mylayers.py 文件源码

def get_output_for(self, upscaled, **kwargs):
        a, b = self.scale_factor
        # get output for pooling and pre-pooling layer
        inp, out =\
                lasagne.layers.get_output([self.pool2d_layer_in,
                                           self.pool2d_layer])
        # upscale the input feature map by scale_factor
        if b > 1:
            upscaled = T.extra_ops.repeat(upscaled, b, 3)
        if a > 1:
            upscaled = T.extra_ops.repeat(upscaled, a, 2)
        # get the shapes for pre-pooling layer and upscaled layer
        sh_pool2d_in = T.shape(inp)
        sh_upscaled = T.shape(upscaled)
        # in case the shape is different left-bottom-pad with zero
        tmp = T.zeros(sh_pool2d_in)

        indx = (slice(None),
                slice(None),
                slice(0, sh_upscaled[2]),
                slice(0, sh_upscaled[3]))
        upscaled = T.set_subtensor(tmp[indx], upscaled)
        # get max pool indices
        indices_pool = T.grad(None, wrt=inp,
                known_grads={out: T.ones_like(out)})
        # mask values using indices_pool
        f = indices_pool * upscaled
        return f