def test_hard_simple_VFE(): hard_simple_VFE_cfg = dict(type='HardSimpleVFE', num_features=5) hard_simple_VFE = build_voxel_encoder(hard_simple_VFE_cfg) features = torch.rand([240000, 10, 5]) num_voxels = torch.randint(1, 10, [240000]) outputs = hard_simple_VFE(features, num_voxels, None) assert outputs.shape == torch.Size([240000, 5])
def __init__(self, pts_voxel_layer=None, pts_voxel_encoder=None, pts_middle_encoder=None, img_backbone=None, img_seg_head=None, pts_backbone=None, pts_neck=None, pts_bbox_head=None, train_cfg=None, test_cfg=None, pretrained=None, pts_fc=[], contrast_criterion=None, max_pts=4096, lambda_contrast=0.1): super(FusionContrastV2, self).__init__() if img_backbone: self.img_backbone = builder.build_backbone(img_backbone) if img_seg_head: self.img_seg_head = builder.build_head(img_seg_head) if pts_voxel_layer: self.pts_voxel_layer = Voxelization(**pts_voxel_layer) if pts_voxel_encoder: self.pts_voxel_encoder = builder.build_voxel_encoder( pts_voxel_encoder) if pts_middle_encoder: self.pts_middle_encoder = builder.build_middle_encoder( pts_middle_encoder) if pts_backbone: self.pts_backbone = builder.build_backbone(pts_backbone) if pts_neck: self.pts_neck = builder.build_neck(pts_neck) if pts_bbox_head: pts_train_cfg = train_cfg.pts if train_cfg else None pts_bbox_head.update(train_cfg=pts_train_cfg) pts_test_cfg = test_cfg.pts if test_cfg else None pts_bbox_head.update(test_cfg=pts_test_cfg) self.pts_bbox_head = builder.build_head(pts_bbox_head) if contrast_criterion: self.contrast_criterion = builder.build_loss(contrast_criterion) self.max_pts = max_pts self.lambda_contrast = lambda_contrast fc_layers = [] for i, (in_c, out_c) in enumerate(zip(pts_fc[:-1], pts_fc[1:])): fc_layers.append(nn.Linear(in_c, out_c)) if i == len(pts_fc) - 2: break fc_layers.append(nn.ReLU(inplace=True)) self.fc_layers = nn.Sequential(*fc_layers) self.train_cfg = train_cfg self.test_cfg = test_cfg self.init_weights(pretrained=pretrained)
def __init__(self, pts_voxel_layer=None, pts_voxel_encoder=None, pts_middle_encoder=None, img_backbone=None, img_seg_head=None, pts_backbone=None, pts_neck=None, pts_bbox_head=None, train_cfg=None, test_cfg=None, pretrained=None, pts_fc=[]): super(FusionBaseline4, self).__init__() if img_backbone: self.img_backbone = builder.build_backbone(img_backbone) if img_seg_head: self.img_seg_head = builder.build_head(img_seg_head) if pts_voxel_layer: self.pts_voxel_layer = Voxelization(**pts_voxel_layer) if pts_voxel_encoder: self.pts_voxel_encoder = builder.build_voxel_encoder( pts_voxel_encoder) if pts_middle_encoder: self.pts_middle_encoder = builder.build_middle_encoder( pts_middle_encoder) if pts_backbone: self.pts_backbone = builder.build_backbone(pts_backbone) if pts_neck is not None: self.pts_neck = builder.build_neck(pts_neck) if pts_bbox_head: pts_train_cfg = train_cfg.pts if train_cfg else None pts_bbox_head.update(train_cfg=pts_train_cfg) pts_test_cfg = test_cfg.pts if test_cfg else None pts_bbox_head.update(test_cfg=pts_test_cfg) self.pts_bbox_head = builder.build_head(pts_bbox_head) fc_layers = [] for i, (in_c, out_c) in enumerate(zip(pts_fc[:-1], pts_fc[1:])): fc_layers.append(nn.Linear(in_c, out_c)) if i == len(pts_fc) - 2: break fc_layers.append(nn.ReLU(inplace=True)) self.fc_layers = nn.Sequential(*fc_layers) self.train_cfg = train_cfg self.test_cfg = test_cfg self.init_weights(pretrained=pretrained)
def __init__(self, pts_voxel_layer=None, pts_voxel_encoder=None, pts_middle_encoder=None, img_backbone=None, img_seg_head=None, pts_backbone=None, pts_neck=None, pts_bbox_head=None, train_cfg=None, test_cfg=None, pretrained=None, vfes=[]): super(FusionBaseline, self).__init__() if img_backbone: self.img_backbone = builder.build_backbone(img_backbone) if img_seg_head: self.img_seg_head = builder.build_head(img_seg_head) if pts_voxel_layer: self.pts_voxel_layer = Voxelization(**pts_voxel_layer) if pts_voxel_encoder: self.pts_voxel_encoder = builder.build_voxel_encoder( pts_voxel_encoder) if pts_middle_encoder: self.pts_middle_encoder = builder.build_middle_encoder( pts_middle_encoder) if pts_backbone: self.pts_backbone = builder.build_backbone(pts_backbone) if pts_neck is not None: self.pts_neck = builder.build_neck(pts_neck) if pts_bbox_head: pts_train_cfg = train_cfg.pts if train_cfg else None pts_bbox_head.update(train_cfg=pts_train_cfg) pts_test_cfg = test_cfg.pts if test_cfg else None pts_bbox_head.update(test_cfg=pts_test_cfg) self.pts_bbox_head = builder.build_head(pts_bbox_head) vfe_layers = [] for in_c, out_c in zip(vfes[:-1], vfes[1:]): vfe_layers.append(VFELayer(in_c, out_c, max_out=False)) self.vfe_layers = nn.Sequential(*vfe_layers) self.train_cfg = train_cfg self.test_cfg = test_cfg self.init_weights(pretrained=pretrained)
def test_pillar_feature_net(): pillar_feature_net_cfg = dict( type='PillarFeatureNet', in_channels=5, feat_channels=[64], with_distance=False, voxel_size=(0.2, 0.2, 8), point_cloud_range=(-51.2, -51.2, -5.0, 51.2, 51.2, 3.0), norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01)) pillar_feature_net = build_voxel_encoder(pillar_feature_net_cfg) features = torch.rand([97297, 20, 5]) num_voxels = torch.randint(1, 100, [97297]) coors = torch.randint(0, 100, [97297, 4]) features = pillar_feature_net(features, num_voxels, coors) assert features.shape == torch.Size([97297, 64])
def __init__(self, pts_voxel_layer=None, pts_voxel_encoder=None, pts_middle_encoder=None, img_backbone=None, img_seg_head=None, pts_backbone=None, pts_neck=None, pts_bbox_head=None, train_cfg=None, test_cfg=None, pretrained=None): super(MultiTaskSep, self).__init__() if img_backbone: self.img_backbone = builder.build_backbone(img_backbone) if img_seg_head: self.img_seg_head = builder.build_head(img_seg_head) if pts_voxel_layer: self.pts_voxel_layer = Voxelization(**pts_voxel_layer) if pts_voxel_encoder: self.pts_voxel_encoder = builder.build_voxel_encoder( pts_voxel_encoder) if pts_middle_encoder: self.pts_middle_encoder = builder.build_middle_encoder( pts_middle_encoder) if pts_backbone: self.pts_backbone = builder.build_backbone(pts_backbone) if pts_neck is not None: self.pts_neck = builder.build_neck(pts_neck) if pts_bbox_head: pts_train_cfg = train_cfg.pts if train_cfg else None pts_bbox_head.update(train_cfg=pts_train_cfg) pts_test_cfg = test_cfg.pts if test_cfg else None pts_bbox_head.update(test_cfg=pts_test_cfg) self.pts_bbox_head = builder.build_head(pts_bbox_head) self.train_cfg = train_cfg self.test_cfg = test_cfg self.init_weights(pretrained=pretrained)