from typing import Tuple, Optional
from webdnn.graph.axis import Axis
from webdnn.graph.operator import Operator
from webdnn.graph.operators.attributes.tensorwise import Tensorwise
from webdnn.graph.operators.util import IntOrTuple, to_tuple
from webdnn.graph.order import OrderNHWC, OrderNCHW, Order
from webdnn.graph.placeholder import Placeholder
from webdnn.graph.variable import Variable
from webdnn.util.assertion import assert_sequence_type
[docs]class Deconvolution2D(Operator):
"""Deconvolution2D(name, ksize, stride, padding)
Spatial deconvolution operator.
Args:
name (str): Operator name.
ksize (int or tuple of int): Kernel size.
stride (int or tuple of int): Stride size.
padding (int or tuple of int): Padding size.
Signature
.. code::
y, = op(x, w)
- **x** - Input variables. It must has 4 axes, :obj:`~webdnn.Axis.N`, :obj:`~webdnn.Axis.C`,
:obj:`~webdnn.Axis.H`, and :obj:`~webdnn.Axis.W`.
- **w** - Kernel variable. It must has :obj:`~webdnn.graph.axis.Axis.N`, :obj:`~webdnn.Axis.C`,
:obj:`~webdnn.Axis.H`, :obj:`~webdnn.Axis.W`. Its size of :obj:`~webdnn.Axis.H` and
:obj:`~webdnn.Axis.W` must be same as kernel size. Its size of :obj:`~webdnn.Axis.C` must be same as
:code:`x`.
- **y** - Output variable. Its order is same as :code:`x`.
"""
def __init__(self, name: Optional[str], ksize: IntOrTuple, stride: IntOrTuple, padding: IntOrTuple):
super().__init__(name)
self.parameters["ksize"] = assert_sequence_type(to_tuple(ksize), (int, Placeholder), message=f"""
[Deconvolution2D] Parameter "ksize" must be integer or tuple of integer""")
self.parameters["stride"] = assert_sequence_type(to_tuple(stride), (int, Placeholder), message=f"""
[Deconvolution2D] Parameter "stride" must be integer or tuple of integer""")
self.parameters["padding"] = assert_sequence_type(to_tuple(padding), (int, Placeholder), message=f"""
[Deconvolution2D] Parameter "padding" must be integer or tuple of integer""")
self.attributes.add(Tensorwise(Axis.N))
def __call__(self, x: Variable, w: Variable):
assert x.order.check_same_axes(OrderNCHW), f"""
[Deconvolution2D] Input variable of Deconvolution2D must have N, C, H, and W axes:
(x.order.axes) = {x.order.axes}"""
assert w.order.check_same_axes(Order([Axis.N, Axis.KH, Axis.KW, Axis.C])), f"""
[Deconvolution2D] Kernel variable of Deconvolution2D must have N, C, KH, and KW axes:
(w.order.axes) = {w.order.axes}"""
if Placeholder.check_resolved(w.shape_dict[Axis.KH]) and Placeholder.check_resolved(w.shape_dict[Axis.KW]):
assert (w.shape_dict[Axis.KH], w.shape_dict[Axis.KW]) == self.ksize, f"""
[Deconvolution2D] Kernel variable of Deconvolution2D must be same spatial size as ksize parameter:
(w.shape_dict[Axis.KH]) = {w.shape_dict[Axis.KH]}
(w.shape_dict[Axis.KW]) = {w.shape_dict[Axis.KW]}
(self.ksize) = {self.ksize}"""
if Placeholder.check_resolved(w.shape_dict[Axis.C]) and Placeholder.check_resolved(x.shape_dict[Axis.C]):
assert w.shape_dict[Axis.C] == x.shape_dict[Axis.C], f"""
[Deconvolution2D] Input and Kernel variables of Deconvolution2D must be same channel size:
(x.shape_dict[Axis.C]) = {x.shape_dict[Axis.C]}
(w.shape_dict[Axis.C]) = {w.shape_dict[Axis.C]}"""
N = x.shape_dict[Axis.N]
H2 = (x.shape_dict[Axis.H] - 1) * self.SH - 2 * self.PH + self.KH
W2 = (x.shape_dict[Axis.W] - 1) * self.SW - 2 * self.PW + self.KW
C2 = w.shape_dict[Axis.N]
y = Variable([N, H2, W2, C2], OrderNHWC)
y.change_order(x.order) # output same order as input to preserve following reshape semantics
self.append_input("x", x)
self.append_input("w", w)
self.append_output("y", y)
return y,
@property
def ksize(self) -> Tuple[int, int]:
return self.parameters["ksize"]
@property
def stride(self) -> Tuple[int, int]:
return self.parameters["stride"]
@property
def padding(self) -> Tuple[int, int]:
return self.parameters["padding"]
@property
def KH(self) -> int:
return self.ksize[0]
@property
def KW(self) -> int:
return self.ksize[1]
@property
def SH(self) -> int:
return self.stride[0]
@property
def SW(self) -> int:
return self.stride[1]
@property
def PH(self) -> int:
return self.padding[0]
@property
def PW(self) -> int:
return self.padding[1]