Merge branch 'refacor-sec-backbone' into 'master'

Refactor SECOND Backbone

See merge request open-mmlab/mmdet.3d!26

.gitlab-ci.yml

@@ -16,7 +16,7 @@ before_script:
 .linting_template: &linting_template_def
   stage: linting
   script:
-    - pip install flake8 yapf isort
+    - pip install flake8==3.7.9 yapf isort
     - flake8 .
     - isort -rc --check-only --diff mmdet3d/ tools/ tests/
     - yapf -r -d mmdet3d/ tools/ tests/ configs/

configs/kitti/dv_mvx-v2_second_secfpn_fpn-fusion_adamw_2x8_80e_kitti-3d-3class.py

@@ -57,7 +57,7 @@ model = dict(
         in_channels=256,
         layer_nums=[5, 5],
         layer_strides=[1, 2],
-        num_filters=[128, 256],
+        out_channels=[128, 256],
     ),
     pts_neck=dict(
         type='SECONDFPN',

configs/kitti/dv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -28,7 +28,7 @@ model = dict(
         in_channels=64,
         layer_nums=[3, 5, 5],
         layer_strides=[2, 2, 2],
-        num_filters=[64, 128, 256],
+        out_channels=[64, 128, 256],
     ),
     neck=dict(
         type='SECONDFPN',

configs/kitti/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py

@@ -26,7 +26,7 @@ model = dict(
         in_channels=256,
         layer_nums=[5, 5],
         layer_strides=[1, 2],
-        num_filters=[128, 256],
+        out_channels=[128, 256],
     ),
     neck=dict(
         type='SECONDFPN',

configs/kitti/dv_second_secfpn_6x8_80e_kitti-3d-car.py

@@ -26,7 +26,7 @@ model = dict(
         in_channels=256,
         layer_nums=[5, 5],
         layer_strides=[1, 2],
-        num_filters=[128, 256],
+        out_channels=[128, 256],
     ),
     neck=dict(
         type='SECONDFPN',

configs/kitti/hv_PartA2_secfpn_4x8_cosine_80e_kitti-3d-3class.py

@@ -22,7 +22,7 @@ model = dict(
         in_channels=256,
         layer_nums=[5, 5],
         layer_strides=[1, 2],
-        num_filters=[128, 256]),
+        out_channels=[128, 256]),
     neck=dict(
         type='SECONDFPN',
         in_channels=[128, 256],

configs/kitti/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -27,7 +27,7 @@ model = dict(
         in_channels=64,
         layer_nums=[3, 5, 5],
         layer_strides=[2, 2, 2],
-        num_filters=[64, 128, 256],
+        out_channels=[64, 128, 256],
     ),
     neck=dict(
         type='SECONDFPN',

configs/kitti/hv_second_secfpn_6x8_80e_kitti-3d-car.py

@@ -26,7 +26,7 @@ model = dict(
         in_channels=256,
         layer_nums=[5, 5],
         layer_strides=[1, 2],
-        num_filters=[128, 256],
+        out_channels=[128, 256],
     ),
     neck=dict(
         type='SECONDFPN',

configs/nus/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py

@@ -34,7 +34,7 @@ model = dict(
         norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
         layer_nums=[3, 5, 5],
         layer_strides=[2, 2, 2],
-        num_filters=[64, 128, 256],
+        out_channels=[64, 128, 256],
     ),
     pts_neck=dict(
         type='SECONDFPN',

mmdet3d/models/backbones/second.py

-from functools import partial
-
 import torch.nn as nn
-from mmcv.cnn import build_norm_layer
+from mmcv.cnn import build_conv_layer, build_norm_layer
 from mmcv.runner import load_checkpoint
 
 from mmdet.models import BACKBONES
 
 
-class Empty(nn.Module):
-
-    def __init__(self, *args, **kwargs):
-        super(Empty, self).__init__()
-
-    def forward(self, *args, **kwargs):
-        if len(args) == 1:
-            return args[0]
-        elif len(args) == 0:
-            return None
-        return args
-
-
 @BACKBONES.register_module()
 class SECOND(nn.Module):
-    """Compare with RPN, RPNV2 support arbitrary number of stage.
+    """Backbone network for SECOND/PointPillars/PartA2/MVXNet
+
+    Args:
+        in_channels (int): Input channels
+        out_channels (list[int]): Output channels for multi-scale feature maps
+        layer_nums (list[int]): Number of layers in each stage
+        layer_strides (list[int]): Strides of each stage
+        norm_cfg (dict): Config dict of normalization layers
+        conv_cfg (dict): Config dict of convolutional layers
     """
 
     def __init__(self,
                  in_channels=128,
+                 out_channels=[128, 128, 256],
                  layer_nums=[3, 5, 5],
                  layer_strides=[2, 2, 2],
-                 num_filters=[128, 128, 256],
-                 norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01)):
+                 norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+                 conv_cfg=dict(type='Conv2d', bias=False)):
         super(SECOND, self).__init__()
         assert len(layer_strides) == len(layer_nums)
-        assert len(num_filters) == len(layer_nums)
-
-        if norm_cfg is not None:
-            Conv2d = partial(nn.Conv2d, bias=False)
-        else:
-            Conv2d = partial(nn.Conv2d, bias=True)
+        assert len(out_channels) == len(layer_nums)
 
-        in_filters = [in_channels, *num_filters[:-1]]
+        in_filters = [in_channels, *out_channels[:-1]]
         # note that when stride > 1, conv2d with same padding isn't
         # equal to pad-conv2d. we should use pad-conv2d.
         blocks = []
-
         for i, layer_num in enumerate(layer_nums):
-            norm_layer = (
-                build_norm_layer(norm_cfg, num_filters[i])[1]
-                if norm_cfg is not None else Empty)
             block = [
-                nn.ZeroPad2d(1),
-                Conv2d(
-                    in_filters[i], num_filters[i], 3, stride=layer_strides[i]),
-                norm_layer,
+                build_conv_layer(
+                    conv_cfg,
+                    in_filters[i],
+                    out_channels[i],
+                    3,
+                    stride=layer_strides[i],
+                    padding=1),
+                build_norm_layer(norm_cfg, out_channels[i])[1],
                 nn.ReLU(inplace=True),
             ]
             for j in range(layer_num):
-                norm_layer = (
-                    build_norm_layer(norm_cfg, num_filters[i])[1]
-                    if norm_cfg is not None else Empty)
                 block.append(
-                    Conv2d(num_filters[i], num_filters[i], 3, padding=1))
-                block.append(norm_layer)
+                    build_conv_layer(
+                        conv_cfg,
+                        out_channels[i],
+                        out_channels[i],
+                        3,
+                        padding=1))
+                block.append(build_norm_layer(norm_cfg, out_channels[i])[1])
                 block.append(nn.ReLU(inplace=True))
 
             block = nn.Sequential(*block)
@@ -71,6 +62,8 @@ class SECOND(nn.Module):
         self.blocks = nn.ModuleList(blocks)
 
     def init_weights(self, pretrained=None):
+        # Do not initialize the conv layers
+        # to follow the original implementation
         if isinstance(pretrained, str):
             from mmdet3d.utils import get_root_logger
             logger = get_root_logger()