Spaces:
Running
on
Zero
Running
on
Zero
| """ | |
| 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 | |