Create app.py

app.py

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+import torch
+import gradio as gr
+from PIL import Image
+from torchvision import transforms
+from statistics import mean
+Image.MAX_IMAGE_PIXELS = None
+
+def read_img_file(f):
+    img = Image.open(f)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    return img
+
+_transform_test_random=transforms.Compose([
+        transforms.RandomCrop((256,256)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
+
+_transform_test_random_vit = transforms.Compose([
+        transforms.RandomCrop((252,252)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
+
+def detect(img, model_choices):
+    if model_choices == "EVA-02 ViT L/14":
+        model = torch.load("./model_eva.pth",map_location="cpu").cpu().eval()
+        _transform = _transform_test_random_vit
+    elif model_choices == "ConvNext Large":
+        model = torch.load("./model_convnext.pth",map_location="cpu").cpu().eval()
+        _transform = _transform_test_random
+    elif model_choices == "EfficientNet-V2 B0":
+        model = torch.load("./model_effnet.pth",map_location="cpu").cpu().eval()
+        _transform = _transform_test_random
+
+    output = ""
+    with torch.inference_mode():
+        tmp=[]
+        for _ in range(10):
+            img_random_crop = _transform(img)
+            outputs = model.forward(img_random_crop.unsqueeze(0))
+            outputs = torch.sigmoid(outputs).cpu().numpy()
+            tmp.append(outputs[0][0])
+        output+=f"{str(tmp)}\n"
+        output+=f"10 try method: {mean(tmp)}\n"
+        # print(tmp)
+        # print("10 try method: ", mean(tmp))
+
+    with torch.inference_mode():
+        img_crop = _transform(img)
+        outputs = model.forward(img_crop.unsqueeze(0))
+        outputs = torch.sigmoid(outputs).cpu().numpy()
+        output+=f"1 try method: {outputs}\n"
+        # print("1 try method: ",outputs)
+    return output
+
+model_choices = ["ConvNext Large", "EVA-02 ViT L/14", "EfficientNet-V2 B0"]
+
+demo = gr.Interface(fn=detect, 
+        inputs=[gr.Image(type="pil", label="Input Image"),
+                 gr.Radio(
+                    model_choices,
+                    type="value",
+                    value="EVA-02 ViT L/14",
+                    label="Choose Detector Model",
+                )], outputs="text")
+demo.launch()