error.py 文件源码

def forward_gpu(self, inputs):
        cupy = cuda.cupy
        x, t = inputs
        if chainer.is_debug():
            self._check_input_values(x, t)

        log_y = log_softmax._log_softmax(x)
        if self.cache_score:
            self.y = cupy.exp(log_y)
        if self.class_weight is not None:
            shape = [1 if d != 1 else -1 for d in six.moves.range(x.ndim)]
            log_y *= cupy.broadcast_to(
                self.class_weight.reshape(shape), x.shape)
        if self.normalize:
            coeff = cupy.maximum(1, (t != self.ignore_label).sum())
        else:
            coeff = max(1, len(t))
        self._coeff = cupy.divide(1.0, coeff, dtype=x.dtype)

        log_y = cupy.rollaxis(log_y, 1, log_y.ndim)
        if self.reduce == 'mean':
            ret = cuda.reduce(
                'S t, raw T log_y, int32 n_channel, raw T coeff, '
                'S ignore_label',
                'T out',
                't == ignore_label ? T(0) : log_y[_j * n_channel + t]',
                'a + b', 'out = a * -coeff[0]', '0', 'crossent_fwd'
            )(t, log_y.reduced_view(), log_y.shape[-1],
              self._coeff, self.ignore_label)
        else:
            ret = cuda.elementwise(
                'S t, raw T log_y, int32 n_channel, T ignore', 'T out',
                '''
                if (t == ignore) {
                  out = 0;
                } else {
                  out = -log_y[i * n_channel + t];
                }
                ''',
                'softmax_crossent_no_reduce_fwd'
            )(t, log_y.reduced_view(), log_y.shape[-1], self.ignore_label)
            ret = ret.reshape(t.shape)
        return ret,