add todo tasks

catdog_segmentation_model/model.py

@@ -9,19 +9,17 @@
 class CatDogUNet:
     def __init__(self):
         filename = "unet_model.ckpt"
-        if not os.path.exists(filename):
-            model_path = os.path.join(
-                "https://connectionsworkshop.blob.core.windows.net/pets", filename
-            )
-            r = requests.get(model_path)
-            with open(filename, "wb") as outfile:
-                outfile.write(r.content)
-        self.model = unet.CatDogUNet(num_classes=1)
+        ### TODO ###
+        ### Download the file containing the weights of the pre-trained model from url
+        ### if the file doesn't exist already locally, and write it to a file.
+        ### After that, create an instance of the model class.
+        ### The code to load the model weights from file and evaluate the model is
+        ### already provided.
         self.model.load_state_dict(torch.load(filename))
         self.model.eval()
 
     def predict(self, image):
-        # transform input image (as required by model)
+        # pre-process input image (as required by model)
         transform_input = transforms.Compose([transforms.Resize((192, 192)),])
         image = image.values
         image = image[:, :, 0:3]  # make sure we have only 3 channels
@@ -31,9 +29,8 @@ def predict(self, image):
         image = transform_input(image)
 
         # make prediction
-        prediction = self.model(image)
-        return prediction
+        ### TODO ###
+        ### Apply the model to the pre-processed input image.
+        ### Return the segmentation mask.
+        return "FIX ME"
 
-
-#  -------------
-# - We need to return an image with class labels

setup.py

@@ -12,7 +12,7 @@
     name="catdog_segmentation_model",
     version="0.0.1",
     description="scivision plugin, using a UNet to segment cat/dog images",
-    url="https://github.com/pwochner/catdog_segmentation_model",
+    url="TODO: INSERT URL OF THE MODEL GITHUB REPO HERE",
     packages=find_packages(),
     install_requires=requirements,
     python_requires=">=3.7",