Update app.py

app.py

@@ -1,102 +1,102 @@
-import gradio as gr
-from options.test_options import TestOptions
-from data import create_dataset
-from models import create_model
-from PIL import Image
-import torchvision.transforms as transforms
-import torch
-import sys
-import matplotlib.pyplot as plt
-"python test.py --model test --name selfie2anime --dataroot selfie2anime/testB --num_test 100 --model_suffix '_B' --no_dropout"
-
-
-title = "MASFNet: Multi-scale Adaptive Sampling Fusion Network for Object Detection in Adverse Weather"
-description = ""
-article = ""
-
-def reset_interface():
-    return gr.update(value=None), gr.update(visible=False)
-
-def inference(img):
-    try:
-        # Debugging: Check if image is correctly received
-        if img is None:
-            print("No image received!")
-            return None
-        import sys
-        sys.argv = ['--model',  '--dataroot', '/home/data/luhaoxiang/wby/cyclegan/img/', '--num_test', '1',  '--no_dropout']
-
-        # Load options and set them up
-        opt = TestOptions().parse() 
-        opt.num_threads = 0   
-        opt.batch_size = 1   
-        opt.serial_batches = True 
-        opt.no_flip = True    
-        opt.display_id = -1   
-        opt.name = 'selfie2anime'
-        opt.model_suffix = '_B'
-        opt.num_test = 1
-        opt.no_dropout = True
-
-        # Create model and set it up
-        dataset = create_dataset(opt)
-        model = create_model(opt)     
-        model.setup(opt)     
-        if opt.eval:
-            model.eval()
-        # Convert PIL image to tensor
-        img_tensor = transforms.ToTensor()(img.convert('RGB')).unsqueeze(0)
-        img_tensor = img_tensor.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))  # Move to GPU if available
-        
-        # Prepare data for the model
-        data = {'A':img_tensor,'A_paths':'/home/data/luhaoxiang/wby/cyclegan/img/'}
-        model.set_input(data)
-        model.test()
-        
-        # Get the output visuals
-        img_out = model.get_current_visuals()
-        output_img_tensor = img_out.get('fake')
-        print(f'type of output_img_tensor: {type(img_out)}')
-        if output_img_tensor is None:
-            print("No output from model!")
-            return None
-        
-        if isinstance(output_img_tensor, torch.Tensor):
-            # 将张量转换回PIL图像
-            output_img = output_img_tensor.squeeze(0).cpu().detach().numpy().transpose(1, 2, 0)
-            output_img = (output_img * 0.5 + 0.5) * 255  # 假设输出在[-1, 1]之间标准化
-            output_img = output_img.astype('uint8')
-            output_img = Image.fromarray(output_img)
-            print(f'type if output_img_tensor: {type(output_img_tensor)}')
-            return output_img
-        else:
-            print(f"意外的输出类型: {type(output_img_tensor)}")
-            return None
-    
-    except Exception as e:
-        print(f"Error during inference: {e}")
-        return None
-
-example_images = [
-    "img/1.png"
-]
-
-with gr.Blocks() as demo:
-    gr.Markdown(f"### {title}")
-    gr.Markdown(description)
-    
-    with gr.Row():
-        with gr.Column():
-            img_input = gr.Image(type="pil", label="Upload an Image")
-            submit_btn = gr.Button("Submit")
-        with gr.Column():
-            output = gr.Image(type="pil", label="Prediction Result")
-
-    submit_btn.click(fn=inference, inputs=img_input, outputs=output)
-    demo.load(reset_interface, None, output)
-    gr.Examples(
-        examples=example_images,
-        inputs=img_input,
-    )
-    
-demo.launch()
+import gradio as gr
+from options.test_options import TestOptions
+from data import create_dataset
+from models import create_model
+from PIL import Image
+import torchvision.transforms as transforms
+import torch
+import sys
+import matplotlib.pyplot as plt
+"python test.py --model test --name selfie2anime --dataroot selfie2anime/testB --num_test 100 --model_suffix '_B' --no_dropout"
+
+
+title = "MASFNet: Multi-scale Adaptive Sampling Fusion Network for Object Detection in Adverse Weather"
+description = ""
+article = ""
+
+def reset_interface():
+    return gr.update(value=None), gr.update(visible=False)
+
+def inference(img):
+    try:
+        # Debugging: Check if image is correctly received
+        if img is None:
+            print("No image received!")
+            return None
+        import sys
+        sys.argv = ['--model',  '--dataroot', '/home/data/luhaoxiang/wby/cyclegan/img/', '--num_test', '1',  '--no_dropout']
+
+        # Load options and set them up
+        opt = TestOptions().parse() 
+        opt.num_threads = 0   
+        opt.batch_size = 1   
+        opt.serial_batches = True 
+        opt.no_flip = True    
+        opt.display_id = -1   
+        opt.name = 'selfie2anime'
+        opt.model_suffix = '_B'
+        opt.num_test = 1
+        opt.no_dropout = True
+
+        # Create model and set it up
+        dataset = create_dataset(opt)
+        model = create_model(opt)     
+        model.setup(opt)     
+        if opt.eval:
+            model.eval()
+        # Convert PIL image to tensor
+        img_tensor = transforms.ToTensor()(img.convert('RGB')).unsqueeze(0)
+        img_tensor = img_tensor.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))  # Move to GPU if available
+        
+        # Prepare data for the model
+        data = {'A':img_tensor,'A_paths':'/home/data/luhaoxiang/wby/cyclegan/img/'}
+        model.set_input(data)
+        model.test()
+        
+        # Get the output visuals
+        img_out = model.get_current_visuals()
+        output_img_tensor = img_out.get('fake')
+        print(f'type of output_img_tensor: {type(img_out)}')
+        if output_img_tensor is None:
+            print("No output from model!")
+            return None
+        
+        if isinstance(output_img_tensor, torch.Tensor):
+            # 将张量转换回PIL图像
+            output_img = output_img_tensor.squeeze(0).cpu().detach().numpy().transpose(1, 2, 0)
+            output_img = (output_img * 0.5 + 0.5) * 255  # 假设输出在[-1, 1]之间标准化
+            output_img = output_img.astype('uint8')
+            output_img = Image.fromarray(output_img)
+            print(f'type if output_img_tensor: {type(output_img_tensor)}')
+            return output_img
+        else:
+            print(f"意外的输出类型: {type(output_img_tensor)}")
+            return None
+    
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        return None
+
+example_images = [
+    "img/1.png"
+]
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"### {title}")
+    gr.Markdown(description)
+    
+    with gr.Row():
+        with gr.Column():
+            img_input = gr.Image(type="pil", label="Upload an Image")
+            submit_btn = gr.Button("Submit...")
+        with gr.Column():
+            output = gr.Image(type="pil", label="Prediction Result")
+
+    submit_btn.click(fn=inference, inputs=img_input, outputs=output)
+    demo.load(reset_interface, None, output)
+    gr.Examples(
+        examples=example_images,
+        inputs=img_input,
+    )
+    
+demo.launch()