargs, + ) -> Optional[str]: + """ + Save weights in local directory. + + Args: + save_directory (`str` or `Path`): + Path to directory in which the model weights and configuration will be saved. + config (`dict`): + Model configuration specified as a key/value dictionary. + data_config (`str` or `Path`): + The path to the data config. + repo_id (`str`, *optional*): + ID of your repository on the Hub. Used only if `push_to_hub=True`. Will default to + the folder name if not provided. + push_to_hub (`bool`, *optional*, defaults to `False`): + Whether or not to push your model to the HuggingFace Hub after saving it. + wandb_repo: Identifier of the repo on wandb. + wandb_ids: Identifier(s) of the model on wandb. + card_template_path: Path to the HuggingFace model card template. Defaults to card in + PVNet library if set to None. + kwargs: + Additional key word arguments passed along to the + [`~ModelHubMixin._from_pretrained`] method. + """ + + save_directory = Path(save_directory) + save_directory.mkdir(parents=True, exist_ok=True) + + # saving model weights/files + self._save_pretrained(save_directory) + + # saving model and data config + if isinstance(config, dict): + with open(save_directory / MODEL_CONFIG_NAME, "w") as f: + yaml.dump(config, f, sort_keys=False, default_flow_style=False) + + # Save cleaned configuration file + if data_config is not None: + new_data_config_path = save_directory / DATA_CONFIG_NAME + + # Replace the input filenames with place holders + make_clean_data_config(data_config, new_data_config_path) + + # Taylor the data config to the model being saved + minimize_data_config(new_data_config_path, new_data_config_path, self) + + card = self.create_hugging_face_model_card( + repo_id, wandb_repo, wandb_ids, card_template_path + ) + + (save_directory / "README.md").write_text(str(card)) + + if push_to_hub: + api = HfApi() + + api.upload_folder( + repo_id=repo_id, + repo_type="model", + folder_path=save_directory, + ) + + # Print the most recent commit hash + c = api.list_repo_commits(repo_id=repo_id, repo_type="model")[0] + + message = ( + f"The latest commit is now: \n" + f" date: {c.created_at} \n" + f" commit hash: {c.commit_id}\n" + f" by: {c.authors}\n" + f" title: {c.title}\n" + ) + + print(message) + + return None + + @staticmethod + def create_hugging_face_model_card( + repo_id: Optional[str] = None, + wandb_repo: Optional[str] = None, + wandb_ids: Optional[Union[list[str], str]] = None, + card_template_path: Optional[Path] = None, + ) -> ModelCard: + """ + Creates Hugging Face model card + + Args: + repo_id (`str`, *optional*): + ID of your repository on the Hub. Used only if `push_to_hub=True`. Will default to + the folder name if not provided. + wandb_repo: Identifier of the repo on wandb. + wandb_ids: Identifier(s) of the model on wandb. + card_template_path: Path to the HuggingFace model card template. Defaults to card in + PVNet library if set to None. + + Returns: + card: ModelCard - Hugging Face model card object + """ + + # Get appropriate model card + model_name = repo_id.split("/")[1] + if model_name == "windnet_india": + model_card = "wind_india_model_card_template.md" + elif model_name == "pvnet_india": + model_card = "pv_india_model_card_template.md" + else: + model_card = "pv_uk_regional_model_card_template.md" + + # Creating and saving model card. + card_data = ModelCardData(language="en", license="mit", library_name="pytorch") + if card_template_path is None: + card_template_path = ( + f"{os.path.dirname(os.path.abspath(__file__))}/model_cards/{model_card}" + ) + + if isinstance(wandb_ids, str): + wandb_ids = [wandb_ids] + + wandb_links = "" + for wandb_id in wandb_ids: + link = f"https://wandb.ai/{wandb_repo}/runs/{wandb_id}" + wandb_links += f" - [{link}]({link})\n" + + # Find package versions for OCF packages + packages_to_display = ["pvnet", "ocf-data-sampler"] + packages_and_versions = { + package_name: pkg_resources.get_distribution(package_name).version + for package_name in packages_to_display + } + + package_versions_markdown = "" + for package, version in packages_and_versions.items(): + package_versions_markdown += f" - {package}=={version}\n" + + return ModelCard.from_template( + card_data, + template_path=card_template_path, + wandb_links=wandb_links, + package_versions=package_versions_markdown + ) + + +class BaseModel(pl.LightningModule, PVNetModelHubMixin): + """Abstract base class for PVNet submodels""" + + def __init__( + self, + history_minutes: int, + forecast_minutes: int, + optimizer: AbstractOptimizer, + output_quantiles: Optional[list[float]] = None, + target_key: str = "gsp", + interval_minutes: int = 30, + timestep_intervals_to_plot: Optional[list[int]] = None, + forecast_minutes_ignore: Optional[int] = 0, + save_validation_results_csv: Optional[bool] = False, + ): + """Abtstract base class for PVNet submodels. + + Args: + history_minutes (int): Length of the GSP history period in minutes + forecast_minutes (int): Length of the GSP forecast period in minutes + optimizer (AbstractOptimizer): Optimizer + output_quantiles: A list of float (0.0, 1.0) quantiles to predict values for. If set to + None the output is a single value. + target_key: The key of the target variable in the batch + interval_minutes: The interval in minutes between each timestep in the data + timestep_intervals_to_plot: Intervals, in timesteps, to plot during training + forecast_minutes_ignore: Number of forecast minutes to ignore when calculating losses. + For example if set to 60, the model doesnt predict the first 60 minutes + save_validation_results_csv: whether to save full csv outputs from validation results. + """ + super().__init__() + + self._optimizer = optimizer + self._target_key = target_key + if timestep_intervals_to_plot is not None: + for interval in timestep_intervals_to_plot: + assert type(interval) in [list, tuple] and len(interval) == 2, ValueError( + f"timestep_intervals_to_plot must be a list of tuples or lists of length 2, " + f"but got {timestep_intervals_to_plot=}" + ) + self.time_step_intervals_to_plot = timestep_intervals_to_plot + + # Model must have lr to allow tuning + # This setting is only used when lr is tuned with callback + self.lr = None + + self.history_minutes = history_minutes + self.forecast_minutes = forecast_minutes + self.output_quantiles = output_quantiles + self.interval_minutes = interval_minutes + self.forecast_minutes_ignore = forecast_minutes_ignore + + # Number of timestemps for 30 minutely data + self.history_len = history_minutes // interval_minutes + self.forecast_len = (forecast_minutes - forecast_minutes_ignore) // interval_minutes + self.forecast_len_ignore = forecast_minutes_ignore // interval_minutes + + self._accumulated_metrics = MetricAccumulator() + self._accumulated_batches = BatchAccumulator(key_to_keep=self._target_key) + self._accumulated_y_hat = PredAccumulator() + self._horizon_maes = MetricAccumulator() + + # Store whether the model should use quantile regression or simply predict the mean + self.use_quantile_regression = self.output_quantiles is not None + + # Store the number of ouput features that the model should predict for + if self.use_quantile_regression: + self.num_output_features = self.forecast_len * len(self.output_quantiles) + else: + self.num_output_features = self.forecast_len + + # save all validation results to array, so we can save these to weights n biases + self.validation_epoch_results = [] + self.save_validation_results_csv = save_validation_results_csv + + def _adapt_batch(self, batch): + """Slice batches into appropriate shapes for model. + + Returns a new batch dictionary with adapted data, leaving the original batch unchanged. + We make some specific assumptions about the original batch and the derived sliced batch: + - We are only limiting the future projections. I.e. we are never shrinking the batch from + the left hand side of the time axis, only slicing it from the right + - We are only shrinking the spatial crop of the satellite and NWP data + + """ + # Create a copy of the batch to avoid modifying the original + new_batch = {key: copy.deepcopy(value) for key, value in batch.items()} + + if "gsp" in new_batch.keys(): + # Slice off the end of the GSP data + gsp_len = self.forecast_len + self.history_len + 1 + new_batch["gsp"] = new_batch["gsp"][:, :gsp_len] + new_batch["gsp_time_utc"] = new_batch["gsp_time_utc"][:, :gsp_len] + + if self.include_sat: + # Slice off the end of the satellite data and spatially crop + # Shape: batch_size, seq_length, channel, height, width + new_batch["satellite_actual"] = center_crop( + new_batch["satellite_actual"][:, : self.sat_sequence_len], + output_size=self.sat_encoder.image_size_pixels, + ) + + if self.include_nwp: + # Slice off the end of the NWP data and spatially crop + for nwp_source in self.nwp_encoders_dict: + # shape: batch_size, seq_len, n_chans, height, width + new_batch["nwp"][nwp_source]["nwp"] = center_crop( + new_batch["nwp"][nwp_source]["nwp"], + output_size=self.nwp_encoders_dict[nwp_source].image_size_pixels, + )[:, : self.nwp_encoders_dict[nwp_source].sequence_length] + + if self.include_sun: + sun_len = self.forecast_len + self.history_len + 1 + # Slice off end of solar coords + for s in ["solar_azimuth", "solar_elevation"]: + if s in new_batch.keys(): + new_batch[s] = new_batch[s][:, :sun_len] + + return new_batch + + def transfer_batch_to_device(self, batch, device, dataloader_idx): + """Method to move custom batches to a given device""" + return copy_batch_to_device(batch, device) + + def _quantiles_to_prediction(self, y_quantiles): + """ + Convert network prediction into a point prediction. + + Note: + Implementation copied from: + https://pytorch-forecasting.readthedocs.io/en/stable/_modules/pytorch_forecasting + /metrics/quantile.html#QuantileLoss.loss + + Args: + y_quantiles: Quantile prediction of network + + Returns: + torch.Tensor: Point prediction + """ + # y_quantiles Shape: batch_size, seq_length, num_quantiles + idx = self.output_quantiles.index(0.5) + y_median = y_quantiles[..., idx] + return y_median + + def _calculate_quantile_loss(self, y_quantiles, y): + """Calculate quantile loss. + + Note: + Implementation copied from: + https://pytorch-forecasting.readthedocs.io/en/stable/_modules/pytorch_forecasting + /metrics/quantile.html#QuantileLoss.loss + + Args: + y_quantiles: Quantile prediction of network + y: Target values + + Returns: + Quantile loss + """ + # calculate quantile loss + losses = [] + for i, q in enumerate(self.output_quantiles): + errors = y - y_quantiles[..., i] + losses.append(torch.max((q - 1) * errors, q * errors).unsqueeze(-1)) + losses = 2 * torch.cat(losses, dim=2) + + return losses.mean() + + def _calculate_common_losses(self, y, y_hat): + """Calculate losses common to train, and val""" + + losses = {} + + if self.use_quantile_regression: + losses["quantile_loss"] = self._calculate_quantile_loss(y_hat, y) + y_hat = self._quantiles_to_prediction(y_hat) + + # calculate mse, mae + mse_loss = F.mse_loss(y_hat, y) + mae_loss = F.l1_loss(y_hat, y) + + # TODO: Compute correlation coef using np.corrcoef(tensor with + # shape (2, num_timesteps))[0, 1] on each example, and taking + # the mean across the batch? + losses.update( + { + "MSE": mse_loss, + "MAE": mae_loss, + } + ) + + return losses + + def _step_mae_and_mse(self, y, y_hat, dict_key_root): + """Calculate the MSE and MAE at each forecast step""" + losses = {} + + mse_each_step = torch.mean((y_hat - y) ** 2, dim=0) + mae_each_step = torch.mean(torch.abs(y_hat - y), dim=0) + + losses.update({f"MSE_{dict_key_root}/step_{i:03}": m for i, m in enumerate(mse_each_step)}) + losses.update({f"MAE_{dict_key_root}/step_{i:03}": m for i, m in enumerate(mae_each_step)}) + + return losses + + def _calculate_val_losses(self, y, y_hat): + """Calculate additional validation losses""" + + losses = {} + + if self.use_quantile_regression: + # Add fraction below each quantile for calibration + for i, quantile in enumerate(self.output_quantiles): + below_quant = y <= y_hat[..., i] + # Mask values small values, which are dominated by night + mask = y >= 0.01 + losses[f"fraction_below_{quantile}_quantile"] = (below_quant[mask]).float().mean() + + # Take median value for remaining metric calculations + y_hat = self._quantiles_to_prediction(y_hat) + + # Log the loss at each time horizon + losses.update(self._step_mae_and_mse(y, y_hat, dict_key_root="horizon")) + + # Log the persistance losses + y_persist = y[:, -1].unsqueeze(1).expand(-1, self.forecast_len) + losses["MAE_persistence/val"] = F.l1_loss(y_persist, y) + losses["MSE_persistence/val"] = F.mse_loss(y_persist, y) + + # Log persistance loss at each time horizon + losses.update(self._step_mae_and_mse(y, y_persist, dict_key_root="persistence")) + return losses + + def _training_accumulate_log(self, batch, batch_idx, losses, y_hat): + """Internal function to accumulate training batches and log results. + + This is used when accummulating grad batches. Should make the variability in logged training + step metrics indpendent on whether we accumulate N batches of size B or just use a larger + batch size of N*B with no accumulaion. + """ + + losses = {k: v.detach().cpu() for k, v in losses.items()} + y_hat = y_hat.detach().cpu() + + self._accumulated_metrics.append(losses) + self._accumulated_batches.append(batch) + self._accumulated_y_hat.append(y_hat) + + if not self.trainer.fit_loop._should_accumulate(): + losses = self._accumulated_metrics.flush() + batch = self._accumulated_batches.flush() + y_hat = self._accumulated_y_hat.flush() + + self.log_dict( + losses, + on_step=True, + on_epoch=True, + ) + + # Number of accumulated grad batches + grad_batch_num = (batch_idx + 1) / self.trainer.accumulate_grad_batches + + # We only create the figure every 8 log steps + # This was reduced as it was creating figures too often + if grad_batch_num % (8 * self.trainer.log_every_n_steps) == 0: + fig = plot_batch_forecasts( + batch, + y_hat, + batch_idx, + quantiles=self.output_quantiles, + key_to_plot=self._target_key, + ) + fig.savefig("latest_logged_train_batch.png") + plt.close(fig) + + def training_step(self, batch, batch_idx): + """Run training step""" + y_hat = self(batch) + + # Batch is adapted in the model forward method, but needs to be adapted here too + batch = self._adapt_batch(batch) + + y = batch[self._target_key][:, -self.forecast_len :] + + losses = self._calculate_common_losses(y, y_hat) + losses = {f"{k}/train": v for k, v in losses.items()} + + self._training_accumulate_log(batch, batch_idx, losses, y_hat) + + if self.use_quantile_regression: + opt_target = losses["quantile_loss/train"] + else: + opt_target = losses["MAE/train"] + return opt_target + + def _log_forecast_plot(self, batch, y_hat, accum_batch_num, timesteps_to_plot, plot_suffix): + """Log forecast plot to wandb""" + fig = plot_batch_forecasts( + batch, + y_hat, + quantiles=self.output_quantiles, + key_to_plot=self._target_key, + ) + + plot_name = f"val_forecast_samples/batch_idx_{accum_batch_num}_{plot_suffix}" + + try: + self.logger.experiment.log({plot_name: wandb.Image(fig)}) + except Exception as e: + print(f"Failed to log {plot_name} to wandb") + print(e) + plt.close(fig) + + def _log_validation_results(self, batch, y_hat, accum_batch_num): + """Append validation results to self.validation_epoch_results""" + + # get truth values, shape (b, forecast_len) + y = batch[self._target_key][:, -self.forecast_len :] + y = y.detach().cpu().numpy() + batch_size = y.shape[0] + + # get prediction values, shape (b, forecast_len, quantiles?) + y_hat = y_hat.detach().cpu().numpy() + + # get time_utc, shape (b, forecast_len) + time_utc_key = f"{self._target_key}_time_utc" + time_utc = batch[time_utc_key][:, -self.forecast_len :].detach().cpu().numpy() + + # get target id and change from (b,1) to (b,) + id_key = f"{self._target_key}_id" + target_id = batch[id_key].detach().cpu().numpy() + target_id = target_id.squeeze() + + for i in range(batch_size): + y_i = y[i] + y_hat_i = y_hat[i] + time_utc_i = time_utc[i] + target_id_i = target_id[i] + + results_dict = { + "y": y_i, + "time_utc": time_utc_i, + } + if self.use_quantile_regression: + results_dict.update( + {f"y_quantile_{q}": y_hat_i[:, i] for i, q in enumerate(self.output_quantiles)} + ) + else: + results_dict["y_hat"] = y_hat_i + + results_df = pd.DataFrame(results_dict) + results_df["id"] = target_id_i + results_df["batch_idx"] = accum_batch_num + results_df["example_idx"] = i + + self.validation_epoch_results.append(results_df) + + def validation_step(self, batch: dict, batch_idx): + """Run validation step""" + + accum_batch_num = batch_idx // self.trainer.accumulate_grad_batches + + y_hat = self(batch) + # Batch is adapted in the model forward method, but needs to be adapted here too + batch = self._adapt_batch(batch) + + y = batch[self._target_key][:, -self.forecast_len :] + + if (batch_idx + 1) % self.trainer.accumulate_grad_batches == 0: + self._log_validation_results(batch, y_hat, accum_batch_num) + + # Expand persistence to be the same shape as y + losses = self._calculate_common_losses(y, y_hat) + losses.update(self._calculate_val_losses(y, y_hat)) + + # Store these to make horizon accuracy plot + self._horizon_maes.append( + {i: losses[f"MAE_horizon/step_{i:03}"].cpu().numpy() for i in range(self.forecast_len)} + ) + + logged_losses = {f"{k}/val": v for k, v in losses.items()} + + self.log_dict( + logged_losses, + on_step=False, + on_epoch=True, + ) + + # Make plots only if using wandb logger + if isinstance(self.logger, pl.loggers.WandbLogger) and accum_batch_num in [0, 1]: + # Store these temporarily under self + if not hasattr(self, "_val_y_hats"): + self._val_y_hats = PredAccumulator() + self._val_batches = BatchAccumulator(key_to_keep=self._target_key) + + self._val_y_hats.append(y_hat) + self._val_batches.append(batch) + + # if batch has accumulated + if (batch_idx + 1) % self.trainer.accumulate_grad_batches == 0: + y_hat = self._val_y_hats.flush() + batch = self._val_batches.flush() + + self._log_forecast_plot( + batch, + y_hat, + accum_batch_num, + timesteps_to_plot=None, + plot_suffix="all", + ) + + if self.time_step_intervals_to_plot is not None: + for interval in self.time_step_intervals_to_plot: + self._log_forecast_plot( + batch, + y_hat, + accum_batch_num, + timesteps_to_plot=interval, + plot_suffix=f"timestep_{interval}", + ) + + del self._val_y_hats + del self._val_batches + + return logged_losses + + def on_validation_epoch_end(self): + """Run on epoch end""" + + try: + # join together validation results, and save to wandb + validation_results_df = pd.concat(self.validation_epoch_results) + validation_results_df["error"] = ( + validation_results_df["y"] - validation_results_df["y_quantile_0.5"] + ) + + if isinstance(self.logger, pl.loggers.WandbLogger): + # log error distribution metrics + wandb.log( + { + "2nd_percentile_median_forecast_error": validation_results_df[ + "error" + ].quantile(0.02), + "5th_percentile_median_forecast_error": validation_results_df[ + "error" + ].quantile(0.05), + "95th_percentile_median_forecast_error": validation_results_df[ + "error" + ].quantile(0.95), + "98th_percentile_median_forecast_error": validation_results_df[ + "error" + ].quantile(0.98), + "95th_percentile_median_forecast_absolute_error": abs( + validation_results_df["error"] + ).quantile(0.95), + "98th_percentile_median_forecast_absolute_error": abs( + validation_results_df["error"] + ).quantile(0.98), + } + ) + # saving validation result csvs + if self.save_validation_results_csv: + with tempfile.TemporaryDirectory() as tempdir: + filename = os.path.join(tempdir, f"validation_results_{self.current_epoch}.csv") + validation_results_df.to_csv(filename, index=False) + + # make and log wand artifact + validation_artifact = wandb.Artifact( + f"validation_results_epoch_{self.current_epoch}", type="dataset" + ) + validation_artifact.add_file(filename) + wandb.log_artifact(validation_artifact) + + except Exception as e: + print("Failed to log validation results to wandb") + print(e) + + self.validation_epoch_results = [] + horizon_maes_dict = self._horizon_maes.flush() + + # Create the horizon accuracy curve + if isinstance(self.logger, pl.loggers.WandbLogger): + per_step_losses = [[i, horizon_maes_dict[i]] for i in range(self.forecast_len)] + try: + table = wandb.Table(data=per_step_losses, columns=["horizon_step", "MAE"]) + wandb.log( + { + "horizon_loss_curve": wandb.plot.line( + table, "horizon_step", "MAE", title="Horizon loss curve" + ) + }, + ) + except Exception as e: + print("Failed to log horizon_loss_curve to wandb") + print(e) + + def configure_optimizers(self): + """Configure the optimizers using learning rate found with LR finder if used""" + if self.lr is not None: + # Use learning rate found by learning rate finder callback + self._optimizer.lr = self.lr + return self._optimizer(self) diff --git a/pvnet/models/baseline/__init__.py b/pvnet/models/baseline/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d932b245fa47816dd0f7e2c9c07d315aaa332b6f --- /dev/null +++ b/pvnet/models/baseline/__init__.py @@ -0,0 +1 @@ +"""Baselines""" diff --git a/pvnet/models/baseline/last_value.py b/pvnet/models/baseline/last_value.py new file mode 100644 index 0000000000000000000000000000000000000000..454b58312799878333bbe95c04739ef5cecc8042 --- /dev/null +++ b/pvnet/models/baseline/last_value.py @@ -0,0 +1,42 @@ +"""Persistence model""" + + +import pvnet +from pvnet.models.base_model import BaseModel +from pvnet.optimizers import AbstractOptimizer + + +class Model(BaseModel): + """Simple baseline model that takes the last gsp yield value and copies it forward.""" + + name = "last_value" + + def __init__( + self, + forecast_minutes: int = 12, + history_minutes: int = 6, + optimizer: AbstractOptimizer = pvnet.optimizers.Adam(), + ): + """Simple baseline model that takes the last gsp yield value and copies it forward. + + Args: + history_minutes (int): Length of the GSP history period in minutes + forecast_minutes (int): Length of the GSP forecast period in minutes + optimizer (AbstractOptimizer): Optimizer + """ + + super().__init__(history_minutes, forecast_minutes, optimizer) + self.save_hyperparameters() + + def forward(self, x: dict): + """Run model forward on dict batch of data""" + # Shape: batch_size, seq_length, n_sites + gsp_yield = x["gsp"] + + # take the last value non forecaster value and the first in the pv yeild + # (this is the pv site we are preditcting for) + y_hat = gsp_yield[:, -self.forecast_len - 1] + + # expand the last valid forward n predict steps + out = y_hat.unsqueeze(1).repeat(1, self.forecast_len) + return out diff --git a/pvnet/models/baseline/readme.md b/pvnet/models/baseline/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..2e1f8926be192fc62469cf75013cc1c5f4124f84 --- /dev/null +++ b/pvnet/models/baseline/readme.md @@ -0,0 +1,5 @@ +# Baseline Models + + - `last_value` - Forecast the sample last historical PV yeild for every forecast step + - `single_value` - Learns a single value estimate and predicts this value for every input and every + forecast step. diff --git a/pvnet/models/baseline/single_value.py b/pvnet/models/baseline/single_value.py new file mode 100644 index 0000000000000000000000000000000000000000..c24aebdc840fada7fc43c278e0558ec0e907226c --- /dev/null +++ b/pvnet/models/baseline/single_value.py @@ -0,0 +1,36 @@ +"""Average value model""" +import torch +from torch import nn + +import pvnet +from pvnet.models.base_model import BaseModel +from pvnet.optimizers import AbstractOptimizer + + +class Model(BaseModel): + """Simple baseline model that predicts always the same value.""" + + name = "single_value" + + def __init__( + self, + forecast_minutes: int = 120, + history_minutes: int = 60, + optimizer: AbstractOptimizer = pvnet.optimizers.Adam(), + ): + """Simple baseline model that predicts always the same value. + + Args: + history_minutes (int): Length of the GSP history period in minutes + forecast_minutes (int): Length of the GSP forecast period in minutes + optimizer (AbstractOptimizer): Optimizer + """ + super().__init__(history_minutes, forecast_minutes, optimizer) + self._value = nn.Parameter(torch.zeros(1), requires_grad=True) + self.save_hyperparameters() + + def forward(self, x: dict): + """Run model forward on dict batch of data""" + # Returns a single value at all steps + y_hat = torch.zeros_like(x["gsp"][:, : self.forecast_len]) + self._value + return y_hat diff --git a/pvnet/models/ensemble.py b/pvnet/models/ensemble.py new file mode 100644 index 0000000000000000000000000000000000000000..6dab2b90e9bc619fbbe661d571e032b5fa988c3a --- /dev/null +++ b/pvnet/models/ensemble.py @@ -0,0 +1,74 @@ +"""Model which uses mutliple prediction heads""" +from typing import Optional + +import torch +from torch import nn + +from pvnet.models.base_model import BaseModel + + +class Ensemble(BaseModel): + """Ensemble of PVNet models""" + + def __init__( + self, + model_list: list[BaseModel], + weights: Optional[list[float]] = None, + ): + """Ensemble of PVNet models + + Args: + model_list: A list of PVNet models to ensemble + weights: A list of weighting to apply to each model. If None, the models are weighted + equally. + """ + + # Surface check all the models are compatible + output_quantiles = [] + history_minutes = [] + forecast_minutes = [] + target_key = [] + interval_minutes = [] + + # Get some model properties from each model + for model in model_list: + output_quantiles.append(model.output_quantiles) + history_minutes.append(model.history_minutes) + forecast_minutes.append(model.forecast_minutes) + target_key.append(model._target_key) + interval_minutes.append(model.interval_minutes) + + # Check these properties are all the same + for param_list in [ + output_quantiles, + history_minutes, + forecast_minutes, + target_key, + interval_minutes, + ]: + assert all([p == param_list[0] for p in param_list]), param_list + + super().__init__( + history_minutes=history_minutes[0], + forecast_minutes=forecast_minutes[0], + optimizer=None, + output_quantiles=output_quantiles[0], + target_key=target_key[0], + interval_minutes=interval_minutes[0], + ) + + self.model_list = nn.ModuleList(model_list) + + if weights is None: + weights = torch.ones(len(model_list)) / len(model_list) + else: + assert len(weights) == len(model_list) + weights = torch.Tensor(weights) / sum(weights) + self.weights = nn.Parameter(weights, requires_grad=False) + + def forward(self, batch): + """Run the model forward""" + y_hat = 0 + for weight, model in zip(self.weights, self.model_list): + y_hat = model(batch) * weight + y_hat + return y_hat diff --git a/pvnet/models/model_cards/pv_india_model_card_template.md b/pvnet/models/model_cards/pv_india_model_card_template.md new file mode 100644 index 0000000000000000000000000000000000000000..75e096d67d7479396e05aba406c0759366449798 --- /dev/null +++ b/pvnet/models/model_cards/pv_india_model_card_template.md @@ -0,0 +1,56 @@ +--- +{{ card_data }} +--- + + + + + + +# PVNet India + +## Model Description + + +This model class uses numerical weather predictions from providers such as ECMWF to forecast the PV power in North West India over the next 48 hours. More information can be found in the model repo [1] and experimental notes [here](https://github.com/openclimatefix/PVNet/tree/main/experiments/india). + + +- **Developed by:** openclimatefix +- **Model type:** Fusion model +- **Language(s) (NLP):** en +- **License:** mit + + +# Training Details + +## Data + + + +The model is trained on data from 2019-2022 and validated on data from 2022-2023. See experimental notes [here](https://github.com/openclimatefix/PVNet/tree/main/experiments/india) + + +### Preprocessing + +Data is prepared with the `ocf_data_sampler/torch_datasets/datasets/site` Dataset [2]. + + +## Results + +The training logs for the current model can be found here: +{{ wandb_links }} + + +### Hardware + +Trained on a single NVIDIA Tesla T4 + +### Software + +This model was trained using the following Open Climate Fix packages: + +- [1] https://github.com/openclimatefix/PVNet +- [2] https://github.com/openclimatefix/ocf-data-sampler + +The versions of these packages can be found below: +{{ package_versions }} diff --git a/pvnet/models/model_cards/pv_uk_regional_model_card_template.md b/pvnet/models/model_cards/pv_uk_regional_model_card_template.md new file mode 100644 index 0000000000000000000000000000000000000000..fee9ef19eec579acd36e5ad47b37872eb67ad0a1 --- /dev/null +++ b/pvnet/models/model_cards/pv_uk_regional_model_card_template.md @@ -0,0 +1,59 @@ +--- +{{ card_data }} +--- + + + + + + +# PVNet2 + +## Model Description + + +This model class uses satellite data, numerical weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in [this google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing). + +- **Developed by:** openclimatefix +- **Model type:** Fusion model +- **Language(s) (NLP):** en +- **License:** mit + + +# Training Details + +## Data + + + +The model is trained on data from 2019-2022 and validated on data from 2022-2023. See experimental notes in the [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) for more details. + + +### Preprocessing + +Data is prepared with the `ocf_data_sampler/torch_datasets/datasets/pvnet_uk` Dataset [2]. + + +## Results + +The training logs for the current model can be found here: +{{ wandb_links }} + +The training logs for all model runs of PVNet2 can be found [here](https://wandb.ai/openclimatefix/pvnet2.1). + +Some experimental notes can be found at in [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) + + +### Hardware + +Trained on a single NVIDIA Tesla T4 + +### Software + +This model was trained using the following Open Climate Fix packages: + +- [1] https://github.com/openclimatefix/PVNet +- [2] https://github.com/openclimatefix/ocf-data-sampler + +The versions of these packages can be found below: +{{ package_versions }} diff --git a/pvnet/models/model_cards/wind_india_model_card_template.md b/pvnet/models/model_cards/wind_india_model_card_template.md new file mode 100644 index 0000000000000000000000000000000000000000..12d4de690d41da3ead14da04a89dcf47dd0cf7c1 --- /dev/null +++ b/pvnet/models/model_cards/wind_india_model_card_template.md @@ -0,0 +1,56 @@ +--- +{{ card_data }} +--- + + + + + + +# WindNet + +## Model Description + + +This model class uses numerical weather predictions from providers such as ECMWF to forecast the wind power in North West India over the next 48 hours at 15 minute granularity. More information can be found in the model repo [1] and experimental notes [here](https://github.com/openclimatefix/PVNet/tree/main/experiments/india). + + +- **Developed by:** openclimatefix +- **Model type:** Fusion model +- **Language(s) (NLP):** en +- **License:** mit + + +# Training Details + +## Data + + + +The model is trained on data from 2019-2022 and validated on data from 2022-2023. See experimental notes [here](https://github.com/openclimatefix/PVNet/tree/main/experiments/india) + + +### Preprocessing + +Data is prepared with the `ocf_data_sampler/torch_datasets/datasets/site` Dataset [2]. + + +## Results + +The training logs for the current model can be found here: +{{ wandb_links }} + + +### Hardware + +Trained on a single NVIDIA Tesla T4 + +### Software + +This model was trained using the following Open Climate Fix packages: + +- [1] https://github.com/openclimatefix/PVNet +- [2] https://github.com/openclimatefix/ocf-data-sampler + +The versions of these packages can be found below: +{{ package_versions }} diff --git a/pvnet/models/multimodal/__init__.py b/pvnet/models/multimodal/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7fab7ba2db1be87d5950ce628dda5556fe78d09d --- /dev/null +++ b/pvnet/models/multimodal/__init__.py @@ -0,0 +1 @@ +"""Multimodal Models""" diff --git a/pvnet/models/multimodal/basic_blocks.py b/pvnet/models/multimodal/basic_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..516300cbe3429e833ef8c9ff57722fcb90976333 --- /dev/null +++ b/pvnet/models/multimodal/basic_blocks.py @@ -0,0 +1,104 @@ +"""Basic layers for composite models""" + +import warnings + +import torch +from torch import _VF, nn + + +class ImageEmbedding(nn.Module): + """A embedding layer which concatenates an ID embedding as a new channel onto 3D inputs.""" + + def __init__(self, num_embeddings, sequence_length, image_size_pixels, **kwargs): + """A embedding layer which concatenates an ID embedding as a new channel onto 3D inputs. + + The embedding is a single 2D image and is appended at each step in the 1st dimension + (assumed to be time). + + Args: + num_embeddings: Size of the dictionary of embeddings + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + **kwargs: See `torch.nn.Embedding` for more possible arguments. + """ + super().__init__() + self.image_size_pixels = image_size_pixels + self.sequence_length = sequence_length + self._embed = nn.Embedding( + num_embeddings=num_embeddings, + embedding_dim=image_size_pixels * image_size_pixels, + **kwargs, + ) + + def forward(self, x, id): + """Append ID embedding to image""" + emb = self._embed(id) + emb = emb.reshape((-1, 1, 1, self.image_size_pixels, self.image_size_pixels)) + emb = emb.repeat(1, 1, self.sequence_length, 1, 1) + x = torch.cat((x, emb), dim=1) + return x + + +class CompleteDropoutNd(nn.Module): + """A layer used to completely drop out all elements of a N-dimensional sample. + + Each sample will be zeroed out independently on every forward call with probability `p` using + samples from a Bernoulli distribution. + + """ + + __constants__ = ["p", "inplace", "n_dim"] + p: float + inplace: bool + n_dim: int + + def __init__(self, n_dim, p=0.5, inplace=False): + """A layer used to completely drop out all elements of a N-dimensional sample. + + Args: + n_dim: Number of dimensions of each sample not including channels. E.g. a sample with + shape (channel, time, height, width) would use `n_dim=3`. + p: probability of a channel to be zeroed. Default: 0.5 + training: apply dropout if is `True`. Default: `True` + inplace: If set to `True`, will do this operation in-place. Default: `False` + """ + super().__init__() + if p < 0 or p > 1: + raise ValueError( + "dropout probability has to be between 0 and 1, " "but got {}".format(p) + ) + self.p = p + self.inplace = inplace + self.n_dim = n_dim + + def forward(self, input: torch.Tensor) -> torch.Tensor: + """Run dropout""" + p = self.p + inp_dim = input.dim() + + if inp_dim not in (self.n_dim + 1, self.n_dim + 2): + warn_msg = ( + f"CompleteDropoutNd: Received a {inp_dim}-D input. Expected either a single sample" + f" with {self.n_dim+1} dimensions, or a batch of samples with {self.n_dim+2}" + " dimensions." + ) + warnings.warn(warn_msg) + + is_batched = inp_dim == self.n_dim + 2 + if not is_batched: + input = input.unsqueeze_(0) if self.inplace else input.unsqueeze(0) + + input = input.unsqueeze_(1) if self.inplace else input.unsqueeze(1) + + result = ( + _VF.feature_dropout_(input, p, self.training) + if self.inplace + else _VF.feature_dropout(input, p, self.training) + ) + + result = result.squeeze_(1) if self.inplace else result.squeeze(1) + + if not is_batched: + result = result.squeeze_(0) if self.inplace else result.squeeze(0) + + return result diff --git a/pvnet/models/multimodal/encoders/__init__.py b/pvnet/models/multimodal/encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..386db39ce50959e004a296e59de73c52342e2ede --- /dev/null +++ b/pvnet/models/multimodal/encoders/__init__.py @@ -0,0 +1 @@ +"""Submodels to encode satellite and NWP inputs""" diff --git a/pvnet/models/multimodal/encoders/basic_blocks.py b/pvnet/models/multimodal/encoders/basic_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..2173267e83c570ae198b62148dad048815cd2aba --- /dev/null +++ b/pvnet/models/multimodal/encoders/basic_blocks.py @@ -0,0 +1,217 @@ +"""Basic blocks for image sequence encoders""" +from abc import ABCMeta, abstractmethod + +import torch +from torch import nn + + +class AbstractNWPSatelliteEncoder(nn.Module, metaclass=ABCMeta): + """Abstract class for NWP/satellite encoder. + + The encoder will take an input of shape (batch_size, sequence_length, channels, height, width) + and return an output of shape (batch_size, out_features). + """ + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + ): + """Abstract class for NWP/satellite encoder. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + """ + super().__init__() + self.out_features = out_features + self.image_size_pixels = image_size_pixels + self.sequence_length = sequence_length + + @abstractmethod + def forward(self): + """Run model forward""" + pass + + +class ResidualConv3dBlock(nn.Module): + """Fully-connected deep network based on ResNet architecture. + + Internally, this network uses ELU activations throughout the residual blocks. + """ + + def __init__( + self, + in_channels, + n_layers: int = 2, + dropout_frac: float = 0.0, + ): + """Fully-connected deep network based on ResNet architecture. + + Args: + in_channels: Number of input channels. + n_layers: Number of layers in residual pathway. + dropout_frac: Probability of an element to be zeroed. + """ + super().__init__() + + layers = [] + for i in range(n_layers): + layers += [ + nn.ELU(), + nn.Conv3d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=(3, 3, 3), + padding=(1, 1, 1), + ), + nn.Dropout3d(p=dropout_frac), + ] + + self.model = nn.Sequential(*layers) + + def forward(self, x): + """Run residual connection""" + return self.model(x) + x + + +class ResidualConv3dBlock2(nn.Module): + """Residual block of 'full pre-activation' similar to the block in figure 4(e) of [1]. + + This was the best performing residual block tested in the study. This implementation differs + from that block just by using LeakyReLU activation to avoid dead neurons, and by including + optional dropout in the residual branch. This is also a 3D fully connected layer residual block + rather than a 2D convolutional block. + + Sources: + [1] https://arxiv.org/pdf/1603.05027.pdf + """ + + def __init__( + self, + in_channels: int, + n_layers: int = 2, + dropout_frac: float = 0.0, + batch_norm: bool = True, + ): + """Residual block of 'full pre-activation' similar to the block in figure 4(e) of [1]. + + Sources: + [1] https://arxiv.org/pdf/1603.05027.pdf + + Args: + in_channels: Number of input channels. + n_layers: Number of layers in residual pathway. + dropout_frac: Probability of an element to be zeroed. + batch_norm: Whether to use batchnorm + """ + super().__init__() + + layers = [] + for i in range(n_layers): + if batch_norm: + layers.append(nn.BatchNorm3d(in_channels)) + layers.extend( + [ + nn.Dropout3d(p=dropout_frac), + nn.LeakyReLU(), + nn.Conv3d( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=(3, 3, 3), + padding=(1, 1, 1), + ), + ] + ) + + self.model = nn.Sequential(*layers) + + def forward(self, x): + """Run model forward""" + return self.model(x) + x + + +class ImageSequenceEncoder(nn.Module): + """Simple network which independently encodes each image in a sequence into 1D features""" + + def __init__( + self, + image_size_pixels: int, + in_channels: int, + number_of_conv2d_layers: int = 4, + conv2d_channels: int = 32, + fc_features: int = 128, + ): + """Simple network which independently encodes each image in a sequence into 1D features. + + For input image with shape [N, C, L, H, W] the output is of shape [N, L, fc_features] where + N is number of samples in batch, C is the number of input channels, L is the length of the + sequence, and H and W are the height and width. + + Args: + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + number_of_conv2d_layers: Number of convolution 2D layers that are used. + conv2d_channels: Number of channels used in each conv2d layer. + fc_features: Number of output nodes for each image in each sequence. + """ + super().__init__() + + # Check that the output shape of the convolutional layers will be at least 1x1 + cnn_spatial_output_size = image_size_pixels - 2 * number_of_conv2d_layers + if not (cnn_spatial_output_size >= 1): + raise ValueError( + f"cannot use this many conv2d layers ({number_of_conv2d_layers}) with this input " + f"spatial size ({image_size_pixels})" + ) + + conv_layers = [] + + conv_layers += [ + nn.Conv2d( + in_channels=in_channels, + out_channels=conv2d_channels, + kernel_size=3, + padding=0, + ), + nn.ELU(), + ] + for i in range(0, number_of_conv2d_layers - 1): + conv_layers += [ + nn.Conv2d( + in_channels=conv2d_channels, + out_channels=conv2d_channels, + kernel_size=3, + padding=0, + ), + nn.ELU(), + ] + + self.conv_layers = nn.Sequential(*conv_layers) + + self.final_block = nn.Sequential( + nn.Linear( + in_features=(cnn_spatial_output_size**2) * conv2d_channels, + out_features=fc_features, + ), + nn.ELU(), + ) + + def forward(self, x): + """Run model forward""" + batch_size, channel, seq_len, height, width = x.shape + + x = torch.swapaxes(x, 1, 2) + x = x.reshape(batch_size * seq_len, channel, height, width) + + out = self.conv_layers(x) + out = out.reshape(batch_size * seq_len, -1) + + out = self.final_block(out) + out = out.reshape(batch_size, seq_len, -1) + + return out diff --git a/pvnet/models/multimodal/encoders/encoders2d.py b/pvnet/models/multimodal/encoders/encoders2d.py new file mode 100644 index 0000000000000000000000000000000000000000..29f8c246f5c46303f3ddd662058985fc656eac6f --- /dev/null +++ b/pvnet/models/multimodal/encoders/encoders2d.py @@ -0,0 +1,413 @@ +"""Encoder modules for the satellite/NWP data. + +These networks naively stack the sequences into extra channels before putting through their +architectures. +""" + +from functools import partial +from typing import Any, Callable, List, Optional, Sequence, Type, Union + +import torch +from torch import Tensor, nn +from torchvision.models.convnext import CNBlock, CNBlockConfig, LayerNorm2d +from torchvision.models.resnet import BasicBlock, Bottleneck, conv1x1 +from torchvision.ops.misc import Conv2dNormActivation +from torchvision.utils import _log_api_usage_once + +from pvnet.models.multimodal.encoders.basic_blocks import AbstractNWPSatelliteEncoder + + +class NaiveEfficientNet(AbstractNWPSatelliteEncoder): + """An implementation of EfficientNet from `efficientnet_pytorch`. + + This model is quite naive, and just stacks the sequence into channels. + """ + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + model_name: str = "efficientnet-b0", + ): + """An implementation of EfficientNet from `efficientnet_pytorch`. + + This model is quite naive, and just stacks the sequence into channels. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + model_name: Name of EfficientNet model to construct. + + Notes: + The `efficientnet_pytorch` package must be installed to use `EncoderNaiveEfficientNet`. + See https://github.com/lukemelas/EfficientNet-PyTorch for install instructions. + """ + + from efficientnet_pytorch import EfficientNet + + super().__init__(sequence_length, image_size_pixels, in_channels, out_features) + + self.model = EfficientNet.from_name( + model_name, + in_channels=in_channels * sequence_length, + image_size=image_size_pixels, + num_classes=out_features, + ) + + def forward(self, x): + """Run model forward""" + bs, s, c, h, w = x.shape + x = x.reshape((bs, s * c, h, w)) + return self.model(x) + + +class NaiveResNet(nn.Module): + """A ResNet model modified from one in torchvision [1]. + + Modified allow different number of input channels. This model is quite naive, and just stacks + the sequence into channels. + + Example use: + ``` + resnet18 = ResNet(BasicBlock, [2, 2, 2, 2]) + resnet50 = ResNet(Bottleneck, [3, 4, 6, 3]) + ``` + + Sources: + [1] https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py + [2] https://pytorch.org/hub/pytorch_vision_resnet + """ + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + layers: List[int] = [2, 2, 2, 2], + block: str = "bottleneck", + zero_init_residual: bool = False, + groups: int = 1, + width_per_group: int = 64, + replace_stride_with_dilation: Optional[List[bool]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ): + """A ResNet model modified from one in torchvision [1]. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + layers: See [1] and [2]. + block: See [1] and [2]. + zero_init_residual: See [1] and [2]. + groups: See [1] and [2]. + width_per_group: See [1] and [2]. + replace_stride_with_dilation: See [1] and [2]. + norm_layer: See [1] and [2]. + + Sources: + [1] https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py + [2] https://pytorch.org/hub/pytorch_vision_resnet + """ + super().__init__() + _log_api_usage_once(self) + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + # Account for stacking sequences into more channels + in_channels = in_channels * sequence_length + + block = { + "basic": BasicBlock, + "bottleneck": Bottleneck, + }[block] + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError( + "replace_stride_with_dilation should be None " + f"or a 3-element tuple, got {replace_stride_with_dilation}" + ) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d( + in_channels, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False + ) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer( + block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0] + ) + self.layer3 = self._make_layer( + block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1] + ) + self.layer4 = self._make_layer( + block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2] + ) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, out_features) + self.final_act = nn.LeakyReLU() + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an + # identity. This improves the model by 0.2~0.3% according to + # https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck) and m.bn3.weight is not None: + nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type] + elif isinstance(m, BasicBlock) and m.bn2.weight is not None: + nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type] + + def _make_layer( + self, + block: Type[Union[BasicBlock, Bottleneck]], + planes: int, + blocks: int, + stride: int = 1, + dilate: bool = False, + ) -> nn.Sequential: + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + self.inplanes, + planes, + stride, + downsample, + self.groups, + self.base_width, + previous_dilation, + norm_layer, + ) + ) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block( + self.inplanes, + planes, + groups=self.groups, + base_width=self.base_width, + dilation=self.dilation, + norm_layer=norm_layer, + ) + ) + + return nn.Sequential(*layers) + + def _forward_impl(self, x: Tensor) -> Tensor: + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + # x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + x = self.final_act(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + """Run model forward""" + bs, s, c, h, w = x.shape + x = x.reshape((bs, s * c, h, w)) + return self._forward_impl(x) + + +class NaiveConvNeXt(nn.Module): + """A NaiveConvNeXt model [1] modified from one in torchvision [2]. + + Mopdified to allow different number of input channels, and smaller spatial inputs. This model is + quite naive, and just stacks the sequence into channels. + + Example usage: + ``` + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 9), + CNBlockConfig(768, None, 3), + ] + + sequence_len = 12 + channels = 2 + pixels=24 + + convnext_tiny = ConvNeXt( + sequence_length=12, + image_size_pixels=24, + in_channels=2, + out_features=128, + block_setting=block_setting, + stochastic_depth_prob=0.1, + ) + ``` + + Sources: + [1] https://arxiv.org/abs/2201.03545 + [2] https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py + [3] https://pytorch.org/vision/main/models/convnext.html + + """ + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float = 0.0, + layer_scale: float = 1e-6, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, + ) -> None: + """A ConvNeXt model [1] modified from one in torchvision [2]. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + block_setting: See [2] and [3]. + stochastic_depth_prob: See [2] and [3]. + layer_scale: See [2] and [3]. + block: See [2] and [3]. + norm_layer: See [2] and [3]. + **kwargs: See [2] and [3]. + + Sources: + [1] https://arxiv.org/abs/2201.03545 + [2] https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py + [3] https://pytorch.org/vision/main/models/convnext.html + """ + super().__init__() + _log_api_usage_once(self) + + if not block_setting: + raise ValueError("The block_setting should not be empty") + elif not ( + isinstance(block_setting, Sequence) + and all([isinstance(s, CNBlockConfig) for s in block_setting]) + ): + raise TypeError("The block_setting should be List[CNBlockConfig]") + + if block is None: + block = CNBlock + + if norm_layer is None: + norm_layer = partial(LayerNorm2d, eps=1e-6) + + layers: List[nn.Module] = [] + + # Account for stacking sequences into more channels + in_channels = in_channels * sequence_length + + # Stem + firstconv_output_channels = block_setting[0].input_channels + layers.append( + Conv2dNormActivation( + in_channels, + firstconv_output_channels, + kernel_size=2, + stride=2, + padding=0, + norm_layer=norm_layer, + activation_layer=None, + bias=True, + ) + ) + + total_stage_blocks = sum(cnf.num_layers for cnf in block_setting) + stage_block_id = 0 + for cnf in block_setting: + # Bottlenecks + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + stage.append(block(cnf.input_channels, layer_scale, sd_prob)) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + if cnf.out_channels is not None: + # Downsampling + layers.append( + nn.Sequential( + norm_layer(cnf.input_channels), + nn.Conv2d(cnf.input_channels, cnf.out_channels, kernel_size=2, stride=2), + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + lastblock = block_setting[-1] + lastconv_output_channels = ( + lastblock.out_channels + if lastblock.out_channels is not None + else lastblock.input_channels + ) + self.classifier = nn.Sequential( + norm_layer(lastconv_output_channels), + nn.Flatten(1), + nn.Linear(lastconv_output_channels, out_features), + ) + + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + """Run model forward""" + bs, s, c, h, w = x.shape + x = x.reshape((bs, s * c, h, w)) + return self._forward_impl(x) diff --git a/pvnet/models/multimodal/encoders/encoders3d.py b/pvnet/models/multimodal/encoders/encoders3d.py new file mode 100644 index 0000000000000000000000000000000000000000..b0df28ab25749bbe7c3b5d02b6dc9aa09696ef9e --- /dev/null +++ b/pvnet/models/multimodal/encoders/encoders3d.py @@ -0,0 +1,402 @@ +"""Encoder modules for the satellite/NWP data based on 3D concolutions. +""" +from typing import List, Union + +import torch +from torch import nn +from torchvision.transforms import CenterCrop + +from pvnet.models.multimodal.encoders.basic_blocks import ( + AbstractNWPSatelliteEncoder, + ResidualConv3dBlock, + ResidualConv3dBlock2, +) + + +class DefaultPVNet(AbstractNWPSatelliteEncoder): + """This is the original encoding module used in PVNet, with a few minor tweaks.""" + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + number_of_conv3d_layers: int = 4, + conv3d_channels: int = 32, + fc_features: int = 128, + spatial_kernel_size: int = 3, + temporal_kernel_size: int = 3, + padding: Union[int, List[int]] = (1, 0, 0), + ): + """This is the original encoding module used in PVNet, with a few minor tweaks. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + number_of_conv3d_layers: Number of convolution 3d layers that are used. + conv3d_channels: Number of channels used in each conv3d layer. + fc_features: number of output nodes out of the hidden fully connected layer. + spatial_kernel_size: The spatial size of the kernel used in the conv3d layers. + temporal_kernel_size: The temporal size of the kernel used in the conv3d layers. + padding: The padding used in the conv3d layers. If an int, the same padding + is used in all dimensions + """ + super().__init__(sequence_length, image_size_pixels, in_channels, out_features) + if isinstance(padding, int): + padding = (padding, padding, padding) + # Check that the output shape of the convolutional layers will be at least 1x1 + cnn_spatial_output_size = ( + image_size_pixels + - ((spatial_kernel_size - 2 * padding[1]) - 1) * number_of_conv3d_layers + ) + cnn_sequence_length = ( + sequence_length + - ((temporal_kernel_size - 2 * padding[0]) - 1) * number_of_conv3d_layers + ) + if not (cnn_spatial_output_size >= 1): + raise ValueError( + f"cannot use this many conv3d layers ({number_of_conv3d_layers}) with this input " + f"spatial size ({image_size_pixels})" + ) + + conv_layers = [] + + conv_layers += [ + nn.Conv3d( + in_channels=in_channels, + out_channels=conv3d_channels, + kernel_size=(temporal_kernel_size, spatial_kernel_size, spatial_kernel_size), + padding=padding, + ), + nn.ELU(), + ] + for i in range(0, number_of_conv3d_layers - 1): + conv_layers += [ + nn.Conv3d( + in_channels=conv3d_channels, + out_channels=conv3d_channels, + kernel_size=(temporal_kernel_size, spatial_kernel_size, spatial_kernel_size), + padding=padding, + ), + nn.ELU(), + ] + + self.conv_layers = nn.Sequential(*conv_layers) + + # Calculate the size of the output of the 3D convolutional layers + cnn_output_size = conv3d_channels * cnn_spatial_output_size**2 * cnn_sequence_length + + self.final_block = nn.Sequential( + nn.Linear(in_features=cnn_output_size, out_features=fc_features), + nn.ELU(), + nn.Linear(in_features=fc_features, out_features=out_features), + nn.ELU(), + ) + + def forward(self, x): + """Run model forward""" + out = self.conv_layers(x) + out = out.reshape(x.shape[0], -1) + + # Fully connected layers + out = self.final_block(out) + + return out + + +class DefaultPVNet2(AbstractNWPSatelliteEncoder): + """The original encoding module used in PVNet, with a few minor tweaks, and batchnorm.""" + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + number_of_conv3d_layers: int = 4, + conv3d_channels: int = 32, + fc_features: int = 128, + batch_norm=True, + fc_dropout=0.2, + ): + """The original encoding module used in PVNet, with a few minor tweaks, and batchnorm. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + number_of_conv3d_layers: Number of convolution 3d layers that are used. + conv3d_channels: Number of channels used in each conv3d layer. + fc_features: number of output nodes out of the hidden fully connected layer. + batch_norm: Whether to include 3D batch normalisation. + fc_dropout: Probability of an element to be zeroed before the last two fully connected + layers. + """ + super().__init__(sequence_length, image_size_pixels, in_channels, out_features) + + # Check that the output shape of the convolutional layers will be at least 1x1 + cnn_spatial_output_size = image_size_pixels - 2 * number_of_conv3d_layers + if not (cnn_spatial_output_size > 0): + raise ValueError( + f"cannot use this many conv3d layers ({number_of_conv3d_layers}) with this input " + f"spatial size ({image_size_pixels})" + ) + + conv_layers = [ + nn.Conv3d( + in_channels=in_channels, + out_channels=conv3d_channels, + kernel_size=(3, 3, 3), + padding=(1, 0, 0), + ), + nn.LeakyReLU(), + ] + if batch_norm: + # Inserted before activation using position -1 + conv_layers.insert(-1, nn.BatchNorm3d(conv3d_channels)) + for i in range(0, number_of_conv3d_layers - 1): + conv_layers += [ + nn.Conv3d( + in_channels=conv3d_channels, + out_channels=conv3d_channels, + kernel_size=(3, 3, 3), + padding=(1, 0, 0), + ), + nn.LeakyReLU(), + ] + if batch_norm: + # Inserted before activation using position -1 + conv_layers.insert(-1, nn.BatchNorm3d(conv3d_channels)) + + self.conv_layers = nn.Sequential(*conv_layers) + + # Calculate the size of the output of the 3D convolutional layers + cnn_output_size = conv3d_channels * cnn_spatial_output_size**2 * sequence_length + + final_block = [ + nn.Linear(in_features=cnn_output_size, out_features=fc_features), + nn.LeakyReLU(), + nn.Linear(in_features=fc_features, out_features=out_features), + nn.LeakyReLU(), + ] + + if fc_dropout > 0: + # Insert after the linear layers + final_block.insert(1, nn.Dropout(fc_dropout)) + final_block.insert(-1, nn.Dropout(fc_dropout)) + + self.final_block = nn.Sequential(*final_block) + + def forward(self, x): + """Run model forward""" + out = self.conv_layers(x) + out = out.reshape(x.shape[0], -1) + + # Fully connected layers + out = self.final_block(out) + + return out + + +class ResConv3DNet2(AbstractNWPSatelliteEncoder): + """3D convolutional network based on ResNet architecture. + + The residual blocks are implemented based on the best performing block in [1]. + + Sources: + [1] https://arxiv.org/pdf/1603.05027.pdf + """ + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + hidden_channels: int = 32, + n_res_blocks: int = 4, + res_block_layers: int = 2, + batch_norm=True, + dropout_frac=0.0, + ): + """Fully connected deep network based on ResNet architecture. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + hidden_channels: Number of channels in middle hidden layers. + n_res_blocks: Number of residual blocks to use. + res_block_layers: Number of Conv3D layers used in each residual block. + batch_norm: Whether to include batch normalisation. + dropout_frac: Probability of an element to be zeroed in the residual pathways. + """ + super().__init__(sequence_length, image_size_pixels, in_channels, out_features) + + model = [ + nn.Conv3d( + in_channels=in_channels, + out_channels=hidden_channels, + kernel_size=(3, 3, 3), + padding=(1, 1, 1), + ), + ] + + for i in range(n_res_blocks): + model.extend( + [ + ResidualConv3dBlock2( + in_channels=hidden_channels, + n_layers=res_block_layers, + dropout_frac=dropout_frac, + batch_norm=batch_norm, + ), + nn.AvgPool3d((1, 2, 2), stride=(1, 2, 2)), + ] + ) + + # Calculate the size of the output of the 3D convolutional layers + final_im_size = image_size_pixels // (2**n_res_blocks) + cnn_output_size = hidden_channels * sequence_length * final_im_size * final_im_size + + model.extend( + [ + nn.ELU(), + nn.Flatten(start_dim=1, end_dim=-1), + nn.Linear(in_features=cnn_output_size, out_features=out_features), + nn.ELU(), + ] + ) + + self.model = nn.Sequential(*model) + + def forward(self, x): + """Run model forward""" + return self.model(x) + + +class EncoderUNET(AbstractNWPSatelliteEncoder): + """An encoder based on emodifed UNet architecture. + + An encoder for satellite and/or NWP data taking inspiration from the kinds of skip + connections in UNet. This differs from an actual UNet in that it does not have upsampling + layers, instead it concats features from different spatial scales, and applies a few extra + conv3d layers. + """ + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + n_downscale: int = 3, + res_block_layers: int = 2, + conv3d_channels: int = 32, + dropout_frac: float = 0.1, + ): + """An encoder based on emodifed UNet architecture. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + n_downscale: Number of conv3d and spatially downscaling layers that are used. + res_block_layers: Number of residual blocks used after each downscale layer. + conv3d_channels: Number of channels used in each conv3d layer. + dropout_frac: Probability of an element to be zeroed in the residual pathways. + """ + cnn_spatial_output = image_size_pixels // (2**n_downscale) + + if not (cnn_spatial_output > 0): + raise ValueError( + f"cannot use this many downscaling layers ({n_downscale}) with this input " + f"spatial size ({image_size_pixels})" + ) + + super().__init__(sequence_length, image_size_pixels, in_channels, out_features) + + self.first_layer = nn.Sequential( + nn.Conv3d( + in_channels=in_channels, + out_channels=conv3d_channels, + kernel_size=(1, 1, 1), + padding=(0, 0, 0), + ), + ResidualConv3dBlock( + in_channels=conv3d_channels, + n_layers=res_block_layers, + dropout_frac=dropout_frac, + ), + ) + + downscale_layers = [] + for _ in range(n_downscale): + downscale_layers += [ + nn.Sequential( + ResidualConv3dBlock( + in_channels=conv3d_channels, + n_layers=res_block_layers, + dropout_frac=dropout_frac, + ), + nn.ELU(), + nn.Conv3d( + in_channels=conv3d_channels, + out_channels=conv3d_channels, + kernel_size=(1, 2, 2), + padding=(0, 0, 0), + stride=(1, 2, 2), + ), + ) + ] + + self.downscale_layers = nn.ModuleList(downscale_layers) + + self.crop_fn = CenterCrop(cnn_spatial_output) + + cat_channels = conv3d_channels * (1 + n_downscale) + self.post_cat_conv = nn.Sequential( + ResidualConv3dBlock( + in_channels=cat_channels, + n_layers=res_block_layers, + ), + nn.ELU(), + nn.Conv3d( + in_channels=cat_channels, + out_channels=conv3d_channels, + kernel_size=(1, 1, 1), + ), + ) + + final_channels = ( + (image_size_pixels // (2**n_downscale)) ** 2 * conv3d_channels * sequence_length + ) + self.final_layer = nn.Sequential( + nn.ELU(), + nn.Linear( + in_features=final_channels, + out_features=out_features, + ), + nn.ELU(), + ) + + def forward(self, x): + """Run model forward""" + out = self.first_layer(x) + outputs = [self.crop_fn(out)] + + for layer in self.downscale_layers: + out = layer(out) + outputs += [self.crop_fn(out)] + + out = torch.cat(outputs, dim=1) + out = self.post_cat_conv(out) + out = torch.flatten(out, start_dim=1) + out = self.final_layer(out) + return out diff --git a/pvnet/models/multimodal/encoders/encodersRNN.py b/pvnet/models/multimodal/encoders/encodersRNN.py new file mode 100644 index 0000000000000000000000000000000000000000..37f7cef68e707be12d1898f7778b23ebf68ead42 --- /dev/null +++ b/pvnet/models/multimodal/encoders/encodersRNN.py @@ -0,0 +1,141 @@ +"""Encoder modules for the satellite/NWP data based on recursive and 2D convolutional layers. +""" + +import torch +from torch import nn + +from pvnet.models.multimodal.encoders.basic_blocks import ( + AbstractNWPSatelliteEncoder, + ImageSequenceEncoder, +) + + +class ConvLSTM(AbstractNWPSatelliteEncoder): + """Convolutional LSTM block from MetNet.""" + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + hidden_channels: int = 32, + num_layers: int = 2, + kernel_size: int = 3, + bias: bool = True, + activation=torch.tanh, + batchnorm=False, + ): + """Convolutional LSTM block from MetNet. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. + hidden_channels: Hidden dimension size. + num_layers: Depth of ConvLSTM cells. + kernel_size: Kernel size. + bias: Whether to add bias. + activation: Activation function for ConvLSTM cells. + batchnorm: Whether to use batch norm. + """ + from metnet.layers.ConvLSTM import ConvLSTM as _ConvLSTM + + super().__init__(sequence_length, image_size_pixels, in_channels, out_features) + + self.conv_lstm = _ConvLSTM( + input_dim=in_channels, + hidden_dim=hidden_channels, + kernel_size=kernel_size, + num_layers=num_layers, + bias=bias, + activation=activation, + batchnorm=batchnorm, + ) + + # Calculate the size of the output of the ConvLSTM network + convlstm_output_size = hidden_channels * image_size_pixels**2 + + self.final_block = nn.Sequential( + nn.Linear(in_features=convlstm_output_size, out_features=out_features), + nn.ELU(), + ) + + def forward(self, x): + """Run model forward""" + + batch_size, channel, seq_len, height, width = x.shape + x = torch.swapaxes(x, 1, 2) + + res, _ = self.conv_lstm(x) + + # Select last state only + out = res[:, -1] + + # Flatten and fully connected layer + out = out.reshape(batch_size, -1) + out = self.final_block(out) + + return out + + +class FlattenLSTM(AbstractNWPSatelliteEncoder): + """Convolutional blocks followed by LSTM.""" + + def __init__( + self, + sequence_length: int, + image_size_pixels: int, + in_channels: int, + out_features: int, + num_layers: int = 2, + number_of_conv2d_layers: int = 4, + conv2d_channels: int = 32, + ): + """Network consisting of 2D spatial convolutional and LSTM sequence encoder. + + Args: + sequence_length: The time sequence length of the data. + image_size_pixels: The spatial size of the image. Assumed square. + in_channels: Number of input channels. + out_features: Number of output features. Also used for LSTM hidden dimension. + num_layers: Number of recurrent layers. E.g., setting num_layers=2 would mean stacking + two LSTMs together to form a stacked LSTM, with the second LSTM taking in outputs of + the first LSTM and computing the final results. + number_of_conv2d_layers: Number of convolution 2D layers that are used. + conv2d_channels: Number of channels used in each conv2d layer. + """ + + super().__init__(sequence_length, image_size_pixels, in_channels, out_features) + + self.lstm = nn.LSTM( + input_size=out_features, + hidden_size=out_features, + num_layers=num_layers, + batch_first=True, + ) + + self.encode_image_sequence = ImageSequenceEncoder( + image_size_pixels=image_size_pixels, + in_channels=in_channels, + number_of_conv2d_layers=number_of_conv2d_layers, + conv2d_channels=conv2d_channels, + fc_features=out_features, + ) + + self.final_block = nn.Sequential( + nn.Linear(in_features=out_features, out_features=out_features), + nn.ELU(), + ) + + def forward(self, x): + """Run model forward""" + encoded_images = self.encode_image_sequence(x) + + _, (_, c_n) = self.lstm(encoded_images) + + # Take only the deepest level hidden cell state + out = self.final_block(c_n[-1]) + + return out diff --git a/pvnet/models/multimodal/linear_networks/__init__.py b/pvnet/models/multimodal/linear_networks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..89926d6a180b0a5c3c5f9d9a60c5b0524e1d76ec --- /dev/null +++ b/pvnet/models/multimodal/linear_networks/__init__.py @@ -0,0 +1 @@ +"""Submodels to combine 1D feature vectors from different sources and make final predictions""" diff --git a/pvnet/models/multimodal/linear_networks/basic_blocks.py b/pvnet/models/multimodal/linear_networks/basic_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..c115c9fb042bc6882a9ce961b543d7d64d9710bd --- /dev/null +++ b/pvnet/models/multimodal/linear_networks/basic_blocks.py @@ -0,0 +1,121 @@ +"""Basic blocks for the lienar networks""" +from abc import ABCMeta, abstractmethod +from collections import OrderedDict + +import torch +from torch import nn + + +class AbstractLinearNetwork(nn.Module, metaclass=ABCMeta): + """Abstract class for a network to combine the features from all the inputs.""" + + def __init__( + self, + in_features: int, + out_features: int, + ): + """Abstract class for a network to combine the features from all the inputs. + + Args: + in_features: Number of input features. + out_features: Number of output features. + """ + super().__init__() + + def cat_modes(self, x): + """Concatenate modes of input data into 1D feature vector""" + if isinstance(x, OrderedDict): + return torch.cat([value for key, value in x.items()], dim=1) + elif isinstance(x, torch.Tensor): + return x + else: + raise ValueError(f"Input of unexpected type {type(x)}") + + @abstractmethod + def forward(self): + """Run model forward""" + pass + + +class ResidualLinearBlock(nn.Module): + """A 1D fully-connected residual block using ELU activations and including optional dropout.""" + + def __init__( + self, + in_features: int, + n_layers: int = 2, + dropout_frac: float = 0.0, + ): + """A 1D fully-connected residual block using ELU activations and including optional dropout. + + Args: + in_features: Number of input features. + n_layers: Number of layers in residual pathway. + dropout_frac: Probability of an element to be zeroed. + """ + super().__init__() + + layers = [] + for i in range(n_layers): + layers += [ + nn.ELU(), + nn.Linear( + in_features=in_features, + out_features=in_features, + ), + nn.Dropout(p=dropout_frac), + ] + self.model = nn.Sequential(*layers) + + def forward(self, x): + """Run model forward""" + return self.model(x) + x + + +class ResidualLinearBlock2(nn.Module): + """Residual block of 'full pre-activation' similar to the block in figure 4(e) of [1]. + + This was the best performing residual block tested in the study. This implementation differs + from that block just by using LeakyReLU activation to avoid dead neuron, and by including + optional dropout in the residual branch. This is also a 1D fully connected layer residual block + rather than a 2D convolutional block. + + Sources: + [1] https://arxiv.org/pdf/1603.05027.pdf + """ + + def __init__( + self, + in_features: int, + n_layers: int = 2, + dropout_frac: float = 0.0, + ): + """Residual block of 'full pre-activation' similar to the block in figure 4(e) of [1]. + + Sources: + [1] https://arxiv.org/pdf/1603.05027.pdf + + Args: + in_features: Number of input features. + n_layers: Number of layers in residual pathway. + dropout_frac: Probability of an element to be zeroed. + """ + super().__init__() + + layers = [] + for i in range(n_layers): + layers += [ + nn.BatchNorm1d(in_features), + nn.Dropout(p=dropout_frac), + nn.LeakyReLU(), + nn.Linear( + in_features=in_features, + out_features=in_features, + ), + ] + + self.model = nn.Sequential(*layers) + + def forward(self, x): + """Run model forward""" + return self.model(x) + x diff --git a/pvnet/models/multimodal/linear_networks/networks.py b/pvnet/models/multimodal/linear_networks/networks.py new file mode 100644 index 0000000000000000000000000000000000000000..69c21d827fabe3aa33df5a87877acd84a96f49e0 --- /dev/null +++ b/pvnet/models/multimodal/linear_networks/networks.py @@ -0,0 +1,332 @@ +"""Linear networks used for the fusion model""" +from torch import nn, rand + +from pvnet.models.multimodal.linear_networks.basic_blocks import ( + AbstractLinearNetwork, + ResidualLinearBlock, + ResidualLinearBlock2, +) + + +class DefaultFCNet(AbstractLinearNetwork): + """Similar to the original FCNet module used in PVNet, with a few minor tweaks. + + This is a 2-layer fully connected block, with internal ELU activations and output ReLU. + """ + + def __init__( + self, + in_features: int, + out_features: int, + fc_hidden_features: int = 128, + ): + """Similar to the original FCNet module used in PVNet, with a few minor tweaks. + + Args: + in_features: Number of input features. + out_features: Number of output features. + fc_hidden_features: Number of features in middle hidden layer. + """ + super().__init__(in_features, out_features) + + self.model = nn.Sequential( + nn.Linear(in_features=in_features, out_features=fc_hidden_features), + nn.ELU(), + nn.Linear(in_features=fc_hidden_features, out_features=out_features), + nn.ReLU(), + ) + + def forward(self, x): + """Run model forward""" + x = self.cat_modes(x) + return self.model(x) + + +class ResFCNet(AbstractLinearNetwork): + """Fully-connected deep network based on ResNet architecture. + + Internally, this network uses ELU activations throughout the residual blocks. + With n_res_blocks=0 this becomes equivalent to `DefaultFCNet`. + """ + + def __init__( + self, + in_features: int, + out_features: int, + fc_hidden_features: int = 128, + n_res_blocks: int = 4, + res_block_layers: int = 2, + dropout_frac: float = 0.2, + ): + """Fully-connected deep network based on ResNet architecture. + + Args: + in_features: Number of input features. + out_features: Number of output features. + fc_hidden_features: Number of features in middle hidden layers. + n_res_blocks: Number of residual blocks to use. + res_block_layers: Number of fully-connected layers used in each residual block. + dropout_frac: Probability of an element to be zeroed in the residual pathways. + """ + super().__init__(in_features, out_features) + + model = [ + nn.Linear(in_features=in_features, out_features=fc_hidden_features), + ] + + for i in range(n_res_blocks): + model += [ + ResidualLinearBlock( + in_features=fc_hidden_features, + n_layers=res_block_layers, + dropout_frac=dropout_frac, + ) + ] + + model += [ + nn.ELU(), + nn.Linear(in_features=fc_hidden_features, out_features=out_features), + nn.LeakyReLU(negative_slope=0.01), + ] + self.model = nn.Sequential(*model) + + def forward(self, x): + """Run model forward""" + x = self.cat_modes(x) + return self.model(x) + + +class ResFCNet2(AbstractLinearNetwork): + """Fully connected deep network based on ResNet architecture. + + This architecture is similar to + `ResFCNet`, except that it uses LeakyReLU activations internally, and batchnorm in the residual + branches. The residual blocks are implemented based on the best performing block in [1]. + + Sources: + [1] https://arxiv.org/pdf/1603.05027.pdf + """ + + def __init__( + self, + in_features: int, + out_features: int, + fc_hidden_features: int = 128, + n_res_blocks: int = 4, + res_block_layers: int = 2, + dropout_frac=0.0, + ): + """Fully connected deep network based on ResNet architecture. + + Args: + in_features: Number of input features. + out_features: Number of output features. + fc_hidden_features: Number of features in middle hidden layers. + n_res_blocks: Number of residual blocks to use. + res_block_layers: Number of fully-connected layers used in each residual block. + dropout_frac: Probability of an element to be zeroed in the residual pathways. + """ + super().__init__(in_features, out_features) + + model = [ + nn.Linear(in_features=in_features, out_features=fc_hidden_features), + ] + + for i in range(n_res_blocks): + model += [ + ResidualLinearBlock2( + in_features=fc_hidden_features, + n_layers=res_block_layers, + dropout_frac=dropout_frac, + ) + ] + + model += [ + nn.LeakyReLU(), + nn.Linear(in_features=fc_hidden_features, out_features=out_features), + nn.LeakyReLU(negative_slope=0.01), + ] + + self.model = nn.Sequential(*model) + + def forward(self, x): + """Run model forward""" + x = self.cat_modes(x) + return self.model(x) + + +class SNN(AbstractLinearNetwork): + """Self normalising neural network implementation borrowed from [1] and proposed in [2]. + + Sources: + [1] https://github.com/tonyduan/snn/blob/master/snn/models.py + [2] https://arxiv.org/pdf/1706.02515v5.pdf + + Args: + in_features: Number of input features. + out_features: Number of output features. + fc_hidden_features: Number of features in middle hidden layers. + n_layers: Number of fully-connected layers used in the network. + dropout_frac: Probability of an element to be zeroed. + + """ + + def __init__( + self, + in_features: int, + out_features: int, + fc_hidden_features: int = 128, + n_layers: int = 10, + dropout_frac: float = 0.0, + ): + """Self normalising neural network implementation borrowed from [1] and proposed in [2]. + + Sources: + [1] https://github.com/tonyduan/snn/blob/master/snn/models.py + [2] https://arxiv.org/pdf/1706.02515v5.pdf + + Args: + in_features: Number of input features. + out_features: Number of output features. + fc_hidden_features: Number of features in middle hidden layers. + n_layers: Number of fully-connected layers used in the network. + dropout_frac: Probability of an element to be zeroed. + + """ + super().__init__(in_features, out_features) + + layers = [ + nn.Linear(in_features, fc_hidden_features, bias=False), + nn.SELU(), + nn.AlphaDropout(p=dropout_frac), + ] + for i in range(1, n_layers - 1): + layers += [ + nn.Linear(fc_hidden_features, fc_hidden_features, bias=False), + nn.SELU(), + nn.AlphaDropout(p=dropout_frac), + ] + layers += [ + nn.Linear(fc_hidden_features, out_features, bias=True), + nn.LeakyReLU(negative_slope=0.01), + ] + + self.network = nn.Sequential(*layers) + self._reset_parameters() + + def forward(self, x): + """Run model forward""" + x = self.cat_modes(x) + return self.network(x) + + def _reset_parameters(self): + for layer in self.network: + if isinstance(layer, nn.Linear): + nn.init.normal_(layer.weight, std=layer.out_features**-0.5) + if layer.bias is not None: + fan_in, _ = nn.init._calculate_fan_in_and_fan_out(layer.weight) + bound = fan_in**-0.5 + nn.init.uniform_(layer.bias, -bound, bound) + + +class TabNet(AbstractLinearNetwork): + """An implmentation of TabNet [1]. + + The implementation comes rom `pytorch_tabnet` and this must be installed for use. + + + Sources: + [1] https://arxiv.org/abs/1908.07442 + """ + + def __init__( + self, + in_features: int, + out_features: int, + n_d=8, + n_a=8, + n_steps=3, + gamma=1.3, + cat_idxs=[], + cat_dims=[], + cat_emb_dim=1, + n_independent=2, + n_shared=2, + epsilon=1e-15, + virtual_batch_size=128, + momentum=0.02, + mask_type="sparsemax", + ): + """An implmentation of TabNet [1]. + + Sources: + [1] https://arxiv.org/abs/1908.07442 + + Args: + in_features: int + Number of input features. + out_features: int + Number of output features. + n_d : int + Dimension of the prediction layer (usually between 4 and 64) + n_a : int + Dimension of the attention layer (usually between 4 and 64) + n_steps : int + Number of successive steps in the network (usually between 3 and 10) + gamma : float + Float above 1, scaling factor for attention updates (usually between 1.0 to 2.0) + cat_idxs : list of int + Index of each categorical column in the dataset + cat_dims : list of int + Number of categories in each categorical column + cat_emb_dim : int or list of int + Size of the embedding of categorical features + if int, all categorical features will have same embedding size + if list of int, every corresponding feature will have specific size + n_independent : int + Number of independent GLU layer in each GLU block (default 2) + n_shared : int + Number of independent GLU layer in each GLU block (default 2) + epsilon : float + Avoid log(0), this should be kept very low + virtual_batch_size : int + Batch size for Ghost Batch Normalization + momentum : float + Float value between 0 and 1 which will be used for momentum in all batch norm + mask_type : str + Either "sparsemax" or "entmax" : this is the masking function to use + """ + from pytorch_tabnet.tab_network import TabNet as _TabNetModel + + super().__init__(in_features, out_features) + + self._tabnet = _TabNetModel( + input_dim=in_features, + output_dim=out_features, + n_d=n_d, + n_a=n_a, + n_steps=n_steps, + gamma=gamma, + cat_idxs=cat_idxs, + cat_dims=cat_dims, + cat_emb_dim=cat_emb_dim, + n_independent=n_independent, + n_shared=n_shared, + epsilon=epsilon, + virtual_batch_size=virtual_batch_size, + momentum=momentum, + mask_type=mask_type, + group_attention_matrix=rand(4, in_features), + ) + + self.activation = nn.LeakyReLU(negative_slope=0.01) + + def forward(self, x): + """Run model forward""" + # TODO: USE THIS LOSS COMPONENT + # loss = self.compute_loss(output, y) + # Add the overall sparsity loss + # loss = loss - self.lambda_sparse * M_loss + x = self.cat_modes(x) + out1, M_loss = self._tabnet(x) + return self.activation(out1) diff --git a/pvnet/models/multimodal/multimodal.py b/pvnet/models/multimodal/multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..2ec6ed710f1177b18afc26627c2b11c3681ff9ff --- /dev/null +++ b/pvnet/models/multimodal/multimodal.py @@ -0,0 +1,417 @@ +"""The default composite model architecture for PVNet""" + +import logging +from collections import OrderedDict +from typing import Any, Optional + +import torch +from omegaconf import DictConfig +from torch import nn + +import pvnet +from pvnet.models.base_model import BaseModel +from pvnet.models.multimodal.basic_blocks import ImageEmbedding +from pvnet.models.multimodal.encoders.basic_blocks import AbstractNWPSatelliteEncoder +from pvnet.models.multimodal.linear_networks.basic_blocks import AbstractLinearNetwork +from pvnet.models.multimodal.site_encoders.basic_blocks import AbstractSitesEncoder +from pvnet.optimizers import AbstractOptimizer + +logger = logging.getLogger(__name__) + + +class Model(BaseModel): + """Neural network which combines information from different sources + + Architecture is roughly as follows: + + - Satellite data, if included, is put through an encoder which transforms it from 4D, with time, + channel, height, and width dimensions to become a 1D feature vector. + - NWP, if included, is put through a similar encoder. + - PV site-level data, if included, is put through an encoder which transforms it from 2D, with + time and system-ID dimensions, to become a 1D feature vector. + - The satellite features*, NWP features*, PV site-level features*, GSP ID embedding*, and sun + paramters* are concatenated into a 1D feature vector and passed through another neural + network to combine them and produce a forecast. + + * if included + """ + + name = "conv3d_sat_nwp" + + def __init__( + self, + output_network: AbstractLinearNetwork, + output_quantiles: Optional[list[float]] = None, + nwp_encoders_dict: Optional[dict[AbstractNWPSatelliteEncoder]] = None, + sat_encoder: Optional[AbstractNWPSatelliteEncoder] = None, + pv_encoder: Optional[AbstractSitesEncoder] = None, + sensor_encoder: Optional[AbstractSitesEncoder] = None, + add_image_embedding_channel: bool = False, + include_gsp_yield_history: bool = True, + include_site_yield_history: Optional[bool] = False, + include_sun: bool = True, + include_time: bool = False, + location_id_mapping: Optional[dict[Any, int]] = None, + embedding_dim: Optional[int] = 16, + forecast_minutes: int = 30, + history_minutes: int = 60, + sat_history_minutes: Optional[int] = None, + min_sat_delay_minutes: Optional[int] = 30, + nwp_forecast_minutes: Optional[DictConfig] = None, + nwp_history_minutes: Optional[DictConfig] = None, + pv_history_minutes: Optional[int] = None, + sensor_history_minutes: Optional[int] = None, + sensor_forecast_minutes: Optional[int] = None, + optimizer: AbstractOptimizer = pvnet.optimizers.Adam(), + target_key: str = "gsp", + interval_minutes: int = 30, + nwp_interval_minutes: Optional[DictConfig] = None, + pv_interval_minutes: int = 5, + sat_interval_minutes: int = 5, + sensor_interval_minutes: int = 30, + timestep_intervals_to_plot: Optional[list[int]] = None, + adapt_batches: Optional[bool] = False, + forecast_minutes_ignore: Optional[int] = 0, + save_validation_results_csv: Optional[bool] = False, + ): + """Neural network which combines information from different sources. + + Notes: + In the args, where it says a module `m` is partially instantiated, it means that a + normal pytorch module will be returned by running `mod = m(**kwargs)`. In this library, + this partial instantiation is generally achieved using partial instantiation via hydra. + However, the arg is still valid as long as `m(**kwargs)` returns a valid pytorch module + - for example if `m` is a regular function. + + Args: + output_network: A partially instantiated pytorch Module class used to combine the 1D + features to produce the forecast. + output_quantiles: A list of float (0.0, 1.0) quantiles to predict values for. If set to + None the output is a single value. + nwp_encoders_dict: A dictionary of partially instantiated pytorch Module class used to + encode the NWP data from 4D into a 1D feature vector from different sources. + sat_encoder: A partially instantiated pytorch Module class used to encode the satellite + data from 4D into a 1D feature vector. + pv_encoder: A partially instantiated pytorch Module class used to encode the site-level + PV data from 2D into a 1D feature vector. + add_image_embedding_channel: Add a channel to the NWP and satellite data with the + embedding of the GSP ID. + include_gsp_yield_history: Include GSP yield data. + include_site_yield_history: Include Site yield data. + include_sun: Include sun azimuth and altitude data. + include_time: Include sine and cosine of dates and times. + location_id_mapping: A dictionary mapping the location ID to an integer. ID embedding is + not used if this is not provided. + embedding_dim: Number of embedding dimensions to use for GSP ID. + forecast_minutes: The amount of minutes that should be forecasted. + history_minutes: The default amount of historical minutes that are used. + sat_history_minutes: Length of recent observations used for satellite inputs. Defaults + to `history_minutes` if not provided. + min_sat_delay_minutes: Minimum delay with respect to t0 of the latest available + satellite image. + nwp_forecast_minutes: Period of future NWP forecast data used as input. Defaults to + `forecast_minutes` if not provided. + nwp_history_minutes: Period of historical NWP forecast used as input. Defaults to + `history_minutes` if not provided. + pv_history_minutes: Length of recent site-level PV data used as + input. Defaults to `history_minutes` if not provided. + optimizer: Optimizer factory function used for network. + target_key: The key of the target variable in the batch. + interval_minutes: The interval between each sample of the target data + nwp_interval_minutes: Dictionary of the intervals between each sample of the NWP + data for each source + pv_interval_minutes: The interval between each sample of the PV data + sat_interval_minutes: The interval between each sample of the satellite data + sensor_interval_minutes: The interval between each sample of the sensor data + timestep_intervals_to_plot: Intervals, in timesteps, to plot in + addition to the full forecast + sensor_encoder: Encoder for sensor data + sensor_history_minutes: Length of recent sensor data used as input. + sensor_forecast_minutes: Length of forecast sensor data used as input. + adapt_batches: If set to true, we attempt to slice the batches to the expected shape for + the model to use. This allows us to overprepare batches and slice from them for the + data we need for a model run. + forecast_minutes_ignore: Number of forecast minutes to ignore when calculating losses. + For example if set to 60, the model doesnt predict the first 60 minutes + save_validation_results_csv: whether to save full csv outputs from validation results. + """ + + self.include_gsp_yield_history = include_gsp_yield_history + self.include_site_yield_history = include_site_yield_history + self.include_sat = sat_encoder is not None + self.include_nwp = nwp_encoders_dict is not None and len(nwp_encoders_dict) != 0 + self.include_pv = pv_encoder is not None + self.include_sun = include_sun + self.include_time = include_time + self.include_sensor = sensor_encoder is not None + self.location_id_mapping = location_id_mapping + self.embedding_dim = embedding_dim + self.add_image_embedding_channel = add_image_embedding_channel + self.interval_minutes = interval_minutes + self.min_sat_delay_minutes = min_sat_delay_minutes + self.adapt_batches = adapt_batches + + if self.location_id_mapping is None: + logger.warning("location_id_mapping` is not provided, " + "defaulting to outdated GSP mapping (0 to 317)") + + # Note 318 is the 2024 UK GSP count, so this is a temporary fix + # for models trained with this default embedding + self.location_id_mapping = {i: i for i in range(318)} + + # in the future location_id_mapping could be None, + # and in this case use_id_embedding should be False + self.use_id_embedding = self.embedding_dim is not None + + if self.use_id_embedding: + num_embeddings = max(self.location_id_mapping.values()) + 1 + + super().__init__( + history_minutes=history_minutes, + forecast_minutes=forecast_minutes, + optimizer=optimizer, + output_quantiles=output_quantiles, + target_key=target_key, + interval_minutes=interval_minutes, + timestep_intervals_to_plot=timestep_intervals_to_plot, + forecast_minutes_ignore=forecast_minutes_ignore, + save_validation_results_csv=save_validation_results_csv + ) + + # Number of features expected by the output_network + # Add to this as network pieces are constructed + fusion_input_features = 0 + + if self.include_sat: + # Param checks + assert sat_history_minutes is not None + + self.sat_sequence_len = ( + sat_history_minutes - min_sat_delay_minutes + ) // sat_interval_minutes + 1 + + self.sat_encoder = sat_encoder( + sequence_length=self.sat_sequence_len, + in_channels=sat_encoder.keywords["in_channels"] + add_image_embedding_channel, + ) + if add_image_embedding_channel: + self.sat_embed = ImageEmbedding( + num_embeddings, self.sat_sequence_len, self.sat_encoder.image_size_pixels + ) + + # Update num features + fusion_input_features += self.sat_encoder.out_features + + if self.include_nwp: + # Param checks + assert nwp_forecast_minutes is not None + assert nwp_history_minutes is not None + + # For each NWP encoder the forecast and history minutes must be set + assert set(nwp_encoders_dict.keys()) == set(nwp_forecast_minutes.keys()) + assert set(nwp_encoders_dict.keys()) == set(nwp_history_minutes.keys()) + + if nwp_interval_minutes is None: + nwp_interval_minutes = dict.fromkeys(nwp_encoders_dict.keys(), 60) + + self.nwp_encoders_dict = torch.nn.ModuleDict() + if add_image_embedding_channel: + self.nwp_embed_dict = torch.nn.ModuleDict() + + for nwp_source in nwp_encoders_dict.keys(): + nwp_sequence_len = ( + nwp_history_minutes[nwp_source] // nwp_interval_minutes[nwp_source] + + nwp_forecast_minutes[nwp_source] // nwp_interval_minutes[nwp_source] + + 1 + ) + + self.nwp_encoders_dict[nwp_source] = nwp_encoders_dict[nwp_source]( + sequence_length=nwp_sequence_len, + in_channels=( + nwp_encoders_dict[nwp_source].keywords["in_channels"] + + add_image_embedding_channel + ), + ) + if add_image_embedding_channel: + self.nwp_embed_dict[nwp_source] = ImageEmbedding( + num_embeddings, + nwp_sequence_len, + self.nwp_encoders_dict[nwp_source].image_size_pixels, + ) + + # Update num features + fusion_input_features += self.nwp_encoders_dict[nwp_source].out_features + + if self.include_pv: + assert pv_history_minutes is not None + + self.pv_encoder = pv_encoder( + sequence_length=pv_history_minutes // pv_interval_minutes + 1, + target_key_to_use=self._target_key, + input_key_to_use="site", + ) + + # Update num features + fusion_input_features += self.pv_encoder.out_features + + if self.include_sensor: + if sensor_history_minutes is None: + sensor_history_minutes = history_minutes + if sensor_forecast_minutes is None: + sensor_forecast_minutes = forecast_minutes + + self.sensor_encoder = sensor_encoder( + sequence_length=sensor_history_minutes // sensor_interval_minutes + + sensor_forecast_minutes // sensor_interval_minutes + + 1, + target_key_to_use=self._target_key, + input_key_to_use="sensor", + ) + + # Update num features + fusion_input_features += self.sensor_encoder.out_features + + if self.use_id_embedding: + self.embed = nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim) + + # Update num features + fusion_input_features += embedding_dim + + if self.include_sun: + self.sun_fc1 = nn.Linear( + in_features=2 + * (self.forecast_len + self.forecast_len_ignore + self.history_len + 1), + out_features=16, + ) + + # Update num features + fusion_input_features += 16 + + if self.include_time: + self.time_fc1 = nn.Linear( + in_features=4 + * (self.forecast_len + self.forecast_len_ignore + self.history_len + 1), + out_features=32, + ) + + # Update num features + fusion_input_features += 32 + + if include_gsp_yield_history: + # Update num features + fusion_input_features += self.history_len + + if include_site_yield_history: + # Update num features + fusion_input_features += self.history_len + 1 + + self.output_network = output_network( + in_features=fusion_input_features, + out_features=self.num_output_features, + ) + + self.save_hyperparameters() + + def forward(self, x): + """Run model forward""" + + if self.adapt_batches: + x = self._adapt_batch(x) + + if self.use_id_embedding: + # eg: x['gsp_id] = [1] with location_id_mapping = {1:0}, would give [0] + id = torch.tensor( + [self.location_id_mapping[i.item()] for i in x[f"{self._target_key}_id"]], + device=self.device, + dtype=torch.int64, + ) + + modes = OrderedDict() + # ******************* Satellite imagery ************************* + if self.include_sat: + # Shape: batch_size, seq_length, channel, height, width + sat_data = x["satellite_actual"][:, : self.sat_sequence_len] + sat_data = torch.swapaxes(sat_data, 1, 2).float() # switch time and channels + + if self.add_image_embedding_channel: + sat_data = self.sat_embed(sat_data, id) + modes["sat"] = self.sat_encoder(sat_data) + + # *********************** NWP Data ************************************ + if self.include_nwp: + # Loop through potentially many NMPs + for nwp_source in self.nwp_encoders_dict: + # shape: batch_size, seq_len, n_chans, height, width + nwp_data = x["nwp"][nwp_source]["nwp"].float() + nwp_data = torch.swapaxes(nwp_data, 1, 2) # switch time and channels + # Some NWP variables can overflow into NaNs when normalised if they have extreme + # tails + nwp_data = torch.clip(nwp_data, min=-50, max=50) + + if self.add_image_embedding_channel: + nwp_data = self.nwp_embed_dict[nwp_source](nwp_data, id) + + nwp_out = self.nwp_encoders_dict[nwp_source](nwp_data) + modes[f"nwp/{nwp_source}"] = nwp_out + + # *********************** Site Data ************************************* + # Add site-level yield history + if self.include_site_yield_history: + site_history = x["site"][:, : self.history_len + 1].float() + site_history = site_history.reshape(site_history.shape[0], -1) + modes["site"] = site_history + + # Add site-level yield history through PV encoder + if self.include_pv: + if self._target_key != "site": + modes["site"] = self.pv_encoder(x) + else: + # Target is PV, so only take the history + # Copy batch + x_tmp = x.copy() + x_tmp["site"] = x_tmp["site"][:, : self.history_len + 1] + modes["site"] = self.pv_encoder(x_tmp) + + # *********************** GSP Data ************************************ + # add gsp yield history + if self.include_gsp_yield_history: + gsp_history = x["gsp"][:, : self.history_len].float() + gsp_history = gsp_history.reshape(gsp_history.shape[0], -1) + modes["gsp"] = gsp_history + + # ********************** Embedding of GSP/Site ID ******************** + if self.use_id_embedding: + modes["id"] = self.embed(id) + + if self.include_sun: + # Use only new direct keys + sun = torch.cat( + ( + x["solar_azimuth"], + x["solar_elevation"], + ), + dim=1, + ).float() + sun = self.sun_fc1(sun) + modes["sun"] = sun + + if self.include_time: + time = torch.cat( + ( + x[f"{self._target_key}_date_sin"], + x[f"{self._target_key}_date_cos"], + x[f"{self._target_key}_time_sin"], + x[f"{self._target_key}_time_cos"], + ), + dim=1, + ).float() + time = self.time_fc1(time) + modes["time"] = time + + out = self.output_network(modes) + + if self.use_quantile_regression: + # Shape: batch_size, seq_length * num_quantiles + out = out.reshape(out.shape[0], self.forecast_len, len(self.output_quantiles)) + + return out diff --git a/pvnet/models/multimodal/readme.md b/pvnet/models/multimodal/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..66f385878eb00bfa4f7e89cace490efcdc6b0c18 --- /dev/null +++ b/pvnet/models/multimodal/readme.md @@ -0,0 +1,11 @@ +## Multimodal model architecture + +These models fusion models to predict GSP power output based on NWP, non-HRV satellite, GSP output history, solor coordinates, and GSP ID. + +The core model is `multimodel.Model`, and its architecture is shown in the diagram below. + +![multimodal_model_diagram](https://github.com/openclimatefix/PVNet/assets/41546094/118393fa-52ec-4bfe-a0a3-268c94c25f1e) + +This model uses encoders which take 4D (time, channel, x, y) inputs of NWP and satellite and encode them into 1D feature vectors. Different encoders are contained inside `encoders`. + +Different choices for the fusion model are contained inside `linear_networks`. diff --git a/pvnet/models/multimodal/site_encoders/__init__.py b/pvnet/models/multimodal/site_encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..225604d3940db496740868960d7141fe3e36e449 --- /dev/null +++ b/pvnet/models/multimodal/site_encoders/__init__.py @@ -0,0 +1 @@ +"""Submodels to encode site-level PV data""" diff --git a/pvnet/models/multimodal/site_encoders/basic_blocks.py b/pvnet/models/multimodal/site_encoders/basic_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..525ba74a9c0ce456123fd40030ab71c8008f2824 --- /dev/null +++ b/pvnet/models/multimodal/site_encoders/basic_blocks.py @@ -0,0 +1,35 @@ +"""Basic blocks for PV-site encoders""" +from abc import ABCMeta, abstractmethod + +from torch import nn + + +class AbstractSitesEncoder(nn.Module, metaclass=ABCMeta): + """Abstract class for encoder for output data from multiple PV sites. + + The encoder will take an input of shape (batch_size, sequence_length, num_sites) + and return an output of shape (batch_size, out_features). + """ + + def __init__( + self, + sequence_length: int, + num_sites: int, + out_features: int, + ): + """Abstract class for PV site-level encoder. + + Args: + sequence_length: The time sequence length of the data. + num_sites: Number of PV sites in the input data. + out_features: Number of output features. + """ + super().__init__() + self.sequence_length = sequence_length + self.num_sites = num_sites + self.out_features = out_features + + @abstractmethod + def forward(self): + """Run model forward""" + pass diff --git a/pvnet/models/multimodal/site_encoders/encoders.py b/pvnet/models/multimodal/site_encoders/encoders.py new file mode 100644 index 0000000000000000000000000000000000000000..543cd1b219e0e3d4692c1b993bd72a0432b0d198 --- /dev/null +++ b/pvnet/models/multimodal/site_encoders/encoders.py @@ -0,0 +1,284 @@ +"""Encoder modules for the site-level PV data. + +""" + +import einops +import torch +from torch import nn + +from pvnet.models.multimodal.linear_networks.networks import ResFCNet2 +from pvnet.models.multimodal.site_encoders.basic_blocks import AbstractSitesEncoder + + +class SimpleLearnedAggregator(AbstractSitesEncoder): + """A simple model which learns a different weighted-average across all PV sites for each GSP. + + Each sequence from each site is independently encodeded through some dense layers wih skip- + connections, then the encoded form of each sequence is aggregated through a learned weighted-sum + and finally put through more dense layers. + + This model was written to be a simplified version of a single-headed attention layer. + """ + + def __init__( + self, + sequence_length: int, + num_sites: int, + out_features: int, + value_dim: int = 10, + value_enc_resblocks: int = 2, + final_resblocks: int = 2, + ): + """A simple sequence encoder and weighted-average model. + + Args: + sequence_length: The time sequence length of the data. + num_sites: Number of PV sites in the input data. + out_features: Number of output features. + value_dim: The number of features in each encoded sequence. Similar to the value + dimension in single- or multi-head attention. + value_dim: The number of features in each encoded sequence. Similar to the value + dimension in single- or multi-head attention. + value_enc_resblocks: Number of residual blocks in the value-encoder sub-network. + final_resblocks: Number of residual blocks in the final sub-network. + """ + + super().__init__(sequence_length, num_sites, out_features) + + # Network used to encode each PV site sequence + self._value_encoder = nn.Sequential( + ResFCNet2( + in_features=sequence_length, + out_features=value_dim, + fc_hidden_features=value_dim, + n_res_blocks=value_enc_resblocks, + res_block_layers=2, + dropout_frac=0, + ), + ) + + # The learned weighted average is stored in an embedding layer for ease of use + self._attention_network = nn.Sequential( + nn.Embedding(318, num_sites), + nn.Softmax(dim=1), + ) + + # Network used to process weighted average + self.output_network = ResFCNet2( + in_features=value_dim, + out_features=out_features, + fc_hidden_features=value_dim, + n_res_blocks=final_resblocks, + res_block_layers=2, + dropout_frac=0, + ) + + def _calculate_attention(self, x): + gsp_ids = x["gsp_id"].squeeze().int() + attention = self._attention_network(gsp_ids) + return attention + + def _encode_value(self, x): + # Shape: [batch size, sequence length, PV site] + pv_site_seqs = x["pv"].float() + batch_size = pv_site_seqs.shape[0] + + pv_site_seqs = pv_site_seqs.swapaxes(1, 2).flatten(0, 1) + + x_seq_enc = self._value_encoder(pv_site_seqs) + x_seq_out = x_seq_enc.unflatten(0, (batch_size, self.num_sites)) + return x_seq_out + + def forward(self, x): + """Run model forward""" + # Output has shape: [batch size, num_sites, value_dim] + encodeded_seqs = self._encode_value(x) + + # Calculate learned averaging weights + attn_avg_weights = self._calculate_attention(x) + + # Take weighted average across num_sites + value_weighted_avg = (encodeded_seqs * attn_avg_weights.unsqueeze(-1)).sum(dim=1) + + # Put through final processing layers + x_out = self.output_network(value_weighted_avg) + + return x_out + + +class SingleAttentionNetwork(AbstractSitesEncoder): + """A simple attention-based model with a single multihead attention layer + + For the attention layer the query is based on the target alone, the key is based on the + input ID and the recent input data, the value is based on the recent input data. + + """ + + def __init__( + self, + sequence_length: int, + num_sites: int, + out_features: int, + kdim: int = 10, + id_embed_dim: int = 10, + num_heads: int = 2, + n_kv_res_blocks: int = 2, + kv_res_block_layers: int = 2, + use_id_in_value: bool = False, + target_id_dim: int = 318, + target_key_to_use: str = "gsp", + input_key_to_use: str = "site", + num_channels: int = 1, + num_sites_in_inference: int = 1, + ): + """A simple attention-based model with a single multihead attention layer + + Args: + sequence_length: The time sequence length of the data. + num_sites: Number of sites in the input data. + out_features: Number of output features. In this network this is also the embed and + value dimension in the multi-head attention layer. + kdim: The dimensions used the keys. + id_embed_dim: Number of dimensiosn used in the site ID embedding layer(s). + num_heads: Number of parallel attention heads. Note that `out_features` will be split + across `num_heads` so `out_features` must be a multiple of `num_heads`. + n_kv_res_blocks: Number of residual blocks to use in the key and value encoders. + kv_res_block_layers: Number of fully-connected layers used in each residual block within + the key and value encoders. + use_id_in_value: Whether to use a site ID embedding in network used to produce the + value for the attention layer. + target_id_dim: The number of unique IDs. + target_key_to_use: The key to use for the target in the attention layer. + input_key_to_use: The key to use for the input in the attention layer. + num_channels: Number of channels in the input data. For single site generation, + this will be 1, as there is not channel dimension, for Sensors, + this will probably be higher than that + num_sites_in_inference: Number of sites to use in inference. + This is used to determine the number of sites to use in the + attention layer, for a single site, 1 works, while for multiple sites + (such as multiple sensors), this would be higher than that + + """ + super().__init__(sequence_length, num_sites, out_features) + self.sequence_length = sequence_length + self.target_id_embedding = nn.Embedding(target_id_dim, out_features) + self.site_id_embedding = nn.Embedding(num_sites, id_embed_dim) + self._ids = nn.parameter.Parameter(torch.arange(num_sites), requires_grad=False) + self.use_id_in_value = use_id_in_value + self.target_key_to_use = target_key_to_use + self.input_key_to_use = input_key_to_use + self.num_channels = num_channels + self.num_sites_in_inference = num_sites_in_inference + + if use_id_in_value: + self.value_id_embedding = nn.Embedding(num_sites, id_embed_dim) + + self._value_encoder = nn.Sequential( + ResFCNet2( + in_features=sequence_length * self.num_channels + + int(use_id_in_value) * id_embed_dim, + out_features=out_features, + fc_hidden_features=sequence_length * self.num_channels, + n_res_blocks=n_kv_res_blocks, + res_block_layers=kv_res_block_layers, + dropout_frac=0, + ), + ) + + self._key_encoder = nn.Sequential( + ResFCNet2( + in_features=id_embed_dim + sequence_length * self.num_channels, + out_features=kdim, + fc_hidden_features=id_embed_dim + sequence_length * self.num_channels, + n_res_blocks=n_kv_res_blocks, + res_block_layers=kv_res_block_layers, + dropout_frac=0, + ), + ) + + self.multihead_attn = nn.MultiheadAttention( + embed_dim=out_features, + kdim=kdim, + vdim=out_features, + num_heads=num_heads, + batch_first=True, + ) + + def _encode_inputs(self, x): + # Shape: [batch size, sequence length, number of sites] + # Shape: [batch size, station_id, sequence length, channels] + input_data = x[f"{self.input_key_to_use}"] + if len(input_data.shape) == 2: # one site per sample + input_data = input_data.unsqueeze(-1) # add dimension of 1 to end to make 3D + if len(input_data.shape) == 4: # Has multiple channels + input_data = input_data[:, :, : self.sequence_length] + input_data = einops.rearrange(input_data, "b id s c -> b (s c) id") + else: + input_data = input_data[:, : self.sequence_length] + site_seqs = input_data.float() + batch_size = site_seqs.shape[0] + site_seqs = site_seqs.swapaxes(1, 2) # [batch size, Site ID, sequence length] + return site_seqs, batch_size + + def _encode_query(self, x): + # Select the first one + if self.target_key_to_use == "gsp": + # GSP seems to have a different structure + ids = x[f"{self.target_key_to_use}_id"] + else: + ids = x[f"{self.input_key_to_use}_id"] + ids = ids.int() + query = self.target_id_embedding(ids).unsqueeze(1) + return query + + def _encode_key(self, x): + site_seqs, batch_size = self._encode_inputs(x) + + # site ID embeddings are the same for each sample + site_id_embed = torch.tile(self.site_id_embedding(self._ids), (batch_size, 1, 1)) + # Each concated (site sequence, site ID embedding) is processed with encoder + x_seq_in = torch.cat((site_seqs, site_id_embed), dim=2).flatten(0, 1) + key = self._key_encoder(x_seq_in) + + # Reshape to [batch size, site, kdim] + key = key.unflatten(0, (batch_size, self.num_sites)) + return key + + def _encode_value(self, x): + site_seqs, batch_size = self._encode_inputs(x) + + if self.use_id_in_value: + # site ID embeddings are the same for each sample + site_id_embed = torch.tile(self.value_id_embedding(self._ids), (batch_size, 1, 1)) + # Each concated (site sequence, site ID embedding) is processed with encoder + x_seq_in = torch.cat((site_seqs, site_id_embed), dim=2).flatten(0, 1) + else: + # Encode each site sequence independently + x_seq_in = site_seqs.flatten(0, 1) + value = self._value_encoder(x_seq_in) + + # Reshape to [batch size, site, vdim] + value = value.unflatten(0, (batch_size, self.num_sites)) + return value + + def _attention_forward(self, x, average_attn_weights=True): + query = self._encode_query(x) + key = self._encode_key(x) + value = self._encode_value(x) + attn_output, attn_weights = self.multihead_attn( + query, key, value, average_attn_weights=average_attn_weights + ) + + return attn_output, attn_weights + + def forward(self, x): + """Run model forward""" + # Do slicing here to only get history + attn_output, attn_output_weights = self._attention_forward(x) + + # Reshape from [batch_size, 1, vdim] to [batch_size, vdim] + x_out = attn_output.squeeze() + if len(x_out.shape) == 1: + x_out = x_out.unsqueeze(0) + + return x_out diff --git a/pvnet/models/multimodal/unimodal_teacher.py b/pvnet/models/multimodal/unimodal_teacher.py new file mode 100644 index 0000000000000000000000000000000000000000..19fbc2664e3f2cc505b8c36b10db28d9f2fff239 --- /dev/null +++ b/pvnet/models/multimodal/unimodal_teacher.py @@ -0,0 +1,447 @@ +"""The default composite model architecture for PVNet""" + +import glob +from collections import OrderedDict +from typing import Any, Optional + +import hydra +import torch +import torch.nn.functional as F +from pyaml_env import parse_config +from torch import nn + +import pvnet +from pvnet.models.base_model import BaseModel +from pvnet.models.multimodal.linear_networks.basic_blocks import AbstractLinearNetwork +from pvnet.optimizers import AbstractOptimizer + + +class Model(BaseModel): + """Neural network which combines information from different sources + + The network is trained via unimodal teachers [1]. + + Architecture is roughly as follows: + + - Satellite data, if included, is put through an encoder which transforms it from 4D, with time, + channel, height, and width dimensions to become a 1D feature vector. + - NWP, if included, is put through a similar encoder. + - PV site-level data, if included, is put through an encoder which transforms it from 2D, with + time and system-ID dimensions, to become a 1D feature vector. + - The satellite features*, NWP features*, PV site-level features*, GSP ID embedding*, and sun + paramters* are concatenated into a 1D feature vector and passed through another neural + network to combine them and produce a forecast. + + * if included + [1] https://arxiv.org/pdf/2305.01233.pdf + """ + + name = "unimodal_teacher" + + def __init__( + self, + output_network: AbstractLinearNetwork, + output_quantiles: Optional[list[float]] = None, + include_gsp_yield_history: bool = True, + include_sun: bool = True, + location_id_mapping: Optional[dict[Any, int]] = None, + embedding_dim: Optional[int] = 16, + forecast_minutes: int = 30, + history_minutes: int = 60, + optimizer: AbstractOptimizer = pvnet.optimizers.Adam(), + mode_teacher_dict: dict = {}, + val_best: bool = True, + cold_start: bool = True, + enc_loss_frac: float = 0.3, + adapt_batches: Optional[bool] = False, + ): + """Neural network which combines information from different sources. + + The network is trained via unimodal teachers [1]. + + [1] https://arxiv.org/pdf/2305.01233.pdf + + Notes: + In the args, where it says a module `m` is partially instantiated, it means that a + normal pytorch module will be returned by running `mod = m(**kwargs)`. In this library, + this partial instantiation is generally achieved using partial instantiation via hydra. + However, the arg is still valid as long as `m(**kwargs)` returns a valid pytorch module + - for example if `m` is a regular function. + + Args: + output_network: A partially instatiated pytorch Module class used to combine the 1D + features to produce the forecast. + output_quantiles: A list of float (0.0, 1.0) quantiles to predict values for. If set to + None the output is a single value. + include_gsp_yield_history: Include GSP yield data. + include_sun: Include sun azimuth and altitude data. + location_id_mapping: A dictionary mapping the location ID to an integer. ID embedding is + not used if this is not provided. + embedding_dim: Number of embedding dimensions to use for GSP ID + forecast_minutes: The amount of minutes that should be forecasted. + history_minutes: The default amount of historical minutes that are used. + optimizer: Optimizer factory function used for network. + mode_teacher_dict: A dictionary of paths to different model checkpoint directories, + which will be used as the unimodal teachers. + val_best: Whether to load the model which performed best on the validation set. Else the + last checkpoint is loaded. + cold_start: Whether to train the uni-modal encoders from scratch. Else start them with + weights from the uni-modal teachers. + enc_loss_frac: Fraction of total loss attributed to the teacher encoders. + adapt_batches: If set to true, we attempt to slice the batches to the expected shape for + the model to use. This allows us to overprepare batches and slice from them for the + data we need for a model run. + """ + + self.include_gsp_yield_history = include_gsp_yield_history + self.include_sun = include_sun + self.location_id_mapping = location_id_mapping + self.embedding_dim = embedding_dim + self.enc_loss_frac = enc_loss_frac + self.include_sat = False + self.include_nwp = False + self.include_pv = False + self.adapt_batches = adapt_batches + + self.use_id_embedding = location_id_mapping is not None + + if self.use_id_embedding: + num_embeddings = max(location_id_mapping.values()) + 1 + + # This is set but modified later based on the teachers + self.add_image_embedding_channel = False + + super().__init__( + history_minutes=history_minutes, + forecast_minutes=forecast_minutes, + optimizer=optimizer, + output_quantiles=output_quantiles, + target_key="gsp", + ) + + # Number of features expected by the output_network + # Add to this as network pices are constructed + fusion_input_features = 0 + + self.teacher_models = torch.nn.ModuleDict() + self.mode_teacher_dict = mode_teacher_dict + + for mode, path in mode_teacher_dict.items(): + # load teacher model and freeze its weights + self.teacher_models[mode] = self.get_unimodal_encoder(path, True, val_best=val_best) + + for param in self.teacher_models[mode].parameters(): + param.requires_grad = False + + # Recreate model as student + mode_student_model = self.get_unimodal_encoder( + path, load_weights=(not cold_start), val_best=val_best + ) + + if mode == "sat": + self.include_sat = True + self.sat_sequence_len = mode_student_model.sat_sequence_len + self.sat_encoder = mode_student_model.sat_encoder + + if mode_student_model.add_image_embedding_channel: + self.sat_embed = mode_student_model.sat_embed + self.add_image_embedding_channel = True + + fusion_input_features += self.sat_encoder.out_features + + elif mode == "site": + self.include_pv = True + self.site_encoder = mode_student_model.site_encoder + fusion_input_features += self.site_encoder.out_features + + elif mode.startswith("nwp"): + nwp_source = mode.removeprefix("nwp/") + + if not self.include_nwp: + self.include_nwp = True + self.nwp_encoders_dict = torch.nn.ModuleDict() + + if mode_student_model.add_image_embedding_channel: + self.add_image_embedding_channel = True + self.nwp_embed_dict = torch.nn.ModuleDict() + + self.nwp_encoders_dict[nwp_source] = mode_student_model.nwp_encoders_dict[ + nwp_source + ] + + if self.add_image_embedding_channel: + self.nwp_embed_dict[nwp_source] = mode_student_model.nwp_embed_dict[nwp_source] + + fusion_input_features += self.nwp_encoders_dict[nwp_source].out_features + + if self.embedding_dim: + self.embed = nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim) + fusion_input_features += embedding_dim + + if self.include_sun: + self.sun_fc1 = nn.Linear( + in_features=2 * (self.forecast_len + self.history_len + 1), + out_features=16, + ) + fusion_input_features += 16 + + if include_gsp_yield_history: + fusion_input_features += self.history_len + + self.output_network = output_network( + in_features=fusion_input_features, + out_features=self.num_output_features, + ) + + self.save_hyperparameters() + + def get_unimodal_encoder(self, path, load_weights, val_best): + """Load a model to function as a unimodal teacher""" + + model_config = parse_config(f"{path}/model_config.yaml") + + # Load the teacher model + encoder = hydra.utils.instantiate(model_config) + + if load_weights: + if val_best: + # Only one epoch (best) saved per model + files = glob.glob(f"{path}/epoch*.ckpt") + assert len(files) == 1 + checkpoint = torch.load(files[0], map_location="cpu") + else: + checkpoint = torch.load(f"{path}/last.ckpt", map_location="cpu") + + encoder.load_state_dict(state_dict=checkpoint["state_dict"]) + return encoder + + def teacher_forward(self, x): + """Run the teacher models and return their encodings""" + + if self.use_id_embedding: + # eg: x['gsp_id] = [1] with location_id_mapping = {1:0}, would give [0] + id = torch.tensor( + [self.location_id_mapping[i.item()] for i in x[f"{self._target_key}_id"]], + device=self.device, + dtype=torch.int64, + ) + + modes = OrderedDict() + for mode, teacher_model in self.teacher_models.items(): + # ******************* Satellite imagery ************************* + if mode == "sat": + # Shape: batch_size, seq_length, channel, height, width + sat_data = x["satellite_actual"][:, : teacher_model.sat_sequence_len] + sat_data = torch.swapaxes(sat_data, 1, 2).float() # switch time and channels + + if self.add_image_embedding_channel: + sat_data = teacher_model.sat_embed(sat_data, id) + + modes[mode] = teacher_model.sat_encoder(sat_data) + + # *********************** NWP Data ************************************ + if mode.startswith("nwp"): + nwp_source = mode.removeprefix("nwp/") + + # shape: batch_size, seq_len, n_chans, height, width + nwp_data = x["nwp"][nwp_source]["nwp"].float() + nwp_data = torch.swapaxes(nwp_data, 1, 2) # switch time and channels + nwp_data = torch.clip(nwp_data, min=-50, max=50) + if teacher_model.add_image_embedding_channel: + nwp_data = teacher_model.nwp_embed_dict[nwp_source](nwp_data, id) + + nwp_out = teacher_model.nwp_encoders_dict[nwp_source](nwp_data) + modes[mode] = nwp_out + + # *********************** PV Data ************************************* + # Add site-level PV yield + if mode == "site": + modes[mode] = teacher_model.site_encoder(x) + + return modes + + def forward(self, x, return_modes=False): + """Run model forward""" + + if self.adapt_batches: + x = self._adapt_batch(x) + + if self.use_id_embedding: + # eg: x['gsp_id] = [1] with location_id_mapping = {1:0}, would give [0] + id = torch.tensor( + [self.location_id_mapping[i.item()] for i in x[f"{self._target_key}_id"]], + device=self.device, + dtype=torch.int64, + ) + + modes = OrderedDict() + # ******************* Satellite imagery ************************* + if self.include_sat: + # Shape: batch_size, seq_length, channel, height, width + sat_data = x["satellite_actual"][:, : self.sat_sequence_len] + sat_data = torch.swapaxes(sat_data, 1, 2).float() # switch time and channels + + if self.add_image_embedding_channel: + sat_data = self.sat_embed(sat_data, id) + modes["sat"] = self.sat_encoder(sat_data) + + # *********************** NWP Data ************************************ + if self.include_nwp: + # Loop through potentially many NMPs + for nwp_source in self.nwp_encoders_dict: + # shape: batch_size, seq_len, n_chans, height, width + nwp_data = x["nwp"][nwp_source]["nwp"].float() + nwp_data = torch.swapaxes(nwp_data, 1, 2) # switch time and channels + # Some NWP variables can overflow into NaNs when normalised if they have extreme + # tails + nwp_data = torch.clip(nwp_data, min=-50, max=50) + + if self.add_image_embedding_channel: + nwp_data = self.nwp_embed_dict[nwp_source](nwp_data, id) + + nwp_out = self.nwp_encoders_dict[nwp_source](nwp_data) + modes[f"nwp/{nwp_source}"] = nwp_out + + # *********************** PV Data ************************************* + # Add site-level PV yield + if self.include_pv: + if self._target_key != "site": + modes["site"] = self.site_encoder(x) + else: + # Target is PV, so only take the history + pv_history = x["pv"][:, : self.history_len].float() + modes["site"] = self.site_encoder(pv_history) + + # *********************** GSP Data ************************************ + # add gsp yield history + if self.include_gsp_yield_history: + gsp_history = x["gsp"][:, : self.history_len].float() + gsp_history = gsp_history.reshape(gsp_history.shape[0], -1) + modes["gsp"] = gsp_history + + # ********************** Embedding of GSP ID ******************** + if self.use_id_embedding: + modes["id"] = self.embed(id) + + if self.include_sun: + # Use only new direct keys + sun = torch.cat( + ( + x["solar_azimuth"], + x["solar_elevation"], + ), + dim=1, + ).float() + sun = self.sun_fc1(sun) + modes["sun"] = sun + + out = self.output_network(modes) + + if self.use_quantile_regression: + # Shape: batch_size, seq_length * num_quantiles + out = out.reshape(out.shape[0], self.forecast_len, len(self.output_quantiles)) + + if return_modes: + return out, modes + else: + return out + + def _calculate_teacher_loss(self, modes, teacher_modes): + enc_losses = {} + for m, enc in teacher_modes.items(): + enc_losses[f"enc_loss/{m}"] = F.l1_loss(enc, modes[m]) + enc_losses["enc_loss/total"] = sum([v for k, v in enc_losses.items()]) + return enc_losses + + def training_step(self, batch, batch_idx): + """Run training step""" + y_hat, modes = self.forward(batch, return_modes=True) + y = batch[self._target_key][:, -self.forecast_len :, 0] + + losses = self._calculate_common_losses(y, y_hat) + + teacher_modes = self.teacher_forward(batch) + teacher_loss = self._calculate_teacher_loss(modes, teacher_modes) + losses.update(teacher_loss) + + if self.use_quantile_regression: + opt_target = losses["quantile_loss"] + else: + opt_target = losses["MAE"] + + t_loss = teacher_loss["enc_loss/total"] + + # The scales of the two losses + l_s = opt_target.detach() + tl_s = max(t_loss.detach(), 1e-9) + + # opt_target = t_loss/tl_s * l_s * self.enc_loss_frac + opt_target * (1-self.enc_loss_frac) + losses["opt_loss"] = t_loss / tl_s * l_s * self.enc_loss_frac + opt_target * ( + 1 - self.enc_loss_frac + ) + + losses = {f"{k}/train": v for k, v in losses.items()} + self._training_accumulate_log(batch, batch_idx, losses, y_hat) + + return losses["opt_loss/train"] + + def convert_to_multimodal_model(self, config): + """Convert the model into a multimodal model class whilst preserving weights""" + config = config.copy() + + if "cold_start" in config: + del config["cold_start"] + + config["_target_"] = "pvnet.models.multimodal.multimodal.Model" + + sources = [] + for mode, path in config["mode_teacher_dict"].items(): + model_config = parse_config(f"{path}/model_config.yaml") + + if mode.startswith("nwp"): + nwp_source = mode.removeprefix("nwp/") + if "nwp_encoders_dict" in config: + for key in ["nwp_encoders_dict", "nwp_history_minutes", "nwp_forecast_minutes"]: + config[key][nwp_source] = model_config[key][nwp_source] + sources.append("nwp") + else: + for key in ["nwp_encoders_dict", "nwp_history_minutes", "nwp_forecast_minutes"]: + config[key] = {nwp_source: model_config[key][nwp_source]} + config["add_image_embedding_channel"] = model_config["add_image_embedding_channel"] + + elif mode == "sat": + for key in [ + "sat_encoder", + "add_image_embedding_channel", + "min_sat_delay_minutes", + "sat_history_minutes", + ]: + config[key] = model_config[key] + sources.append("sat") + + elif mode == "site": + for key in ["site_encoder", "site_history_minutes"]: + config[key] = model_config[key] + sources.append("site") + + del config["mode_teacher_dict"] + + # Load the teacher model + multimodal_model = hydra.utils.instantiate(config) + + if "sat" in sources: + multimodal_model.sat_encoder.load_state_dict(self.sat_encoder.state_dict()) + if "nwp" in sources: + multimodal_model.nwp_encoders_dict.load_state_dict(self.nwp_encoders_dict.state_dict()) + if "site" in sources: + multimodal_model.site_encoder.load_state_dict(self.site_encoder.state_dict()) + + multimodal_model.output_network.load_state_dict(self.output_network.state_dict()) + + if self.embedding_dim: + multimodal_model.embed.load_state_dict(self.embed.state_dict()) + + if self.include_sun: + multimodal_model.sun_fc1.load_state_dict(self.sun_fc1.state_dict()) + + return multimodal_model, config diff --git a/pvnet/models/utils.py b/pvnet/models/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6eb9a892836d55fb5fff65bcd8379332b360e9ea --- /dev/null +++ b/pvnet/models/utils.py @@ -0,0 +1,123 @@ +"""Utility functions""" + +import logging + +import numpy as np +import torch + +logger = logging.getLogger(__name__) + +logger = logging.getLogger(__name__) + + +class PredAccumulator: + """A class for accumulating y-predictions using grad accumulation and small batch size. + + Attributes: + _y_hats (list[torch.Tensor]): List of prediction tensors + """ + + def __init__(self): + """Prediction accumulator""" + self._y_hats = [] + + def __bool__(self): + return len(self._y_hats) > 0 + + def append(self, y_hat: torch.Tensor): + """Append a sub-batch of predictions""" + self._y_hats.append(y_hat) + + def flush(self) -> torch.Tensor: + """Return all appended predictions as single tensor and remove from accumulated store.""" + y_hat = torch.cat(self._y_hats, dim=0) + self._y_hats = [] + return y_hat + + +class DictListAccumulator: + """Abstract class for accumulating dictionaries of lists""" + + @staticmethod + def _dict_list_append(d1, d2): + for k, v in d2.items(): + d1[k].append(v) + + @staticmethod + def _dict_init_list(d): + return {k: [v] for k, v in d.items()} + + +class MetricAccumulator(DictListAccumulator): + """Dictionary of metrics accumulator. + + A class for accumulating, and finding the mean of logging metrics when using grad + accumulation and the batch size is small. + + Attributes: + _metrics (Dict[str, list[float]]): Dictionary containing lists of metrics. + """ + + def __init__(self): + """Dictionary of metrics accumulator.""" + self._metrics = {} + + def __bool__(self): + return self._metrics != {} + + def append(self, loss_dict: dict[str, float]): + """Append lictionary of metrics to self""" + if not self: + self._metrics = self._dict_init_list(loss_dict) + else: + self._dict_list_append(self._metrics, loss_dict) + + def flush(self) -> dict[str, float]: + """Calculate mean of all accumulated metrics and clear""" + mean_metrics = {k: np.mean(v) for k, v in self._metrics.items()} + self._metrics = {} + return mean_metrics + + +class BatchAccumulator(DictListAccumulator): + """A class for accumulating batches when using grad accumulation and the batch size is small. + + Attributes: + _batches (Dict[str, list[torch.Tensor]]): Dictionary containing lists of metrics. + """ + + def __init__(self, key_to_keep: str = "gsp"): + """Batch accumulator""" + self._batches = {} + self.key_to_keep = key_to_keep + + def __bool__(self): + return self._batches != {} + + # @staticmethod + def _filter_batch_dict(self, d): + keep_keys = [ + self.key_to_keep, + f"{self.key_to_keep}_id", + f"{self.key_to_keep}_t0_idx", + f"{self.key_to_keep}_time_utc", + ] + return {k: v for k, v in d.items() if k in keep_keys} + + def append(self, batch: dict[str, list[torch.Tensor]]): + """Append batch to self""" + if not self: + self._batches = self._dict_init_list(self._filter_batch_dict(batch)) + else: + self._dict_list_append(self._batches, self._filter_batch_dict(batch)) + + def flush(self) -> dict[str, list[torch.Tensor]]: + """Concatenate all accumulated batches, return, and clear self""" + batch = {} + for k, v in self._batches.items(): + if k == f"{self.key_to_keep}_t0_idx": + batch[k] = v[0] + else: + batch[k] = torch.cat(v, dim=0) + self._batches = {} + return batch diff --git a/pvnet/optimizers.py b/pvnet/optimizers.py new file mode 100644 index 0000000000000000000000000000000000000000..704f5889175a8a525b3ee75b058899f27224def5 --- /dev/null +++ b/pvnet/optimizers.py @@ -0,0 +1,200 @@ +"""Optimizer factory-function classes. +""" + +from abc import ABC, abstractmethod + +import torch + + +class AbstractOptimizer(ABC): + """Abstract class for optimizer + + Optimizer classes will be used by model like: + > OptimizerGenerator = AbstractOptimizer() + > optimizer = OptimizerGenerator(model) + The returned object `optimizer` must be something that may be returned by `pytorch_lightning`'s + `configure_optimizers()` method. + See : + https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#configure-optimizers + + """ + + @abstractmethod + def __call__(self): + """Abstract call""" + pass + + +class Adam(AbstractOptimizer): + """Adam optimizer""" + + def __init__(self, lr=0.0005, **kwargs): + """Adam optimizer""" + self.lr = lr + self.kwargs = kwargs + + def __call__(self, model): + """Return optimizer""" + return torch.optim.Adam(model.parameters(), lr=self.lr, **self.kwargs) + + +class AdamW(AbstractOptimizer): + """AdamW optimizer""" + + def __init__(self, lr=0.0005, **kwargs): + """AdamW optimizer""" + self.lr = lr + self.kwargs = kwargs + + def __call__(self, model): + """Return optimizer""" + return torch.optim.AdamW(model.parameters(), lr=self.lr, **self.kwargs) + + +def find_submodule_parameters(model, search_modules): + """Finds all parameters within given submodule types + + Args: + model: torch Module to search through + search_modules: List of submodule types to search for + """ + if isinstance(model, search_modules): + return model.parameters() + + children = list(model.children()) + if len(children) == 0: + return [] + else: + params = [] + for c in children: + params += find_submodule_parameters(c, search_modules) + return params + + +def find_other_than_submodule_parameters(model, ignore_modules): + """Finds all parameters not with given submodule types + + Args: + model: torch Module to search through + ignore_modules: List of submodule types to ignore + """ + if isinstance(model, ignore_modules): + return [] + + children = list(model.children()) + if len(children) == 0: + return model.parameters() + else: + params = [] + for c in children: + params += find_other_than_submodule_parameters(c, ignore_modules) + return params + + +class EmbAdamWReduceLROnPlateau(AbstractOptimizer): + """AdamW optimizer and reduce on plateau scheduler""" + + def __init__( + self, lr=0.0005, weight_decay=0.01, patience=3, factor=0.5, threshold=2e-4, **opt_kwargs + ): + """AdamW optimizer and reduce on plateau scheduler""" + self.lr = lr + self.weight_decay = weight_decay + self.patience = patience + self.factor = factor + self.threshold = threshold + self.opt_kwargs = opt_kwargs + + def __call__(self, model): + """Return optimizer""" + + search_modules = (torch.nn.Embedding,) + + no_decay = find_submodule_parameters(model, search_modules) + decay = find_other_than_submodule_parameters(model, search_modules) + + optim_groups = [ + {"params": decay, "weight_decay": self.weight_decay}, + {"params": no_decay, "weight_decay": 0.0}, + ] + opt = torch.optim.AdamW(optim_groups, lr=self.lr, **self.opt_kwargs) + + sch = torch.optim.lr_scheduler.ReduceLROnPlateau( + opt, + factor=self.factor, + patience=self.patience, + threshold=self.threshold, + ) + sch = { + "scheduler": sch, + "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", + } + return [opt], [sch] + + +class AdamWReduceLROnPlateau(AbstractOptimizer): + """AdamW optimizer and reduce on plateau scheduler""" + + def __init__( + self, lr=0.0005, patience=3, factor=0.5, threshold=2e-4, step_freq=None, **opt_kwargs + ): + """AdamW optimizer and reduce on plateau scheduler""" + self._lr = lr + self.patience = patience + self.factor = factor + self.threshold = threshold + self.step_freq = step_freq + self.opt_kwargs = opt_kwargs + + def _call_multi(self, model): + remaining_params = {k: p for k, p in model.named_parameters()} + + group_args = [] + + for key in self._lr.keys(): + if key == "default": + continue + + submodule_params = [] + for param_name in list(remaining_params.keys()): + if param_name.startswith(key): + submodule_params += [remaining_params.pop(param_name)] + + group_args += [{"params": submodule_params, "lr": self._lr[key]}] + + remaining_params = [p for k, p in remaining_params.items()] + group_args += [{"params": remaining_params}] + + opt = torch.optim.AdamW( + group_args, lr=self._lr["default"] if model.lr is None else model.lr, **self.opt_kwargs + ) + sch = { + "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau( + opt, + factor=self.factor, + patience=self.patience, + threshold=self.threshold, + ), + "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", + } + + return [opt], [sch] + + def __call__(self, model): + """Return optimizer""" + if not isinstance(self._lr, float): + return self._call_multi(model) + else: + default_lr = self._lr if model.lr is None else model.lr + opt = torch.optim.AdamW(model.parameters(), lr=default_lr, **self.opt_kwargs) + sch = torch.optim.lr_scheduler.ReduceLROnPlateau( + opt, + factor=self.factor, + patience=self.patience, + threshold=self.threshold, + ) + sch = { + "scheduler": sch, + "monitor": "quantile_loss/val" if model.use_quantile_regression else "MAE/val", + } + return [opt], [sch] diff --git a/pvnet/training.py b/pvnet/training.py new file mode 100644 index 0000000000000000000000000000000000000000..91205c15f5b15eff24167cd65d2568105c9896a1 --- /dev/null +++ b/pvnet/training.py @@ -0,0 +1,183 @@ +"""Training""" +import os +import shutil +from typing import Optional + +import hydra +import torch +from lightning.pytorch import ( + Callback, + LightningDataModule, + LightningModule, + Trainer, + seed_everything, +) +from lightning.pytorch.callbacks import ModelCheckpoint +from lightning.pytorch.loggers import Logger +from lightning.pytorch.loggers.wandb import WandbLogger +from omegaconf import DictConfig, OmegaConf + +from pvnet import utils + +log = utils.get_logger(__name__) + +torch.set_default_dtype(torch.float32) + + +def _callbacks_to_phase(callbacks, phase): + for c in callbacks: + if hasattr(c, "switch_phase"): + c.switch_phase(phase) + + +def resolve_monitor_loss(output_quantiles): + """Return the desired metric to monitor based on whether quantile regression is being used. + + The adds the option to use something like: + monitor: "${resolve_monitor_loss:${model.output_quantiles}}" + + in early stopping and model checkpoint callbacks so the callbacks config does not need to be + modified depending on whether quantile regression is being used or not. + """ + if output_quantiles is None: + return "MAE/val" + else: + return "quantile_loss/val" + + +OmegaConf.register_new_resolver("resolve_monitor_loss", resolve_monitor_loss) + + +def train(config: DictConfig) -> Optional[float]: + """Contains training pipeline. + + Instantiates all PyTorch Lightning objects from config. + + Args: + config (DictConfig): Configuration composed by Hydra. + + Returns: + Optional[float]: Metric score for hyperparameter optimization. + """ + + # Set seed for random number generators in pytorch, numpy and python.random + if "seed" in config: + seed_everything(config.seed, workers=True) + + # Init lightning datamodule + log.info(f"Instantiating datamodule <{config.datamodule._target_}>") + datamodule: LightningDataModule = hydra.utils.instantiate(config.datamodule) + + # Init lightning model + log.info(f"Instantiating model <{config.model._target_}>") + model: LightningModule = hydra.utils.instantiate(config.model) + + # Init lightning loggers + loggers: list[Logger] = [] + if "logger" in config: + for _, lg_conf in config.logger.items(): + if "_target_" in lg_conf: + log.info(f"Instantiating logger <{lg_conf._target_}>") + loggers.append(hydra.utils.instantiate(lg_conf)) + + # Init lightning callbacks + callbacks: list[Callback] = [] + if "callbacks" in config: + for _, cb_conf in config.callbacks.items(): + if "_target_" in cb_conf: + log.info(f"Instantiating callback <{cb_conf._target_}>") + callbacks.append(hydra.utils.instantiate(cb_conf)) + + # Align the wandb id with the checkpoint path + # - only works if wandb logger and model checkpoint used + # - this makes it easy to push the model to huggingface + use_wandb_logger = False + for logger in loggers: + log.info(f"{logger}") + if isinstance(logger, WandbLogger): + use_wandb_logger = True + wandb_logger = logger + break + + if use_wandb_logger: + for callback in callbacks: + log.info(f"{callback}") + if isinstance(callback, ModelCheckpoint): + # Need to call the .experiment property to initialise the logger + wandb_logger.experiment + callback.dirpath = "/".join( + callback.dirpath.split("/")[:-1] + [wandb_logger.version] + ) + # Also save model config here - this makes for easy model push to huggingface + os.makedirs(callback.dirpath, exist_ok=True) + OmegaConf.save(config.model, f"{callback.dirpath}/model_config.yaml") + + # Similarly save the data config + data_config = config.datamodule.configuration + if data_config is None: + # Data config can be none if using presaved batches. We go to the presaved + # batches to get the data config + data_config = f"{config.datamodule.sample_dir}/data_configuration.yaml" + + assert os.path.isfile(data_config), f"Data config file not found: {data_config}" + shutil.copyfile(data_config, f"{callback.dirpath}/data_config.yaml") + + # upload configuration up to wandb + OmegaConf.save(config, "./experiment_config.yaml") + wandb_logger.experiment.save( + f"{callback.dirpath}/data_config.yaml", callback.dirpath + ) + wandb_logger.experiment.save("./experiment_config.yaml") + + break + + should_pretrain = False + for c in callbacks: + should_pretrain |= hasattr(c, "training_phase") and c.training_phase == "pretrain" + + if should_pretrain: + _callbacks_to_phase(callbacks, "pretrain") + + trainer: Trainer = hydra.utils.instantiate( + config.trainer, + logger=loggers, + _convert_="partial", + callbacks=callbacks, + ) + + # TODO: remove this option + if should_pretrain: + # Pre-train the model + raise NotImplementedError("Pre-training is not yet supported") + # The parameter `block_nwp_and_sat` is not available in data-sampler + # If pretraining is re-supported in the future it is likely any pre-training logic should + # go here or perhaps in the callbacks + # datamodule.block_nwp_and_sat = True + + trainer.fit(model=model, datamodule=datamodule) + + _callbacks_to_phase(callbacks, "main") + + trainer.should_stop = False + + # Train the model completely + trainer.fit(model=model, datamodule=datamodule) + + # Make sure everything closed properly + log.info("Finalizing!") + utils.finish( + config=config, + model=model, + datamodule=datamodule, + trainer=trainer, + callbacks=callbacks, + loggers=loggers, + ) + + # Print path to best checkpoint + log.info(f"Best checkpoint path:\n{trainer.checkpoint_callback.best_model_path}") + + # Return metric score for hyperparameter optimization + optimized_metric = config.get("optimized_metric") + if optimized_metric: + return trainer.callback_metrics[optimized_metric] diff --git a/pvnet/utils.py b/pvnet/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..833cbc36f75737bcc35b96fee1699fa0b2cbbd75 --- /dev/null +++ b/pvnet/utils.py @@ -0,0 +1,321 @@ +"""Utils""" +import logging +import warnings +from collections.abc import Sequence +from typing import Optional + +import lightning.pytorch as pl +import matplotlib.pyplot as plt +import pandas as pd +import pylab +import rich.syntax +import rich.tree +import xarray as xr +from lightning.pytorch.loggers import Logger +from lightning.pytorch.utilities import rank_zero_only +from ocf_data_sampler.select.location import Location +from omegaconf import DictConfig, OmegaConf + + +def get_logger(name=__name__, level=logging.INFO) -> logging.Logger: + """Initializes multi-GPU-friendly python logger.""" + + logger = logging.getLogger(name) + logger.setLevel(level) + + # this ensures all logging levels get marked with the rank zero decorator + # otherwise logs would get multiplied for each GPU process in multi-GPU setup + for level in ( + "debug", + "info", + "warning", + "error", + "exception", + "fatal", + "critical", + ): + setattr(logger, level, rank_zero_only(getattr(logger, level))) + + return logger + + +class GSPLocationLookup: + """Query object for GSP location from GSP ID""" + + def __init__(self, x_osgb: xr.DataArray, y_osgb: xr.DataArray): + """Query object for GSP location from GSP ID + + Args: + x_osgb: DataArray of the OSGB x-coordinate for any given GSP ID + y_osgb: DataArray of the OSGB y-coordinate for any given GSP ID + + """ + self.x_osgb = x_osgb + self.y_osgb = y_osgb + + def __call__(self, gsp_id: int) -> Location: + """Returns the locations for the input GSP IDs. + + Args: + gsp_id: Integer ID of the GSP + """ + return Location( + x=self.x_osgb.sel(gsp_id=gsp_id).item(), + y=self.y_osgb.sel(gsp_id=gsp_id).item(), + id=gsp_id, + ) + + +class SiteLocationLookup: + """Query object for site location from site ID""" + + def __init__(self, long: xr.DataArray, lat: xr.DataArray): + """Query object for site location from site ID + + Args: + long: DataArray of the longitude coordinates for any given site ID + lat: DataArray of the latitude coordinates for any given site ID + + """ + self.longitude = long + self.latitude = lat + + def __call__(self, site_id: int) -> Location: + """Returns the locations for the input site IDs. + + Args: + site_id: Integer ID of the site + """ + return Location( + coordinate_system="lon_lat", + x=self.longitude.sel(pv_system_id=site_id).item(), + y=self.latitude.sel(pv_system_id=site_id).item(), + id=site_id, + ) + + +def extras(config: DictConfig) -> None: + """A couple of optional utilities. + + Controlled by main config file: + - disabling warnings + - easier access to debug mode + - forcing debug friendly configuration + + Modifies DictConfig in place. + + Args: + config (DictConfig): Configuration composed by Hydra. + """ + + log = get_logger() + + # enable adding new keys to config + OmegaConf.set_struct(config, False) + + # disable python warnings if + if config.get("ignore_warnings"): + log.info("Disabling python warnings! ") + warnings.filterwarnings("ignore") + + # set if + if config.get("debug"): + log.info("Running in debug mode! ") + config.trainer.fast_dev_run = True + + # force debugger friendly configuration if + if config.trainer.get("fast_dev_run"): + log.info("Forcing debugger friendly configuration! ") + # Debuggers don't like GPUs or multiprocessing + if config.trainer.get("gpus"): + config.trainer.gpus = 0 + if config.datamodule.get("pin_memory"): + config.datamodule.pin_memory = False + if config.datamodule.get("num_workers"): + config.datamodule.num_workers = 0 + + # disable adding new keys to config + OmegaConf.set_struct(config, True) + + +@rank_zero_only +def print_config( + config: DictConfig, + fields: Sequence[str] = ( + "trainer", + "model", + "datamodule", + "callbacks", + "logger", + "seed", + ), + resolve: bool = True, +) -> None: + """Prints content of DictConfig using Rich library and its tree structure. + + Args: + config (DictConfig): Configuration composed by Hydra. + fields (Sequence[str], optional): Determines which main fields from config will + be printed and in what order. + resolve (bool, optional): Whether to resolve reference fields of DictConfig. + """ + + style = "dim" + tree = rich.tree.Tree("CONFIG", style=style, guide_style=style) + + for field in fields: + branch = tree.add(field, style=style, guide_style=style) + + config_section = config.get(field) + branch_content = str(config_section) + if isinstance(config_section, DictConfig): + branch_content = OmegaConf.to_yaml(config_section, resolve=resolve) + + branch.add(rich.syntax.Syntax(branch_content, "yaml")) + + rich.print(tree) + + with open("config_tree.txt", "w") as fp: + rich.print(tree, file=fp) + + +def empty(*args, **kwargs): + """Returns nothing""" + pass + + +@rank_zero_only +def log_hyperparameters( + config: DictConfig, + model: pl.LightningModule, + datamodule: pl.LightningDataModule, + trainer: pl.Trainer, + callbacks: list[pl.Callback], + logger: list[Logger], +) -> None: + """This method controls which parameters from Hydra config are saved by Lightning loggers. + + Additionaly saves: + - number of trainable model parameters + """ + + hparams = {} + + # choose which parts of hydra config will be saved to loggers + hparams["trainer"] = config["trainer"] + hparams["model"] = config["model"] + hparams["datamodule"] = config["datamodule"] + if "seed" in config: + hparams["seed"] = config["seed"] + if "callbacks" in config: + hparams["callbacks"] = config["callbacks"] + + # save number of model parameters + hparams["model/params_total"] = sum(p.numel() for p in model.parameters()) + hparams["model/params_trainable"] = sum( + p.numel() for p in model.parameters() if p.requires_grad + ) + hparams["model/params_not_trainable"] = sum( + p.numel() for p in model.parameters() if not p.requires_grad + ) + + # send hparams to all loggers + trainer.logger.log_hyperparams(hparams) + + # disable logging any more hyperparameters for all loggers + # this is just a trick to prevent trainer from logging hparams of model, + # since we already did that above + trainer.logger.log_hyperparams = empty + + +def finish( + config: DictConfig, + model: pl.LightningModule, + datamodule: pl.LightningDataModule, + trainer: pl.Trainer, + callbacks: list[pl.Callback], + loggers: list[Logger], +) -> None: + """Makes sure everything closed properly.""" + + # without this sweeps with wandb logger might crash! + if any([isinstance(logger, pl.loggers.wandb.WandbLogger) for logger in loggers]): + import wandb + + wandb.finish() + + +def plot_batch_forecasts( + batch, + y_hat, + batch_idx=None, + quantiles=None, + key_to_plot: str = "gsp", + timesteps_to_plot: Optional[list[int]] = None, +): + """Plot a batch of data and the forecast from that batch""" + + def _get_numpy(key): + return batch[key].cpu().numpy().squeeze() + + y_key = key_to_plot + y_id_key = f"{key_to_plot}_id" + time_utc_key = f"{key_to_plot}_time_utc" + y = batch[y_key].cpu().numpy() # Select the one it is trained on + y_hat = y_hat.cpu().numpy() + # Select between the timesteps in timesteps to plot + plotting_name = key_to_plot.upper() + + gsp_ids = batch[y_id_key].cpu().numpy().squeeze() + + times_utc = batch[time_utc_key].cpu().numpy().squeeze().astype("datetime64[ns]") + times_utc = [pd.to_datetime(t) for t in times_utc] + if timesteps_to_plot is not None: + y = y[:, timesteps_to_plot[0] : timesteps_to_plot[1]] + y_hat = y_hat[:, timesteps_to_plot[0] : timesteps_to_plot[1]] + times_utc = [t[timesteps_to_plot[0] : timesteps_to_plot[1]] for t in times_utc] + + batch_size = y.shape[0] + + fig, axes = plt.subplots(4, 4, figsize=(16, 16)) + + for i, ax in enumerate(axes.ravel()): + if i >= batch_size: + ax.axis("off") + continue + ax.plot(times_utc[i], y[i], marker=".", color="k", label=r"$y$") + + if quantiles is None: + ax.plot( + times_utc[i][-len(y_hat[i]) :], y_hat[i], marker=".", color="r", label=r"$\hat{y}$" + ) + else: + cm = pylab.get_cmap("twilight") + for nq, q in enumerate(quantiles): + ax.plot( + times_utc[i][-len(y_hat[i]) :], + y_hat[i, :, nq], + color=cm(q), + label=r"$\hat{y}$" + f"({q})", + alpha=0.7, + ) + + ax.set_title(f"ID: {gsp_ids[i]} | {times_utc[i][0].date()}", fontsize="small") + + xticks = [t for t in times_utc[i] if t.minute == 0][::2] + ax.set_xticks(ticks=xticks, labels=[f"{t.hour:02}" for t in xticks], rotation=90) + ax.grid() + + axes[0, 0].legend(loc="best") + + for ax in axes[-1, :]: + ax.set_xlabel("Time (hour of day)") + + if batch_idx is not None: + title = f"Normed {plotting_name} output : batch_idx={batch_idx}" + else: + title = f"Normed {plotting_name} output" + plt.suptitle(title) + plt.tight_layout() + + return fig diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..644f0b62922fe17aa169f131fa41e3c70720fe1d --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,130 @@ +[project] +name="PVNet" +description = "PVNet" +authors = [{name="Peter Dudfield", email="info@openclimatefix.org"}] +dynamic = ["version", "readme"] +license={file="LICENCE"} + +dependencies = [ + "ocf-data-sampler>=0.2.10", + "numpy", + "pandas", + "matplotlib", + "xarray", + "h5netcdf", + "torch>=2.0.0", + "lightning", + "torchvision", + "pytest", + "pytest-cov", + "typer", + "sqlalchemy", + "fsspec[s3]", + "wandb", + "huggingface-hub", + "tqdm", + "omegaconf", + "hydra-core", + "rich", + "einops", +] + +[tool.setuptools.dynamic] +version = {attr = "pvnet.__version__"} +readme = {file = "README.md", content-type = "text/markdown"} + +[tool.setuptools.package-dir] +"pvnet" = "pvnet" + +[project.optional-dependencies] +dev=[ + "pvlive-api", + "ruff", + "mypy", + "pre-commit", + "pytest", + "pytest-cov", +] +all_models=[ + "pytorch-tabnet", + "efficientnet_pytorch" +] +all=["PVNet[dev,all_models]"] + +[tool.mypy] +exclude = [ + "^tests/", +] +disallow_untyped_defs = true +disallow_any_unimported = true +no_implicit_optional = true +check_untyped_defs = true +warn_return_any = true +warn_unused_ignores = true +show_error_codes = true +warn_unreachable = true + +[[tool.mypy.overrides]] +module = [ +] +ignore_missing_imports = true + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = "-ra -q" +testpaths = [ + "tests", +] + +[tool.ruff] +line-length = 100 +exclude = [ + ".ipynb_checkpoints", + "configs.example", + ".bzr", + ".direnv", + ".eggs", + ".git", + ".hg", + ".mypy_cache", + ".nox", + ".pants.d", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv", + "tests", +] + +# Assume Python 3.10. +target-version = "py310" +fix = false + +[tool.ruff.lint] +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" +# Allow autofix for all enabled rules (when `--fix`) is provided. +fixable = ["A", "B", "C", "D", "E", "F", "I"] +unfixable = [] +select = ["E", "F", "D", "I"] +ignore-init-module-imports = true +# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default. +ignore = ["D200","D202","D210","D212","D415","D105",] + +[tool.ruff.lint.mccabe] +# Unlike Flake8, default to a complexity level of 10. +max-complexity = 10 + +[tool.ruff.lint.pydocstyle] +# Use Google-style docstrings. +convention = "google" + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401", "E402"] diff --git a/pytorch_model.bin b/pytorch_model.bin index 1e67ce22194800fc35efbd93cc477f5933ab9a92..b4765a6eb99291cbbafc3d218fbb0fa4c861fbdc 100644 --- a/pytorch_model.bin +++ b/pytorch_model.bin @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:89b7f251302eea1cf46944ad254770e4ba2ab1ef9d16a7c12c17fdd6d6554361 -size 35254402 +oid sha256:83f96be8259c2e2c3f03b54d28a9c28a556cc531578bde5e159ff13368d22c44 +size 1202599131 diff --git a/run.py b/run.py new file mode 100644 index 0000000000000000000000000000000000000000..dca2fafa854389f6cf1c6c16a770c6f2843460e4 --- /dev/null +++ b/run.py @@ -0,0 +1,62 @@ +"""Run training +""" + +import os + +import torch + +try: + torch.multiprocessing.set_start_method("spawn") + import torch.multiprocessing as mp + + mp.set_start_method("spawn") +except RuntimeError: + pass + +import logging +import sys + +# Tired of seeing these warnings +import warnings + +import hydra +from omegaconf import DictConfig +from sqlalchemy import exc as sa_exc + +warnings.filterwarnings("ignore", category=sa_exc.SAWarning) + +logging.basicConfig(stream=sys.stdout, level=logging.ERROR) + +os.environ["HYDRA_FULL_ERROR"] = "1" + + +# this file can be run for example using +# python run.py experiment=example_simple + + +@hydra.main(config_path="configs/", config_name="config.yaml", version_base="1.2") +def main(config: DictConfig): + """Runs training""" + # Imports should be nested inside @hydra.main to optimize tab completion + # Read more here: https://github.com/facebookresearch/hydra/issues/934 + from pvnet.training import train + from pvnet.utils import extras, print_config + + # A couple of optional utilities: + # - disabling python warnings + # - easier access to debug mode + # - forcing debug friendly configuration + # - forcing multi-gpu friendly configuration + # You can safely get rid of this line if you don't want those + extras(config) + + # Pretty print config using Rich library + if config.get("print_config"): + print_config(config, resolve=True) + + # Train model + return train(config) + + +if __name__ == "__main__": + main() diff --git a/scripts/backtest_sites.py b/scripts/backtest_sites.py new file mode 100644 index 0000000000000000000000000000000000000000..7779da3cb8af4cdb00356cb8060420f413de6cad --- /dev/null +++ b/scripts/backtest_sites.py @@ -0,0 +1,539 @@ +""" +A script to run backtest for PVNet for specific sites + +Use: + +- This script uses hydra to construct the config, just like in `run.py`. So you need to make sure + that the data config is set up appropriate for the model being run in this script +- The PVNet model checkpoint; the time range over which to make predictions are made; + the site ids to produce forecasts for and the output directory where the results + near the top of the script as hard coded user variables. These should be changed. + +``` +python scripts/backtest_sites.py +``` + +""" + +try: + import torch.multiprocessing as mp + + mp.set_start_method("spawn", force=True) + mp.set_sharing_strategy("file_system") +except RuntimeError: + pass + +import json +import logging +import os +import sys + +import hydra +import numpy as np +import pandas as pd +import torch +import xarray as xr +from huggingface_hub import hf_hub_download +from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME +from ocf_data_sampler.sample.base import batch_to_tensor, copy_batch_to_device +from ocf_datapipes.batch import ( + BatchKey, + NumpyBatch, + stack_np_examples_into_batch, +) +from ocf_datapipes.config.load import load_yaml_configuration +from ocf_datapipes.load.pv.pv import OpenPVFromNetCDFIterDataPipe +from ocf_datapipes.training.common import create_t0_and_loc_datapipes +from ocf_datapipes.training.pvnet_site import ( + DictDatasetIterDataPipe, + _get_datapipes_dict, + construct_sliced_data_pipeline, + split_dataset_dict_dp, +) +from ocf_datapipes.utils.consts import ELEVATION_MEAN, ELEVATION_STD +from omegaconf import DictConfig +from torch.utils.data import DataLoader, IterDataPipe, functional_datapipe +from torch.utils.data.datapipes.iter import IterableWrapper +from tqdm import tqdm + +from pvnet.load_model import get_model_from_checkpoints +from pvnet.utils import SiteLocationLookup + +# ------------------------------------------------------------------ +# USER CONFIGURED VARIABLES TO RUN THE SCRIPT + +# Directory path to save results +output_dir = "PLACEHOLDER" + +# Local directory to load the PVNet checkpoint from. By default this should pull the best performing +# checkpoint on the val set +model_chckpoint_dir = "PLACEHOLDER" + +hf_revision = None +hf_token = None +hf_model_id = None + +# Forecasts will be made for all available init times between these +start_datetime = "2022-05-08 00:00" +end_datetime = "2022-05-08 00:30" + +# ------------------------------------------------------------------ +# SET UP LOGGING + +logger = logging.getLogger(__name__) +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + +# ------------------------------------------------------------------ +# DERIVED VARIABLES + +# This will run on GPU if it exists +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# ------------------------------------------------------------------ +# GLOBAL VARIABLES + +# The frequency of the pv site data +FREQ_MINS = 30 + +# When sun as elevation below this, the forecast is set to zero +MIN_DAY_ELEVATION = 0 + +# Add all pv site ids here that you wish to produce forecasts for +ALL_SITE_IDS = [] +# Need to be in ascending order +ALL_SITE_IDS.sort() + +# ------------------------------------------------------------------ +# FUNCTIONS + + +@functional_datapipe("pad_forward_pv") +class PadForwardPVIterDataPipe(IterDataPipe): + """ + Pads forecast pv. + + Sun position is calculated based off of pv time index + and for t0's close to end of pv data can have wrong shape as pv starts + to run out of data to slice for the forecast part. + """ + + def __init__( + self, + pv_dp: IterDataPipe, + forecast_duration: np.timedelta64, + history_duration: np.timedelta64, + time_resolution_minutes: np.timedelta64, + ): + """Init""" + + super().__init__() + self.pv_dp = pv_dp + self.forecast_duration = forecast_duration + self.history_duration = history_duration + self.time_resolution_minutes = time_resolution_minutes + + self.min_seq_length = history_duration // time_resolution_minutes + + def __iter__(self): + """Iter""" + + for xr_data in self.pv_dp: + t_end = ( + xr_data.time_utc.data[0] + + self.history_duration + + self.forecast_duration + + self.time_resolution_minutes + ) + time_idx = np.arange(xr_data.time_utc.data[0], t_end, self.time_resolution_minutes) + + if len(xr_data.time_utc.data) < self.min_seq_length: + raise ValueError("Not enough PV data to predict") + + yield xr_data.reindex(time_utc=time_idx, fill_value=-1) + + +def load_model_from_hf(model_id: str, revision: str, token: str): + """ + Loads model from HuggingFace + """ + + model_file = hf_hub_download( + repo_id=model_id, + filename=PYTORCH_WEIGHTS_NAME, + revision=revision, + token=token, + ) + + # load config file + config_file = hf_hub_download( + repo_id=model_id, + filename=CONFIG_NAME, + revision=revision, + token=token, + ) + + with open(config_file, "r", encoding="utf-8") as f: + config = json.load(f) + + model = hydra.utils.instantiate(config) + + state_dict = torch.load(model_file, map_location=torch.device("cuda")) + model.load_state_dict(state_dict) # type: ignore + model.eval() # type: ignore + + return model + + +def preds_to_dataarray(preds, model, valid_times, site_ids): + """Put numpy array of predictions into a dataarray""" + + if model.use_quantile_regression: + output_labels = [f"forecast_mw_plevel_{int(q*100):02}" for q in model.output_quantiles] + output_labels[output_labels.index("forecast_mw_plevel_50")] = "forecast_mw" + else: + output_labels = ["forecast_mw"] + preds = preds[..., np.newaxis] + + da = xr.DataArray( + data=preds, + dims=["pv_system_id", "target_datetime_utc", "output_label"], + coords=dict( + pv_system_id=site_ids, + target_datetime_utc=valid_times, + output_label=output_labels, + ), + ) + return da + + +# TODO change this to load the PV sites data (metadata?) +def get_sites_ds(config_path: str) -> xr.Dataset: + """Load site data from the path in the data config. + + Args: + config_path: Path to the data configuration file + + Returns: + xarray.Dataset of PVLive truths and capacities + """ + + config = load_yaml_configuration(config_path) + site_datapipe = OpenPVFromNetCDFIterDataPipe(pv=config.input_data.pv) + ds_sites = next(iter(site_datapipe)) + + return ds_sites + + +def get_available_t0_times(start_datetime, end_datetime, config_path): + """Filter a list of t0 init-times to those for which all required input data is available. + + Args: + start_datetime: First potential t0 time + end_datetime: Last potential t0 time + config_path: Path to data config file + + Returns: + pandas.DatetimeIndex of the init-times available for required inputs + """ + + start_datetime = pd.Timestamp(start_datetime) + end_datetime = pd.Timestamp(end_datetime) + # Open all the input data so we can check what of the potential data init times we have input + # data for + datapipes_dict = _get_datapipes_dict(config_path, production=False) + + # Pop out the config file + config = datapipes_dict.pop("config") + + # We are going to abuse the `create_t0_and_loc_datapipes()` function to find the init-times in + # potential_init_times which we have input data for. To do this, we will feed in some fake site + # data which has the potential_init_times as timestamps. This is a bit hacky but works for now + + # Set up init-times we would like to make predictions for + potential_init_times = pd.date_range(start_datetime, end_datetime, freq=f"{FREQ_MINS}min") + + # We buffer the potential init-times so that we don't lose any init-times from the + # start and end. Again this is a hacky step + history_duration = pd.Timedelta(config.input_data.pv.history_minutes, "min") + forecast_duration = pd.Timedelta(config.input_data.pv.forecast_minutes, "min") + buffered_potential_init_times = pd.date_range( + start_datetime - history_duration, end_datetime + forecast_duration, freq=f"{FREQ_MINS}min" + ) + ds_fake_site = ( + buffered_potential_init_times.to_frame().to_xarray().rename({"index": "time_utc"}) + ) + ds_fake_site = ds_fake_site.rename({0: "site_pv_power_mw"}) + ds_fake_site = ds_fake_site.expand_dims("pv_system_id", axis=1) + ds_fake_site = ds_fake_site.assign_coords( + pv_system_id=[0], + latitude=("pv_system_id", [0]), + longitude=("pv_system_id", [0]), + ) + ds_fake_site = ds_fake_site.site_pv_power_mw.astype(float) * 1e-18 + # Overwrite the site data which is already in the datapipes dict + datapipes_dict["pv"] = IterableWrapper([ds_fake_site]) + + # Use create_t0_and_loc_datapipes to get datapipe of init-times + location_pipe, t0_datapipe = create_t0_and_loc_datapipes( + datapipes_dict, + configuration=config, + key_for_t0="pv", + shuffle=False, + ) + + # Create a full list of available init-times. Note that we need to loop over the t0s AND + # locations to avoid the torch datapipes buffer overflow but we don't actually use the location + available_init_times = [t0 for _, t0 in zip(location_pipe, t0_datapipe)] + available_init_times = pd.to_datetime(available_init_times) + + logger.info( + f"{len(available_init_times)} out of {len(potential_init_times)} " + "requested init-times have required input data" + ) + + return available_init_times + + +def get_loctimes_datapipes(config_path): + """Create location and init-time datapipes + + Args: + config_path: Path to data config file + + Returns: + tuple: A tuple of datapipes + - Datapipe yielding locations + - Datapipe yielding init-times + """ + + # Set up ID location query object + ds_sites = get_sites_ds(config_path) + site_id_to_loc = SiteLocationLookup(ds_sites.longitude, ds_sites.latitude) + + # Filter the init-times to times we have all input data for + available_target_times = get_available_t0_times( + start_datetime, + end_datetime, + config_path, + ) + num_t0s = len(available_target_times) + + # Save the init-times which predictions are being made for. This is really helpful to check + # whilst the backtest is running since it takes a long time. This lets you see what init-times + # the backtest will end up producing + available_target_times.to_frame().to_csv(f"{output_dir}/t0_times.csv") + + # Cycle the site locations + location_pipe = IterableWrapper([[site_id_to_loc(site_id) for site_id in ALL_SITE_IDS]]).repeat( + num_t0s + ) + + # Shard and then unbatch the locations so that each worker will generate all samples for all + # sites and for a single init-time + location_pipe = location_pipe.sharding_filter() + location_pipe = location_pipe.unbatch( + unbatch_level=1 + ) # might not need this part since the site datapipe is creating examples + + # Create times datapipe so each worker receives + # len(ALL_SITE_IDS) copies of the same datetime for its batch + t0_datapipe = IterableWrapper( + [[t0 for site_id in ALL_SITE_IDS] for t0 in available_target_times] + ) + t0_datapipe = t0_datapipe.sharding_filter() + t0_datapipe = t0_datapipe.unbatch( + unbatch_level=1 + ) # might not need this part since the site datapipe is creating examples + + t0_datapipe = t0_datapipe.set_length(num_t0s * len(ALL_SITE_IDS)) + location_pipe = location_pipe.set_length(num_t0s * len(ALL_SITE_IDS)) + + return location_pipe, t0_datapipe + + +class ModelPipe: + """A class to conveniently make and process predictions from batches""" + + def __init__(self, model, ds_site: xr.Dataset): + """A class to conveniently make and process predictions from batches + + Args: + model: PVNet site level model + ds_site:xarray dataset of pv site true values and capacities + """ + self.model = model + self.ds_site = ds_site + + def predict_batch(self, batch: NumpyBatch) -> xr.Dataset: + """Run the batch through the model and compile the predictions into an xarray DataArray + + Args: + batch: A batch of samples with inputs for each site for the same init-time + + Returns: + xarray.Dataset of all site and national forecasts for the batch + """ + # Unpack some variables from the batch + id0 = batch[BatchKey.pv_t0_idx] + + t0 = batch[BatchKey.pv_time_utc].cpu().numpy().astype("datetime64[s]")[0, id0] + n_valid_times = len(batch[BatchKey.pv_time_utc][0, id0 + 1 :]) + model = self.model + + # Get valid times for this forecast + valid_times = pd.to_datetime( + [t0 + np.timedelta64((i + 1) * FREQ_MINS, "m") for i in range(n_valid_times)] + ) + + # Get effective capacities for this forecast + site_capacities = self.ds_site.nominal_capacity_wp.values + # Get the solar elevations. We need to un-normalise these from the values in the batch + elevation = batch[BatchKey.pv_solar_elevation] * ELEVATION_STD + ELEVATION_MEAN + # We only need elevation mask for forecasted values, not history + elevation = elevation[:, id0 + 1 :] + + # Make mask dataset for sundown + da_sundown_mask = xr.DataArray( + data=elevation < MIN_DAY_ELEVATION, + dims=["pv_system_id", "target_datetime_utc"], + coords=dict( + pv_system_id=ALL_SITE_IDS, + target_datetime_utc=valid_times, + ), + ) + + with torch.no_grad(): + # Run batch through model to get 0-1 predictions for all sites + device_batch = copy_batch_to_device(batch_to_tensor(batch), device) + y_normed_site = model(device_batch).detach().cpu().numpy() + da_normed_site = preds_to_dataarray(y_normed_site, model, valid_times, ALL_SITE_IDS) + + # Multiply normalised forecasts by capacities and clip negatives + da_abs_site = da_normed_site.clip(0, None) * site_capacities[:, None, None] + + # Apply sundown mask + da_abs_site = da_abs_site.where(~da_sundown_mask).fillna(0.0) + + da_abs_site = da_abs_site.expand_dims(dim="init_time_utc", axis=0).assign_coords( + init_time_utc=np.array([t0], dtype="datetime64[ns]") + ) + + return da_abs_site + + +def get_datapipe(config_path: str) -> NumpyBatch: + """Construct datapipe yielding batches of concurrent samples for all sites + + Args: + config_path: Path to the data configuration file + + Returns: + NumpyBatch: Concurrent batch of samples for each site + """ + + # Construct location and init-time datapipes + location_pipe, t0_datapipe = get_loctimes_datapipes(config_path) + + # Get the number of init-times + # num_batches = len(t0_datapipe) + num_batches = len(t0_datapipe) // len(ALL_SITE_IDS) + # Construct sample datapipes + data_pipeline = construct_sliced_data_pipeline( + config_path, + location_pipe, + t0_datapipe, + ) + + config = load_yaml_configuration(config_path) + data_pipeline["pv"] = data_pipeline["pv"].pad_forward_pv( + forecast_duration=np.timedelta64(config.input_data.pv.forecast_minutes, "m"), + history_duration=np.timedelta64(config.input_data.pv.history_minutes, "m"), + time_resolution_minutes=np.timedelta64(config.input_data.pv.time_resolution_minutes, "m"), + ) + + data_pipeline = DictDatasetIterDataPipe( + {k: v for k, v in data_pipeline.items() if k != "config"}, + ).map(split_dataset_dict_dp) + + data_pipeline = data_pipeline.pvnet_site_convert_to_numpy_batch() + + # Batch so that each worker returns a batch of all locations for a single init-time + # Also convert to tensor for model + data_pipeline = ( + data_pipeline.batch(len(ALL_SITE_IDS)) + .map(stack_np_examples_into_batch) + .map(batch_to_tensor) + ) + data_pipeline = data_pipeline.set_length(num_batches) + + return data_pipeline + + +@hydra.main(config_path="../configs", config_name="config.yaml", version_base="1.2") +def main(config: DictConfig): + """Runs the backtest""" + + dataloader_kwargs = dict( + shuffle=False, + batch_size=None, + sampler=None, + batch_sampler=None, + # Number of workers set in the config file + num_workers=config.datamodule.num_workers, + collate_fn=None, + pin_memory=False, + drop_last=False, + timeout=0, + worker_init_fn=None, + prefetch_factor=config.datamodule.prefetch_factor, + persistent_workers=False, + ) + + # Set up output dir + os.makedirs(output_dir) + + # Create concurrent batch datapipe + # Each batch includes a sample for each of the n sites for a single init-time + batch_pipe = get_datapipe(config.datamodule.configuration) + num_batches = len(batch_pipe) + # Load the site data as an xarray object + ds_site = get_sites_ds(config.datamodule.configuration) + # Create a dataloader for the concurrent batches and use multiprocessing + dataloader = DataLoader(batch_pipe, **dataloader_kwargs) + # Load the PVNet model + if model_chckpoint_dir: + model, *_ = get_model_from_checkpoints([model_chckpoint_dir], val_best=True) + elif hf_model_id: + model = load_model_from_hf(hf_model_id, hf_revision, hf_token) + else: + raise ValueError("Provide a model checkpoint or a HuggingFace model") + + model = model.eval().to(device) + + # Create object to make predictions for each input batch + model_pipe = ModelPipe(model, ds_site) + # Loop through the batches + pbar = tqdm(total=num_batches) + for i, batch in zip(range(num_batches), dataloader): + try: + # Make predictions for the init-time + ds_abs_all = model_pipe.predict_batch(batch) + + t0 = ds_abs_all.init_time_utc.values[0] + + # Save the predictions + filename = f"{output_dir}/{t0}.nc" + ds_abs_all.to_netcdf(filename) + + pbar.update() + except Exception as e: + print(f"Exception {e} at batch {i}") + pass + + # Close down + pbar.close() + del dataloader + + +if __name__ == "__main__": + main() diff --git a/scripts/backtest_uk_gsp.py b/scripts/backtest_uk_gsp.py new file mode 100644 index 0000000000000000000000000000000000000000..e58105b5f0a325d2f614685bac8eca43b8353bef --- /dev/null +++ b/scripts/backtest_uk_gsp.py @@ -0,0 +1,431 @@ +""" +A script to run backtest for PVNet and the summation model for UK regional and national + +Use: + +- This script uses hydra to construct the config, just like in `run.py`. So you need to make sure + that the data config is set up appropriate for the model being run in this script +- The PVNet and summation model checkpoints; the time range over which to make predictions are made; + and the output directory where the results near the top of the script as hard coded user + variables. These should be changed. + + +``` +python backtest_uk_gsp.py +``` + +""" + +try: + import torch.multiprocessing as mp + + mp.set_start_method("spawn", force=True) + mp.set_sharing_strategy("file_system") +except RuntimeError: + pass + +import logging +import os +import sys + +import hydra +import numpy as np +import pandas as pd +import torch +import xarray as xr +from ocf_data_sampler.sample.base import batch_to_tensor, copy_batch_to_device +from ocf_datapipes.batch import ( + BatchKey, + NumpyBatch, +) +from ocf_datapipes.config.load import load_yaml_configuration +from ocf_datapipes.load import OpenGSP +from ocf_datapipes.training.common import _get_datapipes_dict +from ocf_datapipes.training.pvnet_all_gsp import construct_sliced_data_pipeline, create_t0_datapipe +from ocf_datapipes.utils.consts import ELEVATION_MEAN, ELEVATION_STD +from omegaconf import DictConfig + +# TODO: Having this script rely on pvnet_app sets up a circular dependency. The function +# `preds_to_dataarray()` should probably be moved here +from pvnet_app.utils import preds_to_dataarray +from torch.utils.data import DataLoader +from torch.utils.data.datapipes.iter import IterableWrapper +from tqdm import tqdm + +from pvnet.load_model import get_model_from_checkpoints + +# ------------------------------------------------------------------ +# USER CONFIGURED VARIABLES +output_dir = "/mnt/disks/extra_batches/test_backtest" + +# Local directory to load the PVNet checkpoint from. By default this should pull the best performing +# checkpoint on the val set +model_chckpoint_dir = "/home/jamesfulton/repos/PVNet/checkpoints/q911tei5" + +# Local directory to load the summation model checkpoint from. By default this should pull the best +# performing checkpoint on the val set. If set to None a simple sum is used instead +summation_chckpoint_dir = ( + "/home/jamesfulton/repos/PVNet_summation/checkpoints/pvnet_summation/73oa4w9t" +) + +# Forecasts will be made for all available init times between these +start_datetime = "2022-05-08 00:00" +end_datetime = "2022-05-08 00:30" + +# ------------------------------------------------------------------ +# SET UP LOGGING + +logger = logging.getLogger(__name__) +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + +# ------------------------------------------------------------------ +# DERIVED VARIABLES + +# This will run on GPU if it exists +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# ------------------------------------------------------------------ +# GLOBAL VARIABLES + +# The frequency of the GSP data +FREQ_MINS = 30 + +# When sun as elevation below this, the forecast is set to zero +MIN_DAY_ELEVATION = 0 + +# All regional GSP IDs - not including national which is treated separately +ALL_GSP_IDS = np.arange(1, 318) + +# ------------------------------------------------------------------ +# FUNCTIONS + + +def get_gsp_ds(config_path: str) -> xr.Dataset: + """Load GSP data from the path in the data config. + + Args: + config_path: Path to the data configuration file + + Returns: + xarray.Dataset of PVLive truths and capacities + """ + + config = load_yaml_configuration(config_path) + gsp_datapipe = OpenGSP(gsp_pv_power_zarr_path=config.input_data.gsp.gsp_zarr_path) + ds_gsp = next(iter(gsp_datapipe)) + + return ds_gsp + + +def get_available_t0_times(start_datetime, end_datetime, config_path): + """Filter a list of t0 init-times to those for which all required input data is available. + + Args: + start_datetime: First potential t0 time + end_datetime: Last potential t0 time + config_path: Path to data config file + + Returns: + pandas.DatetimeIndex of the init-times available for required inputs + """ + + start_datetime = pd.Timestamp(start_datetime) + end_datetime = pd.Timestamp(end_datetime) + # Open all the input data so we can check what of the potential data init times we have input + # data for + datapipes_dict = _get_datapipes_dict(config_path, production=False) + + # Pop out the config file + config = datapipes_dict.pop("config") + + # We are going to abuse the `create_t0_datapipe()` function to find the init-times in + # potential_init_times which we have input data for. To do this, we will feed in some fake GSP + # data which has the potential_init_times as timestamps. This is a bit hacky but works for now + + # Set up init-times we would like to make predictions for + potential_init_times = pd.date_range(start_datetime, end_datetime, freq=f"{FREQ_MINS}min") + + # We buffer the potential init-times so that we don't lose any init-times from the + # start and end. Again this is a hacky step + history_duration = pd.Timedelta(config.input_data.gsp.history_minutes, "min") + forecast_duration = pd.Timedelta(config.input_data.gsp.forecast_minutes, "min") + buffered_potential_init_times = pd.date_range( + start_datetime - history_duration, end_datetime + forecast_duration, freq=f"{FREQ_MINS}min" + ) + + ds_fake_gsp = buffered_potential_init_times.to_frame().to_xarray().rename({"index": "time_utc"}) + ds_fake_gsp = ds_fake_gsp.rename({0: "gsp_pv_power_mw"}) + ds_fake_gsp = ds_fake_gsp.expand_dims("gsp_id", axis=1) + ds_fake_gsp = ds_fake_gsp.assign_coords( + gsp_id=[0], + x_osgb=("gsp_id", [0]), + y_osgb=("gsp_id", [0]), + ) + ds_fake_gsp = ds_fake_gsp.gsp_pv_power_mw.astype(float) * 1e-18 + + # Overwrite the GSP data which is already in the datapipes dict + datapipes_dict["gsp"] = IterableWrapper([ds_fake_gsp]) + + # Use create_t0_datapipe to get datapipe of init-times + t0_datapipe = create_t0_datapipe( + datapipes_dict, + configuration=config, + shuffle=False, + ) + + # Create a full list of available init-times + available_init_times = pd.to_datetime([t0 for t0 in t0_datapipe]) + + logger.info( + f"{len(available_init_times)} out of {len(potential_init_times)} " + "requested init-times have required input data" + ) + + return available_init_times + + +def get_times_datapipe(config_path): + """Create init-time datapipe + + Args: + config_path: Path to data config file + + Returns: + Datapipe: A Datapipe yielding init-times + """ + + # Filter the init-times to times we have all input data for + available_target_times = get_available_t0_times( + start_datetime, + end_datetime, + config_path, + ) + num_t0s = len(available_target_times) + + # Save the init-times which predictions are being made for. This is really helpful to check + # whilst the backtest is running since it takes a long time. This lets you see what init-times + # the backtest will end up producing + available_target_times.to_frame().to_csv(f"{output_dir}/t0_times.csv") + + # Create times datapipe so each worker receives 317 copies of the same datetime for its batch + t0_datapipe = IterableWrapper(available_target_times) + t0_datapipe = t0_datapipe.sharding_filter() + + t0_datapipe = t0_datapipe.set_length(num_t0s) + + return t0_datapipe + + +class ModelPipe: + """A class to conveniently make and process predictions from batches""" + + def __init__(self, model, summation_model, ds_gsp: xr.Dataset): + """A class to conveniently make and process predictions from batches + + Args: + model: PVNet GSP level model + summation_model: Summation model to make national forecast from GSP level forecasts + ds_gsp:xarray dataset of PVLive true values and capacities + """ + self.model = model + self.summation_model = summation_model + self.ds_gsp = ds_gsp + + def predict_batch(self, batch: NumpyBatch) -> xr.Dataset: + """Run the batch through the model and compile the predictions into an xarray DataArray + + Args: + batch: A batch of samples with inputs for each GSP for the same init-time + + Returns: + xarray.Dataset of all GSP and national forecasts for the batch + """ + + # Unpack some variables from the batch + id0 = batch[BatchKey.gsp_t0_idx] + t0 = batch[BatchKey.gsp_time_utc].cpu().numpy().astype("datetime64[s]")[0, id0] + n_valid_times = len(batch[BatchKey.gsp_time_utc][0, id0 + 1 :]) + ds_gsp = self.ds_gsp + model = self.model + summation_model = self.summation_model + + # Get valid times for this forecast + valid_times = pd.to_datetime( + [t0 + np.timedelta64((i + 1) * FREQ_MINS, "m") for i in range(n_valid_times)] + ) + + # Get effective capacities for this forecast + gsp_capacities = ds_gsp.effective_capacity_mwp.sel( + time_utc=t0, gsp_id=slice(1, None) + ).values + national_capacity = ds_gsp.effective_capacity_mwp.sel(time_utc=t0, gsp_id=0).item() + + # Get the solar elevations. We need to un-normalise these from the values in the batch + elevation = batch[BatchKey.gsp_solar_elevation] * ELEVATION_STD + ELEVATION_MEAN + # We only need elevation mask for forecasted values, not history + elevation = elevation[:, id0 + 1 :] + + # Make mask dataset for sundown + da_sundown_mask = xr.DataArray( + data=elevation < MIN_DAY_ELEVATION, + dims=["gsp_id", "target_datetime_utc"], + coords=dict( + gsp_id=ALL_GSP_IDS, + target_datetime_utc=valid_times, + ), + ) + + with torch.no_grad(): + # Run batch through model to get 0-1 predictions for all GSPs + device_batch = copy_batch_to_device(batch_to_tensor(batch), device) + y_normed_gsp = model(device_batch).detach().cpu().numpy() + + da_normed_gsp = preds_to_dataarray(y_normed_gsp, model, valid_times, ALL_GSP_IDS) + + # Multiply normalised forecasts by capacities and clip negatives + da_abs_gsp = da_normed_gsp.clip(0, None) * gsp_capacities[:, None, None] + + # Apply sundown mask + da_abs_gsp = da_abs_gsp.where(~da_sundown_mask).fillna(0.0) + + # Make national predictions using summation model + if summation_model is not None: + with torch.no_grad(): + # Construct sample for the summation model + summation_inputs = { + "pvnet_outputs": torch.Tensor(y_normed_gsp[np.newaxis]).to(device), + "effective_capacity": ( + torch.Tensor(gsp_capacities / national_capacity) + .to(device) + .unsqueeze(0) + .unsqueeze(-1) + ), + } + + # Run batch through the summation model + y_normed_national = ( + summation_model(summation_inputs).detach().squeeze().cpu().numpy() + ) + + # Convert national predictions to DataArray + da_normed_national = preds_to_dataarray( + y_normed_national[np.newaxis], summation_model, valid_times, gsp_ids=[0] + ) + + # Multiply normalised forecasts by capacities and clip negatives + da_abs_national = da_normed_national.clip(0, None) * national_capacity + + # Apply sundown mask - All GSPs must be masked to mask national + da_abs_national = da_abs_national.where(~da_sundown_mask.all(dim="gsp_id")).fillna(0.0) + + # If no summation model, make national predictions using simple sum + else: + da_abs_national = ( + da_abs_gsp.sum(dim="gsp_id") + .expand_dims(dim="gsp_id", axis=0) + .assign_coords(gsp_id=[0]) + ) + + # Concat the regional GSP and national predictions + da_abs_all = xr.concat([da_abs_national, da_abs_gsp], dim="gsp_id") + ds_abs_all = da_abs_all.to_dataset(name="hindcast") + + ds_abs_all = ds_abs_all.expand_dims(dim="init_time_utc", axis=0).assign_coords( + init_time_utc=[t0] + ) + + return ds_abs_all + + +def get_datapipe(config_path: str) -> NumpyBatch: + """Construct datapipe yielding batches of concurrent samples for all GSPs + + Args: + config_path: Path to the data configuration file + + Returns: + NumpyBatch: Concurrent batch of samples for each GSP + """ + + # Construct location and init-time datapipes + t0_datapipe = get_times_datapipe(config_path) + + # Construct sample datapipes + data_pipeline = construct_sliced_data_pipeline( + config_path, + t0_datapipe, + ) + + # Convert to tensor for model + data_pipeline = data_pipeline.map(batch_to_tensor).set_length(len(t0_datapipe)) + + return data_pipeline + + +@hydra.main(config_path="../configs", config_name="config.yaml", version_base="1.2") +def main(config: DictConfig): + """Runs the backtest""" + + dataloader_kwargs = dict( + shuffle=False, + batch_size=None, + sampler=None, + batch_sampler=None, + # Number of workers set in the config file + num_workers=config.datamodule.num_workers, + collate_fn=None, + pin_memory=False, + drop_last=False, + timeout=0, + worker_init_fn=None, + prefetch_factor=config.datamodule.prefetch_factor, + persistent_workers=False, + ) + + # Set up output dir + os.makedirs(output_dir) + + # Create concurrent batch datapipe + # Each batch includes a sample for each of the 317 GSPs for a single init-time + batch_pipe = get_datapipe(config.datamodule.configuration) + num_batches = len(batch_pipe) + + # Load the GSP data as an xarray object + ds_gsp = get_gsp_ds(config.datamodule.configuration) + + # Create a dataloader for the concurrent batches and use multiprocessing + dataloader = DataLoader(batch_pipe, **dataloader_kwargs) + + # Load the PVNet model and summation model + model, *_ = get_model_from_checkpoints([model_chckpoint_dir], val_best=True) + model = model.eval().to(device) + if summation_chckpoint_dir is None: + summation_model = None + else: + summation_model, *_ = get_model_from_checkpoints([summation_chckpoint_dir], val_best=True) + summation_model = summation_model.eval().to(device) + + # Create object to make predictions for each input batch + model_pipe = ModelPipe(model, summation_model, ds_gsp) + + # Loop through the batches + pbar = tqdm(total=num_batches) + for i, batch in zip(range(num_batches), dataloader): + # Make predictions for the init-time + ds_abs_all = model_pipe.predict_batch(batch) + + t0 = ds_abs_all.init_time_utc.values[0] + + # Save the predictioons + filename = f"{output_dir}/{t0}.nc" + ds_abs_all.to_netcdf(filename) + + pbar.update() + + # Close down + pbar.close() + del dataloader + + +if __name__ == "__main__": + main() diff --git a/scripts/checkpoint_to_huggingface.py b/scripts/checkpoint_to_huggingface.py new file mode 100644 index 0000000000000000000000000000000000000000..d4e0c6dadde907dcae94e6702ffe9d4f0d712b17 --- /dev/null +++ b/scripts/checkpoint_to_huggingface.py @@ -0,0 +1,83 @@ +"""Command line tool to push locally save model checkpoints to huggingface + +use: +python checkpoint_to_huggingface.py "path/to/model/checkpoints" \ + --huggingface-repo="openclimatefix/pvnet_uk_region" \ + --wandb-repo="openclimatefix/pvnet2.1" \ + --local-path="~/tmp/this_model" \ + --no-push-to-hub +""" + +import tempfile + +import typer +import wandb + +from pvnet.load_model import get_model_from_checkpoints + +app = typer.Typer(pretty_exceptions_show_locals=False) + +@app.command() +def push_to_huggingface( + checkpoint_dir_paths: list[str], + huggingface_repo: str = "openclimatefix/pvnet_uk_region", # e.g. openclimatefix/windnet_india + wandb_repo: str = "openclimatefix/pvnet2.1", + val_best: bool = True, + wandb_ids: list[str] = [], + local_path: str = None, + push_to_hub: bool = True, +): + """Push a local model to a huggingface model repo + + Args: + checkpoint_dir_paths: Path(s) of the checkpoint directory(ies) + huggingface_repo: Name of the HuggingFace repo to push the model to + wandb_repo: Name of the wandb repo which has training logs + val_best: Use best model according to val loss, else last saved model + wandb_ids: The wandb ID code(s) + local_path: Where to save the local copy of the model + push_to_hub: Whether to push the model to the hub or just create local version. + """ + + assert push_to_hub or local_path is not None + + is_ensemble = len(checkpoint_dir_paths) > 1 + + # Check if checkpoint dir name is wandb run ID + if wandb_ids == []: + all_wandb_ids = [run.id for run in wandb.Api().runs(path=wandb_repo)] + for path in checkpoint_dir_paths: + dirname = path.split("/")[-1] + if dirname in all_wandb_ids: + wandb_ids.append(dirname) + else: + wandb_ids.append(None) + + model, model_config, data_config = get_model_from_checkpoints(checkpoint_dir_paths, val_best) + + if not is_ensemble: + wandb_ids = wandb_ids[0] + + # Push to hub + if local_path is None: + temp_dir = tempfile.TemporaryDirectory() + model_output_dir = temp_dir.name + else: + model_output_dir = local_path + + model.save_pretrained( + model_output_dir, + config=model_config, + data_config=data_config, + wandb_repo=wandb_repo, + wandb_ids=wandb_ids, + push_to_hub=push_to_hub, + repo_id=huggingface_repo if push_to_hub else None, + ) + + if local_path is None: + temp_dir.cleanup() + + +if __name__ == "__main__": + app() diff --git a/scripts/save_concurrent_samples.py b/scripts/save_concurrent_samples.py new file mode 100644 index 0000000000000000000000000000000000000000..b3beaa68edf7e029a490ec91968f344c3697958e --- /dev/null +++ b/scripts/save_concurrent_samples.py @@ -0,0 +1,189 @@ +""" +Constructs batches where each batch includes all GSPs and only a single timestamp. + +Currently a slightly hacky implementation due to the way the configs are done. This script will use +the same config file currently set to train the model. In the datamodule config it is possible +to set the batch_output_dir and number of train/val batches, they can also be overriden in the +command as shown in the example below. + +use: +``` +python save_concurrent_samples.py \ + +datamodule.sample_output_dir="/mnt/disks/concurrent_batches/concurrent_samples_sat_pred_test" \ + +datamodule.num_train_samples=20 \ + +datamodule.num_val_samples=20 +``` + +""" +# Ensure this block of code runs only in the main process to avoid issues with worker processes. +if __name__ == "__main__": + import torch.multiprocessing as mp + + # Set the start method for torch multiprocessing. Choose either "forkserver" or "spawn" to be + # compatible with dask's multiprocessing. + mp.set_start_method("forkserver") + + # Set the sharing strategy to 'file_system' to handle file descriptor limitations. This is + # important because libraries like Zarr may open many files, which can exhaust the file + # descriptor limit if too many workers are used. + mp.set_sharing_strategy("file_system") + + +import logging +import os +import shutil +import sys +import warnings + +import hydra +import numpy as np +import torch +from ocf_data_sampler.torch_datasets.datasets.pvnet_uk import PVNetUKConcurrentDataset +from omegaconf import DictConfig, OmegaConf +from sqlalchemy import exc as sa_exc +from torch.utils.data import DataLoader, Dataset +from tqdm import tqdm + +from pvnet.utils import print_config + +# ------- filter warning and set up config ------- + +warnings.filterwarnings("ignore", category=sa_exc.SAWarning) + +logger = logging.getLogger(__name__) + +logging.basicConfig(stream=sys.stdout, level=logging.ERROR) + +# ------------------------------------------------- + + +class SaveFuncFactory: + """Factory for creating a function to save a sample to disk.""" + + def __init__(self, save_dir: str): + """Factory for creating a function to save a sample to disk.""" + self.save_dir = save_dir + + def __call__(self, sample, sample_num: int): + """Save a sample to disk""" + torch.save(sample, f"{self.save_dir}/{sample_num:08}.pt") + + +def save_samples_with_dataloader( + dataset: Dataset, + save_dir: str, + num_samples: int, + dataloader_kwargs: dict, +) -> None: + """Save samples from a dataset using a dataloader.""" + save_func = SaveFuncFactory(save_dir) + + gsp_ids = np.array([loc.id for loc in dataset.locations]) + + dataloader = DataLoader(dataset, **dataloader_kwargs) + + pbar = tqdm(total=num_samples) + for i, sample in zip(range(num_samples), dataloader): + check_sample(sample, gsp_ids) + save_func(sample, i) + pbar.update() + pbar.close() + + +def check_sample(sample, gsp_ids): + """Check if sample is valid concurrent batch for all GSPs""" + # Check all GSP IDs are included and in correct order + assert (sample["gsp_id"].flatten().numpy() == gsp_ids).all() + # Check all times are the same + assert len(np.unique(sample["gsp_time_utc"][:, 0].numpy())) == 1 + + +@hydra.main(config_path="../configs/", config_name="config.yaml", version_base="1.2") +def main(config: DictConfig) -> None: + """Constructs and saves validation and training samples.""" + config_dm = config.datamodule + + print_config(config, resolve=False) + + # Set up directory + os.makedirs(config_dm.sample_output_dir, exist_ok=False) + + # Copy across configs which define the samples into the new sample directory + with open(f"{config_dm.sample_output_dir}/datamodule.yaml", "w") as f: + f.write(OmegaConf.to_yaml(config_dm)) + + shutil.copyfile( + config_dm.configuration, f"{config_dm.sample_output_dir}/data_configuration.yaml" + ) + + # Define the keywargs going into the train and val dataloaders + dataloader_kwargs = dict( + shuffle=True, + batch_size=None, + sampler=None, + batch_sampler=None, + num_workers=config_dm.num_workers, + collate_fn=None, + pin_memory=False, # Only using CPU to prepare samples so pinning is not beneficial + drop_last=False, + timeout=0, + worker_init_fn=None, + prefetch_factor=config_dm.prefetch_factor, + persistent_workers=False, # Not needed since we only enter the dataloader loop once + ) + + if config_dm.num_val_samples > 0: + print("----- Saving val samples -----") + + val_output_dir = f"{config_dm.sample_output_dir}/val" + + # Make directory for val samples + os.mkdir(val_output_dir) + + # Get the dataset + val_dataset = PVNetUKConcurrentDataset( + config_dm.configuration, + start_time=config_dm.val_period[0], + end_time=config_dm.val_period[1], + ) + + # Save samples + save_samples_with_dataloader( + dataset=val_dataset, + save_dir=val_output_dir, + num_samples=config_dm.num_val_samples, + dataloader_kwargs=dataloader_kwargs, + ) + + del val_dataset + + if config_dm.num_train_samples > 0: + print("----- Saving train samples -----") + + train_output_dir = f"{config_dm.sample_output_dir}/train" + + # Make directory for train samples + os.mkdir(train_output_dir) + + # Get the dataset + train_dataset = PVNetUKConcurrentDataset( + config_dm.configuration, + start_time=config_dm.train_period[0], + end_time=config_dm.train_period[1], + ) + + # Save samples + save_samples_with_dataloader( + dataset=train_dataset, + save_dir=train_output_dir, + num_samples=config_dm.num_train_samples, + dataloader_kwargs=dataloader_kwargs, + ) + + del train_dataset + + print("----- Saving complete -----") + + +if __name__ == "__main__": + main() diff --git a/scripts/save_samples.py b/scripts/save_samples.py new file mode 100644 index 0000000000000000000000000000000000000000..84ad0bc2ebe3242ac81bf68a1b062a747bfe2c24 --- /dev/null +++ b/scripts/save_samples.py @@ -0,0 +1,218 @@ +""" +Constructs samples and saves them to disk. + +Currently a slightly hacky implementation due to the way the configs are done. This script will use +the same config file currently set to train the model. + +use: +``` +python save_samples.py +``` +if setting all values in the datamodule config file, or + +``` +python save_samples.py \ + +datamodule.sample_output_dir="/mnt/disks/bigbatches/samples_v0" \ + +datamodule.num_train_samples=0 \ + +datamodule.num_val_samples=2 \ + datamodule.num_workers=2 \ + datamodule.prefetch_factor=2 +``` +if wanting to override these values for example +""" + +# Ensure this block of code runs only in the main process to avoid issues with worker processes. +if __name__ == "__main__": + import torch.multiprocessing as mp + + # Set the start method for torch multiprocessing. Choose either "forkserver" or "spawn" to be + # compatible with dask's multiprocessing. + mp.set_start_method("forkserver") + + # Set the sharing strategy to 'file_system' to handle file descriptor limitations. This is + # important because libraries like Zarr may open many files, which can exhaust the file + # descriptor limit if too many workers are used. + mp.set_sharing_strategy("file_system") + + +import logging +import os +import shutil +import sys +import warnings + +import dask +import hydra +from ocf_data_sampler.torch_datasets.datasets import PVNetUKRegionalDataset, SitesDataset +from ocf_data_sampler.torch_datasets.sample.site import SiteSample +from ocf_data_sampler.torch_datasets.sample.uk_regional import UKRegionalSample +from omegaconf import DictConfig, OmegaConf +from sqlalchemy import exc as sa_exc +from torch.utils.data import DataLoader, Dataset +from tqdm import tqdm + +from pvnet.utils import print_config + +dask.config.set(scheduler="threads", num_workers=4) + + +# ------- filter warning and set up config ------- + +warnings.filterwarnings("ignore", category=sa_exc.SAWarning) + +logger = logging.getLogger(__name__) + +logging.basicConfig(stream=sys.stdout, level=logging.ERROR) + +# ------------------------------------------------- + + +class SaveFuncFactory: + """Factory for creating a function to save a sample to disk.""" + + def __init__(self, save_dir: str, renewable: str = "pv_uk"): + """Factory for creating a function to save a sample to disk.""" + self.save_dir = save_dir + self.renewable = renewable + + def __call__(self, sample, sample_num: int): + """Save a sample to disk""" + save_path = f"{self.save_dir}/{sample_num:08}" + + if self.renewable == "pv_uk": + sample_class = UKRegionalSample(sample) + filename = f"{save_path}.pt" + elif self.renewable == "site": + sample_class = SiteSample(sample) + filename = f"{save_path}.nc" + else: + raise ValueError(f"Unknown renewable: {self.renewable}") + # Assign data and save + sample_class._data = sample + sample_class.save(filename) + + +def get_dataset( + config_path: str, start_time: str, end_time: str, renewable: str = "pv_uk" +) -> Dataset: + """Get the dataset for the given renewable type.""" + if renewable == "pv_uk": + dataset_cls = PVNetUKRegionalDataset + elif renewable == "site": + dataset_cls = SitesDataset + else: + raise ValueError(f"Unknown renewable: {renewable}") + + return dataset_cls(config_path, start_time=start_time, end_time=end_time) + + +def save_samples_with_dataloader( + dataset: Dataset, + save_dir: str, + num_samples: int, + dataloader_kwargs: dict, + renewable: str = "pv_uk", +) -> None: + """Save samples from a dataset using a dataloader.""" + save_func = SaveFuncFactory(save_dir, renewable=renewable) + + dataloader = DataLoader(dataset, **dataloader_kwargs) + + pbar = tqdm(total=num_samples) + for i, sample in zip(range(num_samples), dataloader): + save_func(sample, i) + pbar.update() + pbar.close() + + +@hydra.main(config_path="../configs/", config_name="config.yaml", version_base="1.2") +def main(config: DictConfig) -> None: + """Constructs and saves validation and training samples.""" + config_dm = config.datamodule + + print_config(config, resolve=False) + + # Set up directory + os.makedirs(config_dm.sample_output_dir, exist_ok=False) + + # Copy across configs which define the samples into the new sample directory + with open(f"{config_dm.sample_output_dir}/datamodule.yaml", "w") as f: + f.write(OmegaConf.to_yaml(config_dm)) + + shutil.copyfile( + config_dm.configuration, f"{config_dm.sample_output_dir}/data_configuration.yaml" + ) + + # Define the keywargs going into the train and val dataloaders + dataloader_kwargs = dict( + shuffle=True, + batch_size=None, + sampler=None, + batch_sampler=None, + num_workers=config_dm.num_workers, + collate_fn=None, + pin_memory=False, # Only using CPU to prepare samples so pinning is not beneficial + drop_last=False, + timeout=0, + worker_init_fn=None, + prefetch_factor=config_dm.prefetch_factor, + persistent_workers=False, # Not needed since we only enter the dataloader loop once + ) + + if config_dm.num_val_samples > 0: + print("----- Saving val samples -----") + + val_output_dir = f"{config_dm.sample_output_dir}/val" + + # Make directory for val samples + os.mkdir(val_output_dir) + + # Get the dataset + val_dataset = get_dataset( + config_dm.configuration, + *config_dm.val_period, + renewable=config.renewable, + ) + + # Save samples + save_samples_with_dataloader( + dataset=val_dataset, + save_dir=val_output_dir, + num_samples=config_dm.num_val_samples, + dataloader_kwargs=dataloader_kwargs, + renewable=config.renewable, + ) + + del val_dataset + + if config_dm.num_train_samples > 0: + print("----- Saving train samples -----") + + train_output_dir = f"{config_dm.sample_output_dir}/train" + + # Make directory for train samples + os.mkdir(train_output_dir) + + # Get the dataset + train_dataset = get_dataset( + config_dm.configuration, + *config_dm.train_period, + renewable=config.renewable, + ) + + # Save samples + save_samples_with_dataloader( + dataset=train_dataset, + save_dir=train_output_dir, + num_samples=config_dm.num_train_samples, + dataloader_kwargs=dataloader_kwargs, + renewable=config.renewable, + ) + + del train_dataset + + print("----- Saving complete -----") + + +if __name__ == "__main__": + main() diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..6e0feea659ef8efc4cab7514b3da3aef7a489bd5 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,540 @@ +import os +import tempfile +from datetime import timedelta + +import pytest +import pandas as pd +import numpy as np +import xarray as xr +import torch +import hydra + +from pvnet.data import DataModule, SiteDataModule +import pvnet.models.multimodal.encoders.encoders3d +import pvnet.models.multimodal.linear_networks.networks +import pvnet.models.multimodal.site_encoders.encoders +from pvnet.models.multimodal.multimodal import Model + + +xr.set_options(keep_attrs=True) + + +def time_before_present(dt: timedelta): + return pd.Timestamp.now(tz=None) - dt + + +@pytest.fixture +def nwp_data(): + # Load dataset which only contains coordinates, but no data + ds = xr.open_zarr( + f"{os.path.dirname(os.path.abspath(__file__))}/test_data/sample_data/nwp_shell.zarr" + ) + + # Last init time was at least 2 hours ago and hour to 3-hour interval + t0_datetime_utc = time_before_present(timedelta(hours=2)).floor(timedelta(hours=3)) + ds.init_time.values[:] = pd.date_range( + t0_datetime_utc - timedelta(hours=3 * (len(ds.init_time) - 1)), + t0_datetime_utc, + freq=timedelta(hours=3), + ) + + # This is important to avoid saving errors + for v in list(ds.coords.keys()): + if ds.coords[v].dtype == object: + ds[v].encoding.clear() + + for v in list(ds.variables.keys()): + if ds[v].dtype == object: + ds[v].encoding.clear() + + # Add data to dataset + ds["UKV"] = xr.DataArray( + np.zeros([len(ds[c]) for c in ds.coords]), + coords=ds.coords, + ) + + # Add stored attributes to DataArray + ds.UKV.attrs = ds.attrs["_data_attrs"] + del ds.attrs["_data_attrs"] + + return ds + + +@pytest.fixture() +def sat_data(): + # Load dataset which only contains coordinates, but no data + ds = xr.open_zarr( + f"{os.path.dirname(os.path.abspath(__file__))}/test_data/sample_data/non_hrv_shell.zarr" + ) + + # Change times so they lead up to present. Delayed by at most 1 hour + t0_datetime_utc = time_before_present(timedelta(minutes=0)).floor(timedelta(minutes=30)) + t0_datetime_utc = t0_datetime_utc - timedelta(minutes=30) + ds.time.values[:] = pd.date_range( + t0_datetime_utc - timedelta(minutes=5 * (len(ds.time) - 1)), + t0_datetime_utc, + freq=timedelta(minutes=5), + ) + + # Add data to dataset + ds["data"] = xr.DataArray( + np.zeros([len(ds[c]) for c in ds.coords]), + coords=ds.coords, + ) + + # Add stored attributes to DataArray + ds.data.attrs = ds.attrs["_data_attrs"] + del ds.attrs["_data_attrs"] + + return ds + + +def generate_synthetic_sample(): + """ + Generate synthetic sample for testing + """ + now = pd.Timestamp.now(tz=None) + sample = {} + + # NWP define + sample["nwp"] = { + "ukv": { + "nwp": torch.rand(11, 11, 24, 24), + "nwp_init_time_utc": torch.tensor( + [(now - pd.Timedelta(hours=i)).timestamp() for i in range(11)] + ), + "nwp_step": torch.arange(11, dtype=torch.float32), + "nwp_target_time_utc": torch.tensor( + [(now + pd.Timedelta(hours=i)).timestamp() for i in range(11)] + ), + "nwp_y_osgb": torch.linspace(0, 100, 24), + "nwp_x_osgb": torch.linspace(0, 100, 24), + }, + "ecmwf": { + "nwp": torch.rand(11, 12, 12, 12), + "nwp_init_time_utc": torch.tensor( + [(now - pd.Timedelta(hours=i)).timestamp() for i in range(11)] + ), + "nwp_step": torch.arange(11, dtype=torch.float32), + "nwp_target_time_utc": torch.tensor( + [(now + pd.Timedelta(hours=i)).timestamp() for i in range(11)] + ), + }, + "sat_pred": { + "nwp": torch.rand(12, 11, 24, 24), + "nwp_init_time_utc": torch.tensor( + [(now - pd.Timedelta(hours=i)).timestamp() for i in range(12)] + ), + "nwp_step": torch.arange(12, dtype=torch.float32), + "nwp_target_time_utc": torch.tensor( + [(now + pd.Timedelta(hours=i)).timestamp() for i in range(12)] + ), + }, + } + + # Satellite define + sample["satellite_actual"] = torch.rand(7, 11, 24, 24) + sample["satellite_time_utc"] = torch.tensor( + [(now - pd.Timedelta(minutes=5*i)).timestamp() for i in range(7)] + ) + sample["satellite_x_geostationary"] = torch.linspace(0, 100, 24) + sample["satellite_y_geostationary"] = torch.linspace(0, 100, 24) + + # GSP define + sample["gsp"] = torch.rand(21) + sample["gsp_nominal_capacity_mwp"] = torch.tensor(100.0) + sample["gsp_effective_capacity_mwp"] = torch.tensor(85.0) + sample["gsp_time_utc"] = torch.tensor( + [(now + pd.Timedelta(minutes=30*i)).timestamp() for i in range(21)] + ) + sample["gsp_t0_idx"] = float(7) + sample["gsp_id"] = 12 + sample["gsp_x_osgb"] = 123456.0 + sample["gsp_y_osgb"] = 654321.0 + + # Solar position define + sample["solar_azimuth"] = torch.linspace(0, 180, 21) + sample["solar_elevation"] = torch.linspace(-10, 60, 21) + + return sample + + +def generate_synthetic_site_sample(site_id=1, variation_index=0, add_noise=True): + """ + Generate synthetic site sample that matches site sample structure + + Args: + site_id: ID for the site + variation_index: Index to use for coordinate variations + add_noise: Whether to add random noise to data variables + """ + now = pd.Timestamp.now(tz=None) + + # Create time and space coordinates + site_time_coords = pd.date_range(start=now - pd.Timedelta(hours=48), periods=197, freq="15min") + nwp_time_coords = pd.date_range(start=now, periods=50, freq="1h") + nwp_lat = np.linspace(50.0, 60.0, 24) + nwp_lon = np.linspace(-10.0, 2.0, 24) + nwp_channels = np.array(['t2m', 'ssrd', 'ssr', 'sp', 'r', 'tcc', 'u10', 'v10'], dtype='= '3.12' and sys_platform == 'linux'", + "python_full_version >= '3.12' and sys_platform != 'linux'", + "python_full_version == '3.11.*' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and sys_platform != 'linux'", + "python_full_version < '3.11' and sys_platform == 'linux'", + "python_full_version < '3.11' and sys_platform != 'linux'", +] + +[[package]] +name = "aiobotocore" +version = "2.22.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = 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