README.md

@@ -111,7 +111,7 @@ After adjusting the grant name, the paths to conda env and the paths to data sen
 
    The script performs the following steps:
    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.
+   1. Trains multiple versions of GPD, PhaseNet, BasicPhaseAE, and EQTransformer 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   
@@ -126,14 +126,31 @@ After adjusting the grant name, the paths to conda env and the paths to data sen
    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:
+    The default settings for max number of experiments and paths 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 the 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.
-    
+   
+   Sweep configs are used to define the max number of epochs to run and the hyperparameters search space for the following parameters: 
+    * `batch_size`
+    * `learning_rate`
+   
+   Phasenet model has additional available parameters:
+     * `norm` - normalization method, options ('peak', 'std')
+     * `pretrained` - pretrained seisbench models used for transfer learning 
+     * `finetuning` - the type of layers to finetune first, options ('all', 'top', 'encoder', 'decoder')
+     * `lr_reduce_factor` - factor to reduce learning rate after unfreezing layers
+
+   GPD model has additional parameters for filtering: 
+     * `highpass` - highpass filter frequency
+     * `lowpass` - lowpass filter frequency
+
+   The sweep configs are saved in the `experiments` folder.  
+
+
    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

scripts/data.py

@@ -1,8 +1,13 @@
 """
 This file contains functionality related to data.
 """
+import os.path
 
 import seisbench.data as sbd
+import logging
+
+logging.root.setLevel(logging.INFO)
+logger = logging.getLogger('data')
 
 
 def get_dataset_by_name(name):
@@ -30,3 +35,26 @@ def get_custom_dataset(path):
     except AttributeError:
         raise ValueError(f"Unknown dataset '{path}'.")
 
+
+def validate_custom_dataset(data_path):
+    """
+    Validate the dataset
+    :param data_path: path to the dataset
+    :return:
+    """
+    # check if path exists
+    if not os.path.isdir((data_path)):
+        raise ValueError(f"Data path {data_path} does not exist.")
+
+    dataset = sbd.WaveformDataset(data_path)
+    # check if the dataset is split into train, dev and test
+    if len(dataset.train()) == 0:
+        raise ValueError(f"Training set is empty.")
+    if len(dataset.dev()) == 0:
+        raise ValueError(f"Dev set is empty.")
+    if len(dataset.test()) == 0:
+        raise ValueError(f"Test set is empty.")
+
+    logger.info("Custom dataset validated successfully.")
+
+

scripts/hyperparameter_sweep.py

@@ -7,8 +7,9 @@ import os
 import os.path
 import argparse
 from pytorch_lightning.loggers import WandbLogger, CSVLogger
-from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
+
 import pytorch_lightning as pl
 import wandb
 import torch
@@ -20,6 +21,8 @@ import train
 import util
 import config_loader
 
+
+
 torch.multiprocessing.set_sharing_strategy('file_system')
 os.system("ulimit -n unlimited")
 
@@ -31,14 +34,13 @@ 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_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)
+logger.setLevel(logging.INFO)
 
 
 def set_random_seed(seed=3):
@@ -54,6 +56,7 @@ def get_trainer_args(config):
     return trainer_args
 
 
+
 class HyperparameterSweep:
     def __init__(self, project_name, sweep_config):
         self.project_name = project_name
@@ -87,21 +90,42 @@ class HyperparameterSweep:
 
         try:
 
-            logger.debug("Starting a new run...")
+            logger.info("Starting a new run...")
             run = wandb.init(
                 project=self.project_name,
                 config=config_loader.config,
-                save_code=True
+                save_code=True,
+                entity=wandb_user_name
             )
             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)
+            if "pretrained" in wandb.config:
+                weights = wandb.config.get("pretrained")
+                if type(weights) == list:
+                    weights = weights[0]
+                if weights != "false":
+                    model_args["pretrained"] = weights
+            if "norm" in wandb.config:
+                model_args["norm"] = wandb.config.norm
+
             logger.debug(f"Initializing {model_name}")
+            if "finetuning" in wandb.config:
+                # train for a few epochs with some frozen params, then unfreeze and continue training
+                if type(wandb.config.finetuning) == list:
+                    finetuning_strategy = wandb.config.finetuning[0]
+                else:
+                    finetuning_strategy = wandb.config.finetuning
+                model_args['finetuning_strategy'] = finetuning_strategy
+
+            if "lr_reduce_factor" in wandb.config:
+                model_args['steplr_gamma'] = wandb.config.lr_reduce_factor
+
 
             model = models.__getattribute__(model_name + "Lit")(**model_args)
 
@@ -132,10 +156,13 @@ class HyperparameterSweep:
 
             early_stopping_callback = EarlyStopping(
                 monitor="val_loss",
-                patience=3,
+                patience=5,
                 verbose=True,
                 mode="min")
-            callbacks = [checkpoint_callback, early_stopping_callback]
+
+            lr_monitor = LearningRateMonitor(logging_interval='epoch')
+
+            callbacks = [checkpoint_callback, early_stopping_callback, lr_monitor]
 
             trainer = pl.Trainer(
                 default_root_dir=config_loader.models_path,
@@ -143,7 +170,6 @@ class HyperparameterSweep:
                 callbacks=callbacks,
                 **get_trainer_args(wandb.config)
             )
-
             trainer.fit(model, train_loader, dev_loader)
 
         except Exception as e:
@@ -155,7 +181,6 @@ class HyperparameterSweep:
 
 
 def start_sweep(sweep_config):
-
     logger.info("Starting sweep with config: " + str(sweep_config))
     set_random_seed(config_loader.seed)
     sweep_runner = HyperparameterSweep(project_name=wandb_project_name, sweep_config=sweep_config)
@@ -165,7 +190,6 @@ def start_sweep(sweep_config):
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser()
     parser.add_argument("--sweep_config", type=str, required=True)
     args = parser.parse_args()

scripts/input_validate.py

@@ -0,0 +1,142 @@
+from pydantic import BaseModel, ConfigDict, field_validator
+from typing_extensions import Literal
+from typing import Union, List, Optional
+import yaml
+import logging
+
+logging.root.setLevel(logging.INFO)
+logger = logging.getLogger('input_validator')
+
+#todo
+# 1. check if a single value is allowed in a sweep
+# 2. merge input params
+# 3. change names of the classes
+# 4. add constraints for PhaseNet, GPD
+
+
+model_names = Literal["PhaseNet", "GPD", "BasicPhaseAE", "EQTransformer"]
+norm_values = Literal["peak", "std"]
+finetuning_values = Literal["all", "top", "decoder", "encoder"]
+pretrained_values = Literal['diting', 'ethz', 'geofon', 'instance', 'iquique', 'lendb', 'neic',
+                                                    'original', 'scedc', False]
+
+
+class Metric(BaseModel):
+    goal: str
+    name: str
+
+
+class NumericValue(BaseModel):
+    value: Union[int, float, List[Union[int, float]]]
+
+
+class NumericValues(BaseModel):
+    values: List[Union[int, float]]
+
+
+class IntDistribution(BaseModel):
+    distribution: str = "int_uniform"
+    min: int
+    max: int
+
+
+class FloatDistribution(BaseModel):
+    distribution: str = "uniform"
+    min: float
+    max: float
+
+
+class Pretrained(BaseModel):
+    distribution: Optional[str] = "categorical"
+    values: List[pretrained_values] = None
+    value: Union[pretrained_values, List[pretrained_values]] = None
+
+
+class Finetuning(BaseModel):
+    distribution: Optional[str] = "categorical"
+    values: List[finetuning_values] = None
+    value: Union[finetuning_values, List[finetuning_values]] = None
+
+
+class Norm(BaseModel):
+    distribution: Optional[str] = "categorical"
+    values: List[norm_values] = None
+    value: Union[norm_values, List[norm_values]] = None
+
+
+class ModelType(BaseModel):
+    distribution: Optional[str] = "categorical"
+    value: Union[model_names, List[model_names]] = None
+    values: List[model_names] = None
+
+
+class Parameters(BaseModel):
+    model_config = ConfigDict(extra='forbid', protected_namespaces=())
+    model_name: ModelType
+    batch_size: Union[IntDistribution, NumericValue, NumericValues]
+    learning_rate: Union[FloatDistribution, NumericValue, NumericValues]
+    max_epochs: Union[IntDistribution, NumericValue, NumericValues]
+
+
+class PhaseNetParameters(Parameters):
+    model_config = ConfigDict(extra='forbid')
+    norm: Norm = None
+    pretrained: Pretrained = None
+    finetuning: Finetuning = None
+    lr_reduce_factor: Optional[Union[FloatDistribution, NumericValue, NumericValues]] = None
+
+    @field_validator("model_name")
+    def validate_model(cls, v):
+        if "PhaseNet" not in v.value:
+            raise ValueError("Additional parameters implemented for PhaseNet only")
+        return v
+
+
+class GPDParameters(Parameters):
+    model_config = ConfigDict(extra='forbid')
+
+    highpass: Union[NumericValue, NumericValues, FloatDistribution] = None
+    lowpass: Union[NumericValue, NumericValues, FloatDistribution] = None
+
+    @field_validator("model_name")
+    def validate_model(cls, v):
+        if "GPD" not in v.value:
+            raise ValueError("Additional parameters implemented for GPD only")
+
+
+class InputParams(BaseModel):
+    name: str
+    method: str
+    metric: Metric
+    parameters: Union[Parameters, PhaseNetParameters, GPDParameters]
+
+
+def validate_sweep_yaml(yaml_filename, model_name=None):
+    # Load YAML configuration
+    with open(yaml_filename, 'r') as f:
+        sweep_config = yaml.safe_load(f)
+
+    validate_sweep_config(sweep_config, model_name)
+
+
+def validate_sweep_config(sweep_config, model_name=None):
+
+    # Validate sweep config
+
+    input_params = InputParams(**sweep_config)
+
+    # Check consistency of input parameters and sweep configuration
+    sweep_model_name = input_params.parameters.model_name.value
+    if model_name is not None and model_name not in sweep_model_name:
+        info = f"Model name {model_name} is inconsistent with the sweep configuration {sweep_model_name}."
+        logger.info(info)
+        raise ValueError(info)
+    logger.info("Input validation successful.")
+
+
+
+
+
+if __name__ == "__main__":
+    yaml_filename = "../experiments/sweep_phasenet_lumineos_lr_bs.yaml"
+    validate_sweep_yaml(yaml_filename, None)

scripts/models.py

@@ -7,16 +7,26 @@ import seisbench.generate as sbg
 
 import pytorch_lightning as pl
 import torch
+from torch.optim import lr_scheduler
 import torch.nn.functional as F
 import numpy as np
 from abc import abstractmethod, ABC
 
+# import lightning as L
+
+
 # Allows to import this file in both jupyter notebook and code
 try:
     from .augmentations import DuplicateEvent
 except ImportError:
     from augmentations import DuplicateEvent
 
+import os
+import logging
+
+script_name = os.path.splitext(os.path.basename(__file__))[0]
+logger = logging.getLogger(script_name)
+logger.setLevel(logging.DEBUG)
 
 # Phase dict for labelling. We only study P and S phases without differentiating between them.
 phase_dict = {
@@ -131,30 +141,84 @@ class PhaseNetLit(SeisBenchModuleLit):
         self.sigma = sigma
         self.sample_boundaries = sample_boundaries
         self.loss = vector_cross_entropy
-        self.model = sbm.PhaseNet(**kwargs)
+        self.pretrained = kwargs.pop("pretrained", None)
+        self.norm = kwargs.pop("norm", "peak")
+
+        if self.pretrained is not None:
+            self.model = sbm.PhaseNet.from_pretrained(self.pretrained)
+            # self.norm = self.model.norm
+        else:
+            self.model = sbm.PhaseNet(**kwargs)
+
+        self.finetuning_strategy = kwargs.pop("finetuning_strategy", None)
+        self.steplr_gamma = kwargs.pop("steplr_gamma", 0.1)
+
+        self.initial_epochs = 0
+
+        if self.finetuning_strategy is not None:
+            if self.finetuning_strategy == "top":
+                self.initial_epochs = 3
+            elif self.finetuning_strategy in ["decoder", "encoder"]:
+                self.initial_epochs = 6
+
+            self.freeze()
+
 
     def forward(self, x):
         return self.model(x)
 
+
     def shared_step(self, batch):
         x = batch["X"]
         y_true = batch["y"]
         y_pred = self.model(x)
         return self.loss(y_pred, y_true)
 
+
     def training_step(self, batch, batch_idx):
         loss = self.shared_step(batch)
         self.log("train_loss", loss)
         return loss
 
+
     def validation_step(self, batch, batch_idx):
         loss = self.shared_step(batch)
         self.log("val_loss", loss)
         return loss
 
+
     def configure_optimizers(self):
         optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
-        return optimizer
+        if self.finetuning_strategy is not None:
+            scheduler = lr_scheduler.LambdaLR(optimizer, self.lr_lambda)
+        else:
+            scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=3)
+        #
+        return {
+            'optimizer': optimizer,
+            'lr_scheduler': {
+                'scheduler': scheduler,
+                'monitor': 'val_loss',
+                'interval': 'epoch',
+                'reduce_on_plateau': False,
+            },
+        }
+
+
+    def lr_lambda(self, epoch):
+        # reduce lr after x initial epochs
+        if epoch == self.initial_epochs:
+            self.lr *= self.steplr_gamma
+
+        return self.lr
+
+
+    def lr_scheduler_step(self, scheduler, metric):
+        scheduler.step(epoch=self.current_epoch)
+
+
+    # def lr_scheduler_step(self, scheduler, optimizer_idx, metric):
+    #         scheduler.step(epoch=self.current_epoch)
 
     def get_augmentations(self):
         return [
@@ -181,19 +245,21 @@ class PhaseNetLit(SeisBenchModuleLit):
                 strategy="pad",
             ),
             sbg.ChangeDtype(np.float32),
-            sbg.Normalize(demean_axis=-1, amp_norm_axis=-1, amp_norm_type="peak"),
+            sbg.Normalize(demean_axis=-1, amp_norm_axis=-1, amp_norm_type=self.norm),
             sbg.ProbabilisticLabeller(
                 label_columns=phase_dict, sigma=self.sigma, dim=0
             ),
         ]
 
+
     def get_eval_augmentations(self):
         return [
             sbg.SteeredWindow(windowlen=3001, strategy="pad"),
             sbg.ChangeDtype(np.float32),
-            sbg.Normalize(demean_axis=-1, amp_norm_axis=-1, amp_norm_type="peak"),
+            sbg.Normalize(demean_axis=-1, amp_norm_axis=-1, amp_norm_type=self.norm),
         ]
 
+
     def predict_step(self, batch, batch_idx=None, dataloader_idx=None):
         x = batch["X"]
         window_borders = batch["window_borders"]
@@ -219,6 +285,27 @@ class PhaseNetLit(SeisBenchModuleLit):
 
         return score_detection, score_p_or_s, p_sample, s_sample
 
+    def freeze(self):
+        if self.finetuning_strategy == "decoder":  # finetune decoder branch and freeze encoder branch
+            for p in self.model.down_branch.parameters():
+                p.requires_grad = False
+        elif self.finetuning_strategy == "encoder":  # finetune encoder branch and freeze decoder branch
+            for p in self.model.up_branch.parameters():
+                p.requires_grad = False
+        elif self.finetuning_strategy == "top":
+            for p in self.model.out.parameters():
+                p.requires_grad = False
+
+    def unfreeze(self):
+        logger.info("Unfreezing layers")
+        for p in self.model.parameters():
+            p.requires_grad = True
+
+    def on_train_epoch_start(self):
+        # Unfreeze some layers after x initial epochs
+        if self.current_epoch == self.initial_epochs:
+            self.unfreeze()
+
 
 class GPDLit(SeisBenchModuleLit):
     """
@@ -846,7 +933,7 @@ class BasicPhaseAELit(SeisBenchModuleLit):
         # Create overlapping windows
         re = torch.zeros(x.shape[:2] + (7, 600), dtype=x.dtype, device=x.device)
         for i, start in enumerate(range(0, 2401, 400)):
-            re[:, :, i] = x[:, :, start : start + 600]
+            re[:, :, i] = x[:, :, start: start + 600]
         x = re
 
         x = x.permute(0, 2, 1, 3)  # --> (batch, windows, channels, samples)
@@ -862,9 +949,9 @@ class BasicPhaseAELit(SeisBenchModuleLit):
         for i, start in enumerate(range(0, 2401, 400)):
             if start == 0:
                 # Use full window (for start==0, the end will be overwritten)
-                pred[:, :, start : start + 600] = window_pred[:, i]
+                pred[:, :, start: start + 600] = window_pred[:, i]
             else:
-                pred[:, :, start + 100 : start + 600] = window_pred[:, i, :, 100:]
+                pred[:, :, start + 100: start + 600] = window_pred[:, i, :, 100:]
 
         score_detection = torch.zeros(pred.shape[0])
         score_p_or_s = torch.zeros(pred.shape[0])
@@ -1112,7 +1199,6 @@ class DPPPickerLit(SeisBenchModuleLit):
 
 
 def get_model_specific_args(config):
-
     model = config.model_name[0]
     lr = config.learning_rate
     if type(lr) == list:

scripts/mseeds_to_seisbench.py

@@ -57,6 +57,8 @@ def split_events(events, input_path):
             events_stats.loc[i, 'split'] = 'dev'
         else:
             break
+            
+    logger.info(f"Split: {events_stats['split'].value_counts()}")
 
     logger.info(f"Split: {events_stats['split'].value_counts()}")
 
@@ -198,9 +200,10 @@ def convert_mseed_to_seisbench_format(input_path, catalog_path, output_path):
 
     metadata_path = output_path + "/metadata.csv"
     waveforms_path = output_path + "/waveforms.hdf5"
-
+    
     events_to_convert = events_stats[events_stats['pick_count'] > 0]
 
+
     logger.debug("Catalog loaded, starting converting {events_to_convert} events ...")
 
     with sbd.WaveformDataWriter(metadata_path, waveforms_path) as writer:

scripts/pipeline.py

@@ -17,6 +17,8 @@ import eval
 import collect_results
 import importlib
 import config_loader
+import input_validate
+import data
 
 logging.root.setLevel(logging.INFO)
 logger = logging.getLogger('pipeline')
@@ -40,11 +42,22 @@ def load_sweep_config(model_name, args):
     return util.load_sweep_config(sweep_fname)
 
 
-def find_the_best_params(model_name, args):
+def validate_pipeline_input(args):
+
+    # validate input parameters
+    for model_name in args.models:
+        sweep_config = load_sweep_config(model_name, args)
+        input_validate.validate_sweep_config(sweep_config, model_name)
+
+    # validate dataset
+    data.validate_custom_dataset(config_loader.data_path)
+
+
+def find_the_best_params(sweep_config):
     # find the best hyperparams for the model_name
+    model_name = sweep_config['parameters']['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
@@ -91,6 +104,8 @@ def main():
             util.set_dataset(args.dataset)
             importlib.reload(config_loader)
 
+        validate_pipeline_input(args)
+
         logger.info(f"Started pipeline for the {config_loader.dataset_name} dataset.")
 
         # generate labels
@@ -101,7 +116,8 @@ def main():
         # find the best hyperparams for the models
         logger.info("Started training the models.")
         for model_name in args.models:
-            sweep_id = find_the_best_params(model_name, args)
+            sweep_config = load_sweep_config(model_name, args)
+            sweep_id = find_the_best_params(sweep_config)
             generate_predictions(sweep_id, model_name)
 
     # collect results