三维点云xyz经过主干网络得到point-wise的特征feature feature经过分类头和回归头得到point-wise的分类结果rpn_cls和回归结果rpn_reg,分类头和回归头由Conv1d组成 cls_rpn经过sigmoid变换到[0, 1]之间,表示该点为车的score,score大于阈值thres的点被认为是属于车的点,从而构造seg_mask,用于构造RCNN的输入 通过每个点...
Conv2d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm2d(out_channel)) last_channel = out_channel self.group_all = group_all def forward(self, xyz, points): """ Input: xyz: input points position data, [B, C, N] points: input points data, [B, D, N] Return:...
F-ConvNet aggregates point-wise features as frustum- level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustum- level features and supports an end-to-end and continuous ...
这里默认Fold了Conv和BN(我们所说的MergeConvBNs) 有一个TOCO(Tf Lite Optimizing Converter)工具可以直接将训练好的FrozenGraph转化为真正的定点模型 PyTorch Quantization Tool QNNPack 支持PerTensor和PerChannel的量化,采用带zeropoint的rounding Quantize Aware Training attorch.nn.qat torch.nn.intrinsic.qat ...
在handdiff.py中,模型使用了ConvNeXt作为其backbone。ConvNeXt是一个基于卷积神经网络的架构,它在模型中用于提取特征。在代码中,ConvNeXt被实例化为self.backbone,并且根据模型配置的不同(例如 'tiny', 'small', 'base', 'large'),其深度和维度会有所不同。
总体思路跟spatial-wise attention类似,是在全局channel维度上进行的一个attention融合操作,例如,分别计算出channel之间的相似度,再用softmax计算出channel之间的影响因子,最后用影响因子对channel进行融合。最终可以得到类似的feature: \tilde{\mathbf{F}} \in \mathbb{R}^{C_{d} \times N_{d}} 4.3 prediction...
3D ConvNets (as opposed to the classical 2D form) have been applied successfully to 3Dvolumetric representations for both discriminative [ 29 , 18 , 9 ] and generative [ 6 , 2 , 31 , 28 ] problems.Despite their recent success, 3D ConvNets suffer from an inherent drawback when modeling ...