| """ |
| Based on: https://github.com/yanx27/Pointnet_Pointnet2_pytorch/blob/eb64fe0b4c24055559cea26299cb485dcb43d8dd/models/pointnet2_cls_ssg.py |
| |
| MIT License |
| |
| Copyright (c) 2019 benny |
| |
| Permission is hereby granted, free of charge, to any person obtaining a copy |
| of this software and associated documentation files (the "Software"), to deal |
| in the Software without restriction, including without limitation the rights |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
| copies of the Software, and to permit persons to whom the Software is |
| furnished to do so, subject to the following conditions: |
| |
| The above copyright notice and this permission notice shall be included in all |
| copies or substantial portions of the Software. |
| |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| SOFTWARE. |
| """ |
|
|
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from .pointnet2_utils import PointNetSetAbstraction |
|
|
|
|
| class get_model(nn.Module): |
| def __init__(self, num_class, normal_channel=True, width_mult=1): |
| super(get_model, self).__init__() |
| self.width_mult = width_mult |
| in_channel = 6 if normal_channel else 3 |
| self.normal_channel = normal_channel |
| self.sa1 = PointNetSetAbstraction( |
| npoint=512, |
| radius=0.2, |
| nsample=32, |
| in_channel=in_channel, |
| mlp=[64 * width_mult, 64 * width_mult, 128 * width_mult], |
| group_all=False, |
| ) |
| self.sa2 = PointNetSetAbstraction( |
| npoint=128, |
| radius=0.4, |
| nsample=64, |
| in_channel=128 * width_mult + 3, |
| mlp=[128 * width_mult, 128 * width_mult, 256 * width_mult], |
| group_all=False, |
| ) |
| self.sa3 = PointNetSetAbstraction( |
| npoint=None, |
| radius=None, |
| nsample=None, |
| in_channel=256 * width_mult + 3, |
| mlp=[256 * width_mult, 512 * width_mult, 1024 * width_mult], |
| group_all=True, |
| ) |
| self.fc1 = nn.Linear(1024 * width_mult, 512 * width_mult) |
| self.bn1 = nn.BatchNorm1d(512 * width_mult) |
| self.drop1 = nn.Dropout(0.4) |
| self.fc2 = nn.Linear(512 * width_mult, 256 * width_mult) |
| self.bn2 = nn.BatchNorm1d(256 * width_mult) |
| self.drop2 = nn.Dropout(0.4) |
| self.fc3 = nn.Linear(256 * width_mult, num_class) |
|
|
| def forward(self, xyz, features=False): |
| B, _, _ = xyz.shape |
| if self.normal_channel: |
| norm = xyz[:, 3:, :] |
| xyz = xyz[:, :3, :] |
| else: |
| norm = None |
| l1_xyz, l1_points = self.sa1(xyz, norm) |
| l2_xyz, l2_points = self.sa2(l1_xyz, l1_points) |
| l3_xyz, l3_points = self.sa3(l2_xyz, l2_points) |
| x = l3_points.view(B, 1024 * self.width_mult) |
| x = self.drop1(F.relu(self.bn1(self.fc1(x)))) |
| result_features = self.bn2(self.fc2(x)) |
| x = self.drop2(F.relu(result_features)) |
| x = self.fc3(x) |
| x = F.log_softmax(x, -1) |
|
|
| if features: |
| return x, l3_points, result_features |
| else: |
| return x, l3_points |
|
|
|
|
| class get_loss(nn.Module): |
| def __init__(self): |
| super(get_loss, self).__init__() |
|
|
| def forward(self, pred, target, trans_feat): |
| total_loss = F.nll_loss(pred, target) |
|
|
| return total_loss |
|
|
