def garray_to_cudandarray_nested(lst):
lst_flat = flatten(lst)
lst_flat = [gput.garray_to_cudandarray(i)
if not isinstance(i, theano.sandbox.cuda.CudaNdarray) else i
for i in lst_flat]
lst = unflatten(lst, lst_flat)
return lst
def garray_to_cudandarray_nested(lst):
lst_flat = flatten(lst)
lst_flat = [
gput.garray_to_cudandarray(i)
if not isinstance(i, theano.sandbox.cuda.CudaNdarray) else i
for i in lst_flat
]
lst = unflatten(lst, lst_flat)
return lst
def Run_fmin_cg(self, maxiter):
x0 = self.classifier.flat_params.get_value(borrow=True,
return_internal_type=True)
x0 = gnumpy_utils.cudandarray_to_garray(x0)
f_op, params_opt = fmin_cg(f=self.ObjFun,
x0=x0,
fprime=self.ObjFunPrime,
maxiter=maxiter)
params_opt = gnumpy_utils.garray_to_cudandarray(params_opt)
self.classifier.flat_params.set_value(params_opt)
def garray_to_cudandarray_nested(lst):
#print "sizeof(lst): " + str(getsizeof(lst))
for idx,i in enumerate(lst):
lst[idx].shape = tuple(int(q) for q in i.shape)
lst_flat = flatten(lst)
#print "len(lst_flat[0]): " + str(len(lst_flat[0]))
#print "len(lst_flat[1]): " + str(len(lst_flat[1]))
#print "len(lst_flat[2]): " + str(len(lst_flat[2]))
#print "Size of vector in garray_to_cudandarray_nested:" + str(len(lst))
#print "Contents lst_flat[0]: " + str(lst[0].shape)
#print "Contents lst_flat[1]: " + str(lst[1].shape)
#print "Contents lst_flat[2]: " + str(lst[2].shape)
#lst_flat = [gput.garray_to_cudandarray(i) for i in lst_flat[1:len(lst_flat)]]
lst_flat = [gput.garray_to_cudandarray(i) for i in lst_flat]
# #for idx,i in enumerate(lst_flat):
# lst[idx].shape = tuple(int(q) for q in i.shape)
# if lst_flat[idx].stride != None:
# lst_flat[idx].stride = tuple(int(q) for q in i.stride)
# print "Stride was long.\n"
# else:
# print "Stride was None.\n"
# print "sizeof(i): " + str(getsizeof(i))
# tmp = []
# for i in lst_flat:
# print "garray_to_cudandarray_nested:i.shape:" + str(i.shape)
# tmp_ptr = gput.garray_to_cudandarray(i)
# print "garray_to_cudandarray_nested:returned ptr:" + str(tmp_ptr)
# print "garray_to_cudandarray_nested:returned ptr:" + str(tmp_ptr.shape)
#
# tmp.append(tmp_ptr)
# print "loop"
#for i in lst_flat:
# print "sizeof(i): " + str(getsizeof(i))
#print "len(lst_flat[0]): " + str(len(lst_flat[0]))
#print "len(lst_flat[1]): " + str(len(lst_flat[1]))
#print "len(lst_flat[2]): " + str(len(lst_flat[2]))
lst = unflatten(lst, lst_flat)
#print "here"
# lst = unflatten(lst, tmp)
# print "here1"
return lst
def test(shape=(3,4,5)):
"""
Make sure that the gnumpy conversion is exact.
"""
gpu = theano.sandbox.cuda.basic_ops.gpu_from_host
U = gpu(theano.tensor.ftensor3('U'))
ii = theano.function([U], gpu(U+1))
A = gnumpy.rand(*shape)
A_cnd = garray_to_cudandarray(A)
B_cnd = ii(A_cnd)
B = cudandarray_to_garray(B_cnd)
from numpy import array
B2 = array(B_cnd)
u = (A+1).asarray()
v = B.asarray()
w = B2
assert abs(u-v).max() == 0
assert abs(u-w).max() == 0
def test(shape=(3, 4, 5)):
"""
Make sure that the gnumpy conversion is exact from garray to
CudaNdarray back to garray.
"""
gpu = theano.sandbox.cuda.basic_ops.gpu_from_host
U = gpu(theano.tensor.ftensor3('U'))
ii = theano.function([U], gpu(U + 1))
A = gnumpy.rand(*shape)
A_cnd = garray_to_cudandarray(A)
assert A_cnd.shape == A.shape
# dtype always float32
# garray don't have strides
B_cnd = ii(A_cnd)
B = cudandarray_to_garray(B_cnd)
assert A_cnd.shape == A.shape
u = (A + 1).asarray()
v = B.asarray()
w = np.array(B_cnd)
assert (u == v).all()
assert (u == w).all()
def test2(shape=(3, 4, 5)):
"""
Make sure that the gnumpy conversion is exact from CudaNdarray to
garray back to CudaNdarray.
"""
gpu = theano.sandbox.cuda.basic_ops.gpu_from_host
U = gpu(theano.tensor.ftensor3('U'))
theano.function([U], gpu(U + 1))
A = np.random.rand(*shape).astype('float32')
A_cnd = theano.sandbox.cuda.CudaNdarray(A)
A_gar = cudandarray_to_garray(A_cnd)
assert A_cnd.shape == A_gar.shape
# dtype always float32
# garray don't have strides
B = garray_to_cudandarray(A_gar)
assert A_cnd.shape == B.shape
# dtype always float32
assert A_cnd._strides == B._strides
assert A_cnd.gpudata == B.gpudata
v = np.asarray(B)
assert (v == A).all()
def garray_to_cudandarray_nested(lst):
lst_flat = flatten(lst)
lst_flat = [gput.garray_to_cudandarray(i) for i in lst_flat]
lst = unflatten(lst, lst_flat)
return lst
def garray_to_cudandarray_nested(lst):
lst_flat = flatten(lst)
lst_flat = [gput.garray_to_cudandarray(i) for i in lst_flat]
lst = unflatten(lst, lst_flat)
return lst
def ObjFunPrime(self, params):
params = gnumpy_utils.garray_to_cudandarray(params)
grad = self.grad_Tfunc(params)
return gnumpy_utils.cudandarray_to_garray(grad)
def ObjFun(self, params):
params = gnumpy_utils.garray_to_cudandarray(params)
return self.get_cost_at_x(params)
