Refactor of models

models/pointnet2_msg_cls.py

@@ -0,0 +1,110 @@
+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)
+        labels = labels.view(-1)
+        loss = criterion(preds, labels)
+
+        _, classes = torch.max(preds.data, -1)
+        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=3):
+        super().__init__()
+
+        self.SA_modules = nn.ModuleList()
+        self.SA_modules.append(
+            PointnetSAModuleMSG(
+                npoint=512,
+                radii=[0.1, 0.2, 0.4],
+                nsamples=[32, 64, 128],
+                mlps=[[input_channels, 64], [input_channels, 128],
+                      [input_channels, 128]]
+            )
+        )
+
+        input_channels = 64 + 128 + 128
+        self.SA_modules.append(
+            PointnetSAModuleMSG(
+                npoint=128,
+                radii=[0.2, 0.4, 0.8],
+                nsamples=[16, 32, 64],
+                mlps=[[input_channels, 128], [input_channels, 256],
+                      [input_channels, 256]]
+            )
+        )
+        self.SA_modules.append(
+            PointnetSAModule(mlp=[128 + 256 + 256, 256, 512, 1024])
+        )
+
+        self.FC_layer = nn.Sequential(
+            pt_utils.FC(1024, 512, bn=True),
+            nn.Dropout(p=0.5),
+            pt_utils.FC(512, 256, bn=True),
+            nn.Dropout(p=0.5),
+            pt_utils.FC(256, 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
+        )
+        for module in self.SA_modules:
+            xyz, points = module(xyz, points)
+
+        return self.FC_layer(points.squeeze(-1))
+
+
+if __name__ == "__main__":
+    from torch.autograd import Variable
+    import numpy as np
+    import torch.optim as optim
+    import torch.autograd.profiler as profiler
+    B = 2
+    N = 2048
+    inputs = torch.randn(B, N, 6).cuda()
+    labels = torch.from_numpy(np.random.randint(0, 3, size=B)).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()