From 495617279b53ddf65b490b9ed3a9e249e18df175 Mon Sep 17 00:00:00 2001
From: "Cpt.Captain" <cpt.captain@noreply.%(DOMAIN)s>
Date: Fri, 23 Sep 2022 11:48:41 +0200
Subject: [PATCH] remove debug prints and other useless comments

---
 train_lightning.py | 24 +++---------------------
 1 file changed, 3 insertions(+), 21 deletions(-)

diff --git a/train_lightning.py b/train_lightning.py
index 6fb6f8c..b31b8d8 100644
--- a/train_lightning.py
+++ b/train_lightning.py
@@ -32,20 +32,11 @@ import cv2
 
 
 def normalize_and_colormap(img, reduce_dynamic_range=False):
-    # print(img.min())
-    # print(img.max())
-    # print(img.mean())
     ret = (img - img.min()) / (img.max() - img.min()) * 255.0
-    # print(ret.min())
-    # print(ret.max())
-    # print(ret.mean())
     
     # FIXME do I need to compress dynamic range somehow or something?
     if reduce_dynamic_range and img.max() > 5*img.mean():
         ret = (img - img.min()) / (5*img.mean() - img.min()) * 255.0
-    # print(ret.min())
-    # print(ret.max())
-    # print(ret.mean())
         
     if isinstance(ret, torch.Tensor):
         ret = ret.cpu().detach().numpy()
@@ -66,8 +57,6 @@ def log_images(left, right, pred_disp, gt_disp):
     pred_disp = torch.squeeze(pred_disp[:, 0, :, :])
     gt_disp = torch.squeeze(gt_disp[:, 0, :, :])
     
-    # print('gt_disp debug')
-    # print(gt_disp.shape)
     
     singular_batch = False
     if len(left.shape) == 2:
@@ -76,15 +65,12 @@ def log_images(left, right, pred_disp, gt_disp):
         input_left = left.cpu().detach().numpy()
         input_right = right.cpu().detach().numpy()
     else:
-        input_left = left[batch_idx].cpu().detach().numpy()# .transpose(1,2,0)
-        input_right = right[batch_idx].cpu().detach().numpy()# .transpose(1,2,0)
+        input_left = left[batch_idx].cpu().detach().numpy()
+        input_right = right[batch_idx].cpu().detach().numpy()
         
     disp = pred_disp
     disp_error = gt_disp - disp
     
-    # print('gt_disp debug normalize')
-    # print(gt_disp.max(), gt_disp.min())
-    # print(gt_disp.dtype)
 
     if singular_batch:
         wandb_log =  dict(
@@ -110,7 +96,6 @@ def log_images(left, right, pred_disp, gt_disp):
         wandb_log =  dict(
             key='samples', 
             images=[
-                # pred_disp.cpu().detach().numpy().transpose(1,2,0),
                 normalize_and_colormap(pred_disp[batch_idx]),
                 normalize_and_colormap(abs(disp_error[batch_idx])),
                 normalize_and_colormap(gt_disp[batch_idx]),
@@ -118,7 +103,6 @@ def log_images(left, right, pred_disp, gt_disp):
                 input_right,
             ],
             caption=[
-                # f"Disparity \n{pred_disp[batch_idx].min():.{2}f}/{pred_disp[batch_idx].max():.{2}f}",
                 f"Disparity (vis)\n{pred_disp[batch_idx].min():.{2}f}/{pred_disp[batch_idx].max():.{2}f}",
                 f"Disp. Error\n{disp_error[batch_idx].min():.{2}f}/{disp_error[batch_idx].max():.{2}f}\n{abs(disp_error[batch_idx]).mean():.{2}f}",
                 f"GT Disp Vis \n{gt_disp[batch_idx].min():.{2}f}/{gt_disp[batch_idx].max():.{2}f}",
@@ -439,7 +423,7 @@ if __name__ == "__main__":
         # this was used for our blender renders
         pattern_path = '/home/nils/miniprojekt/kinect_syn_ref.png'
     elif 'ctd' in config.training_data_path:
-        # this one is used (i hope) for ctd
+        # this one is used for ctd
         pattern_path = '/home/nils/kinect_from_settings.png'
     
 
@@ -454,8 +438,6 @@ if __name__ == "__main__":
             pattern_path, 
             # lr=0.00017378008287493763,          # found with auto_lr_find=True
             )
-    # NOTE turn this down once it's working, this might use too much space
-    # wandb_logger.watch(model, log_graph=False) #, log='all')
 
     model_checkpoint = ModelCheckpoint(
             monitor="val_loss",