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Unverified Commit 1d719ca3 authored by Scott Cyphers's avatar Scott Cyphers Committed by GitHub
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Make convolution test generator work with python3 (#2633) 

Also fix formatting
parent 56f673bb
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Showing with 150 additions and 90 deletions
test/ref_generators/generate_convolution_ref.py
View file @ 1d719ca3
......@@ -22,7 +22,9 @@ import random
from operator import mul
# Generates an array of random floating point literals of the given length, from a fixed seed.
def random_array_float_literals(length,seed=8086):
def random_array_float_literals(length, seed=8086):
literals = []
random.seed(seed)
......@@ -35,23 +37,27 @@ def random_array_float_literals(length,seed=8086):
return literals
# Elementwise addition on tuples.
def tuple_plus(t1,t2):
def tuple_plus(t1, t2):
assert(len(t1) == len(t2))
res = ()
for (x,y) in zip(list(t1),list(t2)):
for (x, y) in zip(list(t1), list(t2)):
res = res + (x+y,)
return res
# Elementwise multiplication on tuples.
def tuple_times(t1,t2):
def tuple_times(t1, t2):
assert(len(t1) == len(t2))
res = ()
for (x,y) in zip(list(t1),list(t2)):
for (x, y) in zip(list(t1), list(t2)):
res = res + (x*y,)
return res
......@@ -74,6 +80,8 @@ def tuple_times(t1,t2):
# Where the D's are computed according to TensorFlow-style "valid" convolution rules, but *after* padding.
# See https://www.tensorflow.org/api_docs/python/tf/nn/convolution.
#
def convolution_ref(data_batch, filter, move_strides, filter_dilation, below_pads, above_pads, data_dilation):
assert(len(data_batch.shape) == len(filter.shape))
assert(len(data_batch.shape) > 2)
......@@ -85,33 +93,41 @@ def convolution_ref(data_batch, filter, move_strides, filter_dilation, below_pad
# dilate the input batch
new_item_shape = (np.array(data_batch.shape[2:]) - 1) * data_dilation + 1
new_data_batch_shape = list(np.array(data_batch.shape[:2])) + list(new_item_shape)
new_data_batch_shape = list(
np.array(data_batch.shape[:2])) + list(new_item_shape)
new_data_batch = np.zeros(new_data_batch_shape)
for n in range(0, new_data_batch_shape[0]) :
for c in range(0, new_data_batch_shape[1]) :
for n in range(0, new_data_batch_shape[0]):
for c in range(0, new_data_batch_shape[1]):
if new_data_batch.ndim == 3:
new_data_batch[n, c, 0::data_dilation[0]] = data_batch[n][c]
elif new_data_batch.ndim == 4:
new_data_batch[n, c, 0::data_dilation[0], 0::data_dilation[1]] = data_batch[n][c]
new_data_batch[n, c, 0::data_dilation[0],
0::data_dilation[1]] = data_batch[n][c]
elif new_data_batch.ndim == 5:
new_data_batch[n, c, 0::data_dilation[0], 0::data_dilation[1], 0::data_dilation[2]] = data_batch[n][c]
new_data_batch[n, c, 0::data_dilation[0],
0::data_dilation[1], 0::data_dilation[2]] = data_batch[n][c]
elif new_data_batch.ndim == 6:
new_data_batch[n, c, 0::data_dilation[0], 0::data_dilation[1], 0::data_dilation[2], 0::data_dilation[3]] = data_batch[n][c]
new_data_batch[n, c, 0::data_dilation[0], 0::data_dilation[1],
0::data_dilation[2], 0::data_dilation[3]] = data_batch[n][c]
else:
assert(False)
data_batch = new_data_batch
# Pad the input batch wherever the pads are positive.
below_pads_pos = (0,0) + tuple(np.clip(below_pads,0,None)) # Have to add values for the spatial and channel dims.
above_pads_pos = (0,0) + tuple(np.clip(above_pads,0,None)) # Have to add values for the spatial and channel dims.
data_batch = np.pad(data_batch, zip(below_pads_pos,above_pads_pos), mode='constant', constant_values=0)
# Have to add values for the spatial and channel dims.
below_pads_pos = (0, 0) + tuple(np.clip(below_pads, 0, None))
# Have to add values for the spatial and channel dims.
above_pads_pos = (0, 0) + tuple(np.clip(above_pads, 0, None))
data_batch = np.pad(data_batch, list(
zip(below_pads_pos, above_pads_pos)), mode='constant', constant_values=0)
# Slice the input batch wherever the pads are negative.
slice_bottoms = (0,0) + tuple (-np.clip(below_pads,None,0))
slice_tops = (0,0) + tuple (np.clip(above_pads,None,0))
slices = map(lambda p: slice(p[0],p[1] if p[1] < 0 else None),zip(slice_bottoms,slice_tops))
slice_bottoms = (0, 0) + tuple(-np.clip(below_pads, None, 0))
slice_tops = (0, 0) + tuple(np.clip(above_pads, None, 0))
slices = list(map(lambda p: slice(
p[0], p[1] if p[1] < 0 else None), zip(slice_bottoms, slice_tops)))
data_batch = data_batch[slices]
item_count = data_batch.shape[0] # N
......@@ -122,18 +138,20 @@ def convolution_ref(data_batch, filter, move_strides, filter_dilation, below_pad
# This is not used in computation but we will calculate it for a check to make sure the window fits.
window_physical_shape = []
for (d_in,d_virt,dil) in zip(input_item_shape,window_virtual_shape,filter_dilation):
for (d_in, d_virt, dil) in zip(input_item_shape, window_virtual_shape, filter_dilation):
d_phys = (d_virt - 1) * dil + 1
assert(d_phys <= input_item_shape)
assert(d_phys <= d_in)
window_physical_shape.append(d_phys)
output_item_shape = [] # D'1,...,D'n
for (d_in,d_win,dil,mov) in zip (input_item_shape,window_virtual_shape,filter_dilation,move_strides):
d_out = int(math.ceil((float(d_in) - (float(d_win) - 1.0) * float(dil))/float(mov))) # Formula is taken from TF's definition for VALID convolution.
for (d_in, d_win, dil, mov) in zip(input_item_shape, window_virtual_shape, filter_dilation, move_strides):
# Formula is taken from TF's definition for VALID convolution.
d_out = int(
math.ceil((float(d_in) - (float(d_win) - 1.0) * float(dil))/float(mov)))
assert(d_out > 0)
output_item_shape.append(d_out)
output_shape = [item_count,co_count]+output_item_shape # N,Co,D'1,...,D'n
output_shape = [item_count, co_count]+output_item_shape # N,Co,D'1,...,D'n
output_batch = np.zeros(output_shape)
# Walk over the output batch space.
......@@ -151,10 +169,12 @@ def convolution_ref(data_batch, filter, move_strides, filter_dilation, below_pad
ci, filter_pos = filter_index[0], filter_index[1:]
# Build up the coordinate within the space N,Ci,D1,...,Dn that we need to read from in the input batch.
input_index = (item,ci) + (tuple_plus(tuple_times(output_pos,move_strides),tuple_times(filter_pos,filter_dilation)))
input_index = (item, ci) + (tuple_plus(tuple_times(output_pos,
move_strides), tuple_times(filter_pos, filter_dilation)))
# Add to the sum-of-products.
output_batch[output_index] = output_batch[output_index] + filter[(co,) + filter_index] * data_batch[input_index]
output_batch[output_index] = output_batch[output_index] + \
filter[(co,) + filter_index] * data_batch[input_index]
filter_it.iternext()
......@@ -162,6 +182,7 @@ def convolution_ref(data_batch, filter, move_strides, filter_dilation, below_pad
return output_batch
def shape_str(shape):
result = ''
first = True
......@@ -173,6 +194,7 @@ def shape_str(shape):
result = result + (',%d' % d)
return result
def scalar_str(x):
result = ('%.1000g' % x)
# This next part is a bit stupid.
......@@ -182,6 +204,7 @@ def scalar_str(x):
result = "%.8ff" % float(result)
return result
def data_str(data):
result = ''
first = True
......@@ -193,20 +216,31 @@ def data_str(data):
result = result + ',' + scalar_str(x)
return result
def emit_test(t,f):
test_name, input_batch_shape, filters_shape, move_strides, filter_dilation, below_pads, above_pads, data_dilation, bprop = t
input_batch_literals = random_array_float_literals(reduce(mul,input_batch_shape))
filters_literals = random_array_float_literals(reduce(mul, filters_shape))
def shape_size(shape):
result = 1
for l in shape:
result = result * l
return result
input_batch_array = np.array(map(lambda s: np.float32(s),input_batch_literals))
def emit_test(t, f):
test_name, input_batch_shape, filters_shape, move_strides, filter_dilation, below_pads, above_pads, data_dilation, bprop = t
input_batch_literals = random_array_float_literals(
shape_size(input_batch_shape))
filters_literals = random_array_float_literals(shape_size(filters_shape))
input_batch_array = np.array(
list(map(lambda s: np.float32(s), input_batch_literals)))
input_batch_array.shape = input_batch_shape
filters_array = np.array(map(lambda s: np.float32(s),filters_literals))
filters_array = np.array(
list(map(lambda s: np.float32(s), filters_literals)))
filters_array.shape = filters_shape
print ("Generating convolution test '%s'..." % test_name)
print("Generating convolution test '%s'..." % test_name)
output_batch_data = convolution_ref(input_batch_array,filters_array,move_strides,filter_dilation,below_pads,above_pads,data_dilation)
output_batch_data = convolution_ref(
input_batch_array, filters_array, move_strides, filter_dilation, below_pads, above_pads, data_dilation)
template = '''
// !!!!!!!!!!!!!! THIS FILE IS AUTOGENERATED OUTSIDE OF THE BUILD PROCESS !!!!!!!!!!!!!!
......@@ -253,70 +287,95 @@ NGRAPH_TEST (${BACKEND_NAME}, %s)
%sEXPECT_TRUE(autodiff_numeric_compare<float>(backend.get(), make_graph, {a, b}, .01f, .01f));
}
'''
f.write (template % (test_name,
shape_str(input_batch_shape),
shape_str(filters_shape),
shape_str(output_batch_data.shape),
shape_str(move_strides),
shape_str(filter_dilation),
shape_str(below_pads),
shape_str(above_pads),
shape_str(data_dilation),
",".join(map(lambda s: "%.8ff" % float(s),input_batch_literals)),
",".join(map(lambda s: "%.8ff" % float(s),filters_literals)),
data_str(output_batch_data),
bprop));
f.write(template % (test_name,
shape_str(input_batch_shape),
shape_str(filters_shape),
shape_str(output_batch_data.shape),
shape_str(move_strides),
shape_str(filter_dilation),
shape_str(below_pads),
shape_str(above_pads),
shape_str(data_dilation),
",".join(map(lambda s: "%.8ff" %
float(s), input_batch_literals)),
",".join(map(lambda s: "%.8ff" %
float(s), filters_literals)),
data_str(output_batch_data),
bprop))
# filter data
# test name skip list i batch shape filts shape stride dilation below-pads above-pads dilation bprop?
tests = [
("convolution_2d_1item", (1,1,3,5), (2,1,2,2), (1,1), (1,1), (0,0), (0,0), (1,1), ""),
("convolution_2d_1item_padded_1_1x1_1", (1,1,3,5), (2,1,2,2), (1,1), (1,1), (1,1), (1,1), (1,1), ""),
("convolution_2d_1item_padded_2_3x4_5", (1,1,3,5), (2,1,2,2), (1,1), (1,1), (2,3), (4,5), (1,1), ""),
("convolution_2d_2items", (2,1,3,5), (2,1,2,2), (1,1), (1,1), (0,0), (0,0), (1,1), ""),
("convolution_2d_2items_strided", (2,1,3,5), (2,1,2,2), (2,2), (1,1), (0,0), (0,0), (1,1), ""),
("convolution_2d_2items_strided_padded", (2,1,3,5), (2,1,2,2), (2,2), (1,1), (4,2), (5,7), (1,1), ""),
("convolution_2d_2items_strided_padded_same",(2,1,3,5), (2,1,2,2), (2,2), (1,1), (2,2), (2,2), (1,1), ""),
("convolution_2d_2items_dilated", (2,1,3,5), (2,1,2,2), (1,1), (2,2), (0,0), (0,0), (1,1), ""),
("convolution_2d_2items_dilated_padded", (2,1,3,5), (2,1,2,2), (1,1), (2,2), (4,2), (5,7), (1,1), ""),
("convolution_3d_2items", (2,1,3,5,8), (2,1,2,2,3), (1,1,1), (1,1,1), (0,0,0), (0,0,0), (1,1,1), ""),
("convolution_4d_2items", (2,1,3,5,8,7),(2,1,2,2,3,1),(1,1,1,1),(1,1,1,1),(0,0,0,0), (0,0,0,0), (1,1,1,1), "// "),
("convolution_4d_4items", (4,3,3,5,8,7),(4,3,2,2,3,1),(1,1,1,1),(1,1,1,1),(0,0,0,0), (0,0,0,0), (1,1,1,1), "// "),
("convolution_4d_4items_padded_neg", (4,3,3,5,8,7),(4,3,2,2,3,1),(1,1,1,1),(1,1,1,1),(-1,2,-3,2),(1,0,0,-3), (1,1,1,1), "// "),
("convolution_4d_4items_strided", (4,3,3,5,8,7),(4,3,2,2,3,1),(2,1,3,2),(1,1,1,1),(0,0,0,0), (0,0,0,0), (1,1,1,1), "// "),
("convolution_4d_4items_dilated", (4,3,3,5,8,7),(4,3,2,2,3,1),(1,1,1,1),(2,1,3,2),(0,0,0,0), (0,0,0,0), (1,1,1,1), "// "),
("convolution_4d_4items_strided_dilated", (4,3,8,8,8,8),(4,3,2,2,3,1),(3,2,2,3),(2,1,3,2),(0,0,0,0), (0,0,0,0), (1,1,1,1), "// "),
("convolution_4d_4items_strided_dilated_padded",
(4,3,8,8,8,8),(4,3,2,2,3,1),(3,2,2,3),(2,1,3,2),(2,4,6,8), (1,3,5,7), (1,1,1,1), "// "),
("convolution_4d_4items_strided_dilated_padded_neg",
(4,3,8,8,8,8),(4,3,2,2,3,1),(3,2,2,3),(2,1,3,2),(-2,4,0,5), (1,3,-1,-4),(1,1,1,1), "// "),
("convolution_4d_4items_strided_dilated_padded_same",
(4,3,8,8,8,8),(4,3,2,2,3,1),(3,2,2,3),(2,1,3,2),(3,3,3,3), (3,3,3,3), (1,1,1,1), "// "),
("convolution_2d_1item_1o1i_data_dilated",(1,1,3,5), (1,1,2,2), (1,1), (1,1), (0,0), (0,0), (2,2), ""),
("convolution_2d_1item_2o1i_data_dilated",(1,1,3,5), (2,1,2,2), (1,1), (1,1), (0,0), (0,0), (2,2), ""),
("convolution_2d_1item_2o2i_data_dilated",(1,2,3,5), (2,2,2,2), (1,1), (1,1), (0,0), (0,0), (2,2), ""),
("convolution_2d_1item_5o3i_data_dilated",(1,3,3,5), (5,3,2,2), (1,1), (1,1), (0,0), (0,0), (2,2), ""),
("convolution_2d_2item_5o3i_data_dilated",(2,3,3,5), (5,3,2,2), (1,1), (1,1), (0,0), (0,0), (2,2), ""),
("convolution_2d_8item_large_5o3i_data_dilated",
(8,3,16,16), (5,3,2,2), (1,1), (1,1), (0,0), (0,0), (2,2), "// "),
("convolution_2d_8item_large_5o3i_uneven_filter_data_dilated",
(8,3,16,16), (5,3,2,3), (1,1), (1,1), (0,0), (0,0), (2,2), "// "),
("convolution_2d_8item_large_5o3i_uneven_filter_uneven_data_dilation_data_dilated",
(8,3,16,16), (5,3,2,3), (1,1), (1,1), (0,0), (0,0), (2,3), "// "),
("convolution_3d_2item_large_5o3i_uneven_filter_uneven_data_dilation_data_dilated",
(2,3,8,8,8), (5,3,2,3,4), (1,1,1), (1,1,1), (0,0,0), (0,0,0), (2,3,2), "// "),
("convolution_3d_1item_large_5o3i_padded_uneven_filter_uneven_data_dilation_data_dilated",
(1,3,8,8,8), (5,3,2,3,4), (1,1,1), (1,1,1), (2,1,2), (1,2,3), (2,3,2), "// "),
("convolution_3d_2item_large_5o3i_padded_strided_uneven_filter_uneven_data_dilation_data_dilated",
(2,3,8,8,8), (5,3,2,3,4), (2,3,2), (1,1,1), (2,1,2), (1,2,3), (2,3,2), "// "),
("convolution_3d_2item_large_5o3i_padded_strided_uneven_filter_uneven_data_dilation_filter_dilated_data_dilated",
(2,3,8,8,8), (5,3,2,3,4), (2,3,2), (3,2,2), (2,1,2), (1,2,3), (2,3,2), "// "),
]
("convolution_2d_1item", (1, 1, 3, 5), (2, 1, 2, 2),
(1, 1), (1, 1), (0, 0), (0, 0), (1, 1), ""),
("convolution_2d_1item_padded_1_1x1_1", (1, 1, 3, 5), (2, 1, 2, 2),
(1, 1), (1, 1), (1, 1), (1, 1), (1, 1), ""),
("convolution_2d_1item_padded_2_3x4_5", (1, 1, 3, 5), (2, 1, 2, 2),
(1, 1), (1, 1), (2, 3), (4, 5), (1, 1), ""),
("convolution_2d_2items", (2, 1, 3, 5), (2, 1, 2, 2),
(1, 1), (1, 1), (0, 0), (0, 0), (1, 1), ""),
("convolution_2d_2items_strided", (2, 1, 3, 5), (2, 1, 2, 2),
(2, 2), (1, 1), (0, 0), (0, 0), (1, 1), ""),
("convolution_2d_2items_strided_padded", (2, 1, 3, 5), (2, 1, 2, 2),
(2, 2), (1, 1), (4, 2), (5, 7), (1, 1), ""),
("convolution_2d_2items_strided_padded_same", (2, 1, 3, 5), (2, 1, 2, 2),
(2, 2), (1, 1), (2, 2), (2, 2), (1, 1), ""),
("convolution_2d_2items_dilated", (2, 1, 3, 5), (2, 1, 2, 2),
(1, 1), (2, 2), (0, 0), (0, 0), (1, 1), ""),
("convolution_2d_2items_dilated_padded", (2, 1, 3, 5), (2, 1, 2, 2),
(1, 1), (2, 2), (4, 2), (5, 7), (1, 1), ""),
("convolution_3d_2items", (2, 1, 3, 5, 8), (2, 1, 2, 2, 3),
(1, 1, 1), (1, 1, 1), (0, 0, 0), (0, 0, 0), (1, 1, 1), ""),
("convolution_4d_2items", (2, 1, 3, 5, 8, 7), (2, 1, 2, 2, 3, 1),
(1, 1, 1, 1), (1, 1, 1, 1), (0, 0, 0, 0), (0, 0, 0, 0), (1, 1, 1, 1), "// "),
("convolution_4d_4items", (4, 3, 3, 5, 8, 7), (4, 3, 2, 2, 3, 1),
(1, 1, 1, 1), (1, 1, 1, 1), (0, 0, 0, 0), (0, 0, 0, 0), (1, 1, 1, 1), "// "),
("convolution_4d_4items_padded_neg", (4, 3, 3, 5, 8, 7), (4, 3, 2, 2, 3, 1),
(1, 1, 1, 1), (1, 1, 1, 1), (-1, 2, -3, 2), (1, 0, 0, -3), (1, 1, 1, 1), "// "),
("convolution_4d_4items_strided", (4, 3, 3, 5, 8, 7), (4, 3, 2, 2, 3, 1),
(2, 1, 3, 2), (1, 1, 1, 1), (0, 0, 0, 0), (0, 0, 0, 0), (1, 1, 1, 1), "// "),
("convolution_4d_4items_dilated", (4, 3, 3, 5, 8, 7), (4, 3, 2, 2, 3, 1),
(1, 1, 1, 1), (2, 1, 3, 2), (0, 0, 0, 0), (0, 0, 0, 0), (1, 1, 1, 1), "// "),
("convolution_4d_4items_strided_dilated", (4, 3, 8, 8, 8, 8), (4, 3, 2, 2, 3, 1),
(3, 2, 2, 3), (2, 1, 3, 2), (0, 0, 0, 0), (0, 0, 0, 0), (1, 1, 1, 1), "// "),
("convolution_4d_4items_strided_dilated_padded",
(4, 3, 8, 8, 8, 8), (4, 3, 2, 2, 3, 1), (3, 2, 2, 3), (2, 1, 3, 2), (2, 4, 6, 8), (1, 3, 5, 7), (1, 1, 1, 1), "// "),
("convolution_4d_4items_strided_dilated_padded_neg",
(4, 3, 8, 8, 8, 8), (4, 3, 2, 2, 3, 1), (3, 2, 2, 3), (2, 1, 3, 2), (-2, 4, 0, 5), (1, 3, -1, -4), (1, 1, 1, 1), "// "),
("convolution_4d_4items_strided_dilated_padded_same",
(4, 3, 8, 8, 8, 8), (4, 3, 2, 2, 3, 1), (3, 2, 2, 3), (2, 1, 3, 2), (3, 3, 3, 3), (3, 3, 3, 3), (1, 1, 1, 1), "// "),
("convolution_2d_1item_1o1i_data_dilated", (1, 1, 3, 5), (1, 1, 2, 2),
(1, 1), (1, 1), (0, 0), (0, 0), (2, 2), ""),
("convolution_2d_1item_2o1i_data_dilated", (1, 1, 3, 5), (2, 1, 2, 2),
(1, 1), (1, 1), (0, 0), (0, 0), (2, 2), ""),
("convolution_2d_1item_2o2i_data_dilated", (1, 2, 3, 5), (2, 2, 2, 2),
(1, 1), (1, 1), (0, 0), (0, 0), (2, 2), ""),
("convolution_2d_1item_5o3i_data_dilated", (1, 3, 3, 5), (5, 3, 2, 2),
(1, 1), (1, 1), (0, 0), (0, 0), (2, 2), ""),
("convolution_2d_2item_5o3i_data_dilated", (2, 3, 3, 5), (5, 3, 2, 2),
(1, 1), (1, 1), (0, 0), (0, 0), (2, 2), ""),
("convolution_2d_8item_large_5o3i_data_dilated",
(8, 3, 16, 16), (5, 3, 2, 2), (1, 1), (1, 1), (0, 0), (0, 0), (2, 2), "// "),
("convolution_2d_8item_large_5o3i_uneven_filter_data_dilated",
(8, 3, 16, 16), (5, 3, 2, 3), (1, 1), (1, 1), (0, 0), (0, 0), (2, 2), "// "),
("convolution_2d_8item_large_5o3i_uneven_filter_uneven_data_dilation_data_dilated",
(8, 3, 16, 16), (5, 3, 2, 3), (1, 1), (1, 1), (0, 0), (0, 0), (2, 3), "// "),
("convolution_3d_2item_large_5o3i_uneven_filter_uneven_data_dilation_data_dilated",
(2, 3, 8, 8, 8), (5, 3, 2, 3, 4), (1, 1, 1), (1, 1, 1), (0, 0, 0), (0, 0, 0), (2, 3, 2), "// "),
("convolution_3d_1item_large_5o3i_padded_uneven_filter_uneven_data_dilation_data_dilated",
(1, 3, 8, 8, 8), (5, 3, 2, 3, 4), (1, 1, 1), (1, 1, 1), (2, 1, 2), (1, 2, 3), (2, 3, 2), "// "),
("convolution_3d_2item_large_5o3i_padded_strided_uneven_filter_uneven_data_dilation_data_dilated",
(2, 3, 8, 8, 8), (5, 3, 2, 3, 4), (2, 3, 2), (1, 1, 1), (2, 1, 2), (1, 2, 3), (2, 3, 2), "// "),
("convolution_3d_2item_large_5o3i_padded_strided_uneven_filter_uneven_data_dilation_filter_dilated_data_dilated",
(2, 3, 8, 8, 8), (5, 3, 2, 3, 4), (2, 3, 2), (3, 2, 2), (2, 1, 2), (1, 2, 3), (2, 3, 2), "// "),
]
def main():
assert(len(sys.argv)>1)
assert(len(sys.argv) > 1)
f = open(sys.argv[1],'w')
f = open(sys.argv[1], 'w')
f.write('''//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
......@@ -374,7 +433,7 @@ constexpr int tolerance = FLOAT_MANTISSA_BITS - three_quarters_of_available_bits
''')
for t in tests:
emit_test(t,f)
emit_test(t, f)
f.write('''
// clang-format on
......@@ -382,5 +441,6 @@ constexpr int tolerance = FLOAT_MANTISSA_BITS - three_quarters_of_available_bits
f.close()
if __name__ == "__main__":
main()
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