mirror of
https://github.com/wassname/Pointnet2_PyTorch.git
synced 2026-06-27 16:00:07 +08:00
erikwijmans
146 lines
4.2 KiB
Python
146 lines
4.2 KiB
Python
import os, sys
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(BASE_DIR)
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sys.path.append(os.path.join(BASE_DIR, "../utils"))
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import pytorch_utils as pt_utils
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from pointnet2_modules import PointnetSAModule, PointnetFPModule, PointnetSAModuleMSG
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from pointnet2_utils import RandomDropout
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from collections import namedtuple
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def model_fn_decorator(criterion):
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ModelReturn = namedtuple("ModelReturn", ['preds', 'loss', 'acc'])
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def model_fn(model, data, epoch=0, eval=False):
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inputs, labels = data
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inputs = Variable(inputs.cuda(async=True), volatile=eval)
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labels = Variable(labels.cuda(async=True), volatile=eval)
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xyz = inputs[..., :3]
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if inputs.size(2) > 3:
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points = inputs[..., 3:]
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else:
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points = None
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preds = model(xyz, points)
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labels = labels.view(-1)
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loss = criterion(preds, labels)
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_, classes = torch.max(preds.data, -1)
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acc = (classes == labels.data).sum() / labels.numel()
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return ModelReturn(preds, loss, {"acc": acc})
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return model_fn
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class Pointnet2SSG(nn.Module):
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def __init__(self, num_classes, input_channels=9):
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super().__init__()
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self.SA_modules = nn.ModuleList()
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self.SA_modules.append(
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PointnetSAModule(
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npoint=512,
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radius=0.2,
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nsample=64,
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mlp=[input_channels, 64, 64, 128]
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)
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)
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self.SA_modules.append(
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PointnetSAModule(
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npoint=128,
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radius=0.4,
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nsample=64,
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mlp=[128 + 3, 128, 128, 256]
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)
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)
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self.SA_modules.append(PointnetSAModule(mlp=[256 + 3, 256, 512, 1024]))
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self.FC_layer = nn.Sequential(
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pt_utils.FC(1024, 512, bn=True),
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nn.Dropout(p=0.5),
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pt_utils.FC(512, 256, bn=True),
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nn.Dropout(p=0.5),
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pt_utils.FC(256, num_classes, activation=None)
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)
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def forward(self, xyz, points=None):
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for module in self.SA_modules:
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xyz, points = module(xyz, points)
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return self.FC_layer(points.squeeze(1))
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class Pointnet2MSG(nn.Module):
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def __init__(self, num_classes, input_channels=9):
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super().__init__()
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self.SA_modules = nn.ModuleList()
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self.SA_modules.append(
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PointnetSAModuleMSG(
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npoint=512,
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radii=[0.1, 0.2, 0.4],
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nsamples=[32, 64, 128],
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mlps=[[input_channels, 64], [input_channels, 128],
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[input_channels, 128]]
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)
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)
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input_channels = 64 + 128 + 128 + 3
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self.SA_modules.append(
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PointnetSAModuleMSG(
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npoint=128,
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radii=[0.2, 0.4, 0.8],
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nsamples=[16, 32, 64],
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mlps=[[input_channels, 128], [input_channels, 256],
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[input_channels, 256]]
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)
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)
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self.SA_modules.append(
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PointnetSAModule(mlp=[128 + 256 + 256 + 3, 256, 512, 1024])
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)
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self.FC_layer = nn.Sequential(
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pt_utils.FC(1024, 512, bn=True),
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nn.Dropout(p=0.5),
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pt_utils.FC(512, 256, bn=True),
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nn.Dropout(p=0.5),
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pt_utils.FC(256, num_classes, activation=None)
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)
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def forward(self, xyz, points=None):
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for module in self.SA_modules:
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xyz, points = module(xyz, points)
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return self.FC_layer(points.squeeze(1))
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if __name__ == "__main__":
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from torch.autograd import Variable
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import numpy as np
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import torch.optim as optim
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import torch.autograd.profiler as profiler
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B = 2
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N = 2048
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inputs = torch.randn(B, N, 9).cuda()
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labels = torch.from_numpy(np.random.randint(0, 3, size=B)).cuda()
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model = Pointnet2MSG(3)
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model.cuda()
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optimizer = optim.Adam(model.parameters(), lr=1e-2)
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model_fn = model_fn_decorator(nn.CrossEntropyLoss())
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for _ in range(20):
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optimizer.zero_grad()
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_, loss, _ = model_fn(model, (inputs, labels))
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loss.backward()
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print(loss.data[0])
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optimizer.step()
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