def forward()

in models/attentive_nas_dynamic_model.py [0:0]

• 16 lines of code
• 6 McCabe index (conditional complexity)

    def forward(self, x):
        # resize input to target resolution first
        if x.size(-1) != self.active_resolution:
            x = torch.nn.functional.interpolate(x, size=self.active_resolution, mode='bicubic')

        # first conv
        x = self.first_conv(x)
        # blocks
        for stage_id, block_idx in enumerate(self.block_group_info):
            depth = self.runtime_depth[stage_id]
            active_idx = block_idx[:depth]
            for idx in active_idx:
                x = self.blocks[idx](x)

        x = self.last_conv(x)
        x = x.mean(3, keepdim=True).mean(2, keepdim=True)  # global average pooling
        x = torch.squeeze(x)

        if self.active_dropout_rate > 0 and self.training:
            x = torch.nn.functional.dropout(x, p = self.active_dropout_rate)

        x = self.classifier(x)
        return x