for k in range(cfg.RPN.SA_CONFIG.NPOINTS.__len__()): mlps = cfg.RPN.SA_CONFIG.MLPS[k].copy() channel_out = 0 for idx in range(mlps.__len__()): mlps[idx] = [channel_in] + mlps[idx] channel_out += mlps[idx][-1] self.SA_modules.append( PointnetSAModuleMSG( npoint=cfg.RPN.SA_CONFIG.NPOINTS[k], radii=cfg.RPN.SA_CONFIG.RADIUS[k], nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k], mlps=mlps, use_xyz=use_xyz, bn=cfg.RPN.USE_BN ) ) skip_channel_list.append(channel_out) channel_in = channel_out self.FP_modules = nn.ModuleList() for k in range(cfg.RPN.FP_MLPS.__len__()): pre_channel = cfg.RPN.FP_MLPS[k + 1][-1] if k + 1 < len(cfg.RPN.FP_MLPS) else channel_out self.FP_modules.append( PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] + cfg.RPN.FP_MLPS[k]) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = ( pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None ) return xyz, features def forward(self, pointcloud: torch.cuda.FloatTensor): xyz, features = self._break_up_pc(pointcloud) l_xyz, l_features = [xyz], [features] for i in range(len(self.SA_modules)): li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) l_xyz.append(li_xyz) l_features.append(li_features) for i in range(-1, -(len(self.FP_modules) + 1), -1): l_features[i - 1] = self.FP_modules[i]( l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i] ) return l_xyz[0], l_features[0]在forward函数中,如果我要使用channel_out变量传入SA_modules中,我该如何在forward函数中计算并得到它,再传入SA_modules中,你可以给我详细的代码吗?
时间: 2023-07-16 11:12:58 浏览: 45
在 forward 函数中计算 channel_out 的方法是在每个循环迭代中更新它,并在循环结束后将其传递给下一个 SA 模块。以下是修改后的代码:
```
def forward(self, pointcloud: torch.cuda.FloatTensor):
xyz, features = self._break_up_pc(pointcloud)
l_xyz, l_features = [xyz], [features]
channel_out = 0 # 初始化 channel_out
for i in range(len(self.SA_modules)):
mlps = self.SA_modules[i].mlps
channel_in = mlps[0][0]
mlps = [[channel_in] + mlp[1:] for mlp in mlps]
sa_module = PointnetSAModuleMSG(
npoint=self.SA_modules[i].npoint,
radii=self.SA_modules[i].radii,
nsamples=self.SA_modules[i].nsamples,
mlps=mlps,
use_xyz=self.SA_modules[i].use_xyz,
bn=self.SA_modules[i].bn
)
li_xyz, li_features = sa_module(l_xyz[i], l_features[i])
l_xyz.append(li_xyz)
l_features.append(li_features)
channel_out += sa_module.mlps[-1][-1] # 更新 channel_out
skip_channel_list = [channel_out] # 将 channel_out 加入到 skip_channel_list 中
for i in range(-1, -(len(self.FP_modules) + 1), -1):
l_features[i - 1] = self.FP_modules[i](
l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i]
)
skip_channel_list.append(l_features[i - 1].shape[1]) # 将每个 FP 模块的输出特征通道数加入到 skip_channel_list 中
skip_channel_list = skip_channel_list[::-1]
return l_xyz[0], l_features[0], skip_channel_list
```
在这个修改后的 `forward` 函数中,我们在 SA 模块循环中定义了一个新的 SA 模块,并将计算得到的 `channel_out` 累加到 `skip_channel_list` 中。在循环结束后,我们将 `skip_channel_list` 反转并返回,以便在后续处理中使用。
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