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import os
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import pytest
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import sparse
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- from util import ImagePydataSparseTensorLoader , safeCastPydataTensorToInts , TnsFileDumper , plot_image
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+ from util import ImagePydataSparseTensorLoader , safeCastPydataTensorToInts , TnsFileDumper # , plot_image
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# import matplotlib.pyplot as plt
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- @pytest .mark .parametrize ("num" , list (range (1 , 311 )))
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+ @pytest .mark .parametrize ("num" , list (range (1 , 253 )))
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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def bench_edge_detection_pydata (tacoBench , num , pt1 , plot ):
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loader = ImagePydataSparseTensorLoader ()
@@ -43,7 +43,7 @@ def dense_bench():
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t2 = round (loader .max [num ]* (pt1 + 0.05 ), 2 )
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#plot_image(loader.img[num], bin_img1, bin_img2, xor_img, sparse_xor_img, t1, t2)
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- @pytest .mark .parametrize ("num" , list (range (1 , 311 )))
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+ @pytest .mark .parametrize ("num" , list (range (1 , 253 )))
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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def bench_edge_detection_dense (tacoBench , num , pt1 ):
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loader = ImagePydataSparseTensorLoader ()
@@ -55,7 +55,7 @@ def dense_bench():
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return xor_img
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tacoBench (dense_bench )
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- @pytest .mark .parametrize ("num" , list (range (1 , 311 )))
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+ @pytest .mark .parametrize ("num" , list (range (1 , 253 )))
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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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loader = ImagePydataSparseTensorLoader ()
@@ -98,7 +98,7 @@ def dense_bench():
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assert (sparse_xor_img .nnz == np .sum (xor_img != 0 ))
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- @pytest .mark .parametrize ("num" , list (range (1 , 311 )))
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+ @pytest .mark .parametrize ("num" , list (range (1 , 253 )))
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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def bench_edge_detection_fused_dense (tacoBench , num , pt1 ):
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loader = ImagePydataSparseTensorLoader ()
@@ -114,7 +114,7 @@ def dense_bench():
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tacoBench (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 , 311 )))
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+ @pytest .mark .parametrize ("num" , list (range (1 , 253 )))
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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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 ):
@@ -167,7 +167,7 @@ def dense_bench():
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assert (sparse_xor_img .nnz == np .sum (xor_img != 0 ))
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- @pytest .mark .parametrize ("num" , list (range (1 , 311 )))
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+ @pytest .mark .parametrize ("num" , list (range (1 , 253 )))
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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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 ):
@@ -236,7 +236,7 @@ def dense_bench():
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assert (sparse_xor_img .nnz == np .sum (xor_img != 1 ))
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@pytest .mark .skip (reason = "for getting the input matrices statistics only" )
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- @pytest .mark .parametrize ("num" , list (range (1 , 311 )))
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+ @pytest .mark .parametrize ("num" , list (range (1 , 253 )))
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@pytest .mark .parametrize ("pt1" , [0.75 ])
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def bench_edge_detection_statistics (tacoBench , num , pt1 ):
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loader = ImagePydataSparseTensorLoader ()
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