epos-ai/platform-demo-scripts
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Added logging MAE for the best runs, option to run a pipeline on a specific dataset, template bash scripts, GPLv3 license. Modified behavior of generating eval targets, it is skipped if targets already exist.

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This commit is contained in:
Krystyna Milian 2023-10-12 13:25:34 +02:00
parent aa39980573
commit c62ec2cacb
18 changed files with 311 additions and 368 deletions
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1
.gitignore vendored
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@ -3,6 +3,7 @@ __pycache__/
*/.ipynb_checkpoints/
.ipynb_checkpoints/
.env
*.out
weights/
datasets/
wip

62
README.md
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@ -2,12 +2,13 @@
This repo contains notebooks and scripts demonstrating how to:
- Prepare data for training a seisbench model detecting P and S waves (i.e. transform mseeds into [SeisBench data format](https://seisbench.readthedocs.io/en/stable/pages/data_format.html)), check the [notebook](utils/Transforming%20mseeds%20from%20Bogdanka%20to%20Seisbench%20format.ipynb) and the [script](utils/mseeds_to_seisbench.py)
- [to update] Explore available data, check the [notebook](notebooks/Explore%20igf%20data.ipynb)
- Prepare data for training a seisbench model detecting P and S waves (i.e. transform mseeds into [SeisBench data format](https://seisbench.readthedocs.io/en/stable/pages/data_format.html)), check the [notebook](notebooks/Transforming%20mseeds%20from%20Bogdanka%20to%20Seisbench%20format.ipynb) and the [script](scripts/mseeds_to_seisbench.py)
[//]: # (- [to update] Explore available data, check the [notebook](notebooks/Explore%20igf%20data.ipynb))
- Train various cnn models available in seisbench library and compare their performance of detecting P and S waves, check the [script](scripts/pipeline.py)
- [to update] Validate model performance, check the [notebook](notebooks/Check%20model%20performance%20depending%20on%20station-random%20window.ipynb)
- [to update] Use model for detecting P phase, check the [notebook](notebooks/Present%20model%20predictions.ipynb)
[//]: # (- [to update] Validate model performance, check the [notebook](notebooks/Check%20model%20performance%20depending%20on%20station-random%20window.ipynb))
[//]: # (- [to update] Use model for detecting P phase, check the [notebook](notebooks/Present%20model%20predictions.ipynb))
### Acknowledgments
@ -69,10 +70,13 @@ poetry shell
WANDB_USER="your user"
WANDB_PROJECT="training_seisbench_models"
BENCHMARK_DEFAULT_WORKER=2
```
2. Transform data into seisbench format.
To utilize functionality of Seisbench library, data need to be transformed to [SeisBench data format](https://seisbench.readthedocs.io/en/stable/pages/data_format.html)). If your data is in the MSEED format, you can use the prepared script `mseeds_to_seisbench.py` to perform the transformation. Please make sure that your data has the same structure as the data used in this project.
To utilize functionality of Seisbench library, data need to be transformed to [SeisBench data format](https://seisbench.readthedocs.io/en/stable/pages/data_format.html)).
If your data is stored in the MSEED format and catalog in the QuakeML format, you can use the prepared script `mseeds_to_seisbench.py` to perform the transformation. Please make sure that your data has the same structure as the data used in this project.
The script assumes that:
* the data is stored in the following directory structure:
`input_path/year/station_network_code/station_code/trace_channel.D` e.g.
@ -80,24 +84,20 @@ poetry shell
* the file names follow the pattern:
`station_network_code.station_code..trace_channel.D.year.day_of_year`
e.g. `PL.ALBE..EHE.D.2018.282`
* events catalog is stored in quakeML format
Run the script `mseeds_to_seisbench` located in the `utils` directory
Run the `mseeds_to_seisbench.py` script with the following arguments:
```
cd utils
python mseeds_to_seisbench.py --input_path $input_path --catalog_path $catalog_path --output_path $output_path
```
If you want to run the script on a cluster, you can use the script `convert_data.sh` as a template (adjust the grant name, computing name and paths) and send the job to queue using sbatch command on login node of e.g. Ares:
If you want to run the script on a cluster, you can use the template script `convert_data_template.sh`.
After adjusting the grant name, the paths to conda env and the paths to data send the job to queue using sbatch command on a login node of e.g. Ares:
```
cd utils
sbatch convert_data.sh
sbatch convert_data_template.sh
```
If your data has a different structure or format, use the notebooks to gain an understanding of the Seisbench format and what needs to be done to transform your data:
If your data has a different structure or format, check the notebooks to gain an understanding of the Seisbench format and what needs to be done to transform your data:
* [Seisbench example](https://colab.research.google.com/github/seisbench/seisbench/blob/main/examples/01a_dataset_basics.ipynb) or
* [Transforming mseeds from Bogdanka to Seisbench format](utils/Transforming mseeds from Bogdanka to Seisbench format.ipynb) notebook
* [Transforming mseeds from Bogdanka to Seisbench format](notebooks/Transforming mseeds from Bogdanka to Seisbench format.ipynb) notebook
3. Adjust the `config.json` and specify:
@ -110,8 +110,8 @@ poetry shell
`python pipeline.py`
The script performs the following steps:
* Generates evaluation targets in `datasets/<dataset_name>/targets` directory.
* Trains multiple versions of GPD, PhaseNet and ... models to find the best hyperparameters, producing the lowest validation loss.
1. Generates evaluation targets in `datasets/<dataset_name>/targets` directory.
1. Trains multiple versions of GPD, PhaseNet and ... models to find the best hyperparameters, producing the lowest validation loss.
This step utilizes the Weights & Biases platform to perform the hyperparameters search (called sweeping) and track the training process and store the results.
The results are available at
@ -121,23 +121,37 @@ poetry shell
* Weights of the best checkpoint of each model are saved as `<dataset_name>_<model_name>_sweep=<sweep_id>-run=<run_id>-epoch=<epoch_number>-val_loss=<val_loss>.ckpt`
* Metrics and hyperparams are saved in <run_id> folders
* Uses the best performing model of each type to generate predictions. The predictons are saved in the `scripts/pred/<dataset_name>_<model_name>/<run_id>` directory.
* Evaluates the performance of each model by comparing the predictions with the evaluation targets.
The results are saved in the `scripts/pred/results.csv` file.
1. Uses the best performing model of each type to generate predictions. The predictons are saved in the `scripts/pred/<dataset_name>_<model_name>/<run_id>` directory.
1. Evaluates the performance of each model by comparing the predictions with the evaluation targets and calculating MAE metrics.
The results are saved in the `scripts/pred/results.csv` file. They are additionally logged in Weights & Biases platform as summary metrics of corresponding runs.
<br/>
The default settings are saved in config.json file. To change the settings, edit the config.json file or pass the new settings as arguments to the script. For example, to change the sweep configuration file for GPD model, run:
```python pipeline.py --gpd_config <new config file>```
The default settings are saved in config.json file. To change the settings, edit the config.json file or pass the new settings as arguments to the script.
For example, to change the sweep configuration file for GPD model, run:
`python pipeline.py --gpd_config <new config file>`
The new config file should be placed in the `experiments` folder or as specified in the `configs_path` parameter in the config.json file.
If you have multiple datasets, you can run the pipeline for each dataset separately by specifying the dataset name as an argument:
```python pipeline.py --dataset <dataset_name>```
### Troubleshooting
* Problem with reading the catalog file: please make sure that your quakeML xml file has the following opening and closing tags:
```
<?xml version="1.0"?>
<q:quakeml xmlns="http://quakeml.org/xmlns/bed/1.2" xmlns:q="http://quakeml.org/xmlns/quakeml/1.2">
....
</q:quakeml>
```
* `wandb: ERROR Run .. errored: OSError(24, 'Too many open files')`
-> https://github.com/wandb/wandb/issues/2825
### Licence
TODO
The code is licenced under the GNU General Public License v3.0. See the [LICENSE](LICENSE.txt) file for details.
### Copyright

8
config.json
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@ -1,6 +1,6 @@
{
"dataset_name": "bogdanka",
"data_path": "datasets/bogdanka/seisbench_format/",
"dataset_name": "lumineos",
"data_path": "datasets/lumineos/seisbench_format/",
"targets_path": "datasets/targets",
"models_path": "weights",
"configs_path": "experiments",
@ -9,7 +9,9 @@
"seed": 10,
"sweep_files": {
"GPD": "sweep_gpd.yaml",
"PhaseNet": "sweep_phasenet.yaml"
"PhaseNet": "sweep_phasenet.yaml",
"BasicPhaseAE": "sweep_basicphase_ae.yaml",
"EQTransformer": "sweep_eqtransformer.yaml"
},
"experiment_count": 20
}

19
experiments/sweep_basicphase_ae.yaml Normal file
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@ -0,0 +1,19 @@
method: bayes
metric:
goal: minimize
name: val_loss
parameters:
model_name:
value:
- BasicPhaseAE
batch_size:
distribution: int_uniform
max: 1024
min: 256
max_epochs:
value:
- 20
learning_rate:
distribution: uniform
max: 0.02
min: 0.001

20
experiments/sweep_eqtransformer.yaml Normal file
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@ -0,0 +1,20 @@
name: EQTransformer
method: bayes
metric:
goal: minimize
name: val_loss
parameters:
model_name:
value:
- EQTransformer
batch_size:
distribution: int_uniform
max: 1024
min: 256
max_epochs:
value:
- 30
learning_rate:
distribution: uniform
max: 0.02
min: 0.005

4
experiments/sweep_gpd.yaml
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@ -13,14 +13,14 @@ parameters:
min: 256
max_epochs:
value:
- 3
- 30
learning_rate:
distribution: uniform
max: 0.02
min: 0.005
highpass:
value:
- 2
- 1
lowpass:
value:
- 10

2
experiments/sweep_phasenet.yaml
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@ -13,7 +13,7 @@ parameters:
min: 256
max_epochs:
value:
- 15
- 30
learning_rate:
distribution: uniform
max: 0.02

6
utils/Transforming mseeds from Bogdanka to Seisbench format.ipynb → notebooks/Transforming mseeds from Bogdanka to Seisbench format.ipynb
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@ -18,9 +18,7 @@
"import seisbench.data as sbd\n",
"import seisbench.util as sbu\n",
"import numpy as np\n",
"\n",
"\n",
"import utils\n"
"\n"
]
},
{
@ -1126,7 +1124,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
"version": "3.10.6"
}
},
"nbformat": 4,

31
utils/Transforming mseeds from Lumineos to SeisBench dataset.ipynb → notebooks/Transforming mseeds from Lumineos to SeisBench dataset.ipynb
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@ -36,6 +36,16 @@
"\n"
]
},
{
"cell_type": "markdown",
"id": "70c64dc6-e4dd-4c01-939d-a28914866f5d",
"metadata": {},
"source": [
"##### The catalog has a custom format with the following properties: \n",
"###### 'Datetime', 'X', 'Y', 'Depth', 'Mw', 'Phases', 'mseed_name'\n",
"###### Phases is a string with detected phases seperated by comma: <Phase> <Station> <Datetime> e.g. \"Pg BRDW 2020-01-01 10:09:44.400, Sg BRDW 2020-01-01 10:09:45.696\""
]
},
{
"cell_type": "code",
"execution_count": 2,
@ -106,6 +116,27 @@
"catalog.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "03257d45-299d-4ed1-bc64-03303d2a9873",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Pg BRDW 2020-01-01 10:09:44.400, Sg BRDW 2020-01-01 10:09:45.696, Pg GROD 2020-01-01 10:09:45.206, Sg GROD 2020-01-01 10:09:46.655, Pg GUZI 2020-01-01 10:09:45.116, Sg GUZI 2020-01-01 10:09:46.561, Pg JEDR 2020-01-01 10:09:44.920, Sg JEDR 2020-01-01 10:09:46.285, Pg MOSK2 2020-01-01 10:09:45.417, Sg MOSK2 2020-01-01 10:09:46.921, Pg NWLU 2020-01-01 10:09:45.686, Sg NWLU 2020-01-01 10:09:47.175, Pg PCHB 2020-01-01 10:09:45.213, Sg PCHB 2020-01-01 10:09:46.565, Pg PPOL 2020-01-01 10:09:44.755, Sg PPOL 2020-01-01 10:09:46.069, Pg RUDN 2020-01-01 10:09:44.502, Sg RUDN 2020-01-01 10:09:45.756, Pg RYNR 2020-01-01 10:09:43.442, Sg RYNR 2020-01-01 10:09:44.394, Pg RZEC 2020-01-01 10:09:46.075, Sg RZEC 2020-01-01 10:09:47.587, Pg SGOR 2020-01-01 10:09:45.817, Sg SGOR 2020-01-01 10:09:47.284, Pg TRBC2 2020-01-01 10:09:44.833, Sg TRBC2 2020-01-01 10:09:46.095, Pg TRN2 2020-01-01 10:09:44.488, Sg TRN2 2020-01-01 10:09:45.698, Pg TRZS 2020-01-01 10:09:46.232, Sg TRZS 2020-01-01 10:09:47.727, Pg ZMST 2020-01-01 10:09:43.592, Sg ZMST 2020-01-01 10:09:44.553, Pg LUBW 2020-01-01 10:09:43.119, Sg LUBW 2020-01-01 10:09:43.929'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"catalog.Phases[0]"
]
},
{
"cell_type": "markdown",
"id": "fe0627b1-6fa0-4b5a-8a60-d80626b5c9be",

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scripts/convert_data_template.sh Normal file
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@ -0,0 +1,19 @@
#!/bin/bash
#SBATCH --job-name=mseeds_to_seisbench
#SBATCH --time=1:00:00
#SBATCH --account= ### to fill
#SBATCH --partition plgrid
#SBATCH --cpus-per-task=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem=24gb
## activate conda environment
source /path/to/mambaforge/bin/activate ### to adjust
conda activate epos-ai-train
input_path="/path/to/folder/with/mseed/files"
catalog_path="/path/to/catolog.xml"
output_path="/path/to/output/in/seisbench_format"
python mseeds_to_seisbench.py --input_path $input_path --catalog_path $catalog_path --output_path $output_path

16
scripts/generate_eval_targets.py
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@ -39,10 +39,15 @@ from pathlib import Path
import pandas as pd
import numpy as np
from tqdm import tqdm
import logging
from models import phase_dict
logging.root.setLevel(logging.INFO)
logger = logging.getLogger('targets generator')
def main(dataset_name, output, tasks, sampling_rate, noise_before_events):
np.random.seed(42)
tasks = [str(i) in tasks.split(",") for i in range(1, 4)]
@ -64,7 +69,7 @@ def main(dataset_name, output, tasks, sampling_rate, noise_before_events):
dataset = sbd.WaveformDataset(dataset_name, **dataset_args)
output = Path(output)
output.mkdir(parents=True, exist_ok=False)
output.mkdir(parents=True, exist_ok=True)
if "split" in dataset.metadata.columns:
dataset.filter(dataset["split"].isin(["dev", "test"]), inplace=True)
@ -72,9 +77,16 @@ def main(dataset_name, output, tasks, sampling_rate, noise_before_events):
dataset.preload_waveforms(pbar=True)
if tasks[0]:
if not Path.exists(output / "task1.csv"):
generate_task1(dataset, output, sampling_rate, noise_before_events)
else:
logger.info(f"{output}/task1.csv already exists. Skipping generation of targets.")
if tasks[1] or tasks[2]:
if not Path.exists(output / "task23.csv"):
generate_task23(dataset, output, sampling_rate)
else:
logger.info(f"{output}/task23.csv already exists. Skipping generation of targets.")
def generate_task1(dataset, output, sampling_rate, noise_before_events):

33
scripts/hyperparameter_sweep.py
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@ -18,9 +18,7 @@ from dotenv import load_dotenv
import models
import train
import util
from config_loader import config as common_config
from config_loader import models_path, dataset_name, seed, experiment_count
import config_loader
torch.multiprocessing.set_sharing_strategy('file_system')
os.system("ulimit -n unlimited")
@ -35,8 +33,6 @@ if host is None:
wandb.login(key=wandb_api_key, host=host)
# wandb.login(key=wandb_api_key)
wandb_project_name = os.environ.get("WANDB_PROJECT")
wandb_user_name = os.environ.get("WANDB_USER")
@ -68,11 +64,9 @@ class HyperparameterSweep:
# Create the sweep
self.sweep_id = wandb.sweep(self.sweep_config, project=self.project_name)
logger.info("Created sweep with ID: " + self.sweep_id)
# Run the sweep
wandb.agent(self.sweep_id, function=self.run_experiment, count=experiment_count)
wandb.agent(self.sweep_id, function=self.run_experiment, count=config_loader.experiment_count)
def all_runs_finished(self):
@ -96,13 +90,14 @@ class HyperparameterSweep:
logger.debug("Starting a new run...")
run = wandb.init(
project=self.project_name,
config=common_config,
)
wandb.run.log_code(
".",
include_fn=lambda path: path.endswith(os.path.basename(__file__))
config=config_loader.config,
save_code=True
)
run.log_code(
root=".",
include_fn=lambda path: path.endswith(".py") or path.endswith(".sh"),
exclude_fn=lambda path: path.endswith("template.sh")
) # not working as expected
model_name = wandb.config.model_name[0]
model_args = models.get_model_specific_args(wandb.config)
@ -116,8 +111,8 @@ class HyperparameterSweep:
wandb_logger.watch(model)
# CSV logger - also used for saving configuration as yaml
experiment_name = f"{dataset_name}_{model_name}"
csv_logger = CSVLogger(models_path, experiment_name, version=run.id)
experiment_name = f"{config_loader.dataset_name}_{model_name}"
csv_logger = CSVLogger(config_loader.models_path, experiment_name, version=run.id)
csv_logger.log_hyperparams(wandb.config)
loggers = [wandb_logger, csv_logger]
@ -131,7 +126,7 @@ class HyperparameterSweep:
filename=experiment_signature + "-{epoch}-{val_loss:.3f}",
monitor="val_loss",
mode="min",
dirpath=f"{models_path}/{experiment_name}/",
dirpath=f"{config_loader.models_path}/{experiment_name}/",
) # save_top_k=1, monitor="val_loss", mode="min": save the best model in terms of validation loss
checkpoint_callback.STARTING_VERSION = 1
@ -143,7 +138,7 @@ class HyperparameterSweep:
callbacks = [checkpoint_callback, early_stopping_callback]
trainer = pl.Trainer(
default_root_dir=models_path,
default_root_dir=config_loader.models_path,
logger=loggers,
callbacks=callbacks,
**get_trainer_args(wandb.config)
@ -162,7 +157,7 @@ class HyperparameterSweep:
def start_sweep(sweep_config):
logger.info("Starting sweep with config: " + str(sweep_config))
set_random_seed(seed)
set_random_seed(config_loader.seed)
sweep_runner = HyperparameterSweep(project_name=wandb_project_name, sweep_config=sweep_config)
sweep_runner.run_sweep()

0
utils/mseeds_to_seisbench.py → scripts/mseeds_to_seisbench.py
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43
scripts/pipeline.py
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@ -15,21 +15,25 @@ import generate_eval_targets
import hyperparameter_sweep
import eval
import collect_results
from config_loader import data_path, targets_path, sampling_rate, dataset_name, sweep_files
import importlib
import config_loader
logging.root.setLevel(logging.INFO)
logger = logging.getLogger('pipeline')
def load_sweep_config(model_name, args):
if model_name == "PhaseNet" and args.phasenet_config is not None:
sweep_fname = args.phasenet_config
elif model_name == "GPD" and args.gpd_config is not None:
sweep_fname = args.gpd_config
elif model_name == "BasicPhaseAE" and args.basic_phase_ae_config is not None:
sweep_fname = args.basic_phase_ae_config
elif model_name == "EQTransformer" and args.eqtransformer_config is not None:
sweep_fname = args.eqtransformer_config
else:
# use the default sweep config for the model
sweep_fname = sweep_files[model_name]
sweep_fname = config_loader.sweep_files[model_name]
logger.info(f"Loading sweep config: {sweep_fname}")
@ -37,7 +41,6 @@ def load_sweep_config(model_name, args):
def find_the_best_params(model_name, args):
# find the best hyperparams for the model_name
logger.info(f"Starting searching for the best hyperparams for the model: {model_name}")
@ -58,9 +61,9 @@ def find_the_best_params(model_name, args):
def generate_predictions(sweep_id, model_name):
experiment_name = f"{dataset_name}_{model_name}"
experiment_name = f"{config_loader.dataset_name}_{model_name}"
eval.main(weights=experiment_name,
targets=targets_path,
targets=config_loader.targets_path,
sets='dev,test',
batchsize=128,
num_workers=4,
@ -73,22 +76,42 @@ def main():
parser = argparse.ArgumentParser()
parser.add_argument("--phasenet_config", type=str, required=False)
parser.add_argument("--gpd_config", type=str, required=False)
parser.add_argument("--basic_phase_ae_config", type=str, required=False)
parser.add_argument("--eqtransformer_config", type=str, required=False)
parser.add_argument("--dataset", type=str, required=False)
args = parser.parse_args()
if args.dataset is not None:
util.set_dataset(args.dataset)
importlib.reload(config_loader)
logger.info(f"Started pipeline for the {config_loader.dataset_name} dataset.")
# generate labels
logger.info("Started generating labels for the dataset.")
generate_eval_targets.main(data_path, targets_path, "2,3", sampling_rate, None)
generate_eval_targets.main(config_loader.data_path, config_loader.targets_path, "2,3", config_loader.sampling_rate,
None)
# find the best hyperparams for the models
logger.info("Started training the models.")
for model_name in ["GPD", "PhaseNet"]:
for model_name in ["GPD", "PhaseNet", "BasicPhaseAE", "EQTransformer"]:
if config_loader.dataset_name == "lumineos" and model_name == "EQTransformer":
break
sweep_id = find_the_best_params(model_name, args)
generate_predictions(sweep_id, model_name)
# collect results
logger.info("Collecting results.")
collect_results.traverse_path("pred", "pred/results.csv")
logger.info("Results saved in pred/results.csv")
results_path = "pred/results.csv"
collect_results.traverse_path("pred", results_path)
logger.info(f"Results saved in {results_path}")
# log calculated metrics (MAE) on w&b
logger.info("Logging MAE metrics on w&b.")
util.log_metrics(results_path)
logger.info("Pipeline finished")
if __name__ == "__main__":
main()

20
scripts/run_pipeline_template.sh Normal file
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@ -0,0 +1,20 @@
#!/bin/bash
#SBATCH --job-name=job_name
#SBATCH --time=10:00:00
#SBATCH --account= ### to fill
#SBATCH --partition=plgrid-gpu-v100
#SBATCH --cpus-per-task=1
#SBATCH --ntasks-per-node=1
#SBATCH --gres=gpu:1
source path/to/mambaforge/bin/activate ### to change
conda activate epos-ai-train
python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
python -c "import torch; print('Number of CUDA devices:', torch.cuda.device_count())"
python -c "import torch; print('Name of GPU:', torch.cuda.get_device_name(torch.cuda.current_device()))"
python pipeline.py --dataset "lumineos"
python pipeline.py --dataset "bogdanka"

96
scripts/util.py
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@ -1,5 +1,10 @@
"""
This script offers general functionality required in multiple places.
-----------------
Copyright © 2023 ACK Cyfronet AGH, Poland.
This work was partially funded by EPOS Project funded in frame of PL-POIR4.2
-----------------
This script runs the pipeline for the training and evaluation of the models.
"""
import numpy as np
@ -7,13 +12,15 @@ import pandas as pd
import os
import logging
import glob
import json
import wandb
from dotenv import load_dotenv
import sys
from config_loader import models_path, configs_path
from config_loader import models_path, configs_path, config_path
import yaml
load_dotenv()
load_dotenv()
logging.basicConfig()
logging.getLogger().setLevel(logging.INFO)
@ -38,7 +45,15 @@ def load_best_model_data(sweep_id, weights):
# Get best run parameters
best_run = sweep.best_run()
run_id = best_run.id
matching_models = glob.glob(f"{models_path}/{weights}/*run={run_id}*ckpt")
run = api.run(f"{wandb_user}/{wandb_project_name}/runs/{run_id}")
dataset = run.config["dataset_name"]
model = run.config["model_name"][0]
experiment = f"{dataset}_{model}"
checkpoints_path = f"{models_path}/{experiment}/*run={run_id}*ckpt"
logging.debug(f"Searching for checkpoints in dir: {checkpoints_path}")
matching_models = glob.glob(checkpoints_path)
if len(matching_models) != 1:
raise ValueError("Unable to determine the best checkpoint for run_id: " + run_id)
best_checkpoint_path = matching_models[0]
@ -62,31 +77,6 @@ def load_best_model_data(sweep_id, weights):
return best_checkpoint_path, run_id
def load_best_model(model_cls, weights, version):
"""
Determines the model with lowest validation loss from the csv logs and loads it
:param model_cls: Class of the lightning module to load
:param weights: Path to weights as in cmd arguments
:param version: String of version file
:return: Instance of lightning module that was loaded from the best checkpoint
"""
metrics = pd.read_csv(weights / version / "metrics.csv")
idx = np.nanargmin(metrics["val_loss"])
min_row = metrics.iloc[idx]
# For default checkpoint filename, see https://github.com/Lightning-AI/lightning/pull/11805
# and https://github.com/Lightning-AI/lightning/issues/16636.
# For example, 'epoch=0-step=1.ckpt' means the 1st step has finish, but the 1st epoch hasn't
checkpoint = f"epoch={min_row['epoch']:.0f}-step={min_row['step']+1:.0f}.ckpt"
# For default save path of checkpoints, see https://github.com/Lightning-AI/lightning/pull/12372
checkpoint_path = weights / version / "checkpoints" / checkpoint
return model_cls.load_from_checkpoint(checkpoint_path)
default_workers = os.getenv("BENCHMARK_DEFAULT_WORKERS", None)
if default_workers is None:
logging.warning(
@ -117,3 +107,51 @@ def load_sweep_config(sweep_fname):
sys.exit(1)
return sweep_config
def log_metrics(results_file):
"""
:param results_file: csv file with calculated metrics
:return:
"""
api = wandb.Api()
wandb_project_name = os.environ.get("WANDB_PROJECT")
wandb_user = os.environ.get("WANDB_USER")
results = pd.read_csv(results_file)
for run_id in results["version"].unique():
try:
run = api.run(f"{wandb_user}/{wandb_project_name}/{run_id}")
metrics_to_log = {}
run_results = results[results["version"] == run_id]
for col in run_results.columns:
if 'mae' in col:
metrics_to_log[col] = run_results[col].values[0]
run.summary[col] = run_results[col].values[0]
run.summary.update()
logging.info(f"Logged metrics for run: {run_id}, {metrics_to_log}")
except Exception as e:
print(f"An error occurred: {e}, {type(e).__name__}, {e.args}")
def set_dataset(dataset_name):
"""
Sets the dataset name in the config file
:param dataset_name:
:return:
"""
with open(config_path, "r+") as f:
config = json.load(f)
config["dataset_name"] = dataset_name
config["data_path"] = f"datasets/{dataset_name}/seisbench_format/"
f.seek(0) # rewind
json.dump(config, f, indent=4)
f.truncate()

19
utils/convert_data.sh
View File

@ -1,19 +0,0 @@
#!/bin/bash
#SBATCH --job-name=mseeds_to_seisbench
#SBATCH --time=1:00:00
#SBATCH --account=plgeposai22gpu-gpu
#SBATCH --partition plgrid
#SBATCH --cpus-per-task=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem=24gb
## activate conda environment
source /net/pr2/projects/plgrid/plggeposai/kmilian/mambaforge/bin/activate
conda activate epos-ai-train
input_path="/net/pr2/projects/plgrid/plggeposai/datasets/bogdanka"
catalog_path="/net/pr2/projects/plgrid/plggeposai/datasets/bogdanka/BOIS_all.xml"
output_path="/net/pr2/projects/plgrid/plggeposai/kmilian/platform-demo-scripts/datasets/bogdanka/seisbench_format"
python mseeds_to_seisbench.py --input_path $input_path --catalog_path $catalog_path --output_path $output_path

230
utils/utils.py
View File

@ -1,230 +0,0 @@
import os
import pandas as pd
import glob
from pathlib import Path
import obspy
from obspy.core.event import read_events
import seisbench.data as sbd
import seisbench.util as sbu
import sys
import logging
logging.basicConfig(filename="output.out",
filemode='a',
format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.DEBUG)
logger = logging.getLogger('converter')
def create_traces_catalog(directory, years):
for year in years:
directory = f"{directory}/{year}"
files = glob.glob(directory)
traces = []
for i, f in enumerate(files):
st = obspy.read(f)
for tr in st.traces:
# trace_id = tr.id
# start = tr.meta.starttime
# end = tr.meta.endtime
trs = pd.Series({
'trace_id': tr.id,
'trace_st': tr.meta.starttime,
'trace_end': tr.meta.endtime,
'stream_fname': f
})
traces.append(trs)
traces_catalog = pd.DataFrame(pd.concat(traces)).transpose()
traces_catalog.to_csv("data/bogdanka/traces_catalog.csv", append=True, index=False)
def split_events(events, input_path):
logger.info("Splitting available events into train, dev and test sets ...")
events_stats = pd.DataFrame()
events_stats.index.name = "event"
for i, event in enumerate(events):
#check if mseed exists
actual_picks = 0
for pick in event.picks:
trace_params = get_trace_params(pick)
trace_path = get_trace_path(input_path, trace_params)
if os.path.isfile(trace_path):
actual_picks += 1
events_stats.loc[i, "pick_count"] = actual_picks
events_stats['pick_count_cumsum'] = events_stats.pick_count.cumsum()
train_th = 0.7 * events_stats.pick_count_cumsum.values[-1]
dev_th = 0.85 * events_stats.pick_count_cumsum.values[-1]
events_stats['split'] = 'test'
for i, event in events_stats.iterrows():
if event['pick_count_cumsum'] < train_th:
events_stats.loc[i, 'split'] = 'train'
elif event['pick_count_cumsum'] < dev_th:
events_stats.loc[i, 'split'] = 'dev'
else:
break
return events_stats
def get_event_params(event):
origin = event.preferred_origin()
if origin is None:
return {}
# print(origin)
mag = event.preferred_magnitude()
source_id = str(event.resource_id)
event_params = {
"source_id": source_id,
"source_origin_uncertainty_sec": origin.time_errors["uncertainty"],
"source_latitude_deg": origin.latitude,
"source_latitude_uncertainty_km": origin.latitude_errors["uncertainty"],
"source_longitude_deg": origin.longitude,
"source_longitude_uncertainty_km": origin.longitude_errors["uncertainty"],
"source_depth_km": origin.depth / 1e3,
"source_depth_uncertainty_km": origin.depth_errors["uncertainty"] / 1e3 if origin.depth_errors[
"uncertainty"] is not None else None,
}
if mag is not None:
event_params["source_magnitude"] = mag.mag
event_params["source_magnitude_uncertainty"] = mag.mag_errors["uncertainty"]
event_params["source_magnitude_type"] = mag.magnitude_type
event_params["source_magnitude_author"] = mag.creation_info.agency_id if mag.creation_info is not None else None
return event_params
def get_trace_params(pick):
net = pick.waveform_id.network_code
sta = pick.waveform_id.station_code
trace_params = {
"station_network_code": net,
"station_code": sta,
"trace_channel": pick.waveform_id.channel_code,
"station_location_code": pick.waveform_id.location_code,
"time": pick.time
}
return trace_params
def find_trace(pick_time, traces):
for tr in traces:
if pick_time > tr.stats.endtime:
continue
if pick_time >= tr.stats.starttime:
# print(pick_time, " - selected trace: ", tr)
return tr
logger.warning(f"no matching trace for peak: {pick_time}")
return None
def get_trace_path(input_path, trace_params):
year = trace_params["time"].year
day_of_year = pd.Timestamp(str(trace_params["time"])).day_of_year
net = trace_params["station_network_code"]
station = trace_params["station_code"]
tr_channel = trace_params["trace_channel"]
path = f"{input_path}/{year}/{net}/{station}/{tr_channel}.D/{net}.{station}..{tr_channel}.D.{year}.{day_of_year}"
return path
def load_trace(input_path, trace_params):
trace_path = get_trace_path(input_path, trace_params)
trace = None
if not os.path.isfile(trace_path):
logger.w(trace_path + " not found")
else:
stream = obspy.read(trace_path)
if len(stream.traces) > 1:
trace = find_trace(trace_params["time"], stream.traces)
elif len(stream.traces) == 0:
logger.warning(f"no data in: {trace_path}")
else:
trace = stream.traces[0]
return trace
def load_stream(input_path, trace_params, time_before=60, time_after=60):
trace_path = get_trace_path(input_path, trace_params)
sampling_rate, stream = None, None
pick_time = trace_params["time"]
if not os.path.isfile(trace_path):
print(trace_path + " not found")
else:
stream = obspy.read(trace_path)
stream = stream.slice(pick_time - time_before, pick_time + time_after)
if len(stream.traces) == 0:
print(f"no data in: {trace_path}")
else:
sampling_rate = stream.traces[0].stats.sampling_rate
return sampling_rate, stream
def convert_mseed_to_seisbench_format():
input_path = "/net/pr2/projects/plgrid/plggeposai"
logger.info("Loading events catalog ...")
events = read_events(input_path + "/BOIS_all.xml")
events_stats = split_events(events)
output_path = input_path + "/seisbench_format"
metadata_path = output_path + "/metadata.csv"
waveforms_path = output_path + "/waveforms.hdf5"
with sbd.WaveformDataWriter(metadata_path, waveforms_path) as writer:
writer.data_format = {
"dimension_order": "CW",
"component_order": "ZNE",
}
for i, event in enumerate(events):
logger.debug(f"Converting {i} event")
event_params = get_event_params(event)
event_params["split"] = events_stats.loc[i, "split"]
# b = False
for pick in event.picks:
trace_params = get_trace_params(pick)
sampling_rate, stream = load_stream(input_path, trace_params)
if stream is None:
continue
actual_t_start, data, _ = sbu.stream_to_array(
stream,
component_order=writer.data_format["component_order"],
)
trace_params["trace_sampling_rate_hz"] = sampling_rate
trace_params["trace_start_time"] = str(actual_t_start)
pick_time = obspy.core.utcdatetime.UTCDateTime(trace_params["time"])
pick_idx = (pick_time - actual_t_start) * sampling_rate
trace_params[f"trace_{pick.phase_hint}_arrival_sample"] = int(pick_idx)
writer.add_trace({**event_params, **trace_params}, data)
if __name__ == "__main__":
convert_mseed_to_seisbench_format()
# create_traces_catalog("/net/pr2/projects/plgrid/plggeposai/", ["2018", "2019"])