average_pooling_2d.py 文件源码

def backward_gpu(self, x, gy):
        if (cuda.cudnn_enabled and self.use_cudnn and
                pooling_2d._check_cudnn_acceptable_type(x[0].dtype)):
            return super(AveragePooling2D, self).backward_gpu(x, gy)

        n, c, h, w = x[0].shape
        y_h, y_w = gy[0].shape[2:]
        gx = cuda.cupy.empty_like(x[0])
        coeff = 1. / (self.kh * self.kw)
        cuda.elementwise(
            'raw T gy, int32 h, int32 w,'
            'int32 out_h, int32 out_w, int32 kh, int32 kw,'
            'int32 sy, int32 sx, int32 ph, int32 pw, T coeff',
            'T gx',
            '''
               int c0 = i / (h * w);
               int y  = i / w % h + ph;
               int x  = i % w + pw;
               int out_y_0 = max(0,     (y - kh + sy) / sy);
               int out_y_1 = min(out_h, (y      + sy) / sy);
               int out_x_0 = max(0,     (x - kw + sx) / sx);
               int out_x_1 = min(out_w, (x      + sx) / sx);
               int hc0  = out_h * c0;

               T val = 0;
               for (int out_y = out_y_0; out_y < out_y_1; ++out_y) {
                 for (int out_x = out_x_0; out_x < out_x_1; ++out_x) {
                   val = val + gy[out_x + out_w * (out_y + hc0)];
                 }
               }
               gx = val * coeff;
            ''', 'avg_pool_bwd')(gy[0].reduced_view(),
                                 h, w, y_h, y_w, self.kh, self.kw,
                                 self.sy, self.sx, self.ph, self.pw, coeff,
                                 gx)
        return gx,