remove test

convert_siglip_to_hf.py

@@ -260,23 +260,6 @@ def convert_siglip_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=
     #
     # pixel_values = processor(image, return_tensors="pt").pixel_values
 
-    filepath = hf_hub_download(repo_id="nielsr/test-image", filename="pixel_values_siglip.npy", repo_type="dataset")
-    pixel_values = np.load(filepath)
-    pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2)
-    filepath = hf_hub_download(repo_id="nielsr/test-image", filename="input_ids_siglip.npy", repo_type="dataset")
-    input_ids = np.load(filepath)
-    input_ids = torch.from_numpy(input_ids)
-
-    with torch.no_grad():
-        outputs = model(input_ids=input_ids, pixel_values=pixel_values)
-
-    # assert values
-    expected_slice = torch.tensor(
-        [[-2.9621, -2.1672, -1.7837], [-0.2713, 0.2910, -10.6595], [-13.6617, -13.1611, -17.4408]]
-    )
-    assert torch.allclose(outputs.logits_per_image[:3, :3], expected_slice, atol=1e-4)
-    print("Looks ok!")
-
     if pytorch_dump_folder_path is not None:
         Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
         print(f"Saving model {model_name} to {pytorch_dump_folder_path}")