Use config options and auto-downloaded weights

.github/workflows/screenshots.yml

@@ -11,15 +11,19 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out the repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.head_ref }}
 
       - name: Set up Python
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
 
       - name: Install your custom tools
-        run: pip install .
+        run: |
+          python -m pip install --upgrade pip
+          pip install .
 
       - name: Generate terminal images with rich-codex
         uses: ewels/rich-codex@v1

casanovo/casanovo.py

@@ -133,7 +133,7 @@ def sequence(
     to sequence peptides.
     """
     output = setup_logging(output, verbosity)
-    config = setup_model(model, config, output, False)
+    config, model = setup_model(model, config, output, False)
     with ModelRunner(config, model) as runner:
         logger.info("Sequencing peptides from:")
         for peak_file in peak_path:
@@ -164,7 +164,7 @@ def evaluate(
     such as those provided by MassIVE-KB.
     """
     output = setup_logging(output, verbosity)
-    config = setup_model(model, config, output, False)
+    config, model = setup_model(model, config, output, False)
     with ModelRunner(config, model) as runner:
         logger.info("Sequencing and evaluating peptides from:")
         for peak_file in annotated_peak_path:
@@ -207,7 +207,7 @@ def train(
     provided by MassIVE-KB, from which to train a new Casnovo model.
     """
     output = setup_logging(output, verbosity)
-    config = setup_model(model, config, output, True)
+    config, model = setup_model(model, config, output, True)
     with ModelRunner(config, model) as runner:
         logger.info("Training a model from:")
         for peak_file in train_peak_path:
@@ -378,7 +378,7 @@ def setup_model(
     for key, value in config.items():
         logger.debug("%s = %s", str(key), str(value))
 
-    return config
+    return config, model
 
 
 def _get_model_weights() -> str:

casanovo/denovo/model_runner.py

@@ -7,6 +7,7 @@
 import uuid
 from pathlib import Path
 from typing import Iterable, List, Optional, Union
+import warnings
 
 import lightning.pytorch as pl
 import numpy as np
@@ -217,6 +218,7 @@ def initialize_model(self, train: bool) -> None:
             max_charge=self.config.max_charge,
             precursor_mass_tol=self.config.precursor_mass_tol,
             isotope_error_range=self.config.isotope_error_range,
+            min_peptide_len=self.config.min_peptide_len,
             n_beams=self.config.n_beams,
             top_match=self.config.top_match,
             n_log=self.config.n_log,
@@ -229,6 +231,24 @@ def initialize_model(self, train: bool) -> None:
             calculate_precision=self.config.calculate_precision,
         )
 
+        # Reconfigurable non-architecture related parameters for a loaded model
+        loaded_model_params = dict(
+            max_length=self.config.max_length,
+            precursor_mass_tol=self.config.precursor_mass_tol,
+            isotope_error_range=self.config.isotope_error_range,
+            n_beams=self.config.n_beams,
+            min_peptide_len=self.config.min_peptide_len,
+            top_match=self.config.top_match,
+            n_log=self.config.n_log,
+            tb_summarywriter=self.config.tb_summarywriter,
+            warmup_iters=self.config.warmup_iters,
+            max_iters=self.config.max_iters,
+            lr=self.config.learning_rate,
+            weight_decay=self.config.weight_decay,
+            out_writer=self.writer,
+            calculate_precision=self.config.calculate_precision,
+        )
+
         from_scratch = (
             self.config.train_from_scratch,
             self.model_filename is None,
@@ -254,13 +274,33 @@ def initialize_model(self, train: bool) -> None:
             self.model = Spec2Pep.load_from_checkpoint(
                 self.model_filename,
                 map_location=device,
+                **loaded_model_params,
             )
-        except RuntimeError:
-            self.model = Spec2Pep.load_from_checkpoint(
-                self.model_filename,
-                map_location=device,
-                **model_params,
+
+            architecture_params = set(model_params.keys()) - set(
+                loaded_model_params.keys()
             )
+            for param in architecture_params:
+                if model_params[param] != self.model.hparams[param]:
+                    warnings.warn(
+                        f"Mismatching {param} parameter in "
+                        f"model checkpoint ({self.model.hparams[param]}) "
+                        f"vs. config file ({model_params[param]}), "
+                        f"using the checkpoint."
+                    )
+        except RuntimeError:
+            # This only doesn't work if the weights are from an older version
+            try:
+                self.model = Spec2Pep.load_from_checkpoint(
+                    self.model_filename,
+                    map_location=device,
+                    **model_params,
+                )
+            except RuntimeError:
+                raise RuntimeError(
+                    "Weights file incompatible "
+                    "with the current version of Casanovo. "
+                )
 
     def initialize_data_module(
         self,