sparse_manager.py 文件源码

def feed(self, im):
        assert im.dtype == np.uint8
        im = cv2.resize(im, dsize=self.im_shape, interpolation=cv2.INTER_AREA)
        im = im - 127.5

        ss = [None]*len(self.Vs)
        cs = [None]*len(self.Vs)
        inp = im
        for i, Vn in enumerate(self.Vs):
            n = i + 1
            if self.Vs[i].use_feedback and (i+1) < len(self.Vs):
                context = self.gen_context(self.Vs[i+1], self.im_shape[0]/self.Vs[0].Xb/(2**i), self.im_shape[1]/self.Vs[0].Yb/(2**i))
            else:
                context = None  # top level doesn't have any feedback
            s, c = self.Vs[i].sparsify(inp, context=context)
            ss[i] = s
            cs[i] = c

            if c is None:  # sparsify returns None if a layer isn't trained enough to return a response
                break
            if n < len(self.Vs):
                # input for next level
                inp = self.group_NxN_input(c, 2, self.Vs[0].K+1, self.im_shape[0]/self.Vs[0].Xb/(2**i), self.im_shape[1]/self.Vs[0].Yb/(2**i))

        return ss, cs