Upload 6 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+gradio_check1.png filter=lfs diff=lfs merge=lfs -text
+gradio_check13.png filter=lfs diff=lfs merge=lfs -text

dehazing_gen.py

@@ -0,0 +1,86 @@
+import torch
+import torch.nn as nn
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, downsample: bool = True, use_act: bool = True,
+                 use_dropout: bool = False, **kwargs):
+        super(ConvBlock, self).__init__()
+
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, padding_mode="reflect", **kwargs)
+
+            if downsample
+            else nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, **kwargs),
+
+            nn.InstanceNorm2d(num_features=out_channels),
+            nn.ReLU(inplace=True) if use_act else nn.Identity()
+        )
+        if use_dropout:
+            self.conv_block = nn.Sequential(self.conv_block, nn.Dropout(p=0.5))
+
+    def forward(self, x):
+        return self.conv_block(x)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, features: int):
+        super(ResidualBlock, self).__init__()
+        self.residual_block = nn.Sequential(
+            ConvBlock(in_channels=features, out_channels=features, kernel_size=3, padding=1),
+            ConvBlock(in_channels=features, out_channels=features, kernel_size=3, padding=1, use_act=False),
+        )
+
+    def forward(self, x):
+        return x + self.residual_block(x)
+
+
+class CycleGenerator(nn.Module):
+    def __init__(self, img_channels: int = 3, latent_dim: int = 64, num_residuals: int = 9):
+        super(CycleGenerator, self).__init__()
+
+        self.base = nn.Sequential(
+            nn.Conv2d(in_channels=img_channels, out_channels=latent_dim, kernel_size=7, stride=1, padding=3,
+                      padding_mode="reflect"),
+            nn.ReLU(inplace=True)
+        )
+
+        self.down_blocks = nn.ModuleList(
+            [
+                ConvBlock(in_channels=latent_dim, out_channels=latent_dim * 2, kernel_size=3, stride=2, padding=1),
+                ConvBlock(in_channels=latent_dim * 2, out_channels=latent_dim * 4, kernel_size=3, stride=2, padding=1),
+            ]
+        )
+
+        self.residual_blocks = nn.Sequential(
+            *[ResidualBlock(features=latent_dim * 4) for _ in range(num_residuals)]
+        )
+
+        self.up_blocks = nn.ModuleList(
+            [
+                ConvBlock(in_channels=latent_dim * 4, out_channels=latent_dim * 2, kernel_size=3, stride=2, padding=1,
+                          output_padding=1,
+                          downsample=False),
+                ConvBlock(in_channels=latent_dim * 2, out_channels=latent_dim, kernel_size=3, stride=2, padding=1,
+                          output_padding=1,
+                          downsample=False),
+            ]
+        )
+
+        self.head = nn.Conv2d(in_channels=latent_dim, out_channels=img_channels, kernel_size=7, stride=1, padding=3,
+                              padding_mode="reflect")
+
+    def forward(self, x):
+        x = self.base(x)
+
+        for layer in self.down_blocks:
+            x = layer(x)
+
+        x = self.residual_blocks(x)
+
+        for layer in self.up_blocks:
+            x = layer(x)
+
+        x = self.head(x)
+
+        return torch.tanh(x)

genC.pth.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83c998de8c48e350a93fd204db7bf56739b7fd1e068a8bc73e80baf326c3ea30
+size 156833914