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)