net.py 文件源码

def __call__(self, x, test):
        h = F.relu(self.b1(self.c1(x), test=test))
        h = self.b2(self.c2(h), test=test)
        if x.data.shape != h.data.shape:
            xp = chainer.cuda.get_array_module(x.data)
            n, c, hh, ww = x.data.shape
            pad_c = h.data.shape[1] - c
            p = xp.zeros((n, pad_c, hh, ww), dtype=xp.float32)
            p = chainer.Variable(p, volatile=test)
            x = F.concat((p, x))
            if x.data.shape[2:] != h.data.shape[2:]:
                x = F.average_pooling_2d(x, 1, 2)
        return h + x