Remove prints

src/membrain_pick/clustering/mean_shift_inference.py

@@ -45,7 +45,6 @@ def mean_shift_for_scores(
         )
     else:
         raise ValueError("Unknown method for mean shift clustering.")
-    print("Found", out_pos.shape[0], "clusters.")
     return out_pos, out_p_num
 
 

src/membrain_pick/dataloading/mesh_partitioning.py

@@ -508,7 +508,7 @@ def load_from_cache(cur_cache_path: str) -> Optional[Dict[str, np.ndarray]]:
         The loaded partitioning data if successful, None otherwise.
     """
     if os.path.isfile(cur_cache_path):
-        print(f"Loading partitioning data from {cur_cache_path}")
+        # print(f"Loading partitioning data from {cur_cache_path}")
         return np.load(cur_cache_path)
     else:
         return None

src/membrain_pick/mesh_projections/mesh_conversion_wrappers.py

@@ -154,7 +154,6 @@ def mesh_for_tomo_mb_folder(
     mb_files = [
         os.path.join(mb_folder, f) for f in os.listdir(mb_folder) if f.endswith(".mrc")
     ]
-    print(mb_files)
 
     if tomo is None:
         tomo = load_tomogram(tomo_file)

src/membrain_pick/napari_utils/surforama_cli_utils.py

@@ -213,7 +213,6 @@ def normalize_surface_values(surface_values, value_range=None):
             np.percentile(surface_values, cutoff_pct * 100),
             np.percentile(surface_values, (1 - cutoff_pct) * 100),
         )
-    print("Normalized value range: ", value_range)
     normalized_values = (surface_values - value_range[0]) / (
         value_range[1] - value_range[0] + np.finfo(float).eps
     )

src/membrain_pick/networks/diffusion_net/geometry.py

@@ -471,7 +471,7 @@ def get_operators(verts, faces, k_eig=128, op_cache_dir=None, normals=None, over
                 # If we're overwriting, or there aren't enough eigenvalues, just delete it; we'll create a new
                 # entry below more eigenvalues
                 if overwrite_cache: 
-                    print("  overwriting cache by request")
+                    # print("  overwriting cache by request")
                     os.remove(search_path)
                     break
 
@@ -516,7 +516,7 @@ def read_sp_mat(prefix):
                 break
 
             except FileNotFoundError:
-                print("  cache miss -- constructing operators")
+                # print("  cache miss -- constructing operators")
                 break
 
             except Exception as E:

src/membrain_pick/optimization/diffusion_training_pylit.py

@@ -2,6 +2,7 @@
 import pytorch_lightning as pl
 import torch
 from torch.optim import Adam, SGD
+import matplotlib.pyplot as plt
 
 from membrain_pick.networks.diffusion_net import DiffusionNet
 from membrain_pick.clustering.mean_shift_utils import MeanShiftForwarder
@@ -30,11 +31,17 @@ def __init__(self,
                  fixed_time=None, 
                  one_D_conv_first=False, 
                  clamp_diffusion=False, 
+                 out_plot_file=None,
                  visualize_diffusion=False, 
                  visualize_grad_rotations=False, 
                  visualize_grad_features=False):
         super().__init__()
         self.max_epochs = max_epochs
+        self.epoch_losses = {
+            "train": [],
+            "val": []
+        }
+        self.out_plot_file = out_plot_file
         # Initialize the DiffusionNet with the given arguments.
         self.model = DiffusionNet(C_in=C_in, 
                                   C_out=C_out, 
@@ -132,7 +139,7 @@ def training_step(self, batch, batch_idx):
         # Log training loss
         self.total_train_loss += loss.detach()
         self.train_batches += 1
-        print(f"Training loss: {loss}")
+        # print(f"Training loss: {loss}")
         return loss
 
     def validation_step(self, batch, batch_idx):
@@ -156,12 +163,25 @@ def validation_step(self, batch, batch_idx):
     def on_train_epoch_end(self):
         # Log the average training loss
         avg_train_loss = self.total_train_loss / self.train_batches
+        print("Train epoch loss: ", avg_train_loss)
+        self.epoch_losses["train"].append(avg_train_loss)
         self.log('train_loss', avg_train_loss)
 
     def on_validation_epoch_end(self):
         # Log the average validation loss
         avg_val_loss = self.total_val_loss / self.val_batches
+        print("Validation epoch loss: ", avg_val_loss)
+        self.epoch_losses["val"].append(avg_val_loss)
         self.log('val_loss', avg_val_loss)
+        self.plot_losses()
+
+
+    def plot_losses(self):
+        plt.figure()
+        plt.plot(self.epoch_losses["train"], label="Train loss")
+        plt.plot(self.epoch_losses["val"], label="Validation loss")
+        plt.legend()
+        plt.savefig(self.out_plot_file)
 
 
 def unpack_batch(batch):

src/membrain_pick/orientation.py

@@ -1,3 +1,4 @@
+import logging
 import os
 import numpy as np
 import scipy.spatial as spatial
@@ -14,6 +15,7 @@
 from membrain_seg.segmentation.dataloading.data_utils import load_tomogram
 
 
+
 def orientation_from_mesh(coordinates, mesh):
     """
     Get the orientation of a point cloud from a mesh.
@@ -39,7 +41,7 @@ def orientation_from_mesh(coordinates, mesh):
     distances, vertex_indices = tree.query(coordinates)
 
     if np.any(distances > 200):
-        print(
+        logging.warning(
             "Warning: Some points are more than 200 units away from the mesh. This might be an error. Check rescaling factors."
         )
 

src/membrain_pick/train.py

@@ -48,6 +48,8 @@ def train(
     val_path = os.path.join(data_dir, "val")
     cache_dir_mb = os.path.join(training_dir, "mesh_cache")
     log_dir = os.path.join(training_dir, "logs")
+    out_plot_file = os.path.join(training_dir, "plots", f"training_curves_{project_name}_{sub_name}.png")
+    os.makedirs(os.path.join(training_dir, "plots"), exist_ok=True)
 
     # Create the data module
     data_module = MemSegDiffusionNetDataModule(
@@ -85,6 +87,7 @@ def train(
         device=device,
         one_D_conv_first=one_D_conv_first,
         max_epochs=max_epochs,
+        out_plot_file=out_plot_file,
     )
 
     checkpointing_name = project_name + "_" + sub_name