format

script.py

@@ -12,7 +12,7 @@ import numpy as np
 
 # Import your model and anything else you want
 # You can even install other packages included in your repo
-# However, during the evaluation the container will not have access to the internet. 
+# However, during the evaluation the container will not have access to the internet.
 # So you must include everything you need in your model repo.
 
 
@@ -23,23 +23,21 @@ import torch
 def preprocess_v1(file_like):
     file_like.seek(0)
     decoder = VideoDecoder(file_like)
-    frames = decoder[0 : -1 : 20]
+    frames = decoder[0:-1:20]
     frames = frames.float() / 255.0
     return frames
 
 
-
-
 def preprocess(file_like):
     # Open the video file
     file_like.seek(0)
     container = av.open(file_like)
     frames = []
     for frame in container.decode(video=0):
-        frame_array = frame.to_ndarray(format='rgb24')
+        frame_array = frame.to_ndarray(format="rgb24")
         frame_tensor = torch.from_numpy(frame_array).permute(2, 0, 1).float()
         frames.append(frame_tensor)
-    
+
     video_tensor = torch.stack(frames)
     return video_tensor
 
@@ -48,16 +46,16 @@ class Model(torch.nn.Module):
     def __init__(self):
         super(Model, self).__init__()
         self.fc1 = torch.nn.Linear(10, 5)
-        self.threshold = 0.
+        self.threshold = 0.0
 
     def forward(self, x):
-       ## generates a random float the same size as x
-       return torch.randn(x.shape[0]).to(x.device)
+        ## generates a random float the same size as x
+        return torch.randn(x.shape[0]).to(x.device)
 
 
 # load the dataset. dataset will be automatically downloaded to /tmp/data during evaluation
 DATASET_PATH = "/tmp/data"
-dataset_remote = load_dataset(DATASET_PATH, split = "test", streaming = True)
+dataset_remote = load_dataset(DATASET_PATH, split="test", streaming=True)
 
 
 # load your model
@@ -92,11 +90,11 @@ for el in tqdm.tqdm(dataset_remote):
         # append your prediction
         # "id" and "pred" are required. "score" will not be used in scoring but we encourage you to include it. We'll use it for analysis of the results
 
-        out.append(dict(id = el["id"], pred = pred, score = score))
+        out.append(dict(id=el["id"], pred=pred, score=score))
     except Exception as e:
         print(e)
         print("failed", el["id"])
-        out.append(dict(id = el["id"]))
+        out.append(dict(id=el["id"]))
 
 # save the final result and that's it
-pd.DataFrame(out).to_csv("submission.csv",index = False)
+pd.DataFrame(out).to_csv("submission.csv", index=False)