Method __init__

depth_upsampling/models/msg/msg.py:15–41

(self, upsampling_factor)

    http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2016_depth.pdf
    """
    def __init__(self, upsampling_factor):
        super().__init__()
        # initialize indexes for layers
        self.upsampling_factor = upsampling_factor
        m = int(np.log2(upsampling_factor))

        # RGB-branch
        self.rgb_encoder1 = nn.Sequential(ConvPReLu(3, 49, 7, stride=1, padding=3),
                                          ConvPReLu(49, 32))
        self.rgb_encoder_blocks = nn.ModuleList()
        for i in range(m-1):
            self.rgb_encoder_blocks.append(nn.Sequential(ConvPReLu(32, 32),
                                                         nn.MaxPool2d(3, 2, padding=1)))

        # D-branch
        self.depth_decoder1 = nn.Sequential(ConvPReLu(1, 64, 5, stride=1, padding=2),
                                            DeconvPReLu(64, 32, 5, stride=2, padding=2))
        self.depth_decoder_blocks = nn.ModuleList()
        for i in range(m-1):
            self.depth_decoder_blocks.append(nn.Sequential(ConvPReLu(64, 32, 5, stride=1, padding=2),
                                                           ConvPReLu(32, 32, 5, stride=1, padding=2),
                                                           DeconvPReLu(32, 32, 5, stride=2, padding=2)))

        self.depth_decoder_n = nn.Sequential(ConvPReLu(64, 32, 5, stride=1, padding=2),
                                             ConvPReLu(32, 32, 5, stride=1, padding=2),
                                             ConvPReLu(32, 32, 5, stride=1, padding=2),
                                             ConvPReLu(32, 1, 5, stride=1, padding=2))

    def forward(self, batch):
        rgb_img = batch[dataset_keys.COLOR_IMG] / 255