Refactor of models

2026-07-20 12:10:53 +08:00 · 2018-02-22 20:03:52 -05:00
parent 8e0f03d9ba
commit 2730bbf963
4 changed files with 230 additions and 105 deletions
@@ -0,0 +1,145 @@
+import os, sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, "../utils"))
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import pytorch_utils as pt_utils
+from pointnet2_modules import PointnetSAModule, PointnetFPModule, PointnetSAModuleMSG
+from pointnet2_utils import RandomDropout
+from collections import namedtuple
+
+
+def model_fn_decorator(criterion):
+    ModelReturn = namedtuple("ModelReturn", ['preds', 'loss', 'acc'])
+
+    def model_fn(model, data, epoch=0, eval=False):
+        inputs, labels = data
+        inputs = Variable(inputs.cuda(async=True), volatile=eval)
+        labels = Variable(labels.cuda(async=True), volatile=eval)
+
+        xyz = inputs[..., :3]
+        if inputs.size(2) > 3:
+            points = inputs[..., 3:]
+        else:
+            points = None
+
+        preds = model(xyz, points)
+        loss = criterion(preds.view(labels.numel(), -1), labels.view(-1))
+
+        _, classes = torch.max(preds.data, 2)
+        acc = (classes == labels.data).sum() / labels.numel()
+
+        return ModelReturn(preds, loss, {"acc": acc})
+
+    return model_fn
+
+
+class Pointnet2MSG(nn.Module):
+
+    def __init__(self, num_classes, input_channels=9):
+        super().__init__()
+
+        self.SA_modules = nn.ModuleList()
+        c_in = input_channels
+        self.SA_modules.append(
+            PointnetSAModuleMSG(
+                npoint=1024,
+                radii=[0.05, 0.1],
+                nsamples=[16, 32],
+                mlps=[[c_in, 16, 16, 32], [c_in, 32, 32, 64]]
+            )
+        )
+        c_out_0 = 32 + 64
+
+        c_in = c_out_0
+        self.SA_modules.append(
+            PointnetSAModuleMSG(
+                npoint=256,
+                radii=[0.1, 0.2],
+                nsamples=[16, 32],
+                mlps=[[c_in, 64, 64, 128], [c_in, 64, 96, 128]]
+            )
+        )
+        c_out_1 = 128 + 128
+
+        c_in = c_out_1
+        self.SA_modules.append(
+            PointnetSAModuleMSG(
+                npoint=64,
+                radii=[0.2, 0.4],
+                nsamples=[16, 32],
+                mlps=[[c_in, 128, 196, 256], [c_in, 128, 196, 256]]
+            )
+        )
+        c_out_2 = 256 + 256
+
+        c_in = c_out_2
+        self.SA_modules.append(
+            PointnetSAModuleMSG(
+                npoint=16,
+                radii=[0.4, 0.8],
+                nsamples=[16, 32],
+                mlps=[[c_in, 256, 256, 512], [c_in, 256, 384, 512]]
+            )
+        )
+        c_out_3 = 512 + 512
+
+        self.FP_modules = nn.ModuleList()
+        self.FP_modules.append(
+            PointnetFPModule(mlp=[256 + input_channels, 128, 128])
+        )
+        self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_0, 256, 256]))
+        self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_1, 512, 512]))
+        self.FP_modules.append(
+            PointnetFPModule(mlp=[c_out_3 + c_out_2, 512, 512])
+        )
+
+        self.FC_layer = nn.Sequential(
+            pt_utils.Conv1d(128, 128, bn=True), nn.Dropout(),
+            pt_utils.Conv1d(128, num_classes, activation=None)
+        )
+
+    def forward(self, xyz, points=None):
+        xyz = xyz.contiguous()
+        points = (
+            points.transpose(1, 2).contiguous() if points is not None else None
+        )
+
+        l_xyz, l_points = [xyz], [points]
+        for i in range(len(self.SA_modules)):
+            li_xyz, li_points = self.SA_modules[i](l_xyz[i], l_points[i])
+            l_xyz.append(li_xyz)
+            l_points.append(li_points)
+
+        for i in range(-1, -(len(self.FP_modules) + 1), -1):
+            l_points[i - 1] = self.FP_modules[i](
+                l_xyz[i - 1], l_xyz[i], l_points[i - 1], l_points[i]
+            )
+
+        return self.FC_layer(l_points[0]).transpose(1, 2).contiguous()
+
+
+if __name__ == "__main__":
+    from torch.autograd import Variable
+    import numpy as np
+    import torch.optim as optim
+    B = 2
+    N = 32
+    inputs = torch.randn(B, N, 6).cuda()
+    labels = torch.from_numpy(np.random.randint(0, 3,
+                                                size=B * N)).view(B, N).cuda()
+    model = Pointnet2MSG(3, input_channels=3)
+    model.cuda()
+
+    optimizer = optim.Adam(model.parameters(), lr=1e-2)
+
+    model_fn = model_fn_decorator(nn.CrossEntropyLoss())
+    for _ in range(20):
+        optimizer.zero_grad()
+        _, loss, _ = model_fn(model, (inputs, labels))
+        loss.backward()
+        print(loss.data[0])
+        optimizer.step()