def _make_block()

in src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/efficientnet_builder.py [0:0]

• 32 lines of code
• 12 McCabe index (conditional complexity)

    def _make_block(self, ba):
        bt = ba.pop('block_type')
        ba['in_chs'] = self.in_chs
        ba['out_chs'] = self._round_channels(ba['out_chs'])
        if 'fake_in_chs' in ba and ba['fake_in_chs']:
            # FIXME this is a hack to work around mismatch in origin impl input filters for EdgeTPU
            ba['fake_in_chs'] = self._round_channels(ba['fake_in_chs'])
        ba['norm_layer'] = self.norm_layer
        ba['norm_kwargs'] = self.norm_kwargs
        ba['pad_type'] = self.pad_type
        # block act fn overrides the model default
        ba['act_layer'] = ba['act_layer'] if ba['act_layer'] is not None else self.act_layer
        assert ba['act_layer'] is not None
        if bt == 'ir':
            ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
            ba['se_kwargs'] = self.se_kwargs
            if ba.get('num_experts', 0) > 0:
                block = CondConvResidual(**ba)
            else:
                block = InvertedResidual(**ba)
        elif bt == 'ds' or bt == 'dsa':
            ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
            ba['se_kwargs'] = self.se_kwargs
            block = DepthwiseSeparableConv(**ba)
        elif bt == 'er':
            ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
            ba['se_kwargs'] = self.se_kwargs
            block = EdgeResidual(**ba)
        elif bt == 'cn':
            block = ConvBnAct(**ba)
        else:
            assert False, 'Uknkown block type (%s) while building model.' % bt
        self.in_chs = ba['out_chs']  # update in_chs for arg of next block
        return block