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提交 f44feaaa authored 作者: lamblin's avatar lamblin
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Merge pull request #616 from nouiz/gpu_conv_faster

Gpu conv faster
上级 f4b3c8ad ecd6f2b3
隐藏空白字符变更
内嵌 并排
正在显示 包含 181 行增加 和 143 行删除
theano/sandbox/cuda/__init__.py
浏览文件 @ f44feaaa
......@@ -309,8 +309,15 @@ def use(device,
device = 0
try:
if device != 'gpu':
assert isinstance(device, int)
gpu_init(device)
use.device_number = device
use.device_number = device
else:
# This mean we let the driver select the GPU.
# But default it is always number 0.
# If the driver is in exclusive mode, it will always show
# device 0 event if it use something else.
use.device_number = 0
if test_driver:
import theano.sandbox.cuda.tests.test_driver
theano.sandbox.cuda.tests.test_driver.test_nvidia_driver1()
......
theano/sandbox/cuda/blas.py
浏览文件 @ f44feaaa
......@@ -718,10 +718,11 @@ class GpuConv(GpuOp):
node_ = copy.copy(node)
assert node.op is node_.op
if node_.op.max_threads_dim0 is None:
op = copy.copy(node_.op)
device_id = theano.sandbox.cuda.use.device_number[3:]
if device_id == '':
device_id = 0
cuda = theano.sandbox.cuda
device_id = cuda.use.device_number
if device_id is None:
cuda.use("gpu", False, False, False, False, True)
device_id = cuda.use.device_number
cuda_ndarray = theano.sandbox.cuda.cuda_ndarray.cuda_ndarray
prop = cuda_ndarray.device_properties(device_id)
node_.op.max_threads_dim0 = prop['maxThreadsDim0']
......
theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py
浏览文件 @ f44feaaa
......@@ -35,9 +35,9 @@ device_id = theano.sandbox.cuda.use.device_number
if device_id is None:
cuda_ndarray.shared_constructor(numpy.zeros(2, dtype='float32'))
device_id = theano.sandbox.cuda.use.device_number
device_id = device_id[3:]
if device_id == '':
device_id = 0
if device_id is None:
cuda.use("gpu", False, False, False, False, True)
device_id = theano.sandbox.cuda.use.device_number
cuda_ndarray = theano.sandbox.cuda.cuda_ndarray.cuda_ndarray
device_prop = cuda_ndarray.device_properties(device_id)
......@@ -55,7 +55,7 @@ def py_conv_valid_numpy(img, kern):
#rr, cc is the upper-left corner of img patches
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()
out[b, k, rr, cc] = innerprod
......@@ -127,9 +127,10 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
# See `test_example` (above) for an example of how to use this directly.
#
# :param kshape: (4d)The shape of the kernel at run time.
# :param compile_kshp: (2d) hardcode the shape of the kernel in the generated code
# This is supposed to be faster, but we need to check
# That we raise an error if the input have the wrong shape.
# :param compile_kshp: (2d) hardcode the shape of the kernel in
# the generated code This is supposed to be
# faster, but we need to check That we raise
# an error if the input have the wrong shape.
#
if ones:
assert not random
......@@ -195,7 +196,8 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
if mode == 'valid':
approx_fp = cpuval.size * ishape[1] * kshape[2] * kshape[3] * 2
else:
approx_fp = ishape[0] * kshape[0] * kshape[1] * kshape[2] * kshape[3] * ishape[2] * ishape[3] * 2
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)
......@@ -204,14 +206,21 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
print >> sys.stdout, '%12.5f %7.2f %7.2f %7.1f' % (approx_fp,
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
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()
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)
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]
......@@ -275,17 +284,16 @@ def exec_conv(version, shapes, verbose, random, mode,
def get_basic_shapes():
return [
#basic test of image and kernel shape
((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))
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
, ((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))
, ((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))]
((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)),
((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),
......@@ -293,40 +301,56 @@ def get_shapes(imshp=(1, 1), kshp=(1, 1), subsample=(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
, ((3, 1)+imshp, (2, 1)+kshp,subsample, img_stride, kern_stride)#batch and nkern
, ((3, 2)+imshp, (1, 2)+kshp,subsample, img_stride, kern_stride)#batch and stack
, ((1, 2)+imshp, (2, 2)+kshp,subsample, img_stride, kern_stride)#stack and nkern
, ((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
]
return [
#stack only
((1, 2) + imshp, (1, 2) + kshp, subsample, img_stride, kern_stride),
#batch only
((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
((3, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
#batch and stack
((3, 2) + imshp, (1, 2) + kshp, subsample, img_stride, kern_stride),
#stack and nkern
((1, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
#batch, nkern and stack
((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)
]
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)
shapes = get_shapes((1 * scales_img[0], 1 * scales_img[1]),
(1 * scales_kern[0], 1 * scales_kern[1]),
subsample, img_stride, kern_stride)
#basic test of stack, batch and nkern paramter with image and kernel shape
shapes +=get_shapes((2*scales_img[0],2*scales_img[1]),
(2*scales_kern[0],2*scales_kern[1]),subsample, img_stride, kern_stride)
shapes += get_shapes((2 * scales_img[0], 2 * scales_img[1]),
(2 * scales_kern[0], 2 * scales_kern[1]),
subsample, img_stride, kern_stride)
#basic test of stack, batch and nkern paramter with image and kernel shape
shapes +=get_shapes((3*scales_img[0],3*scales_img[1]),
(2*scales_kern[0],2*scales_kern[1]),subsample, img_stride, kern_stride)
shapes += get_shapes((3 * scales_img[0], 3 * scales_img[1]),
(2 * scales_kern[0], 2 * scales_kern[1]),
subsample, img_stride, kern_stride)
#basic test of stack, batch and nkern paramter with not square image.
shapes +=get_shapes((4*scales_img[0],3*scales_img[1]),
(2*scales_kern[0],2*scales_kern[1]),subsample, img_stride, kern_stride)
shapes += get_shapes((4 * scales_img[0], 3 * scales_img[1]),
(2 * scales_kern[0], 2 * scales_kern[1]),
subsample, img_stride, kern_stride)
#basic test of stack, batch and nkern paramter with not square image.
shapes +=get_shapes((3*scales_img[0],4*scales_img[1]),
(2*scales_kern[0],2*scales_kern[1]),subsample, img_stride, kern_stride)
shapes += get_shapes((3 * scales_img[0], 4 * scales_img[1]),
(2 * scales_kern[0], 2 * scales_kern[1]),
subsample, img_stride, kern_stride)
#basic test of stack, batch and nkern paramter with not square kernel.
shapes +=get_shapes((4*scales_img[0],4*scales_img[1]),
(3*scales_kern[0],2*scales_kern[1]),subsample, img_stride, kern_stride)
shapes += get_shapes((4 * scales_img[0], 4 * scales_img[1]),
(3 * scales_kern[0], 2 * scales_kern[1]),
subsample, img_stride, kern_stride)
#basic test of stack, batch and nkern paramter with not square kernel.
shapes +=get_shapes((4*scales_img[0],4*scales_img[1]),
(2*scales_kern[0],3*scales_kern[1]),subsample, img_stride, kern_stride)
shapes += get_shapes((4 * scales_img[0], 4 * scales_img[1]),
(2 * scales_kern[0], 3 * scales_kern[1]),
subsample, img_stride, kern_stride)
return shapes
......@@ -348,34 +372,37 @@ def get_valid_shapes():
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
((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 then 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
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
]
return shapes
......@@ -483,7 +510,7 @@ def test_valid_5():
random = False
shapes2 = []
print len(shapes)
# 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:]) +
......@@ -496,7 +523,7 @@ def test_valid_5():
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
print len(shapes2)
# print len(shapes2)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
......@@ -517,7 +544,7 @@ def test_valid_7_8_13():
random = False
shapes2 = []
print len(shapes)
# 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:]) +
......@@ -530,7 +557,7 @@ def test_valid_7_8_13():
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
print len(shapes2)
# print len(shapes2)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
......@@ -548,7 +575,7 @@ def test_valid_9_10():
random = False
shapes2 = []
print len(shapes)
# 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:]) +
......@@ -560,7 +587,7 @@ def test_valid_9_10():
if subshape == (1, 1):
shapes2.append((ishape, kshape, subshape, istride, kstride))
shapes = shapes2
print len(shapes2)
# print len(shapes2)
exec_conv(version, shapes, verbose, random, 'valid',
print_=print_, ones=ones, rtol=1.1e-5)
......@@ -600,39 +627,42 @@ 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
# ((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 then 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=shapes[:277]
......@@ -646,13 +676,12 @@ def test_full():
def test_subsample():
# implement when
shapes = [
((1, 1, 1, 1), (1, 1, 1, 1), (1,1), (1,1), (1,1))
, ((1, 1, 1, 1), (1, 1, 1, 1), (2,2), (1,1), (1,1))
, ((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 = [((1, 1, 1, 1), (1, 1, 1, 1), (1, 1), (1, 1), (1, 1)),
((1, 1, 1, 1), (1, 1, 1, 1), (2, 2), (1, 1), (1, 1)),
((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))
......@@ -676,7 +705,8 @@ def test_subsample():
## See #616
#def test_logical_shapes():
# # implement when
# print >> sys.stderr, "WARNING TODO: test_logical_shapes not implemented (i.e. imshp_logical, kshp_logical, kshp_logical_top_aligned)"
# print >> sys.stderr, ("WARNING TODO: test_logical_shapes not implemented"
# " (i.e. imshp_logical, kshp_logical, kshp_logical_top_aligned)")
class TestConv2DGPU(unittest.TestCase):
......@@ -745,43 +775,43 @@ 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:]
......
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