Refactor PSF model optimizer handling with configurable selection and optional TensorFlow Addons

README.md

@@ -13,3 +13,15 @@ This repository includes:
 - A differentiable PSF model entirely built in [Tensorflow](https://github.com/tensorflow/tensorflow).
 - A [numpy-based PSF simulator](https://github.com/CosmoStat/wf-psf/tree/dummy_main/src/wf_psf/sims).
 - All the scripts, jobs and notebooks required to reproduce the results in [arXiv:2203.04908](http://arxiv.org/abs/2203.04908) and [arXiv:2111.12541](https://arxiv.org/abs/2111.12541).
+
+---------------------------------------------------------------------------
+NOTICE ABOUT THIRD-PARTY CODE
+
+This repository contains code copied from TensorFlow Addons
+(https://github.com/tensorflow/addons), specifically the 
+`interpolate_spline` and `types.py` modules.
+
+Those files are licensed under the Apache License, Version 2.0.
+The copyright and license headers are preserved in each copied file.
+
+All other code in this repository remains under the MIT License.

THIRD_PARTY_LICENSES/TFA_LICENSE.txt

@@ -0,0 +1,203 @@
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changelog.d/20260123_164629_jennifer.pollack_configurable_optimizer.md

@@ -0,0 +1,43 @@
+<!--
+A new scriv changelog fragment.
+
+Uncomment the section that is right (remove the HTML comment wrapper).
+For top level release notes, leave all the headers commented out.
+-->
+
+<!--
+### Breaking changes
+
+- A bullet item for the Breaking changes category.
+
+-->
+
+### New features
+
+- Added configurable optimizer selection system via new `optimizer.py` module with `get_optimizer` function
+- Optimizer configuration now supports multiple input types: `RecursiveNamespace` from configs, dictionaries, or string names
+- Added support for hyperparameter overrides (learning rate, beta1/beta2, epsilon, amsgrad) via YAML or programmatic configuration
+- RectifiedAdam optimizer now dynamically imports TensorFlow Addons only when explicitly specified in configuration
+
+
+### Bug fixes
+
+- Fixed TensorFlow 2.11 compatibility by automatically using `tf.keras.optimizers.legacy.Adam` for TF < 2.11
+
+
+<!--
+### Performance improvements
+
+- A bullet item for the Performance improvements category.
+
+-->
+
+### Internal changes
+
+- Refactored `build_PSF_model` to accept either Keras optimizer instances or configuration passed through `get_optimizer`
+- Added `interpolation.py` and `types.py` modules with vendored code from TensorFlow Addons repository
+- Replaced `tfa.image.interpolate_spline` with local `tfa_interpolate_spline_rbf` implementation
+- Added comprehensive unit tests in `test_optimizer.py` and `test_interpolation.py`
+- Updated README and added THIRD_PARTY_LICENSE directory with TensorFlow Addons license
+- Training now runs on TensorFlow 2.11 without requiring TensorFlow Addons installation
+- Removed TensorFlow Addons as a required dependency; RectifiedAdam optimizer now requires explicit TFA installation if needed

config/metrics_config.yaml

@@ -141,6 +141,15 @@ metrics:
     # Batch size to use for the evaluation.
     batch_size: 16
 
+    # Metrics and model evaluation configuration
+    optimizer:
+      name: 'adam'           # Only standard Adam used for metrics
+      learning_rate: 1e-2
+      beta_1: 0.9
+      beta_2: 0.999
+      epsilon: 1e-07
+      amsgrad: False
+
     # Save RMS error for each super resolved PSF in the test dataset in addition to the mean across the FOV."
     # Flag to get Super-Resolution pixel PSF RMSE for each individual test star. 
     # If `True`, the relative pixel RMSE of each star is added to ther saving dictionary.

config/training_config.yaml

@@ -153,6 +153,10 @@ training:
     # Default: 'mask_mse'. Choose 'mse' if the dataset does not include a mask.
     loss: 'mask_mse'
 
+    # Optimizer to use during training. Options are: 'adam' or 'rectified_adam'.
+    optimizer: 
+      name: 'rectified_adam'                     
+
     multi_cycle_params:
 
       # Number of training cycles to perform. Each cycle may use different learning rates or number of epochs.

pyproject.toml

@@ -14,7 +14,6 @@ dependencies = [
     "numpy>=1.26.4,<2.0",
     "scipy",
     "tensorflow==2.11.0",
-    "tensorflow-addons",
     "tensorflow-estimator",
     "zernike",
     "opencv-python",
@@ -41,6 +40,10 @@ docs = [
     "scriv",
 ]
 
+addons = [
+    "tensorflow-addons",
+]
+
 lint = [
     "ruff",
 ]

src/wf_psf/metrics/metrics.py

@@ -381,8 +381,9 @@ def compute_shape_metrics(
     output_Q=1,
     output_dim=64,
     batch_size=16,
-    opt_stars_rel_pix_rmse=False,
+    optimizer_settings=None,
     dataset_dict=None,
+    opt_stars_rel_pix_rmse=False,
 ):
     """Compute the pixel, shape and size RMSE of a PSF model.
 
@@ -418,15 +419,18 @@ def compute_shape_metrics(
         Output dimension of the square PSF stamps.
     batch_size: int
         Batch size to process the PSF estimations.
-    opt_stars_rel_pix_rmse: bool
-        If `True`, the relative pixel RMSE of each star is added to ther saving dictionary.
-        The summary statistics are always computed.
-        Default is `False`.
+    optimizer_settings: RecursiveNamespace, dict, str, optional
+        Optimizer configuration (from YAML or programmatically), or string name.
     dataset_dict: dict
         Dictionary containing the dataset information. If provided, and if the `'super_res_stars'`
         key is present, the noiseless super resolved stars from the dataset are used to compute
         the metrics. Otherwise, the stars are generated from the gt model.
         Default is `None`.
+    opt_stars_rel_pix_rmse: bool
+        If `True`, the relative pixel RMSE of each star is added to ther saving dictionary.
+        The summary statistics are always computed.
+        Default is `False`.
+
 
     Returns
     -------
@@ -445,8 +449,12 @@ def compute_shape_metrics(
     gt_tf_semiparam_field.set_output_Q(output_Q=output_Q, output_dim=output_dim)
 
     # Need to compile the models again
-    tf_semiparam_field = build_PSF_model(tf_semiparam_field)
-    gt_tf_semiparam_field = build_PSF_model(gt_tf_semiparam_field)
+    tf_semiparam_field = build_PSF_model(
+        tf_semiparam_field, optimizer=optimizer_settings
+    )
+    gt_tf_semiparam_field = build_PSF_model(
+        gt_tf_semiparam_field, optimizer=optimizer_settings
+    )
 
     # Generate SED data list
     packed_SED_data = [

src/wf_psf/metrics/metrics_interface.py

@@ -296,9 +296,10 @@ def evaluate_metrics_shape(
             tf_pos=dataset["positions"],
             n_bins_lda=self.trained_model.model_params.n_bins_lda,
             n_bins_gt=self.metrics_params.ground_truth_model.model_params.n_bins_lda,
-            batch_size=self.metrics_params.metrics_hparams.batch_size,
             output_Q=self.metrics_params.metrics_hparams.output_Q,
             output_dim=self.metrics_params.metrics_hparams.output_dim,
+            batch_size=self.metrics_params.metrics_hparams.batch_size,
+            optimizer_settings=self.metrics_params.metrics_hparams.optimizer,
             opt_stars_rel_pix_rmse=self.metrics_params.metrics_hparams.opt_stars_rel_pix_rmse,
             dataset_dict=dataset,
         )

src/wf_psf/psf_models/psf_models.py

@@ -11,6 +11,7 @@
 import tensorflow as tf
 from wf_psf.sims.psf_simulator import PSFSimulator
 from wf_psf.utils.utils import zernike_generator
+from wf_psf.utils.optimizer import is_optimizer_instance, get_optimizer
 import glob
 import logging
 
@@ -160,12 +161,12 @@ def build_PSF_model(model_inst, optimizer=None, loss=None, metrics=None):
     if loss is None:
         loss = tf.keras.losses.MeanSquaredError()
 
-    # Define optimizer function
-    if optimizer is None:
-        optimizer = tf.keras.optimizers.legacy.Adam(
-            learning_rate=1e-2, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False
-        )
-
+    # Handle optimizer: either config object or a Keras optimizer instance
+    if is_optimizer_instance(optimizer):
+        pass
+    else:
+        optimizer = get_optimizer(optimizer_config=optimizer)
+    
     # Define metric functions
     if metrics is None:
         metrics = [tf.keras.metrics.MeanSquaredError()]