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Fix number of images in image.py
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numpy/image.py

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Original file line numberDiff line numberDiff line change
@@ -3,11 +3,11 @@
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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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10-
@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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58-
@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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170-
@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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