From be93795833fb37696e968378dc7653c7a8dce2c8 Mon Sep 17 00:00:00 2001
From: Stephan Seitz <stephan.seitz@fau.de>
Date: Wed, 22 Jan 2020 21:58:29 +0100
Subject: [PATCH] Cherry-pick changes from interpolation-refactoring
---
pystencils_tests/test_interpolation.py | 150 ++++++++++++-------------
1 file changed, 72 insertions(+), 78 deletions(-)
diff --git a/pystencils_tests/test_interpolation.py b/pystencils_tests/test_interpolation.py
index 0776a7de0..92f20a236 100644
--- a/pystencils_tests/test_interpolation.py
+++ b/pystencils_tests/test_interpolation.py
@@ -11,6 +11,8 @@ import itertools
from os.path import dirname, join
import numpy as np
+import pycuda.autoinit # NOQA
+import pycuda.gpuarray as gpuarray
import pytest
import sympy
@@ -108,93 +110,79 @@ def test_rotate_interpolation(address_mode):
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
+@pytest.mark.parametrize('dtype', (np.int32, np.float32, np.float64))
+@pytest.mark.parametrize('address_mode', ('border', 'wrap', 'clamp', 'mirror'))
+@pytest.mark.parametrize('use_textures', ('use_textures', False))
+def test_rotate_interpolation_gpu(dtype, address_mode, use_textures):
rotation_angle = sympy.pi / 5
scale = 1
- 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()
+ if dtype == np.int32:
+ lenna_gpu = gpuarray.to_gpu(
+ np.ascontiguousarray(lenna * 255, dtype))
+ else:
+ lenna_gpu = gpuarray.to_gpu(
+ np.ascontiguousarray(lenna, dtype))
+ 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))
@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
+@pytest.mark.parametrize('dtype', [np.float64, np.float32, np.int32])
+@pytest.mark.parametrize('use_textures', ('use_textures', False,))
+def test_shift_interpolation_gpu(address_mode, dtype, use_textures):
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
- else:
- 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))
+ if dtype == np.int32:
+ lenna_gpu = gpuarray.to_gpu(
+ np.ascontiguousarray(lenna * 255, dtype))
+ else:
+ lenna_gpu = gpuarray.to_gpu(
+ np.ascontiguousarray(lenna, dtype))
+
+ 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
+ else:
+ 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'])
@@ -222,7 +210,7 @@ def test_rotate_interpolation_size_change(address_mode):
@pytest.mark.parametrize('address_mode, target',
- itertools.product(['border', 'wrap', 'clamp', 'mirror'], ['cpu', 'gpu']))
+ itertools.product(['border', 'wrap', 'clamp', 'mirror'], ['cpu']))
def test_field_interpolated(address_mode, target):
x_f, y_f = pystencils.fields('x,y: float64 [2d]')
@@ -230,7 +218,7 @@ def test_field_interpolated(address_mode, target):
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)
+ ast = pystencils.create_kernel(assignments, target=target)
print(ast)
print(pystencils.show_code(ast))
kernel = ast.compile()
@@ -238,5 +226,11 @@ def test_field_interpolated(address_mode, target):
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)
+
+
+def test_spatial_derivative():
+ x, y = pystencils.fields('x, y: float32[2d]')
+ tx, ty = pystencils.fields('t_x, t_y: float32[2d]')
+
+ diff = sympy.diff(x.interpolated_access((tx.center, ty.center)), tx.center)
+ print("diff: " + str(diff))
--
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