lib/models/patchnet.py [55:68]:
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        output_dict = {}

        # get [h, w] of input patch  [for global pooling]
        _, _, h, w = patch.shape

        # mask generation
        depth_map = patch[:, 2, :, :]
        threshold = depth_map.mean(-1).mean(-1) + self.cfg['threshold_offset']
        threshold = threshold.unsqueeze(-1).unsqueeze(-1).repeat(1, h, w)
        zeros, ones = torch.zeros_like(depth_map), torch.ones_like(depth_map)
        mask = torch.where(depth_map < threshold, ones, zeros)
        mask_xyz_mean = mask_global_avg_pooling_2d(patch, mask)
        patch = patch - mask_xyz_mean
        mask_xyz_mean = mask_xyz_mean.squeeze(-1).squeeze(-1)
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lib/models/patchnet_pct.py [77:90]:
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        output_dict = {}

        # get [h, w] of input patch  [for global pooling]
        _, _, h, w = patch.shape

        # mask generation
        depth_map = patch[:, 2, :, :]
        threshold = depth_map.mean(-1).mean(-1) + self.cfg['threshold_offset']
        threshold = threshold.unsqueeze(-1).unsqueeze(-1).repeat(1, h, w)
        zeros, ones = torch.zeros_like(depth_map), torch.ones_like(depth_map)
        mask = torch.where(depth_map < threshold, ones, zeros)
        mask_xyz_mean = mask_global_avg_pooling_2d(patch, mask)
        patch = patch - mask_xyz_mean
        mask_xyz_mean = mask_xyz_mean.squeeze(-1).squeeze(-1)
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