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modules/module_lib/pointnet2_utils/tools/pointnet2_msg.py

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+import torch
+import torch.nn as nn
+import sys
+sys.path.append('..')
+from pointnet2.pointnet2_modules import PointnetFPModule, PointnetSAModuleMSG
+import pointnet2.pytorch_utils as pt_utils
+
+
+def get_model(input_channels=0):
+    return Pointnet2MSG(input_channels=input_channels)
+
+
+NPOINTS = [4096, 1024, 256, 64]
+RADIUS = [[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]]
+NSAMPLE = [[16, 32], [16, 32], [16, 32], [16, 32]]
+MLPS = [[[16, 16, 32], [32, 32, 64]], [[64, 64, 128], [64, 96, 128]],
+        [[128, 196, 256], [128, 196, 256]], [[256, 256, 512], [256, 384, 512]]]
+FP_MLPS = [[128, 128], [256, 256], [512, 512], [512, 512]]
+CLS_FC = [128]
+DP_RATIO = 0.5
+
+
+class Pointnet2MSG(nn.Module):
+    def __init__(self, input_channels=6):
+        super().__init__()
+
+        self.SA_modules = nn.ModuleList()
+        channel_in = input_channels
+
+        skip_channel_list = [input_channels]
+        for k in range(NPOINTS.__len__()):
+            mlps = 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=NPOINTS[k],
+                    radii=RADIUS[k],
+                    nsamples=NSAMPLE[k],
+                    mlps=mlps,
+                    use_xyz=True,
+                    bn=True
+                )
+            )
+            skip_channel_list.append(channel_out)
+            channel_in = channel_out
+
+        self.FP_modules = nn.ModuleList()
+
+        for k in range(FP_MLPS.__len__()):
+            pre_channel = FP_MLPS[k + 1][-1] if k + 1 < len(FP_MLPS) else channel_out
+            self.FP_modules.append(
+                PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] + FP_MLPS[k])
+            )
+
+        cls_layers = []
+        pre_channel = FP_MLPS[0][-1]
+        for k in range(0, CLS_FC.__len__()):
+            cls_layers.append(pt_utils.Conv1d(pre_channel, CLS_FC[k], bn=True))
+            pre_channel = CLS_FC[k]
+        cls_layers.append(pt_utils.Conv1d(pre_channel, 1, activation=None))
+        cls_layers.insert(1, nn.Dropout(0.5))
+        self.cls_layer = nn.Sequential(*cls_layers)
+
+    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])
+
+            print(li_xyz.shape, li_features.shape)
+
+            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]
+            )
+
+        pred_cls = self.cls_layer(l_features[0]).transpose(1, 2).contiguous()  # (B, N, 1)
+        return pred_cls
+
+if __name__ == '__main__':
+    net = Pointnet2MSG(0).cuda()
+    pts = torch.randn(2, 1024, 3).cuda()
+
+    pre = net(pts)
+    print(pre.shape)