Python basic.get_scalar_constant_value函数代码示例

def local_concatenateGrad_mkl(node):
    if not mkl_available():
        return

    if not isinstance(node.op, Split):
        return

    if node.inputs[0].type.ndim != 4:
        return

    try:
        gz, axis, splits, = node.inputs
        if not isinstance(axis, integer_types):
            try:
                axis = int(get_scalar_constant_value(axis))
            except NotScalarConstantError:
                return

        if isinstance(axis, integer_types):
            # MKL Concatenate only supports axis=1
            if axis != 1:
                return

        # Retrieve the inputs to Join op
        #                         inp_0             inp_1    inp
        #                         |                 |        |
        # Splits <- MakeVector <- [Subtensor...] <- Shape <- inputs
        if not isinstance(splits.owner.op, theano.tensor.opt.MakeVector):
            return

        tensors = []
        for inp_0 in splits.owner.inputs:
            if not isinstance(inp_0.owner.op, theano.tensor.subtensor.Subtensor):
                return

            inp_1 = inp_0.owner.inputs[0]
            if not isinstance(inp_1.owner.op, theano.compile.ops.Shape):
                return

            inp = inp_1.owner.inputs[0]
            tensors.append(inp)

        tensors_internal = [U2IConcatenate()(x) for x in tensors]
        new_inputs = [axis] + tensors_internal
        z_internal = mkl_concatenate.Concatenate()(*new_inputs)
        gz_internal = I2UGrad()(z_internal, gz)

        concatenateGradOut = mkl_concatenate.ConcatenateGrad()(gz_internal, axis, *tensors_internal)
        gx_user = [U2IGrad()(_x, _gz) for _x, _gz in zip(tensors, concatenateGradOut)]

        rval = gx_user
        return rval
    except Exception as e:
        msg = ('Failed to apply local opt to Op %s. '
               'Exception message: %s\n') % (node.op, str(e))
        _logger.warning(msg)
        return

def local_0_dot_x(node):
    if not isinstance(node.op, T.Dot):
        return False

    x = node.inputs[0]
    y = node.inputs[1]
    replace = False
    try:
        if get_scalar_constant_value(x) == 0:
            replace = True
    except NotScalarConstantError:
        pass

    try:
        if get_scalar_constant_value(y) == 0:
            replace = True
    except NotScalarConstantError:
        pass

    if replace:
        constant_zero = T.constant(0, dtype=node.outputs[0].type.dtype)
        if x.ndim == 2 and y.ndim == 2:
            constant_zero = assert_(constant_zero,
                                    T.eq(x.shape[1], y.shape[0]))
            return [T.alloc(constant_zero, x.shape[0], y.shape[1])]
        elif x.ndim == 1 and y.ndim == 2:
            constant_zero = assert_(constant_zero,
                                    T.eq(x.shape[0], y.shape[0]))
            return [T.alloc(constant_zero, y.shape[1])]
        elif x.ndim == 2 and y.ndim == 1:
            constant_zero = assert_(constant_zero,
                                    T.eq(x.shape[1], y.shape[0]))
            return [T.alloc(constant_zero, x.shape[0])]
        elif x.ndim == 1 and y.ndim == 1:
            constant_zero = assert_(constant_zero,
                                    T.eq(x.shape[0], y.shape[0]))
            return [constant_zero]
        else:
            _logger.warning("Optimization Warning: "
                            "Optimization theano/opt.py:local_0_dot_x Found "
                            "that it could apply, but was not implemented "
                            "for dot product with these input types:\n"
                            "(%s, %s)",
                            x.type, y.type)

def scalarconsts_rest(inputs):
    """Partition a list of variables into two kinds:
    scalar constants, and the rest."""
    consts = []
    origconsts = []
    nonconsts = []
    for i in inputs:
        try:
            v = get_scalar_constant_value(i)
            consts.append(v)
            origconsts.append(i)
        except NotScalarConstantError:
            nonconsts.append(i)
    return consts, origconsts, nonconsts

def local_max_and_argmax(node):
    """
    If we don't use the argmax, change it to a max only.
    """
    if node.op == T._max_and_argmax:
        if len(node.outputs[1].clients) == 0:
            #MaxAndArgmax support variable axis,
            #but CAReduce support only constant axis.
            try:
                axis = get_scalar_constant_value(node.inputs[1])
            except NotScalarConstantError:
                return False

            new = CAReduce(scal.maximum, axis)(node.inputs[0])
            return [new, None]

 def shape_dim_i(x, i):
     #print 'shape keys', shape_of.keys()
     #print 'args (x, i):', x, i
     try:
         return x.data.shape[i]
     except AttributeError:
         pass
     try:
         return int(get_scalar_constant_value(shape_of[x][i]))
     except NotScalarConstantError:
         pass
     try:
         return shape_of[x][i].eval()
     except:
         return -1 # an unsatisfiable shape

    def apply(self, fgraph):
        did_something = True
        while did_something:
            nodelist = fgraph.toposort()
            did_something = False
            for node in nodelist:
                if node.op == T._max_and_argmax:
                    if len(node.outputs[1].clients) == 0:
                        try:
                            axis = get_scalar_constant_value(node.inputs[1])
                        except NotScalarConstantError:
                            return False

                        new = CAReduce(scal.maximum, axis)(node.inputs[0])
                        try:
                            fgraph.replace_all_validate(((node.outputs[0], new),), reason=self.__class__.__name__)
                            did_something = True
                            break
                        except InconsistencyError, e:
                            pass

    def make_node(self, x, repeats):
        x = basic.as_tensor_variable(x)
        repeats = basic.as_tensor_variable(repeats)

        if repeats.dtype not in tensor.integer_dtypes:
            raise TypeError("repeats.dtype must be an integer.")

        # Some dtypes are not supported by numpy's implementation of repeat.
        # Until another one is available, we should fail at graph construction
        # time, not wait for execution.
        ptr_bitwidth = theano.configdefaults.local_bitwidth()
        if ptr_bitwidth == 64:
            numpy_unsupported_dtypes = ("uint64",)
        if ptr_bitwidth == 32:
            numpy_unsupported_dtypes = ("uint32", "int64", "uint64")

        if repeats.dtype in numpy_unsupported_dtypes:
            raise TypeError(
                (
                    "dtypes %s are not supported by numpy.repeat "
                    "for the 'repeats' parameter, " % str(numpy_unsupported_dtypes)
                ),
                repeats.dtype,
            )

        if self.axis is None:
            broadcastable = [False]
        else:
            try:
                const_reps = basic.get_scalar_constant_value(repeats)
            except basic.NotScalarConstantError:
                const_reps = None
            if const_reps == 1:
                broadcastable = x.broadcastable
            else:
                broadcastable = list(x.broadcastable)
                broadcastable[self.axis] = False

        out_type = theano.tensor.TensorType(x.dtype, broadcastable)

        return theano.Apply(self, [x, repeats], [out_type()])

def isNaN_or_Inf_or_None(x):
    isNone = x is None
    try:
        isNaN = numpy.isnan(x)
        isInf = numpy.isinf(x)
        isStr = isinstance(x, string_types)
    except Exception:
        isNaN = False
        isInf = False
        isStr = False
    if not isNaN and not isInf:
        try:
            val = get_scalar_constant_value(x)
            isInf = numpy.isinf(val)
            isNaN = numpy.isnan(val)
        except Exception:
            isNaN = False
            isInf = False
    if isinstance(x, gof.Constant) and isinstance(x.data, string_types):
        isStr = True
    else:
        isStr = False
    return isNone or isNaN or isInf or isStr

def local_concatenate_mkl(node):
    if not mkl_available():
        return

    if not isinstance(node.op, Join):
        return

    if node.inputs[1].type.ndim != 4:
        return

    try:
        axis, tensors = node.inputs[0], node.inputs[1:]

        if not isinstance(axis, integer_types):
            try:
                axis = int(get_scalar_constant_value(axis))
            except NotScalarConstantError:
                return

        if isinstance(axis, integer_types):
            # MKL Concatenate only supports axis=1
            if axis != 1:
                return

        tensors_internal = [U2IConcatenate()(x) for x in tensors]
        new_inputs = [axis] + tensors_internal
        concatenateOut = mkl_concatenate.Concatenate()(*new_inputs)
        z_user = I2U()(concatenateOut)
        rval = z_user

        return [rval]
    except Exception as e:
        msg = ('Failed to apply local opt to Op %s. '
               'Exception message: %s\n') % (node.op, str(e))
        _logger.warning(msg)
        return

def local_max_and_argmax(node):
    """
    If we don't use the argmax, change it to a max only.
    """
    if node.op == T._max_and_argmax:
        if len(node.outputs[1].clients) == 0:
            # MaxAndArgmax support variable axis,
            # but CAReduce support only constant axis.
            if node.inputs[1].data is None:
                axis = None
            else:
                try:
                    axis = get_scalar_constant_value(node.inputs[1])
                except NotScalarConstantError:
                    axis = node.inputs[1]
                    if not isinstance(axis, T.TensorConstant):
                        return False
                    axis = axis.data

            new = CAReduce(scal.maximum, axis)(node.inputs[0])
            return [new, None]

        if len(node.outputs[0].clients) == 0:
            return [None, T._argmax(node.inputs[0], node.inputs[1])]