first commit

2025-05-13 09:03:38 +08:00
commit b98753bfbb
121 changed files with 8665 additions and 0 deletions
--- a/modules/pointnet++_encoder.py
+++ b/modules/pointnet++_encoder.py
@@ -0,0 +1,149 @@
+import torch
+import torch.nn as nn
+import os
+import sys
+path = os.path.abspath(__file__)
+for i in range(2):
+    path = os.path.dirname(path)
+PROJECT_ROOT = path                 
+sys.path.append(PROJECT_ROOT)
+import PytorchBoot.stereotype as stereotype
+from modules.module_lib.pointnet2_modules import PointnetSAModuleMSG
+
+
+ClsMSG_CFG_Dense = {
+    'NPOINTS': [512, 256, 128, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
+    'NSAMPLE': [[32, 64], [16, 32], [8, 16], [None, None]],
+    '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]]],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Light = {
+    'NPOINTS': [512, 256, 128, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
+    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [None, None]],
+    '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]]],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Light_2048 = {
+    'NPOINTS': [512, 256, 128, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
+    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [None, None]],
+    'MLPS': [[[16, 16, 32], [32, 32, 64]],
+         [[64, 64, 128], [64, 96, 128]],
+         [[128, 196, 256], [128, 196, 256]],
+         [[256, 256, 1024], [256, 512, 1024]]],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Strong = {
+    'NPOINTS': [512, 256, 128, 64, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16],[0.16, 0.32], [None, None]],
+    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [16, 32], [None, None]],
+    'MLPS': [[[16, 16, 32], [32, 32, 64]],
+         [[64, 64, 128], [64, 96, 128]],
+         [[128, 196, 256], [128, 196, 256]],
+         [[256, 256, 512], [256, 512, 512]],
+         [[512, 512, 2048], [512, 1024, 2048]]
+         ],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Lighter = {
+    'NPOINTS': [512, 256, 128, 64, None],
+    'RADIUS': [[0.01], [0.02], [0.04], [0.08], [None]],
+    'NSAMPLE': [[64], [32], [16], [8], [None]],
+    'MLPS': [[[32, 32, 64]],
+             [[64, 64, 128]],
+             [[128, 196, 256]],
+             [[256, 256, 512]],
+             [[512, 512, 1024]]],
+    'DP_RATIO': 0.5,
+}
+
+
+def select_params(name):
+    if name == 'light':
+        return ClsMSG_CFG_Light
+    elif name == 'lighter':
+        return ClsMSG_CFG_Lighter
+    elif name == 'dense':
+        return ClsMSG_CFG_Dense
+    elif name == 'light_2048':
+        return ClsMSG_CFG_Light_2048
+    elif name == 'strong':
+        return ClsMSG_CFG_Strong
+    else:
+        raise NotImplementedError
+
+
+def break_up_pc(pc):
+    xyz = pc[..., 0:3].contiguous()
+    features = (
+        pc[..., 3:].transpose(1, 2).contiguous()
+        if pc.size(-1) > 3 else None
+    )
+
+    return xyz, features
+
+
+@stereotype.module("pointnet++_encoder")
+class PointNet2Encoder(nn.Module):
+    def encode_points(self, pts, require_per_point_feat=False):
+        return self.forward(pts)
+
+    def __init__(self, config:dict):
+        super().__init__()
+
+        channel_in = config.get("in_dim", 3) - 3
+        params_name = config.get("params_name", "light")
+
+        self.SA_modules = nn.ModuleList()
+        selected_params = select_params(params_name)
+        for k in range(selected_params['NPOINTS'].__len__()):
+            mlps = selected_params['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=selected_params['NPOINTS'][k],
+                    radii=selected_params['RADIUS'][k],
+                    nsamples=selected_params['NSAMPLE'][k],
+                    mlps=mlps,
+                    use_xyz=True,
+                    bn=True
+                )
+            )
+            channel_in = channel_out
+
+    def forward(self, point_cloud: torch.cuda.FloatTensor):
+        xyz, features = break_up_pc(point_cloud)
+
+        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)
+        return l_features[-1].squeeze(-1)
+
+
+if __name__ == '__main__':
+    seed = 100
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    net = PointNet2Encoder(config={"in_dim": 3, "params_name": "strong"}).cuda()
+    pts = torch.randn(2, 2444, 3).cuda()
+    print(torch.mean(pts, dim=1))
+    pre = net.encode_points(pts)
+    print(pre.shape)