Merge branch 'pillarnet' of https://github.com/agent-sgs/OpenPCDet-raw …

…into agent-sgs-pillarnet

.gitignore

@@ -7,7 +7,6 @@ data/
 venv/
 *.idea/
 *.so
-*.yaml
 *.sh
 *.pth
 *.pkl

pcdet/models/backbones_2d/__init__.py

@@ -1,5 +1,6 @@
-from .base_bev_backbone import BaseBEVBackbone
+from .base_bev_backbone import BaseBEVBackbone, BaseBEVBackboneV1
 
 __all__ = {
-    'BaseBEVBackbone': BaseBEVBackbone
+    'BaseBEVBackbone': BaseBEVBackbone,
+    'BaseBEVBackboneV1': BaseBEVBackboneV1
 }

pcdet/models/backbones_2d/base_bev_backbone.py

@@ -110,3 +110,95 @@ def forward(self, data_dict):
         data_dict['spatial_features_2d'] = x
 
         return data_dict
+
+
+class BaseBEVBackboneV1(nn.Module):
+    def __init__(self, model_cfg, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        layer_nums = self.model_cfg.LAYER_NUMS
+        num_filters = self.model_cfg.NUM_FILTERS
+        assert len(layer_nums) == len(num_filters) == 2
+
+        num_upsample_filters = self.model_cfg.NUM_UPSAMPLE_FILTERS
+        upsample_strides = self.model_cfg.UPSAMPLE_STRIDES
+        assert len(num_upsample_filters) == len(upsample_strides)
+
+        num_levels = len(layer_nums)
+        self.blocks = nn.ModuleList()
+        self.deblocks = nn.ModuleList()
+        for idx in range(num_levels):
+            cur_layers = [
+                nn.ZeroPad2d(1),
+                nn.Conv2d(
+                    num_filters[idx], num_filters[idx], kernel_size=3,
+                    stride=1, padding=0, bias=False
+                ),
+                nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                nn.ReLU()
+            ]
+            for k in range(layer_nums[idx]):
+                cur_layers.extend([
+                    nn.Conv2d(num_filters[idx], num_filters[idx], kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(num_filters[idx], eps=1e-3, momentum=0.01),
+                    nn.ReLU()
+                ])
+            self.blocks.append(nn.Sequential(*cur_layers))
+            if len(upsample_strides) > 0:
+                stride = upsample_strides[idx]
+                if stride >= 1:
+                    self.deblocks.append(nn.Sequential(
+                        nn.ConvTranspose2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            upsample_strides[idx],
+                            stride=upsample_strides[idx], bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+                else:
+                    stride = np.round(1 / stride).astype(np.int)
+                    self.deblocks.append(nn.Sequential(
+                        nn.Conv2d(
+                            num_filters[idx], num_upsample_filters[idx],
+                            stride,
+                            stride=stride, bias=False
+                        ),
+                        nn.BatchNorm2d(num_upsample_filters[idx], eps=1e-3, momentum=0.01),
+                        nn.ReLU()
+                    ))
+
+        c_in = sum(num_upsample_filters)
+        if len(upsample_strides) > num_levels:
+            self.deblocks.append(nn.Sequential(
+                nn.ConvTranspose2d(c_in, c_in, upsample_strides[-1], stride=upsample_strides[-1], bias=False),
+                nn.BatchNorm2d(c_in, eps=1e-3, momentum=0.01),
+                nn.ReLU(),
+            ))
+
+        self.num_bev_features = c_in
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                spatial_features
+        Returns:
+        """
+        spatial_features = data_dict['multi_scale_2d_features']
+
+        x_conv4 = spatial_features['x_conv4']
+        x_conv5 = spatial_features['x_conv5']
+
+        ups = [self.deblocks[0](x_conv4)]
+
+        x = self.blocks[1](x_conv5)
+        ups.append(self.deblocks[1](x))
+
+        x = torch.cat(ups, dim=1)
+        x = self.blocks[0](x)
+
+        data_dict['spatial_features_2d'] = x
+
+        return data_dict

pcdet/models/backbones_3d/__init__.py

@@ -1,5 +1,6 @@
 from .pointnet2_backbone import PointNet2Backbone, PointNet2MSG
 from .spconv_backbone import VoxelBackBone8x, VoxelResBackBone8x
+from .spconv_backbone_2d import PillarBackBone8x, PillarRes18BackBone8x
 from .spconv_backbone_focal import VoxelBackBone8xFocal
 from .spconv_unet import UNetV2
 
@@ -9,5 +10,7 @@
     'PointNet2Backbone': PointNet2Backbone,
     'PointNet2MSG': PointNet2MSG,
     'VoxelResBackBone8x': VoxelResBackBone8x,
-    'VoxelBackBone8xFocal': VoxelBackBone8xFocal
+    'VoxelBackBone8xFocal': VoxelBackBone8xFocal,
+    'PillarBackBone8x': PillarBackBone8x,
+    'PillarRes18BackBone8x': PillarRes18BackBone8x
 }