resolves issue #238: remove custom_encoder option

CHANGELOG.md

@@ -25,22 +25,18 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 - `every_n_train_steps` has been renamed to `val_check_interval` in accordance to the corresponding Pytorch Lightning parameter.
 - Training batches are randomly shuffled.
 
-### Fixed
-
-- Casanovo runs on CPU and can passes all tests.
-- Enable gradients during prediction and validation to avoid NaNs from occuring as a temporary workaround until a new Pytorch version is available.
-- Upgrade to depthcharge v0.2.3 for `PeptideTransformerDecoder` hotfix.
-
 ### Removed
 
 - Remove config option for a custom Pytorch Lightning logger.
+- Remove superfluous `custom_encoder` config option.
 
 ### Fixed
 
-- Casanovo now runs on CPU and can passes all tests.
-- Upgrade to Depthcharge v0.2.0 to fix sinusoidal encoding.
+- Casanovo runs on CPU and can pass all tests.
 - Correctly refer to input peak files by their full file path.
 - Specifying custom residues to retrain Casanovo is now possible.
+- Upgrade to depthcharge v0.2.3 to fix sinusoidal encoding and for the `PeptideTransformerDecoder` hotfix.
+- Enable gradients during prediction and validation to avoid NaNs from occuring as a temporary workaround until a new Pytorch version is available.
 
 ## [3.3.0] - 2023-04-04
 

casanovo/config.yaml

@@ -79,9 +79,6 @@ dropout: 0.0
 # Number of dimensions to use for encoding peak intensity
 # Projected up to ``dim_model`` by default and summed with the peak m/z encoding
 dim_intensity:
-# Option to provide a pre-trained spectrum encoder when training
-# Trained from scratch by default
-custom_encoder:
 # Max decoded peptide length
 max_length: 100
 # Number of warmup iterations for learning rate scheduler

casanovo/denovo/model.py

@@ -43,9 +43,6 @@ class Spec2Pep(pl.LightningModule, ModelMixin):
         (``dim_model - dim_intensity``) are reserved for encoding the m/z value.
         If ``None``, the intensity will be projected up to ``dim_model`` using a
         linear layer, then summed with the m/z encoding for each peak.
-    custom_encoder : Optional[Union[SpectrumEncoder, PairedSpectrumEncoder]]
-        A pretrained encoder to use. The ``dim_model`` of the encoder must be
-        the same as that specified by the ``dim_model`` parameter here.
     max_length : int
         The maximum peptide length to decode.
     residues: Union[Dict[str, float], str]
@@ -97,7 +94,6 @@ def __init__(
         n_layers: int = 9,
         dropout: float = 0.0,
         dim_intensity: Optional[int] = None,
-        custom_encoder: Optional[SpectrumEncoder] = None,
         max_length: int = 100,
         residues: Union[Dict[str, float], str] = "canonical",
         max_charge: int = 5,
@@ -120,17 +116,14 @@ def __init__(
         self.save_hyperparameters()
 
         # Build the model.
-        if custom_encoder is not None:
-            self.encoder = custom_encoder
-        else:
-            self.encoder = SpectrumEncoder(
-                dim_model=dim_model,
-                n_head=n_head,
-                dim_feedforward=dim_feedforward,
-                n_layers=n_layers,
-                dropout=dropout,
-                dim_intensity=dim_intensity,
-            )
+        self.encoder = SpectrumEncoder(
+            dim_model=dim_model,
+            n_head=n_head,
+            dim_feedforward=dim_feedforward,
+            n_layers=n_layers,
+            dropout=dropout,
+            dim_intensity=dim_intensity,
+        )
         self.decoder = PeptideDecoder(
             dim_model=dim_model,
             n_head=n_head,

casanovo/denovo/model_runner.py

@@ -212,7 +212,6 @@ def initialize_model(self, train: bool) -> None:
             n_layers=self.config.n_layers,
             dropout=self.config.dropout,
             dim_intensity=self.config.dim_intensity,
-            custom_encoder=self.config.custom_encoder,
             max_length=self.config.max_length,
             residues=self.config.residues,
             max_charge=self.config.max_charge,