QualityMetrics.evaluate

petric.py

@@ -57,7 +57,7 @@ def __call__(self, algo: Algorithm):
             algo.x.write(str(self.outdir / 'iter_final.hv'))
 
 
-class TensorBoard(cbks.Callback):
+class StatsLog(cbks.Callback):
     """Log image slices & objective value"""
     def __init__(self, verbose=1, transverse_slice=None, coronal_slice=None, vmax=None, logdir=OUTDIR):
         super().__init__(verbose)
@@ -98,27 +98,26 @@ def __init__(self, reference_image, whole_object_mask, background_mask, **kwargs
         self.ref_im_arr = reference_image.as_array()
         self.norm = self.ref_im_arr[self.background_indices].mean()
 
-    def __call__(self, algorithm):
-        iteration = algorithm.iteration
-        if iteration % algorithm.update_objective_interval != 0 and iteration != algorithm.max_iteration:
+    def __call__(self, algo: Algorithm):
+        iteration = algo.iteration
+        if iteration % algo.update_objective_interval != 0 and iteration != algo.max_iteration:
             return
-        self.log(algorithm.x, iteration)
+        for tag, value in self.evaluate(algo.x).items():
+            self.tb_summary_writer.add_scalar(tag, value, iteration)
 
-    def log(self, test_im, iteration):
+    def evaluate(self, test_im: STIR.ImageData) -> dict[str, float]:
         assert not any(self.filter.values()), "Filtering not implemented"
         test_im_arr = test_im.as_array()
-        self.tb_summary_writer.add_scalar(
-            "RMSE_whole_object",
-            np.sqrt(mse(self.ref_im_arr[self.whole_object_indices], test_im_arr[self.whole_object_indices])) /
-            self.norm, iteration)
-        self.tb_summary_writer.add_scalar(
-            "RMSE_background",
-            np.sqrt(mse(self.ref_im_arr[self.background_indices], test_im_arr[self.background_indices])) / self.norm,
-            iteration)
-        for voi_name, voi_indices in sorted(self.voi_indices.items()):
-            self.tb_summary_writer.add_scalar(
-                f"AEM_VOI_{voi_name}",
-                np.abs(test_im_arr[voi_indices].mean() - self.ref_im_arr[voi_indices].mean()) / self.norm, iteration)
+        whole = {
+            "RMSE_whole_object": np.sqrt(
+                mse(self.ref_im_arr[self.whole_object_indices], test_im_arr[self.whole_object_indices])) / self.norm,
+            "RMSE_background": np.sqrt(
+                mse(self.ref_im_arr[self.background_indices], test_im_arr[self.background_indices])) / self.norm}
+        local = {
+            f"AEM_VOI_{voi_name}": np.abs(test_im_arr[voi_indices].mean() - self.ref_im_arr[voi_indices].mean()) /
+            self.norm
+            for voi_name, voi_indices in sorted(self.voi_indices.items())}
+        return {**whole, **local}
 
 
 class MetricsWithTimeout(cbks.Callback):
@@ -129,20 +128,20 @@ def __init__(self, seconds=300, outdir=OUTDIR, transverse_slice=None, coronal_sl
         self.callbacks = [
             cbks.ProgressCallback(),
             SaveIters(outdir=outdir),
-            (tb_cbk := TensorBoard(logdir=outdir, transverse_slice=transverse_slice, coronal_slice=coronal_slice))]
+            (tb_cbk := StatsLog(logdir=outdir, transverse_slice=transverse_slice, coronal_slice=coronal_slice))]
         self.tb = tb_cbk.tb
         self.reset()
 
     def reset(self, seconds=None):
         self.limit = time() + (self._seconds if seconds is None else seconds)
 
-    def __call__(self, algorithm: Algorithm):
+    def __call__(self, algo: Algorithm):
         if (now := time()) > self.limit:
             log.warning("Timeout reached. Stopping algorithm.")
             raise StopIteration
         if self.callbacks:
             for c in self.callbacks:
-                c(algorithm)
+                c(algo)
             self.limit += time() - now
 
     @staticmethod
@@ -221,7 +220,7 @@ def get_image(fname):
 
 if SRCDIR.is_dir():
     # create list of existing data
-    # Note: as MetricsWithTimeout initialises Tensorboard, this will currently create directories in OUTDIR accordingly
+    # NB: `MetricsWithTimeout` initialises `SaveIters` which creates `outdir`
     data_dirs_metrics = [(SRCDIR / "Siemens_mMR_NEMA_IQ", OUTDIR / "mMR_NEMA",
                           [MetricsWithTimeout(outdir=OUTDIR / "mMR_NEMA", transverse_slice=72, coronal_slice=109)]),
                          (SRCDIR / "NeuroLF_Hoffman_Dataset", OUTDIR / "NeuroLF_Hoffman",