{"env_info": "sys.platform: linux\nPython: 3.9.0 (default, Nov 15 2020, 14:28:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/lustre/share/cuda-11.1\nNVCC: Build cuda_11.1.TC455_06.29069683_0\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.11.0+cu113\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 v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - LAPACK is enabled (usually provided by MKL)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.3\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 8.2\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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 -DEDGE_PROFILER_USE_KINETO -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-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=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0+cu113\nOpenCV: 4.6.0\nMMCV: 1.4.8\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\nMMSegmentation: 0.27.0+", "seed": 1392677960, "exp_name": "mask2former_dcnv3_huge_1024_80k_cityscapes_extra.py", "mmseg_version": "0.27.0+", "config": "num_things_classes = 8\nnum_stuff_classes = 11\nnum_classes = 19\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n    type='EncoderDecoderMask2Former',\n    pretrained=None,\n    backbone=dict(\n        type='DCNv3_5_new2',\n        deform_points=9,\n        embed_dim=320,\n        depths=[6, 6, 32, 6],\n        num_heads=[10, 20, 40, 80],\n        deform_padding=True,\n        dilation_rates=[1],\n        kernel_size=5,\n        mlp_ratio=4.0,\n        drop_path_rate=0.5,\n        res_post_norm=True,\n        level2_post_norm=True,\n        op_types=['D', 'D', 'D', 'D'],\n        with_cp=True,\n        dcnv3_core_type='MSDeformAttnGrid4_softmax_ab',\n        out_indices=(0, 1, 2, 3),\n        init_cfg=None),\n    decode_head=dict(\n        type='Mask2FormerHead',\n        in_channels=[320, 640, 1280, 2560],\n        feat_channels=256,\n        out_channels=256,\n        in_index=[0, 1, 2, 3],\n        num_things_classes=8,\n        num_stuff_classes=11,\n        num_queries=100,\n        num_transformer_feat_level=3,\n        pixel_decoder=dict(\n            type='MSDeformAttnPixelDecoder',\n            num_outs=3,\n            norm_cfg=dict(type='GN', num_groups=32),\n            act_cfg=dict(type='ReLU'),\n            encoder=dict(\n                type='DetrTransformerEncoder',\n                num_layers=6,\n                transformerlayers=dict(\n                    type='BaseTransformerLayer',\n                    attn_cfgs=dict(\n                        type='MultiScaleDeformableAttention',\n                        embed_dims=256,\n                        num_heads=8,\n                        num_levels=3,\n                        num_points=4,\n                        im2col_step=64,\n                        dropout=0.0,\n                        batch_first=False,\n                        norm_cfg=None,\n                        init_cfg=None),\n                    ffn_cfgs=dict(\n                        type='FFN',\n                        embed_dims=256,\n                        feedforward_channels=1024,\n                        num_fcs=2,\n                        ffn_drop=0.0,\n                        act_cfg=dict(type='ReLU', inplace=True),\n                        with_cp=False),\n                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),\n                init_cfg=None),\n            positional_encoding=dict(\n                type='SinePositionalEncoding', num_feats=128, normalize=True),\n            init_cfg=None),\n        enforce_decoder_input_project=False,\n        positional_encoding=dict(\n            type='SinePositionalEncoding', num_feats=128, normalize=True),\n        transformer_decoder=dict(\n            type='DetrTransformerDecoder',\n            return_intermediate=True,\n            num_layers=9,\n            transformerlayers=dict(\n                type='DetrTransformerDecoderLayer',\n                attn_cfgs=dict(\n                    type='MultiheadAttention',\n                    embed_dims=256,\n                    num_heads=8,\n                    attn_drop=0.0,\n                    proj_drop=0.0,\n                    dropout_layer=None,\n                    batch_first=False),\n                ffn_cfgs=dict(\n                    embed_dims=256,\n                    feedforward_channels=2048,\n                    num_fcs=2,\n                    act_cfg=dict(type='ReLU', inplace=True),\n                    ffn_drop=0.0,\n                    dropout_layer=None,\n                    add_identity=True,\n                    with_cp=False),\n                feedforward_channels=2048,\n                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',\n                                 'ffn', 'norm')),\n            init_cfg=None),\n        loss_cls=dict(\n            type='CrossEntropyLoss',\n            use_sigmoid=False,\n            loss_weight=2.0,\n            reduction='mean',\n            class_weight=[\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                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.1\n            ]),\n        loss_mask=dict(\n            type='CrossEntropyLoss',\n            use_sigmoid=True,\n            reduction='mean',\n            loss_weight=5.0),\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=5.0),\n        train_cfg=dict(\n            num_points=12544,\n            oversample_ratio=3.0,\n            importance_sample_ratio=0.75,\n            assigner=dict(\n                type='MaskHungarianAssigner',\n                cls_cost=dict(type='ClassificationCost', weight=2.0),\n                mask_cost=dict(\n                    type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),\n                dice_cost=dict(\n                    type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),\n            sampler=dict(type='MaskPseudoSampler')),\n        test_cfg=dict(\n            panoptic_on=True,\n            semantic_on=False,\n            instance_on=True,\n            max_per_image=100,\n            iou_thr=0.8,\n            filter_low_score=True,\n            mode='slide',\n            crop_size=(1024, 1024),\n            stride=(512, 512))),\n    train_cfg=dict(\n        num_points=12544,\n        oversample_ratio=3.0,\n        importance_sample_ratio=0.75,\n        assigner=dict(\n            type='MaskHungarianAssigner',\n            cls_cost=dict(type='ClassificationCost', weight=2.0),\n            mask_cost=dict(\n                type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),\n            dice_cost=dict(\n                type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),\n        sampler=dict(type='MaskPseudoSampler')),\n    test_cfg=dict(\n        panoptic_on=True,\n        semantic_on=False,\n        instance_on=True,\n        max_per_image=100,\n        iou_thr=0.8,\n        filter_low_score=True,\n        mode='slide',\n        crop_size=(1024, 1024),\n        stride=(512, 512)),\n    init_cfg=None)\ndataset_type = 'CityscapesDataset'\ndata_root = 'data/cityscapes/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (1024, 1024)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations'),\n    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),\n    dict(type='RandomCrop', crop_size=(1024, 1024), cat_max_ratio=0.75),\n    dict(type='RandomFlip', prob=0.5),\n    dict(type='PhotoMetricDistortion'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='Pad', size=(1024, 1024), pad_val=0, seg_pad_val=255),\n    dict(type='ToMask'),\n    dict(type='DefaultFormatBundle'),\n    dict(\n        type='Collect',\n        keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(2048, 1024),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='ResizeToMultiple', size_divisor=32),\n            dict(type='RandomFlip'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=2,\n    train=dict(\n        type='CityscapesDataset',\n        data_root='data/cityscapes/',\n        img_dir=['leftImg8bit/train', 'leftImg8bit/train_extra'],\n        ann_dir=['gtFine/train', 'refinement_final/train_extra'],\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations'),\n            dict(\n                type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),\n            dict(\n                type='RandomCrop', crop_size=(1024, 1024), cat_max_ratio=0.75),\n            dict(type='RandomFlip', prob=0.5),\n            dict(type='PhotoMetricDistortion'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size=(1024, 1024), pad_val=0, seg_pad_val=255),\n            dict(type='ToMask'),\n            dict(type='DefaultFormatBundle'),\n            dict(\n                type='Collect',\n                keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels'])\n        ]),\n    val=dict(\n        type='CityscapesDataset',\n        data_root='data/cityscapes/',\n        img_dir='leftImg8bit/val',\n        ann_dir='gtFine/val',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(2048, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='ResizeToMultiple', size_divisor=32),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]),\n    test=dict(\n        type='CityscapesDataset',\n        data_root='data/cityscapes/',\n        img_dir='leftImg8bit/val',\n        ann_dir='gtFine/val',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(2048, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='ResizeToMultiple', size_divisor=32),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nlog_config = dict(\n    interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'work_dirs/mask2former_dcnv3_huge_896_80k_mapillary/iter_80000.pth'\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\noptimizer = dict(\n    type='AdamW',\n    lr=1e-05,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    constructor='InternLayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(\n        num_layers=50, layer_decay_rate=0.95, depths=[6, 6, 32, 6]))\noptimizer_config = dict()\nlr_config = dict(\n    policy='poly',\n    warmup='linear',\n    warmup_iters=1500,\n    warmup_ratio=1e-06,\n    power=1.0,\n    min_lr=0.0,\n    by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=80000)\ncheckpoint_config = dict(by_epoch=False, interval=2000, max_keep_ckpts=40)\nevaluation = dict(\n    interval=2000, metric='mIoU', pre_eval=True, save_best='mIoU')\nwork_dir = './work_dirs/mask2former_dcnv3_huge_1024_80k_cityscapes_extra'\ngpu_ids = range(0, 8)\nauto_resume = False\ndevice = 'cuda'\nseed = 1392677960\n", "CLASSES": ["road", "sidewalk", "building", "wall", "fence", "pole", "traffic light", "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car", "truck", "bus", "train", "motorcycle", "bicycle"], "PALETTE": [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156], [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0], [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60], [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100], [0, 0, 230], [119, 11, 32]], "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 32803, "data_time": 0.0829, "decode.loss_cls": 0.80396, "decode.loss_mask": 0.58431, "decode.loss_dice": 1.16701, "decode.d0.loss_cls": 0.70572, "decode.d0.loss_mask": 0.652, "decode.d0.loss_dice": 1.29051, "decode.d1.loss_cls": 0.77879, "decode.d1.loss_mask": 0.58585, "decode.d1.loss_dice": 1.20919, "decode.d2.loss_cls": 0.78636, "decode.d2.loss_mask": 0.58579, "decode.d2.loss_dice": 1.19043, "decode.d3.loss_cls": 0.80664, "decode.d3.loss_mask": 0.58317, "decode.d3.loss_dice": 1.17829, "decode.d4.loss_cls": 0.81399, "decode.d4.loss_mask": 0.58387, "decode.d4.loss_dice": 1.16659, "decode.d5.loss_cls": 0.80569, "decode.d5.loss_mask": 0.58691, "decode.d5.loss_dice": 1.17193, "decode.d6.loss_cls": 0.80396, "decode.d6.loss_mask": 0.58094, "decode.d6.loss_dice": 1.16513, "decode.d7.loss_cls": 0.79989, "decode.d7.loss_mask": 0.58476, "decode.d7.loss_dice": 1.16653, "decode.d8.loss_cls": 0.80165, "decode.d8.loss_mask": 0.58725, "decode.d8.loss_dice": 1.16857, "loss": 25.69566, "time": 2.28404}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 32803, "data_time": 0.05074, "decode.loss_cls": 0.7187, "decode.loss_mask": 0.54132, "decode.loss_dice": 1.16491, "decode.d0.loss_cls": 0.69177, "decode.d0.loss_mask": 0.63994, "decode.d0.loss_dice": 1.28859, "decode.d1.loss_cls": 0.73647, "decode.d1.loss_mask": 0.55057, "decode.d1.loss_dice": 1.20405, "decode.d2.loss_cls": 0.73345, "decode.d2.loss_mask": 0.551, "decode.d2.loss_dice": 1.18995, "decode.d3.loss_cls": 0.72941, "decode.d3.loss_mask": 0.5499, "decode.d3.loss_dice": 1.17334, "decode.d4.loss_cls": 0.73026, "decode.d4.loss_mask": 0.54853, "decode.d4.loss_dice": 1.16963, "decode.d5.loss_cls": 0.73242, "decode.d5.loss_mask": 0.54816, "decode.d5.loss_dice": 1.16857, "decode.d6.loss_cls": 0.71873, "decode.d6.loss_mask": 0.54755, "decode.d6.loss_dice": 1.16191, "decode.d7.loss_cls": 0.72435, "decode.d7.loss_mask": 0.54708, "decode.d7.loss_dice": 1.16463, "decode.d8.loss_cls": 0.71936, "decode.d8.loss_mask": 0.54777, "decode.d8.loss_dice": 1.1681, "loss": 24.66042, "time": 2.22}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0, "memory": 32803, "data_time": 0.04963, "decode.loss_cls": 0.62215, "decode.loss_mask": 0.46585, "decode.loss_dice": 1.16655, "decode.d0.loss_cls": 0.6932, "decode.d0.loss_mask": 0.55401, "decode.d0.loss_dice": 1.27074, "decode.d1.loss_cls": 0.67791, "decode.d1.loss_mask": 0.47835, "decode.d1.loss_dice": 1.20891, "decode.d2.loss_cls": 0.6547, "decode.d2.loss_mask": 0.47996, "decode.d2.loss_dice": 1.18296, "decode.d3.loss_cls": 0.64164, "decode.d3.loss_mask": 0.47635, "decode.d3.loss_dice": 1.1728, "decode.d4.loss_cls": 0.6399, "decode.d4.loss_mask": 0.47468, "decode.d4.loss_dice": 1.17222, "decode.d5.loss_cls": 0.65131, "decode.d5.loss_mask": 0.47084, "decode.d5.loss_dice": 1.16751, "decode.d6.loss_cls": 0.63145, "decode.d6.loss_mask": 0.46795, "decode.d6.loss_dice": 1.16903, "decode.d7.loss_cls": 0.63387, "decode.d7.loss_mask": 0.46499, "decode.d7.loss_dice": 1.16386, "decode.d8.loss_cls": 0.6367, "decode.d8.loss_mask": 0.46518, "decode.d8.loss_dice": 1.17334, "loss": 23.1289, "time": 2.23966}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0, "memory": 32803, "data_time": 0.0446, "decode.loss_cls": 0.56988, "decode.loss_mask": 0.43662, "decode.loss_dice": 1.19121, "decode.d0.loss_cls": 0.71526, "decode.d0.loss_mask": 0.50178, "decode.d0.loss_dice": 1.29169, "decode.d1.loss_cls": 0.63735, "decode.d1.loss_mask": 0.44392, "decode.d1.loss_dice": 1.23587, "decode.d2.loss_cls": 0.61356, "decode.d2.loss_mask": 0.44306, "decode.d2.loss_dice": 1.20222, "decode.d3.loss_cls": 0.58261, "decode.d3.loss_mask": 0.44271, "decode.d3.loss_dice": 1.18976, "decode.d4.loss_cls": 0.5861, "decode.d4.loss_mask": 0.44198, "decode.d4.loss_dice": 1.1963, "decode.d5.loss_cls": 0.56843, "decode.d5.loss_mask": 0.43879, "decode.d5.loss_dice": 1.19726, "decode.d6.loss_cls": 0.56337, "decode.d6.loss_mask": 0.43763, "decode.d6.loss_dice": 1.19455, "decode.d7.loss_cls": 0.56785, "decode.d7.loss_mask": 0.43421, "decode.d7.loss_dice": 1.19821, "decode.d8.loss_cls": 0.56316, "decode.d8.loss_mask": 0.43552, "decode.d8.loss_dice": 1.19546, "loss": 22.51634, "time": 2.22685}
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