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提交 5ed22311 authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Some flake8 fixes.

上级 c508befc
隐藏空白字符变更
内嵌 并排
正在显示 包含 147 行增加 和 192 行删除
theano/sandbox/gpuarray/tests/test_conv_cuda_ndarray.py
浏览文件 @ 5ed22311
......@@ -6,37 +6,36 @@ import sys
import time
import unittest
import numpy
from six.moves import xrange
from nose.plugins.skip import SkipTest
imported_scipy_convolve2d = False
try:
from scipy.signal import convolve2d
imported_scipy_convolve2d = True
except ImportError:
pass
import theano
from theano import tensor
from theano.tests.unittest_tools import seed_rng
# We let that import do the init of the back-end if needed.
from .test_basic_ops import (mode_with_gpu,
mode_without_gpu)
from .test_basic_ops import mode_with_gpu
from ..type import GpuArrayType
from ..conv import GpuConv
from theano.sandbox.gpuarray import dnn
import pygpu
imported_scipy_convolve2d = False
try:
from scipy.signal import convolve2d
imported_scipy_convolve2d = True
except ImportError:
pass
gftensor4 = GpuArrayType('float32', [False] * 4)
def py_conv_valid_numpy(img, kern):
assert img.shape[1] == kern.shape[1]
outshp = (img.shape[0], kern.shape[0],
img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1)
img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1)
out = numpy.zeros(outshp, dtype='float32')
for b in xrange(out.shape[0]):
for k in xrange(out.shape[1]):
......@@ -60,7 +59,7 @@ def py_conv_full_numpy(img, kern):
padded_img = numpy.zeros((img.shape[0], img.shape[1], pad_rows, pad_cols),
dtype=img.dtype)
padded_img[:, :, kern.shape[2] - 1: kern.shape[2] - 1 + img.shape[2],
kern.shape[3] - 1: kern.shape[3] - 1 + img.shape[3]] = img
kern.shape[3] - 1: kern.shape[3] - 1 + img.shape[3]] = img
return py_conv_valid_numpy(padded_img, kern)
......@@ -73,10 +72,10 @@ def py_conv(img, kern, mode, subsample):
return py_conv_scipy(img, kern, mode, subsample)
elif mode == 'valid':
return py_conv_valid_numpy(img, kern)[:, :, ::subsample[0],
::subsample[1]]
::subsample[1]]
elif mode == 'full':
return py_conv_full_numpy(img, kern)[:, :, ::subsample[0],
::subsample[1]]
::subsample[1]]
else:
raise Exception("Can't execute this kernel.")
......@@ -85,12 +84,12 @@ def py_conv_scipy(img, kern, mode, subsample):
assert img.shape[1] == kern.shape[1]
if mode == 'valid':
outshp = (img.shape[0], kern.shape[0],
img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1)
img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1)
else:
outshp = (img.shape[0], kern.shape[0],
img.shape[2] + kern.shape[2] - 1,
img.shape[3] + kern.shape[3] - 1)
img.shape[2] + kern.shape[2] - 1,
img.shape[3] + kern.shape[3] - 1)
out = numpy.zeros(outshp, dtype='float32')
for b in xrange(out.shape[0]):
for k in xrange(out.shape[1]):
......@@ -133,9 +132,9 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
dtype='float32')
else:
npy_img = theano._asarray(numpy.arange(
numpy.prod(ishape)).reshape(ishape), dtype='float32') + 1
numpy.prod(ishape)).reshape(ishape), dtype='float32') + 1
npy_kern = -(theano._asarray(numpy.arange(
numpy.prod(kshape)).reshape(kshape), dtype='float32') + 1)
numpy.prod(kshape)).reshape(kshape), dtype='float32') + 1)
img = pygpu.array(npy_img)
kern = pygpu.array(npy_kern)
......@@ -191,15 +190,17 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
cpu_mflops = approx_fp / (t1 - t0)
gpu_mflops = approx_fp / (t2 - t1)
if verbose > 0:
print('%15s' % str(ishape), '%15s' % str(kshape), end=' ', file=sys.stdout)
print('%12.5f %7.2f %7.2f %7.1f' % (approx_fp,
cpu_mflops, gpu_mflops, (t1 - t0) / (t2 - t1)), file=sys.stdout)
print('%15s' % str(ishape), '%15s' % str(kshape), end=' ',
file=sys.stdout)
print('%12.5f %7.2f %7.2f %7.1f' %
(approx_fp, cpu_mflops, gpu_mflops, (t1 - t0) / (t2 - t1)),
file=sys.stdout)
if not rval:
print(('test_' + mode + ' id=' + str(id) +
' FAILED for ishape, kshape, mode, subsample,' +
' img_stride, kern_stride, version', ishape,
kshape, mode, subsample, img_stride, kern_stride,
version), file=sys.stdout)
print('test_' + mode + ' id=' + str(id) +
' FAILED for ishape, kshape, mode, subsample,' +
' img_stride, kern_stride, version', ishape,
kshape, mode, subsample, img_stride, kern_stride,
version, file=sys.stdout)
diff = cpuval - gpuval
diffabs = numpy.absolute(diff)
pr_diff = diffabs / numpy.absolute(cpuval)
......@@ -210,7 +211,7 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
nb_close, "/", diff.size))
print("max relatif diff:", (pr_diff.max(), "avg rel diff:",
numpy.average(pr_diff)))
if not rval and print_ != False:
if not rval and print_ is not False:
if npy_img.shape[0] > 5:
print("img", npy_img[0])
print("kern", npy_kern[0])
......@@ -242,19 +243,20 @@ def exec_conv(version, shapes, verbose, random, mode,
istride, kstride) in enumerate(shapes):
ret = False
try:
ret = _params_allgood(ishape,
kshape,
mode,
subsample=subshape,
img_stride=istride,
kern_stride=kstride,
version=ver,
verbose=verbose,
random=random,
id=id,
print_=print_,
rtol=rtol,
ones=ones)
ret = _params_allgood(
ishape,
kshape,
mode,
subsample=subshape,
img_stride=istride,
kern_stride=kstride,
version=ver,
verbose=verbose,
random=random,
id=id,
print_=print_,
rtol=rtol,
ones=ones)
except Exception as e:
print(ver, id, (ishape, kshape, subshape, istride, kstride))
print(e)
......@@ -273,11 +275,11 @@ def exec_conv(version, shapes, verbose, random, mode,
def get_basic_shapes():
# basic test of image and kernel shape
# basic test of image and kernel shape
return [((1, 1, 1, 1), (1, 1, 1, 1), (1, 1), (1, 1), (1, 1)),
((1, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 3, 3), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
# basic test for unsquare kernel and image
# basic test for unsquare kernel and image
((1, 1, 2, 4), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 3, 4), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 4, 3), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
......@@ -297,17 +299,17 @@ def get_shapes(imshp=(1, 1), kshp=(1, 1), subsample=(1, 1),
((3, 1) + imshp, (1, 1) + kshp, subsample, img_stride, kern_stride),
# nkern only
((1, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
#batch and nkern
# batch and nkern
((3, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
#batch and stack
# batch and stack
((3, 2) + imshp, (1, 2) + kshp, subsample, img_stride, kern_stride),
#stack and nkern
# stack and nkern
((1, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
#batch, nkern and stack
# batch, nkern and stack
((2, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
#batch, nkern and stack
# batch, nkern and stack
((3, 2) + imshp, (4, 2) + kshp, subsample, img_stride, kern_stride)
]
]
def get_shapes2(scales_img=(1, 1), scales_kern=(1, 1), subsample=(1, 1),
......@@ -344,7 +346,6 @@ def get_shapes2(scales_img=(1, 1), scales_kern=(1, 1), subsample=(1, 1),
def get_valid_shapes():
# img shape, kern shape, subsample shape
shapes = get_basic_shapes()
......@@ -360,39 +361,36 @@ def get_valid_shapes():
# test subsample done in a separate fct
shapes += [
# other test
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
, ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
, ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
, ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
, ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize
, ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image
, ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image, non-square kern
, ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
, ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)) # a big one
, ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # MNIST LeNET layer 1
, ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)) # layer 1 backprop to weights
, ((60, 20, 28, 28), (10, 20, 5, 5), (1, 1), (2, 2), (1, 1)) # added a test case that fail from test_nnet.py.test_conv_nnet2
, ((10, 5, 28, 28), (10, 5, 5, 5), (1, 1), (2, 2), (1, 1)) # test precedent but reduced that triger the error
# Test more than maxThreadsDim0
, ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
, ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
]
shapes += [ ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 1 layers
, ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 2 layers
, ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 1 full
, ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 2 valid
# , ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
, ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 1 layers
, ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 2 layers
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 full
# , ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 1
# , ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 2
]
# other test
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize
((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image
((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image, non-square kern
((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)), # a big one
((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # MNIST LeNET layer 1
((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)), # layer 1 backprop to weights
((60, 20, 28, 28), (10, 20, 5, 5), (1, 1), (2, 2), (1, 1)), # added a test case that fail from test_nnet.py.test_conv_nnet2
((10, 5, 28, 28), (10, 5, 5, 5), (1, 1), (2, 2), (1, 1)), # test precedent but reduced that triger the error
# Test more than maxThreadsDim0
((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
]
shapes += [((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 1 layers
((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 2 layers
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 1 full
((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 1 layers
((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 2 layers
((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 full
]
return shapes
......@@ -428,43 +426,35 @@ def test_full():
shapes += [
# other test
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
, ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
, ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
, ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
, ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize
, ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image
, ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image, non-square kern
, ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
, ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)) # a big one
, ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # MNIST LeNET layer 1
, ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)) # layer 1 backprop to weights
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize
((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image
((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image, non-square kern
((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)), # a big one
((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # MNIST LeNET layer 1
((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)), # layer 1 backprop to weights
# other test
, ((3, 1, 1, 1), (2, 1, 5, 3), (1, 1), (1, 1), (1, 1)) # kernel bigger then image
, ((3, 2, 1, 1), (4, 2, 1, 1), (1, 1), (1, 1), (1, 1))
, ((3, 2, 4, 4), (4, 2, 2, 6), (1, 1), (1, 1), (1, 1))
, ((3, 2, 4, 4), (4, 2, 8, 6), (1, 1), (1, 1), (1, 1)) # kernel bigger then image
, ((4, 2, 10, 10), (3, 2, 2, 12), (1, 1), (1, 1), (1, 1))
]
((3, 1, 1, 1), (2, 1, 5, 3), (1, 1), (1, 1), (1, 1)), # kernel bigger then image
((3, 2, 1, 1), (4, 2, 1, 1), (1, 1), (1, 1), (1, 1)),
((3, 2, 4, 4), (4, 2, 2, 6), (1, 1), (1, 1), (1, 1)),
((3, 2, 4, 4), (4, 2, 8, 6), (1, 1), (1, 1), (1, 1)), # kernel bigger then image
((4, 2, 10, 10), (3, 2, 2, 12), (1, 1), (1, 1), (1, 1)),
]
shapes += [
# ((60,1,28,28),(20,1,5,5), (1, 1), (1, 1), (1, 1))#test_lenet_28 1 layers
# , ((60,20,12,12),(30,20,5,5), (1, 1), (1, 1), (1, 1))#test_lenet_28 2 layers
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 1 full
# , ((20,60,12,12),(30,60,8,8), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
# , ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
# , ((10,1,64,64),(20,1,7,7), (1, 1), (1, 1), (1, 1))#test_lenet_64 1 layers
# , ((10,20,29,29),(30,20,7,7), (1, 1), (1, 1), (1, 1))#test_lenet_64 2 layers
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 full
# , ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 1
# , ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 2
# Test more than maxThreadsDim0
, ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
, ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
]
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 1 full
((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 full
# Test more than maxThreadsDim0
((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
]
version = [-1]
verbose = 0
......@@ -481,7 +471,7 @@ def test_subsample():
((4, 2, 10, 10), (3, 2, 2, 2), (1, 3), (1, 1), (1, 1)),
((4, 2, 10, 10), (3, 2, 2, 2), (3, 3), (1, 1), (1, 1)),
((4, 2, 10, 10), (3, 2, 2, 2), (3, 1), (1, 1), (1, 1))
]
]
shapes += get_shapes2(scales_img=(2, 2), subsample=(1, 1))
shapes += get_shapes2(scales_img=(2, 2), subsample=(1, 2))
shapes += get_shapes2(scales_img=(2, 2), subsample=(2, 1))
......@@ -562,7 +552,6 @@ class TestConv2DGPU(unittest.TestCase):
for mode in ['valid', 'full']:
for shapes in [((3, 2, 8, 8), (4, 2, 5, 5), (8, 8)),
((3, 2, 8, 8), (4, 2, 5, 5), (5, 8)),
#((3, 2, 8, 8), (4, 2, 5, 5), (8, 5)),
# We use only the number of columns.
]:
......@@ -580,47 +569,45 @@ def benchmark():
shapes_valid = [
# test_lenet_28 shape
((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)) # valid
, ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # valid
, ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 60, 28, 28), (20, 60, 24, 24), (1, 1), (1, 1), (1, 1)) # valid
((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)), # valid
((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
((1, 60, 28, 28), (20, 60, 24, 24), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_32 shape
, ((20, 60, 14, 14), (30, 60, 10, 10), (1, 1), (1, 1), (1, 1)) # valid
, ((60, 20, 14, 14), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # valid
, ((60, 1, 32, 32), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 60, 32, 32), (20, 60, 28, 28), (1, 1), (1, 1), (1, 1)) # valid
((20, 60, 14, 14), (30, 60, 10, 10), (1, 1), (1, 1), (1, 1)), # valid
((60, 20, 14, 14), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
((60, 1, 32, 32), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
((1, 60, 32, 32), (20, 60, 28, 28), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_64 shape
, ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # valid
, ((20, 10, 29, 29), (30, 10, 23, 23), (1, 1), (1, 1), (1, 1)) # valid
, ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 10, 64, 64), (20, 10, 58, 58), (1, 1), (1, 1), (1, 1)) # valid
((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
((20, 10, 29, 29), (30, 10, 23, 23), (1, 1), (1, 1), (1, 1)), # valid
((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
((1, 10, 64, 64), (20, 10, 58, 58), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_108 shape
, ((10, 20, 51, 51), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # valid
, ((20, 10, 51, 51), (30, 10, 45, 45), (1, 1), (1, 1), (1, 1)) # valid
, ((10, 1, 108, 108), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 10, 108, 108), (20, 10, 102, 102), (1, 1), (1, 1), (1, 1)) # valid
((10, 20, 51, 51), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
((20, 10, 51, 51), (30, 10, 45, 45), (1, 1), (1, 1), (1, 1)), # valid
((10, 1, 108, 108), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
((1, 10, 108, 108), (20, 10, 102, 102), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_256 shape
, ((2, 20, 124, 124), (30, 20, 9, 9), (1, 1), (1, 1), (1, 1)) # valid
, ((20, 2, 124, 124), (30, 2, 116, 116), (1, 1), (1, 1), (1, 1)) # valid
, ((2, 1, 256, 256), (20, 1, 9, 9), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 2, 256, 256), (20, 2, 248, 248), (1, 1), (1, 1), (1, 1)) # valid
]
((2, 20, 124, 124), (30, 20, 9, 9), (1, 1), (1, 1), (1, 1)), # valid
((20, 2, 124, 124), (30, 2, 116, 116), (1, 1), (1, 1), (1, 1)), # valid
((2, 1, 256, 256), (20, 1, 9, 9), (1, 1), (1, 1), (1, 1)), # valid
((1, 2, 256, 256), (20, 2, 248, 248), (1, 1), (1, 1), (1, 1)), # valid
]
shapes_full = [
# test_lenet_28 shape
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # full
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # full
# test_lenet_32 shape
, ((60, 30, 10, 10), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # full conv_full_patch_stack_padded' N=1
((60, 30, 10, 10), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # full conv_full_patch_stack_padded' N=1
# test_lenet_64 shape
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # full conv_full_patch_stack_padded' N=3
((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # full conv_full_patch_stack_padded' N=3
# test_lenet_108 shape
, ((10, 30, 45, 45), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # full 'conv_full_patch_stack_padded' N=9
((10, 30, 45, 45), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # full 'conv_full_patch_stack_padded' N=9
# test_lenet_256 shape
, ((2, 30, 116, 116), (20, 30, 9, 9), (1, 1), (1, 1), (1, 1)) # full conv_reference_full
]
((2, 30, 116, 116), (20, 30, 9, 9), (1, 1), (1, 1), (1, 1)), # full conv_reference_full
]
# shapes_valid=shapes_valid[-1:]
# shapes_full=shapes_full[-1:]
version = [-1]
verbose = 1
random = True
......@@ -640,6 +627,6 @@ def test_stack_rows_segfault_070312():
kern = theano.shared(numpy.random.rand(1, 80, 9, 9).astype('float32'))
out = theano.shared(numpy.random.rand(1, 2, 2, 3).astype('float32'))
op = theano.tensor.nnet.conv.ConvOp(imshp=(80, 96, 96), kshp=(9, 9),
nkern=1, bsize=1)
nkern=1, bsize=1)
f = theano.function([], [], updates=[(out, op(img, kern))], mode=mode_with_gpu)
f()
theano/sandbox/gpuarray/tests/test_neighbours.py
浏览文件 @ 5ed22311
import unittest
from theano.tensor.nnet.tests import test_neighbours
# We let that import do the init of the back-end if needed.
from .test_basic_ops import (mode_with_gpu,
mode_without_gpu)
from .test_basic_ops import mode_with_gpu
from ..neighbours import GpuImages2Neibs
......
theano/sandbox/gpuarray/tests/test_nnet.py
浏览文件 @ 5ed22311
from __future__ import print_function
from nose.plugins.skip import SkipTest
import numpy
import unittest
......@@ -7,8 +7,6 @@ import theano
import theano.tensor as T
import theano.tests.unittest_tools as utt
from theano.sandbox import gpuarray
# We let that import do the init of the back-end if needed.
from .test_basic_ops import (mode_with_gpu,
mode_without_gpu)
......@@ -36,15 +34,13 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
n_in = 4098
n_out = 4099
x = T.fmatrix('x')
y = T.lvector('y')
b = T.fvector('b')
#W = T.fmatrix('W')
# we precompute the dot with big shape before to allow the test of
# GpuCrossentropySoftmax1HotWithBiasDx to don't fail with the error
#(the launch timed out and was terminated) on GPU card not
# (the launch timed out and was terminated) on GPU card not
# powerful enough. We need the big shape to check for corner
# case.
dot_result = T.fmatrix('dot_result')
......@@ -54,7 +50,6 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
xx = numpy.asarray(numpy.random.rand(batch_size, n_in),
dtype=numpy.float32)
#?????yy = numpy.ones((batch_size,),dtype='float32')
yy = numpy.ones((batch_size,), dtype='int32')
b_values = numpy.zeros((n_out,), dtype='float32')
W_values = numpy.asarray(numpy.random.rand(n_in, n_out), dtype='float32')
......@@ -71,8 +66,6 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
classify_gpu = theano.function(inputs=[y, b, dot_result],
outputs=[loss, y_pred, dW],
mode=mode_with_gpu)
# theano.printing.debugprint(classify)
# theano.printing.debugprint(classify_gpu)
assert any([isinstance(node.op,
T.nnet.CrossentropySoftmaxArgmax1HotWithBias)
......@@ -97,12 +90,10 @@ def test_GpuCrossentropySoftmax1HotWithBiasDx():
We check that we loop when their is too much threads
"""
n_in = 1000
batch_size = 4097
n_out = 1250
if not isinstance(mode_with_gpu, theano.compile.DebugMode):
n_in = 4098
n_out = 4099
# Seed numpy.random with config.unittests.rseed
......@@ -137,25 +128,7 @@ def test_GpuCrossentropySoftmax1HotWithBiasDx():
rtol = 1e-5
atol = 1e-6
if not numpy.allclose(cpu_out, gpu_out, rtol=rtol, atol=atol):
abs_err, rel_err = T.numeric_grad.abs_rel_err(cpu_out, gpu_out)
scaled_err = numpy.minimum(abs_err / atol, rel_err / rtol)
max_i = scaled_err.argmax()
print('max err index:', max_i, max_i / batch_size, end=' ')
print(max_i % batch_size, max_i / n_out, max_i & n_out)
print('At that index:')
print('err:', scaled_err.flatten()[max_i])
print('absolute error:', abs_err.flatten()[max_i])
print('relative error:', rel_err.flatten()[max_i])
print('cpu_out:', cpu_out.flatten()[max_i])
print('gpu_out:', gpu_out.flatten()[max_i])
print('softmax_output_value:', softmax_output_value.flatten()[max_i])
print('dnll_value:', dnll_value[max_i / n_out])
print('y_idx_value:', y_idx_value[max_i / n_out])
assert False, "numpy.allclose(cpu_out, gpu_out, rtol=%s, atol=%s)" % (
rtol, atol)
utt.assert_allclose(cpu_out, gpu_out, rtol=rtol, atol=atol)
def test_softmax_with_bias_float16():
......@@ -166,6 +139,7 @@ def test_softmax_with_bias_float16():
softmax_with_bias_unittest_template(dtypeInput='float32',
dtypeBias='float16')
def test_softmax_with_bias_float32():
softmax_with_bias_unittest_template(dtypeInput='float32',
dtypeBias='float32')
......@@ -188,6 +162,7 @@ def softmax_with_bias_unittest_template(dtypeInput, dtypeBias):
TODO: check that we loop when there are too many threads. (THIS IS
NOT IMPLEMENTED)
"""
x = T.matrix('x', dtype=dtypeInput)
b = T.vector('b', dtype=dtypeBias)
......@@ -228,9 +203,11 @@ def softmax_with_bias_unittest_template(dtypeInput, dtypeBias):
def test_softmax_float16():
softmax_unittest_template('float16')
def test_softmax_float32():
softmax_unittest_template('float32')
def test_softmax_float64():
softmax_unittest_template('float64')
......
theano/sandbox/gpuarray/tests/test_type.py
浏览文件 @ 5ed22311
import operator
import numpy
import theano
......@@ -25,7 +23,6 @@ def test_deep_copy():
def test_values_eq_approx():
a = rand_gpuarray(20, dtype='float32')
g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
assert GpuArrayType.values_eq_approx(a, a)
b = a.copy()
b[0] = numpy.asarray(b[0]) + 1.
......
theano/tests/test_flake8.py
浏览文件 @ 5ed22311
......@@ -160,12 +160,8 @@ whitelist_flake8 = [
"sandbox/gpuarray/__init__.py",
"sandbox/gpuarray/tests/test_subtensor.py",
"sandbox/gpuarray/tests/test_scan.py",
"sandbox/gpuarray/tests/test_neighbours.py",
"sandbox/gpuarray/tests/test_conv_cuda_ndarray.py",
"sandbox/gpuarray/tests/test_type.py",
"sandbox/gpuarray/tests/test_opt.py",
"sandbox/gpuarray/tests/test_elemwise.py",
"sandbox/gpuarray/tests/test_nnet.py",
"scan_module/scan_utils.py",
"scan_module/scan_views.py",
"scan_module/scan.py",
......
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