initial commit with the pipeline for training and evaluating seisbench models

.gitignore

@@ -3,8 +3,11 @@ __pycache__/
 */.ipynb_checkpoints/
 .ipynb_checkpoints/
 .env
-models/
-data/
+weights/
+datasets/
 wip
 artifacts/
-wandb/
+wandb/
+scripts/pred/
+scripts/pred_resampled/
+scripts/lightning_logs/

README.md

@@ -2,20 +2,59 @@
 
 
 This repo contains notebooks and scripts demonstrating how to:
-- Prepare IGF data for training model detecting P phase (i.e. transform mseeds into [SeisBench data format](https://seisbench.readthedocs.io/en/stable/pages/data_format.html)), check the [notebook](utils/Transforming%20mseeds%20to%20SeisBench%20dataset.ipynb).
-The original data can be downloaded from the [drive](https://drive.google.com/drive/folders/1InVI9DLaD7gdzraM2jMzeIrtiBSu-UIK?usp=drive_link)
+- Prepare IGF data for training a seisbench model detecting P phase (i.e. transform mseeds into [SeisBench data format](https://seisbench.readthedocs.io/en/stable/pages/data_format.html)), check the [notebook](utils/Transforming%20mseeds%20to%20SeisBench%20dataset.ipynb).
 
 - Explore available data, check the [notebook](notebooks/Explore%20igf%20data.ipynb)
-- Train cnn model (Seisbench PhaseNet) to detect P phase, check the [script](scripts/train.py)
-- Use Weights & Biases to search for the best training hyperparams, check the [script](scripts/training_wandb_sweep.py)
-- Validate model performance, check the [notebook](notebooks/Check%20model%20performance%20depending%20on%20station-random%20window.ipynb)
-- Use model for detecting P phase, check the [notebook](notebooks/Present%20model%20predictions.ipynb)
+- Train various cnn models available in seisbench library and compare their performance of detecting P phase, 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)
 
 
+### Acknowledgments
+This code is based on the [pick-benchmark](https://github.com/seisbench/pick-benchmark), the repository accompanying the paper:
+[Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers](https://github.com/seisbench/pick-benchmark#:~:text=Which%20picker%20fits%20my%20data%3F%20A%20quantitative%20evaluation%20of%20deep%20learning%20based%20seismic%20pickers)
 ### Usage
 
 1. Install all dependencies with poetry, run: 
-`poetry install`
-2. Prepare .env file with content: 
 
-    `WANDB_API_KEY="your key"`
+   `poetry install`
+2. Prepare .env file with content:
+   ```
+   WANDB_HOST="https://epos-ai.grid.cyfronet.pl/"
+   WANDB_API_KEY="your key"
+   WANDB_USER="your user"
+   WANDB_PROJECT="training_seisbench_models_on_igf_data"
+   BENCHMARK_DEFAULT_WORKER=2
+
+3. Transform data into seisbench format.
+   * Download original data from the [drive](https://drive.google.com/drive/folders/1InVI9DLaD7gdzraM2jMzeIrtiBSu-UIK?usp=drive_link)
+   * Run the notebook: `utils/Transforming mseeds to SeisBench dataset.ipynb`
+
+4. Initialize poetry environment:
+
+   `poetry shell`
+
+5. Run the pipeline script:
+
+   `python pipeline.py`
+
+   The script performs the following steps:
+   * Generates evaluation targets
+     * 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   
+     `https://epos-ai.grid.cyfronet.pl/<your user name>/<your project name>`
+   * Uses the best performing model of each type to generate predictions
+   * Evaluates the performance of each model by comparing the predictions with the evaluation targets
+   * Saves the results in the `scripts/pred` directory  
+   * 
+   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` or as specified in the `configs_path` parameter in the config.json file.
+
+### Troubleshooting
+
+* `wandb: ERROR Run .. errored: OSError(24, 'Too many open files')`
+-> https://github.com/wandb/wandb/issues/2825

config.json

@@ -0,0 +1,15 @@
+{
+  "dataset_name": "igf",
+  "data_path": "datasets/igf/seisbench_format/",
+  "targets_path": "datasets/targets/igf",
+  "models_path": "weights",
+  "configs_path": "experiments",
+  "sampling_rate": 100,
+  "num_workers": 1,
+  "seed": 10,
+  "sweep_files": {
+    "GPD": "sweep_gpd.yaml",
+    "PhaseNet": "sweep_phasenet.yaml"
+  },
+  "experiment_count": 20
+}

experiments/config.json

@@ -1,11 +0,0 @@
-{
-  "epochs": 10,
-  "batch_size": 256,
-  "dataset": "igf_1",
-  "sampling_rate": 100,
-  "model_names": "EQTransformer,BasicPhaseAE,GPD",
-  "model_name": "PhaseNet",
-  "learning_rate": 0.01,
-  "pretrained": null,
-  "sampling_rate": 100
-}

experiments/sweep_gpd.yaml

@@ -0,0 +1,26 @@
+name: GPD_fixed_highpass:2-10
+method: bayes
+metric:
+  goal: minimize
+  name: val_loss
+parameters:
+  model_name:
+    value:
+      - GPD
+  batch_size:
+    distribution: int_uniform
+    max: 1024
+    min: 256
+  max_epochs:
+    value:
+      - 3
+  learning_rate:
+    distribution: uniform
+    max: 0.02
+    min: 0.005
+  highpass:
+    value:
+      - 2
+  lowpass:
+    value:
+      - 10

experiments/sweep_gpd_highpass.yaml

@@ -0,0 +1,27 @@
+name: GPD_fixed_highpass:2-10
+method: bayes
+metric:
+  goal: minimize
+  name: val_loss
+parameters:
+  model_name:
+    value:
+      - GPD
+  batch_size:
+    distribution: int_uniform
+    max: 1024
+    min: 256
+  max_epochs:
+    value:
+      - 15
+  learning_rate:
+    distribution: uniform
+    max: 0.02
+    min: 0.005
+  highpass:
+    distribution: uniform
+    min: 0.5
+    max: 2.0
+  lowpass:
+    value:
+      - 10

experiments/sweep_phasenet.yaml

@@ -0,0 +1,20 @@
+name: PhaseNet-lr0.005-0.02-bs256-1024
+method: bayes
+metric:
+  goal: minimize
+  name: val_loss
+parameters:
+  model_name:
+    value:
+      - PhaseNet
+  batch_size:
+    distribution: int_uniform
+    max: 1024
+    min: 256
+  max_epochs:
+    value:
+      - 15
+  learning_rate:
+    distribution: uniform
+    max: 0.02
+    min: 0.005

pyproject.toml

@@ -14,6 +14,8 @@ pandas = "^2.0.3"
 obspy = "^1.4.0"
 wandb = "^0.15.4"
 torchmetrics = "^0.11.4"
+ipykernel = "^6.24.0"
+jupyterlab = "^4.0.2"
 
 [tool.poetry.dev-dependencies]
 

scripts/augmentations.py

@@ -0,0 +1,133 @@
+"""
+This file contains augmentations required for the models that are too specific to be merged into SeisBench.
+"""
+
+import numpy as np
+import copy
+
+
+class DuplicateEvent:
+    """
+    Adds a rescaled version of the event to the empty part of the trace after the event.
+    Event position and empty space are determined from a detection.
+    Detections can be generated for example with :py:class:`~seisbench.generate.labeling.DetectionLabeller`.
+
+    This implementation is modelled after the `implementation for EQTransformer <https://github.com/smousavi05/EQTransformer/blob/98676017f971efbb6f4475f42e415c3868d00c03/EQTransformer/core/EqT_utils.py#L255>`_.
+
+    .. warning::
+        This augmentation does **not** modify the metadata, as representing multiple picks of
+        the same type is currently not supported. Workflows should therefore always first generate
+        labels from metadata and then pass the labels in the key `label_keys`. These keys are automatically
+        adjusted by addition of the labels.
+
+    .. warning::
+        This implementation currently has strict shape requirements:
+
+        - (1, samples) for detection
+        - (channels, samples) for data
+        - (labels, samples) for labels
+
+    :param inv_scale: The scale factor is defined by as 1/u, where u is uniform.
+                      `inv_scale` defines the minimum and maximum values for u.
+                      Defaults to (1, 10), e.g., scaling by factor 1 to 1/10.
+    :param detection_key: Key to read detection from.
+                          If key is a tuple, detection will be read from the first key and written to the second one.
+    :param key: The keys for reading from and writing to the state dict.
+                If key is a single string, the corresponding entry in state dict is modified.
+                Otherwise, a 2-tuple is expected, with the first string indicating the key
+                to read from and the second one the key to write to.
+    :param label_keys: Keys for the label columns.
+                       Labels of the original and duplicate events will be added and capped at 1.
+                       Note that this will lead to invalid noise traces.
+                       Value can either be a single key specification or a list of key specifications.
+                       Each key specification is either a string, for identical input and output keys,
+                       or as a tuple of two strings, input and output keys.
+                       Defaults to None.
+    """
+
+    def __init__(
+        self, inv_scale=(1, 10), detection_key="detections", key="X", label_keys=None
+    ):
+        if isinstance(detection_key, str):
+            self.detection_key = (detection_key, detection_key)
+        else:
+            self.detection_key = detection_key
+
+        if isinstance(key, str):
+            self.key = (key, key)
+        else:
+            self.key = key
+
+        # Single key
+        if not isinstance(label_keys, list):
+            if label_keys is None:
+                label_keys = []
+            else:
+                label_keys = [label_keys]
+
+        # Resolve identical input and output keys
+        self.label_keys = []
+        for key in label_keys:
+            if isinstance(key, tuple):
+                self.label_keys.append(key)
+            else:
+                self.label_keys.append((key, key))
+
+        self.inv_scale = inv_scale
+
+    def __call__(self, state_dict):
+        x, metadata = state_dict[self.key[0]]
+        detection, _ = state_dict[self.detection_key[0]]
+        detection_mask = detection[0] > 0.5
+
+        if detection.shape[-1] != x.shape[-1]:
+            raise ValueError("Number of samples in trace and detection disagree.")
+
+        if self.key[0] != self.key[1]:
+            # Ensure metadata is not modified inplace unless input and output key are anyhow identical
+            metadata = copy.deepcopy(metadata)
+
+        if detection_mask.any():
+            n_samples = x.shape[-1]
+            event_samples = np.arange(n_samples)[detection_mask]
+            event_start, event_end = np.min(event_samples), np.max(event_samples) + 1
+
+            if event_end + 20 < n_samples:
+                second_start = np.random.randint(event_end + 20, n_samples)
+                scale = 1 / np.random.uniform(*self.inv_scale)
+
+                if self.key[0] != self.key[1]:
+                    # Avoid inplace modification if input and output keys differ
+                    x = x.copy()
+
+                space = min(event_end - event_start, n_samples - second_start)
+                x[:, second_start : second_start + space] += (
+                    x[:, event_start : event_start + space] * scale
+                )
+
+                shift = second_start - event_start
+
+                for label_key in self.label_keys + [self.detection_key]:
+                    y, metadata = state_dict[label_key[0]]
+                    if y.shape[-1] != n_samples:
+                        raise ValueError(
+                            f"Number of samples disagree between trace and label key '{label_key[0]}'."
+                        )
+
+                    if label_key[0] != label_key[1]:
+                        metadata = copy.deepcopy(metadata)
+                        y = y.copy()
+
+                    y[:, shift:] += y[:, :-shift]
+                    y = np.minimum(y, 1)
+                    state_dict[label_key[1]] = (y, metadata)
+        else:
+            # Copy entries
+            for label_key in self.label_keys + [self.detection_key]:
+                y, metadata = state_dict[label_key[0]]
+                if label_key[0] != label_key[1]:
+                    metadata = copy.deepcopy(metadata)
+                    y = y.copy()
+                state_dict[label_key[1]] = (y, metadata)
+
+        state_dict[self.key[1]] = (x, metadata)

scripts/collect_results.py

@@ -0,0 +1,335 @@
+"""
+This script collects results in a folder, calculates performance metrics and writes them to csv.
+"""
+
+import argparse
+from pathlib import Path
+import logging
+import pandas as pd
+import numpy as np
+from sklearn.metrics import (
+    precision_recall_curve,
+    precision_recall_fscore_support,
+    roc_auc_score,
+    matthews_corrcoef,
+)
+from tqdm import tqdm
+
+
+def traverse_path(path, output, cross=False, resampled=False, baer=False):
+    """
+    Traverses the given path and extracts results for each experiment and version
+
+    :param path: Root path
+    :param output: Path to write results csv to
+    :param cross: If true, expects cross-domain results.
+    :return: None
+    """
+    path = Path(path)
+
+    results = []
+
+    exp_dirs = [x for x in path.iterdir() if x.is_dir()]
+    for exp_dir in tqdm(exp_dirs):
+        itr = exp_dir.iterdir()
+        if baer:
+            itr = [exp_dir]  # Missing version directory in the structure
+        for version_dir in itr:
+            if not version_dir.is_dir():
+                pass
+
+            results.append(
+                process_version(
+                    version_dir, cross=cross, resampled=resampled, baer=baer
+                )
+            )
+
+    results = pd.DataFrame(results)
+    if cross:
+        sort_keys = ["data", "model", "target", "lr", "version"]
+    else:
+        sort_keys = ["data", "model", "lr", "version"]
+    results.sort_values(sort_keys, inplace=True)
+    results.to_csv(output, index=False)
+
+
+def process_version(version_dir: Path, cross: bool, resampled: bool, baer: bool):
+    """
+    Extracts statistics for the given version of the given experiment.
+
+    :param version_dir: Path to the specific version
+    :param cross: If true, expects cross-domain results.
+    :return: Results dictionary
+    """
+    stats = parse_exp_name(version_dir, cross=cross, resampled=resampled, baer=baer)
+
+    stats.update(eval_task1(version_dir))
+    stats.update(eval_task23(version_dir))
+
+    return stats
+
+
+def parse_exp_name(version_dir, cross, resampled, baer):
+    if baer:
+        exp_name = version_dir.name
+        version = "0"
+    else:
+        exp_name = version_dir.parent.name
+        version = version_dir.name.split("_")[-1]
+
+    parts = exp_name.split("_")
+    target = None
+    sampling_rate = None
+    if cross or baer:
+        if len(parts) == 4:
+            data, model, lr, target = parts
+        else:
+            data, model, target = parts
+            lr = "0.001"
+    elif resampled:
+        if len(parts) == 5:
+            data, model, lr, target, sampling_rate = parts
+        else:
+            data, model, target, sampling_rate = parts
+            lr = "0.001"
+    else:
+        if len(parts) == 3:
+            data, model, lr = parts
+        else:
+            data, model, *_ = parts
+            lr = "0.001"
+
+    # lr = float(lr)
+
+    stats = {
+        "experiment": exp_name,
+        "data": data,
+        "model": model,
+        "lr": None,
+        "version": version,
+    }
+
+    if cross or baer:
+        stats["target"] = target
+    if resampled:
+        stats["target"] = target
+        stats["sampling_rate"] = sampling_rate
+
+    return stats
+
+
+def eval_task1(version_dir: Path):
+    if not (
+        (version_dir / "dev_task1.csv").is_file()
+        and (version_dir / "test_task1.csv").is_file()
+    ):
+        logging.warning(f"Directory {version_dir} does not contain task 1")
+        return {}
+
+    stats = {}
+
+    dev_pred = pd.read_csv(version_dir / "dev_task1.csv")
+    dev_pred["trace_type_bin"] = dev_pred["trace_type"] == "earthquake"
+    test_pred = pd.read_csv(version_dir / "test_task1.csv")
+    test_pred["trace_type_bin"] = test_pred["trace_type"] == "earthquake"
+
+    prec, recall, thr = precision_recall_curve(
+        dev_pred["trace_type_bin"], dev_pred["score_detection"]
+    )
+
+    f1 = 2 * prec * recall / (prec + recall)
+    auc = roc_auc_score(dev_pred["trace_type_bin"], dev_pred["score_detection"])
+
+    opt_index = np.nanargmax(f1)  # F1 optimal threshold index
+    opt_thr = thr[opt_index]  # F1 optimal threshold value
+
+    dev_stats = {
+        "dev_det_precision": prec[opt_index],
+        "dev_det_recall": recall[opt_index],
+        "dev_det_f1": f1[opt_index],
+        "dev_det_auc": auc,
+        "det_threshold": opt_thr,
+    }
+    stats.update(dev_stats)
+
+    prec, recall, f1, _ = precision_recall_fscore_support(
+        test_pred["trace_type_bin"],
+        test_pred["score_detection"] > opt_thr,
+        average="binary",
+    )
+    auc = roc_auc_score(test_pred["trace_type_bin"], test_pred["score_detection"])
+    test_stats = {
+        "test_det_precision": prec,
+        "test_det_recall": recall,
+        "test_det_f1": f1,
+        "test_det_auc": auc,
+    }
+    stats.update(test_stats)
+
+    return stats
+
+
+def eval_task23(version_dir: Path):
+    print(version_dir / "dev_task23.csv")
+    if not (
+        (version_dir / "dev_task23.csv").is_file()
+        and (version_dir / "test_task23.csv").is_file()
+    ):
+        logging.warning(f"Directory {version_dir} does not contain tasks 2 and 3")
+        return {}
+
+    stats = {}
+
+    dev_pred = pd.read_csv(version_dir / "dev_task23.csv")
+    dev_pred["phase_label_bin"] = dev_pred["phase_label"] == "P"
+    test_pred = pd.read_csv(version_dir / "test_task23.csv")
+    test_pred["phase_label_bin"] = test_pred["phase_label"] == "P"
+
+    def add_aux_columns(pred):
+        for col in ["s_sample_pred", "score_p_or_s"]:
+            if col not in pred.columns:
+                pred[col] = np.nan
+
+    add_aux_columns(dev_pred)
+    add_aux_columns(test_pred)
+
+    def nanmask(pred):
+        """
+        Returns all entries that are nan in score_p_or_s, p_sample_pred and s_sample_pred
+        """
+        mask = np.logical_and(
+            np.isnan(pred["p_sample_pred"]), np.isnan(pred["s_sample_pred"])
+        )
+        mask = np.logical_and(mask, np.isnan(pred["score_p_or_s"]))
+        return mask
+
+    if nanmask(dev_pred).all():
+        logging.warning(f"{version_dir} contains NaN predictions for tasks 2 and 3")
+        return {}
+
+    dev_pred = dev_pred[~nanmask(dev_pred)]
+    test_pred = test_pred[~nanmask(test_pred)]
+
+    skip_task2 = False
+    if (
+        np.logical_or(
+            np.isnan(dev_pred["score_p_or_s"]), np.isinf(dev_pred["score_p_or_s"])
+        ).all()
+        or np.logical_or(
+            np.isnan(test_pred["score_p_or_s"]), np.isinf(test_pred["score_p_or_s"])
+        ).all()
+    ):
+        # For unfortunate combinations of nans and infs, otherwise weird scores can occur
+        skip_task2 = True
+
+    # Clipping removes infinitely likely P waves, usually resulting from models trained without S arrivals
+    dev_pred["score_p_or_s"] = np.clip(dev_pred["score_p_or_s"].values, -1e100, 1e100)
+    test_pred["score_p_or_s"] = np.clip(test_pred["score_p_or_s"].values, -1e100, 1e100)
+
+    dev_pred_restricted = dev_pred[~np.isnan(dev_pred["score_p_or_s"])]
+    test_pred_restricted = test_pred[~np.isnan(test_pred["score_p_or_s"])]
+    if len(dev_pred_restricted) > 0 and not skip_task2:
+        prec, recall, thr = precision_recall_curve(
+            dev_pred_restricted["phase_label_bin"], dev_pred_restricted["score_p_or_s"]
+        )
+
+        f1 = 2 * prec * recall / (prec + recall)
+
+        opt_index = np.nanargmax(f1)  # F1 optimal threshold index
+        opt_thr = thr[opt_index]  # F1 optimal threshold value
+
+        # Determine (approximately) optimal MCC threshold using 50 candidates
+        mcc_thrs = np.sort(dev_pred["score_p_or_s"].values)
+        mcc_thrs = mcc_thrs[np.linspace(0, len(mcc_thrs) - 1, 50, dtype=int)]
+        mccs = []
+        for thr in mcc_thrs:
+            mccs.append(
+                matthews_corrcoef(
+                    dev_pred["phase_label_bin"], dev_pred["score_p_or_s"] > thr
+                )
+            )
+        mcc = np.max(mccs)
+        mcc_thr = mcc_thrs[np.argmax(mccs)]
+
+        dev_stats = {
+            "dev_phase_precision": prec[opt_index],
+            "dev_phase_recall": recall[opt_index],
+            "dev_phase_f1": f1[opt_index],
+            "phase_threshold": opt_thr,
+            "dev_phase_mcc": mcc,
+            "phase_threshold_mcc": mcc_thr,
+        }
+        stats.update(dev_stats)
+
+        prec, recall, f1, _ = precision_recall_fscore_support(
+            test_pred_restricted["phase_label_bin"],
+            test_pred_restricted["score_p_or_s"] > opt_thr,
+            average="binary",
+        )
+        mcc = matthews_corrcoef(
+            test_pred["phase_label_bin"], test_pred["score_p_or_s"] > mcc_thr
+        )
+        test_stats = {
+            "test_phase_precision": prec,
+            "test_phase_recall": recall,
+            "test_phase_f1": f1,
+            "test_phase_mcc": mcc,
+        }
+        stats.update(test_stats)
+
+    for pred, set_str in [(dev_pred, "dev"), (test_pred, "test")]:
+        for i, phase in enumerate(["P", "S"]):
+            pred_phase = pred[pred["phase_label"] == phase]
+            pred_col = f"{phase.lower()}_sample_pred"
+
+            if len(pred_phase) == 0:
+                continue
+
+            diff = (pred_phase[pred_col] - pred_phase["phase_onset"]) / pred_phase[
+                "sampling_rate"
+            ]
+
+            stats[f"{set_str}_{phase}_mean_s"] = np.mean(diff)
+            stats[f"{set_str}_{phase}_std_s"] = np.sqrt(np.mean(diff**2))
+            stats[f"{set_str}_{phase}_mae_s"] = np.mean(np.abs(diff))
+
+    return stats
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Collects results from all experiments in a folder and outputs them in condensed csv format."
+    )
+    parser.add_argument(
+        "path",
+        type=str,
+        help="Root path of predictions",
+    )
+    parser.add_argument(
+        "output",
+        type=str,
+        help="Path for the output csv",
+    )
+    parser.add_argument(
+        "--cross", action="store_true", help="If true, expects cross-domain results."
+    )
+    parser.add_argument(
+        "--resampled",
+        action="store_true",
+        help="If true, expects cross-domain cross-sampling rate results.",
+    )
+    parser.add_argument(
+        "--baer",
+        action="store_true",
+        help="If true, expects results from Baer-Kradolfer picker.",
+    )
+
+    args = parser.parse_args()
+
+    traverse_path(
+        args.path,
+        args.output,
+        cross=args.cross,
+        resampled=args.resampled,
+        baer=args.baer,
+    )

scripts/config_loader.py

@@ -0,0 +1,26 @@
+import json
+from pathlib import Path
+
+
+def load_config(file_path):
+    with open(file_path, 'r') as config_file:
+        config = json.load(config_file)
+    return config
+
+
+project_path = str(Path.cwd().parent)
+config_path = project_path + "/config.json"
+
+config = load_config(config_path)
+
+data_path = f"{project_path}/{config['data_path']}"
+models_path = f"{project_path}/{config['models_path']}"
+targets_path = f"{project_path}/{config['targets_path']}"
+dataset_name = config['dataset_name']
+configs_path = f"{project_path}/{config['configs_path']}"
+
+sweep_files = config['sweep_files']
+sampling_rate = config['sampling_rate']
+num_workers = config['num_workers']
+seed = config['seed']
+experiment_count = config['experiment_count']

scripts/data.py

@@ -0,0 +1,32 @@
+"""
+This file contains functionality related to data.
+"""
+
+import seisbench.data as sbd
+
+
+def get_dataset_by_name(name):
+    """
+    Resolve dataset name to class from seisbench.data.
+
+    :param name: Name of dataset as defined in seisbench.data.
+    :return: Dataset class from seisbench.data
+    """
+    try:
+        return sbd.__getattribute__(name)
+    except AttributeError:
+        raise ValueError(f"Unknown dataset '{name}'.")
+
+
+def get_custom_dataset(path):
+    """
+    Return custom dataset in seisbench format
+    :param path:
+    :return: Dataset class
+    """
+
+    try:
+        return sbd.WaveformDataset(path)
+    except AttributeError:
+        raise ValueError(f"Unknown dataset '{path}'.")
+

scripts/eval.py

@@ -0,0 +1,248 @@
+"""
+This script implements functionality for evaluating models.
+Given a model and a set of targets, it calculates and outputs predictions.
+"""
+
+import seisbench.generate as sbg
+
+import argparse
+import pandas as pd
+import yaml
+from pathlib import Path
+import pytorch_lightning as pl
+from torch.utils.data import DataLoader
+import torch
+
+import models, data
+import logging
+from util import default_workers, load_best_model_data
+import time
+import datetime
+from config_loader import models_path, project_path
+import os
+
+data_aliases = {
+    "ethz": "ETHZ",
+    "geofon": "GEOFON",
+    "stead": "STEAD",
+    "neic": "NEIC",
+    "instance": "InstanceCountsCombined",
+    "iquique": "Iquique",
+    "lendb": "LenDB",
+    "scedc": "SCEDC"
+}
+
+
+def main(weights, targets, sets, batchsize, num_workers, sampling_rate=None, sweep_id=None, test_run=False):
+    weights = Path(weights)
+    targets = Path(os.path.abspath(targets))
+    print(targets)
+    # print()
+    sets = sets.split(",")
+
+    checkpoint_path, version = load_best_model_data(sweep_id, weights)
+    logging.warning("Starting evaluation of model: \n" + checkpoint_path)
+
+    config_path = f"{models_path}/{weights}/{version}/hparams.yaml"
+    with open(config_path, "r") as f:
+        # config = yaml.safe_load(f)
+        config = yaml.full_load(f)
+
+    model_name = config["model_name"][0] if type(config["model_name"]) == list else config["model_name"]
+
+    model_cls = models.__getattribute__(model_name + "Lit")
+    model = model_cls.load_from_checkpoint(checkpoint_path)
+
+    data_name = data_aliases[targets.name] if targets.name in data_aliases else None
+
+    if data_name != config["dataset_name"] and targets.name in data_aliases:
+        logging.warning("Detected cross-domain evaluation")
+        pred_root = "pred_cross"
+        parts = weights.name.split()
+        weight_path_name = "_".join(parts[:2] + [targets.name] + parts[2:])
+
+    else:
+        pred_root = "pred"
+        weight_path_name = weights.name
+
+    if data_name is not None:
+        dataset = data.get_dataset_by_name(data_name)(
+            sampling_rate=100, component_order="ZNE", dimension_order="NCW", cache="full"
+        )
+    else:
+        data_path = project_path + '/' + config['data_path']
+        print("Loading dataset: ", data_path)
+        dataset = data.get_custom_dataset(data_path)
+
+    if sampling_rate is not None:
+        dataset.sampling_rate = sampling_rate
+        pred_root = pred_root + "_resampled"
+        weight_path_name = weight_path_name + f"_{sampling_rate}"
+
+    for eval_set in sets:
+        split = dataset.get_split(eval_set)
+        if targets.name == "instance":
+            logging.warning(
+                "Overwriting noise trace_names to allow correct identification"
+            )
+            # Replace trace names for noise entries
+            split._metadata["trace_name"].values[
+                -len(split.datasets[-1]) :
+            ] = split._metadata["trace_name"][-len(split.datasets[-1]) :].apply(
+                lambda x: "noise_" + x
+            )
+            split._build_trace_name_to_idx_dict()
+
+        logging.warning(f"Starting set {eval_set}")
+        split.preload_waveforms(pbar=True)
+        print("eval set shape", split.metadata.shape)
+
+        for task in ["1", "23"]:
+            task_csv = targets / f"task{task}.csv"
+
+            print(task_csv)
+
+            if not task_csv.is_file():
+                continue
+
+            logging.warning(f"Starting task {task}")
+
+            task_targets = pd.read_csv(task_csv)
+            task_targets = task_targets[task_targets["trace_split"] == eval_set]
+            if task == "1" and targets.name == "instance":
+                border = _identify_instance_dataset_border(task_targets)
+                task_targets["trace_name"].values[border:] = task_targets["trace_name"][
+                    border:
+                ].apply(lambda x: "noise_" + x)
+
+            if sampling_rate is not None:
+                for key in ["start_sample", "end_sample", "phase_onset"]:
+                    if key not in task_targets.columns:
+                        continue
+                    task_targets[key] = (
+                        task_targets[key]
+                        * sampling_rate
+                        / task_targets["sampling_rate"]
+                    )
+                task_targets[sampling_rate] = sampling_rate
+
+            restrict_to_phase = config.get("restrict_to_phase", None)
+            if restrict_to_phase is not None and "phase_label" in task_targets.columns:
+                mask = task_targets["phase_label"].isin(list(restrict_to_phase))
+                task_targets = task_targets[mask]
+
+            if restrict_to_phase is not None and task == "1":
+                logging.warning("Skipping task 1 as restrict_to_phase is set.")
+                continue
+
+            generator = sbg.SteeredGenerator(split, task_targets)
+            generator.add_augmentations(model.get_eval_augmentations())
+
+            loader = DataLoader(
+                generator, batch_size=batchsize, shuffle=False, num_workers=num_workers
+            )
+            # trainer = pl.Trainer(accelerator="gpu", devices=1)
+            trainer = pl.Trainer()
+
+            predictions = trainer.predict(model, loader)
+
+            # Merge batches
+            merged_predictions = []
+            for i, _ in enumerate(predictions[0]):
+                merged_predictions.append(torch.cat([x[i] for x in predictions]))
+
+            merged_predictions = [x.cpu().numpy() for x in merged_predictions]
+            task_targets["score_detection"] = merged_predictions[0]
+            task_targets["score_p_or_s"] = merged_predictions[1]
+            task_targets["p_sample_pred"] = (
+                merged_predictions[2] + task_targets["start_sample"]
+            )
+            task_targets["s_sample_pred"] = (
+                merged_predictions[3] + task_targets["start_sample"]
+            )
+
+            pred_path = (
+                weights.parent.parent
+                / pred_root
+                / weight_path_name
+                / version
+                / f"{eval_set}_task{task}.csv"
+            )
+            pred_path.parent.mkdir(exist_ok=True, parents=True)
+            task_targets.to_csv(pred_path, index=False)
+
+
+def _identify_instance_dataset_border(task_targets):
+    """
+    Calculates the dataset border between Signal and Noise for instance,
+    assuming it is the only place where the bucket number does not increase
+    """
+    buckets = task_targets["trace_name"].apply(lambda x: int(x.split("$")[0][6:]))
+
+    last_bucket = 0
+    for i, bucket in enumerate(buckets):
+        if bucket < last_bucket:
+            return i
+        last_bucket = bucket
+
+
+if __name__ == "__main__":
+    code_start_time = time.perf_counter()
+    parser = argparse.ArgumentParser(
+        description="Evaluate a trained model using a set of targets."
+    )
+    parser.add_argument(
+        "weights",
+        type=str,
+        help="Path to weights. Expected to be in models_path directory."
+        "The script will automatically load the configuration and the model. "
+        "The script always uses the checkpoint with lowest validation loss."
+        "If sweep_id is provided the script considers only the checkpoints generated by that sweep."
+        "Predictions will be written into the weights path as csv."
+        "Note: Due to pytorch lightning internals, there exist two weights folders, "
+        "{weights} and {weight}_{weights}. Please use the former as parameter",
+    )
+    parser.add_argument(
+        "targets",
+        type=str,
+        help="Path to evaluation targets folder. "
+        "The script will detect which tasks are present base on file names.",
+    )
+    parser.add_argument(
+        "--sets",
+        type=str,
+        default="dev,test",
+        help="Sets on which to evaluate, separated by commata. Defaults to dev and test.",
+    )
+    parser.add_argument("--batchsize", type=int, default=1024, help="Batch size")
+    parser.add_argument(
+        "--num_workers",
+        default=default_workers,
+        type=int,
+        help="Number of workers for data loader",
+    )
+    parser.add_argument(
+        "--sampling_rate", type=float, help="Overwrites the sampling rate in the data"
+    )
+    parser.add_argument(
+        "--sweep_id", type=str, help="wandb sweep_id", required=False, default=None
+    )
+    parser.add_argument(
+        "--test_run", action="store_true", required=False, default=False
+    )
+    args = parser.parse_args()
+
+    main(
+        args.weights,
+        args.targets,
+        args.sets,
+        batchsize=args.batchsize,
+        num_workers=args.num_workers,
+        sampling_rate=args.sampling_rate,
+        sweep_id=args.sweep_id,
+        test_run=args.test_run
+    )
+    running_time = str(
+        datetime.timedelta(seconds=time.perf_counter() - code_start_time)
+    )
+    print(f"Running time: {running_time}")

scripts/generate_eval_targets.py

@@ -0,0 +1,321 @@
+"""
+This script generates evaluation targets for the following three tasks:
+
+- Earthquake detection (Task 1): Given a 30~s window, does the window contain an earthquake signal?
+- Phase identification (Task 2): Given a 10~s window containing exactly one phase onset, identify which phase type.
+- Onset time determination (Task 3): Given a 10~s window containing exactly one phase onset, identify the onset time.
+
+Each target for evaluation will consist of the following information:
+
+- trace name (as in dataset)
+- trace index (in dataset)
+- split (as in dataset)
+- sampling rate (at which all information is presented)
+- start_sample
+- end_sample
+- trace_type (only task 1: earthquake/noise)
+- phase_label (only task 2/3: P/S)
+- full_phase_label (only task 2/3: phase label as in the dataset, might be Pn, Pg, etc.)
+- phase_onset_sample (only task 2/3: onset sample of the phase relative to full trace)
+
+It needs to be provided with a dataset and writes a folder with two CSV files, one for task 1 and one for tasks 2 and 3.
+Each file will describe targets for train, dev and test, derived from the respective splits.
+
+When using these tasks for evaluation, the models can make use of waveforms from the context, i.e.,
+before/after the start and end samples. However, make sure this does not add further bias in the evaluation,
+for example by always centring the windows on the picks using context.
+
+.. warning::
+    For comparability, it is strongly advised to use published evaluation targets, instead of generating new ones.
+
+.. warning::
+    This implementation is not optimized and loads the full waveform data for its computations.
+    This will lead to very high memory usage, as the full dataset will be stored in memory.
+"""
+import seisbench.data as sbd
+
+import argparse
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+from models import phase_dict
+
+
+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)]
+
+    if not any(tasks):
+        raise ValueError(f"No task defined. Got tasks='{tasks}'.")
+
+    dataset_args = {
+        "sampling_rate": sampling_rate,
+        "dimension_order": "NCW",
+        "cache": "full",
+    }
+
+    try:
+        # Check if dataset is available in SeisBench
+        dataset = sbd.__getattribute__(dataset_name)(**dataset_args)
+    except AttributeError:
+        # Otherwise interpret data_in as path
+        dataset = sbd.WaveformDataset(dataset_name, **dataset_args)
+
+    output = Path(output)
+    output.mkdir(parents=True, exist_ok=False)
+
+    if "split" in dataset.metadata.columns:
+        dataset.filter(dataset["split"].isin(["dev", "test"]), inplace=True)
+
+    dataset.preload_waveforms(pbar=True)
+
+    if tasks[0]:
+        generate_task1(dataset, output, sampling_rate, noise_before_events)
+    if tasks[1] or tasks[2]:
+        generate_task23(dataset, output, sampling_rate)
+
+
+def generate_task1(dataset, output, sampling_rate, noise_before_events):
+    np.random.seed(42)
+    windowlen = 30 * sampling_rate  # 30 s windows
+    labels = []
+
+    for i in tqdm(range(len(dataset)), total=len(dataset)):
+        waveforms, metadata = dataset.get_sample(i)
+
+        if "split" in metadata:
+            trace_split = metadata["split"]
+        else:
+            trace_split = ""
+
+        def checkphase(metadata, phase, phase_label, target_phase, npts):
+            return (
+                phase in metadata
+                and phase_label == target_phase
+                and not np.isnan(metadata[phase])
+                and 0 <= metadata[phase] < npts
+            )
+
+        p_arrivals = [
+            metadata[phase]
+            for phase, phase_label in phase_dict.items()
+            if checkphase(metadata, phase, phase_label, "P", waveforms.shape[-1])
+        ]
+        s_arrivals = [
+            metadata[phase]
+            for phase, phase_label in phase_dict.items()
+            if checkphase(metadata, phase, phase_label, "S", waveforms.shape[-1])
+        ]
+
+        if len(p_arrivals) == 0 and len(s_arrivals) == 0:
+            start_sample, end_sample = select_window_containing(
+                waveforms.shape[-1], windowlen
+            )
+            sample = {
+                "trace_name": metadata["trace_name"],
+                "trace_idx": i,
+                "trace_split": trace_split,
+                "sampling_rate": sampling_rate,
+                "start_sample": start_sample,
+                "end_sample": end_sample,
+                "trace_type": "noise",
+            }
+            labels += [sample]
+
+        else:
+            first_arrival = min(p_arrivals + s_arrivals)
+
+            start_sample, end_sample = select_window_containing(
+                waveforms.shape[-1], windowlen, containing=first_arrival
+            )
+            if end_sample - start_sample <= windowlen:
+                sample = {
+                    "trace_name": metadata["trace_name"],
+                    "trace_idx": i,
+                    "trace_split": trace_split,
+                    "sampling_rate": sampling_rate,
+                    "start_sample": start_sample,
+                    "end_sample": end_sample,
+                    "trace_type": "earthquake",
+                }
+                labels += [sample]
+
+            if noise_before_events and first_arrival > windowlen:
+                start_sample, end_sample = select_window_containing(
+                    min(waveforms.shape[-1], first_arrival), windowlen
+                )
+                if end_sample - start_sample <= windowlen:
+                    sample = {
+                        "trace_name": metadata["trace_name"],
+                        "trace_idx": i,
+                        "trace_split": trace_split,
+                        "sampling_rate": sampling_rate,
+                        "start_sample": start_sample,
+                        "end_sample": end_sample,
+                        "trace_type": "noise",
+                    }
+                    labels += [sample]
+
+    labels = pd.DataFrame(labels)
+    diff = labels["end_sample"] - labels["start_sample"]
+    labels = labels[diff > 100]
+    labels.to_csv(output / "task1.csv", index=False)
+
+
+def generate_task23(dataset, output, sampling_rate):
+    np.random.seed(42)
+    windowlen = 10 * sampling_rate  # 30 s windows
+    labels = []
+
+    for idx in tqdm(range(len(dataset)), total=len(dataset)):
+        waveforms, metadata = dataset.get_sample(idx)
+
+        if "split" in metadata:
+            trace_split = metadata["split"]
+        else:
+            trace_split = ""
+
+        def checkphase(metadata, phase, npts):
+            return (
+                phase in metadata
+                and not np.isnan(metadata[phase])
+                and 0 <= metadata[phase] < npts
+            )
+
+        # Example entry: (1031, "P", "Pg")
+        arrivals = sorted(
+            [
+                (metadata[phase], phase_label, phase.split("_")[1])
+                for phase, phase_label in phase_dict.items()
+                if checkphase(metadata, phase, waveforms.shape[-1])
+            ]
+        )
+
+        if len(arrivals) == 0:
+            # Trace has no arrivals
+            continue
+
+        for i, (onset, phase, full_phase) in enumerate(arrivals):
+            if i == 0:
+                onset_before = 0
+            else:
+                onset_before = int(arrivals[i - 1][0]) + int(
+                    0.5 * sampling_rate
+                )  # 0.5 s minimum spacing
+
+            if i == len(arrivals) - 1:
+                onset_after = np.inf
+            else:
+                onset_after = int(arrivals[i + 1][0]) - int(
+                    0.5 * sampling_rate
+                )  # 0.5 s minimum spacing
+
+            if (
+                onset_after - onset_before < windowlen
+                or onset_before > onset
+                or onset_after < onset
+            ):
+                # Impossible to isolate pick
+                continue
+
+            else:
+                onset_after = min(onset_after, waveforms.shape[-1])
+                # Shift everything to a "virtual" start at onset_before
+                start_sample, end_sample = select_window_containing(
+                    onset_after - onset_before,
+                    windowlen=windowlen,
+                    containing=onset - onset_before,
+                    bounds=(50, 50),
+                )
+                start_sample += onset_before
+                end_sample += onset_before
+                if end_sample - start_sample <= windowlen:
+                    sample = {
+                        "trace_name": metadata["trace_name"],
+                        "trace_idx": idx,
+                        "trace_split": trace_split,
+                        "sampling_rate": sampling_rate,
+                        "start_sample": start_sample,
+                        "end_sample": end_sample,
+                        "phase_label": phase,
+                        "full_phase_label": full_phase,
+                        "phase_onset": onset,
+                    }
+
+                    labels += [sample]
+
+    labels = pd.DataFrame(labels)
+    diff = labels["end_sample"] - labels["start_sample"]
+    labels = labels[diff > 100]
+    labels.to_csv(output / "task23.csv", index=False)
+
+
+def select_window_containing(npts, windowlen, containing=None, bounds=(100, 100)):
+    """
+    Selects a window from a larger trace.
+
+    :param npts: Number of points of the full trace
+    :param windowlen: Desired windowlen
+    :param containing: Sample number that should be contained. If None, any window within the trace is valid.
+    :param bounds: The containing sample may not be in the first/last samples indicated here.
+    :return: Start sample, end_sample
+    """
+    if npts <= windowlen:
+        # If npts is smaller than the window length, always return the full window
+        return 0, npts
+
+    else:
+        if containing is None:
+            start_sample = np.random.randint(0, npts - windowlen + 1)
+            return start_sample, start_sample + windowlen
+
+        else:
+            earliest_start = max(0, containing - windowlen + bounds[1])
+            latest_start = min(npts - windowlen, containing - bounds[0])
+            if latest_start <= earliest_start:
+                # Again, return full window
+                return 0, npts
+
+            else:
+                start_sample = np.random.randint(earliest_start, latest_start + 1)
+                return start_sample, start_sample + windowlen
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate evaluation targets. See the docstring for details."
+    )
+    parser.add_argument(
+        "dataset", type=str, help="Path to input dataset or SeisBench dataset name"
+    )
+    parser.add_argument(
+        "output", type=str, help="Path to write target files to. Must not exist."
+    )
+    parser.add_argument(
+        "--tasks",
+        type=str,
+        default="1,2,3",
+        help="Which tasks to generate data for. By default generates data for all tasks.",
+    )
+    parser.add_argument(
+        "--sampling_rate",
+        type=float,
+        default=100,
+        help="Sampling rate in Hz to generate targets for.",
+    )
+    parser.add_argument(
+        "--no_noise_before_events",
+        action="store_true",
+        help="If set, does not extract noise from windows before the first arrival.",
+    )
+    args = parser.parse_args()
+
+    main(
+        args.dataset,
+        args.output,
+        args.tasks,
+        args.sampling_rate,
+        not args.no_noise_before_events,
+    )

scripts/hyperparameter_sweep.py

@@ -0,0 +1,178 @@
+# -----------------
+# Copyright © 2023 ACK Cyfronet AGH, Poland.
+# This work was partially funded by EPOS Project funded in frame of PL-POIR4.2
+# -----------------
+
+import os.path
+import argparse
+from pytorch_lightning.loggers import WandbLogger, CSVLogger
+from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.callbacks.early_stopping import EarlyStopping
+import pytorch_lightning as pl
+import wandb
+import torch
+import traceback
+import logging
+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
+
+
+torch.multiprocessing.set_sharing_strategy('file_system')
+
+load_dotenv()
+wandb_api_key = os.environ.get('WANDB_API_KEY')
+if wandb_api_key is None:
+    raise ValueError("WANDB_API_KEY environment variable is not set.")
+host = os.environ.get("WANDB_HOST")
+if host is None:
+    raise ValueError("WANDB_HOST environment variable is not set.")
+
+
+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")
+
+script_name = os.path.splitext(os.path.basename(__file__))[0]
+logger = logging.getLogger(script_name)
+logger.setLevel(logging.WARNING)
+
+
+def set_random_seed(seed=3):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+def get_trainer_args(config):
+    trainer_args = {'max_epochs': config.max_epochs[0]}
+    return trainer_args
+
+
+class HyperparameterSweep:
+    def __init__(self, project_name, sweep_config):
+        self.project_name = project_name
+        self.sweep_config = sweep_config
+        self.sweep_id = None
+
+    def run_sweep(self):
+
+        # 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)
+
+    def all_runs_finished(self):
+
+        sweep_path = f"{wandb_user_name}/{wandb_project_name}/{self.sweep_id}"
+        logger.debug(f"Sweep path: {sweep_path}")
+        sweep_runs = wandb.Api().sweep(sweep_path).runs
+        all_finished = all(run.state == "finished" for run in sweep_runs)
+        if all_finished:
+            logger.info("All runs finished successfully.")
+
+        all_not_running = all(run.state != "running" for run in sweep_runs)
+        if all_not_running and not all_finished:
+            logger.warning("Some runs are not finished but failed or crashed.")
+
+        return all_not_running
+
+    def run_experiment(self):
+
+        try:
+
+            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__))
+            )
+
+            model_name = wandb.config.model_name[0]
+            model_args = models.get_model_specific_args(wandb.config)
+            logger.debug(f"Initializing {model_name}")
+
+            model = models.__getattribute__(model_name + "Lit")(**model_args)
+
+            train_loader, dev_loader = train.prepare_data(wandb.config, model, test_run=False)
+
+            wandb_logger = WandbLogger(project=self.project_name, log_model="all")
+            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)
+            csv_logger.log_hyperparams(wandb.config)
+
+            loggers = [wandb_logger, csv_logger]
+
+            experiment_signature = f"{experiment_name}_sweep={self.sweep_id}-run={run.id}"
+
+            logger.debug("Experiment signature: " + experiment_signature)
+
+            checkpoint_callback = ModelCheckpoint(
+                save_top_k=1,
+                filename=experiment_signature + "-{epoch}-{val_loss:.3f}",
+                monitor="val_loss",
+                mode="min",
+                dirpath=f"{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
+
+            early_stopping_callback = EarlyStopping(
+                monitor="val_loss",
+                patience=3,
+                verbose=True,
+                mode="min")
+            callbacks = [checkpoint_callback, early_stopping_callback]
+
+            trainer = pl.Trainer(
+                default_root_dir=models_path,
+                logger=loggers,
+                callbacks=callbacks,
+                **get_trainer_args(wandb.config)
+            )
+
+            trainer.fit(model, train_loader, dev_loader)
+
+        except Exception as e:
+            logger.error("caught error: ", str(e))
+            traceback_str = traceback.format_exc()
+            logger.error(traceback_str)
+
+        run.finish()
+
+
+def start_sweep(sweep_config):
+
+    logger.info("Starting sweep with config: " + str(sweep_config))
+    set_random_seed(seed)
+    sweep_runner = HyperparameterSweep(project_name=wandb_project_name, sweep_config=sweep_config)
+    sweep_runner.run_sweep()
+
+    return sweep_runner
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--sweep_config", type=str, required=True)
+    args = parser.parse_args()
+
+    sweep_config = util.load_sweep_config(args.sweep_config)
+    start_sweep(sweep_config)
+

scripts/pipeline.py

@@ -0,0 +1,92 @@
+"""
+-----------------
+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 logging
+import time
+import argparse
+
+import util
+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
+
+logger = logging.getLogger('pipeline')
+logger.setLevel(logging.INFO)
+
+
+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
+    else:
+        # use the default sweep config for the model
+        sweep_fname = sweep_files[model_name]
+
+    logger.info(f"Loading sweep config: {sweep_fname}")
+
+    return util.load_sweep_config(sweep_fname)
+
+
+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}")
+
+    sweep_config = load_sweep_config(model_name, args)
+    sweep_runner = hyperparameter_sweep.start_sweep(sweep_config)
+
+    # wait for all runs to finish
+
+    all_finished = sweep_runner.all_runs_finished()
+    while not all_finished:
+        logger.info("Waiting for sweep runs to finish...")
+        # Sleep for a few seconds before checking again
+        time.sleep(30)
+        all_finished = sweep_runner.all_runs_finished()
+
+    logger.info(f"Finished the sweep: {sweep_runner.sweep_id}")
+    return sweep_runner.sweep_id
+
+
+def generate_predictions(sweep_id, model_name):
+    experiment_name = f"{dataset_name}_{model_name}"
+    eval.main(weights=experiment_name,
+              targets=targets_path,
+              sets='dev,test',
+              batchsize=128,
+              num_workers=4,
+              # sampling_rate=sampling_rate,
+              sweep_id=sweep_id
+              )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--phasenet_config", type=str, required=False)
+    parser.add_argument("--gpd_config", type=str, required=False)
+    args = parser.parse_args()
+
+    # generate labels
+    generate_eval_targets.main(data_path, targets_path, "2,3", sampling_rate, None)
+
+    # find the best hyperparams for the models
+    for model_name in ["GPD", "PhaseNet"]:
+        sweep_id = find_the_best_params(model_name, args)
+        generate_predictions(sweep_id, model_name)
+
+    # collect results
+    collect_results.traverse_path("pred", "pred/results.csv")
+
+
+if __name__ == "__main__":
+    main()
+

scripts/train.py

@@ -1,339 +1,245 @@
-import os.path
-import wandb
-import seisbench.data as sbd
+"""
+This script handles the training of models base on model configuration files.
+"""
+
 import seisbench.generate as sbg
-import seisbench.models as sbm
 from seisbench.util import worker_seeding
-import numpy as np
-import torch
-from torch.utils.data import DataLoader
-import torch.nn.functional as f
-import torch.nn as nn
-from torchmetrics import Metric
-from torch import Tensor, tensor
+
+import pytorch_lightning as pl
+# from pytorch_lightning.loggers import TensorBoardLogger, CSVLogger
+from pytorch_lightning.loggers import WandbLogger, CSVLogger
+
+# https://github.com/Lightning-AI/lightning/pull/12554
+# https://github.com/Lightning-AI/lightning/issues/11796
+from pytorch_lightning.callbacks import ModelCheckpoint
+import argparse
 import json
-from dotenv import load_dotenv
+import numpy as np
+from torch.utils.data import DataLoader
+import torch
+import os
+import logging
+from pathlib import Path
 
-load_dotenv()
-wandb_api_key = os.environ.get('WANDB_API_KEY')
-if wandb_api_key is None:
-    raise ValueError("WANDB_API_KEY environment variable is not set.")
+import models, data, util
+import time
+import datetime
+import wandb
+#
+# load_dotenv()
+# wandb_api_key = os.environ.get('WANDB_API_KEY')
+# if wandb_api_key is None:
+#     raise ValueError("WANDB_API_KEY environment variable is not set.")
+#
+# wandb.login(key=wandb_api_key)
 
-wandb.login(key=wandb_api_key)
+def train(config, experiment_name, test_run):
+    """
+    Runs the model training defined by the config.
 
-project_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    Config parameters:
+
+        - model: Model used as in the models.py file, but without the Lit suffix
+        - data: Dataset used, as in seisbench.data
+        - model_args: Arguments passed to the constructor of the model lightning module
+        - trainer_args: Arguments passed to the lightning trainer
+        - batch_size: Batch size for training and validation
+        - num_workers: Number of workers for data loading.
+          If not set, uses environment variable BENCHMARK_DEFAULT_WORKERS
+        - restrict_to_phase: Filters datasets only to examples containing the given phase.
+          By default, uses all phases.
+        - training_fraction: Fraction of training blocks to use as float between 0 and 1. Defaults to 1.
+
+    :param config: Configuration parameters for training
+    :param test_run: If true, makes a test run with less data and less logging. Intended for debug purposes.
+    """
+    model = models.__getattribute__(config["model"] + "Lit")(
+        **config.get("model_args", {})
+    )
+
+    train_loader, dev_loader = prepare_data(config, model, test_run)
+
+    # CSV logger - also used for saving configuration as yaml
+    csv_logger = CSVLogger("weights", experiment_name)
+    csv_logger.log_hyperparams(config)
+    loggers = [csv_logger]
+
+    default_root_dir = os.path.join(
+        "weights"
+    )  # Experiment name is parsed from the loggers
+    if not test_run:
+        # tb_logger = TensorBoardLogger("tb_logs", experiment_name)
+        # tb_logger.log_hyperparams(config)
+        # loggers += [tb_logger]
+        wandb_logger = WandbLogger()
+        wandb_logger.watch(model)
+
+        loggers +=[wandb_logger]
+
+    checkpoint_callback = ModelCheckpoint(
+        save_top_k=1, filename="{epoch}-{step}", monitor="val_loss", mode="min"
+    )  # save_top_k=1, monitor="val_loss", mode="min": save the best model in terms of validation loss
+    callbacks = [checkpoint_callback]
+
+    ## Uncomment the following 2 lines to enable
+    # device_stats = DeviceStatsMonitor()
+    # callbacks.append(device_stats)
+
+    trainer = pl.Trainer(
+        default_root_dir=default_root_dir,
+        logger=loggers,
+        callbacks=callbacks,
+        **config.get("trainer_args", {}),
+    )
+
+    trainer.fit(model, train_loader, dev_loader)
 
 
-class PickMAE(Metric):
-    higher_is_better: bool = False
-    mae_error: Tensor
+def prepare_data(config, model, test_run):
+    """
+    Returns the training and validation data loaders
+    :param config:
+    :param model:
+    :param test_run:
+    :return:
+    """
+    batch_size = config.get("batch_size", 1024)
+    if type(batch_size) == list:
+        batch_size = batch_size[0]
 
-    def __init__(self, sampling_rate):
-        super().__init__()
-        self.add_state("mae_error", default=torch.tensor(0), dist_reduce_fx="sum")
-        self.sampling_rate = sampling_rate
+    num_workers = config.get("num_workers", util.default_workers)
+    try:
+        dataset = data.get_dataset_by_name(config["dataset_name"])(
+            sampling_rate=100, component_order="ZNE", dimension_order="NCW", cache="full"
+        )
+    except ValueError:
+        data_path = str(Path.cwd().parent) + '/' + config['data_path']
+        print(data_path)
+        dataset = data.get_custom_dataset(data_path)
 
-    def update(self, preds: torch.Tensor, target: torch.Tensor):
+    restrict_to_phase = config.get("restrict_to_phase", None)
+    if restrict_to_phase is not None:
+        mask = generate_phase_mask(dataset, restrict_to_phase)
+        dataset.filter(mask, inplace=True)
 
-        assert preds.shape == target.shape
+    if "split" not in dataset.metadata.columns:
+        logging.warning("No split defined, adding auxiliary split.")
+        split = np.array(["train"] * len(dataset))
+        split[int(0.6 * len(dataset)) : int(0.7 * len(dataset))] = "dev"
+        split[int(0.7 * len(dataset)) :] = "test"
 
-        pred_pick_idx = torch.argmax(preds[:, 0, :], dim=1).type(torch.FloatTensor)
-        true_pick_idx = torch.argmax(target[:, 0, :], dim=-1).type(torch.FloatTensor)
+        dataset._metadata["split"] = split
 
-        mae = nn.L1Loss()
-        self.mae_error = mae(pred_pick_idx, true_pick_idx) / self.sampling_rate #mae in seconds
+    train_data = dataset.train()
+    dev_data = dataset.dev()
 
-    def compute(self):
-        return self.mae_error.float()
+    if test_run:
+        # Only use a small part of the dataset
+        train_mask = np.zeros(len(train_data), dtype=bool)
+        train_mask[:5000] = True
+        train_data.filter(train_mask, inplace=True)
+
+        dev_mask = np.zeros(len(dev_data), dtype=bool)
+        dev_mask[:5000] = True
+        dev_data.filter(dev_mask, inplace=True)
+
+    training_fraction = config.get("training_fraction", 1.0)
+    apply_training_fraction(training_fraction, train_data)
+
+    train_data.preload_waveforms(pbar=True)
+    dev_data.preload_waveforms(pbar=True)
+
+    train_generator = sbg.GenericGenerator(train_data)
+    dev_generator = sbg.GenericGenerator(dev_data)
+
+    train_generator.add_augmentations(model.get_train_augmentations())
+    dev_generator.add_augmentations(model.get_val_augmentations())
+
+    train_loader = DataLoader(
+        train_generator,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=num_workers,
+        worker_init_fn=worker_seeding,
+        drop_last=True,  # Avoid crashes from batch norm layers for batch size 1
+    )
+    dev_loader = DataLoader(
+        dev_generator,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        worker_init_fn=worker_seeding,
+    )
+
+    return train_loader, dev_loader
 
 
-class EarlyStopper:
-    def __init__(self, patience=1, min_delta=0):
-        self.patience = patience
-        self.min_delta = min_delta
-        self.counter = 0
-        self.min_validation_loss = np.inf
+def apply_training_fraction(training_fraction, train_data):
+    """
+    Reduces the size of train_data to train_fraction by inplace filtering.
+    Filter blockwise for efficient memory savings.
 
-    def early_stop(self, validation_loss):
-        if validation_loss < self.min_validation_loss:
-            self.min_validation_loss = validation_loss
-            self.counter = 0
-        elif validation_loss > (self.min_validation_loss + self.min_delta):
-            self.counter += 1
-            if self.counter >= self.patience:
-                return True
-        return False
+    :param training_fraction: Training fraction between 0 and 1.
+    :param train_data: Training dataset
+    :return: None
+    """
+
+    if not 0.0 < training_fraction <= 1.0:
+        raise ValueError("Training fraction needs to be between 0 and 1.")
+
+    if training_fraction < 1:
+        blocks = train_data["trace_name"].apply(lambda x: x.split("$")[0])
+        unique_blocks = blocks.unique()
+        np.random.shuffle(unique_blocks)
+        target_blocks = unique_blocks[: int(training_fraction * len(unique_blocks))]
+        target_blocks = set(target_blocks)
+        mask = blocks.isin(target_blocks)
+        train_data.filter(mask, inplace=True)
 
 
-def get_data_generator(split, sampling_rate, path, sb_dataset="ethz", station=None, window='random'):
+def generate_phase_mask(dataset, phases):
+    mask = np.zeros(len(dataset), dtype=bool)
 
-    if path is not None:
-        data = sbd.WaveformDataset(path, sampling_rate=sampling_rate)
-        phase_dict = {
-            "trace_Pg_arrival_sample": "P"
-        }
-    elif sb_dataset == "ethz":
-        data = sbd.ETHZ(sampling_rate=sampling_rate, force=True)
-
-        phase_dict = {
-            "trace_p_arrival_sample": "P",
-            "trace_pP_arrival_sample": "P",
-            "trace_P_arrival_sample": "P",
-            "trace_P1_arrival_sample": "P",
-            "trace_Pg_arrival_sample": "P",
-            "trace_Pn_arrival_sample": "P",
-            "trace_PmP_arrival_sample": "P",
-            "trace_pwP_arrival_sample": "P",
-            "trace_pwPm_arrival_sample": "P",
-            # "trace_s_arrival_sample": "S",
-            # "trace_S_arrival_sample": "S",
-            # "trace_S1_arrival_sample": "S",
-            # "trace_Sg_arrival_sample": "S",
-            # "trace_SmS_arrival_sample": "S",
-            # "trace_Sn_arrival_sample": "S",
-        }
-
-    dataset = data.get_split(split)
-    dataset.filter(dataset.metadata.trace_Pg_arrival_sample.notna())
-
-    print(split, dataset.metadata.shape, sampling_rate)
-
-    if station is not None:
-        dataset.filter(dataset.metadata.station_code==station)
-
-    data_generator = sbg.GenericGenerator(dataset)
-    if window == 'random':
-        print("using random window")
-        window_selector = sbg.RandomWindow(windowlen=3001, strategy="pad")
-    else:
-        window_selector = sbg.FixedWindow(windowlen=3001, p0=0, strategy="pad")
-
-    augmentations = [
-        sbg.WindowAroundSample(list(phase_dict.keys()), samples_before=3000, windowlen=6000, selection="random",
-                               strategy="variable"),
-        window_selector,
-        sbg.Normalize(demean_axis=-1, amp_norm_axis=-1, amp_norm_type="peak"),
-        sbg.ChangeDtype(np.float32),
-        sbg.ProbabilisticLabeller(label_columns=phase_dict, sigma=30, dim=0)
-    ]
-
-    data_generator.add_augmentations(augmentations)
-
-    return data_generator
-
-
-def get_data_generators(sampling_rate=100, path=project_path+"/data/igf/seisbench_format", sb_dataset="ethz", station=None,
-                        window='random'):
-
-    train_generator = get_data_generator("train", sampling_rate, path, sb_dataset, station, window)
-    dev_generator = get_data_generator("dev", sampling_rate, path, sb_dataset, station, window)
-    test_generator = get_data_generator("test", sampling_rate, path, sb_dataset, station, window)
-
-    return train_generator, dev_generator, test_generator
-
-
-def get_data_loaders(batch_size=256, sampling_rate=100, path=project_path+"/data/igf/seisbench_format", sb_dataset="ethz",
-                     window='random'):
-
-    train_generator, dev_generator, test_generator = get_data_generators(sampling_rate, path, sb_dataset, window=window)
-    num_workers = 0  # The number of threads used for loading data
-
-    train_loader = DataLoader(train_generator, batch_size=batch_size, shuffle=True, num_workers=num_workers,
-                              worker_init_fn=worker_seeding)
-    dev_loader = DataLoader(dev_generator, batch_size=batch_size, shuffle=False, num_workers=num_workers,
-                            worker_init_fn=worker_seeding)
-
-    test_loader = DataLoader(test_generator, batch_size=batch_size, shuffle=False, num_workers=num_workers,
-                            worker_init_fn=worker_seeding)
-
-    return train_loader, dev_loader, test_loader
-
-
-def load_model(name="PhaseNet", pretrained=None, classes=2, modify_output=True):
-
-    if name == "PhaseNet":
-
-        if pretrained is not None and pretrained:
-            model = sbm.PhaseNet(phases="PN", norm="peak").from_pretrained(pretrained)
+    for key, phase in models.phase_dict.items():
+        if phase not in phases:
+            continue
         else:
-            model = sbm.PhaseNet(phases="PN", norm="peak")
+            if key in dataset.metadata:
+                mask = np.logical_or(mask, ~np.isnan(dataset.metadata[key]))
 
-        if modify_output:
-            model.out = nn.Conv1d(model.filters_root, classes, 1, padding="same")
-
-    return model
-
-
-def train_one_epoch(model, dataloader, optimizer, pick_mae):
-    size = len(dataloader.dataset)
-    for batch_id, batch in enumerate(dataloader):
-
-        # Compute prediction and loss
-
-        pred = model(batch["X"].to(model.device))
-
-        loss = loss_fn(pred, batch["y"].to(model.device))
-
-        # Compute cross entropy loss
-        cross_entropy_loss = f.cross_entropy(pred, batch["y"])
-
-        # Compute mae
-        mae = pick_mae(pred, batch['y'])
-
-        wandb.log({"loss": loss})
-        wandb.log({"batch cross entropy loss": cross_entropy_loss})
-        wandb.log({"p_mae": mae})
-
-
-        # Backpropagation
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-        if batch_id % 5 == 0:
-            loss, current = loss.item(), batch_id * batch["X"].shape[0]
-            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
-            print(f"mae: {mae:>7f}")
-
-
-def test_one_epoch(model, dataloader, pick_mae, wandb_log=True):
-
-    num_batches = len(dataloader)
-    test_loss = 0
-    test_mae = 0
-
-    with torch.no_grad():
-        for batch in dataloader:
-            pred = model(batch["X"].to(model.device))
-
-            test_loss += loss_fn(pred, batch["y"].to(model.device)).item()
-            test_mae += pick_mae(pred, batch['y'])
-            test_cross_entropy_loss = f.cross_entropy(pred, batch["y"])
-            if wandb_log:
-                wandb.log({"batch cross entropy test loss": test_cross_entropy_loss})
-
-        test_loss /= num_batches
-        test_mae /= num_batches
-
-        wandb.log({"test_p_mae": test_mae, "test_loss": test_loss})
-
-    print(f"Test avg loss: {test_loss:>8f}")
-    print(f"Test avg mae: {test_mae:>7f}\n")
-
-    return test_loss, test_mae
-
-
-def train_model(model, path_to_trained_model, train_loader, dev_loader):
-
-    wandb.watch(model, log_freq=10)
-
-    optimizer = torch.optim.Adam(model.parameters(), lr=wandb.config.learning_rate)
-    early_stopper = EarlyStopper(patience=3, min_delta=10)
-    pick_mae = PickMAE(wandb.config.sampling_rate)
-
-    best_loss = np.inf
-    best_metrics = {}
-
-    for t in range(wandb.config.epochs):
-        print(f"Epoch {t + 1}\n-------------------------------")
-        train_one_epoch(model, train_loader, optimizer, pick_mae)
-        test_loss, test_mae = test_one_epoch(model, dev_loader, pick_mae)
-
-        if test_loss < best_loss:
-            best_loss = test_loss
-            best_metrics = {"test_p_mae": test_mae, "test_loss": test_loss}
-            torch.save(model.state_dict(), path_to_trained_model)
-
-        if early_stopper.early_stop(test_loss):
-            break
-
-    print("Best model: ", str(best_metrics))
-
-
-def loss_fn(y_pred, y_true, eps=1e-5):
-    # vector cross entropy loss
-    h = y_true * torch.log(y_pred + eps)
-    h = h.mean(-1).sum(-1)  # Mean along sample dimension and sum along pick dimension
-    h = h.mean()  # Mean over batch axis
-    return -h
-
-
-def train_phasenet_on_sb_data():
-
-    config = {
-        "epochs": 3,
-        "batch_size": 256,
-        "dataset": "ethz",
-        "sampling_rate": 100,
-        "model_name": "PhaseNet"
-    }
-
-    run = wandb.init(
-        # set the wandb project where this run will be logged
-        project="training_seisbench_models_on_igf_data",
-        # track hyperparameters and run metadata
-        config=config
-    )
-
-    wandb.run.log_code(".", include_fn=lambda path: path.endswith("training_wandb_sweep.py"))
-
-    train_loader, dev_loader, test = get_data_loaders(batch_size=wandb.config.batch_size,
-                                                      sampling_rate=wandb.config.sampling_rate,
-                                                      path=None,
-                                                      sb_dataset=wandb.config.dataset)
-
-    model = load_model(name=wandb.config.model_name, pretrained=None, modify_output=True)
-    path_to_trained_model = f"{project_path}/models/{wandb.config.model_name}_trained_on_{wandb.config.data_set}.pt"
-    train_model(model, path_to_trained_model,
-                train_loader, dev_loader)
-
-    artifact = wandb.Artifact('model', type='model')
-    artifact.add_file(path_to_trained_model)
-    run.log_artifact(artifact)
-
-    run.finish()
-
-
-def load_config(config_path):
-    with open(config_path, 'r') as f:
-        config = json.load(f)
-    return config
-
-
-def train_sbmodel_on_igf_data():
-
-    config_path = project_path + "/experiments/config.json"
-    config = load_config(config_path)
-
-    run = wandb.init(
-        # set the wandb project where this run will be logged
-        project="training_seisbench_models_on_igf_data",
-        # track hyperparameters and run metadata
-        config=config
-    )
-    wandb.run.log_code(".", include_fn=lambda path: path.endswith("training_wandb_sweep.py"))
-
-    print(wandb.config.batch_size, wandb.config.sampling_rate)
-    train_loader, dev_loader, test_loader = get_data_loaders(batch_size=wandb.config.batch_size,
-                                                      sampling_rate=wandb.config.sampling_rate
-                                                      )
-
-    model_name = wandb.config.model_name
-    pretrained = wandb.config.pretrained
-
-    print(model_name, pretrained)
-    model = load_model(name=model_name, pretrained=pretrained)
-    path_to_trained_model = f"{project_path}/models/{model_name}_pretrained_on_{pretrained}_finetuned_on_{wandb.config.dataset}.pt"
-    train_model(model, path_to_trained_model, train_loader, dev_loader)
-
-    artifact = wandb.Artifact('model', type='model')
-    artifact.add_file(path_to_trained_model)
-    run.log_artifact(artifact)
-
-    run.finish()
+    return mask
 
 
 if __name__ == "__main__":
-    # train_phasenet_on_sb_data()
-    train_sbmodel_on_igf_data()
+    code_start_time = time.perf_counter()
 
+    torch.manual_seed(42)
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--test_run", action="store_true")
+    parser.add_argument("--lr", default=None, type=float)
+    args = parser.parse_args()
+
+    with open(args.config, "r") as f:
+        config = json.load(f)
+
+    experiment_name = os.path.basename(args.config)[:-5]
+    if args.lr is not None:
+        logging.warning(f"Overwriting learning rate to {args.lr}")
+        experiment_name += f"_{args.lr}"
+        config["model_args"]["lr"] = args.lr
+
+    run = wandb.init(
+        # set the wandb project where this run will be logged
+        project="training_seisbench_models_on_igf_data_with_pick-benchmark",
+        # track hyperparameters and run metadata
+        config=config
+    )
+
+    if args.test_run:
+        experiment_name = experiment_name + "_test"
+    train(config, experiment_name, test_run=args.test_run)
+
+    running_time = str(
+        datetime.timedelta(seconds=time.perf_counter() - code_start_time)
+    )
+    print(f"Running time: {running_time}")

scripts/training_wandb_sweep.py

@@ -1,62 +0,0 @@
-import os.path
-import wandb
-import yaml
-
-from train import get_data_loaders, load_model, train_model
-
-from dotenv import load_dotenv
-
-load_dotenv()
-wandb_api_key = os.environ.get('WANDB_API_KEY')
-if wandb_api_key is None:
-    raise ValueError("WANDB_API_KEY environment variable is not set.")
-
-wandb.login(key=wandb_api_key)
-
-project_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sweep_config_path = project_path + "/experiments/sweep4.yaml"
-
-with open(sweep_config_path) as file:
-    sweep_configuration = yaml.load(file, Loader=yaml.FullLoader)
-
-sweep_id = wandb.sweep(
-  sweep=sweep_configuration,
-  project='training_seisbench_models_on_igf_data'
-  )
-sampling_rate = 100
-
-def tune_training_hyperparams():
-
-    run = wandb.init(
-        # set the wandb project where this run will be logged
-        project="training_seisbench_models_on_igf_data",
-        # track hyperparameters and run metadata
-        config={"sampling_rate":sampling_rate}
-    )
-
-    wandb.run.log_code(".", include_fn=lambda path: path.endswith("training_wandb_sweep.py"))
-
-    train_loader, dev_loader, test_loader = get_data_loaders(batch_size=wandb.config.batch_size,
-                                                      sampling_rate=wandb.config.sampling_rate,
-                                                      sb_dataset=wandb.config.dataset)
-
-    model_name = wandb.config.model_name
-    pretrained = wandb.config.pretrained
-    print(wandb.config)
-    print(model_name, pretrained, type(pretrained), wandb.config.sampling_rate)
-    if not pretrained:
-        pretrained
-    model = load_model(name=model_name, pretrained=pretrained)
-    path_to_trained_model = f"{project_path}/models/{model_name}_pretrained_on_{pretrained}_finetuned_on_{wandb.config.dataset}.pt"
-    train_model(model, path_to_trained_model, train_loader, dev_loader)
-
-    artifact = wandb.Artifact('model', type='model')
-    artifact.add_file(path_to_trained_model)
-    run.log_artifact(artifact)
-
-    run.finish()
-
-
-if __name__ == "__main__":
-
-    wandb.agent(sweep_id, function=tune_training_hyperparams, count=10)

scripts/util.py

@@ -0,0 +1,119 @@
+"""
+This script offers general functionality required in multiple places.
+"""
+
+import numpy as np
+import pandas as pd
+import os
+import logging
+import glob
+import wandb
+from dotenv import load_dotenv
+import sys
+from config_loader import models_path, configs_path
+import yaml
+load_dotenv()
+
+
+logging.basicConfig()
+logging.getLogger().setLevel(logging.DEBUG)
+
+
+def load_best_model_data(sweep_id, weights):
+    """
+    Determines the model with the lowest validation loss.
+    If sweep_id is not provided the best model is determined based on the validation loss in checkpoint filenames in weights directory.
+
+    :param sweep_id:
+    :param weights:
+    :return:
+    """
+
+    if sweep_id is not None:
+        wandb_project_name = os.environ.get("WANDB_PROJECT")
+        wandb_user = os.environ.get("WANDB_USER")
+        api = wandb.Api()
+        sweep = api.sweep(f"{wandb_user}/{wandb_project_name}/{sweep_id}")
+
+        # 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")
+        if len(matching_models)!=1:
+            raise ValueError("Unable to determine the best checkpoint for run_id: " + run_id)
+        best_checkpoint_path = matching_models[0]
+
+    else:
+        checkpoints_path = f"{models_path}/{weights}/*ckpt"
+        logging.debug(f"Searching for checkpoints in dir: {checkpoints_path}")
+
+        checkpoints = glob.glob(checkpoints_path)
+        val_losses = []
+
+        for ckpt in checkpoints:
+            i = ckpt.index("val_loss=")
+            val_losses.append(float(ckpt[i + 9:-5]))
+
+        best_checkpoint_path = checkpoints[np.argmin(val_losses)]
+        run_id_st = best_checkpoint_path.index("run=") + 4
+        run_id_end = best_checkpoint_path.index("-epoch=")
+        run_id = best_checkpoint_path[run_id_st:run_id_end]
+
+    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(
+        "BENCHMARK_DEFAULT_WORKERS not set. "
+        "Will use 12 workers if not specified otherwise in configuration."
+    )
+    default_workers = 12
+else:
+    default_workers = int(default_workers)
+
+
+def load_sweep_config(sweep_fname):
+    """
+    Loads sweep config from yaml file
+
+    :param sweep_fname: sweep yaml file, expected to be in configs_path
+    :return: Dictionary containing sweep config
+    """
+
+    sweep_config_path = f"{configs_path}/{sweep_fname}"
+
+    try:
+        with open(sweep_config_path, "r") as file:
+            sweep_config = yaml.load(file, Loader=yaml.FullLoader)
+    except FileNotFoundError:
+        logging.error(f"Could not find sweep config file: {sweep_fname}. "
+                      f"Please make sure the file exists and is in {configs_path} directory.")
+        sys.exit(1)
+
+    return sweep_config