tensorboard logger

casanovo/denovo/model_runner.py

@@ -4,23 +4,18 @@
 import glob
 import logging
 import os
-import re
 import tempfile
 import warnings
 from pathlib import Path
 from typing import Iterable, List, Optional, Union
 from datetime import datetime
 
-import depthcharge.masses
 import lightning.pytorch as pl
 import torch
+import torch.utils.data
 
 from lightning.pytorch.strategies import DDPStrategy
-from lightning.pytorch.callbacks import (
-    ModelCheckpoint,
-    LearningRateMonitor,
-    EarlyStopping,
-)
+from lightning.pytorch.callbacks import ModelCheckpoint
 from lightning.pytorch.loggers import TensorBoardLogger
 
 from depthcharge.tokenizers import PeptideTokenizer
@@ -161,7 +156,9 @@ def train(
             self.loaders.val_dataloader(),
         )
 
-    def log_metrics(self, test_index: AnnotatedSpectrumIndex) -> None:
+    def log_metrics(
+        self, test_dataloader: torch.utils.data.DataLoader
+    ) -> None:
         """Log peptide precision and amino acid precision
 
         Calculate and log peptide precision and amino acid precision
@@ -172,7 +169,7 @@ def log_metrics(self, test_index: AnnotatedSpectrumIndex) -> None:
         test_index : AnnotatedSpectrumIndex
             Index containing the annotated spectra used to generate model
             predictions
-        """
+
         model_output = [psm.sequence for psm in self.writer.psms]
         spectrum_annotations = [
             test_index[i][4] for i in range(test_index.n_spectra)
@@ -187,6 +184,9 @@ def log_metrics(self, test_index: AnnotatedSpectrumIndex) -> None:
 
         logger.info("Peptide Precision: %.2f%%", 100 * pep_precision)
         logger.info("Amino Acid Precision: %.2f%%", 100 * aa_precision)
+        """
+        # TODO: Fix log_metrics, wait for eval bug fix to be merged in
+        return
 
     def predict(
         self,
@@ -234,7 +234,7 @@ def predict(
         self.trainer.predict(self.model, self.loaders.test_dataloader())
 
         if evaluate:
-            self.log_metrics(test_index)
+            self.log_metrics(self.loaders.test_dataloader())
 
     def initialize_trainer(self, train: bool) -> None:
         """Initialize the lightning Trainer.
@@ -259,11 +259,11 @@ def initialize_trainer(self, train: bool) -> None:
             else:
                 devices = self.config.devices
 
-            if self.config.tb_summarywriter is not None:
+            # TODO: CSV logger
+            if self.config.tb_summarywriter:
                 logger = TensorBoardLogger(
-                    self.config.tb_summarywriter,
-                    version=None,
-                    name=f'model_{datetime.now().strftime("%Y%m%d_%H%M")}',
+                    self.output_dir,
+                    version="tensorboard",
                     default_hp_metric=False,
                 )
             else: