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提交 7bec4ff8 authored 作者: Frederic's avatar Frederic
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Updated NEWS.txt

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NEWS.txt
浏览文件 @ 7bec4ff8
......@@ -10,7 +10,7 @@ Documentation
Interface changes
* In 0.5, we removed the deprecated sharedvar.value property.
Now we raise an error if you access it.
Now we raise an error if you access it. (Frederic B.)
* theano.function does not accept duplicate inputs, so function([x, x], ...)
does not work anymore. (Pascal L.)
* theano.function now raises an error if some of the provided inputs are
......@@ -23,15 +23,16 @@ New Features
* debugprint new param ids=["CHAR", "id", "int", ""]
This makes the identifier printed to be the python id, a unique char, a
unique int, or not have it printed. We changed the default to be "CHAR"
as this is more readable.
as this is more readable. (Frederic B.)
* debugprint new param stop_on_name=[False, True]. If True, we don't print
anything below an intermediate variable that has a name. Defaults to False.
* debugprint does not print anymore the "|" symbol in a column after the last input.
(Frederic B.)
* debugprint does not print anymore the "|" symbol in a column after the last input. (Frederic B.)
* If you use Enthought Python Distribution (EPD) now we use its blas
implementation by default.
implementation by default. (Frederic B.)
Sparse Sandbox graduate
* Remove0 op: it remove store element with value 0.
* Remove0 op: it remove store element with value 0. (Frederic B.)
Sparse Sandbox Addition (Not reviewed/documented/tested, but used by some people)
* They are all in the theano.sparse.sandbox.sp2 module
......@@ -50,7 +51,9 @@ Crash Fix
empty string (Frederic B.)
* When importing theano on a computer without GPU with the Theano
flags 'device' or 'init_gpu_device' set to gpu* (Frederic B., reported by Luo Heng)
* Optimization print useless error when scipy is not available. (Frederic B.)
* Gpu conv crash/slowdown on newer hardware? (James B.)
* Better error handling in gpu conv (Frederic B.)
=============
Release Notes
......
theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py
浏览文件 @ 7bec4ff8
......@@ -24,12 +24,13 @@ if cuda_ndarray.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
#needed as the gpu conv don't have a perform implementation.
if theano.config.mode=='FAST_COMPILE':
if theano.config.mode == 'FAST_COMPILE':
theano_mode = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
else:
theano_mode = theano.compile.mode.get_default_mode().including('gpu')
cuda_tensor4 = cuda_ndarray.CudaNdarrayType([False]*4)
cuda_tensor4 = cuda_ndarray.CudaNdarrayType([False] * 4)
def py_conv_valid_numpy(img, kern):
assert img.shape[1] == kern.shape[1]
......@@ -42,19 +43,27 @@ def py_conv_valid_numpy(img, kern):
for rr in xrange(out.shape[2]):
for cc in xrange(out.shape[3]):
#rr, cc is the upper-left corner of img patches
imgpatch = img[b,:,rr:rr+kern.shape[2], cc:cc+kern.shape[3]]
imgpatch = img[b, :, rr:rr + kern.shape[2],
cc:cc + kern.shape[3]]
#print img.shape, kern.shape, imgpatch.shape, rr+kern.shape[2]-1, rr-1, -1
innerprod = (imgpatch[:,::-1,::-1] * kern[k,:,:,:]).sum()
innerprod = (imgpatch[:, ::-1, ::-1] *
kern[k, :, :, :]).sum()
out[b, k, rr, cc] = innerprod
return out
def py_conv_full_numpy(img, kern):
# manually pad the img with zeros all around, and then run it through py_conv_valid
pad_rows = 2*(kern.shape[2]-1) + img.shape[2]
pad_cols = 2*(kern.shape[3]-1) + img.shape[3]
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
# manually pad the img with zeros all around, and then run it
# through py_conv_valid
pad_rows = 2 * (kern.shape[2] - 1) + img.shape[2]
pad_cols = 2 * (kern.shape[3] - 1) + img.shape[3]
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
return py_conv_valid_numpy(padded_img, kern)
def py_conv(img, kern, mode, subsample):
"""
use a scipy or numpy implementation depending is scipy is available.
......@@ -62,13 +71,16 @@ def py_conv(img, kern, mode, subsample):
"""
if imported_scipy_convolve2d:
return py_conv_scipy(img, kern, mode, subsample)
elif mode=='valid':
return py_conv_valid_numpy(img,kern)[:,:,::subsample[0],::subsample[1]]
elif mode=='full':
return py_conv_full_numpy(img,kern)[:,:,::subsample[0],::subsample[1]]
elif mode == 'valid':
return py_conv_valid_numpy(img, kern)[:, :, ::subsample[0],
::subsample[1]]
elif mode == 'full':
return py_conv_full_numpy(img, kern)[:, :, ::subsample[0],
::subsample[1]]
else:
raise Exception("Can't execute this kernel.")
def py_conv_scipy(img, kern, mode, subsample):
assert img.shape[1] == kern.shape[1]
if mode == 'valid':
......@@ -83,17 +95,20 @@ def py_conv_scipy(img, kern, mode, subsample):
for b in xrange(out.shape[0]):
for k in xrange(out.shape[1]):
for s in xrange(img.shape[1]):
out[b,k,:,:] += convolve2d(img[b,s,:,:]
, kern[k,s,:,:]
, mode)
return out[:,:,::subsample[0], ::subsample[1]]
out[b, k, :, :] += convolve2d(img[b, s, :, :],
kern[k, s, :, :],
mode)
return out[:, :, ::subsample[0], ::subsample[1]]
def _params_allgood_header():
print "ishape kshape #Mflops CPU Mflops GPU Mflops Speedup"
def _params_allgood(ishape, kshape, mode, subsample=(1,1), img_stride=(1,1),
kern_stride=(1,1), version=-1, verbose=0, random=True, print_=None,
id=None, rtol=1e-5, atol = 1e-8, nb_iter=0, ones=False, compile_kshp=None):
def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
kern_stride=(1, 1), version=-1, verbose=0, random=True,
print_=None, id=None, rtol=1e-5, atol=1e-8,
nb_iter=0, ones=False, compile_kshp=None):
#
# This function is the core of several of the big unit-test drivers,
# but it can also be used very directly on its own to test a specific
......@@ -111,22 +126,27 @@ def _params_allgood(ishape, kshape, mode, subsample=(1,1), img_stride=(1,1),
npy_img = theano._asarray(numpy.ones(ishape), dtype='float32')
npy_kern = -theano._asarray(numpy.ones(kshape), dtype='float32')
elif random:
npy_img = theano._asarray(numpy.random.rand(*ishape)+1, dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape)-2, dtype='float32')
npy_img = theano._asarray(numpy.random.rand(*ishape) + 1,
dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape) - 2,
dtype='float32')
else:
npy_img = theano._asarray(numpy.arange(numpy.prod(ishape)).reshape(ishape), dtype='float32')+1
npy_kern = -(theano._asarray(numpy.arange(numpy.prod(kshape)).reshape(kshape), dtype='float32')+1)
npy_img = theano._asarray(numpy.arange(
numpy.prod(ishape)).reshape(ishape), dtype='float32') + 1
npy_kern = -(theano._asarray(numpy.arange(
numpy.prod(kshape)).reshape(kshape), dtype='float32') + 1)
img = cuda_ndarray.CudaNdarray(npy_img)
kern = cuda_ndarray.CudaNdarray(npy_kern)
#we take the stride after the transfert as we make c_contiguous data on the GPU.
if img_stride!=(1,1):
img=img[:,:,::img_stride[0],::img_stride[1]]
npy_img = npy_img[:,:,::img_stride[0],::img_stride[1]]
if kern_stride!=(1,1):
kern=kern[:,:,::kern_stride[0],::kern_stride[1]]
npy_kern = npy_kern[:,:,::kern_stride[0],::kern_stride[1]]
#we take the stride after the transfert as we make c_contiguous
#data on the GPU.
if img_stride != (1, 1):
img = img[:, :, ::img_stride[0], ::img_stride[1]]
npy_img = npy_img[:, :, ::img_stride[0], ::img_stride[1]]
if kern_stride != (1, 1):
kern = kern[:, :, ::kern_stride[0], ::kern_stride[1]]
npy_kern = npy_kern[:, :, ::kern_stride[0], ::kern_stride[1]]
t2 = None
rval = True
......@@ -139,20 +159,23 @@ def _params_allgood(ishape, kshape, mode, subsample=(1,1), img_stride=(1,1),
op = theano.sandbox.cuda.blas.GpuConv(border_mode=mode,
subsample=subsample,
version=version,
verbose=verbose, kshp=compile_kshp)(i,k)
f=theano.function([i,k],op, mode=theano_mode)
gpuval = f(img,kern)
verbose=verbose,
kshp=compile_kshp)(i, k)
f = theano.function([i, k], op, mode=theano_mode)
gpuval = f(img, kern)
t2 = time.time()
for i in range(nb_iter):
gpuval2 = f(img,kern)
assert numpy.allclose(numpy.asarray(gpuval),numpy.asarray(gpuval2))
assert (numpy.asarray(gpuval)==numpy.asarray(gpuval2)).all()
gpuval2 = f(img, kern)
assert numpy.allclose(numpy.asarray(gpuval),
numpy.asarray(gpuval2))
assert (numpy.asarray(gpuval) == numpy.asarray(gpuval2)).all()
gpuval = numpy.asarray(gpuval)
if gpuval.shape != cpuval.shape:
print >> sys.stdout, "ERROR: shape mismatch", gpuval.shape, cpuval.shape
print >> sys.stdout, "ERROR: shape mismatch",
print >> sys.stdout, gpuval.shape, cpuval.shape
rval = False
if rval:
rval = numpy.allclose(cpuval, gpuval, rtol = rtol)
rval = numpy.allclose(cpuval, gpuval, rtol=rtol)
assert numpy.all(numpy.isfinite(gpuval))
except NotImplementedError, e:
print >> sys.stdout, '_params_allgood Failed allclose', e
......@@ -164,49 +187,52 @@ def _params_allgood(ishape, kshape, mode, subsample=(1,1), img_stride=(1,1),
else:
approx_fp = ishape[0] * kshape[0] * kshape[1] * kshape[2] * kshape[3] * ishape[2] * ishape[3] * 2
approx_fp /= 1e6
cpu_mflops = approx_fp / (t1-t0)
gpu_mflops = approx_fp / (t2-t1)
if verbose>0:
print >> sys.stdout, '%15s'% str(ishape), '%15s'% str(kshape),
cpu_mflops = approx_fp / (t1 - t0)
gpu_mflops = approx_fp / (t2 - t1)
if verbose > 0:
print >> sys.stdout, '%15s' % str(ishape), '%15s' % str(kshape),
print >> sys.stdout, '%12.5f %7.2f %7.2f %7.1f' % (approx_fp,
cpu_mflops, gpu_mflops,(t1-t0)/(t2-t1))
cpu_mflops, gpu_mflops, (t1 - t0) / (t2 - t1))
if not rval:
print >> sys.stdout, '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
diff=cpuval-gpuval
diffabs=numpy.absolute(diff)
pr_diff=diffabs/numpy.absolute(cpuval)
nb_close=(diffabs <= (atol + rtol * numpy.absolute(gpuval))).sum()
diff = cpuval - gpuval
diffabs = numpy.absolute(diff)
pr_diff = diffabs / numpy.absolute(cpuval)
nb_close = (diffabs <= (atol + rtol * numpy.absolute(gpuval))).sum()
print "max absolute diff:",diffabs.max(),"avg abs diff:",numpy.average(diffabs)
print "median abs diff:", numpy.median(diffabs), "nb close:",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 npy_img.shape[0]>5:
print "img",npy_img[0]
print "kern",npy_kern[0]
print "gpu",gpuval[0][0]
print "cpu",cpuval[0][0]
print "diff",diff[0][0]
if not rval and print_ != False:
if npy_img.shape[0] > 5:
print "img", npy_img[0]
print "kern", npy_kern[0]
print "gpu", gpuval[0][0]
print "cpu", cpuval[0][0]
print "diff", diff[0][0]
else:
print "img",npy_img
print "kern",npy_kern
print "gpu",gpuval
print "cpu",cpuval
print "diff",diff
print "img", npy_img
print "kern", npy_kern
print "gpu", gpuval
print "cpu", cpuval
print "diff", diff
return rval
def exec_conv(version, shapes, verbose, random, mode,
print_=None, rtol=1e-5, ones=False):
if verbose>0:
if verbose > 0:
_params_allgood_header()
nb_failed = 0
nb_tests = 0
failed_version=set()
failed_id=[]
for ver in version:# I put -1 in case we forget to add version in the test to.
for id,(ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
ret=False
failed_version = set()
failed_id = []
# I put -1 in case we forget to add version in the test to.
for ver in version:
for id, (ishape, kshape, subshape,
istride, kstride) in enumerate(shapes):
ret = False
try:
ret = _params_allgood(ishape,
kshape,
......@@ -222,19 +248,21 @@ def exec_conv(version, shapes, verbose, random, mode,
rtol=rtol,
ones=ones)
except Exception, e:
print ver, id,(ishape, kshape, subshape, istride, kstride)
print ver, id, (ishape, kshape, subshape, istride, kstride)
print e
pass
if not ret:
failed_version.add(ver)
failed_id.append(id)
nb_failed+=1
nb_tests+=1
if nb_failed>0:
print "nb_failed",nb_failed,"on",nb_tests, "failed_version",failed_version, "failed_id",failed_id
assert nb_failed==0, nb_failed
nb_failed += 1
nb_tests += 1
if nb_failed > 0:
print "nb_failed", nb_failed, "on", nb_tests,
print "failed_version", failed_version, "failed_id", failed_id
assert nb_failed == 0, nb_failed
else:
print 'Executed',nb_tests,'different shapes'
print 'Executed', nb_tests, 'different shapes'
def get_basic_shapes():
return [
......@@ -249,8 +277,12 @@ def get_basic_shapes():
, ((1, 1, 4, 4), (1, 1, 3, 2), (1,1), (1,1), (1,1))
, ((1, 1, 4, 4), (1, 1, 2, 3), (1,1), (1,1), (1,1))]
def get_shapes(imshp=(1,1), kshp=(1,1), subsample=(1,1), img_stride=(1,1), kern_stride=(1,1)):
""" all possible case if we one or more of stack size, batch size, nkern. We use the gived image shape, kernel shape and subsmaple shape."""
def get_shapes(imshp=(1, 1), kshp=(1, 1), subsample=(1, 1),
img_stride=(1, 1), kern_stride=(1, 1)):
""" all possible case if we one or more of stack size, batch size,
nkern. We use the gived image shape, kernel shape and subsmaple
shape."""
return [ ((1, 2)+imshp, (1, 2)+kshp,subsample, img_stride, kern_stride)#stack only
, ((3, 1)+imshp, (1, 1)+kshp,subsample, img_stride, kern_stride)#batch only
, ((1, 1)+imshp, (2, 1)+kshp,subsample, img_stride, kern_stride)#nkern only
......@@ -260,7 +292,10 @@ def get_shapes(imshp=(1,1), kshp=(1,1), subsample=(1,1), img_stride=(1,1), kern_
, ((2, 2)+imshp, (2, 2)+kshp,subsample, img_stride, kern_stride)#batch, nkern and stack
, ((3, 2)+imshp, (4, 2)+kshp,subsample, img_stride, kern_stride)#batch, nkern and stack
]
def get_shapes2(scales_img=(1,1), scales_kern=(1,1), subsample=(1,1), img_stride=(1,1), kern_stride=(1,1)):
def get_shapes2(scales_img=(1, 1), scales_kern=(1, 1), subsample=(1, 1),
img_stride=(1, 1), kern_stride=(1, 1)):
#basic test of stack, batch and nkern paramter
shapes =get_shapes((1*scales_img[0],1*scales_img[1]),
(1*scales_kern[0],1*scales_kern[1]),subsample, img_stride, kern_stride)
......@@ -284,19 +319,20 @@ def get_shapes2(scales_img=(1,1), scales_kern=(1,1), subsample=(1,1), img_stride
(2*scales_kern[0],3*scales_kern[1]),subsample, img_stride, kern_stride)
return shapes
def get_valid_shapes():
# img shape, kern shape, subsample shape
shapes = get_basic_shapes()
shapes +=get_shapes2()
shapes += get_shapes2()
#test image stride
shapes += get_shapes2(scales_img=(2,2),img_stride=(1,2))
shapes += get_shapes2(scales_img=(2,2),img_stride=(2,1))
shapes += get_shapes2(scales_img=(2,2),img_stride=(2,2))
shapes += get_shapes2(scales_img=(2,2),img_stride=(-1,-1))
shapes += get_shapes2(scales_img=(2,2),kern_stride=(-1,-1))
shapes += get_shapes2(scales_img=(2, 2), img_stride=(1, 2))
shapes += get_shapes2(scales_img=(2, 2), img_stride=(2, 1))
shapes += get_shapes2(scales_img=(2, 2), img_stride=(2, 2))
shapes += get_shapes2(scales_img=(2, 2), img_stride=(-1, -1))
shapes += get_shapes2(scales_img=(2, 2), kern_stride=(-1, -1))
#test subsample done in a separate fct
......@@ -333,161 +369,192 @@ def get_valid_shapes():
]
return shapes
def test_valid_0_2():
shapes = get_valid_shapes()
version=[0,2]
verbose=0
version = [0, 2]
verbose = 0
random = True
print_ = False
ones = False
if ones:
random = False
shapes2=[]
shapes2 = []
for id,(ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape=[ishape[0]]+[kshape[0]]+list(numpy.asarray(ishape[2:])-numpy.asarray(kshape[2:])+numpy.asarray([1,1]))
if oshape[3]> 512:
for id, (ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape = [ishape[0]] + [kshape[0]] + list(numpy.asarray(ishape[2:]) -
numpy.asarray(kshape[2:]) +
numpy.asarray([1, 1]))
if oshape[3] > 512:
continue
if ishape[1]>1:
if ishape[1] > 1:
continue
if (numpy.prod(ishape[2:])+numpy.prod(kshape[2:]))*4>(16*1024-150):
if ((numpy.prod(ishape[2:]) + numpy.prod(kshape[2:])) * 4 >
(16 * 1024 - 150)):
continue
if subshape==(1,1):
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
exec_conv(version, shapes, verbose, random, 'valid', print_=print_, ones=ones, rtol=1.1e-5)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
def test_valid_1_3_11_12():
shapes = get_valid_shapes()
version=[1,3,11,12]
verbose=0
version = [1, 3, 11, 12]
verbose = 0
random = True
print_ = False
ones = False
if ones:
random = False
shapes2=[]
shapes2 = []
for id,(ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape=[ishape[0]]+[kshape[0]]+list(numpy.asarray(ishape[2:])-numpy.asarray(kshape[2:])+numpy.asarray([1,1]))
if oshape[3]> 512:
for id, (ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape = [ishape[0]] + [kshape[0]] + list(numpy.asarray(ishape[2:]) -
numpy.asarray(kshape[2:]) +
numpy.asarray([1, 1]))
if oshape[3] > 512:
continue
if (numpy.prod(ishape[2:])+numpy.prod(kshape[2:]))*4>(16*1024-150):
if ((numpy.prod(ishape[2:]) + numpy.prod(kshape[2:])) * 4 >
(16 * 1024 - 150)):
continue
if subshape==(1,1):
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
exec_conv(version, shapes, verbose, random, 'valid', print_=print_, ones=ones, rtol=1.1e-5)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
def test_valid_4():
shapes = get_valid_shapes()
version=[4]
verbose=0
version = [4]
verbose = 0
random = True
print_ = False
ones = False
if ones:
random = False
shapes2=[]
shapes2 = []
for id,(ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape=[ishape[0]]+[kshape[0]]+list(numpy.asarray(ishape[2:])-numpy.asarray(kshape[2:])+numpy.asarray([1,1]))
if oshape[3]> 512:
for id, (ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape = [ishape[0]] + [kshape[0]] + list(numpy.asarray(ishape[2:]) -
numpy.asarray(kshape[2:]) +
numpy.asarray([1, 1]))
if oshape[3] > 512:
continue
if ishape[1]>1:
if ishape[1] > 1:
continue
if (kshape[2]*ishape[3]*4+numpy.prod(kshape[2:])*4)>(16*1024-150):
if ((kshape[2] * ishape[3] * 4 + numpy.prod(kshape[2:]) * 4) >
(16 * 1024 - 150)):
continue
if subshape==(1,1):
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
exec_conv(version, shapes, verbose, random, 'valid', print_=print_, ones=ones, rtol=1.1e-5)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
def test_valid_5():
shapes = get_valid_shapes()
version=[5]
verbose=0
version = [5]
verbose = 0
random = True
print_ = False
ones = False
if ones:
random = False
shapes2=[]
shapes2 = []
print len(shapes)
for id,(ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape=[ishape[0]]+[kshape[0]]+list(numpy.asarray(ishape[2:])-numpy.asarray(kshape[2:])+numpy.asarray([1,1]))
if oshape[3]> 512:
for id, (ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape = [ishape[0]] + [kshape[0]] + list(numpy.asarray(ishape[2:]) -
numpy.asarray(kshape[2:]) +
numpy.asarray([1, 1]))
if oshape[3] > 512:
continue
if (kshape[2]*ishape[3]*4+numpy.prod(kshape[2:])*4)>(16*1024-150):
if ((kshape[2] * ishape[3] * 4 + numpy.prod(kshape[2:]) * 4) >
(16 * 1024 - 150)):
continue
if subshape==(1,1):
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
print len(shapes2)
exec_conv(version, shapes, verbose, random, 'valid', print_=print_, ones=ones, rtol=1.1e-5)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
def test_valid_7_8_13():
shapes = get_valid_shapes()
# This is to test the "new" lower shared memory usage.
shapes.append(((10,30,60,60),(20,30,40,40), (1,1), (1,1), (1,1)))
version=[7,8,13]
verbose=0
shapes.append(((10, 30, 60, 60), (20, 30, 40, 40),
(1, 1), (1, 1), (1, 1)))
version = [7, 8, 13]
verbose = 0
random = True
print_ = False
ones = False
if ones:
random = False
shapes2=[]
shapes2 = []
print len(shapes)
for id,(ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape=[ishape[0]]+[kshape[0]]+list(numpy.asarray(ishape[2:])-numpy.asarray(kshape[2:])+numpy.asarray([1,1]))
if oshape[2]*oshape[3]>512:
for id, (ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape = [ishape[0]] + [kshape[0]] + list(numpy.asarray(ishape[2:]) -
numpy.asarray(kshape[2:]) +
numpy.asarray([1, 1]))
if oshape[2] * oshape[3] > 512:
continue
if max(numpy.prod(ishape[2:])*4+2*kshape[3]*4, oshape[2]*oshape[3]*4*2)>(16*1024-150):
if max(numpy.prod(ishape[2:]) * 4 + 2 * kshape[3] * 4,
oshape[2] * oshape[3] * 4 * 2) > (16 * 1024 - 150):
continue
if subshape==(1,1):
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
print len(shapes2)
exec_conv(version, shapes, verbose, random, 'valid', print_=print_, ones=ones, rtol=1.1e-5)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
def test_valid_9_10():
shapes = get_valid_shapes()
version=[9,10]
verbose=0
version = [9, 10]
verbose = 0
random = True
print_ = False
ones = False
if ones:
random = False
shapes2=[]
shapes2 = []
print len(shapes)
for id,(ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape=[ishape[0]]+[kshape[0]]+list(numpy.asarray(ishape[2:])-numpy.asarray(kshape[2:])+numpy.asarray([1,1]))
if oshape[3]> 512:
for id, (ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
oshape = [ishape[0]] + [kshape[0]] + list(numpy.asarray(ishape[2:]) -
numpy.asarray(kshape[2:]) +
numpy.asarray([1, 1]))
if oshape[3] > 512:
continue
if (kshape[3]*4+ishape[3])>(16*1024-150):
if (kshape[3] * 4 + ishape[3]) > (16 * 1024 - 150):
continue
if subshape==(1,1):
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
print len(shapes2)
exec_conv(version, shapes, verbose, random, 'valid', print_=print_, ones=ones, rtol=1.1e-5)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
def test_valid():
shapes = get_valid_shapes()
......@@ -495,8 +562,8 @@ def test_valid():
#shapes=shapes[400:426]
# I put -1 in case we forget to add version in the test to.
# I put -2 to test the reference version.
version=[-2,-1,6]
verbose=0
version = [-2, -1, 6]
verbose = 0
# version=[1]
random = True
......@@ -505,17 +572,19 @@ def test_valid():
if ones:
random = False
exec_conv(version, shapes, verbose, random, 'valid', print_=print_, ones=ones, rtol=1.1e-5)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
def test_full():
shapes = get_basic_shapes()
shapes +=get_shapes2()
shapes += get_shapes2()
#test image stride
shapes += get_shapes2(scales_img=(2,2),img_stride=(1,2))
shapes += get_shapes2(scales_img=(2,2),img_stride=(2,1))
shapes += get_shapes2(scales_img=(2,2),img_stride=(2,2))
shapes += get_shapes2(scales_img=(2,2),img_stride=(-1,-1))
shapes += get_shapes2(scales_img=(2,2),kern_stride=(-1,-1))
shapes += get_shapes2(scales_img=(2, 2), img_stride=(1, 2))
shapes += get_shapes2(scales_img=(2, 2), img_stride=(2, 1))
shapes += get_shapes2(scales_img=(2, 2), img_stride=(2, 2))
shapes += get_shapes2(scales_img=(2, 2), img_stride=(-1, -1))
shapes += get_shapes2(scales_img=(2, 2), kern_stride=(-1, -1))
#test subsample done in a separate fct
......@@ -557,13 +626,14 @@ def test_full():
]
# shapes=shapes[:277]
version=[-2,-1,0,1,2,3,4,5]
verbose=0
version = [-2, -1, 0, 1, 2, 3, 4, 5]
verbose = 0
# version=[4]
random=True
random = True
exec_conv(version, shapes, verbose, random, 'full')
def test_subsample():
# implement when
shapes = [
......@@ -573,14 +643,14 @@ def test_subsample():
, ((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))
shapes += get_shapes2(scales_img=(2,2),subsample=(2,2))
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))
shapes += get_shapes2(scales_img=(2, 2), subsample=(2, 2))
#We put only the version that implement the subsample to make the test faster.
version_valid = [-2,-1,1,3,11,12]
version_full = [-2,-1]
version_valid = [-2, -1, 1, 3, 11, 12]
version_full = [-2, -1]
verbose = 0
random = True
print_ = False
......@@ -588,8 +658,10 @@ def test_subsample():
if ones:
random = False
exec_conv(version_valid, shapes, verbose, random, 'valid', print_=print_, ones=ones)
exec_conv(version_full, shapes, verbose, random, 'full', print_=print_, ones=ones)
exec_conv(version_valid, shapes, verbose, random, 'valid',
print_=print_, ones=ones)
exec_conv(version_full, shapes, verbose, random, 'full',
print_=print_, ones=ones)
## See #616
#def test_logical_shapes():
......@@ -614,7 +686,8 @@ class TestConv2DGPU(unittest.TestCase):
theano_mode_orig = theano_mode
try:
if theano.config.mode in ['DebugMode', 'DEBUG_MODE']:
theano_mode = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
theano_mode = theano.compile.mode.get_mode(
'FAST_RUN').including('gpu')
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)),
......@@ -622,16 +695,21 @@ class TestConv2DGPU(unittest.TestCase):
# We use only the number of columns.
]:
self.assertRaises(ValueError, _params_allgood, shapes[0], shapes[1],
verbose=verbose, random=random, mode=mode,
print_=print_, ones=ones, compile_kshp=shapes[2])
self.assertRaises(ValueError, _params_allgood,
shapes[0], shapes[1],
verbose=verbose, random=random,
mode=mode,
print_=print_, ones=ones,
compile_kshp=shapes[2])
finally:
theano_mode = theano_mode_orig
def _test_dummy():
ishape = (1, 1, 5, 5)
kshape = (1, 1, 3, 3)
mode = 'valid'
subsample = (1,1)
subsample = (1, 1)
npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
......@@ -696,14 +774,14 @@ def benchmark():
,((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
version = [-1]
verbose = 1
random = True
exec_conv(version, shapes_valid, verbose, random, 'valid', print_=None, rtol=1e-3)
exec_conv(version, shapes_valid, verbose, random, 'valid',
print_=None, rtol=1e-3)
exec_conv(version, shapes_full, verbose, random, 'full')
......@@ -719,5 +797,3 @@ def test_stack_rows_segfault_070312():
nkern=1, bsize=1)
f = theano.function([], [], updates={out: op(img, kern)})
f()
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