Pointnet2_PyTorch/models/pointnet2_msg_sem.py

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, use_xyz=True):
        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]],
                use_xyz=use_xyz
            )
        )
        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 if use_xyz else 0), 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()

    # with use_xyz=False
    inputs = torch.randn(B, N, 3).cuda()
    labels = torch.from_numpy(np.random.randint(0, 3,
                                                size=B * N)).view(B, N).cuda()
    model = Pointnet2MSG(3, input_channels=3, use_xyz=False)
    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()