Merge branch 'add-flop-counter' into 'master'

update docstrings in core

See merge request open-mmlab/mmdet.3d!103

mmdet3d/models/necks/second_fpn.py

@@ -4,7 +4,6 @@ from mmcv.cnn import (build_norm_layer, build_upsample_layer, constant_init,
                       is_norm, kaiming_init)
 
 from mmdet.models import NECKS
-from .. import builder
 
 
 @NECKS.register_module()
@@ -47,6 +46,7 @@ class SECONDFPN(nn.Module):
         self.deblocks = nn.ModuleList(deblocks)
 
     def init_weights(self):
+        """Initialize weights of FPN"""
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
                 kaiming_init(m)
@@ -54,53 +54,14 @@ class SECONDFPN(nn.Module):
                 constant_init(m, 1)
 
     def forward(self, x):
-        assert len(x) == len(self.in_channels)
-        ups = [deblock(x[i]) for i, deblock in enumerate(self.deblocks)]
-
-        if len(ups) > 1:
-            out = torch.cat(ups, dim=1)
-        else:
-            out = ups[0]
-        return [out]
-
-
-@NECKS.register_module()
-class SECONDFusionFPN(SECONDFPN):
-    """FPN used in multi-modality SECOND/PointPillars
-
-    Args:
-        in_channels (list[int]): Input channels of multi-scale feature maps
-        out_channels (list[int]): Output channels of feature maps
-        upsample_strides (list[int]): Strides used to upsample the feature maps
-        norm_cfg (dict): Config dict of normalization layers
-        upsample_cfg (dict): Config dict of upsample layers
-        downsample_rates (list[int]): The downsample rate of feature map in
-            comparison to the original voxelization input
-        fusion_layer (dict): Config dict of fusion layers
-    """
+        """Forward function
 
-    def __init__(self,
-                 in_channels=[128, 128, 256],
-                 out_channels=[256, 256, 256],
-                 upsample_strides=[1, 2, 4],
-                 norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
-                 upsample_cfg=dict(type='deconv', bias=False),
-                 downsample_rates=[40, 8, 8],
-                 fusion_layer=None):
-        super(SECONDFusionFPN,
-              self).__init__(in_channels, out_channels, upsample_strides,
-                             norm_cfg, upsample_cfg)
-        self.fusion_layer = None
-        if fusion_layer is not None:
-            self.fusion_layer = builder.build_fusion_layer(fusion_layer)
-        self.downsample_rates = downsample_rates
+        Args:
+            x (torch.Tensor): 4D Tensor in (N, C, H, W) shape.
 
-    def forward(self,
-                x,
-                coors=None,
-                points=None,
-                img_feats=None,
-                img_metas=None):
+        Returns:
+            list[torch.Tensor]: Multi-level feature maps.
+        """
         assert len(x) == len(self.in_channels)
         ups = [deblock(x[i]) for i, deblock in enumerate(self.deblocks)]
 
@@ -108,16 +69,4 @@ class SECONDFusionFPN(SECONDFPN):
             out = torch.cat(ups, dim=1)
         else:
             out = ups[0]
-        if (self.fusion_layer is not None and img_feats is not None):
-            downsample_pts_coors = torch.zeros_like(coors)
-            downsample_pts_coors[:, 0] = coors[:, 0]
-            downsample_pts_coors[:, 1] = (
-                coors[:, 1] / self.downsample_rates[0])
-            downsample_pts_coors[:, 2] = (
-                coors[:, 2] / self.downsample_rates[1])
-            downsample_pts_coors[:, 3] = (
-                coors[:, 3] / self.downsample_rates[2])
-            # fusion for each point
-            out = self.fusion_layer(img_feats, points, out,
-                                    downsample_pts_coors, img_metas)
         return [out]

mmdet3d/models/roi_heads/base_3droi_head.py

@@ -26,26 +26,32 @@ class Base3DRoIHead(nn.Module, metaclass=ABCMeta):
 
     @property
     def with_bbox(self):
+        """bool: whether the RoIHead has box head"""
         return hasattr(self, 'bbox_head') and self.bbox_head is not None
 
     @property
     def with_mask(self):
+        """bool: whether the RoIHead has mask head"""
         return hasattr(self, 'mask_head') and self.mask_head is not None
 
     @abstractmethod
     def init_weights(self, pretrained):
+        """Initialize the module with pre-trained weights."""
         pass
 
     @abstractmethod
     def init_bbox_head(self):
+        """Initialize the box head."""
         pass
 
     @abstractmethod
     def init_mask_head(self):
+        """Initialize maek head."""
         pass
 
     @abstractmethod
     def init_assigner_sampler(self):
+        """Initialize assigner and sampler"""
         pass
 
     @abstractmethod

mmdet3d/models/roi_heads/bbox_heads/parta2_bbox_head.py

@@ -4,13 +4,12 @@ import torch.nn as nn
 from mmcv.cnn import ConvModule, normal_init, xavier_init
 
 import mmdet3d.ops.spconv as spconv
-from mmdet3d.core import build_bbox_coder, xywhr2xyxyr
 from mmdet3d.core.bbox.structures import (LiDARInstance3DBoxes,
-                                          rotation_3d_in_axis)
+                                          rotation_3d_in_axis, xywhr2xyxyr)
 from mmdet3d.models.builder import build_loss
 from mmdet3d.ops import make_sparse_convmodule
 from mmdet3d.ops.iou3d.iou3d_utils import nms_gpu, nms_normal_gpu
-from mmdet.core import multi_apply
+from mmdet.core import build_bbox_coder, multi_apply
 from mmdet.models import HEADS
 
 
@@ -224,6 +223,7 @@ class PartA2BboxHead(nn.Module):
         self.init_weights()
 
     def init_weights(self):
+        """Initialize weights of the bbox head"""
         for m in self.modules():
             if isinstance(m, (nn.Conv2d, nn.Conv1d)):
                 xavier_init(m, distribution='uniform')
@@ -390,6 +390,22 @@ class PartA2BboxHead(nn.Module):
                 bbox_weights)
 
     def _get_target_single(self, pos_bboxes, pos_gt_bboxes, ious, cfg):
+        """Generate training targets for a single sample.
+
+        Args:
+            pos_bboxes (torch.Tensor): Positive boxes with shape
+                (N, 7).
+            pos_gt_bboxes (torch.Tensor): Ground truth boxes with shape
+                (M, 7).
+            ious (torch.Tensor): IoU between `pos_bboxes` and `pos_gt_bboxes`
+                in shape (N, M).
+            cfg (dict): Training configs.
+
+        Returns:
+            tuple: Target for positive boxes.
+                (label, bbox_targets, pos_gt_bboxes, reg_mask, label_weights,
+                bbox_weights)
+        """
         cls_pos_mask = ious > cfg.cls_pos_thr
         cls_neg_mask = ious < cfg.cls_neg_thr
         interval_mask = (cls_pos_mask == 0) & (cls_neg_mask == 0)
@@ -540,6 +556,26 @@ class PartA2BboxHead(nn.Module):
                         nms_thr,
                         input_meta,
                         use_rotate_nms=True):
+        """Multi-class NMS for box head
+
+        Note:
+            This function has large overlap with the `box3d_multiclass_nms`
+            implemented in `mmdet3d.core.post_processing`. We are considering
+            merging these two functions in the future.
+
+        Args:
+            box_probs (torch.Tensor): Predicted boxes probabitilies in
+                shape (N,).
+            box_preds (torch.Tensor): Predicted boxes in shape (N, 7+C).
+            score_thr (float): Threshold of scores.
+            nms_thr (float): Threshold for NMS.
+            input_meta (dict): Meta informations of the current sample.
+            use_rotate_nms (bool, optional): Whether to use rotated nms.
+                Defaults to True.
+
+        Returns:
+            torch.Tensor: Selected indices.
+        """
         if use_rotate_nms:
             nms_func = nms_gpu
         else:

mmdet3d/models/roi_heads/part_aggregation_roi_head.py

@@ -44,15 +44,21 @@ class PartAggregationROIHead(Base3DRoIHead):
         self.init_assigner_sampler()
 
     def init_weights(self, pretrained):
+        """Initialize weights, skip since ``PartAggregationROIHead``
+        does not need to initialize weights"""
         pass
 
     def init_mask_head(self):
+        """Initialize mask head, skip since ``PartAggregationROIHead``
+        does not have one."""
         pass
 
     def init_bbox_head(self, bbox_head):
+        """Initialize box head"""
         self.bbox_head = build_head(bbox_head)
 
     def init_assigner_sampler(self):
+        """Initialize assigner and sampler"""
         self.bbox_assigner = None
         self.bbox_sampler = None
         if self.train_cfg:
@@ -66,6 +72,7 @@ class PartAggregationROIHead(Base3DRoIHead):
 
     @property
     def with_semantic(self):
+        """bool: whether the head has semantic branch"""
         return hasattr(self,
                        'semantic_head') and self.semantic_head is not None
 
@@ -152,6 +159,18 @@ class PartAggregationROIHead(Base3DRoIHead):
 
     def _bbox_forward_train(self, seg_feats, part_feats, voxels_dict,
                             sampling_results):
+        """Forward training function of roi_extractor and bbox_head.
+
+        Args:
+            seg_feats (torch.Tensor): Point-wise semantic features.
+            part_feats (torch.Tensor): Point-wise part prediction features.
+            voxels_dict (dict): Contains information of voxels.
+            sampling_results (:obj:`SamplingResult`): Sampled results used
+                for training.
+
+        Returns:
+            dict: Forward results including losses and predictions.
+        """
         rois = bbox3d2roi([res.bboxes for res in sampling_results])
         bbox_results = self._bbox_forward(seg_feats, part_feats, voxels_dict,
                                           rois)
@@ -166,7 +185,8 @@ class PartAggregationROIHead(Base3DRoIHead):
         return bbox_results
 
     def _bbox_forward(self, seg_feats, part_feats, voxels_dict, rois):
-        """Forward function of roi_extractor and bbox_head.
+        """Forward function of roi_extractor and bbox_head used in both
+        training and testing.
 
         Args:
             seg_feats (torch.Tensor): Point-wise semantic features.
@@ -196,6 +216,18 @@ class PartAggregationROIHead(Base3DRoIHead):
         return bbox_results
 
     def _assign_and_sample(self, proposal_list, gt_bboxes_3d, gt_labels_3d):
+        """Assign and sample proposals for training
+
+        Args:
+            proposal_list (list[dict]): Proposals produced by RPN.
+            gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
+                boxes.
+            gt_labels_3d (list[torch.Tensor]): Ground truth labels
+
+        Returns:
+            list[:obj:`SamplingResult`]: Sampled results of each training
+                sample.
+        """
         sampling_results = []
         # bbox assign
         for batch_idx in range(len(proposal_list)):
@@ -258,6 +290,18 @@ class PartAggregationROIHead(Base3DRoIHead):
 
     def _semantic_forward_train(self, x, voxels_dict, gt_bboxes_3d,
                                 gt_labels_3d):
+        """Train semantic head
+
+        Args:
+            x (torch.Tensor): Point-wise semantic features for segmentation
+            voxels_dict (dict): Contains information of voxels.
+            gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
+                boxes.
+            gt_labels_3d (list[torch.Tensor]): Ground truth labels
+
+        Returns:
+            dict: Segmentation results including losses
+        """
         semantic_results = self.semantic_head(x)
         semantic_targets = self.semantic_head.get_targets(
             voxels_dict, gt_bboxes_3d, gt_labels_3d)

mmdet3d/models/roi_heads/roi_extractors/single_roiaware_extractor.py

@@ -20,6 +20,7 @@ class Single3DRoIAwareExtractor(nn.Module):
         self.roi_layer = self.build_roi_layers(roi_layer)
 
     def build_roi_layers(self, layer_cfg):
+        """Build roi layers using `layer_cfg`"""
         cfg = layer_cfg.copy()
         layer_type = cfg.pop('type')
         assert hasattr(ops, layer_type)

mmdet3d/models/voxel_encoders/pillar_encoder.py

@@ -15,16 +15,22 @@ class PillarFeatureNet(nn.Module):
     through PFNLayers.
 
     Args:
-        in_channels (int). Number of input features,
-            either x, y, z or x, y, z, r.
-        feat_channels (list[int]). Number of features in each of the
-            N PFNLayers.
-        with_distance (bool). Whether to include Euclidean distance
-            to points.
-        voxel_size (list[float]). Size of voxels, only utilize x and y
-            size.
-        point_cloud_range (list[float]). Point cloud range, only
-            utilizes x and y min.
+        in_channels (int, optional): Number of input features,
+            either x, y, z or x, y, z, r. Defaults to 4.
+        feat_channels (tuple, optional): Number of features in each of the
+            N PFNLayers. Defaults to (64, ).
+        with_distance (bool, optional): Whether to include Euclidean distance
+            to points. Defaults to False.
+        with_cluster_center (bool, optional): [description]. Defaults to True.
+        with_voxel_center (bool, optional): [description]. Defaults to True.
+        voxel_size (tuple[float], optional): Size of voxels, only utilize x
+            and y size. Defaults to (0.2, 0.2, 4).
+        point_cloud_range (tuple[float], optional): Point cloud range, only
+            utilizes x and y min. Defaults to (0, -40, -3, 70.4, 40, 1).
+        norm_cfg ([type], optional): [description].
+            Defaults to dict(type='BN1d', eps=1e-3, momentum=0.01).
+        mode (str, optional): The mode to gather point features. Options are
+            'max' or 'avg'. Defaults to 'max'.
     """
 
     def __init__(self,
@@ -77,6 +83,17 @@ class PillarFeatureNet(nn.Module):
         self.point_cloud_range = point_cloud_range
 
     def forward(self, features, num_points, coors):
+        """Forward function
+
+        Args:
+            features (torch.Tensor): Point features or raw points in shape
+                (N, M, C).
+            num_points (torch.Tensor): Number of points in each pillar.
+            coors (torch.Tensor): Coordinates of each voxel
+
+        Returns:
+            torch.Tensor: Features of pillars.
+        """
         features_ls = [features]
         # Find distance of x, y, and z from cluster center
         if self._with_cluster_center:
@@ -119,6 +136,31 @@ class PillarFeatureNet(nn.Module):
 
 @VOXEL_ENCODERS.register_module()
 class DynamicPillarFeatureNet(PillarFeatureNet):
+    """Pillar Feature Net using dynamic voxelization
+
+    The network prepares the pillar features and performs forward pass
+    through PFNLayers. The main difference is that it is used for
+    dynamic voxels, which contains different number of points inside a voxel
+    without limits.
+
+    Args:
+        in_channels (int, optional): Number of input features,
+            either x, y, z or x, y, z, r. Defaults to 4.
+        feat_channels (tuple, optional): Number of features in each of the
+            N PFNLayers. Defaults to (64, ).
+        with_distance (bool, optional): Whether to include Euclidean distance
+            to points. Defaults to False.
+        with_cluster_center (bool, optional): [description]. Defaults to True.
+        with_voxel_center (bool, optional): [description]. Defaults to True.
+        voxel_size (tuple[float], optional): Size of voxels, only utilize x
+            and y size. Defaults to (0.2, 0.2, 4).
+        point_cloud_range (tuple[float], optional): Point cloud range, only
+            utilizes x and y min. Defaults to (0, -40, -3, 70.4, 40, 1).
+        norm_cfg ([type], optional): [description].
+            Defaults to dict(type='BN1d', eps=1e-3, momentum=0.01).
+        mode (str, optional): The mode to gather point features. Options are
+            'max' or 'avg'. Defaults to 'max'.
+    """
 
     def __init__(self,
                  in_channels=4,
@@ -130,11 +172,6 @@ class DynamicPillarFeatureNet(PillarFeatureNet):
                  point_cloud_range=(0, -40, -3, 70.4, 40, 1),
                  norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
                  mode='max'):
-        """
-        Dynamic Pillar Feature Net for Dynamic Voxelization.
-        The difference is in the forward part
-        """
-
         super(DynamicPillarFeatureNet, self).__init__(
             in_channels,
             feat_channels,
@@ -168,6 +205,19 @@ class DynamicPillarFeatureNet(PillarFeatureNet):
             voxel_size, point_cloud_range, average_points=True)
 
     def map_voxel_center_to_point(self, pts_coors, voxel_mean, voxel_coors):
+        """Map the centers of voxels to its corresponding points
+
+        Args:
+            pts_coors (torch.Tensor): The coordinates of each points, shape
+                (M, 3), where M is the number of points.
+            voxel_mean (torch.Tensor): The mean or aggreagated features of a
+                voxel, shape (N, C), where N is the number of voxels.
+            voxel_coors (torch.Tensor): The coordinates of each voxel.
+
+        Returns:
+            torch.Tensor: Corresponding voxel centers of each points, shape
+                (M, C), where M is the numver of points.
+        """
         # Step 1: scatter voxel into canvas
         # Calculate necessary things for canvas creation
         canvas_y = int(
@@ -194,9 +244,15 @@ class DynamicPillarFeatureNet(PillarFeatureNet):
         return center_per_point
 
     def forward(self, features, coors):
-        """
-        features (torch.Tensor): NxC
-        coors (torch.Tensor): Nx(1+NDim)
+        """Forward function
+
+        Args:
+            features (torch.Tensor): Point features or raw points in shape
+                (N, M, C).
+            coors (torch.Tensor): Coordinates of each voxel
+
+        Returns:
+            torch.Tensor: Features of pillars.
         """
         features_ls = [features]
         # Find distance of x, y, and z from cluster center

mmdet3d/models/voxel_encoders/utils.py

@@ -28,6 +28,21 @@ def get_paddings_indicator(actual_num, max_num, axis=0):
 
 
 class VFELayer(nn.Module):
+    """ Voxel Feature Encoder layer.
+
+    The voxel encoder is composed of a series of these layers.
+    This module do not support average pooling and only support to use
+    max pooling to gather features inside a VFE.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        norm_cfg (dict): Config dict of normalization layers
+        max_out (bool): Whether aggregate the features of points inside
+            each voxel and only return voxel features.
+        cat_max (bool): Whether concatenate the aggregated features
+            and pointwise features.
+    """
 
     def __init__(self,
                  in_channels,
@@ -44,6 +59,23 @@ class VFELayer(nn.Module):
         self.linear = nn.Linear(in_channels, out_channels, bias=False)
 
     def forward(self, inputs):
+        """Forward function
+
+        Args:
+            inputs (torch.Tensor): Voxels features of shape (N, M, C).
+                N is the number of voxels, M is the number of points in
+                voxels, C is the number of channels of point features.
+
+        Returns:
+            torch.Tensor: Voxel features. There are three mode under which the
+                features have different meaning.
+                - `max_out=False`: Return point-wise features in
+                    shape (N, M, C).
+                - `max_out=True` and `cat_max=False`: Return aggregated
+                    voxel features in shape (N, C)
+                - `max_out=True` and `cat_max=True`: Return concatenated
+                    point-wise features in shape (N, M, C).
+        """
         # [K, T, 7] tensordot [7, units] = [K, T, units]
         voxel_count = inputs.shape[1]
         x = self.linear(inputs)
@@ -68,6 +100,20 @@ class VFELayer(nn.Module):
 
 
 class PFNLayer(nn.Module):
+    """ Pillar Feature Net Layer.
+
+    The Pillar Feature Net is composed of a series of these layers, but the
+    PointPillars paper results only used a single PFNLayer.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        norm_cfg (dict): Config dict of normalization layers
+        last_layer (bool): If last_layer, there is no concatenation of
+            features.
+        mode (str): Pooling model to gather features inside voxels.
+            Default to 'max'.
+    """
 
     def __init__(self,
                  in_channels,
@@ -75,20 +121,6 @@ class PFNLayer(nn.Module):
                  norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
                  last_layer=False,
                  mode='max'):
-        """ Pillar Feature Net Layer.
-
-        The Pillar Feature Net is composed of a series of these layers, but the
-        PointPillars paper results only used a single PFNLayer.
-
-        Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            norm_cfg (dict): Config dict of normalization layers
-            last_layer (bool): If last_layer, there is no concatenation of
-                features.
-            mode (str): Pooling model to gather features inside voxels.
-                Default to 'max'.
-        """
 
         super().__init__()
         self.name = 'PFNLayer'
@@ -104,7 +136,20 @@ class PFNLayer(nn.Module):
         self.mode = mode
 
     def forward(self, inputs, num_voxels=None, aligned_distance=None):
+        """Forward function
 
+        Args:
+            inputs (torch.Tensor): Pillar/Voxel inputs with shape (N, M, C).
+                N is the number of voxels, M is the number of points in
+                voxels, C is the number of channels of point features.
+            num_voxels (torch.Tensor, optional): Number of points in each
+                voxel. Defaults to None.
+            aligned_distance (torch.Tensor, optional): The distance of
+                each points to the voxel center. Defaults to None.
+
+        Returns:
+            torch.Tensor: Features of Pillars.
+        """
         x = self.linear(inputs)
         x = self.norm(x.permute(0, 2, 1).contiguous()).permute(0, 2,
                                                                1).contiguous()

mmdet3d/models/voxel_encoders/voxel_encoder.py

@@ -19,8 +19,19 @@ class HardSimpleVFE(nn.Module):
         super(HardSimpleVFE, self).__init__()
 
     def forward(self, features, num_points, coors):
-        # features: [concated_num_points, num_voxel_size, 3(4)]
-        # num_points: [concated_num_points]
+        """Forward function
+
+        Args:
+            features (torch.Tensor): point features in shape
+                (N, M, 3(4)). N is the number of voxels and M is the maximum
+                number of points inside a single voxel.
+            num_points (torch.Tensor): Number of points in each voxel,
+                 shape (N, ).
+            coors (torch.Tensor): Coordinates of voxels.
+
+        Returns:
+            torch.Tensor: Mean of points inside each voxel in shape (N, 3(4))
+        """
         points_mean = features[:, :, :4].sum(
             dim=1, keepdim=False) / num_points.type_as(features).view(-1, 1)
         return points_mean.contiguous()
@@ -46,6 +57,17 @@ class DynamicSimpleVFE(nn.Module):
 
     @torch.no_grad()
     def forward(self, features, coors):
+        """Forward function
+
+        Args:
+            features (torch.Tensor): point features in shape
+                (N, 3(4)). N is the number of points.
+            coors (torch.Tensor): Coordinates of voxels.
+
+        Returns:
+            torch.Tensor: Mean of points inside each voxel in shape (M, 3(4)).
+                M is the number of voxels.
+        """
         # This function is used from the start of the voxelnet
         # num_points: [concated_num_points]
         features, features_coors = self.scatter(features, coors)

mmdet3d/utils/collect_env.py

@@ -13,6 +13,7 @@ import mmdet3d
 
 
 def collect_env():
+    """Collect and print the information of running enviroments."""
     env_info = {}
     env_info['sys.platform'] = sys.platform
     env_info['Python'] = sys.version.replace('\n', '')