Ejemplo n.º 1
0
    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)
Ejemplo n.º 2
0
def test_imvoxel_neck():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')

    neck_cfg = dict(
        type='OutdoorImVoxelNeck', in_channels=64, out_channels=256)
    neck = build_neck(neck_cfg).cuda()
    inputs = torch.rand([1, 64, 216, 248, 12], device='cuda')
    outputs = neck(inputs)
    assert outputs[0].shape == (1, 256, 248, 216)
Ejemplo n.º 3
0
def test_centerpoint_fpn():

    second_cfg = dict(
        type='SECOND',
        in_channels=64,
        out_channels=[64, 128, 256],
        layer_nums=[3, 5, 5],
        layer_strides=[2, 2, 2],
        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
        conv_cfg=dict(type='Conv2d', bias=False))

    second = build_backbone(second_cfg)

    # centerpoint usage of fpn
    centerpoint_fpn_cfg = dict(
        type='SECONDFPN',
        in_channels=[64, 128, 256],
        out_channels=[128, 128, 128],
        upsample_strides=[0.5, 1, 2],
        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
        upsample_cfg=dict(type='deconv', bias=False),
        use_conv_for_no_stride=True)

    # original usage of fpn
    fpn_cfg = dict(
        type='SECONDFPN',
        in_channels=[64, 128, 256],
        upsample_strides=[1, 2, 4],
        out_channels=[128, 128, 128])

    second_fpn = build_neck(fpn_cfg)

    centerpoint_second_fpn = build_neck(centerpoint_fpn_cfg)

    input = torch.rand([4, 64, 512, 512])
    sec_output = second(input)
    centerpoint_output = centerpoint_second_fpn(sec_output)
    second_output = second_fpn(sec_output)
    assert centerpoint_output[0].shape == torch.Size([4, 384, 128, 128])
    assert second_output[0].shape == torch.Size([4, 384, 256, 256])
Ejemplo n.º 4
0
    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)
Ejemplo n.º 5
0
    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)
Ejemplo n.º 6
0
    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)