Adding normalization to the validation step.

code/data.py

@@ -122,12 +122,12 @@ class TestDataModule(pl.LightningDataModule):
             # Load the pyramid/00 per file
 
             #For JSC Training.
-            pli_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/pli/NTransmittance'
-            cyto_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/stained'
+            #pli_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/pli/NTransmittance'
+            #cyto_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/stained'
 
             #For Local Machine Training.
-            #pli_path = '/media/tushar/A2246889246861F1/Master Thesis MAIA/example-data/pli/NTransmittance'
-            #cyto_path = '/media/tushar/A2246889246861F1/Master Thesis MAIA/example-data/stained'
+            pli_path = '/media/tushar/A2246889246861F1/Master Thesis MAIA/example-data/pli/NTransmittance'
+            cyto_path = '/media/tushar/A2246889246861F1/Master Thesis MAIA/example-data/stained'
 
             pli_files_list = [file for file in os.listdir(pli_path) if
                               file.endswith(('.h5', '.hdf', '.h4', '.hdf4', '.he2', '.hdf5', '.he5'))]

code/model.py

@@ -52,6 +52,9 @@ class TestModule(pl.LightningModule):
         cyto_imag_generated = self.forward(batch['pli_image'])
         loss = self.loss_f(cyto_imag_generated, batch['cyto_image'])
         self.log("val_loss", loss)
+        batch['pli_image'] = batch['pli_image'] + 0.5
+        batch['cyto_image'] = batch['cyto_image'] + 0.5
+        cyto_imag_generated = cyto_imag_generated +0.5
         if batch_idx == 0:
             grid = make_grid([batch['pli_image'][0], batch['cyto_image'][0], cyto_imag_generated[0]])
             self.logger.experiment.add_image('Grid_images', grid, self.current_epoch, dataformats="CHW")