def forward_gpu(self, x):
        n, out_c, out_h, out_w = x[0].shape
        c = self.W.shape[1]
        h = get_deconv_outsize(out_h, self.kh, self.sy, self.ph)
        w = get_deconv_outsize(out_w, self.kw, self.sx, self.pw)
        if cudnn.enabled and self.use_cudnn:
            handle = cudnn.get_default_handle()
            x_desc = cudnn.get_tensor_desc(x[0], out_h, out_w)
            y = cuda.empty((n, c, h, w), dtype=numpy.float32)
            y_desc = cudnn.get_tensor_desc(y, h, w)

            self.filter_desc = cudnn.get_filter4d_desc(self.W)
            self.conv_desc = cudnn.get_conv2d_desc(
                (self.ph, self.pw), (self.sy, self.sx))
            if self.b is not None:
                self.bias_desc = cudnn.get_conv_bias_desc(self.b)

            libcudnn.cudnnConvolutionBackwardData(
                handle, 1, self.filter_desc.value, cudnn.get_ptr(self.W),
                x_desc.value, cudnn.get_ptr(x[0]), self.conv_desc.value,
                0, y_desc.value, cudnn.get_ptr(y))
            if self.b is not None:
                libcudnn.cudnnAddTensor(
                    handle, libcudnn.cudnnAddMode['CUDNN_ADD_SAME_C'],
                    1, self.bias_desc.value, cudnn.get_ptr(self.b),
                    1, y_desc.value, cudnn.get_ptr(y))
        else:
            handle = cuda.get_cublas_handle()
            # TODO(beam2d): Use streams
            W_mat = self.W.reshape(out_c, c * self.kh * self.kw)
            x_mats = x[0].reshape(n, out_c, out_h * out_w)
            gcol = cuda.empty((n, c, self.kh, self.kw, out_h, out_w), dtype=numpy.float32)
            gcol_mats = gcol.reshape(n, c * self.kh * self.kw, out_h * out_w)
            for i in moves.range(n):
                cuda.culinalg.dot(W_mat, x_mats[i], transa='T', handle=handle,
                                  out=gcol_mats[i])
            y = conv.col2im_gpu(
                gcol, self.sy, self.sx, self.ph, self.pw, h, w)
            # TODO(beam2d): Support unshared bias
            if self.b is not None:
                cuda.elementwise(
                    'float* y, const float* b, int c, int hw',
                    'y[i] += b[i / hw % c]',
                    'conv_bias_fwd')(y, self.b, c, h * w)
        return y,
Пример #2
0
    def forward_gpu(self, x):
        n, c, h, w = x[0].shape
        out_h = conv.get_conv_outsize(h, self.kh, self.sy, self.ph)
        out_w = conv.get_conv_outsize(w, self.kw, self.sx, self.pw)
        out_c = self.W.shape[0]
        y = cuda.empty((n, out_c, out_h, out_w), dtype=numpy.float32)

        if cudnn.enabled and self.use_cudnn:
            handle = cudnn.get_default_handle()
            x_desc = cudnn.get_tensor_desc(x[0], h, w)
            y_desc = cudnn.get_tensor_desc(y, out_h, out_w)

            self.filter_desc = cudnn.get_filter4d_desc(self.W)
            self.conv_desc = cudnn.get_conv2d_desc(
                (self.ph, self.pw), (self.sy, self.sx))
            if self.b is not None:
                self.bias_desc = cudnn.get_conv_bias_desc(self.b)

            algo = libcudnn.cudnnGetConvolutionForwardAlgorithm(
                handle, x_desc.value, self.filter_desc.value,
                self.conv_desc.value, y_desc.value, _fwd_pref,
                self.max_workspace_size)
            workspace_size = libcudnn.cudnnGetConvolutionForwardWorkspaceSize(
                handle, x_desc.value, self.filter_desc.value,
                self.conv_desc.value, y_desc.value, algo).value
            workspace = cuda.empty(
                (max(workspace_size // 4, 1),), dtype=numpy.float32)

            libcudnn.cudnnConvolutionForward(
                handle, 1, x_desc.value, cudnn.get_ptr(x[0]),
                self.filter_desc.value, cudnn.get_ptr(self.W),
                self.conv_desc.value, algo, cudnn.get_ptr(
                    workspace), workspace_size,
                0, y_desc.value, cudnn.get_ptr(y))

            # TODO(beam2d): Support unshared bias
            if self.b is not None:
                libcudnn.cudnnAddTensor(
                    handle, libcudnn.cudnnAddMode['CUDNN_ADD_SAME_C'],
                    1, self.bias_desc.value, cudnn.get_ptr(self.b),
                    1, y_desc.value, cudnn.get_ptr(y))
        else:
            # Implementation using im2col
            self.col = conv.im2col_gpu(
                x[0], self.kh, self.kw, self.sy, self.sx, self.ph, self.pw)

            # TODO(beam2d): Use streams
            handle = cuda.get_cublas_handle()
            W_mat = self.W.reshape(out_c, c * self.kh * self.kw)
            col_mats = self.col.reshape(
                n, c * self.kh * self.kw, out_h * out_w)
            y_mats = y.reshape(n, out_c, out_h * out_w)
            for i in moves.range(n):
                cuda.culinalg.dot(W_mat, col_mats[i], handle=handle,
                                  out=y_mats[i])

            # TODO(beam2d): Support unshared bias
            if self.b is not None:
                cuda.elementwise(
                    'float* y, const float* b, int c, int hw',
                    'y[i] += b[i / hw % c]',
                    'conv_bias_fwd')(y, self.b, out_c, out_h * out_w)

        return y,