def _make_layer()

in easycv/models/backbones/resnest.py [0:0]

• 103 lines of code
• 9 McCabe index (conditional complexity)

    def _make_layer(self,
                    block,
                    planes,
                    blocks,
                    stride=1,
                    dilation=1,
                    norm_layer=None,
                    dropblock_prob=0.0,
                    is_first=True):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            down_layers = []
            if self.avg_down:
                if dilation == 1:
                    down_layers.append(
                        nn.AvgPool2d(
                            kernel_size=stride,
                            stride=stride,
                            ceil_mode=True,
                            count_include_pad=False))
                else:
                    down_layers.append(
                        nn.AvgPool2d(
                            kernel_size=1,
                            stride=1,
                            ceil_mode=True,
                            count_include_pad=False))
                down_layers.append(
                    nn.Conv2d(
                        self.inplanes,
                        planes * block.expansion,
                        kernel_size=1,
                        stride=1,
                        bias=False))
            else:
                down_layers.append(
                    nn.Conv2d(
                        self.inplanes,
                        planes * block.expansion,
                        kernel_size=1,
                        stride=stride,
                        bias=False))
            down_layers.append(norm_layer(planes * block.expansion))
            downsample = nn.Sequential(*down_layers)

        layers = []
        if dilation == 1 or dilation == 2:
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    stride,
                    downsample=downsample,
                    radix=self.radix,
                    cardinality=self.cardinality,
                    bottleneck_width=self.bottleneck_width,
                    avd=self.avd,
                    avd_first=self.avd_first,
                    dilation=1,
                    is_first=is_first,
                    rectified_conv=self.rectified_conv,
                    rectify_avg=self.rectify_avg,
                    norm_layer=norm_layer,
                    dropblock_prob=dropblock_prob,
                    last_gamma=self.last_gamma))
        elif dilation == 4:
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    stride,
                    downsample=downsample,
                    radix=self.radix,
                    cardinality=self.cardinality,
                    bottleneck_width=self.bottleneck_width,
                    avd=self.avd,
                    avd_first=self.avd_first,
                    dilation=2,
                    is_first=is_first,
                    rectified_conv=self.rectified_conv,
                    rectify_avg=self.rectify_avg,
                    norm_layer=norm_layer,
                    dropblock_prob=dropblock_prob,
                    last_gamma=self.last_gamma))
        else:
            raise RuntimeError('=> unknown dilation size: {}'.format(dilation))

        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    radix=self.radix,
                    cardinality=self.cardinality,
                    bottleneck_width=self.bottleneck_width,
                    avd=self.avd,
                    avd_first=self.avd_first,
                    dilation=dilation,
                    rectified_conv=self.rectified_conv,
                    rectify_avg=self.rectify_avg,
                    norm_layer=norm_layer,
                    dropblock_prob=dropblock_prob,
                    last_gamma=self.last_gamma))

        return nn.Sequential(*layers)