Update the implementation of ssim() loss function, reduce the computational complexity from O(n) to O(1) since create_window() is called only once in main function #886
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Updated the function ssim(), the optimized version of ssim() is ssim_optimized(), which reduce the computational complexity. After modified, the function create_window() just need to be called once in the main function in train.py, no need to be called in ssim() in every iteration.
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.
Updated the function ssim(), the optimized version of ssim() is ssim_optimized(), which reduce the computational complexity. After modified, the function create_window() just need to be called once in the main function in train.py, no need to be called in ssim() in every iteration.
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. Updated the calculation of loss function, call the ssim_optimized(), instead of the original ssim().
tryhiseyyysum
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AsherJingkongChen
suggested changes
Oct 15, 2024
| 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]) |
| #----------------------create window------------------ | ||
| window_size=11 | ||
| channel=3 | ||
| window=create_window(window_size, channel) |
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Your window is not passed anywhere. It is a local variable in train.py
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| 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) |
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In the loss_utils.py: Updated the function ssim(), the optimized version of ssim() is ssim_optimized(), which reduce the computational complexity. After modified, the function create_window() just need to be called once in the main function in train.py, no need to be called in ssim() in every iteration.
In the 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.
Updated the calculation of loss function, call the ssim_optimized(), instead of the original ssim().