Spaces:
Runtime error
Runtime error
File size: 2,688 Bytes
d380b77 |
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 |
import numpy as np
import torch
import torch.nn.functional as F
class SSIM(torch.nn.Module):
"""SSIM. Modified from:
https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
"""
def __init__(self, window_size=11, size_average=True):
super().__init__()
self.window_size = window_size
self.size_average = size_average
self.channel = 1
self.register_buffer('window', self._create_window(window_size, self.channel))
def forward(self, img1, img2):
assert len(img1.shape) == 4
channel = img1.size()[1]
if channel == self.channel and self.window.data.type() == img1.data.type():
window = self.window
else:
window = self._create_window(self.window_size, channel)
# window = window.to(img1.get_device())
window = window.type_as(img1)
self.window = window
self.channel = channel
return self._ssim(img1, img2, window, self.window_size, channel, self.size_average)
def _gaussian(self, window_size, sigma):
gauss = torch.Tensor([
np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)
])
return gauss / gauss.sum()
def _create_window(self, window_size, channel):
_1D_window = self._gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
return _2D_window.expand(channel, 1, window_size, window_size).contiguous()
def _ssim(self, 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()
return ssim_map.mean(1).mean(1).mean(1)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
return
|