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78d2329 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | 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")
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