Python compat.izip函数代码示例

            def p(node, args, outs):
                # copy inputs if not inplace
                if not self.inplace:
                    for _, _, val in state_buffers:
                        val[0] = val[0].copy()
                    for buf in non_numeric_states_bufs:
                        buf[0] = buf[0].copy()

                # reset all switches if any
                for sw in self.switches:
                    sw.set_value(numpy.int8(0), borrow=True)
                # set aux shared variables
                for var, val in aux_buffers:
                    var.set_value(val[0], borrow=True)
                # set state shared variables
                for var, length, val in state_buffers:
                    var.set_value(val[0], borrow=True)
                    length.set_value(val[0].shape[0], borrow=True)
                self.index.set_value(numpy.int64(0))
                # grab fixed arguments
                fix_args = [x[0] for x in non_tensor_buffers]
                for dx in xrange(node_input_storage[0][0]):
                    extra_args = [x[0] for x in non_numeric_states_bufs]
                    rvals = self.fn(*(fix_args + extra_args))
                    for buf, rval in izip(non_numeric_states_bufs, rvals):
                        buf[0] = rval
                for pos in xrange(n_numeric_values):
                    buf = state_buffers[pos][0].get_value(borrow=True)
                    mintap = self.mintaps[pos]
                    node_output_storage[pos][0] = buf
                for out_buf, in_buf in izip(
                        node_output_storage[n_numeric_values:],
                        non_numeric_states_bufs):
                    out_buf[0] = in_buf[0]

    def __eq__(self, other):
        # Check if we are dealing with same type of objects
        if not type(self) == type(other):
            return False
        if self.options != other.options:
            return False
        if self.mintals != other.mintaps:
            return False
        # Check if the number of different types of arguments is the same
        diff_args = ['inputs', 'outputs', 'lengths', 'mintaps', 'switches']
        for arg in diff_args:
            if len(getattr(self, arg)) != len(getattr(other, arg)):
                return False
        for x, y in izip(self.inputs, other.inputs):
            if x.type != y.type:
                return False
        for x, y in izip(self.lengths, other.lengths):
            if x.type != y.type:
                return False

        s_ins = [self.index] + self.inputs + self.lengths + self.switches
        o_ins = [other.index] + other.inputs + other.lengths + other.switches
        givens = dict(izip(s_ins, o_ins))
        # This part might be slow
        for x, y in izip(self.outputs, other.outputs):
            if not gof.graph.is_same_graph(x, y, givens=givens):
                return False
        return True

 def infer_shape(self, node, input_shapes):
     for inp, inp_shp in izip(node.inputs, input_shapes):
         assert inp_shp is None or len(inp_shp) == inp.type.ndim
     n_outs = len(self.outputs)
     if self.as_repeatUntil is not None:
         return [(Shape_i(0)(o),) + x[1:] for o, x
                 in izip(node.outputs, input_shapes[1: n_outs + 1])]
     else:
         return input_shapes[1: n_outs + 1]

def map_storage(fgraph, order, input_storage, output_storage):
    """Ensure there is storage (a length-1 list) for inputs, outputs, and interior nodes.

    :param fgraph: The current fgraph.  This function uses the inputs and outputs attributes.
    :param order: an iterable over Apply instances (in program running order)
    :param input_storage: None or existing input storage (see below)
    :param output_storage: None or existing output storage (see below)

    :rtype: 3-tuple
    :returns: (list of storage for inputs, list of storage for outputs, and the `storage_map`)


    This function iterates over the nodes in `order` and ensures that for every
    input and output `Variable`, there is a unique storage container.  This is
    returned as a dictionary Variable->storage called the `storage_map`.

    This function also returns `input_storage` which is a list of storages corresponding to fgraph.inputs.
    This function also returns `output_storage` which is a list of storages corresponding to fgraph.outputs.

    """
    # each Apply argument's data is stored in a list of length 1 (these lists act like pointers)

    # input_storage is a list of data-containers for the inputs.
    if input_storage is None:
        input_storage = [[None] for input in fgraph.inputs]
    else:
        assert len(fgraph.inputs) == len(input_storage)

    storage_map = {}
    for r, storage in izip(fgraph.inputs, input_storage):
        storage_map[r] = storage
#     for orphan in fgraph.orphans:
#         if not isinstance(orphan, Constant):
#             raise TypeError("Cannot link a graph with non-constant orphans.", orphan)
#         storage_map[orphan] = [orphan.data]

    if output_storage is not None:
        assert len(fgraph.outputs) == len(output_storage)
        for r, storage in izip(fgraph.outputs, output_storage):
            storage_map[r] = storage

    for node in order:
        for r in node.inputs:
            if r not in storage_map:
                assert isinstance(r, graph.Constant)
                storage_map[r] = [r.data]
        for r in node.outputs:
            storage_map.setdefault(r, [None])
    for r in fgraph.outputs:
        if isinstance(r, graph.Constant):
            storage_map.setdefault(r, [r.data])

    if output_storage is None:
        output_storage = [storage_map[r] for r in fgraph.outputs]

    return input_storage, output_storage, storage_map

    def make_thunk(self, node, storage_map, _, _2, impl=None):
        # TODO support broadcast!
        # TODO assert all input have the same shape
        fct_name = "pycuda_elemwise_%s" % str(self.scalar_op)
        in_name = ["i" + str(id) for id in range(len(node.inputs))]
        out_name = ["o" + str(id) for id in range(self.nout)]

        c_code = self.scalar_op.c_code(node, "some_name",
                                       tuple([n + "[i]" for n in in_name]),
                                       tuple(n + "[i]" for n in out_name), {})
        c_code_param = ", ".join(
            [_replace_npy_types(var.type.dtype_specs()[1]) + " *" + name
             for var, name in chain(izip(node.inputs, in_name),
                                    izip(node.outputs, out_name))] +
            ["int size"])

        mod = SourceModule("""
  __global__ void %s(%s)
  {
    int i = (blockIdx.x+blockIdx.y*gridDim.x)*(blockDim.x*blockDim.y);
    i += threadIdx.x + threadIdx.y*blockDim.x;
    if(i<size){
        %s
    }
  }
  """ % (fct_name, c_code_param, c_code))
        pycuda_fct = mod.get_function(fct_name)
        inputs = [storage_map[v] for v in node.inputs]
        outputs = [storage_map[v] for v in node.outputs]

        def thunk():
            z = outputs[0]
            if (z[0] is None or
                    z[0].shape != inputs[0][0].shape or
                    not z[0].is_c_contiguous()):
                z[0] = theano.sandbox.cuda.CudaNdarray.zeros(
                    inputs[0][0].shape)
            if inputs[0][0].shape != inputs[1][0].shape:
                raise TypeError("PycudaElemwiseSourceModuleMakeThunkOp:"
                                " inputs don't have the same shape!")

            if inputs[0][0].size > 512:
                grid = (int(numpy.ceil(inputs[0][0].size / 512.)), 1)
                block = (512, 1, 1)
            else:
                grid = (1, 1)
                block = (inputs[0][0].shape[0], inputs[0][0].shape[1], 1)
            pycuda_fct(inputs[0][0], inputs[1][0], z[0],
                       numpy.intc(inputs[1][0].size), block=block,
                       grid=grid)
        thunk.inputs = inputs
        thunk.outputs = outputs
        thunk.lazy = False

        return thunk

 def f():
     for inputs in input_lists[1:]:
         for input1, input2 in izip(inputs0, inputs):
             input2.storage[0] = copy(input1.storage[0])
     for x in to_reset:
         x[0] = None
     pre(self, [input.data for input in input_lists[0]], order, thunk_groups)
     for i, (thunks, node) in enumerate(izip(thunk_groups, order)):
         try:
             wrapper(i, node, *thunks)
         except Exception:
             raise_with_op(node, *thunks)

def infer_shape(outs, inputs, input_shapes):
    """
    Compute the shape of the outputs given the shape of the inputs
    of a theano graph.

    We do it this way to avoid compiling the inner function just to get the
    shape. Changes to ShapeFeature could require changes in this function.

    """
    # We use a ShapeFeature because it has all the necessary logic
    # inside.  We don't use the full ShapeFeature interface, but we
    # let it initialize itself with an empty fgraph, otherwise we will
    # need to do it manually
    for inp, inp_shp in izip(inputs, input_shapes):
        if inp_shp is not None and len(inp_shp) != inp.ndim:
            assert len(inp_shp) == inp.ndim

    shape_feature = tensor.opt.ShapeFeature()
    shape_feature.on_attach(theano.gof.FunctionGraph([], []))

    # Initialize shape_of with the input shapes
    for inp, inp_shp in izip(inputs, input_shapes):
        shape_feature.set_shape(inp, inp_shp)

    def local_traverse(out):
        """
        Go back in the graph, from out, adding computable shapes to shape_of.

        """

        if out in shape_feature.shape_of:
            # Its shape is already known
            return
        elif out.owner is None:
            # This is an input of the graph
            shape_feature.init_r(out)
        else:
            # Recurse over inputs
            for inp in out.owner.inputs:
                if not inp in shape_feature.shape_of:
                    local_traverse(inp)

            # shape_feature.on_import does not actually use an fgraph
            # It will call infer_shape and set_shape appropriately
            dummy_fgraph = None
            shape_feature.on_import(dummy_fgraph, out.owner, reason="dummy")

    ret = []
    for o in outs:
        local_traverse(o)
        ret.append(shape_feature.shape_of[o])
    return ret

    def grad(self, inputs, output_grads):
        # OpFromGraph doesn't implement a connection_pattern, so for
        # now we regard all inputs and outputs as connected. This will
        # compute the right numerical value for the gradients but
        # could fail to raise the disconnected inputs error in some
        # cases.
        if hasattr(self, "grad_ops"):
            grad_ops = self.grad_ops
        else:
            gs = theano.gradient.grad(cost=None,
                                      known_grads=dict(izip(self.new_outputs,
                                                            output_grads)),
                                      wrt=self.new_inputs,
                                      disconnected_inputs='ignore')

            grad_ops = []
            for g in gs:
                if g is None:
                    grad_ops.append(lambda *args: None)
                else:
                    # It is normal if some inputs are not needed in order
                    # to compute the gradient, so we ignore them.
                    grad_ops.append(OpFromGraph(self.new_inputs + output_grads,
                                                [g],
                                                on_unused_input='ignore'))
            self.grad_ops = grad_ops

        return [go(*(inputs + output_grads)) for go in grad_ops]

 def perform(self, node, inputs, outputs):
     variables = self.fn(*inputs)
     assert len(variables) == len(outputs)
     for output, variable in izip(outputs, variables):
         # TODO: when function's output-borrowing semantics are correct,
         # we wont need this copy anymore
         output[0] = variable.copy()

    def __init__(self, inputs, outputs, **kwargs):
        if not isinstance(outputs, list):
            raise TypeError("outputs must be list", outputs)
        for i in inputs + outputs:
            if not isinstance(i, gof.Variable):
                raise TypeError("inputs and outputs must be Variable instances", i)
        if "updates" in kwargs or "givens" in kwargs:
            raise TypeError("updates and givens are not allowed in kwargs")

        # To support correctly shared variables the inner fct should
        # not see them. Otherwise their is problem with the gradient.
        self.shared_inputs = [var for var in gof.graph.inputs(outputs) if isinstance(var, SharedVariable)]
        shared_vars = [var.type() for var in self.shared_inputs]
        new = rebuild_collect_shared(
            outputs,
            inputs=inputs + shared_vars,
            replace=dict(izip(self.shared_inputs, shared_vars)),
            copy_inputs_over=False,
        )
        (new_inputs, new_outputs, [clone_d, update_d, update_expr, shared_inputs]) = new
        assert len(new_inputs) == len(inputs) + len(self.shared_inputs)
        assert len(new_outputs) == len(outputs)
        assert not update_d
        assert not update_expr
        assert not shared_inputs

        self.new_inputs = new_inputs
        self.new_outputs = new_outputs
        self.inputs = inputs
        self.outputs = outputs
        self.kwargs = kwargs
        self.input_types = [input.type for input in inputs]
        self.output_types = [output.type for output in outputs]

    def make_node(self, c, *args):
        assert len(args) == 2 * self.n_outs, (
            "Wrong number of arguments to make_node: "
            "expected %d, got %d" % (2 * self.n_outs, len(args))
        )
        if not self.gpu:
            # When gpu is true, we are given only cuda ndarrays, and we want
            # to keep them be cuda ndarrays
            c = theano.tensor.as_tensor_variable(c)
            nw_args = []
            for x in args:
                if isinstance(x, theano.Variable):
                    nw_args.append(x)
                else:
                    nw_args.append(theano.tensor.as_tensor_variable(x))
            args = nw_args
        ts = args[:self.n_outs]
        fs = args[self.n_outs:]

        for t, f in izip(ts, fs):
            if t.type != f.type:
                raise TypeError(('IfElse requires same types for true and '
                                'false return values'), t, f, t.type, f.type)
        if c.ndim > 0:
            raise TypeError(('Condition given to the op has to be a scalar '
                             'with 0 standing for False, anything else '
                             'for True'))
        return Apply(self, [c] + list(args), [t.type() for t in ts])

def shape_of_variables(fgraph, input_shapes):
    """
    Compute the numeric shape of all intermediate variables given input shapes.

    Parameters
    ----------
    fgraph
        The theano.FunctionGraph in question.
    input_shapes : dict
        A dict mapping input to shape.

    Returns
    -------
    shapes : dict
        A dict mapping variable to shape

    .. warning:: This modifies the fgraph. Not pure.

    Examples
    --------
    >>> import theano
    >>> x = theano.tensor.matrix('x')
    >>> y = x[512:]; y.name = 'y'
    >>> fgraph = theano.FunctionGraph([x], [y], clone=False)
    >>> d = shape_of_variables(fgraph, {x: (1024, 1024)})
    >>> d[y]
    (array(512), array(1024))
    >>> d[x]
    (array(1024), array(1024))
    """

    if not hasattr(fgraph, 'shape_feature'):
        fgraph.attach_feature(theano.tensor.opt.ShapeFeature())

    input_dims = [dimension for inp in fgraph.inputs
                  for dimension in fgraph.shape_feature.shape_of[inp]]

    output_dims = [dimension for shape in fgraph.shape_feature.shape_of.values()
                   for dimension in shape]

    compute_shapes = theano.function(input_dims, output_dims)

    if any([i not in fgraph.inputs for i in input_shapes.keys()]):
        raise ValueError(
            "input_shapes keys aren't in the fgraph.inputs. FunctionGraph()"
            " interface changed. Now by default, it clones the graph it receives."
            " To have the old behavior, give it this new parameter `clone=False`.")

    numeric_input_dims = [dim for inp in fgraph.inputs
                          for dim in input_shapes[inp]]
    numeric_output_dims = compute_shapes(*numeric_input_dims)

    sym_to_num_dict = dict(izip(output_dims, numeric_output_dims))

    l = {}
    for var in fgraph.shape_feature.shape_of:
        l[var] = tuple(sym_to_num_dict[sym]
                       for sym in fgraph.shape_feature.shape_of[var])
    return l

    def __init__(
        self, inputs, outputs,
        inline=False,
        lop_overrides='default',
        grad_overrides='default',
        rop_overrides='default',
        name=None, **kwargs
    ):
        if not isinstance(outputs, list):
            raise TypeError('outputs must be list, got %s' % type(outputs))
        for i in inputs + outputs:
            if not isinstance(i, gof.Variable):
                raise TypeError(
                    'inputs and outputs must be Variable instances', i)
        if 'updates' in kwargs or 'givens' in kwargs:
            raise TypeError('updates and givens are not allowed here')
        self.is_inline = inline
        # To correctly support shared variables the inner fct should
        # not see them. Otherwise there is a problem with the gradient.
        self.shared_inputs = [var for var in gof.graph.inputs(outputs)
                              if isinstance(var, SharedVariable)]
        shared_vars = [var.type() for var in self.shared_inputs]

        new = rebuild_collect_shared(outputs, inputs=inputs + shared_vars,
                                     replace=dict(izip(
                                         self.shared_inputs, shared_vars)),
                                     copy_inputs_over=False)
        (local_inputs, local_outputs,
         [clone_d, update_d, update_expr, shared_inputs]) = new
        assert len(local_inputs) == len(inputs) + len(self.shared_inputs)
        assert len(local_outputs) == len(outputs)
        assert not update_d
        assert not update_expr
        assert not shared_inputs

        self.local_inputs = local_inputs
        self.local_outputs = local_outputs
        self.inputs = inputs
        self.outputs = outputs
        self.kwargs = kwargs
        self.input_types = [inp.type for inp in inputs]
        self.output_types = [out.type for out in outputs]
        if lop_overrides != 'default':
            if grad_overrides != 'default':
                raise ValueError('lop_overrides and grad_overrides are mutually exclusive')
            else:
                self.set_lop_overrides(lop_overrides)
                self._lop_type = 'lop'
        elif grad_overrides != 'default':
            self.set_lop_overrides(grad_overrides)
            self._lop_type = 'grad'
        else:
            self.set_lop_overrides('default')
            self._lop_type = 'lop'
        self.set_rop_overrides(rop_overrides)

        if name is not None:
            assert isinstance(name, str), 'name must be None or string object'
        self.name = name

 def streamline_nice_errors_f():
     for x in no_recycling:
         x[0] = None
     try:
         for thunk, node in izip(thunks, order):
             thunk()
     except Exception:
         raise_with_op(node, thunk)

def test_known_grads():

    # Tests that the grad method with no known_grads
    # matches what happens if you put its own known_grads
    # in for each variable

    full_range = theano.tensor.arange(10)
    x = theano.tensor.scalar('x')
    t = theano.tensor.iscalar('t')
    ft = full_range[t]
    ft.name = 'ft'
    coeffs = theano.tensor.vector('c')
    ct = coeffs[t]
    ct.name = 'ct'
    p = x ** ft
    p.name = 'p'
    y = ct * p
    y.name = 'y'
    cost = theano.tensor.sqr(y)
    cost.name = 'cost'

    layers = [
            [cost],
            [y],
            [ct, p],
            [ct, x, ft],
            [coeffs, t, full_range, x]
            ]

    inputs = [coeffs, t, x]

    rng = np.random.RandomState([2012, 11, 15])
    values = [rng.randn(10), rng.randint(10), rng.randn() ]
    values = [np.cast[ipt.dtype](value) for ipt, value in zip(inputs, values)]

    true_grads = theano.tensor.grad(cost, inputs, disconnected_inputs='ignore')
    true_grads = theano.function(inputs, true_grads)
    true_grads = true_grads(*values)

    for layer in layers:
        print('Testing by separately computing ', layer)
        first = theano.tensor.grad(cost, layer, disconnected_inputs='ignore')
        known = dict(izip(layer, first))
        full = theano.tensor.grad(cost=None,
                known_grads=known, wrt=inputs, disconnected_inputs='ignore')
        full = theano.function(inputs, full)
        full = full(*values)
        assert len(true_grads) == len(full)
        for a, b, var in zip(true_grads, full, inputs):
            if not np.allclose(a, b):
                print('Failure')
                print(a)
                print(b)
                print(var)
                print(layer)
                for v in known:
                    print(v, ':', theano.function(inputs, known[v])(*values))
                assert False