{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar  2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB\nCUDA_HOME: /nvme/share/cuda-11.1\nNVCC: Cuda compilation tools, release 11.1, V11.1.74\nGCC: gcc (GCC) 5.4.0\nPyTorch: 1.9.1+cu111\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.1\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n  - CuDNN 8.0.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.10.1+cu111\nOpenCV: 4.8.1\nMMCV: 1.5.2\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMDetection: 2.26.0\nMMSegmentation: 0.29.1\nMMDetection3D: 1.0.0rc6+a1e0484\nspconv2.0: False", "config": "custom_imports = dict(imports=['projects.bevformer', 'projects.lanesegnet'])\npoint_cloud_range = [-51.2, -25.6, -2.3, 51.2, 25.6, 1.7]\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\nclass_names = ['lane_segment', 'ped_crossing']\nclass_nums = 2\ninput_modality = dict(\n    use_lidar=False,\n    use_camera=True,\n    use_radar=False,\n    use_map=False,\n    use_external=False)\nnum_cams = 7\npts_dim = 3\ndataset_type = 'OpenLaneV2_subset_A_MapElementBucket_Dataset'\ndata_root = 'data/OpenLane-V2/'\npara_method = 'fix_pts_interp'\nmethod_para = dict(n_points=10)\ncode_size = 90\n_dim_ = 256\n_pos_dim_ = 128\n_ffn_dim_ = 512\n_ffn_cfg_ = ({\n    'type': 'FFN',\n    'embed_dims': 256,\n    'feedforward_channels': 512,\n    'num_fcs': 2,\n    'ffn_drop': 0.1,\n    'act_cfg': {\n        'type': 'ReLU',\n        'inplace': True\n    }\n}, )\n_num_levels_ = 4\nbev_h_ = 100\nbev_w_ = 200\nmodel = dict(\n    type='LaneSegNet',\n    img_backbone=dict(\n        type='ResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(1, 2, 3),\n        frozen_stages=1,\n        norm_cfg=dict(type='BN', requires_grad=False),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n    img_neck=dict(\n        type='FPN',\n        in_channels=[512, 1024, 2048],\n        out_channels=256,\n        start_level=0,\n        add_extra_convs='on_output',\n        num_outs=4,\n        relu_before_extra_convs=True),\n    bev_constructor=dict(\n        type='BEVFormerConstructer',\n        num_feature_levels=4,\n        num_cams=7,\n        embed_dims=256,\n        rotate_prev_bev=True,\n        use_shift=True,\n        use_can_bus=True,\n        pc_range=[-51.2, -25.6, -2.3, 51.2, 25.6, 1.7],\n        bev_h=100,\n        bev_w=200,\n        rotate_center=[50, 100],\n        encoder=dict(\n            type='BEVFormerEncoder',\n            num_layers=3,\n            pc_range=[-51.2, -25.6, -2.3, 51.2, 25.6, 1.7],\n            num_points_in_pillar=4,\n            return_intermediate=False,\n            transformerlayers=dict(\n                type='BEVFormerLayer',\n                attn_cfgs=[\n                    dict(\n                        type='TemporalSelfAttention',\n                        embed_dims=256,\n                        num_levels=1),\n                    dict(\n                        type='SpatialCrossAttention',\n                        embed_dims=256,\n                        num_cams=7,\n                        pc_range=[-51.2, -25.6, -2.3, 51.2, 25.6, 1.7],\n                        deformable_attention=dict(\n                            type='MSDeformableAttention3D',\n                            embed_dims=256,\n                            num_points=8,\n                            num_levels=4))\n                ],\n                ffn_cfgs=({\n                    'type': 'FFN',\n                    'embed_dims': 256,\n                    'feedforward_channels': 512,\n                    'num_fcs': 2,\n                    'ffn_drop': 0.1,\n                    'act_cfg': {\n                        'type': 'ReLU',\n                        'inplace': True\n                    }\n                }, ),\n                operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n                                 'ffn', 'norm'))),\n        positional_encoding=dict(\n            type='LearnedPositionalEncoding',\n            num_feats=128,\n            row_num_embed=100,\n            col_num_embed=200)),\n    lane_head=dict(\n        type='LaneSegHead',\n        num_classes=2,\n        num_lane_type_classes=3,\n        in_channels=256,\n        num_query=200,\n        bev_h=100,\n        bev_w=200,\n        pc_range=[-51.2, -25.6, -2.3, 51.2, 25.6, 1.7],\n        pts_dim=3,\n        sync_cls_avg_factor=False,\n        with_box_refine=True,\n        code_size=90,\n        code_weights=[\n            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0\n        ],\n        transformer=dict(\n            type='LaneSegNetTransformer',\n            embed_dims=256,\n            points_num=10,\n            pts_dim=3,\n            decoder=dict(\n                type='LaneSegNetDecoder',\n                num_layers=6,\n                return_intermediate=True,\n                pc_range=[-51.2, -25.6, -2.3, 51.2, 25.6, 1.7],\n                pts_dim=3,\n                transformerlayers=dict(\n                    type='CustomDetrTransformerDecoderLayer',\n                    attn_cfgs=[\n                        dict(\n                            type='MultiheadAttention',\n                            embed_dims=256,\n                            num_heads=8,\n                            dropout=0.1),\n                        dict(\n                            type='LaneAttention',\n                            embed_dims=256,\n                            num_heads=8,\n                            num_points=32,\n                            num_levels=1)\n                    ],\n                    ffn_cfgs=({\n                        'type': 'FFN',\n                        'embed_dims': 256,\n                        'feedforward_channels': 512,\n                        'num_fcs': 2,\n                        'ffn_drop': 0.1,\n                        'act_cfg': {\n                            'type': 'ReLU',\n                            'inplace': True\n                        }\n                    }, ),\n                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n                                     'ffn', 'norm')))),\n        bbox_coder=dict(type='LaneSegmentPseudoCoder'),\n        loss_cls=dict(\n            type='FocalLoss',\n            use_sigmoid=True,\n            gamma=2.0,\n            alpha=0.25,\n            loss_weight=1.5),\n        loss_bbox=dict(type='L1Loss', loss_weight=0.025),\n        loss_lane_type=dict(\n            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.1),\n        loss_mask=dict(\n            type='CrossEntropyLoss',\n            use_sigmoid=True,\n            reduction='mean',\n            loss_weight=3.0),\n        loss_dice=dict(\n            type='DiceLoss',\n            use_sigmoid=True,\n            activate=True,\n            reduction='mean',\n            naive_dice=True,\n            eps=1.0,\n            loss_weight=3.0)),\n    lclc_head=dict(\n        type='RelationshipHead',\n        in_channels_o1=256,\n        in_channels_o2=256,\n        shared_param=False,\n        loss_rel=dict(\n            type='FocalLoss',\n            use_sigmoid=True,\n            gamma=2.0,\n            alpha=0.25,\n            loss_weight=5)),\n    train_cfg=dict(\n        lane=dict(\n            assigner=dict(\n                type='LaneSegmentHungarianAssigner3D',\n                cls_cost=dict(type='FocalLossCost', weight=1.5),\n                reg_cost=dict(type='LaneL1Cost', weight=0.025),\n                mask_cost=dict(\n                    type='CrossEntropyLossCost', weight=3.0, use_sigmoid=True),\n                dice_cost=dict(\n                    type='DiceCost', weight=3.0, pred_act=False, eps=1.0),\n                pc_range=[-51.2, -25.6, -2.3, 51.2, 25.6, 1.7]))))\ntrain_pipeline = [\n    dict(type='CustomLoadMultiViewImageFromFiles', to_float32=True),\n    dict(\n        type='LoadAnnotations3DLaneSegment',\n        with_lane_3d=True,\n        with_lane_label_3d=True,\n        with_lane_adj=True,\n        with_lane_type=True,\n        with_bbox=False,\n        with_label=False,\n        with_lane_lcte_adj=False),\n    dict(type='PhotoMetricDistortionMultiViewImage'),\n    dict(type='CropFrontViewImageForAv2'),\n    dict(type='RandomScaleImageMultiViewImage', scales=[0.5]),\n    dict(\n        type='NormalizeMultiviewImage',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='PadMultiViewImageSame2Max', size_divisor=32),\n    dict(type='GridMaskMultiViewImage'),\n    dict(\n        type='LaneSegmentParameterize3D',\n        method='fix_pts_interp',\n        method_para=dict(n_points=10)),\n    dict(type='GenerateLaneSegmentMask', points_num=10, bev_h=100, bev_w=200),\n    dict(\n        type='CustomFormatBundle3DLane',\n        class_names=['lane_segment', 'ped_crossing']),\n    dict(\n        type='CustomCollect3D',\n        keys=[\n            'img', 'gt_lanes_3d', 'gt_lane_labels_3d', 'gt_lane_adj',\n            'gt_instance_masks', 'gt_lane_left_type', 'gt_lane_right_type'\n        ])\n]\ntest_pipeline = [\n    dict(type='CustomLoadMultiViewImageFromFiles', to_float32=True),\n    dict(type='CropFrontViewImageForAv2'),\n    dict(type='RandomScaleImageMultiViewImage', scales=[0.5]),\n    dict(\n        type='NormalizeMultiviewImage',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='PadMultiViewImageSame2Max', size_divisor=32),\n    dict(\n        type='CustomFormatBundle3DLane',\n        class_names=['lane_segment', 'ped_crossing']),\n    dict(type='CustomCollect3D', keys=['img'])\n]\ndata = dict(\n    samples_per_gpu=1,\n    workers_per_gpu=8,\n    train=dict(\n        type='OpenLaneV2_subset_A_MapElementBucket_Dataset',\n        data_root='data/OpenLane-V2/',\n        ann_file='data/OpenLane-V2/data_dict_subset_A_train_lanesegnet.pkl',\n        pipeline=[\n            dict(type='CustomLoadMultiViewImageFromFiles', to_float32=True),\n            dict(\n                type='LoadAnnotations3DLaneSegment',\n                with_lane_3d=True,\n                with_lane_label_3d=True,\n                with_lane_adj=True,\n                with_lane_type=True,\n                with_bbox=False,\n                with_label=False,\n                with_lane_lcte_adj=False),\n            dict(type='PhotoMetricDistortionMultiViewImage'),\n            dict(type='CropFrontViewImageForAv2'),\n            dict(type='RandomScaleImageMultiViewImage', scales=[0.5]),\n            dict(\n                type='NormalizeMultiviewImage',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='PadMultiViewImageSame2Max', size_divisor=32),\n            dict(type='GridMaskMultiViewImage'),\n            dict(\n                type='LaneSegmentParameterize3D',\n                method='fix_pts_interp',\n                method_para=dict(n_points=10)),\n            dict(\n                type='GenerateLaneSegmentMask',\n                points_num=10,\n                bev_h=100,\n                bev_w=200),\n            dict(\n                type='CustomFormatBundle3DLane',\n                class_names=['lane_segment', 'ped_crossing']),\n            dict(\n                type='CustomCollect3D',\n                keys=[\n                    'img', 'gt_lanes_3d', 'gt_lane_labels_3d', 'gt_lane_adj',\n                    'gt_instance_masks', 'gt_lane_left_type',\n                    'gt_lane_right_type'\n                ])\n        ],\n        classes=['lane_segment', 'ped_crossing'],\n        modality=dict(\n            use_lidar=False,\n            use_camera=True,\n            use_radar=False,\n            use_map=False,\n            use_external=False),\n        split='train',\n        filter_map_change=True,\n        points_num=10,\n        test_mode=False),\n    val=dict(\n        type='OpenLaneV2_subset_A_MapElementBucket_Dataset',\n        data_root='data/OpenLane-V2/',\n        ann_file='data/OpenLane-V2/data_dict_subset_A_val_lanesegnet.pkl',\n        pipeline=[\n            dict(type='CustomLoadMultiViewImageFromFiles', to_float32=True),\n            dict(type='CropFrontViewImageForAv2'),\n            dict(type='RandomScaleImageMultiViewImage', scales=[0.5]),\n            dict(\n                type='NormalizeMultiviewImage',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='PadMultiViewImageSame2Max', size_divisor=32),\n            dict(\n                type='CustomFormatBundle3DLane',\n                class_names=['lane_segment', 'ped_crossing']),\n            dict(type='CustomCollect3D', keys=['img'])\n        ],\n        classes=['lane_segment', 'ped_crossing'],\n        modality=dict(\n            use_lidar=False,\n            use_camera=True,\n            use_radar=False,\n            use_map=False,\n            use_external=False),\n        split='val',\n        points_num=10,\n        test_mode=True),\n    test=dict(\n        type='OpenLaneV2_subset_A_MapElementBucket_Dataset',\n        data_root='data/OpenLane-V2/',\n        ann_file='data/OpenLane-V2/data_dict_subset_A_val_lanesegnet.pkl',\n        pipeline=[\n            dict(type='CustomLoadMultiViewImageFromFiles', to_float32=True),\n            dict(type='CropFrontViewImageForAv2'),\n            dict(type='RandomScaleImageMultiViewImage', scales=[0.5]),\n            dict(\n                type='NormalizeMultiviewImage',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='PadMultiViewImageSame2Max', size_divisor=32),\n            dict(\n                type='CustomFormatBundle3DLane',\n                class_names=['lane_segment', 'ped_crossing']),\n            dict(type='CustomCollect3D', keys=['img'])\n        ],\n        classes=['lane_segment', 'ped_crossing'],\n        modality=dict(\n            use_lidar=False,\n            use_camera=True,\n            use_radar=False,\n            use_map=False,\n            use_external=False),\n        split='val',\n        points_num=10,\n        test_mode=True))\noptimizer = dict(\n    type='AdamW',\n    lr=0.0002,\n    paramwise_cfg=dict(custom_keys=dict(img_backbone=dict(lr_mult=0.1))),\n    weight_decay=0.01)\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n    policy='CosineAnnealing',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.3333333333333333,\n    min_lr_ratio=0.001)\ntotal_epochs = 24\nevaluation = dict(\n    interval=24,\n    pipeline=[\n        dict(type='CustomLoadMultiViewImageFromFiles', to_float32=True),\n        dict(type='CropFrontViewImageForAv2'),\n        dict(type='RandomScaleImageMultiViewImage', scales=[0.5]),\n        dict(\n            type='NormalizeMultiviewImage',\n            mean=[123.675, 116.28, 103.53],\n            std=[58.395, 57.12, 57.375],\n            to_rgb=True),\n        dict(type='PadMultiViewImageSame2Max', size_divisor=32),\n        dict(\n            type='CustomFormatBundle3DLane',\n            class_names=['lane_segment', 'ped_crossing']),\n        dict(type='CustomCollect3D', keys=['img'])\n    ])\nrunner = dict(type='EpochBasedRunner', max_epochs=24)\nlog_config = dict(\n    interval=50,\n    hooks=[dict(type='TextLoggerHook'),\n           dict(type='TensorboardLoggerHook')])\ncheckpoint_config = dict(interval=1, max_keep_ckpts=1)\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nwork_dir = 'work_dirs/lanesegnet'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nauto_scale_lr = dict(base_batch_size=8)\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "lanesegnet_r50_8x1_24e_olv2_subset_A.py"}
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