def ensure_float(val, name):
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
if not val.type.dtype == 'float32':
raise TypeError("%s: type is not float32" % (name,))
return val
def wrapper(*args):
x = as_sparse_variable(args[0])
xs = [scalar.as_scalar(arg) for arg in args[1:]]
data, ind, ptr, shape = csm_properties(x)
data = tensor_op(data, *xs)
return CSM(x.format)(data, ind, ptr, shape)
def wrapper(*args):
x = as_sparse_variable(args[0])
xs = [scalar.as_scalar(arg) for arg in args[1:]]
data, ind, ptr, shape = csm_properties(x)
data = tensor_op(data, *xs)
return CSM(x.format)(data, ind, ptr, shape)
def ensure_float(val, name):
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name, ))
if not val.type.dtype == 'float32':
raise TypeError("%s: type is not float32" % (name, ))
return val
def make_node(self, kern, topgrad, desc, h, w):
kern = as_cuda_ndarray_variable(kern)
topgrad = as_cuda_ndarray_variable(topgrad)
if kern.type.ndim != 4:
raise TypeError('kern must be 4D tensor')
if topgrad.type.ndim != 4:
raise TypeError('topgrad must be 4D tensor')
if not isinstance(desc.type, CDataType) \
or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
raise TypeError('desc must be cudnnConvolutionDescriptor_t')
h = as_scalar(h)
w = as_scalar(w)
broadcastable = [topgrad.type.broadcastable[0],
kern.type.broadcastable[1],
False, False]
return Apply(self, [kern, topgrad, desc, h, w],
[CudaNdarrayType(broadcastable)()])
def ensure_dt(val, default, name, dtype):
if val is None:
val = default.clone()
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
if not val.type.dtype == dtype:
val = val.astype(dtype)
return val
def make_node(self, kern, topgrad, desc, h, w):
kern = as_cuda_ndarray_variable(kern)
topgrad = as_cuda_ndarray_variable(topgrad)
if kern.type.ndim != 4:
raise TypeError('kern must be 4D tensor')
if topgrad.type.ndim != 4:
raise TypeError('topgrad must be 4D tensor')
if not isinstance(desc.type, CDataType) \
or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
raise TypeError('desc must be cudnnConvolutionDescriptor_t')
h = as_scalar(h)
w = as_scalar(w)
broadcastable = [topgrad.type.broadcastable[0],
kern.type.broadcastable[1],
False, False]
return Apply(self, [kern, topgrad, desc, h, w],
[CudaNdarrayType(broadcastable)()])
def ensure_dt(val, default, name, dtype):
if val is None:
val = default.clone()
if not isinstance(val, Variable):
val = constant(val)
if hasattr(val, 'ndim') and val.ndim == 0:
val = as_scalar(val)
if not isinstance(val.type, theano.scalar.Scalar):
raise TypeError("%s: expected a scalar value" % (name,))
if not val.type.dtype == dtype:
val = val.astype(dtype)
return val
def make_node(self, pvals, unis, n=1):
pvals = tt.as_tensor_variable(pvals)
unis = tt.as_tensor_variable(unis)
if pvals.ndim != 2:
raise NotImplementedError("pvals ndim should be 2", pvals.ndim)
if unis.ndim != 1:
raise NotImplementedError("unis ndim should be 1", unis.ndim)
if self.odtype == "auto":
odtype = "int64"
else:
odtype = self.odtype
out = tt.tensor(dtype=odtype, broadcastable=pvals.type.broadcastable)
return Apply(self, [pvals, unis, as_scalar(n)], [out])
def make_node(self, pvals, unis, n=1):
pvals = T.as_tensor_variable(pvals)
unis = T.as_tensor_variable(unis)
if pvals.ndim != 2:
raise NotImplementedError('pvals ndim should be 2', pvals.ndim)
if unis.ndim != 1:
raise NotImplementedError('unis ndim should be 1', unis.ndim)
if self.odtype == 'auto':
odtype = pvals.dtype
else:
odtype = self.odtype
out = T.tensor(dtype=odtype, broadcastable=pvals.type.broadcastable)
return Apply(self, [pvals, unis, as_scalar(n)], [out])
def make_node(self, pvals, unis, n=1):
pvals = T.as_tensor_variable(pvals)
unis = T.as_tensor_variable(unis)
if pvals.ndim != 2:
raise NotImplementedError('pvals ndim should be 2', pvals.ndim)
if unis.ndim != 1:
raise NotImplementedError('unis ndim should be 1', unis.ndim)
if self.odtype == 'auto':
odtype = pvals.dtype
else:
odtype = self.odtype
out = T.tensor(dtype=odtype, broadcastable=pvals.type.broadcastable)
return Apply(self, [pvals, unis, as_scalar(n)], [out])
def make_node(self, pvals, unis, n):
assert pvals.dtype == "float32"
assert unis.dtype == "float32"
ctx_name = infer_context_name(pvals, unis)
pvals = as_gpuarray_variable(pvals, ctx_name)
unis = as_gpuarray_variable(unis, ctx_name)
if pvals.ndim != 2:
raise NotImplementedError("pvals ndim should be 2", pvals.ndim)
if unis.ndim != 1:
raise NotImplementedError("unis ndim should be 1", unis.ndim)
if self.odtype == "auto":
odtype = "int64"
else:
odtype = self.odtype
assert odtype == "int64", odtype
br = (pvals.broadcastable[1], pvals.broadcastable[0])
out = GpuArrayType(broadcastable=br, dtype=odtype, context_name=ctx_name)()
return Apply(self, [pvals, unis, as_scalar(n)], [out])
def make_node(self, pvals, unis, n):
assert pvals.dtype == 'float32'
assert unis.dtype == 'float32'
ctx_name = infer_context_name(pvals, unis)
pvals = as_gpuarray_variable(pvals, ctx_name)
unis = as_gpuarray_variable(unis, ctx_name)
if pvals.ndim != 2:
raise NotImplementedError('pvals ndim should be 2', pvals.ndim)
if unis.ndim != 1:
raise NotImplementedError('unis ndim should be 1', unis.ndim)
if self.odtype == 'auto':
odtype = 'int64'
else:
odtype = self.odtype
assert odtype == 'int64', odtype
br = (pvals.broadcastable[1], pvals.broadcastable[0])
out = GpuArrayType(broadcastable=br,
dtype=odtype,
context_name=ctx_name)()
return Apply(self, [pvals, unis, as_scalar(n)], [out])
def make_node(self, val):
from theano.scalar import as_scalar
from theano import Apply
val = as_scalar(val).astype('uint64')
return Apply(self, [val], [self.rtype()])
def make_node(self, a, b):
return Apply(self, [scalar.as_scalar(a), scalar.as_scalar(b)], [scalar.float64()])
def make_node(self, a, b):
return Apply(
self, [scalar.as_scalar(a), scalar.as_scalar(b)], [scalar.float64()]
)
def make_node(self, x, scal):
x = as_tensor_variable(x)
scal = as_scalar(scal)
return Apply(self, [x, scal], [x.type()])
def make_node(self, val):
from theano.scalar import as_scalar
from theano import Apply
val = as_scalar(val).astype('uint64')
return Apply(self, [val], [self.rtype()])
def make_node(self, x, scal):
x = as_tensor_variable(x)
scal = as_scalar(scal)
return Apply(self, [x, scal], [x.type()])
def make_node(self, input):
input = scalar.as_scalar(input)
output = input.type()
return Apply(self, [input], [output])
def make_node(self, x):
x = t.as_scalar(x)
return theano.Apply(self, [x], [x.type()])
