225 lines
9.9 KiB
Python
225 lines
9.9 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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from ...ops.roiaware_pool3d import roiaware_pool3d_utils
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from ...utils.spconv_utils import spconv
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from .roi_head_template import RoIHeadTemplate
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class PartA2FCHead(RoIHeadTemplate):
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def __init__(self, input_channels, model_cfg, num_class=1, **kwargs):
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super().__init__(num_class=num_class, model_cfg=model_cfg)
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self.model_cfg = model_cfg
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self.SA_modules = nn.ModuleList()
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block = self.post_act_block
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c0 = self.model_cfg.ROI_AWARE_POOL.NUM_FEATURES // 2
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self.conv_part = spconv.SparseSequential(
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block(4, 64, 3, padding=1, indice_key='rcnn_subm1'),
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block(64, c0, 3, padding=1, indice_key='rcnn_subm1_1'),
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)
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self.conv_rpn = spconv.SparseSequential(
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block(input_channels, 64, 3, padding=1, indice_key='rcnn_subm2'),
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block(64, c0, 3, padding=1, indice_key='rcnn_subm1_2'),
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)
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shared_fc_list = []
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pool_size = self.model_cfg.ROI_AWARE_POOL.POOL_SIZE
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pre_channel = self.model_cfg.ROI_AWARE_POOL.NUM_FEATURES * pool_size * pool_size * pool_size
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for k in range(0, self.model_cfg.SHARED_FC.__len__()):
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shared_fc_list.extend([
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nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False),
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nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]),
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nn.ReLU()
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])
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pre_channel = self.model_cfg.SHARED_FC[k]
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if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
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shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
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self.shared_fc_layer = nn.Sequential(*shared_fc_list)
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self.cls_layers = self.make_fc_layers(
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input_channels=pre_channel, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC
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)
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self.reg_layers = self.make_fc_layers(
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input_channels=pre_channel,
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output_channels=self.box_coder.code_size * self.num_class,
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fc_list=self.model_cfg.REG_FC
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)
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self.roiaware_pool3d_layer = roiaware_pool3d_utils.RoIAwarePool3d(
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out_size=self.model_cfg.ROI_AWARE_POOL.POOL_SIZE,
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max_pts_each_voxel=self.model_cfg.ROI_AWARE_POOL.MAX_POINTS_PER_VOXEL
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)
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self.init_weights(weight_init='xavier')
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def init_weights(self, weight_init='xavier'):
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if weight_init == 'kaiming':
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init_func = nn.init.kaiming_normal_
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elif weight_init == 'xavier':
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init_func = nn.init.xavier_normal_
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elif weight_init == 'normal':
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init_func = nn.init.normal_
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else:
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raise NotImplementedError
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
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if weight_init == 'normal':
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init_func(m.weight, mean=0, std=0.001)
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else:
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init_func(m.weight)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001)
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def post_act_block(self, in_channels, out_channels, kernel_size, indice_key, stride=1, padding=0, conv_type='subm'):
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if conv_type == 'subm':
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m = spconv.SparseSequential(
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spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key),
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nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
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nn.ReLU(),
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)
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elif conv_type == 'spconv':
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m = spconv.SparseSequential(
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spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
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bias=False, indice_key=indice_key),
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nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
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nn.ReLU(),
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)
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elif conv_type == 'inverseconv':
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m = spconv.SparseSequential(
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spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size,
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indice_key=indice_key, bias=False),
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nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),
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nn.ReLU(),
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)
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else:
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raise NotImplementedError
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return m
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def roiaware_pool(self, batch_dict):
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"""
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Args:
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batch_dict:
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batch_size:
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rois: (B, num_rois, 7 + C)
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point_coords: (num_points, 4) [bs_idx, x, y, z]
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point_features: (num_points, C)
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point_cls_scores: (N1 + N2 + N3 + ..., 1)
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point_part_offset: (N1 + N2 + N3 + ..., 3)
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Returns:
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"""
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batch_size = batch_dict['batch_size']
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batch_idx = batch_dict['point_coords'][:, 0]
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point_coords = batch_dict['point_coords'][:, 1:4]
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point_features = batch_dict['point_features']
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part_features = torch.cat((
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batch_dict['point_part_offset'] if not self.model_cfg.get('DISABLE_PART', False) else point_coords,
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batch_dict['point_cls_scores'].view(-1, 1).detach()
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), dim=1)
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part_features[part_features[:, -1] < self.model_cfg.SEG_MASK_SCORE_THRESH, 0:3] = 0
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rois = batch_dict['rois']
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pooled_part_features_list, pooled_rpn_features_list = [], []
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for bs_idx in range(batch_size):
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bs_mask = (batch_idx == bs_idx)
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cur_point_coords = point_coords[bs_mask]
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cur_part_features = part_features[bs_mask]
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cur_rpn_features = point_features[bs_mask]
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cur_roi = rois[bs_idx][:, 0:7].contiguous() # (N, 7)
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pooled_part_features = self.roiaware_pool3d_layer.forward(
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cur_roi, cur_point_coords, cur_part_features, pool_method='avg'
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) # (N, out_x, out_y, out_z, 4)
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pooled_rpn_features = self.roiaware_pool3d_layer.forward(
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cur_roi, cur_point_coords, cur_rpn_features, pool_method='max'
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) # (N, out_x, out_y, out_z, C)
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pooled_part_features_list.append(pooled_part_features)
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pooled_rpn_features_list.append(pooled_rpn_features)
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pooled_part_features = torch.cat(pooled_part_features_list, dim=0) # (B * N, out_x, out_y, out_z, 4)
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pooled_rpn_features = torch.cat(pooled_rpn_features_list, dim=0) # (B * N, out_x, out_y, out_z, C)
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return pooled_part_features, pooled_rpn_features
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@staticmethod
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def fake_sparse_idx(sparse_idx, batch_size_rcnn):
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print('Warning: Sparse_Idx_Shape(%s) \r' % (str(sparse_idx.shape)), end='', flush=True)
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# at most one sample is non-empty, then fake the first voxels of each sample(BN needs at least
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# two values each channel) as non-empty for the below calculation
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sparse_idx = sparse_idx.new_zeros((batch_size_rcnn, 3))
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bs_idxs = torch.arange(batch_size_rcnn).type_as(sparse_idx).view(-1, 1)
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sparse_idx = torch.cat((bs_idxs, sparse_idx), dim=1)
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return sparse_idx
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def forward(self, batch_dict):
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"""
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Args:
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batch_dict:
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Returns:
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"""
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targets_dict = self.proposal_layer(
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batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
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)
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if self.training:
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targets_dict = self.assign_targets(batch_dict)
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batch_dict['rois'] = targets_dict['rois']
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batch_dict['roi_labels'] = targets_dict['roi_labels']
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# RoI aware pooling
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pooled_part_features, pooled_rpn_features = self.roiaware_pool(batch_dict)
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batch_size_rcnn = pooled_part_features.shape[0] # (B * N, out_x, out_y, out_z, 4)
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# transform to sparse tensors
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sparse_shape = np.array(pooled_part_features.shape[1:4], dtype=np.int32)
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sparse_idx = pooled_part_features.sum(dim=-1).nonzero() # (non_empty_num, 4) ==> [bs_idx, x_idx, y_idx, z_idx]
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if sparse_idx.shape[0] < 3:
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sparse_idx = self.fake_sparse_idx(sparse_idx, batch_size_rcnn)
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if self.training:
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# these are invalid samples
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targets_dict['rcnn_cls_labels'].fill_(-1)
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targets_dict['reg_valid_mask'].fill_(-1)
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part_features = pooled_part_features[sparse_idx[:, 0], sparse_idx[:, 1], sparse_idx[:, 2], sparse_idx[:, 3]]
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rpn_features = pooled_rpn_features[sparse_idx[:, 0], sparse_idx[:, 1], sparse_idx[:, 2], sparse_idx[:, 3]]
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coords = sparse_idx.int().contiguous()
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part_features = spconv.SparseConvTensor(part_features, coords, sparse_shape, batch_size_rcnn)
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rpn_features = spconv.SparseConvTensor(rpn_features, coords, sparse_shape, batch_size_rcnn)
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# forward rcnn network
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x_part = self.conv_part(part_features)
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x_rpn = self.conv_rpn(rpn_features)
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merged_feature = torch.cat((x_rpn.features, x_part.features), dim=1) # (N, C)
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shared_feature = spconv.SparseConvTensor(merged_feature, coords, sparse_shape, batch_size_rcnn)
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shared_feature = shared_feature.dense().view(batch_size_rcnn, -1, 1)
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shared_feature = self.shared_fc_layer(shared_feature)
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rcnn_cls = self.cls_layers(shared_feature).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2)
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rcnn_reg = self.reg_layers(shared_feature).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C)
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if not self.training:
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batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
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batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
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)
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batch_dict['batch_cls_preds'] = batch_cls_preds
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batch_dict['batch_box_preds'] = batch_box_preds
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batch_dict['cls_preds_normalized'] = False
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else:
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targets_dict['rcnn_cls'] = rcnn_cls
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targets_dict['rcnn_reg'] = rcnn_reg
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self.forward_ret_dict = targets_dict
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return batch_dict
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