def __call__(self, x):
"""Applies the convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of the convolution.
"""
if self.W.data is None:
self._initialize_params(x.shape[1])
return dilated_convolution_2d.dilated_convolution_2d(
x, self.W_bar, self.b, self.stride, self.pad, self.dilate)
def forward(self, x):
"""Applies the convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of the convolution.
"""
if self.W.data is None:
self._initialize_params(x.shape[1])
return dilated_convolution_2d.dilated_convolution_2d(
x, self.W, self.b, self.stride, self.pad, self.dilate)
def __call__(self, x):
"""Applies the convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of the convolution.
"""
if self.has_uninitialized_params:
with cuda.get_device(self._device_id):
self._initialize_params(x.shape[1])
return dilated_convolution_2d.dilated_convolution_2d(
x, self.W, self.b, self.stride,
self.pad, self.dilate, self.use_cudnn)
def __call__(self, x):
"""Applies the convolution layer.
Args:
x (~chainer.Variable): Input image.
Returns:
~chainer.Variable: Output of the convolution.
"""
if self.has_uninitialized_params:
with cuda.get_device(self._device_id):
self._initialize_params(x.shape[1])
return dilated_convolution_2d.dilated_convolution_2d(
x, self.W, self.b, self.stride, self.pad, self.dilate,
self.use_cudnn)
