{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0,1: GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.1.TC455_06.29190527_0\nGCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\nPyTorch: 1.10.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\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: AVX512\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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37\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 -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-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=1.10.0, 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: 0.11.1\nOpenCV: 4.5.4\nMMCV: 1.4.6\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMRotate: 0.1.0+", "config": "dataset_type = 'DOTADataset'\ndata_root = '/remote-home/xiaoyi/datasets/dotav1_ms/split_ms_dota1_0/'\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),\n    dict(type='RResize', img_scale=(1024, 1024)),\n    dict(\n        type='RRandomFlip',\n        flip_ratio=[0.25, 0.25, 0.25],\n        direction=['horizontal', 'vertical', 'diagonal']),\n    dict(\n        type='PolyRandomRotate',\n        rotate_ratio=0.5,\n        angles_range=180,\n        auto_bound=False,\n        rect_classes=[9, 11],\n        version='oc'),\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'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1024, 1024),\n        flip=False,\n        transforms=[\n            dict(type='RResize'),\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'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=0,\n    train=dict(\n        type='DOTADataset',\n        ann_file=\n        '/remote-home/xiaoyi/datasets/dotav1_ms/split_ms_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/remote-home/xiaoyi/datasets/dotav1_ms/split_ms_dota1_0/trainval/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True),\n            dict(type='RResize', img_scale=(1024, 1024)),\n            dict(\n                type='RRandomFlip',\n                flip_ratio=[0.25, 0.25, 0.25],\n                direction=['horizontal', 'vertical', 'diagonal']),\n            dict(\n                type='PolyRandomRotate',\n                rotate_ratio=0.5,\n                angles_range=180,\n                auto_bound=False,\n                rect_classes=[9, 11],\n                version='oc'),\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'])\n        ],\n        version='oc'),\n    val=dict(\n        type='DOTADataset',\n        ann_file=\n        '/remote-home/xiaoyi/datasets/dotav1_ms/split_ms_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/remote-home/xiaoyi/datasets/dotav1_ms/split_ms_dota1_0/trainval/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1024, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='RResize'),\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'])\n                ])\n        ],\n        version='oc'),\n    test=dict(\n        type='DOTADataset',\n        ann_file=\n        '/remote-home/xiaoyi/datasets/dotav1_ms/split_ms_dota1_0/test/images/',\n        img_prefix=\n        '/remote-home/xiaoyi/datasets/dotav1_ms/split_ms_dota1_0/test/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1024, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='RResize'),\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'])\n                ])\n        ],\n        version='oc'))\nevaluation = dict(interval=24, metric='mAP')\noptimizer = dict(\n    type='AdamW',\n    lr=0.0001,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    paramwise_cfg=dict(\n        custom_keys=dict(\n            absolute_pos_embed=dict(decay_mult=0.0),\n            relative_position_bias_table=dict(decay_mult=0.0),\n            norm=dict(decay_mult=0.0))))\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.3333333333333333,\n    step=[18, 22])\nrunner = dict(type='EpochBasedRunner', max_epochs=24)\ncheckpoint_config = dict(interval=12)\nlog_config = dict(\n    interval=50,\n    hooks=[dict(type='TextLoggerHook'),\n           dict(type='TensorboardLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nangle_version = 'oc'\nmodel = dict(\n    type='R3Det',\n    backbone=dict(\n        type='SwinTransformer',\n        embed_dims=96,\n        depths=[2, 2, 6, 2],\n        num_heads=[3, 6, 12, 24],\n        window_size=7,\n        mlp_ratio=4,\n        qkv_bias=True,\n        qk_scale=None,\n        drop_rate=0.0,\n        attn_drop_rate=0.0,\n        drop_path_rate=0.2,\n        patch_norm=True,\n        out_indices=(0, 1, 2, 3),\n        with_cp=False,\n        convert_weights=True,\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint=\n            '/remote-home/xiaoyi/mmrotate-main/configs/swin_tiny_patch4_window7_224.pth'\n        )),\n    neck=dict(\n        type='FPN',\n        in_channels=[96, 192, 384, 768],\n        out_channels=256,\n        start_level=1,\n        add_extra_convs='on_input',\n        num_outs=5),\n    bbox_head=dict(\n        type='KFIoURRetinaHead',\n        num_classes=15,\n        in_channels=256,\n        stacked_convs=4,\n        feat_channels=256,\n        anchor_generator=dict(\n            type='RotatedAnchorGenerator',\n            octave_base_scale=4,\n            scales_per_octave=3,\n            ratios=[1.0, 0.5, 2.0],\n            strides=[8, 16, 32, 64, 128]),\n        bbox_coder=dict(\n            type='DeltaXYWHAOBBoxCoder',\n            angle_range='oc',\n            norm_factor=None,\n            edge_swap=False,\n            proj_xy=False,\n            target_means=(0.0, 0.0, 0.0, 0.0, 0.0),\n            target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),\n        loss_cls=dict(\n            type='FocalLoss',\n            use_sigmoid=True,\n            gamma=2.0,\n            alpha=0.25,\n            loss_weight=1.0),\n        loss_bbox=dict(type='KFLoss', fun='ln', loss_weight=5.0)),\n    frm_cfgs=[dict(in_channels=256, featmap_strides=[8, 16, 32, 64, 128])],\n    num_refine_stages=1,\n    refine_heads=[\n        dict(\n            type='KFIoURRetinaRefineHead',\n            num_classes=15,\n            in_channels=256,\n            stacked_convs=4,\n            feat_channels=256,\n            assign_by_circumhbbox=None,\n            anchor_generator=dict(\n                type='PseudoAnchorGenerator', strides=[8, 16, 32, 64, 128]),\n            bbox_coder=dict(\n                type='DeltaXYWHAOBBoxCoder',\n                angle_range='oc',\n                norm_factor=None,\n                edge_swap=False,\n                proj_xy=False,\n                target_means=(0.0, 0.0, 0.0, 0.0, 0.0),\n                target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),\n            loss_cls=dict(\n                type='FocalLoss',\n                use_sigmoid=True,\n                gamma=2.0,\n                alpha=0.25,\n                loss_weight=1.0),\n            loss_bbox=dict(type='KFLoss', fun='ln', loss_weight=5.0))\n    ],\n    train_cfg=dict(\n        s0=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.5,\n                neg_iou_thr=0.4,\n                min_pos_iou=0,\n                ignore_iof_thr=-1,\n                iou_calculator=dict(type='RBboxOverlaps2D')),\n            allowed_border=-1,\n            pos_weight=-1,\n            debug=False),\n        sr=[\n            dict(\n                assigner=dict(\n                    type='MaxIoUAssigner',\n                    pos_iou_thr=0.6,\n                    neg_iou_thr=0.5,\n                    min_pos_iou=0,\n                    ignore_iof_thr=-1,\n                    iou_calculator=dict(type='RBboxOverlaps2D')),\n                allowed_border=-1,\n                pos_weight=-1,\n                debug=False)\n        ],\n        stage_loss_weights=[1.0]),\n    test_cfg=dict(\n        nms_pre=2000,\n        min_bbox_size=0,\n        score_thr=0.05,\n        nms=dict(iou_thr=0.1),\n        max_per_img=2000))\nfind_unused_parameters = True\nwork_dir = './work_dirs/r3det_kfiou_ln_swin_tiny_adamw_fpn_2x_dota_ms_rr_oc'\nauto_resume = False\ngpu_ids = [0]\n", "seed": 1691121235, "exp_name": "r3det_kfiou_ln_swin_tiny_adamw_fpn_2x_dota_ms_rr_oc.py"}
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