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pcdet/models/roi_heads/pvrcnn_head.py

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+import torch.nn as nn
+
+from ...ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
+from ...utils import common_utils
+from .roi_head_template import RoIHeadTemplate
+
+
+class PVRCNNHead(RoIHeadTemplate):
+    def __init__(self, input_channels, model_cfg, num_class=1, **kwargs):
+        super().__init__(num_class=num_class, model_cfg=model_cfg)
+        self.model_cfg = model_cfg
+
+        self.roi_grid_pool_layer, num_c_out = pointnet2_stack_modules.build_local_aggregation_module(
+            input_channels=input_channels, config=self.model_cfg.ROI_GRID_POOL
+        )
+
+        GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * num_c_out
+
+        shared_fc_list = []
+        for k in range(0, self.model_cfg.SHARED_FC.__len__()):
+            shared_fc_list.extend([
+                nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False),
+                nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]),
+                nn.ReLU()
+            ])
+            pre_channel = self.model_cfg.SHARED_FC[k]
+
+            if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
+                shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
+
+        self.shared_fc_layer = nn.Sequential(*shared_fc_list)
+
+        self.cls_layers = self.make_fc_layers(
+            input_channels=pre_channel, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC
+        )
+        self.reg_layers = self.make_fc_layers(
+            input_channels=pre_channel,
+            output_channels=self.box_coder.code_size * self.num_class,
+            fc_list=self.model_cfg.REG_FC
+        )
+        self.init_weights(weight_init='xavier')
+
+    def init_weights(self, weight_init='xavier'):
+        if weight_init == 'kaiming':
+            init_func = nn.init.kaiming_normal_
+        elif weight_init == 'xavier':
+            init_func = nn.init.xavier_normal_
+        elif weight_init == 'normal':
+            init_func = nn.init.normal_
+        else:
+            raise NotImplementedError
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                if weight_init == 'normal':
+                    init_func(m.weight, mean=0, std=0.001)
+                else:
+                    init_func(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001)
+
+    def roi_grid_pool(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size:
+                rois: (B, num_rois, 7 + C)
+                point_coords: (num_points, 4)  [bs_idx, x, y, z]
+                point_features: (num_points, C)
+                point_cls_scores: (N1 + N2 + N3 + ..., 1)
+                point_part_offset: (N1 + N2 + N3 + ..., 3)
+        Returns:
+
+        """
+        batch_size = batch_dict['batch_size']
+        rois = batch_dict['rois']
+        point_coords = batch_dict['point_coords']
+        point_features = batch_dict['point_features']
+
+        point_features = point_features * batch_dict['point_cls_scores'].view(-1, 1)
+
+        global_roi_grid_points, local_roi_grid_points = self.get_global_grid_points_of_roi(
+            rois, grid_size=self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        )  # (BxN, 6x6x6, 3)
+        global_roi_grid_points = global_roi_grid_points.view(batch_size, -1, 3)  # (B, Nx6x6x6, 3)
+
+        xyz = point_coords[:, 1:4]
+        xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+        batch_idx = point_coords[:, 0]
+        for k in range(batch_size):
+            xyz_batch_cnt[k] = (batch_idx == k).sum()
+
+        new_xyz = global_roi_grid_points.view(-1, 3)
+        new_xyz_batch_cnt = xyz.new_zeros(batch_size).int().fill_(global_roi_grid_points.shape[1])
+        pooled_points, pooled_features = self.roi_grid_pool_layer(
+            xyz=xyz.contiguous(),
+            xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz,
+            new_xyz_batch_cnt=new_xyz_batch_cnt,
+            features=point_features.contiguous(),
+        )  # (M1 + M2 ..., C)
+
+        pooled_features = pooled_features.view(
+            -1, self.model_cfg.ROI_GRID_POOL.GRID_SIZE ** 3,
+            pooled_features.shape[-1]
+        )  # (BxN, 6x6x6, C)
+        return pooled_features
+
+    def get_global_grid_points_of_roi(self, rois, grid_size):
+        rois = rois.view(-1, rois.shape[-1])
+        batch_size_rcnn = rois.shape[0]
+
+        local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size)  # (B, 6x6x6, 3)
+        global_roi_grid_points = common_utils.rotate_points_along_z(
+            local_roi_grid_points.clone(), rois[:, 6]
+        ).squeeze(dim=1)
+        global_center = rois[:, 0:3].clone()
+        global_roi_grid_points += global_center.unsqueeze(dim=1)
+        return global_roi_grid_points, local_roi_grid_points
+
+    @staticmethod
+    def get_dense_grid_points(rois, batch_size_rcnn, grid_size):
+        faked_features = rois.new_ones((grid_size, grid_size, grid_size))
+        dense_idx = faked_features.nonzero()  # (N, 3) [x_idx, y_idx, z_idx]
+        dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float()  # (B, 6x6x6, 3)
+
+        local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6]
+        roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \
+                          - (local_roi_size.unsqueeze(dim=1) / 2)  # (B, 6x6x6, 3)
+        return roi_grid_points
+
+    def forward(self, batch_dict):
+        """
+        :param input_data: input dict
+        :return:
+        """
+
+        targets_dict = self.proposal_layer(
+            batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
+        )
+        if self.training:
+            targets_dict = batch_dict.get('roi_targets_dict', None)
+            if targets_dict is None:
+                targets_dict = self.assign_targets(batch_dict)
+                batch_dict['rois'] = targets_dict['rois']
+                batch_dict['roi_labels'] = targets_dict['roi_labels']
+
+        # RoI aware pooling
+        pooled_features = self.roi_grid_pool(batch_dict)  # (BxN, 6x6x6, C)
+
+        grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
+        batch_size_rcnn = pooled_features.shape[0]
+        pooled_features = pooled_features.permute(0, 2, 1).\
+            contiguous().view(batch_size_rcnn, -1, grid_size, grid_size, grid_size)  # (BxN, C, 6, 6, 6)
+
+        shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1))
+        rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, 1 or 2)
+        rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1)  # (B, C)
+
+        if not self.training:
+            batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
+                batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg
+            )
+            batch_dict['batch_cls_preds'] = batch_cls_preds
+            batch_dict['batch_box_preds'] = batch_box_preds
+            batch_dict['cls_preds_normalized'] = False
+        else:
+            targets_dict['rcnn_cls'] = rcnn_cls
+            targets_dict['rcnn_reg'] = rcnn_reg
+
+            self.forward_ret_dict = targets_dict
+
+        return batch_dict