def _oper_cpu(cls, x, in_shape, out_shape, karnel, stride, padding):
col = im2col(x, out_shape[1:], karnel, stride, padding)
n, ic, kh, kw, oh, ow = col.shape
col = col.reshape(n, ic, kh * kw, oh, ow)
value = np.mean(col, axis=2)
ret = cls._create_node(value)
ret.attrs._x = x
ret.attrs._in_shape = in_shape
ret.attrs._out_shape = out_shape
ret.attrs._kernel = karnel
ret.attrs._stride = stride
ret.attrs._padding = padding
return ret
def _backward_cpu(self, context, dy, **kwargs):
col = im2col(dy, self.attrs._in_shape[1:], self.attrs._kernel,
self.attrs._stride, self.attrs._padding)
if isinstance(self.attrs._x, Node):
dx = np.tensordot(col, self.attrs._w, ([1, 2, 3], [1, 2, 3]))
dx = np.rollaxis(dx, 3, 1)
self.attrs._x._update_diff(context, dx, **kwargs)
if isinstance(self.attrs._w, Node):
self.attrs._w._update_diff(context, np.tensordot(
self.attrs._x, col, ([0, 2, 3], [0, 4, 5])), **kwargs)
if isinstance(self.attrs._b, Node):
self.attrs._b._update_diff(context, np.sum(dy, (0, 2, 3), keepdims=True), **kwargs)
def _oper_cpu(cls, x, w, b, in_shape, out_shape, kernel, stride, padding):
col = im2col(to_value(x), out_shape[1:], kernel, stride, padding)
value = np.rollaxis(
np.tensordot(col, to_value(w), ([1, 2, 3], [1, 2, 3])), 3, 1)
if b is not None:
value += b
ret = cls._create_node(value)
ret.attrs._col = col
ret.attrs._x = x
ret.attrs._w = w
ret.attrs._b = b
ret.attrs._in_shape = in_shape
ret.attrs._out_shape = out_shape
ret.attrs._kernel = kernel
ret.attrs._stride = stride
ret.attrs._padding = padding
return ret
def _oper_cpu(cls, x, w, b, in_shape, out_shape, kernel, stride, padding,
dilation, groups):
N, in_channels, in_h, in_w = x.shape
k_h, k_w = kernel
out_channels = w.shape[0]
iCg = in_channels // groups
oCg = out_channels // groups
col = im2col(to_value(x), out_shape[1:], kernel, stride, padding,
dilation)
out_h, out_w = col.shape[-2:]
col = col.transpose(1, 2, 3, 0, 4, 5)
col = col.reshape(groups, iCg * k_h * k_w, N * out_h * out_w)
w_new = w.reshape(groups, oCg, iCg * k_h * k_w)
value = np.matmul(to_value(w_new), col)
value = value.reshape(groups * oCg, N, out_h, out_w)
value = value.transpose(1, 0, 2, 3)
if b is not None:
value += b.reshape(1, b.size, 1, 1)
ret = cls._create_node(value)
ret.attrs._col = col
ret.attrs._x = x
ret.attrs._w = w
ret.attrs._b = b
ret.attrs._in_shape = in_shape
ret.attrs._out_shape = out_shape
ret.attrs._kernel = kernel
ret.attrs._stride = stride
ret.attrs._padding = padding
ret.attrs._dilation = dilation
ret.attrs._groups = groups
ret.attrs._iCg = iCg
ret.attrs._oCg = oCg
return ret