259 lines
9.2 KiB
Python
259 lines
9.2 KiB
Python
"""
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PointPillars fork from SECOND.
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Code written by Alex Lang and Oscar Beijbom, 2018.
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Licensed under MIT License [see LICENSE].
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"""
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from typing import Any, Dict
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import torch
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from mmcv.cnn import build_norm_layer
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from torch import nn
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from torch.nn import functional as F
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from mmdet3d.models.builder import build_backbone
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from mmdet.models import BACKBONES
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__all__ = ["PillarFeatureNet", "PointPillarsScatter", "PointPillarsEncoder"]
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def get_paddings_indicator(actual_num, max_num, axis=0):
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"""Create boolean mask by actually number of a padded tensor.
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Args:
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actual_num ([type]): [description]
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max_num ([type]): [description]
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Returns:
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[type]: [description]
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"""
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actual_num = torch.unsqueeze(actual_num, axis + 1)
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# tiled_actual_num: [N, M, 1]
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max_num_shape = [1] * len(actual_num.shape)
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max_num_shape[axis + 1] = -1
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max_num = torch.arange(max_num, dtype=torch.int, device=actual_num.device).view(
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max_num_shape
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)
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# tiled_actual_num: [[3,3,3,3,3], [4,4,4,4,4], [2,2,2,2,2]]
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# tiled_max_num: [[0,1,2,3,4], [0,1,2,3,4], [0,1,2,3,4]]
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paddings_indicator = actual_num.int() > max_num
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# paddings_indicator shape: [batch_size, max_num]
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return paddings_indicator
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class PFNLayer(nn.Module):
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def __init__(self, in_channels, out_channels, norm_cfg=None, last_layer=False):
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"""
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Pillar Feature Net Layer.
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The Pillar Feature Net could be composed of a series of these layers, but the PointPillars paper results only
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used a single PFNLayer. This layer performs a similar role as second.pytorch.voxelnet.VFELayer.
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:param in_channels: <int>. Number of input channels.
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:param out_channels: <int>. Number of output channels.
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:param last_layer: <bool>. If last_layer, there is no concatenation of features.
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"""
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super().__init__()
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self.name = "PFNLayer"
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self.last_vfe = last_layer
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if not self.last_vfe:
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out_channels = out_channels // 2
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self.units = out_channels
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if norm_cfg is None:
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norm_cfg = dict(type="BN1d", eps=1e-3, momentum=0.01)
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self.norm_cfg = norm_cfg
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self.linear = nn.Linear(in_channels, self.units, bias=False)
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self.norm = build_norm_layer(self.norm_cfg, self.units)[1]
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def forward(self, inputs):
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x = self.linear(inputs)
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torch.backends.cudnn.enabled = False
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x = self.norm(x.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()
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torch.backends.cudnn.enabled = True
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x = F.relu(x)
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x_max = torch.max(x, dim=1, keepdim=True)[0]
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if self.last_vfe:
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return x_max
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else:
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x_repeat = x_max.repeat(1, inputs.shape[1], 1)
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x_concatenated = torch.cat([x, x_repeat], dim=2)
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return x_concatenated
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@BACKBONES.register_module()
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class PillarFeatureNet(nn.Module):
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def __init__(
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self,
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in_channels=4,
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feat_channels=(64,),
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with_distance=False,
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voxel_size=(0.2, 0.2, 4),
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point_cloud_range=(0, -40, -3, 70.4, 40, 1),
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norm_cfg=None,
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):
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"""
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Pillar Feature Net.
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The network prepares the pillar features and performs forward pass through PFNLayers. This net performs a
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similar role to SECOND's second.pytorch.voxelnet.VoxelFeatureExtractor.
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:param num_input_features: <int>. Number of input features, either x, y, z or x, y, z, r.
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:param num_filters: (<int>: N). Number of features in each of the N PFNLayers.
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:param with_distance: <bool>. Whether to include Euclidean distance to points.
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:param voxel_size: (<float>: 3). Size of voxels, only utilize x and y size.
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:param pc_range: (<float>: 6). Point cloud range, only utilize x and y min.
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"""
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super().__init__()
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self.name = "PillarFeatureNet"
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assert len(feat_channels) > 0
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self.in_channels = in_channels
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in_channels += 5
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if with_distance:
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in_channels += 1
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self._with_distance = with_distance
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# Create PillarFeatureNet layers
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feat_channels = [in_channels] + list(feat_channels)
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pfn_layers = []
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for i in range(len(feat_channels) - 1):
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in_filters = feat_channels[i]
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out_filters = feat_channels[i + 1]
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if i < len(feat_channels) - 2:
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last_layer = False
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else:
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last_layer = True
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pfn_layers.append(
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PFNLayer(
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in_filters, out_filters, norm_cfg=norm_cfg, last_layer=last_layer
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)
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)
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self.pfn_layers = nn.ModuleList(pfn_layers)
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# Need pillar (voxel) size and x/y offset in order to calculate pillar offset
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self.vx = voxel_size[0]
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self.vy = voxel_size[1]
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self.x_offset = self.vx / 2 + point_cloud_range[0]
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self.y_offset = self.vy / 2 + point_cloud_range[1]
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def forward(self, features, num_voxels, coors):
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device = features.device
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dtype = features.dtype
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# Find distance of x, y, and z from cluster center
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# features = features[:, :, :self.num_input]
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points_mean = features[:, :, :3].sum(dim=1, keepdim=True) / num_voxels.type_as(
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features
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).view(-1, 1, 1)
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f_cluster = features[:, :, :3] - points_mean
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# Find distance of x, y, and z from pillar center
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# f_center = features[:, :, :2]
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# modified according to xyz coords
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f_center = torch.zeros_like(features[:, :, :2])
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f_center[:, :, 0] = features[:, :, 0] - (
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coors[:, 1].to(dtype).unsqueeze(1) * self.vx + self.x_offset
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)
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f_center[:, :, 1] = features[:, :, 1] - (
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coors[:, 2].to(dtype).unsqueeze(1) * self.vy + self.y_offset
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)
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# Combine together feature decorations
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features_ls = [features, f_cluster, f_center]
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if self._with_distance:
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points_dist = torch.norm(features[:, :, :3], 2, 2, keepdim=True)
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features_ls.append(points_dist)
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features = torch.cat(features_ls, dim=-1)
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# The feature decorations were calculated without regard to whether pillar was empty. Need to ensure that
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# empty pillars remain set to zeros.
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voxel_count = features.shape[1]
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mask = get_paddings_indicator(num_voxels, voxel_count, axis=0)
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mask = torch.unsqueeze(mask, -1).type_as(features)
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features *= mask
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# Forward pass through PFNLayers
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for pfn in self.pfn_layers:
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features = pfn(features)
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return features.squeeze()
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@BACKBONES.register_module()
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class PointPillarsScatter(nn.Module):
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def __init__(self, in_channels=64, output_shape=(512, 512), **kwargs):
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"""
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Point Pillar's Scatter.
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Converts learned features from dense tensor to sparse pseudo image. This replaces SECOND's
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second.pytorch.voxelnet.SparseMiddleExtractor.
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:param output_shape: ([int]: 4). Required output shape of features.
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:param num_input_features: <int>. Number of input features.
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"""
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super().__init__()
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self.in_channels = in_channels
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self.output_shape = output_shape
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self.nx = output_shape[0]
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self.ny = output_shape[1]
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def extra_repr(self):
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return (
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f"in_channels={self.in_channels}, output_shape={tuple(self.output_shape)}"
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)
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def forward(self, voxel_features, coords, batch_size):
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# batch_canvas will be the final output.
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batch_canvas = []
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for batch_itt in range(batch_size):
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# Create the canvas for this sample
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canvas = torch.zeros(
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self.in_channels,
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self.nx * self.ny,
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dtype=voxel_features.dtype,
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device=voxel_features.device,
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)
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# Only include non-empty pillars
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batch_mask = coords[:, 0] == batch_itt
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this_coords = coords[batch_mask, :]
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# modified -> xyz coords
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indices = this_coords[:, 1] * self.ny + this_coords[:, 2]
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indices = indices.type(torch.long)
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voxels = voxel_features[batch_mask, :]
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voxels = voxels.t()
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# Now scatter the blob back to the canvas.
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canvas[:, indices] = voxels
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# Append to a list for later stacking.
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batch_canvas.append(canvas)
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# Stack to 3-dim tensor (batch-size, nchannels, nrows*ncols)
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batch_canvas = torch.stack(batch_canvas, 0)
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# Undo the column stacking to final 4-dim tensor
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batch_canvas = batch_canvas.view(batch_size, self.in_channels, self.nx, self.ny)
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return batch_canvas
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@BACKBONES.register_module()
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class PointPillarsEncoder(nn.Module):
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def __init__(
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self,
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pts_voxel_encoder: Dict[str, Any],
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pts_middle_encoder: Dict[str, Any],
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**kwargs,
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):
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super().__init__()
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self.pts_voxel_encoder = build_backbone(pts_voxel_encoder)
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self.pts_middle_encoder = build_backbone(pts_middle_encoder)
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def forward(self, feats, coords, batch_size, sizes):
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x = self.pts_voxel_encoder(feats, sizes, coords)
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x = self.pts_middle_encoder(x, coords, batch_size)
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return x
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