def __call__(self, x):
"""Applies N-dimensional convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of convolution.
"""
return convolution_nd.convolution_nd(x, self.W_bar, self.b, self.stride, self.pad, cover_all=self.cover_all)
def __call__(self, x):
"""Applies N-dimensional convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of convolution.
"""
return convolution_nd.convolution_nd(
x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all)
def forward(self, x):
"""Applies N-dimensional convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of convolution.
"""
return convolution_nd.convolution_nd(
x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all,
dilate=self.dilate, groups=self.groups)
def forward(self, x):
"""Applies N-dimensional convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of convolution.
"""
if self.W.array is None:
self._initialize_params(x.shape[1])
return convolution_nd.convolution_nd(
x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all,
dilate=self.dilate, groups=self.groups)
def forward(self, x):
"""Applies N-dimensional convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of convolution.
"""
if self.W.array is None:
self._initialize_params(x.shape[1])
return convolution_nd.convolution_nd(
x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all,
dilate=self.dilate, groups=self.groups)
def forward(self, x):
"""Applies N-dimensional convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of convolution.
"""
return convolution_nd.convolution_nd(x,
self.W,
self.b,
self.stride,
self.pad,
cover_all=self.cover_all,
dilate=self.dilate,
groups=self.groups)