Remove train_from_scratch config option

Instead of having to specify `train_from_scratch` in the config file, training will proceed from an existing model weights file if this is given as an argument to `casanovo train`.

Fixes #263.

CHANGELOG.md

@@ -6,6 +6,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 ## [Unreleased]
 
+### Changed
+
+- Instead of having to specify `train_from_scratch` in the config file, training will proceed from an existing model weights file if this is given as an argument to `casanovo train`.
+
 ## [4.0.0] - 2023-12-22
 
 ### Added

casanovo/config.py

@@ -64,7 +64,6 @@ class Config:
         top_match=int,
         max_epochs=int,
         num_sanity_val_steps=int,
-        train_from_scratch=bool,
         save_top_k=int,
         model_save_folder_path=str,
         val_check_interval=int,

casanovo/config.yaml

@@ -99,8 +99,6 @@ train_batch_size: 32
 max_epochs: 30
 # Number of validation steps to run before training begins
 num_sanity_val_steps: 0
-# Set to "False" to further train a pre-trained Casanovo model
-train_from_scratch: True
 # Calculate peptide and amino acid precision during training. this
 # is expensive, so we recommend against it.
 calculate_precision: False

casanovo/denovo/model_runner.py

@@ -251,16 +251,16 @@ def initialize_model(self, train: bool) -> None:
             calculate_precision=self.config.calculate_precision,
         )
 
-        from_scratch = (
-            self.config.train_from_scratch,
-            self.model_filename is None,
-        )
-        if train and any(from_scratch):
-            self.model = Spec2Pep(**model_params)
-            return
-        elif self.model_filename is None:
-            logger.error("A model file must be provided")
-            raise ValueError("A model file must be provided")
+        if self.model_filename is None:
+            # Train a model from scratch if no model file is provided.
+            if train:
+                self.model = Spec2Pep(**model_params)
+                return
+            # Else we're not training, so a model file must be provided.
+            else:
+                logger.error("A model file must be provided")
+                raise ValueError("A model file must be provided")
+        # Else a model file is provided (to continue training or for inference).
 
         if not Path(self.model_filename).exists():
             logger.error(

tests/conftest.py

@@ -222,7 +222,6 @@ def tiny_config(tmp_path):
         "weight_decay": 1e-5,
         "train_batch_size": 32,
         "num_sanity_val_steps": 0,
-        "train_from_scratch": True,
         "calculate_precision": False,
         "residues": {
             "G": 57.021464,

tests/unit_tests/test_runner.py

@@ -6,35 +6,39 @@
 from casanovo.denovo.model_runner import ModelRunner
 
 
-def test_initialize_model(tmp_path):
-    """Test that"""
+def test_initialize_model(tmp_path, mgf_small):
+    """Test initializing a new or existing model."""
     config = Config()
-    config.train_from_scratch = False
+    # No model filename given, so train from scratch.
     ModelRunner(config=config).initialize_model(train=True)
 
+    # No model filename given during inference = error.
     with pytest.raises(ValueError):
         ModelRunner(config=config).initialize_model(train=False)
 
-    with pytest.raises(FileNotFoundError):
-        runner = ModelRunner(config=config, model_filename="blah")
-        runner.initialize_model(train=True)
-
+    # Non-existing model filename given during inference = error.
     with pytest.raises(FileNotFoundError):
         runner = ModelRunner(config=config, model_filename="blah")
         runner.initialize_model(train=False)
 
-    # This should work now:
-    config.train_from_scratch = True
-    runner = ModelRunner(config=config, model_filename="blah")
+    # Train a quick model.
+    config.max_epochs = 1
+    config.n_layers = 1
+    ckpt = tmp_path / "existing.ckpt"
+    with ModelRunner(config=config) as runner:
+        runner.train([mgf_small], [mgf_small])
+        runner.trainer.save_checkpoint(ckpt)
+
+    # Resume training from previous model.
+    runner = ModelRunner(config=config, model_filename=str(ckpt))
     runner.initialize_model(train=True)
 
-    # But this should still fail:
-    with pytest.raises(FileNotFoundError):
-        runner = ModelRunner(config=config, model_filename="blah")
-        runner.initialize_model(train=False)
+    # Inference with previous model.
+    runner = ModelRunner(config=config, model_filename=str(ckpt))
+    runner.initialize_model(train=False)
 
     # If the model initialization throws and EOFError, then the Spec2Pep model
-    # has tried to load the weights:
+    # has tried to load the weights.
     weights = tmp_path / "blah"
     weights.touch()
     with pytest.raises(EOFError):
@@ -43,7 +47,7 @@ def test_initialize_model(tmp_path):
 
 
 def test_save_and_load_weights(tmp_path, mgf_small, tiny_config):
-    """Test saving aloading weights"""
+    """Test saving and loading weights"""
     config = Config(tiny_config)
     config.max_epochs = 1
     config.n_layers = 1