{"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: GeForce GTX 1080 Ti\nCUDA_HOME: /cluster/apps/cuda/10.1\nNVCC: Cuda compilation tools, release 10.1, V10.1.243\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.7.0\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 v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.1\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_37,code=compute_37\n  - CuDNN 7.6.3\n  - Magma 2.5.2\n  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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: 0.8.0\nOpenCV: 4.5.5\nMMCV: 1.4.4\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 10.1\nMMRotate: 0.1.0+unknown", "config": "dataset_type = 'DOTADataset'\ndata_root = '/cluster/home/it_stu198/main/datasets/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        version='le90'),\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='le90'),\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=2,\n    train=dict(\n        type='DOTADataset',\n        ann_file=\n        '/cluster/home/it_stu198/main/datasets/split_ms_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/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                version='le90'),\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='le90'),\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='le90'),\n    val=dict(\n        type='DOTADataset',\n        ann_file=\n        '/cluster/home/it_stu198/main/datasets/split_ms_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/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='le90'),\n    test=dict(\n        type='DOTADataset',\n        ann_file=\n        '/cluster/home/it_stu198/main/datasets/split_ms_dota1_0/test/images/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/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='le90'))\nevaluation = dict(interval=12, metric='mAP')\noptimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)\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=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=12)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nangle_version = 'le90'\nmodel = dict(\n    type='RotatedRetinaNet',\n    backbone=dict(\n        type='ResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        zero_init_residual=False,\n        norm_cfg=dict(type='BN', requires_grad=True),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n    neck=dict(\n        type='FPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        start_level=1,\n        add_extra_convs='on_input',\n        num_outs=5),\n    bbox_head=dict(\n        type='RotatedRetinaHead',\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='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='le90',\n            norm_factor=None,\n            edge_swap=True,\n            proj_xy=True,\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='L1Loss', loss_weight=1.0)),\n    train_cfg=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    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))\nwork_dir = './work_dirs/rotated_retinanet_obb_r50_fpn_1x_dota_ms_rr_le90'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 518, "exp_name": "rotated_retinanet_obb_r50_fpn_1x_dota_ms_rr_le90.py"}
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{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00166, "memory": 3387, "data_time": 0.00611, "loss_cls": 1.18484, "loss_bbox": 1.15718, "loss": 2.34202, "grad_norm": 3.68512, "time": 0.35272}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00183, "memory": 3387, "data_time": 0.00632, "loss_cls": 1.04386, "loss_bbox": 1.01186, "loss": 2.05572, "grad_norm": 4.85514, "time": 0.3515}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.002, "memory": 3387, "data_time": 0.00635, "loss_cls": 1.17605, "loss_bbox": 1.03407, "loss": 2.21012, "grad_norm": 4.36363, "time": 0.35404}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00216, "memory": 3387, "data_time": 0.00668, "loss_cls": 1.14194, "loss_bbox": 1.05355, "loss": 2.19549, "grad_norm": 2.1964, "time": 0.35544}
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{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0025, "memory": 3387, "data_time": 0.00645, "loss_cls": 1.17309, "loss_bbox": 1.01541, "loss": 2.1885, "grad_norm": 1.92189, "time": 0.35817}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0025, "memory": 3387, "data_time": 0.0062, "loss_cls": 1.16122, "loss_bbox": 1.09639, "loss": 2.25761, "grad_norm": 1.12367, "time": 0.35919}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0025, "memory": 3387, "data_time": 0.00624, "loss_cls": 1.19565, "loss_bbox": 1.06755, "loss": 2.2632, "grad_norm": 1.44339, "time": 0.35769}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0025, "memory": 3387, "data_time": 0.00615, "loss_cls": 1.20048, "loss_bbox": 1.10665, "loss": 2.30714, "grad_norm": 1.4196, "time": 0.35892}
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{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0025, "memory": 3491, "data_time": 0.00909, "loss_cls": 1.11641, "loss_bbox": 1.06504, "loss": 2.18145, "grad_norm": 1.63431, "time": 0.36133}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0025, "memory": 3491, "data_time": 0.00861, "loss_cls": 1.12516, "loss_bbox": 1.073, "loss": 2.19816, "grad_norm": 1.67976, "time": 0.35631}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0025, "memory": 3587, "data_time": 0.00873, "loss_cls": 1.17553, "loss_bbox": 1.09818, "loss": 2.27371, "grad_norm": 2.22377, "time": 0.36397}
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{"mode": "train", "epoch": 1, "iter": 2050, "lr": 0.0025, "memory": 3587, "data_time": 0.00919, "loss_cls": 1.09053, "loss_bbox": 1.00007, "loss": 2.0906, "grad_norm": 1.40499, "time": 0.35256}
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{"mode": "train", "epoch": 1, "iter": 2150, "lr": 0.0025, "memory": 3587, "data_time": 0.00851, "loss_cls": 1.17555, "loss_bbox": 1.04544, "loss": 2.22099, "grad_norm": 1.0427, "time": 0.35187}
{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.0025, "memory": 3587, "data_time": 0.0082, "loss_cls": 1.15185, "loss_bbox": 1.05634, "loss": 2.20819, "grad_norm": 1.27369, "time": 0.35054}
{"mode": "train", "epoch": 1, "iter": 2250, "lr": 0.0025, "memory": 3587, "data_time": 0.00828, "loss_cls": 1.12697, "loss_bbox": 1.08639, "loss": 2.21337, "grad_norm": 2.46688, "time": 0.35814}
{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.0025, "memory": 3587, "data_time": 0.0079, "loss_cls": 1.13492, "loss_bbox": 1.10084, "loss": 2.23576, "grad_norm": 2.27831, "time": 0.35831}
{"mode": "train", "epoch": 1, "iter": 2350, "lr": 0.0025, "memory": 3587, "data_time": 0.00801, "loss_cls": 1.17329, "loss_bbox": 1.20999, "loss": 2.38328, "grad_norm": 2.07689, "time": 0.3563}
{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.0025, "memory": 3587, "data_time": 0.00796, "loss_cls": 1.12894, "loss_bbox": 1.00674, "loss": 2.13568, "grad_norm": 1.26706, "time": 0.35445}
{"mode": "train", "epoch": 1, "iter": 2450, "lr": 0.0025, "memory": 3587, "data_time": 0.00782, "loss_cls": 1.08245, "loss_bbox": 1.01543, "loss": 2.09788, "grad_norm": 1.27203, "time": 0.35288}
{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.0025, "memory": 3587, "data_time": 0.00838, "loss_cls": 1.17506, "loss_bbox": 1.11099, "loss": 2.28605, "grad_norm": 1.7239, "time": 0.34969}
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