add single frame config

README.md

@@ -50,6 +50,7 @@ bash tools/dist_test.sh configs/mv2d/exp/mv2d_r50_frcnn_two_frames_1408x512_ep24
 |                                             config                                              |  mAP  |  NDS  |  checkpoint  |
 |:-----------------------------------------------------------------------------------------------:|:-----:|:-----:|:------------:|
 |    [MV2D-T_R50_1408x512_ep72](./configs/mv2d/exp/mv2d_r50_frcnn_two_frames_1408x512_ep72.py)    | 0.453 | 0.543 | [download](https://drive.google.com/file/d/10zwn2UWb2IzIWqJK1a2y466ZSWoLkD-e/view?usp=drive_link) |  
+| [MV2D-S_R50_1408x512_ep72](./configs/mv2d/exp/mv2d_r50_frcnn_single_frame_roi_1408x512_ep72.py) | 0.398 | 0.470 | [download](https://drive.google.com/file/d/139Lsn-UY78ukfOkVlPh6ywnoY_TvdwZn/view?usp=drive_link) |  
 
 
 ## Acknowledgement

configs/mv2d/exp/mv2d_r50_frcnn_single_frame_roi_1408x512_ep24.py

@@ -0,0 +1,194 @@
+_base_ = [
+    '../data/single_frame.py', '../detectors/maskrcnn_r50.py'
+]
+
+point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
+post_range = [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
+roi_size = 7
+roi_srides = [16]
+
+model = dict(
+    type='MV2D',
+    use_grid_mask=dict(
+        use_h=True,
+        use_w=True,
+        rotate=1,
+        offset=False,
+        ratio_range=(0.4, 0.6),
+        mode=1,
+        prob=0.7,
+        interv_ratio=0.8
+    ),
+    # NOTE:
+    # the FPN in faster r-cnn starts from p2
+    # we use p4 (downsample rate: 16)
+    base_detector=dict(
+        backbone=dict(
+            with_cp=False,
+            dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+            stage_with_dcn=(False, False, True, True),
+        ),
+    ),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 256, 256, 256, 256],
+        out_channels=256,
+        start_level=2,
+        end_level=2,
+        num_outs=1,
+    ),
+    roi_head=dict(
+        type='MV2DSHead',
+        pc_range=point_cloud_range,
+        force_fp32=True,
+        use_denoise=False,
+
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=roi_size, sampling_ratio=-1),
+            featmap_strides=roi_srides,
+            out_channels=512, ),
+        bbox_head=dict(
+            type='CrossAttentionBoxHead',
+            num_classes=10,
+            pc_range=point_cloud_range,
+            transformer=dict(
+                type='MV2DTransformer',
+                decoder=dict(
+                    type='PETRTransformerDecoder',
+                    return_intermediate=True,
+                    num_layers=6,
+                    transformerlayers=dict(
+                        type='PETRTransformerDecoderLayer',
+                        attn_cfgs=[
+                            dict(
+                                type='FlattenMHSelfAttention',
+                                embed_dims=256,
+                                num_heads=8,
+                                dropout=0.1),
+                            dict(
+                                type='PETRMultiheadAttention',
+                                embed_dims=256,
+                                num_heads=8,
+                                dropout=0.1),
+                        ],
+                        feedforward_channels=2048,
+                        ffn_dropout=0.1,
+                        with_cp=False,  ###use checkpoint to save memory
+                        operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                         'ffn', 'norm')),
+                )),
+            bbox_coder=dict(
+                type='NMSFreeCoder',
+                post_center_range=post_range,
+                pc_range=point_cloud_range,
+                max_num=300,
+                num_classes=10),
+            code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.5, 2.0, 2.0],
+            loss_cls=dict(
+                type='FocalLoss',
+                use_sigmoid=True,
+                gamma=2.0,
+                alpha=0.25,
+                loss_weight=2.0,
+            ),
+            loss_bbox=dict(type='L1Loss', loss_weight=0.25),
+        ),
+        query_generator=dict(
+            with_avg_pool=True,
+            num_shared_convs=1,
+            num_shared_fcs=1,
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=roi_size,
+            extra_encoding=dict(
+                num_layers=2,
+                feat_channels=[512, 256],
+                features=[dict(type='intrinsic', in_channels=16,)]
+            ),
+        ),
+        pe=dict(
+            positional_encoding=dict(
+                type='SinePositionalEncoding3D', num_feats=128, normalize=True),
+            strides=roi_srides,
+            position_range=post_range,
+            depth_num=64,
+            with_fpe=True,
+        ),
+        box_correlation=dict(
+            correlation_mode='topk_matched:1:0.0:0.0',
+        ),
+    ),
+    train_cfg=dict(
+        complement_2d_gt=0.4,
+        detection_proposal=dict(
+            score_thr=0.05,
+            nms_pre=1000,
+            max_per_img=75,
+            nms=dict(type='nms', iou_threshold=0.6, class_agnostic=True, ),
+            min_bbox_size=8),
+        rcnn=dict(
+            stage_loss_weights=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
+            assigner=dict(
+                type='HungarianAssigner3D',
+                cls_cost=dict(type='FocalLossCost', weight=2.0),
+                reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
+                iou_cost=dict(type='IoUCost', weight=0.0),
+                # Fake cost. This is just to make it compatible with DETR head.
+                pc_range=point_cloud_range),
+            sampler_cfg=dict(type='PseudoSampler'),
+            pos_weight=-1,
+            debug=False)
+    ),
+    test_cfg=dict(
+        detection_proposal=dict(
+            score_thr=0.05,
+            nms_pre=1000,
+            max_per_img=75,
+            nms=dict(type='nms', iou_threshold=0.6, class_agnostic=True, ),
+            min_bbox_size=8),
+        rcnn=dict(
+            score_thr=0.0,
+            nms=dict(nms_thr=1.0, use_rotate_nms=True, ),
+            max_per_scene=300,
+        ))
+)
+
+data = dict(
+    workers_per_gpu=4,
+)
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=2e-4,
+    paramwise_cfg=dict(
+        custom_keys={
+            'base_detector.backbone': dict(lr_mult=0.25),
+        }
+    ),
+    weight_decay=0.01)
+
+optimizer_config = dict(
+    _delete_=True,
+    grad_clip=dict(max_norm=35, norm_type=2)
+)
+
+lr_config = dict(
+    _delete_=True,
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    min_lr_ratio=1e-3,
+)
+total_epochs = 24
+runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
+evaluation = dict(interval=3, )
+find_unused_parameters = False
+log_config = dict(interval=50)
+
+
+
+
+

configs/mv2d/exp/mv2d_r50_frcnn_single_frame_roi_1408x512_ep72.py

@@ -0,0 +1,194 @@
+_base_ = [
+    '../data/single_frame.py', '../detectors/maskrcnn_r50.py'
+]
+
+point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
+post_range = [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
+roi_size = 7
+roi_srides = [16]
+
+model = dict(
+    type='MV2D',
+    use_grid_mask=dict(
+        use_h=True,
+        use_w=True,
+        rotate=1,
+        offset=False,
+        ratio_range=(0.4, 0.6),
+        mode=1,
+        prob=0.7,
+        interv_ratio=0.8
+    ),
+    # NOTE:
+    # the FPN in faster r-cnn starts from p2
+    # we use p4 (downsample rate: 16)
+    base_detector=dict(
+        backbone=dict(
+            with_cp=False,
+            dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+            stage_with_dcn=(False, False, True, True),
+        ),
+    ),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 256, 256, 256, 256],
+        out_channels=256,
+        start_level=2,
+        end_level=2,
+        num_outs=1,
+    ),
+    roi_head=dict(
+        type='MV2DSHead',
+        pc_range=point_cloud_range,
+        force_fp32=True,
+        use_denoise=False,
+
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=roi_size, sampling_ratio=-1),
+            featmap_strides=roi_srides,
+            out_channels=512, ),
+        bbox_head=dict(
+            type='CrossAttentionBoxHead',
+            num_classes=10,
+            pc_range=point_cloud_range,
+            transformer=dict(
+                type='MV2DTransformer',
+                decoder=dict(
+                    type='PETRTransformerDecoder',
+                    return_intermediate=True,
+                    num_layers=6,
+                    transformerlayers=dict(
+                        type='PETRTransformerDecoderLayer',
+                        attn_cfgs=[
+                            dict(
+                                type='FlattenMHSelfAttention',
+                                embed_dims=256,
+                                num_heads=8,
+                                dropout=0.1),
+                            dict(
+                                type='PETRMultiheadAttention',
+                                embed_dims=256,
+                                num_heads=8,
+                                dropout=0.1),
+                        ],
+                        feedforward_channels=2048,
+                        ffn_dropout=0.1,
+                        with_cp=False,  ###use checkpoint to save memory
+                        operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                         'ffn', 'norm')),
+                )),
+            bbox_coder=dict(
+                type='NMSFreeCoder',
+                post_center_range=post_range,
+                pc_range=point_cloud_range,
+                max_num=300,
+                num_classes=10),
+            code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.5, 2.0, 2.0],
+            loss_cls=dict(
+                type='FocalLoss',
+                use_sigmoid=True,
+                gamma=2.0,
+                alpha=0.25,
+                loss_weight=2.0,
+            ),
+            loss_bbox=dict(type='L1Loss', loss_weight=0.25),
+        ),
+        query_generator=dict(
+            with_avg_pool=True,
+            num_shared_convs=1,
+            num_shared_fcs=1,
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=roi_size,
+            extra_encoding=dict(
+                num_layers=2,
+                feat_channels=[512, 256],
+                features=[dict(type='intrinsic', in_channels=16,)]
+            ),
+        ),
+        pe=dict(
+            positional_encoding=dict(
+                type='SinePositionalEncoding3D', num_feats=128, normalize=True),
+            strides=roi_srides,
+            position_range=post_range,
+            depth_num=64,
+            with_fpe=True,
+        ),
+        box_correlation=dict(
+            correlation_mode='topk_matched:1:0.0:0.0',
+        ),
+    ),
+    train_cfg=dict(
+        complement_2d_gt=0.4,
+        detection_proposal=dict(
+            score_thr=0.05,
+            nms_pre=1000,
+            max_per_img=75,
+            nms=dict(type='nms', iou_threshold=0.6, class_agnostic=True, ),
+            min_bbox_size=8),
+        rcnn=dict(
+            stage_loss_weights=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
+            assigner=dict(
+                type='HungarianAssigner3D',
+                cls_cost=dict(type='FocalLossCost', weight=2.0),
+                reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
+                iou_cost=dict(type='IoUCost', weight=0.0),
+                # Fake cost. This is just to make it compatible with DETR head.
+                pc_range=point_cloud_range),
+            sampler_cfg=dict(type='PseudoSampler'),
+            pos_weight=-1,
+            debug=False)
+    ),
+    test_cfg=dict(
+        detection_proposal=dict(
+            score_thr=0.05,
+            nms_pre=1000,
+            max_per_img=75,
+            nms=dict(type='nms', iou_threshold=0.6, class_agnostic=True, ),
+            min_bbox_size=8),
+        rcnn=dict(
+            score_thr=0.0,
+            nms=dict(nms_thr=1.0, use_rotate_nms=True, ),
+            max_per_scene=300,
+        ))
+)
+
+data = dict(
+    workers_per_gpu=4,
+)
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=2e-4,
+    paramwise_cfg=dict(
+        custom_keys={
+            'base_detector.backbone': dict(lr_mult=0.25),
+        }
+    ),
+    weight_decay=0.01)
+
+optimizer_config = dict(
+    _delete_=True,
+    grad_clip=dict(max_norm=35, norm_type=2)
+)
+
+lr_config = dict(
+    _delete_=True,
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    min_lr_ratio=1e-3,
+)
+total_epochs = 72
+runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
+evaluation = dict(interval=6, )
+find_unused_parameters = False
+log_config = dict(interval=50)
+
+
+
+
+