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299 lines
7.6 KiB
Python
299 lines
7.6 KiB
Python
from math import exp
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import torch
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from torch.nn import functional as F
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def gaussian(window_size, sigma):
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gauss = torch.Tensor(
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[
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exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2))
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for x in range(window_size)
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]
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)
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return gauss / gauss.sum()
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def create_window(window_size, channel=1):
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_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
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_2D_window = (
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_1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0).to(device)
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)
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window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
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return window
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def create_window_3d(window_size, channel=1):
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_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
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_2D_window = _1D_window.mm(_1D_window.t())
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_3D_window = _2D_window.unsqueeze(2) @ (_1D_window.t())
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window = (
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_3D_window.expand(1, channel, window_size, window_size, window_size)
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.contiguous()
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.to(device)
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)
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return window
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def ssim(
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img1,
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img2,
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window_size=11,
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window=None,
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size_average=True,
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full=False,
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val_range=None,
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):
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# Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
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if val_range is None:
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max_val = 255 if torch.max(img1) > 128 else 1
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min_val = -1 if torch.min(img1) < -0.5 else 0
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L = max_val - min_val
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else:
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L = val_range
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padd = 0
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(_, channel, height, width) = img1.size()
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if window is None:
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real_size = min(window_size, height, width)
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window = create_window(real_size, channel=channel).to(img1.device)
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# mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
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# mu2 = F.conv2d(img2, window, padding=padd, groups=channel)
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mu1 = F.conv2d(
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F.pad(img1, (5, 5, 5, 5), mode="replicate"),
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window,
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padding=padd,
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groups=channel,
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)
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mu2 = F.conv2d(
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F.pad(img2, (5, 5, 5, 5), mode="replicate"),
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window,
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padding=padd,
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groups=channel,
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)
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mu1_sq = mu1.pow(2)
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mu2_sq = mu2.pow(2)
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mu1_mu2 = mu1 * mu2
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sigma1_sq = (
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F.conv2d(
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F.pad(img1 * img1, (5, 5, 5, 5), "replicate"),
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window,
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padding=padd,
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groups=channel,
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)
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- mu1_sq
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)
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sigma2_sq = (
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F.conv2d(
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F.pad(img2 * img2, (5, 5, 5, 5), "replicate"),
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window,
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padding=padd,
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groups=channel,
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)
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- mu2_sq
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)
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sigma12 = (
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F.conv2d(
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F.pad(img1 * img2, (5, 5, 5, 5), "replicate"),
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window,
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padding=padd,
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groups=channel,
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)
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- mu1_mu2
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)
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C1 = (0.01 * L) ** 2
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C2 = (0.03 * L) ** 2
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v1 = 2.0 * sigma12 + C2
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v2 = sigma1_sq + sigma2_sq + C2
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cs = torch.mean(v1 / v2) # contrast sensitivity
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ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
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ret = ssim_map.mean() if size_average else ssim_map.mean(1).mean(1).mean(1)
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if full:
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return ret, cs
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return ret
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def ssim_matlab(
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img1,
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img2,
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window_size=11,
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window=None,
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size_average=True,
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full=False,
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val_range=None,
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):
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# Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
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if val_range is None:
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max_val = 255 if torch.max(img1) > 128 else 1
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min_val = -1 if torch.min(img1) < -0.5 else 0
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L = max_val - min_val
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else:
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L = val_range
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padd = 0
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(_, _, height, width) = img1.size()
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if window is None:
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real_size = min(window_size, height, width)
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window = create_window_3d(real_size, channel=1).to(img1.device)
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# Channel is set to 1 since we consider color images as volumetric images
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img1 = img1.unsqueeze(1)
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img2 = img2.unsqueeze(1)
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mu1 = F.conv3d(
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F.pad(img1, (5, 5, 5, 5, 5, 5), mode="replicate"),
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window,
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padding=padd,
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groups=1,
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)
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mu2 = F.conv3d(
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F.pad(img2, (5, 5, 5, 5, 5, 5), mode="replicate"),
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window,
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padding=padd,
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groups=1,
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)
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mu1_sq = mu1.pow(2)
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mu2_sq = mu2.pow(2)
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mu1_mu2 = mu1 * mu2
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sigma1_sq = (
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F.conv3d(
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F.pad(img1 * img1, (5, 5, 5, 5, 5, 5), "replicate"),
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window,
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padding=padd,
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groups=1,
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)
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- mu1_sq
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)
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sigma2_sq = (
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F.conv3d(
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F.pad(img2 * img2, (5, 5, 5, 5, 5, 5), "replicate"),
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window,
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padding=padd,
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groups=1,
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)
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- mu2_sq
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)
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sigma12 = (
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F.conv3d(
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F.pad(img1 * img2, (5, 5, 5, 5, 5, 5), "replicate"),
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window,
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padding=padd,
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groups=1,
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)
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- mu1_mu2
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)
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C1 = (0.01 * L) ** 2
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C2 = (0.03 * L) ** 2
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v1 = 2.0 * sigma12 + C2
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v2 = sigma1_sq + sigma2_sq + C2
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cs = torch.mean(v1 / v2) # contrast sensitivity
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ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
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ret = ssim_map.mean() if size_average else ssim_map.mean(1).mean(1).mean(1)
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if full:
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return ret, cs
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return ret
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def msssim(
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img1, img2, window_size=11, size_average=True, val_range=None, normalize=False
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):
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device = img1.device
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weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to(device)
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levels = weights.size()[0]
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mssim = []
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mcs = []
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for _ in range(levels):
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sim, cs = ssim(
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img1,
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img2,
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window_size=window_size,
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size_average=size_average,
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full=True,
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val_range=val_range,
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)
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mssim.append(sim)
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mcs.append(cs)
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img1 = F.avg_pool2d(img1, (2, 2))
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img2 = F.avg_pool2d(img2, (2, 2))
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mssim = torch.stack(mssim)
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mcs = torch.stack(mcs)
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# Normalize (to avoid NaNs during training unstable models, not compliant with original definition)
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if normalize:
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mssim = (mssim + 1) / 2
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mcs = (mcs + 1) / 2
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pow1 = mcs**weights
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pow2 = mssim**weights
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# From Matlab implementation https://ece.uwaterloo.ca/~z70wang/research/iwssim/
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output = torch.prod(pow1[:-1] * pow2[-1])
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return output
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class SSIM(torch.nn.Module):
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def __init__(self, window_size=11, size_average=True, val_range=None):
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super().__init__()
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self.window_size = window_size
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self.size_average = size_average
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self.val_range = val_range
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# Assume 3 channel for SSIM
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self.channel = 3
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self.window = create_window(window_size, channel=self.channel)
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def forward(self, img1, img2):
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(_, channel, _, _) = img1.size()
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if channel == self.channel and self.window.dtype == img1.dtype:
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window = self.window
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else:
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window = (
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create_window(self.window_size, channel)
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.to(img1.device)
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.type(img1.dtype)
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)
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self.window = window
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self.channel = channel
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_ssim = ssim(
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img1,
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img2,
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window=window,
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window_size=self.window_size,
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size_average=self.size_average,
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)
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dssim = (1 - _ssim) / 2
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return dssim
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class MSSSIM(torch.nn.Module):
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def __init__(self, window_size=11, size_average=True, channel=3):
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super().__init__()
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self.window_size = window_size
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self.size_average = size_average
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self.channel = channel
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def forward(self, img1, img2):
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return msssim(
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img1, img2, window_size=self.window_size, size_average=self.size_average
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)
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