Update train.py

Updated the main function, call the create_window() function for just once, since the window_size is fixed to 11 and the channel is fixed to 3 according to the source code.

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
@@ -29,27 +30,28 @@ 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)
-    gaussians = GaussianModel(dataset.sh_degree)
-    scene = Scene(dataset, gaussians)
-    gaussians.training_setup(opt)
+    tb_writer = prepare_output_and_logger(dataset)  # Tensorboard writer
+    gaussians = GaussianModel(dataset.sh_degree)    #高斯模型
+    scene = Scene(dataset, gaussians)             #场景
+    gaussians.training_setup(opt)           #训练设置
     if checkpoint:
         (model_params, first_iter) = torch.load(checkpoint)
         gaussians.restore(model_params, opt)
 
-    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
-    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
+    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0] #背景颜色
+    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda") #背景颜色
 
-    iter_start = torch.cuda.Event(enable_timing = True)
-    iter_end = torch.cuda.Event(enable_timing = True)
+    iter_start = torch.cuda.Event(enable_timing = True)  #开始时间
+    iter_end = torch.cuda.Event(enable_timing = True)    #结束时间
 
     viewpoint_stack = None
     ema_loss_for_log = 0.0
     progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
     first_iter += 1
     for iteration in range(first_iter, opt.iterations + 1):        
-        if network_gui.conn == None:
+        if network_gui.conn == None:    
             network_gui.try_connect()
         while network_gui.conn != None:
             try:
@@ -64,9 +66,9 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
             except Exception as e:
                 network_gui.conn = None
 
-        iter_start.record()
+        iter_start.record()  #记录开始时间
 
-        gaussians.update_learning_rate(iteration)
+        gaussians.update_learning_rate(iteration)   #更新学习率
 
         # Every 1000 its we increase the levels of SH up to a maximum degree
         if iteration % 1000 == 0:
@@ -83,14 +85,22 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
 
         bg = torch.rand((3), device="cuda") if opt.random_background else background
 
-        render_pkg = render(viewpoint_cam, gaussians, pipe, bg)
+        render_pkg = render(viewpoint_cam, gaussians, pipe, bg)  #渲染
         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()  # 记录训练结束时间
+    total_time = end_time - start_time  # 计算总时间
+    print(f"\nTraining complete. Total training time: {total_time:.2f} seconds.")  # 打印总时间
+
+
 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)