Support multi-modal 3D detection on NuScenes #1339

Add support for multi-modal NuScenes Detection

pcdet/models/detectors/__init__.py

@@ -13,6 +13,8 @@ from .mppnet import MPPNet
 from .mppnet_e2e import MPPNetE2E
 from .pillarnet import PillarNet
 from .voxelnext import VoxelNeXt
+from .transfusion import TransFusion
+from .bevfusion import BevFusion
 
 __all__ = {
     'Detector3DTemplate': Detector3DTemplate,
@@ -30,7 +32,9 @@ __all__ = {
     'MPPNet': MPPNet,
     'MPPNetE2E': MPPNetE2E,
     'PillarNet': PillarNet,
-    'VoxelNeXt': VoxelNeXt
+    'VoxelNeXt': VoxelNeXt,
+    'TransFusion': TransFusion,
+    'BevFusion': BevFusion,
 }
 
 

pcdet/models/detectors/bevfusion.py

+from .detector3d_template import Detector3DTemplate
+from .. import backbones_image, view_transforms
+from ..backbones_image import img_neck
+from ..backbones_2d import fuser
+
+class BevFusion(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_topology = [
+            'vfe', 'backbone_3d', 'map_to_bev_module', 'pfe',
+            'image_backbone','neck','vtransform','fuser',
+            'backbone_2d', 'dense_head',  'point_head', 'roi_head'
+        ]
+        self.module_list = self.build_networks()
+       
+    def build_neck(self,model_info_dict):
+        if self.model_cfg.get('NECK', None) is None:
+            return None, model_info_dict
+        neck_module = img_neck.__all__[self.model_cfg.NECK.NAME](
+            model_cfg=self.model_cfg.NECK
+        )
+        model_info_dict['module_list'].append(neck_module)
+
+        return neck_module, model_info_dict
+    
+    def build_vtransform(self,model_info_dict):
+        if self.model_cfg.get('VTRANSFORM', None) is None:
+            return None, model_info_dict
+        
+        vtransform_module = view_transforms.__all__[self.model_cfg.VTRANSFORM.NAME](
+            model_cfg=self.model_cfg.VTRANSFORM
+        )
+        model_info_dict['module_list'].append(vtransform_module)
+
+        return vtransform_module, model_info_dict
+
+    def build_image_backbone(self, model_info_dict):
+        if self.model_cfg.get('IMAGE_BACKBONE', None) is None:
+            return None, model_info_dict
+        image_backbone_module = backbones_image.__all__[self.model_cfg.IMAGE_BACKBONE.NAME](
+            model_cfg=self.model_cfg.IMAGE_BACKBONE
+        )
+        image_backbone_module.init_weights()
+        model_info_dict['module_list'].append(image_backbone_module)
+
+        return image_backbone_module, model_info_dict
+    
+    def build_fuser(self, model_info_dict):
+        if self.model_cfg.get('FUSER', None) is None:
+            return None, model_info_dict
+    
+        fuser_module = fuser.__all__[self.model_cfg.FUSER.NAME](
+            model_cfg=self.model_cfg.FUSER
+        )
+        model_info_dict['module_list'].append(fuser_module)
+        model_info_dict['num_bev_features'] = self.model_cfg.FUSER.OUT_CHANNEL
+        return fuser_module, model_info_dict
+
+    def forward(self, batch_dict):
+
+        for i,cur_module in enumerate(self.module_list):
+            batch_dict = cur_module(batch_dict)
+        
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss(batch_dict)
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self,batch_dict):
+        disp_dict = {}
+
+        loss_trans, tb_dict = batch_dict['loss'],batch_dict['tb_dict']
+        tb_dict = {
+            'loss_trans': loss_trans.item(),
+            **tb_dict
+        }
+
+        loss = loss_trans
+        return loss, tb_dict, disp_dict
+
+    def post_processing(self, batch_dict):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        final_pred_dict = batch_dict['final_box_dicts']
+        recall_dict = {}
+        for index in range(batch_size):
+            pred_boxes = final_pred_dict[index]['pred_boxes']
+
+            recall_dict = self.generate_recall_record(
+                box_preds=pred_boxes,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+        return final_pred_dict, recall_dict

pcdet/models/detectors/transfusion.py

+from .detector3d_template import Detector3DTemplate
+
+
+class TransFusion(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss(batch_dict)
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self,batch_dict):
+        disp_dict = {}
+
+        loss_trans, tb_dict = batch_dict['loss'],batch_dict['tb_dict']
+        tb_dict = {
+            'loss_trans': loss_trans.item(),
+            **tb_dict
+        }
+
+        loss = loss_trans
+        return loss, tb_dict, disp_dict
+
+    def post_processing(self, batch_dict):
+        post_process_cfg = self.model_cfg.POST_PROCESSING
+        batch_size = batch_dict['batch_size']
+        final_pred_dict = batch_dict['final_box_dicts']
+        recall_dict = {}
+        for index in range(batch_size):
+            pred_boxes = final_pred_dict[index]['pred_boxes']
+
+            recall_dict = self.generate_recall_record(
+                box_preds=pred_boxes,
+                recall_dict=recall_dict, batch_index=index, data_dict=batch_dict,
+                thresh_list=post_process_cfg.RECALL_THRESH_LIST
+            )
+
+        return final_pred_dict, recall_dict

pcdet/models/model_utils/transfusion_utils.py

+import torch
+from torch import nn
+import torch.nn.functional as F
+
+def clip_sigmoid(x, eps=1e-4):
+    y = torch.clamp(x.sigmoid_(), min=eps, max=1 - eps)
+    return y
+
+
+class PositionEmbeddingLearned(nn.Module):
+    """
+    Absolute pos embedding, learned.
+    """
+
+    def __init__(self, input_channel, num_pos_feats=288):
+        super().__init__()
+        self.position_embedding_head = nn.Sequential(
+            nn.Conv1d(input_channel, num_pos_feats, kernel_size=1),
+            nn.BatchNorm1d(num_pos_feats),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(num_pos_feats, num_pos_feats, kernel_size=1))
+
+    def forward(self, xyz):
+        xyz = xyz.transpose(1, 2).contiguous()
+        position_embedding = self.position_embedding_head(xyz)
+        return position_embedding
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
+                 self_posembed=None, cross_posembed=None, cross_only=False):
+        super().__init__()
+        self.cross_only = cross_only
+        if not self.cross_only:
+            self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        def _get_activation_fn(activation):
+            """Return an activation function given a string"""
+            if activation == "relu":
+                return F.relu
+            if activation == "gelu":
+                return F.gelu
+            if activation == "glu":
+                return F.glu
+            raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+        self.activation = _get_activation_fn(activation)
+
+        self.self_posembed = self_posembed
+        self.cross_posembed = cross_posembed
+
+    def with_pos_embed(self, tensor, pos_embed):
+        return tensor if pos_embed is None else tensor + pos_embed
+
+    def forward(self, query, key, query_pos, key_pos, key_padding_mask=None, attn_mask=None):
+        # NxCxP to PxNxC
+        if self.self_posembed is not None:
+            query_pos_embed = self.self_posembed(query_pos).permute(2, 0, 1)
+        else:
+            query_pos_embed = None
+        if self.cross_posembed is not None:
+            key_pos_embed = self.cross_posembed(key_pos).permute(2, 0, 1)
+        else:
+            key_pos_embed = None
+
+        query = query.permute(2, 0, 1)
+        key = key.permute(2, 0, 1)
+
+        if not self.cross_only:
+            q = k = v = self.with_pos_embed(query, query_pos_embed)
+            query2 = self.self_attn(q, k, value=v)[0]
+            query = query + self.dropout1(query2)
+            query = self.norm1(query)
+        
+        query2 = self.multihead_attn(query=self.with_pos_embed(query, query_pos_embed),
+                                     key=self.with_pos_embed(key, key_pos_embed),
+                                     value=self.with_pos_embed(key, key_pos_embed), 
+                                     key_padding_mask=key_padding_mask, attn_mask=attn_mask)[0]
+
+        query = query + self.dropout2(query2)
+        query = self.norm2(query)
+
+        query2 = self.linear2(self.dropout(self.activation(self.linear1(query))))
+        query = query + self.dropout3(query2)
+        query = self.norm3(query)
+
+        # NxCxP to PxNxC
+        query = query.permute(1, 2, 0)
+        return query
+

pcdet/models/view_transforms/__init__.py

+from .depth_lss import DepthLSSTransform
+__all__ = {
+    'DepthLSSTransform': DepthLSSTransform,
+}
\ No newline at end of file

pcdet/models/view_transforms/depth_lss.py

+import torch
+from torch import nn
+from pcdet.ops.bev_pool import bev_pool
+
+
+def gen_dx_bx(xbound, ybound, zbound):
+    dx = torch.Tensor([row[2] for row in [xbound, ybound, zbound]])
+    bx = torch.Tensor([row[0] + row[2] / 2.0 for row in [xbound, ybound, zbound]])
+    nx = torch.LongTensor(
+        [(row[1] - row[0]) / row[2] for row in [xbound, ybound, zbound]]
+    )
+    return dx, bx, nx
+
+
+class DepthLSSTransform(nn.Module):
+    """
+        This module implements LSS, which lists images into 3D and then splats onto bev features.
+        This code is adapted from https://github.com/mit-han-lab/bevfusion/ with minimal modifications.
+    """
+    def __init__(self, model_cfg):
+        super().__init__()
+        self.model_cfg = model_cfg
+        in_channel = self.model_cfg.IN_CHANNEL
+        out_channel = self.model_cfg.OUT_CHANNEL
+        self.image_size = self.model_cfg.IMAGE_SIZE
+        self.feature_size = self.model_cfg.FEATURE_SIZE
+        xbound = self.model_cfg.XBOUND
+        ybound = self.model_cfg.YBOUND
+        zbound = self.model_cfg.ZBOUND
+        self.dbound = self.model_cfg.DBOUND
+        downsample = self.model_cfg.DOWNSAMPLE
+
+        dx, bx, nx = gen_dx_bx(xbound, ybound, zbound)
+        self.dx = nn.Parameter(dx, requires_grad=False)
+        self.bx = nn.Parameter(bx, requires_grad=False)
+        self.nx = nn.Parameter(nx, requires_grad=False)
+
+        self.C = out_channel
+        self.frustum = self.create_frustum()
+        self.D = self.frustum.shape[0]
+
+        self.dtransform = nn.Sequential(
+            nn.Conv2d(1, 8, 1),
+            nn.BatchNorm2d(8),
+            nn.ReLU(True),
+            nn.Conv2d(8, 32, 5, stride=4, padding=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(True),
+            nn.Conv2d(32, 64, 5, stride=2, padding=2),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+        )
+        self.depthnet = nn.Sequential(
+            nn.Conv2d(in_channel + 64, in_channel, 3, padding=1),
+            nn.BatchNorm2d(in_channel),
+            nn.ReLU(True),
+            nn.Conv2d(in_channel, in_channel, 3, padding=1),
+            nn.BatchNorm2d(in_channel),
+            nn.ReLU(True),
+            nn.Conv2d(in_channel, self.D + self.C, 1),
+        )
+        if downsample > 1:
+            assert downsample == 2, downsample
+            self.downsample = nn.Sequential(
+                nn.Conv2d(out_channel, out_channel, 3, padding=1, bias=False),
+                nn.BatchNorm2d(out_channel),
+                nn.ReLU(True),
+                nn.Conv2d(out_channel, out_channel, 3, stride=downsample, padding=1, bias=False),
+                nn.BatchNorm2d(out_channel),
+                nn.ReLU(True),
+                nn.Conv2d(out_channel, out_channel, 3, padding=1, bias=False),
+                nn.BatchNorm2d(out_channel),
+                nn.ReLU(True),
+            )
+        else:
+            self.downsample = nn.Identity()
+    
+    def create_frustum(self):
+        iH, iW = self.image_size
+        fH, fW = self.feature_size
+
+        ds = torch.arange(*self.dbound, dtype=torch.float).view(-1, 1, 1).expand(-1, fH, fW)
+        D, _, _ = ds.shape
+        xs = torch.linspace(0, iW - 1, fW, dtype=torch.float).view(1, 1, fW).expand(D, fH, fW)
+        ys = torch.linspace(0, iH - 1, fH, dtype=torch.float).view(1, fH, 1).expand(D, fH, fW)
+        frustum = torch.stack((xs, ys, ds), -1)
+        
+        return nn.Parameter(frustum, requires_grad=False)
+
+    def get_geometry(self, camera2lidar_rots, camera2lidar_trans, intrins, post_rots, post_trans, **kwargs):
+
+        camera2lidar_rots = camera2lidar_rots.to(torch.float)
+        camera2lidar_trans = camera2lidar_trans.to(torch.float)
+        intrins = intrins.to(torch.float)
+        post_rots = post_rots.to(torch.float)
+        post_trans = post_trans.to(torch.float)
+
+        B, N, _ = camera2lidar_trans.shape
+
+        # undo post-transformation
+        # B x N x D x H x W x 3
+        points = self.frustum - post_trans.view(B, N, 1, 1, 1, 3)
+        points = torch.inverse(post_rots).view(B, N, 1, 1, 1, 3, 3).matmul(points.unsqueeze(-1))
+        
+        # cam_to_lidar
+        points = torch.cat((points[:, :, :, :, :, :2] * points[:, :, :, :, :, 2:3], points[:, :, :, :, :, 2:3]), 5)
+        combine = camera2lidar_rots.matmul(torch.inverse(intrins))
+        points = combine.view(B, N, 1, 1, 1, 3, 3).matmul(points).squeeze(-1)
+        points += camera2lidar_trans.view(B, N, 1, 1, 1, 3)
+
+        if "extra_rots" in kwargs:
+            extra_rots = kwargs["extra_rots"]
+            points = extra_rots.view(B, 1, 1, 1, 1, 3, 3).repeat(1, N, 1, 1, 1, 1, 1) \
+                .matmul(points.unsqueeze(-1)).squeeze(-1)
+            
+        if "extra_trans" in kwargs:
+            extra_trans = kwargs["extra_trans"]
+            points += extra_trans.view(B, 1, 1, 1, 1, 3).repeat(1, N, 1, 1, 1, 1)
+
+        return points
+
+    def bev_pool(self, geom_feats, x):
+        geom_feats = geom_feats.to(torch.float)
+        x = x.to(torch.float)
+
+        B, N, D, H, W, C = x.shape
+        Nprime = B * N * D * H * W
+
+        # flatten x
+        x = x.reshape(Nprime, C)
+
+        # flatten indices
+        geom_feats = ((geom_feats - (self.bx - self.dx / 2.0)) / self.dx).long()
+        geom_feats = geom_feats.view(Nprime, 3)
+        batch_ix = torch.cat([torch.full([Nprime // B, 1], ix, device=x.device, dtype=torch.long) for ix in range(B)])
+        geom_feats = torch.cat((geom_feats, batch_ix), 1)
+
+        # filter out points that are outside box
+        kept = (
+            (geom_feats[:, 0] >= 0)
+            & (geom_feats[:, 0] < self.nx[0])
+            & (geom_feats[:, 1] >= 0)
+            & (geom_feats[:, 1] < self.nx[1])
+            & (geom_feats[:, 2] >= 0)
+            & (geom_feats[:, 2] < self.nx[2])
+        )
+        x = x[kept]
+        geom_feats = geom_feats[kept]
+        x = bev_pool(x, geom_feats, B, self.nx[2], self.nx[0], self.nx[1])
+
+        # collapse Z
+        final = torch.cat(x.unbind(dim=2), 1)
+
+        return final
+
+    def get_cam_feats(self, x, d):
+        B, N, C, fH, fW = x.shape
+
+        d = d.view(B * N, *d.shape[2:])
+        x = x.view(B * N, C, fH, fW)
+
+        d = self.dtransform(d)
+        x = torch.cat([d, x], dim=1)
+        x = self.depthnet(x)
+
+        depth = x[:, : self.D].softmax(dim=1)
+        x = depth.unsqueeze(1) * x[:, self.D : (self.D + self.C)].unsqueeze(2)
+
+        x = x.view(B, N, self.C, self.D, fH, fW)
+        x = x.permute(0, 1, 3, 4, 5, 2)
+        return x
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                image_fpn (list[tensor]): image features after image neck
+
+        Returns:
+            batch_dict:
+                spatial_features_img (tensor): bev features from image modality
+        """
+        x = batch_dict['image_fpn'] 
+        x = x[0]
+        BN, C, H, W = x.size()
+        img = x.view(int(BN/6), 6, C, H, W)
+
+        camera_intrinsics = batch_dict['camera_intrinsics']
+        camera2lidar = batch_dict['camera2lidar']
+        img_aug_matrix = batch_dict['img_aug_matrix']
+        lidar_aug_matrix = batch_dict['lidar_aug_matrix']
+        lidar2image = batch_dict['lidar2image']
+
+        intrins = camera_intrinsics[..., :3, :3]
+        post_rots = img_aug_matrix[..., :3, :3]
+        post_trans = img_aug_matrix[..., :3, 3]
+        camera2lidar_rots = camera2lidar[..., :3, :3]
+        camera2lidar_trans = camera2lidar[..., :3, 3]
+
+        points = batch_dict['points']
+
+        batch_size = BN // 6
+        depth = torch.zeros(batch_size, img.shape[1], 1, *self.image_size).to(points[0].device)
+
+        for b in range(batch_size):
+            batch_mask = points[:,0] == b
+            cur_coords = points[batch_mask][:, 1:4]
+            cur_img_aug_matrix = img_aug_matrix[b]
+            cur_lidar_aug_matrix = lidar_aug_matrix[b]
+            cur_lidar2image = lidar2image[b]
+
+            # inverse aug
+            cur_coords -= cur_lidar_aug_matrix[:3, 3]
+            cur_coords = torch.inverse(cur_lidar_aug_matrix[:3, :3]).matmul(
+                cur_coords.transpose(1, 0)
+            )
+            # lidar2image
+            cur_coords = cur_lidar2image[:, :3, :3].matmul(cur_coords)
+            cur_coords += cur_lidar2image[:, :3, 3].reshape(-1, 3, 1)
+            # get 2d coords
+            dist = cur_coords[:, 2, :]
+            cur_coords[:, 2, :] = torch.clamp(cur_coords[:, 2, :], 1e-5, 1e5)
+            cur_coords[:, :2, :] /= cur_coords[:, 2:3, :]
+
+            # do image aug
+            cur_coords = cur_img_aug_matrix[:, :3, :3].matmul(cur_coords)
+            cur_coords += cur_img_aug_matrix[:, :3, 3].reshape(-1, 3, 1)
+            cur_coords = cur_coords[:, :2, :].transpose(1, 2)
+
+            # normalize coords for grid sample
+            cur_coords = cur_coords[..., [1, 0]]
+
+            # filter points outside of images
+            on_img = (
+                (cur_coords[..., 0] < self.image_size[0])
+                & (cur_coords[..., 0] >= 0)
+                & (cur_coords[..., 1] < self.image_size[1])
+                & (cur_coords[..., 1] >= 0)
+            )
+            for c in range(on_img.shape[0]):
+                masked_coords = cur_coords[c, on_img[c]].long()
+                masked_dist = dist[c, on_img[c]]
+                depth[b, c, 0, masked_coords[:, 0], masked_coords[:, 1]] = masked_dist
+
+        extra_rots = lidar_aug_matrix[..., :3, :3]
+        extra_trans = lidar_aug_matrix[..., :3, 3]
+        geom = self.get_geometry(
+            camera2lidar_rots, camera2lidar_trans, intrins, post_rots, 
+            post_trans, extra_rots=extra_rots, extra_trans=extra_trans,
+        )
+        # use points depth to assist the depth prediction in images
+        x = self.get_cam_feats(img, depth)
+        x = self.bev_pool(geom, x)
+        x = self.downsample(x)
+        # convert bev features from (b, c, x, y) to (b, c, y, x)
+        x = x.permute(0, 1, 3, 2)
+        batch_dict['spatial_features_img'] = x
+        return batch_dict
\ No newline at end of file

pcdet/ops/bev_pool/__init__.py

+from .bev_pool import bev_pool
\ No newline at end of file

pcdet/ops/bev_pool/bev_pool.py

+import torch
+
+from . import bev_pool_ext
+
+__all__ = ["bev_pool"]
+
+
+class QuickCumsum(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, geom_feats, ranks):
+        x = x.cumsum(0)
+        kept = torch.ones(x.shape[0], device=x.device, dtype=torch.bool)
+        kept[:-1] = ranks[1:] != ranks[:-1]
+
+        x, geom_feats = x[kept], geom_feats[kept]
+        x = torch.cat((x[:1], x[1:] - x[:-1]))
+
+        # save kept for backward
+        ctx.save_for_backward(kept)
+
+        # no gradient for geom_feats
+        ctx.mark_non_differentiable(geom_feats)
+
+        return x, geom_feats
+
+    @staticmethod
+    def backward(ctx, gradx, gradgeom):
+        (kept,) = ctx.saved_tensors
+        back = torch.cumsum(kept, 0)
+        back[kept] -= 1
+
+        val = gradx[back]
+
+        return val, None, None
+
+
+class QuickCumsumCuda(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, geom_feats, ranks, B, D, H, W):
+        kept = torch.ones(x.shape[0], device=x.device, dtype=torch.bool)
+        kept[1:] = ranks[1:] != ranks[:-1]
+        interval_starts = torch.where(kept)[0].int()
+        interval_lengths = torch.zeros_like(interval_starts)
+        interval_lengths[:-1] = interval_starts[1:] - interval_starts[:-1]
+        interval_lengths[-1] = x.shape[0] - interval_starts[-1]
+        geom_feats = geom_feats.int()
+
+        out = bev_pool_ext.bev_pool_forward(
+            x,
+            geom_feats,
+            interval_lengths,
+            interval_starts,
+            B,
+            D,
+            H,
+            W,
+        )
+
+        ctx.save_for_backward(interval_starts, interval_lengths, geom_feats)
+        ctx.saved_shapes = B, D, H, W
+        return out
+
+    @staticmethod
+    def backward(ctx, out_grad):
+        interval_starts, interval_lengths, geom_feats = ctx.saved_tensors
+        B, D, H, W = ctx.saved_shapes
+
+        out_grad = out_grad.contiguous()
+        x_grad = bev_pool_ext.bev_pool_backward(
+            out_grad,
+            geom_feats,
+            interval_lengths,
+            interval_starts,
+            B,
+            D,
+            H,
+            W,
+        )
+
+        return x_grad, None, None, None, None, None, None
+
+
+def bev_pool(feats, coords, B, D, H, W):
+    assert feats.shape[0] == coords.shape[0]
+
+    ranks = (
+        coords[:, 0] * (W * D * B)
+        + coords[:, 1] * (D * B)
+        + coords[:, 2] * B
+        + coords[:, 3]
+    )
+    indices = ranks.argsort()
+    feats, coords, ranks = feats[indices], coords[indices], ranks[indices]
+
+    x = QuickCumsumCuda.apply(feats, coords, ranks, B, D, H, W)
+    x = x.permute(0, 4, 1, 2, 3).contiguous()
+    return x

pcdet/ops/bev_pool/src/bev_pool.cpp

+#include <torch/torch.h>
+#include <c10/cuda/CUDAGuard.h>
+
+// CUDA function declarations
+void bev_pool(int b, int d, int h, int w, int n, int c, int n_intervals, const float* x,
+    const int* geom_feats, const int* interval_starts, const int* interval_lengths, float* out);
+
+void bev_pool_grad(int b, int d, int h, int w, int n, int c, int n_intervals, const float* out_grad,
+  const int* geom_feats, const int* interval_starts, const int* interval_lengths, float* x_grad);
+
+
+/*
+  Function: pillar pooling (forward, cuda)
+  Args:
+    x                : input features, FloatTensor[n, c]
+    geom_feats       : input coordinates, IntTensor[n, 4]
+    interval_lengths : starting position for pooled point, IntTensor[n_intervals]
+    interval_starts  : how many points in each pooled point, IntTensor[n_intervals]
+  Return:
+    out              : output features, FloatTensor[b, d, h, w, c]
+*/
+at::Tensor bev_pool_forward(
+  const at::Tensor _x,
+  const at::Tensor _geom_feats, 
+  const at::Tensor _interval_lengths, 
+  const at::Tensor _interval_starts,
+  int b, int d, int h, int w
+) {
+  int n = _x.size(0);
+  int c = _x.size(1);
+  int n_intervals = _interval_lengths.size(0);
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(_x));
+  const float* x = _x.data_ptr<float>();
+  const int* geom_feats = _geom_feats.data_ptr<int>();
+  const int* interval_lengths = _interval_lengths.data_ptr<int>();
+  const int* interval_starts = _interval_starts.data_ptr<int>();
+  
+  auto options =
+      torch::TensorOptions().dtype(_x.dtype()).device(_x.device());
+  at::Tensor _out = torch::zeros({b, d, h, w, c}, options);
+  float* out = _out.data_ptr<float>();
+  bev_pool(
+    b, d, h, w, n, c, n_intervals, x,
+    geom_feats, interval_starts, interval_lengths, out
+  );
+  return _out;
+}
+
+
+/*
+  Function: pillar pooling (backward, cuda)
+  Args:
+    out_grad         : input features, FloatTensor[b, d, h, w, c]
+    geom_feats       : input coordinates, IntTensor[n, 4]
+    interval_lengths : starting position for pooled point, IntTensor[n_intervals]
+    interval_starts  : how many points in each pooled point, IntTensor[n_intervals]
+  Return:
+    x_grad           : output features, FloatTensor[n, 4]
+*/
+at::Tensor bev_pool_backward(
+  const at::Tensor _out_grad,
+  const at::Tensor _geom_feats, 
+  const at::Tensor _interval_lengths, 
+  const at::Tensor _interval_starts,
+  int b, int d, int h, int w
+) {
+  int n = _geom_feats.size(0);
+  int c = _out_grad.size(4);
+  int n_intervals = _interval_lengths.size(0);
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(_out_grad));
+  const float* out_grad = _out_grad.data_ptr<float>();
+  const int* geom_feats = _geom_feats.data_ptr<int>();
+  const int* interval_lengths = _interval_lengths.data_ptr<int>();
+  const int* interval_starts = _interval_starts.data_ptr<int>();
+
+  auto options =
+      torch::TensorOptions().dtype(_out_grad.dtype()).device(_out_grad.device());
+  at::Tensor _x_grad = torch::zeros({n, c}, options);
+  float* x_grad = _x_grad.data_ptr<float>();
+  
+  bev_pool_grad(
+    b, d, h, w, n, c, n_intervals, out_grad,
+    geom_feats, interval_starts, interval_lengths, x_grad
+  );
+  
+  return _x_grad;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("bev_pool_forward", &bev_pool_forward,
+        "bev_pool_forward");
+  m.def("bev_pool_backward", &bev_pool_backward,
+        "bev_pool_backward");
+}

pcdet/ops/bev_pool/src/bev_pool_cuda.cu

+#include <stdio.h>
+#include <stdlib.h>
+
+/*
+  Function: pillar pooling
+  Args:
+    b                : batch size
+    d                : depth of the feature map
+    h                : height of pooled feature map
+    w                : width of pooled feature map
+    n                : number of input points
+    c                : number of channels
+    n_intervals      : number of unique points
+    x                : input features, FloatTensor[n, c]
+    geom_feats       : input coordinates, IntTensor[n, 4]
+    interval_lengths : starting position for pooled point, IntTensor[n_intervals]
+    interval_starts  : how many points in each pooled point, IntTensor[n_intervals]
+    out              : output features, FloatTensor[b, d, h, w, c]
+*/
+__global__ void bev_pool_kernel(int b, int d, int h, int w, int n, int c, int n_intervals,
+                                  const float *__restrict__ x,
+                                  const int *__restrict__ geom_feats,
+                                  const int *__restrict__ interval_starts,
+                                  const int *__restrict__ interval_lengths,
+                                  float* __restrict__ out) {
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  int index = idx / c;
+  int cur_c = idx % c;
+  if (index >= n_intervals) return;
+  int interval_start = interval_starts[index];
+  int interval_length = interval_lengths[index];
+  const int* cur_geom_feats = geom_feats + interval_start * 4;
+  const float* cur_x = x + interval_start * c + cur_c;
+  float* cur_out = out + cur_geom_feats[3] * d * h * w * c + 
+    cur_geom_feats[2] * h * w * c + cur_geom_feats[0] * w * c + 
+    cur_geom_feats[1] * c + cur_c;
+  float psum = 0;
+  for(int i = 0; i < interval_length; i++){
+    psum += cur_x[i * c];
+  }
+  *cur_out = psum;
+}
+
+
+/*
+  Function: pillar pooling backward
+  Args:
+    b                : batch size
+    d                : depth of the feature map
+    h                : height of pooled feature map
+    w                : width of pooled feature map
+    n                : number of input points
+    c                : number of channels
+    n_intervals      : number of unique points
+    out_grad         : gradient of the BEV fmap from top, FloatTensor[b, d, h, w, c]
+    geom_feats       : input coordinates, IntTensor[n, 4]
+    interval_lengths : starting position for pooled point, IntTensor[n_intervals]
+    interval_starts  : how many points in each pooled point, IntTensor[n_intervals]
+    x_grad           : gradient of the image fmap, FloatTensor
+*/
+__global__ void bev_pool_grad_kernel(int b, int d, int h, int w, int n, int c, int n_intervals,
+                                  const float *__restrict__ out_grad,
+                                  const int *__restrict__ geom_feats,
+                                  const int *__restrict__ interval_starts,
+                                  const int *__restrict__ interval_lengths,
+                                  float* __restrict__ x_grad) {
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  int index = idx / c;
+  int cur_c = idx % c;
+  if (index >= n_intervals) return;
+  int interval_start = interval_starts[index];
+  int interval_length = interval_lengths[index];
+  
+  const int* cur_geom_feats = geom_feats + interval_start * 4;
+  float* cur_x_grad = x_grad + interval_start * c + cur_c;
+  
+  const float* cur_out_grad = out_grad + cur_geom_feats[3] * d * h * w * c + 
+    cur_geom_feats[2] * h * w * c + cur_geom_feats[0] * w * c + 
+    cur_geom_feats[1] * c + cur_c;
+  for(int i = 0; i < interval_length; i++){
+    cur_x_grad[i * c] = *cur_out_grad;
+  }
+  
+}
+
+void bev_pool(int b, int d, int h, int w, int n, int c, int n_intervals, const float* x,
+  const int* geom_feats, const int* interval_starts, const int* interval_lengths, float* out) {
+  bev_pool_kernel<<<(int)ceil(((double)n_intervals * c / 256)), 256>>>(
+    b, d, h, w, n, c, n_intervals, x, geom_feats, interval_starts, interval_lengths, out
+  );
+}
+
+void bev_pool_grad(int b, int d, int h, int w, int n, int c, int n_intervals, const float* out_grad,
+  const int* geom_feats, const int* interval_starts, const int* interval_lengths, float* x_grad) {
+  bev_pool_grad_kernel<<<(int)ceil(((double)n_intervals * c / 256)), 256>>>(
+    b, d, h, w, n, c, n_intervals, out_grad, geom_feats, interval_starts, interval_lengths, x_grad
+  );
+}

pcdet/utils/common_utils.py

@@ -57,6 +57,24 @@ def rotate_points_along_z(points, angle):
     return points_rot.numpy() if is_numpy else points_rot
 
 
+def angle2matrix(angle):
+    """
+    Args:
+        angle: angle along z-axis, angle increases x ==> y
+    Returns:
+        rot_matrix: (3x3 Tensor) rotation matrix
+    """
+
+    cosa = torch.cos(angle)
+    sina = torch.sin(angle)
+    rot_matrix = torch.tensor([
+        [cosa, -sina, 0],
+        [sina, cosa,  0],
+        [   0,    0,  1]
+    ])
+    return rot_matrix
+
+
 def mask_points_by_range(points, limit_range):
     mask = (points[:, 0] >= limit_range[0]) & (points[:, 0] <= limit_range[3]) \
            & (points[:, 1] >= limit_range[1]) & (points[:, 1] <= limit_range[4])

pcdet/utils/loss_utils.py

@@ -560,4 +560,49 @@ class IouRegLossSparse(nn.Module):
             loss += (1. - iou).sum()
 
         loss =  loss / (mask.sum() + 1e-4)
-        return loss
+        return loss
\ No newline at end of file
+
+class L1Loss(nn.Module):
+    def __init__(self):
+        super(L1Loss, self).__init__()
+       
+    def forward(self, pred, target):
+        if target.numel() == 0:
+            return pred.sum() * 0
+        assert pred.size() == target.size()
+        loss = torch.abs(pred - target)
+        return loss
+
+
+class GaussianFocalLoss(nn.Module):
+    """GaussianFocalLoss is a variant of focal loss.
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/1808.01244>`_
+    Code is modified from `kp_utils.py
+    <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_  # noqa: E501
+    Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
+    not 0/1 binary target.
+
+    Args:
+        alpha (float): Power of prediction.
+        gamma (float): Power of target for negative samples.
+        reduction (str): Options are "none", "mean" and "sum".
+        loss_weight (float): Loss weight of current loss.
+    """
+
+    def __init__(self,
+                 alpha=2.0,
+                 gamma=4.0):
+        super(GaussianFocalLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(self, pred, target):
+        eps = 1e-12
+        pos_weights = target.eq(1)
+        neg_weights = (1 - target).pow(self.gamma)
+        pos_loss = -(pred + eps).log() * (1 - pred).pow(self.alpha) * pos_weights
+        neg_loss = -(1 - pred + eps).log() * pred.pow(self.alpha) * neg_weights
+
+        return pos_loss + neg_loss
\ No newline at end of file

requirements.txt

@@ -10,3 +10,4 @@ tqdm
 torchvision
 SharedArray
 opencv-python
+pyquaternion
\ No newline at end of file

setup.py

@@ -117,5 +117,13 @@ if __name__ == '__main__':
 
                 ],
             ),
+            make_cuda_ext(
+                name="bev_pool_ext",
+                module="pcdet.ops.bev_pool",
+                sources=[
+                    "src/bev_pool.cpp",
+                    "src/bev_pool_cuda.cu",
+                ],
+            ),
         ],
     )

tools/cfgs/nuscenes_models/bevfusion.yaml

+CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
+              'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
+
+DATA_CONFIG:
+    _BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
+    POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
+    CAMERA_CONFIG:
+        USE_CAMERA: True
+        IMAGE:
+            FINAL_DIM: [256,704]
+            RESIZE_LIM_TRAIN: [0.38, 0.55]
+            RESIZE_LIM_TEST: [0.48, 0.48]
+
+    DATA_AUGMENTOR:
+        DISABLE_AUG_LIST: ['placeholder']
+        AUG_CONFIG_LIST:
+            - NAME: random_world_flip
+              ALONG_AXIS_LIST: ['x', 'y']
+
+            - NAME: random_world_rotation
+              WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
+
+            - NAME: random_world_scaling
+              WORLD_SCALE_RANGE: [0.9, 1.1]
+
+            - NAME: random_world_translation
+              NOISE_TRANSLATE_STD: [0.5, 0.5, 0.5]
+            
+            - NAME: imgaug
+              ROT_LIM: [-5.4, 5.4]
+              RAND_FLIP: True
+
+    DATA_PROCESSOR:
+        - NAME: mask_points_and_boxes_outside_range
+          REMOVE_OUTSIDE_BOXES: True
+
+        - NAME: shuffle_points
+          SHUFFLE_ENABLED: {
+            'train': True,
+            'test': True
+          }
+
+        - NAME: transform_points_to_voxels
+          VOXEL_SIZE: [0.075, 0.075, 0.2]
+          MAX_POINTS_PER_VOXEL: 10
+          MAX_NUMBER_OF_VOXELS: {
+            'train': 120000,
+            'test': 160000
+          }
+
+        - NAME: image_calibrate
+        
+        - NAME: image_normalize
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+
+
+MODEL:
+    NAME: BevFusion
+
+    VFE:
+        NAME: MeanVFE
+
+    BACKBONE_3D:
+        NAME: VoxelResBackBone8x
+        USE_BIAS: False
+
+    MAP_TO_BEV:
+        NAME: HeightCompression
+        NUM_BEV_FEATURES: 256
+    
+    IMAGE_BACKBONE:
+        NAME: SwinTransformer
+        EMBED_DIMS: 96
+        DEPTHS: [2, 2, 6, 2]
+        NUM_HEADS: [3, 6, 12, 24]
+        WINDOW_SIZE: 7
+        MLP_RATIO: 4
+        DROP_RATE: 0.
+        ATTN_DROP_RATE: 0.
+        DROP_PATH_RATE: 0.2
+        PATCH_NORM: True
+        OUT_INDICES: [1, 2, 3]
+        WITH_CP: False
+        CONVERT_WEIGHTS: True
+        INIT_CFG:
+            type: Pretrained
+            checkpoint: swint-nuimages-pretrained.pth
+    
+    NECK:
+        NAME: GeneralizedLSSFPN
+        IN_CHANNELS: [192, 384, 768]
+        OUT_CHANNELS: 256
+        START_LEVEL: 0
+        END_LEVEL: -1
+        NUM_OUTS: 3
+    
+    VTRANSFORM:
+        NAME: DepthLSSTransform
+        IMAGE_SIZE: [256, 704]
+        IN_CHANNEL: 256
+        OUT_CHANNEL: 80
+        FEATURE_SIZE: [32, 88]
+        XBOUND: [-54.0, 54.0, 0.3]
+        YBOUND: [-54.0, 54.0, 0.3]
+        ZBOUND: [-10.0, 10.0, 20.0]
+        DBOUND: [1.0, 60.0, 0.5]
+        DOWNSAMPLE: 2
+    
+    FUSER:
+        NAME: ConvFuser
+        IN_CHANNEL: 336
+        OUT_CHANNEL: 256
+    
+    BACKBONE_2D:
+        NAME: BaseBEVBackbone
+        LAYER_NUMS: [5, 5]
+        LAYER_STRIDES: [1, 2]
+        NUM_FILTERS: [128, 256]
+        UPSAMPLE_STRIDES: [1, 2]
+        NUM_UPSAMPLE_FILTERS: [256, 256]
+        USE_CONV_FOR_NO_STRIDE: True
+
+
+    DENSE_HEAD:
+        CLASS_AGNOSTIC: False
+        NAME: TransFusionHead
+
+        USE_BIAS_BEFORE_NORM: False
+
+        NUM_PROPOSALS: 200
+        HIDDEN_CHANNEL: 128
+        NUM_CLASSES: 10
+        NUM_HEADS: 8
+        NMS_KERNEL_SIZE: 3
+        FFN_CHANNEL: 256
+        DROPOUT: 0.1
+        BN_MOMENTUM: 0.1
+        ACTIVATION: relu
+
+        NUM_HM_CONV: 2
+        SEPARATE_HEAD_CFG:
+            HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel']
+            HEAD_DICT: {
+                'center': {'out_channels': 2, 'num_conv': 2},
+                'height': {'out_channels': 1, 'num_conv': 2},
+                'dim': {'out_channels': 3, 'num_conv': 2},
+                'rot': {'out_channels': 2, 'num_conv': 2},
+                'vel': {'out_channels': 2, 'num_conv': 2},
+            }
+      
+        TARGET_ASSIGNER_CONFIG:
+            FEATURE_MAP_STRIDE: 8
+            DATASET: nuScenes
+            GAUSSIAN_OVERLAP: 0.1
+            MIN_RADIUS: 2
+            HUNGARIAN_ASSIGNER:
+                cls_cost: {'gamma': 2.0, 'alpha': 0.25, 'weight': 0.15}
+                reg_cost: {'weight': 0.25}
+                iou_cost: {'weight': 0.25}
+        
+        LOSS_CONFIG:
+            LOSS_WEIGHTS: {
+                    'cls_weight': 1.0,
+                    'bbox_weight': 0.25,
+                    'hm_weight': 1.0,
+                    'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
+                }
+            LOSS_CLS:
+                use_sigmoid: True
+                gamma: 2.0
+                alpha: 0.25
+          
+        POST_PROCESSING:
+            SCORE_THRESH: 0.0
+            POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
+
+    POST_PROCESSING:
+        RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
+        SCORE_THRESH: 0.1
+        OUTPUT_RAW_SCORE: False
+
+        EVAL_METRIC: kitti
+
+
+
+OPTIMIZATION:
+    BATCH_SIZE_PER_GPU: 3
+    NUM_EPOCHS: 6
+
+    OPTIMIZER: adam_cosineanneal
+    LR: 0.0001
+    WEIGHT_DECAY: 0.01
+    MOMENTUM: 0.9
+    BETAS: [0.9, 0.999]
+
+    MOMS: [0.9, 0.8052631]
+    PCT_START: 0.4
+    WARMUP_ITER: 500
+
+    DECAY_STEP_LIST: [35, 45]
+    LR_WARMUP: False
+    WARMUP_EPOCH: 1
+
+    GRAD_NORM_CLIP: 35
+
+    LOSS_SCALE_FP16: 32
+  
\ No newline at end of file

tools/cfgs/nuscenes_models/transfusion_lidar.yaml

+CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
+              'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
+
+DATA_CONFIG:
+    _BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
+    POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
+
+    DATA_AUGMENTOR:
+        DISABLE_AUG_LIST: ['placeholder']
+        AUG_CONFIG_LIST:
+            - NAME: gt_sampling
+              DB_INFO_PATH:
+                  - nuscenes_dbinfos_10sweeps_withvelo.pkl
+              PREPARE: {
+                filter_by_min_points: [
+                    'car:5','truck:5', 'construction_vehicle:5', 'bus:5', 'trailer:5',
+                    'barrier:5', 'motorcycle:5', 'bicycle:5', 'pedestrian:5', 'traffic_cone:5'
+                ],
+              }
+
+              SAMPLE_GROUPS: [
+                  'car:2','truck:3', 'construction_vehicle:7', 'bus:4', 'trailer:6',
+                  'barrier:2', 'motorcycle:6', 'bicycle:6', 'pedestrian:2', 'traffic_cone:2'
+              ]
+
+              NUM_POINT_FEATURES: 5
+              DATABASE_WITH_FAKELIDAR: False
+              REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
+              LIMIT_WHOLE_SCENE: True
+        
+
+            - NAME: random_world_flip
+              ALONG_AXIS_LIST: ['x', 'y']
+
+            - NAME: random_world_rotation
+              WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
+
+            - NAME: random_world_scaling
+              WORLD_SCALE_RANGE: [0.9, 1.1]
+
+            - NAME: random_world_translation
+              NOISE_TRANSLATE_STD: [0.5, 0.5, 0.5]
+      
+    DATA_PROCESSOR:
+        - NAME: mask_points_and_boxes_outside_range
+          REMOVE_OUTSIDE_BOXES: True
+        - NAME: shuffle_points
+          SHUFFLE_ENABLED: {
+            'train': True,
+            'test': True
+          }
+
+        - NAME: transform_points_to_voxels
+          VOXEL_SIZE: [0.075, 0.075, 0.2]
+          MAX_POINTS_PER_VOXEL: 10
+          MAX_NUMBER_OF_VOXELS: {
+            'train': 120000,
+            'test': 160000
+          }
+
+MODEL:
+    NAME: TransFusion
+
+    VFE:
+        NAME: MeanVFE
+
+    BACKBONE_3D:
+        NAME: VoxelResBackBone8x
+        USE_BIAS: False
+
+    MAP_TO_BEV:
+        NAME: HeightCompression
+        NUM_BEV_FEATURES: 256
+
+    BACKBONE_2D:
+        NAME: BaseBEVBackbone
+        LAYER_NUMS: [5, 5]
+        LAYER_STRIDES: [1, 2]
+        NUM_FILTERS: [128, 256]
+        UPSAMPLE_STRIDES: [1, 2]
+        NUM_UPSAMPLE_FILTERS: [256, 256]
+        USE_CONV_FOR_NO_STRIDE: True
+
+
+    DENSE_HEAD:
+        CLASS_AGNOSTIC: False
+        NAME: TransFusionHead
+
+        USE_BIAS_BEFORE_NORM: False
+
+        NUM_PROPOSALS: 200
+        HIDDEN_CHANNEL: 128
+        NUM_CLASSES: 10
+        NUM_HEADS: 8
+        NMS_KERNEL_SIZE: 3
+        FFN_CHANNEL: 256
+        DROPOUT: 0.1
+        BN_MOMENTUM: 0.1
+        ACTIVATION: relu
+
+        NUM_HM_CONV: 2
+        SEPARATE_HEAD_CFG:
+            HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel']
+            HEAD_DICT: {
+                'center': {'out_channels': 2, 'num_conv': 2},
+                'height': {'out_channels': 1, 'num_conv': 2},
+                'dim': {'out_channels': 3, 'num_conv': 2},
+                'rot': {'out_channels': 2, 'num_conv': 2},
+                'vel': {'out_channels': 2, 'num_conv': 2},
+            }
+    
+        TARGET_ASSIGNER_CONFIG:
+            FEATURE_MAP_STRIDE: 8
+            DATASET: nuScenes
+            GAUSSIAN_OVERLAP: 0.1
+            MIN_RADIUS: 2
+            HUNGARIAN_ASSIGNER:
+                cls_cost: {'gamma': 2.0, 'alpha': 0.25, 'weight': 0.15}
+                reg_cost: {'weight': 0.25}
+                iou_cost: {'weight': 0.25}
+      
+        LOSS_CONFIG:
+            LOSS_WEIGHTS: {
+                    'cls_weight': 1.0,
+                    'bbox_weight': 0.25,
+                    'hm_weight': 1.0,
+                    'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
+                }
+            LOSS_CLS:
+                use_sigmoid: True
+                gamma: 2.0
+                alpha: 0.25
+          
+        POST_PROCESSING:
+            SCORE_THRESH: 0.0
+            POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
+
+    POST_PROCESSING:
+        RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
+        SCORE_THRESH: 0.1
+        OUTPUT_RAW_SCORE: False
+        EVAL_METRIC: kitti
+
+
+
+OPTIMIZATION:
+    BATCH_SIZE_PER_GPU: 4
+    NUM_EPOCHS: 20
+
+    OPTIMIZER: adam_onecycle
+    LR: 0.001
+    WEIGHT_DECAY: 0.01
+    MOMENTUM: 0.9
+    BETAS: [0.9, 0.999]
+
+    MOMS: [0.9, 0.8052631]
+    PCT_START: 0.4
+    DIV_FACTOR: 10
+    DECAY_STEP_LIST: [35, 45]
+    LR_DECAY: 0.1
+    LR_CLIP: 0.0000001
+
+    LR_WARMUP: False
+    WARMUP_EPOCH: 1
+
+    GRAD_NORM_CLIP: 35
+
+HOOK:
+    DisableAugmentationHook:
+        DISABLE_AUG_LIST: ['gt_sampling']
+        NUM_LAST_EPOCHS: 5
\ No newline at end of file

tools/train.py

@@ -195,7 +195,8 @@ def main():
         ckpt_save_time_interval=args.ckpt_save_time_interval,
         use_logger_to_record=not args.use_tqdm_to_record, 
         show_gpu_stat=not args.wo_gpu_stat,
-        use_amp=args.use_amp
+        use_amp=args.use_amp,
+        cfg=cfg
     )
 
     if hasattr(train_set, 'use_shared_memory') and train_set.use_shared_memory:

tools/train_utils/optimization/__init__.py

@@ -5,7 +5,7 @@ import torch.optim as optim
 import torch.optim.lr_scheduler as lr_sched
 
 from .fastai_optim import OptimWrapper
-from .learning_schedules_fastai import CosineWarmupLR, OneCycle
+from .learning_schedules_fastai import CosineWarmupLR, OneCycle, CosineAnnealing
 
 
 def build_optimizer(model, optim_cfg):
@@ -16,7 +16,7 @@ def build_optimizer(model, optim_cfg):
             model.parameters(), lr=optim_cfg.LR, weight_decay=optim_cfg.WEIGHT_DECAY,
             momentum=optim_cfg.MOMENTUM
         )
-    elif optim_cfg.OPTIMIZER == 'adam_onecycle':
+    elif optim_cfg.OPTIMIZER in ['adam_onecycle','adam_cosineanneal']:
         def children(m: nn.Module):
             return list(m.children())
 
@@ -25,8 +25,9 @@ def build_optimizer(model, optim_cfg):
 
         flatten_model = lambda m: sum(map(flatten_model, m.children()), []) if num_children(m) else [m]
         get_layer_groups = lambda m: [nn.Sequential(*flatten_model(m))]
-
-        optimizer_func = partial(optim.Adam, betas=(0.9, 0.99))
+        betas = optim_cfg.get('BETAS', (0.9, 0.99))
+        betas = tuple(betas)
+        optimizer_func = partial(optim.Adam, betas=betas)
         optimizer = OptimWrapper.create(
             optimizer_func, 3e-3, get_layer_groups(model), wd=optim_cfg.WEIGHT_DECAY, true_wd=True, bn_wd=True
         )
@@ -51,6 +52,10 @@ def build_scheduler(optimizer, total_iters_each_epoch, total_epochs, last_epoch,
         lr_scheduler = OneCycle(
             optimizer, total_steps, optim_cfg.LR, list(optim_cfg.MOMS), optim_cfg.DIV_FACTOR, optim_cfg.PCT_START
         )
+    elif optim_cfg.OPTIMIZER == 'adam_cosineanneal':
+        lr_scheduler = CosineAnnealing(
+            optimizer, total_steps, total_epochs, optim_cfg.LR, list(optim_cfg.MOMS), optim_cfg.PCT_START, optim_cfg.WARMUP_ITER
+        )
     else:
         lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd, last_epoch=last_epoch)
 

tools/train_utils/optimization/learning_schedules_fastai.py

@@ -41,7 +41,7 @@ class LRSchedulerStep(object):
                 self.mom_phases.append((int(start * total_step), total_step, lambda_func))
         assert self.mom_phases[0][0] == 0
 
-    def step(self, step):
+    def step(self, step, epoch=None):
         for start, end, func in self.lr_phases:
             if step >= start:
                 self.optimizer.lr = func((step - start) / (end - start))
@@ -83,12 +83,60 @@ class CosineWarmupLR(lr_sched._LRScheduler):
         self.eta_min = eta_min
         super(CosineWarmupLR, self).__init__(optimizer, last_epoch)
 
-    def get_lr(self):
+    def get_lr(self, epoch=None):
         return [self.eta_min + (base_lr - self.eta_min) *
                 (1 - math.cos(math.pi * self.last_epoch / self.T_max)) / 2
                 for base_lr in self.base_lrs]
 
 
+def linear_warmup(end, lr_max, pct):
+    k = (1 - pct / end) * (1 - 0.33333333)
+    warmup_lr = lr_max * (1 - k)
+    return warmup_lr
+
+
+class CosineAnnealing(LRSchedulerStep):
+    def __init__(self, fai_optimizer, total_step, total_epoch, lr_max, moms, pct_start, warmup_iter):
+        self.lr_max = lr_max
+        self.moms = moms
+        self.pct_start = pct_start
+
+        mom_phases = ((0, partial(annealing_cos, *self.moms)),
+                      (self.pct_start, partial(annealing_cos,
+                                               *self.moms[::-1])))
+        fai_optimizer.lr, fai_optimizer.mom = lr_max, self.moms[0]
+
+        self.optimizer = fai_optimizer
+        self.total_step = total_step 
+        self.warmup_iter = warmup_iter
+        self.total_epoch = total_epoch
+
+        self.mom_phases = []
+        for i, (start, lambda_func) in enumerate(mom_phases):
+            if len(self.mom_phases) != 0:
+                assert self.mom_phases[-1][0] < start
+            if isinstance(lambda_func, str):
+                lambda_func = eval(lambda_func)
+            if i < len(mom_phases) - 1:
+                self.mom_phases.append((int(start * total_step), int(mom_phases[i + 1][0] * total_step), lambda_func))
+            else:
+                self.mom_phases.append((int(start * total_step), total_step, lambda_func))
+        assert self.mom_phases[0][0] == 0
+    
+    def step(self, step, epoch):
+        # update lr
+        if step < self.warmup_iter:
+            self.optimizer.lr = linear_warmup(self.warmup_iter, self.lr_max, step)
+        else:
+            target_lr = self.lr_max * 0.001
+            cos_lr = annealing_cos(self.lr_max, target_lr, epoch / self.total_epoch)
+            self.optimizer.lr = cos_lr
+        # update mom
+        for start, end, func in self.mom_phases:
+            if step >= start:
+                self.optimizer.mom = func((step - start) / (end - start))
+
+
 class FakeOptim:
     def __init__(self):
         self.lr = 0