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AOD-Net_最好加入后处理/AOD-Net with PONO/model.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class AODnet(nn.Module):
+    def __init__(self):
+        super(AODnet, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=1, stride=1, padding=0)
+        self.conv2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(in_channels=6, out_channels=3, kernel_size=5, stride=1, padding=2)
+        self.conv4 = nn.Conv2d(in_channels=6, out_channels=3, kernel_size=7, stride=1, padding=3)
+        self.conv5 = nn.Conv2d(in_channels=12, out_channels=3, kernel_size=3, stride=1, padding=1)
+        self.b = 1
+
+    def forward(self, x):
+        x1 = F.relu(self.conv1(x))
+        x2 = F.relu(self.conv2(x1))
+        cat1 = torch.cat((x1, x2), 1)
+        x3 = F.relu(self.conv3(cat1))
+        cat2 = torch.cat((x2, x3), 1)
+        x4 = F.relu(self.conv4(cat2))
+        cat3 = torch.cat((x1, x2, x3, x4), 1)
+        k = F.relu(self.conv5(cat3))
+
+        if k.size() != x.size():
+            raise Exception("k, haze image are different size!")
+
+        output = k * x - k + self.b
+        return F.relu(output)
+
+class AOD_pono_net(nn.Module):
+    def __init__(self):
+        super(AOD_pono_net, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=1, stride=1, padding=0)
+        self.conv2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(in_channels=6, out_channels=3, kernel_size=5, stride=1, padding=2)
+        self.conv4 = nn.Conv2d(in_channels=6, out_channels=3, kernel_size=7, stride=1, padding=3)
+        self.conv5 = nn.Conv2d(in_channels=12, out_channels=3, kernel_size=3, stride=1, padding=1)
+        self.b = 1
+
+        self.pono = PONO(affine=False)
+        self.ms = MS()
+
+    def forward(self, x):
+        x1 = F.relu(self.conv1(x))
+        x2 = F.relu(self.conv2(x1))
+        cat1 = torch.cat((x1, x2), 1)
+        x1, mean1, std1 = self.pono(x1)
+        x2, mean2, std2 = self.pono(x2)
+        x3 = F.relu(self.conv3(cat1))
+        cat2 = torch.cat((x2, x3), 1)
+        x3 = self.ms(x3, mean1, std1)
+        x4 = F.relu(self.conv4(cat2))
+        x4 = self.ms(x4, mean2, std2)
+        cat3 = torch.cat((x1, x2, x3, x4), 1)
+        k = F.relu(self.conv5(cat3))
+
+        if k.size() != x.size():
+            raise Exception("k, haze image are different size!")
+
+        output = k * x - k + self.b
+        return F.relu(output)
+
+class PONO(nn.Module):
+    def __init__(self, input_size=None, return_stats=False, affine=True, eps=1e-5):
+        super(PONO, self).__init__()
+        self.return_stats = return_stats
+        self.input_size = input_size
+        self.eps = eps
+        self.affine = affine
+
+        if affine:
+            self.beta = nn.Parameter(torch.zeros(1, 1, *input_size))
+            self.gamma = nn.Parameter(torch.ones(1, 1, *input_size))
+        else:
+            self.beta, self.gamma = None, None
+
+    def forward(self, x):
+        mean = x.mean(dim=1, keepdim=True)
+        std = (x.var(dim=1, keepdim=True) + self.eps).sqrt()
+        x = (x - mean) / std
+        if self.affine:
+            x = x * self.gamma + self.beta
+        return x, mean, std
+
+class MS(nn.Module):
+    def __init__(self, beta=None, gamma=None):
+        super(MS, self).__init__()
+        self.gamma, self.beta = gamma, beta
+
+    def forward(self, x, beta=None, gamma=None):
+        beta = self.beta if beta is None else beta
+        gamma = self.gamma if gamma is None else gamma
+        if gamma is not None:
+            x.mul_(gamma)
+        if beta is not None:
+            x.add_(beta)
+        return x