Add application file

app.py

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+import gradio as gr
+from torchvision import transforms
+import torch
+import torch.nn as nn
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(device)
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels):
+        super(ResidualBlock, self).__init__()
+        self.block = nn.Sequential(
+            nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1, bias=False),
+            nn.InstanceNorm2d(channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1, bias=False),
+            nn.InstanceNorm2d(channels)
+        )
+    
+    def forward(self, x):
+        return x + self.block(x)
+
+# 強化版生成器：利用下採樣、殘差塊和上採樣結構
+class StrongGenerator(nn.Module):
+    def __init__(self, num_residual_blocks=6):
+        super(StrongGenerator, self).__init__()
+        # 初始卷積層
+        model = [
+            nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3, bias=False),
+            nn.InstanceNorm2d(64),
+            nn.ReLU(inplace=True)
+        ]
+        
+        # 下採樣：連續兩次卷積降維
+        in_channels = 64
+        for _ in range(2):
+            out_channels = in_channels * 2
+            model += [
+                nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False),
+                nn.InstanceNorm2d(out_channels),
+                nn.ReLU(inplace=True)
+            ]
+            in_channels = out_channels
+        
+        # 多個殘差塊
+        for _ in range(num_residual_blocks):
+            model += [ResidualBlock(in_channels)]
+        
+        # 上採樣：連續兩次反捲積提升解析度
+        for _ in range(2):
+            out_channels = in_channels // 2
+            model += [
+                nn.ConvTranspose2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
+                nn.InstanceNorm2d(out_channels),
+                nn.ReLU(inplace=True)
+            ]
+            in_channels = out_channels
+        
+        # 輸出層
+        model += [
+            nn.Conv2d(in_channels, 3, kernel_size=7, stride=1, padding=3),
+            nn.Tanh()
+        ]
+        
+        self.model = nn.Sequential(*model)
+    
+    def forward(self, x):
+        return self.model(x)
+
+generator = StrongGenerator().to(device)
+
+
+# 載入訓練好的 Generator 模型（此處以第 10 個 epoch 為例，請根據實際情況修改）
+generator.load_state_dict(torch.load("./generator_epoch_10.pth", map_location=device))
+generator.eval()
+
+def restore_image(mosaic_image):
+    # 與訓練時相同的圖像轉換
+    transform_in = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
+    ])
+    input_tensor = transform_in(mosaic_image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        restored_tensor = generator(input_tensor)
+    restored_tensor = restored_tensor.squeeze(0).cpu()
+    restored_tensor = (restored_tensor * 0.5 + 0.5).clamp(0, 1)
+    restored_image = transforms.ToPILImage()(restored_tensor)
+    return restored_image
+
+iface = gr.Interface(
+    fn=restore_image,
+    inputs=gr.Image(type="pil"),
+    outputs="image",
+    title="Dog Image Mosaic Restoration",
+    description="上傳打碼後的狗狗圖像，模型將嘗試還原原始圖像。"
+)
+iface.launch()