Newer
Older
# -*- coding: utf-8 -*-
#
# Copyright © 2019 Stephan Seitz <stephan.seitz@fau.de>
#
# Distributed under terms of the GPLv3 license.
"""
"""
from os.path import dirname, join
import numpy as np
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
import sympy
import pystencils
from pystencils.interpolation_astnodes import LinearInterpolator
from pystencils.spatial_coordinates import x_, y_
type_map = {
np.float32: 'float32',
np.float64: 'float64',
np.int32: 'int32',
}
try:
import pyconrad.autoinit
except Exception:
import unittest.mock
pyconrad = unittest.mock.MagicMock()
LENNA_FILE = join(dirname(__file__), 'test_data', 'lenna.png')
try:
import skimage.io
lenna = skimage.io.imread(LENNA_FILE, as_gray=True).astype(np.float64)
pyconrad.imshow(lenna)
except Exception:
lenna = np.random.rand(20, 30)
def test_interpolation():
x_f, y_f = pystencils.fields('x,y: float64 [2d]')
assignments = pystencils.AssignmentCollection({
y_f.center(): LinearInterpolator(x_f).at([x_ + 2.7, y_ + 7.2])
})
print(assignments)
ast = pystencils.create_kernel(assignments)
print(ast)
print(pystencils.show_code(ast))
kernel = ast.compile()
pyconrad.imshow(lenna)
out = np.zeros_like(lenna)
kernel(x=lenna, y=out)
pyconrad.imshow(out, "out")
def test_scale_interpolation():
x_f, y_f = pystencils.fields('x,y: float64 [2d]')
for address_mode in ['border', 'wrap', 'clamp', 'mirror']:
assignments = pystencils.AssignmentCollection({
y_f.center(): LinearInterpolator(x_f, address_mode=address_mode).at([0.5 * x_ + 2.7, 0.25 * y_ + 7.2])
})
print(assignments)
ast = pystencils.create_kernel(assignments)
print(ast)
print(pystencils.show_code(ast))
kernel = ast.compile()
out = np.zeros_like(lenna)
kernel(x=lenna, y=out)
pyconrad.imshow(out, "out " + address_mode)
@pytest.mark.parametrize('address_mode',
['border',
'clamp',
pytest.param('warp', marks=pytest.mark.xfail(
reason="Fails on newer SymPy version due to complex conjugate()")),
pytest.param('mirror', marks=pytest.mark.xfail(
reason="Fails on newer SymPy version due to complex conjugate()")),
])
def test_rotate_interpolation(address_mode):
"""
'wrap', 'mirror' currently fails on new sympy due to conjugate()
"""
x_f, y_f = pystencils.fields('x,y: float64 [2d]')
rotation_angle = sympy.pi / 5
transformed = sympy.rot_axis3(rotation_angle)[:2, :2] * sympy.Matrix((x_, y_))
assignments = pystencils.AssignmentCollection({
y_f.center(): LinearInterpolator(x_f, address_mode=address_mode).at(transformed)
})
print(assignments)
ast = pystencils.create_kernel(assignments)
print(ast)
print(pystencils.show_code(ast))
kernel = ast.compile()
out = np.zeros_like(lenna)
kernel(x=lenna, y=out)
pyconrad.imshow(out, "out " + address_mode)
@pytest.mark.parametrize('address_mode', ['border', 'wrap', 'clamp', 'mirror'])
def test_rotate_interpolation_gpu(address_mode):
pytest.importorskip('pycuda')
import pycuda.autoinit # NOQA
import pycuda.gpuarray as gpuarray
rotation_angle = sympy.pi / 5
scale = 1
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
previous_result = None
for dtype in [np.int32, np.float32, np.float64]:
if dtype == np.int32:
lenna_gpu = gpuarray.to_gpu(
np.ascontiguousarray(lenna * 255, dtype))
else:
lenna_gpu = gpuarray.to_gpu(
np.ascontiguousarray(lenna, dtype))
for use_textures in [True, False]:
x_f, y_f = pystencils.fields('x,y: %s [2d]' % type_map[dtype], ghost_layers=0)
transformed = scale * sympy.rot_axis3(rotation_angle)[:2, :2] * \
sympy.Matrix((x_, y_)) - sympy.Matrix([2, 2])
assignments = pystencils.AssignmentCollection({
y_f.center(): LinearInterpolator(x_f, address_mode=address_mode).at(transformed)
})
print(assignments)
ast = pystencils.create_kernel(assignments, target='gpu', use_textures_for_interpolation=use_textures)
print(ast)
print(pystencils.show_code(ast))
kernel = ast.compile()
out = gpuarray.zeros_like(lenna_gpu)
kernel(x=lenna_gpu, y=out)
pyconrad.imshow(out,
f"out {address_mode} texture:{use_textures} {type_map[dtype]}")
skimage.io.imsave(f"/tmp/out {address_mode} texture:{use_textures} {type_map[dtype]}.tif",
np.ascontiguousarray(out.get(), np.float32))
if previous_result is not None:
try:
assert np.allclose(previous_result[4:-4, 4:-4], out.get()[4:-4, 4:-4], rtol=100, atol=1e-3)
except AssertionError: # NOQA
print("Max error: %f" % np.max(previous_result - out.get()))
# pyconrad.imshow(previous_result - out.get(), "Difference image")
# raise e
previous_result = out.get()
@pytest.mark.parametrize('address_mode', ['border', 'wrap', 'clamp', 'mirror'])
def test_shift_interpolation_gpu(address_mode):
pytest.importorskip('pycuda')
import pycuda.autoinit # NOQA
import pycuda.gpuarray as gpuarray
rotation_angle = 0 # sympy.pi / 5
scale = 1
# shift = - sympy.Matrix([1.5, 1.5])
shift = sympy.Matrix((0.0, 0.0))
for dtype in [np.float64, np.float32, np.int32]:
if dtype == np.int32:
lenna_gpu = gpuarray.to_gpu(
np.ascontiguousarray(lenna * 255, dtype))
else:
lenna_gpu = gpuarray.to_gpu(
np.ascontiguousarray(lenna, dtype))
for use_textures in [True, False]:
x_f, y_f = pystencils.fields('x,y: %s [2d]' % type_map[dtype], ghost_layers=0)
if use_textures:
transformed = scale * sympy.rot_axis3(rotation_angle)[:2, :2] * sympy.Matrix((x_, y_)) + shift
transformed = scale * sympy.rot_axis3(rotation_angle)[:2, :2] * sympy.Matrix((x_, y_)) + shift
assignments = pystencils.AssignmentCollection({
y_f.center(): LinearInterpolator(x_f, address_mode=address_mode).at(transformed)
})
# print(assignments)
ast = pystencils.create_kernel(assignments, target='gpu', use_textures_for_interpolation=use_textures)
# print(ast)
print(pystencils.show_code(ast))
kernel = ast.compile()
out = gpuarray.zeros_like(lenna_gpu)
kernel(x=lenna_gpu, y=out)
pyconrad.imshow(out,
f"out {address_mode} texture:{use_textures} {type_map[dtype]}")
skimage.io.imsave(f"/tmp/out {address_mode} texture:{use_textures} {type_map[dtype]}.tif",
np.ascontiguousarray(out.get(), np.float32))
@pytest.mark.parametrize('address_mode', ['border', 'clamp'])
def test_rotate_interpolation_size_change(address_mode):
"""
'wrap', 'mirror' currently fails on new sympy due to conjugate()
"""
x_f, y_f = pystencils.fields('x,y: float64 [2d]')
rotation_angle = sympy.pi / 5
transformed = sympy.rot_axis3(rotation_angle)[:2, :2] * sympy.Matrix((x_, y_))
assignments = pystencils.AssignmentCollection({
y_f.center(): LinearInterpolator(x_f, address_mode=address_mode).at(transformed)
})
print(assignments)
ast = pystencils.create_kernel(assignments)
print(ast)
print(pystencils.show_code(ast))
kernel = ast.compile()
out = np.zeros((100, 150), np.float64)
kernel(x=lenna, y=out)
pyconrad.imshow(out, "small out " + address_mode)
@pytest.mark.parametrize('address_mode, target',
itertools.product(['border', 'wrap', 'clamp', 'mirror'], ['cpu', 'gpu']))
def test_field_interpolated(address_mode, target):
x_f, y_f = pystencils.fields('x,y: float64 [2d]')
assignments = pystencils.AssignmentCollection({
y_f.center(): x_f.interpolated_access([0.5 * x_ + 2.7, 0.25 * y_ + 7.2], address_mode=address_mode)
})
print(assignments)
ast = pystencils.create_kernel(assignments)
print(ast)
print(pystencils.show_code(ast))
kernel = ast.compile()
out = np.zeros_like(lenna)
kernel(x=lenna, y=out)
pyconrad.imshow(out, "out " + address_mode)
kernel(x=lenna, y=out)
pyconrad.imshow(out, "out " + address_mode)