update

app.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import albumentations as A
+from albumentations.pytorch.transforms import ToTensorV2
+from timm import create_model
+import torch
+import gradio as gr
+
+
+# In[ ]:
+
+
+class TestDataset(torch.utils.data.Dataset):
+    def __init__(self,image,transforms = None):
+        self.image = [image]
+        self.transforms = transforms
+    
+    def __getitem__(self,idx):
+        image = self.image[idx]
+        if self.transforms: 
+            augmented  = self.transforms(image=image)
+            image      = augmented["image"]
+        return {'image':image}
+    def __len__(self):
+        return len(self.image)
+
+def get_test_transform():
+    MEAN = [0.5176, 0.4169, 0.3637]
+    STD  = [0.3010, 0.2723, 0.2672]
+    return A.Compose([
+              #A.resize((256,256)),
+              A.Normalize(MEAN,STD),
+              ToTensorV2(transpose_mask=False,p=1.0)
+            ])
+
+
+# In[ ]:
+
+
+def predict_image(image):
+    test_dataset = TestDataset(image,transforms = get_test_transform())
+    test_loader  = torch.utils.data.DataLoader(test_dataset,
+                                               batch_size  = 1,
+                                               pin_memory  = False,
+                                               num_workers = 8,
+                                               shuffle     = False)
+    # Loading weights
+    for data in test_loader:
+        for key,value in data.items():
+            data[key] = value.to('cpu')
+        # Appending Output and Targets:
+        output = torch.sigmoid(model(data['image'])).cpu().detach().numpy()
+    dict_ = {'Down':float(1-output[0][0]),'Upside':float(output[0][0])}
+    return dict_
+
+
+# In[ ]:
+
+
+model = create_model('resnet18',pretrained = False,num_classes = 1)
+checkpoint = torch.load('model.pt',map_location = 'cpu')
+model.load_state_dict(checkpoint,strict = False)
+
+
+# In[ ]:
+
+
+title = "Upside-Down Detector"
+interpretation='default'
+enable_queue=True
+gr.Interface(fn=predict_image,inputs=gr.inputs.Image(shape=(256, 256)),outputs=gr.outputs.Label(num_top_classes=2),title=title,interpretation=interpretation).launch(share = True)
+

requirements.txt

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+albumentations
+timm