def __call__(self, x, W=None, b=None):
if self.has_uninitialized_params:
with cuda.get_device(self._device_id):
self._initialize_params(x.shape[1])
if W is not None:
return deconvolution_2d.deconvolution_2d(
x,
W,
b,
self.stride,
self.pad,
self.outsize,
self.use_cudnn,
deterministic=self.deterministic)
return deconvolution_2d.deconvolution_2d(
x,
self.W,
self.b,
self.stride,
self.pad,
self.outsize,
self.use_cudnn,
deterministic=self.deterministic)
def __call__(self, x, W=None, b=None):
if self.has_uninitialized_params:
with cuda.get_device(self._device_id):
self._initialize_params(x.shape[1])
if W is not None:
return deconvolution_2d.deconvolution_2d(
x, W, b, self.stride, self.pad,
self.outsize, self.use_cudnn,
deterministic=self.deterministic)
return deconvolution_2d.deconvolution_2d(
x, self.W, self.b, self.stride, self.pad,
self.outsize, self.use_cudnn,
deterministic=self.deterministic)
def __call__(self, x):
if self.W.data is None:
self._initialize_params(x.shape[1])
return deconvolution_2d.deconvolution_2d(x,
self.W_bar,
self.b,
self.stride,
self.pad,
groups=self.groups)
def __call__(self, x):
return deconvolution_2d.deconvolution_2d(
x,
self.W,
self.b,
self.stride,
self.pad,
self.outsize,
self.use_cudnn,
deterministic=self.deterministic)
def forward(self, x):
if self.W.array is None:
self._initialize_params(x.shape[1])
return deconvolution_2d.deconvolution_2d(x,
self.W,
self.b,
self.stride,
self.pad,
self.outsize,
groups=self.groups)
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 deconvolution_2d.deconvolution_2d(x, self.W_bar, self.b,
self.stride, self.pad)
def __call__(self, x):
if self.has_uninitialized_params:
with cuda.get_device_from_id(self._device_id):
self._initialize_params(x.shape[1])
return deconvolution_2d.deconvolution_2d(
x,
self.W,
self.b,
self.stride,
self.pad,
self.outsize,
self.use_cudnn,
deterministic=self.deterministic)
def __call__(self, x):
if self.W.data is None:
self._initialize_params(x.shape[1])
return deconvolution_2d.deconvolution_2d(
x, self.W, self.b, self.stride, self.pad, self.outsize,
group=self.group)
def __call__(self, x):
return deconvolution_2d.deconvolution_2d(
x, self.W, self.b, self.stride, self.pad,
self.outsize, self.use_cudnn)
def forward(self, x):
if self.W.array is None:
self._initialize_params(x.shape[1])
return deconvolution_2d.deconvolution_2d(
x, self.W, self.b, self.stride, self.pad, self.outsize,
dilate=self.dilate, groups=self.groups)
