def test_dnn_conv_inplace():
"""This test that we have inplace work correctly even when
GpuAllocEmpty get merged together.
"""
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
img_shp = [2, 5, 6, 8]
kern_shp = [3, 5, 5, 6]
img = T.ftensor4('img')
kern = T.ftensor4('kern')
out = T.ftensor4('out')
desc1 = dnn.GpuDnnConvDesc(border_mode='valid',
conv_mode='conv')(img.shape, kern.shape)
desc2 = dnn.GpuDnnConvDesc(border_mode='valid',
conv_mode='cross')(img.shape, kern.shape)
# Test forward op
o1 = dnn.dnn_conv(img, kern, conv_mode='conv')
o2 = dnn.dnn_conv(img, kern, conv_mode='cross')
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
d1, d2 = f(
numpy.random.rand(*img_shp).astype('float32'),
numpy.random.rand(*kern_shp).astype('float32'))
topo = f.maker.fgraph.toposort()
convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]
assert len(convs) == 2
assert all([node.op.inplace for node in convs])
assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
# Test grad w op
out = gpu_alloc_empty(*kern.shape)
o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc1)
o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc2)
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradW)]
assert len(convs) == 2
assert all([node.op.inplace for node in convs])
assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
# Test grad i op
out = gpu_alloc_empty(*img.shape)
o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc1)
o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc2)
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradI)]
assert len(convs) == 2
assert all([node.op.inplace for node in convs])
assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
def test_conv_gradi(self):
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4('img')
kerns = T.ftensor4('kerns')
out = T.ftensor4('out')
img_val = numpy.asarray(numpy.random.rand(3, 4, 5, 6), dtype='float32')
kern_vals = numpy.asarray(numpy.random.rand(3, 4, 5, 6),
dtype='float32')
for params in product(
['valid'], # Should this work for 'full'?
[(1, 1)],
['conv', 'cross']):
temp_kerns = kerns.dimshuffle(1, 0, 2, 3)
shape = (img_val.shape[0], kern_vals.shape[1],
img_val.shape[2] + kern_vals.shape[2] - 1,
img_val.shape[3] + kern_vals.shape[3] - 1)
out_vals = numpy.zeros(shape, dtype='float32')
desc = dnn.GpuDnnConvDesc(border_mode=params[0],
subsample=params[1],
conv_mode=params[2])(out.shape,
temp_kerns.shape)
conv_grad_i = dnn.GpuDnnConvGradI()(
temp_kerns,
img,
out,
desc,
)
self._compile_and_check([temp_kerns, img, out], [conv_grad_i],
[kern_vals, img_val, out_vals],
dnn.GpuDnnConvGradI)
def dconvi(img, kern, out):
desc = dnn.GpuDnnConvDesc(border_mode='valid',
subsample=(1, 1),
conv_mode='conv')(img.shape, kern.shape)
return dnn.GpuDnnConvGradI()(kern,
out,
img,
desc,
alpha=-1.0,
beta=0.0)
def test_dnn_conv_merge():
"""This test that we merge correctly multiple dnn_conv.
This can is more difficult due to GpuEmptyAlloc that aren't
merged.
"""
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
img_shp = [2, 5, 6, 8]
kern_shp = [3, 5, 5, 6]
img = T.ftensor4('img')
kern = T.ftensor4('kern')
out = T.ftensor4('out')
desc = dnn.GpuDnnConvDesc(
border_mode='valid')(img.shape, kern.shape)
# Test forward op
o1 = dnn.dnn_conv(img, kern)
o2 = dnn.dnn_conv(img, kern)
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
d1, d2 = f(numpy.random.rand(*img_shp).astype('float32'),
numpy.random.rand(*kern_shp).astype('float32'))
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]) == 1
# Test grad w op
o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc)
o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc)
f = theano.function([img, kern, out], [o1, o2], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradW)]) == 1
# Test grad i op
o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc)
o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc)
f = theano.function([img, kern, out], [o1, o2], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradI)]) == 1
def test_conv_gradi(self):
img = T.ftensor4('img')
kerns = T.ftensor4('kerns')
img_val = numpy.asarray(numpy.random.rand(3, 4, 5, 6), dtype='float32')
kern_vals = numpy.asarray(numpy.random.rand(3, 4, 5, 6),
dtype='float32')
for params in product(
['valid'], # Should this work for 'full'?
[(1, 1)],
['conv', 'cross']):
print params
temp_kerns = kerns.dimshuffle(1, 0, 2, 3)
shape = theano.tensor.stack(img.shape[0], temp_kerns.shape[1],
img.shape[2] + temp_kerns.shape[2] - 1,
img.shape[3] + temp_kerns.shape[3] - 1)
desc = dnn.GpuDnnConvDesc(border_mode=params[0],
subsample=params[1],
conv_mode=params[2])(shape,
temp_kerns.shape)
conv_grad_i = dnn.GpuDnnConvGradI()(temp_kerns, img, desc,
shape[2], shape[3])
self._compile_and_check([temp_kerns, img], [conv_grad_i],
[kern_vals, img_val], dnn.GpuDnnConvGradI)
