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{"mode": "train", "epoch": 7, "iter": 6100, "lr": 0.0002, "memory": 15370, "data_time": 0.06048, "loss_rpn_cls": 0.03195, "loss_rpn_bbox": 0.04799, "loss_cls": 0.21685, "acc": 92.70361, "loss_bbox": 0.23161, "loss_mask": 0.2586, "loss": 0.787, "time": 0.48832}
{"mode": "train", "epoch": 7, "iter": 6150, "lr": 0.0002, "memory": 15370, "data_time": 0.065, "loss_rpn_cls": 0.03459, "loss_rpn_bbox": 0.04767, "loss_cls": 0.23546, "acc": 92.25513, "loss_bbox": 0.24847, "loss_mask": 0.26123, "loss": 0.82742, "time": 0.48868}
{"mode": "train", "epoch": 7, "iter": 6200, "lr": 0.0002, "memory": 15370, "data_time": 0.06532, "loss_rpn_cls": 0.03525, "loss_rpn_bbox": 0.0486, "loss_cls": 0.23136, "acc": 92.3147, "loss_bbox": 0.2415, "loss_mask": 0.26208, "loss": 0.81879, "time": 0.49649}
{"mode": "train", "epoch": 7, "iter": 6250, "lr": 0.0002, "memory": 15370, "data_time": 0.07072, "loss_rpn_cls": 0.03202, "loss_rpn_bbox": 0.04488, "loss_cls": 0.22723, "acc": 92.44092, "loss_bbox": 0.24274, "loss_mask": 0.26156, "loss": 0.80842, "time": 0.49928}
{"mode": "train", "epoch": 7, "iter": 6300, "lr": 0.0002, "memory": 15370, "data_time": 0.06943, "loss_rpn_cls": 0.03277, "loss_rpn_bbox": 0.04741, "loss_cls": 0.23183, "acc": 92.29883, "loss_bbox": 0.24678, "loss_mask": 0.26179, "loss": 0.82059, "time": 0.49367}
{"mode": "train", "epoch": 7, "iter": 6350, "lr": 0.0002, "memory": 15370, "data_time": 0.07797, "loss_rpn_cls": 0.03318, "loss_rpn_bbox": 0.04946, "loss_cls": 0.23799, "acc": 92.19019, "loss_bbox": 0.25066, "loss_mask": 0.25981, "loss": 0.83109, "time": 0.51019}
{"mode": "train", "epoch": 7, "iter": 6400, "lr": 0.0002, "memory": 15370, "data_time": 0.06813, "loss_rpn_cls": 0.02962, "loss_rpn_bbox": 0.04362, "loss_cls": 0.22214, "acc": 92.56421, "loss_bbox": 0.23982, "loss_mask": 0.25338, "loss": 0.78858, "time": 0.5025}
{"mode": "train", "epoch": 7, "iter": 6450, "lr": 0.0002, "memory": 15370, "data_time": 0.06563, "loss_rpn_cls": 0.03438, "loss_rpn_bbox": 0.04822, "loss_cls": 0.23145, "acc": 92.3916, "loss_bbox": 0.24751, "loss_mask": 0.26224, "loss": 0.82381, "time": 0.49848}
{"mode": "train", "epoch": 7, "iter": 6500, "lr": 0.0002, "memory": 15370, "data_time": 0.06383, "loss_rpn_cls": 0.03105, "loss_rpn_bbox": 0.04625, "loss_cls": 0.22574, "acc": 92.36206, "loss_bbox": 0.24634, "loss_mask": 0.26001, "loss": 0.80938, "time": 0.48962}
{"mode": "train", "epoch": 7, "iter": 6550, "lr": 0.0002, "memory": 15370, "data_time": 0.06981, "loss_rpn_cls": 0.03487, "loss_rpn_bbox": 0.04817, "loss_cls": 0.23937, "acc": 92.14893, "loss_bbox": 0.24713, "loss_mask": 0.26214, "loss": 0.83169, "time": 0.50664}
{"mode": "train", "epoch": 7, "iter": 6600, "lr": 0.0002, "memory": 15370, "data_time": 0.06768, "loss_rpn_cls": 0.0352, "loss_rpn_bbox": 0.04924, "loss_cls": 0.23974, "acc": 92.11792, "loss_bbox": 0.25368, "loss_mask": 0.26482, "loss": 0.84269, "time": 0.51161}
{"mode": "train", "epoch": 7, "iter": 6650, "lr": 0.0002, "memory": 15370, "data_time": 0.07415, "loss_rpn_cls": 0.0328, "loss_rpn_bbox": 0.0456, "loss_cls": 0.22245, "acc": 92.54395, "loss_bbox": 0.23646, "loss_mask": 0.25565, "loss": 0.79297, "time": 0.49695}
{"mode": "train", "epoch": 7, "iter": 6700, "lr": 0.0002, "memory": 15370, "data_time": 0.05632, "loss_rpn_cls": 0.03186, "loss_rpn_bbox": 0.04622, "loss_cls": 0.22747, "acc": 92.30518, "loss_bbox": 0.25072, "loss_mask": 0.25976, "loss": 0.81604, "time": 0.49423}
{"mode": "train", "epoch": 7, "iter": 6750, "lr": 0.0002, "memory": 15370, "data_time": 0.06312, "loss_rpn_cls": 0.0347, "loss_rpn_bbox": 0.04656, "loss_cls": 0.2307, "acc": 92.4397, "loss_bbox": 0.24583, "loss_mask": 0.26396, "loss": 0.82175, "time": 0.49842}
{"mode": "train", "epoch": 7, "iter": 6800, "lr": 0.0002, "memory": 15370, "data_time": 0.07089, "loss_rpn_cls": 0.03878, "loss_rpn_bbox": 0.04923, "loss_cls": 0.24477, "acc": 92.10254, "loss_bbox": 0.25002, "loss_mask": 0.27295, "loss": 0.85575, "time": 0.49278}
{"mode": "train", "epoch": 7, "iter": 6850, "lr": 0.0002, "memory": 15370, "data_time": 0.06842, "loss_rpn_cls": 0.03745, "loss_rpn_bbox": 0.05115, "loss_cls": 0.24814, "acc": 92.01709, "loss_bbox": 0.25231, "loss_mask": 0.27408, "loss": 0.86313, "time": 0.49622}
{"mode": "train", "epoch": 7, "iter": 6900, "lr": 0.0002, "memory": 15370, "data_time": 0.0632, "loss_rpn_cls": 0.03324, "loss_rpn_bbox": 0.04852, "loss_cls": 0.235, "acc": 92.25171, "loss_bbox": 0.24818, "loss_mask": 0.25957, "loss": 0.82451, "time": 0.511}
{"mode": "train", "epoch": 7, "iter": 6950, "lr": 0.0002, "memory": 15370, "data_time": 0.06464, "loss_rpn_cls": 0.03568, "loss_rpn_bbox": 0.04609, "loss_cls": 0.23031, "acc": 92.57397, "loss_bbox": 0.23646, "loss_mask": 0.25741, "loss": 0.80596, "time": 0.49232}
{"mode": "train", "epoch": 7, "iter": 7000, "lr": 0.0002, "memory": 15370, "data_time": 0.06107, "loss_rpn_cls": 0.03309, "loss_rpn_bbox": 0.04895, "loss_cls": 0.23746, "acc": 92.27222, "loss_bbox": 0.24001, "loss_mask": 0.25995, "loss": 0.81946, "time": 0.50187}
{"mode": "train", "epoch": 7, "iter": 7050, "lr": 0.0002, "memory": 15370, "data_time": 0.07452, "loss_rpn_cls": 0.03318, "loss_rpn_bbox": 0.04801, "loss_cls": 0.23357, "acc": 92.25513, "loss_bbox": 0.24413, "loss_mask": 0.26235, "loss": 0.82124, "time": 0.50503}
{"mode": "train", "epoch": 7, "iter": 7100, "lr": 0.0002, "memory": 15370, "data_time": 0.07193, "loss_rpn_cls": 0.03732, "loss_rpn_bbox": 0.05228, "loss_cls": 0.23259, "acc": 92.28223, "loss_bbox": 0.24785, "loss_mask": 0.26493, "loss": 0.83497, "time": 0.49703}
{"mode": "train", "epoch": 7, "iter": 7150, "lr": 0.0002, "memory": 15370, "data_time": 0.06583, "loss_rpn_cls": 0.02974, "loss_rpn_bbox": 0.04329, "loss_cls": 0.22341, "acc": 92.65112, "loss_bbox": 0.23424, "loss_mask": 0.25816, "loss": 0.78884, "time": 0.48077}
{"mode": "train", "epoch": 7, "iter": 7200, "lr": 0.0002, "memory": 15370, "data_time": 0.0631, "loss_rpn_cls": 0.0325, "loss_rpn_bbox": 0.0478, "loss_cls": 0.23116, "acc": 92.24658, "loss_bbox": 0.2467, "loss_mask": 0.26572, "loss": 0.82387, "time": 0.50335}
{"mode": "train", "epoch": 7, "iter": 7250, "lr": 0.0002, "memory": 15370, "data_time": 0.07023, "loss_rpn_cls": 0.03374, "loss_rpn_bbox": 0.04858, "loss_cls": 0.23422, "acc": 92.28467, "loss_bbox": 0.25052, "loss_mask": 0.2633, "loss": 0.83037, "time": 0.5048}
{"mode": "train", "epoch": 7, "iter": 7300, "lr": 0.0002, "memory": 15370, "data_time": 0.05748, "loss_rpn_cls": 0.0319, "loss_rpn_bbox": 0.04599, "loss_cls": 0.22665, "acc": 92.34717, "loss_bbox": 0.24338, "loss_mask": 0.25731, "loss": 0.80523, "time": 0.5047}
{"mode": "val", "epoch": 7, "iter": 1250, "lr": 0.0002, "bbox_mAP": 0.3483, "bbox_mAP_50": 0.555, "bbox_mAP_75": 0.3754, "bbox_mAP_s": 0.1972, "bbox_mAP_m": 0.3717, "bbox_mAP_l": 0.4707, "bbox_mAP_copypaste": "0.3483 0.5550 0.3754 0.1972 0.3717 0.4707", "segm_mAP": 0.3243, "segm_mAP_50": 0.5261, "segm_mAP_75": 0.3456, "segm_mAP_s": 0.1479, "segm_mAP_m": 0.341, "segm_mAP_l": 0.4881, "segm_mAP_copypaste": "0.3243 0.5261 0.3456 0.1479 0.3410 0.4881"}
{"mmdet_version": "2.28.2", "CLASSES": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "env_info": "sys.platform: linux\nPython: 3.7.16 (default, Jan 17 2023, 22:20:44) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/lustre/share/cuda-11.3\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\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: 4.7.0\nMMCV: 1.7.0\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.28.2+", "config": "model = dict(\n    type='MaskRCNN',\n    backbone=dict(\n        type='ViTAdapter',\n        patch_size=16,\n        embed_dim=192,\n        depth=12,\n        num_heads=3,\n        mlp_ratio=4,\n        drop_path_rate=0.1,\n        conv_inplane=64,\n        n_points=4,\n        deform_num_heads=6,\n        cffn_ratio=0.25,\n        deform_ratio=1.0,\n        layer_scale=False,\n        interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]],\n        window_attn=[\n            True, True, False, True, True, False, True, True, False, True,\n            True, False\n        ],\n        window_size=[14, 14, None, 14, 14, None, 14, 14, None, 14, 14, None],\n        pretrained='pretrained/deit_tiny_patch16_224-a1311bcf.pth'),\n    neck=dict(\n        type='FPN',\n        in_channels=[192, 192, 192, 192],\n        out_channels=256,\n        num_outs=5),\n    rpn_head=dict(\n        type='RPNHead',\n        in_channels=256,\n        feat_channels=256,\n        anchor_generator=dict(\n            type='AnchorGenerator',\n            scales=[8],\n            ratios=[0.5, 1.0, 2.0],\n            strides=[4, 8, 16, 32, 64]),\n        bbox_coder=dict(\n            type='DeltaXYWHBBoxCoder',\n            target_means=[0.0, 0.0, 0.0, 0.0],\n            target_stds=[1.0, 1.0, 1.0, 1.0]),\n        loss_cls=dict(\n            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n    roi_head=dict(\n        type='StandardRoIHead',\n        bbox_roi_extractor=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        bbox_head=dict(\n            type='Shared2FCBBoxHead',\n            in_channels=256,\n            fc_out_channels=1024,\n            roi_feat_size=7,\n            num_classes=80,\n            bbox_coder=dict(\n                type='DeltaXYWHBBoxCoder',\n                target_means=[0.0, 0.0, 0.0, 0.0],\n                target_stds=[0.1, 0.1, 0.2, 0.2]),\n            reg_class_agnostic=False,\n            loss_cls=dict(\n                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n        mask_roi_extractor=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        mask_head=dict(\n            type='FCNMaskHead',\n            num_convs=4,\n            in_channels=256,\n            conv_out_channels=256,\n            num_classes=80,\n            loss_mask=dict(\n                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n    train_cfg=dict(\n        rpn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.7,\n                neg_iou_thr=0.3,\n                min_pos_iou=0.3,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=256,\n                pos_fraction=0.5,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=False),\n            allowed_border=-1,\n            pos_weight=-1,\n            debug=False),\n        rpn_proposal=dict(\n            nms_pre=2000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.5,\n                neg_iou_thr=0.5,\n                min_pos_iou=0.5,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=512,\n                pos_fraction=0.25,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=True),\n            mask_size=28,\n            pos_weight=-1,\n            debug=False)),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=1000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            score_thr=0.05,\n            nms=dict(type='nms', iou_threshold=0.5),\n            max_per_img=100,\n            mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\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_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1333, 800),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\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='Pad', size_divisor=32),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=4,\n    workers_per_gpu=4,\n    train=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_train2017.json',\n        img_prefix='data/coco/train2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n            dict(type='RandomFlip', flip_ratio=0.5),\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_divisor=32),\n            dict(type='DefaultFormatBundle'),\n            dict(\n                type='Collect',\n                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n        ]),\n    val=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_val2017.json',\n        img_prefix='data/coco/val2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\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='Pad', size_divisor=32),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]),\n    test=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_val2017.json',\n        img_prefix='data/coco/val2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\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='Pad', size_divisor=32),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nevaluation = dict(metric=['bbox', 'segm'])\noptimizer = dict(\n    type='FusedAdam',\n    lr=0.0002,\n    weight_decay=0.01,\n    paramwise_cfg=dict(\n        custom_keys=dict(\n            level_embed=dict(decay_mult=0.0),\n            pos_embed=dict(decay_mult=0.0),\n            norm=dict(decay_mult=0.0),\n            bias=dict(decay_mult=0.0))))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.001,\n    step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1, max_keep_ckpts=3, save_last=True)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\npretrained = 'pretrained/deit_tiny_patch16_224-a1311bcf.pth'\nwork_dir = './work_dirs/mask_rcnn_deit_adapter_tiny_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 4)\ndevice = 'cuda'\n", "seed": 2080713130, "exp_name": "mask_rcnn_deit_adapter_tiny_fpn_1x_coco.py", "epoch": 7, "iter": 51310, "mmcv_version": "1.7.0", "time": "Fri Apr 14 17:33:06 2023", "hook_msgs": {"last_ckpt": "/mnt/petrelfs/chenzhe1/workspace/ViT-Adapter-V2/detection/work_dirs/mask_rcnn_deit_adapter_tiny_fpn_1x_coco/epoch_6.pth"}}
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