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| import torch | |
| import torch.nn.functional as F | |
| import torch.nn as nn | |
| from torch.autograd import Variable | |
| from math import exp | |
| from fused_ssim import fused_ssim | |
| from pytorch_msssim import SSIM | |
| import time | |
| # Reference Implementation is taken from the following: | |
| # https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py | |
| # https://github.com/graphdeco-inria/gaussian-splatting/blob/main/utils/loss_utils.py | |
| def gaussian(window_size, sigma): | |
| gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) | |
| return gauss / gauss.sum() | |
| def create_window(window_size, channel): | |
| _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) | |
| window = create_window(window_size, channel) | |
| 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) | |
| def _ssim(img1, img2, window, window_size, channel, size_average=True): | |
| mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) | |
| mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) | |
| mu1_sq = mu1.pow(2) | |
| mu2_sq = mu2.pow(2) | |
| mu1_mu2 = mu1 * mu2 | |
| sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq | |
| sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq | |
| sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 | |
| C1 = 0.01 ** 2 | |
| C2 = 0.03 ** 2 | |
| ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) | |
| if size_average: | |
| return ssim_map.mean() | |
| else: | |
| return ssim_map.mean(1).mean(1).mean(1) | |
| if __name__ == "__main__": | |
| torch.manual_seed(0) | |
| B, CH, H, W = 5, 5, 1080, 1920 | |
| pm_ssim = SSIM(data_range=1.0, channel=CH) | |
| iterations = 100 | |
| for _ in range(iterations): | |
| with torch.no_grad(): | |
| img1_og = nn.Parameter(torch.rand([B, CH, H, W], device="cuda")) | |
| img2_og = torch.rand([B, CH, H, W], device="cuda") | |
| img1_mine_same = nn.Parameter(img1_og.clone()) | |
| img2_mine_same = img2_og.clone() | |
| img1_mine_valid = nn.Parameter(img1_og.clone()) | |
| img2_mine_valid = img2_og.clone() | |
| img1_pm = nn.Parameter(img1_og.clone()) | |
| img2_pm = img2_og.clone() | |
| og_ssim_val = ssim(img1_og, img2_og) | |
| mine_ssim_val_same = fused_ssim(img1_mine_same, img2_mine_same) | |
| mine_ssim_val_valid = fused_ssim(img1_mine_valid, img2_mine_valid, "valid") | |
| pm_ssim_val = pm_ssim(img1_pm, img2_pm) | |
| assert torch.isclose(og_ssim_val, mine_ssim_val_same) | |
| assert torch.isclose(mine_ssim_val_valid, pm_ssim_val) | |
| og_ssim_val.backward() | |
| mine_ssim_val_same.backward() | |
| mine_ssim_val_valid.backward() | |
| pm_ssim_val.backward() | |
| assert torch.isclose(img1_og.grad, img1_mine_same.grad).all() | |
| assert torch.isclose(img1_mine_valid.grad, img1_pm.grad).all() | |
| img1 = nn.Parameter(torch.rand([B, CH, H, W], device="cuda")) | |
| img2 = torch.rand([B, CH, H, W], device="cuda") | |
| # benchmark og | |
| begin = time.time() | |
| for _ in range(iterations): | |
| og_ssim_val = ssim(img1, img2) | |
| torch.cuda.synchronize() | |
| end = time.time() | |
| og_time_forward = (end - begin) / iterations * 1000 | |
| print("Reference Time (Forward):", og_time_forward, "ms") | |
| begin = time.time() | |
| for _ in range(iterations): | |
| og_ssim_val = ssim(img1, img2) | |
| og_ssim_val.backward() | |
| torch.cuda.synchronize() | |
| end = time.time() | |
| og_time_backward = (end - begin) / iterations * 1000 - og_time_forward | |
| print("Reference Time (Backward):", og_time_backward, "ms") | |
| # benchmark pytorch_mssim (pm) | |
| begin = time.time() | |
| for _ in range(iterations): | |
| pm_ssim_val = pm_ssim(img1, img2) | |
| torch.cuda.synchronize() | |
| end = time.time() | |
| pm_time_forward = (end - begin) / iterations * 1000 | |
| print("pytorch_mssim Time (Forward):", pm_time_forward, "ms") | |
| begin = time.time() | |
| for _ in range(iterations): | |
| pm_ssim_val = pm_ssim(img1, img2) | |
| pm_ssim_val.backward() | |
| torch.cuda.synchronize() | |
| end = time.time() | |
| pm_time_backward = (end - begin) / iterations * 1000 - pm_time_forward | |
| print("pytorch_mssim Time (Backward):", pm_time_backward, "ms") | |
| # benchmark mine | |
| begin = time.time() | |
| for _ in range(iterations): | |
| mine_ssim_val = fused_ssim(img1, img2) | |
| torch.cuda.synchronize() | |
| end = time.time() | |
| mine_time_forward = (end - begin) / iterations * 1000 | |
| print("fused-ssim Time (Forward):", mine_time_forward, "ms") | |
| begin = time.time() | |
| for _ in range(iterations): | |
| mine_ssim_val = fused_ssim(img1, img2) | |
| mine_ssim_val.backward() | |
| torch.cuda.synchronize() | |
| end = time.time() | |
| mine_time_backward = (end - begin) / iterations * 1000 - mine_time_forward | |
| print("fused-ssim Time (Backward):", mine_time_backward, "ms") | |
| begin = time.time() | |
| for _ in range(iterations): | |
| mine_ssim_val = fused_ssim(img1, img2, train=False) | |
| torch.cuda.synchronize() | |
| end = time.time() | |
| mine_time_infer = (end - begin) / iterations * 1000 | |
| print("fused-ssim Time (Inference):", mine_time_infer, "ms") | |