def query()

in lib/model/HGPIFuNetwNML.py [0:0]

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

    def query(self, points, calibs, transforms=None, labels=None, update_pred=True, update_phi=True):
        '''
        given 3d points, we obtain 2d projection of these given the camera matrices.
        filter needs to be called beforehand.
        the prediction is stored to self.preds
        args:
            points: [B, 3, N] 3d points in world space
            calibs: [B, 3, 4] calibration matrices for each image
            transforms: [B, 2, 3] image space coordinate transforms
            labels: [B, C, N] ground truth labels (for supervision only)
        return:
            [B, C, N] prediction
        '''
        xyz = self.projection(points, calibs, transforms)
        xy = xyz[:, :2, :]

        # if the point is outside bounding box, return outside.
        in_bb = (xyz >= -1) & (xyz <= 1)
        in_bb = in_bb[:, 0, :] & in_bb[:, 1, :] & in_bb[:, 2, :]
        in_bb = in_bb[:, None, :].detach().float()

        if labels is not None:
            self.labels = in_bb * labels

        sp_feat = self.spatial_enc(xyz, calibs=calibs)

        intermediate_preds_list = []

        phi = None
        for i, im_feat in enumerate(self.im_feat_list):
            point_local_feat_list = [self.index(im_feat, xy), sp_feat]       
            point_local_feat = torch.cat(point_local_feat_list, 1)
            pred, phi = self.mlp(point_local_feat)
            pred = in_bb * pred

            intermediate_preds_list.append(pred)
        
        if update_phi:
            self.phi = phi

        if update_pred:
            self.intermediate_preds_list = intermediate_preds_list
            self.preds = self.intermediate_preds_list[-1]