fastdev.nn.pointnet¶

PointNet implementation in PyTorch.

Adapted from: https://github.com/yanx27/Pointnet_Pointnet2_pytorch/

Module Contents¶

class fastdev.nn.pointnet.PointNetEncoder(input_dim: int = 3, feature_dim: int = 1024, global_feature: bool = True, point_transform: bool = False, feature_transform: bool = False, pretrained_filename: Literal['models/pointnn/pointnet_cls_241031.safetensors'] | None = None)[source]¶

Bases: torch.nn.Module

Encoder for PointNet.

This implementation differs from the original repository by adding an option to apply a transformation to the input points (default: False). STNkd is reused for both point and feature transformations, with STN3d being a special case of STNkd.

Forward method:

Args:: x (torch.Tensor): Input tensor of shape (batch_size, channels, num_points).
Returns:: torch.Tensor: Extracted features. torch.Tensor: Transformation matrix applied to the points. torch.Tensor: Transformed features.

Reference: https://github.com/yanx27/Pointnet_Pointnet2_pytorch/blob/master/models/pointnet_utils.py

Parameters:

input_dim (int)
feature_dim (int)
global_feature (bool)
point_transform (bool)
feature_transform (bool)
pretrained_filename (Optional[Literal['models/pointnn/pointnet_cls_241031.safetensors']])

global_feat = True[source]¶

point_transform = False[source]¶

feature_transform = False[source]¶

conv1[source]¶

conv2[source]¶

conv3[source]¶

bn1[source]¶

bn2[source]¶

bn3[source]¶

relu[source]¶

forward(x: torch.Tensor)[source]¶

Forward pass.

Parameters:: x (torch.Tensor) – Input tensor of shape (batch_size, channels, num_points).
Returns:: Extracted features. torch.Tensor: Transformation matrix applied to the points. torch.Tensor: Transformation matrix applied to the features.
Return type:: torch.Tensor

class fastdev.nn.pointnet.PointNetCls(k: int = 40, normal_channel: bool = True, point_transform: bool = False, feature_transform: bool = False, pretrained_filename: Literal['models/pointnn/pointnet_cls_241031.safetensors'] | None = None)[source]¶

Bases: torch.nn.Module

Parameters:

k (int)
normal_channel (bool)
point_transform (bool)
feature_transform (bool)
pretrained_filename (Optional[Literal['models/pointnn/pointnet_cls_241031.safetensors']])