Merge 25b71e16d9 into 8a70a8cd6f

train.py

@@ -11,8 +11,9 @@
 
 import os
 import torch
+import time
 from random import randint
-from utils.loss_utils import l1_loss, ssim
+from utils.loss_utils import l1_loss, ssim, ssim_optimized, create_window
 from gaussian_renderer import render, network_gui
 import sys
 from scene import Scene, GaussianModel
@@ -29,8 +30,9 @@ except ImportError:
     TENSORBOARD_FOUND = False
 
 def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
+    start_time=time.time()
     first_iter = 0
-    tb_writer = prepare_output_and_logger(dataset)
+    tb_writer = prepare_output_and_logger(dataset)  # Tensorboard writer
     gaussians = GaussianModel(dataset.sh_degree)    
     scene = Scene(dataset, gaussians)             
     gaussians.training_setup(opt)           
@@ -87,10 +89,18 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
         image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
         
         # Loss
+        # gt_image = viewpoint_cam.original_image.cuda()
+        # Ll1 = l1_loss(image, gt_image)
+        # loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
+        # loss.backward()
+
+
+        # ----------------modify-------------
         gt_image = viewpoint_cam.original_image.cuda()
         Ll1 = l1_loss(image, gt_image)
-        loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
+        loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim_optimized(image, gt_image, window=window))
         loss.backward()
+        #-------------------------------------
 
         iter_end.record()
 
@@ -131,13 +141,18 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
                 print("\n[ITER {}] Saving Checkpoint".format(iteration))
                 torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
 
+    end_time = time.time()  
+    total_time = end_time - start_time  
+    print(f"\nTraining complete. Total training time: {total_time:.2f} seconds.")  
+
+
 def prepare_output_and_logger(args):    
     if not args.model_path:
         if os.getenv('OAR_JOB_ID'):
             unique_str=os.getenv('OAR_JOB_ID')
         else:
             unique_str = str(uuid.uuid4())
-        args.model_path = os.path.join("./output/", unique_str[0:10])
+        args.model_path = os.path.join("/mnt/data1/3dgs_modify_output/", unique_str[0:10])
         
     # Set up output folder
     print("Output folder: {}".format(args.model_path))
@@ -191,6 +206,11 @@ def training_report(tb_writer, iteration, Ll1, loss, l1_loss, elapsed, testing_i
         torch.cuda.empty_cache()
 
 if __name__ == "__main__":
+    #----------------------create window------------------
+    window_size=11
+    channel=3
+    window=create_window(window_size, channel)
+    #--------------------------------
     # Set up command line argument parser
     parser = ArgumentParser(description="Training script parameters")
     lp = ModelParams(parser)

utils/loss_utils.py

@@ -25,13 +25,14 @@ def gaussian(window_size, sigma):
     return gauss / gauss.sum()
 
 def create_window(window_size, channel):
+    """Create a 2D Gaussian window."""
     _1D_window = gaussian(window_size, 1.5).unsqueeze(1) 
     _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
     window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
     return window
 
 def ssim(img1, img2, window_size=11, size_average=True):
-    channel = img1.size(-3)
+    channel = img1.size(-3)   #channel=3
     window = create_window(window_size, channel)
 
     if img1.is_cuda:
@@ -62,3 +63,12 @@ def _ssim(img1, img2, window, window_size, channel, size_average=True):
     else:
         return ssim_map.mean(1).mean(1).mean(1)
 
+#-----------------modify------------------------------------
+def ssim_optimized(img1, img2, window=None, window_size=11, size_average=True):
+    channel = img1.size(-3)
+    if window is None:
+        window = create_window(window_size, channel).to(img1.device).type_as(img1)
+    if img1.is_cuda:
+        window = window.cuda(img1.get_device())
+    window = window.type_as(img1)
+    return _ssim(img1, img2, window, window_size, channel, size_average)