def bench_edge_detection_dense(tacoBench, num, pt1):
loader = ImagePydataSparseTensorLoader()
bin_img1 = loader.dense_image(num, pt1, 1)
bin_img2 = loader.dense_image(num, pt1 + 0.05, 2)
def dense_bench():
xor_img = np.logical_xor(bin_img1, bin_img2).astype('int')
return xor_img
tacoBench(dense_bench)
def bench_edge_detection_fused_dense(tacoBench, num, pt1):
loader = ImagePydataSparseTensorLoader()
bin_img1 = loader.dense_image(num, pt1, 1)
bin_img2 = loader.dense_image(num, pt1 + 0.05, 2)
bin_window = loader.dense_window(num)
def dense_bench():
bi1 = np.logical_and(bin_img1, bin_window).astype('int')
bi2 = np.logical_and(bin_img2, bin_window).astype('int')
xor_img = np.logical_xor(bin_img1, bin_img2).astype('int')
return xor_img
tacoBench(dense_bench)
def bench_edge_detection_window_dense(tacoBench, num, pt1, window_size):
loader = ImagePydataSparseTensorLoader()
bin_img1 = loader.dense_image(num, pt1, 1)
bin_img2 = loader.dense_image(num, pt1 + 0.05, 2)
mid0 = int(bin_img1.shape[0]/2)
mid1 = int(bin_img1.shape[1]/2)
win_len0 = int(window_size * bin_img1.shape[0])
win_len1 = int(window_size * bin_img1.shape[1])
def dense_bench():
win1 = bin_img1[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
win2 = bin_img2[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
xor_img = np.logical_xor(win1, win2).astype('int')
return xor_img
tacoBench(dense_bench)
def bench_edge_detection_pydata(tacoBench, num, pt1, plot):
loader = ImagePydataSparseTensorLoader()
sparse_bin_img1 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1, 1))
sparse_bin_img2 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1+0.05, 2))
bin_img1 = loader.dense_image(num, pt1, 1)
bin_img2 = loader.dense_image(num, pt1 + 0.05, 2)
if plot:
print(sparse_bin_img1.shape)
print(sparse_bin_img2.shape)
def sparse_bench():
sparse_xor_img = np.logical_xor(sparse_bin_img1, sparse_bin_img2).astype('int')
return sparse_xor_img
def dense_bench():
xor_img = np.logical_xor(bin_img1, bin_img2).astype('int')
return xor_img
ret = tacoBench(sparse_bench)
sparse_xor_img = sparse_bench()
xor_img = dense_bench()
assert(sparse_xor_img.nnz == np.sum(xor_img != 0))
if plot:
num_elements = float(np.prod(bin_img1.shape))
print("Sparse xor NNZ = ", sparse_xor_img.nnz, "\t", "Dense xor NNZ = ", np.sum(xor_img != 0))
print("Sparsity img 1 ", np.sum(bin_img1 != 0) / num_elements)
print("Sparsity img 2 ", np.sum(bin_img2 != 0) / num_elements)
print("Sparsity xor ", np.sum(xor_img != 0) / num_elements)
sparse_xor_img = sparse_xor_img.todense()
t1 = round(loader.max[num]*pt1, 2)
t2 = round(loader.max[num]*(pt1 + 0.05), 2)
def bench_edge_detection_statistics(tacoBench, num, pt1):
loader = ImagePydataSparseTensorLoader()
sparse_bin_img1 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1, 1))
sparse_bin_img2 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1+0.05, 2))
sparse_bin_window = loader.sparse_window(num, 3)
print(sparse_bin_img1.shape)
print(sparse_bin_img2.shape)
extra_info = dict()
extra_info['nnz1'] = sparse_bin_img1.nnz
extra_info['nnz2'] = sparse_bin_img2.nnz
extra_info['nnz3'] = sparse_bin_window.nnz
extra_info['dimx'] = sparse_bin_window.shape[0]
extra_info['dimy'] = sparse_bin_window.shape[1]
def sparse_bench():
sbi1 = np.logical_and(sparse_bin_img1, sparse_bin_window)
sbi2 = np.logical_and(sparse_bin_img2, sparse_bin_window)
sparse_xor_img = np.logical_xor(sbi1, sbi2).astype('int')
return sparse_xor_img
tacoBench(sparse_bench, extra_info)
def bench_edge_detection_window_pydata(tacoBench, num, pt1, window_size, plot):
loader = ImagePydataSparseTensorLoader()
sparse_bin_img1 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1, 1))
sparse_bin_img2 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1+0.05, 2))
bin_img1 = loader.dense_image(num, pt1, 1)
bin_img2 = loader.dense_image(num, pt1 + 0.05, 2)
mid0 = int(bin_img1.shape[0]/2)
mid1 = int(bin_img1.shape[1]/2)
win_len0 = int(window_size * bin_img1.shape[0])
win_len1 = int(window_size * bin_img1.shape[1])
if plot:
print(sparse_bin_img1.shape)
print(sparse_bin_img2.shape)
def sparse_bench():
swin1 = sparse_bin_img1[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
swin2 = sparse_bin_img2[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
sparse_xor_img = np.logical_xor(swin1, swin2).astype('int')
return sparse_xor_img
def dense_bench():
win1 = bin_img1[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
win2 = bin_img2[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
xor_img = np.logical_xor(win1, win2).astype('int')
return xor_img
ret = tacoBench(sparse_bench)
sparse_xor_img = sparse_bench()
xor_img = dense_bench()
if plot:
print(sparse_xor_img)
print("sparse img1 nnz =", sparse_bin_img1.nnz, " ", np.sum(bin_img1 != 0))
print("sparse img2 nnz =", sparse_bin_img2.nnz, " ", np.sum(bin_img2 != 0))
num_elements = float(np.prod(bin_img1.shape))
print("Sparse xor NNZ = ", sparse_xor_img.nnz, "\t", "Dense xor NNZ = ", np.sum(xor_img != 0))
print("Sparsity img 1 ", np.sum(bin_img1 != 0) / num_elements)
print("Sparsity img 2 ", np.sum(bin_img2 != 0) / num_elements)
print("Sparsity xor ", np.sum(xor_img != 0) / num_elements)
sparse_xor_img = sparse_xor_img.todense()
t1 = round(loader.max[num]*pt1, 2)
t2 = round(loader.max[num]*(pt1 + 0.05), 2)
print(xor_img)
#plot_image(loader.img[num], bin_img1, bin_img2, xor_img, sparse_xor_img, t1, t2)
assert(sparse_xor_img.nnz == np.sum(xor_img != 0))
def bench_test_fused_pydata(tacoBench, num, pt1):
loader = ImagePydataSparseTensorLoader()
sparse_bin_img1 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1, 1))
sparse_bin_img2 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1+0.05, 2))
sparse_bin_window = loader.sparse_window(num, 3)
bin_img1 = loader.dense_image(num, pt1, 1)
bin_img2 = loader.dense_image(num, pt1 + 0.05, 2)
bin_window = loader.dense_window(num)
def sparse_bench():
return testOp(sparse_bin_img1, sparse_bin_img2, sparse_bin_window).astype('int')
def dense_bench():
return testOp(bin_img1, bin_img2, bin_window).astype('int')
ret = tacoBench(sparse_bench)
sparse_xor_img = sparse_bench()
xor_img = dense_bench()
# Write result to TNS file to see what's different
shape = xor_img.shape
result = sparse.COO.from_numpy(xor_img, fill_value=0)
dok = sparse.DOK(result)
TnsFileDumper().dump_dict_to_file(shape, dok.data, os.path.join("temp", "numpy-result-{}.tns".format(num)))
num_elements = float(np.prod(bin_img1.shape))
f = sparse_xor_img.fill_value
print("shape1", sparse_bin_img1.shape)
print("shape2", sparse_bin_img2.shape)
print("sparse img1 nnz =", sparse_bin_img1.nnz, " ", np.sum(bin_img1 != 0))
print("sparse img2 nnz =", sparse_bin_img2.nnz, " ", np.sum(bin_img2 != 0))
print("sparse win nnz =", sparse_bin_window.nnz, " ", np.sum(bin_window != 0))
print("Total num elements", num_elements)
print("Fill value", f)
print("Sparse xor NNF = ", sparse_xor_img.nnz, "\t", "Dense xor NNF = ", np.sum(xor_img != int(f)))
print("Dense xor NNZ = ", np.sum(xor_img != 0))
assert(sparse_xor_img.nnz == np.sum(xor_img != 1))