utils/gluon/utils/resnetv1.py [267:286]:
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            self.avgpool = gluon.nn.GlobalAvgPool2D()
            self.drop = gluon.nn.Dropout(final_drop) if final_drop > 0. else lambda x: (x)
            self.classifer = gluon.nn.Conv2D(in_channels=int(k*num_out[3]), channels=classes,
                                       kernel_size=1, prefix='classifier_')
            self.flat = gluon.nn.Flatten()

    def _make_layer(self, stage_index, block, blocks, num_out, num_mid, ratio=0., strides=1):

        # -1 stands for None
        mid_planes = (num_mid - int(ratio * num_mid), int(ratio * num_mid))
        mid_planes = tuple(c if c != 0 else -1 for c in mid_planes)
        out_planes = (num_out - int(ratio * num_out), int(ratio * num_out))
        out_planes = tuple(c if c != 0 else -1 for c in out_planes)

        for i in range(0, blocks):
            name = 'L%d_B%d' % (stage_index, i)
            setattr(self, name, block(self.inplanes, mid_planes, out_planes,
                                      groups=self.groups,
                                      strides=(strides if i == 0 else 1),
                                      norm_kwargs=self.norm_kwargs,
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utils/gluon/utils/resnetv2.py [176:195]:
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            self.avgpool = gluon.nn.GlobalAvgPool2D()
            self.drop = gluon.nn.Dropout(final_drop) if final_drop > 0. else lambda x: (x)
            self.classifer = gluon.nn.Conv2D(in_channels=int(k*num_out[3]), channels=classes,
                                       kernel_size=1, prefix='classifier_')
            self.flat = gluon.nn.Flatten()

    def _make_layer(self, stage_index, block, blocks, num_out, num_mid, ratio=0., strides=1):

        # -1 stands for None
        mid_planes = (num_mid - int(ratio * num_mid), int(ratio * num_mid))
        mid_planes = tuple(c if c != 0 else -1 for c in mid_planes)
        out_planes = (num_out - int(ratio * num_out), int(ratio * num_out))
        out_planes = tuple(c if c != 0 else -1 for c in out_planes)

        for i in range(0, blocks):
            name = 'L%d_B%d' % (stage_index, i)
            setattr(self, name, block(self.inplanes, mid_planes, out_planes,
                                      groups=self.groups,
                                      strides=(strides if i == 0 else 1),
                                      norm_kwargs=self.norm_kwargs,
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