@@ -116,7 +116,7 @@ def dense_bench():
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#TODO: Add in a benchmark that uses windowing for medical imaging as well.
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@pytest .mark .parametrize ("num" , list (range (1 , 99 )))
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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- @pytest .mark .parametrize ("window_size" , [0.25 , 0.2 , 0.15 , 0.1 ])
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+ @pytest .mark .parametrize ("window_size" , [0.45 , 0.4 , 0.35 , 0.3 ])
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def bench_edge_detection_window_pydata (tacoBench , num , pt1 , window_size , plot ):
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loader = ImagePydataSparseTensorLoader ()
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sparse_bin_img1 = safeCastPydataTensorToInts (loader .sparse_image (num , pt1 , 1 ))
@@ -169,7 +169,7 @@ def dense_bench():
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@pytest .mark .parametrize ("num" , list (range (1 , 99 )))
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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- @pytest .mark .parametrize ("window_size" , [0.25 , 0.2 , 0.15 , 0.1 ])
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+ @pytest .mark .parametrize ("window_size" , [0.45 , 0.4 , 0.35 , 0.3 ])
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def bench_edge_detection_window_dense (tacoBench , num , pt1 , window_size ):
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loader = ImagePydataSparseTensorLoader ()
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bin_img1 = loader .dense_image (num , pt1 , 1 )
@@ -264,7 +264,7 @@ def sparse_bench():
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@pytest .mark .skip (reasoun = "For image generation only" )
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@pytest .mark .parametrize ("num" , [42 , 44 , 50 , 63 , 92 ])
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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- def bench_edge_detection_fused_pydata (tacoBench , num , pt1 , plot ):
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+ def bench_edge_detection_plot (tacoBench , num , pt1 , plot ):
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loader = ImagePydataSparseTensorLoader ()
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sparse_bin_img1 = safeCastPydataTensorToInts (loader .sparse_image (num , pt1 , 1 ))
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sparse_bin_img2 = safeCastPydataTensorToInts (loader .sparse_image (num , pt1 + 0.05 , 2 ))
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