{"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_1024_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='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_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/split_1024_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='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_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/split_1024_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_1024_dota1_0/test/images/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/split_1024_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=0.005, 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='ReDet',\n    backbone=dict(\n        type='ReResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        style='pytorch',\n        pretrained='work_dirs/pretrain/re_resnet50_c8_batch256-25b16846.pth'),\n    neck=dict(\n        type='ReFPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        num_outs=5),\n    rpn_head=dict(\n        type='RotatedRPNHead',\n        in_channels=256,\n        feat_channels=256,\n        version='le90',\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(\n            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),\n    roi_head=dict(\n        type='RoITransRoIHead',\n        version='le90',\n        num_stages=2,\n        stage_loss_weights=[1, 1],\n        bbox_roi_extractor=[\n            dict(\n                type='SingleRoIExtractor',\n                roi_layer=dict(\n                    type='RoIAlign', output_size=7, sampling_ratio=0),\n                out_channels=256,\n                featmap_strides=[4, 8, 16, 32]),\n            dict(\n                type='RotatedSingleRoIExtractor',\n                roi_layer=dict(\n                    type='RiRoIAlignRotated',\n                    out_size=7,\n                    num_samples=2,\n                    num_orientations=8,\n                    clockwise=True),\n                out_channels=256,\n                featmap_strides=[4, 8, 16, 32])\n        ],\n        bbox_head=[\n            dict(\n                type='RotatedShared2FCBBoxHead',\n                in_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=15,\n                bbox_coder=dict(\n                    type='DeltaXYWHAHBBoxCoder',\n                    angle_range='le90',\n                    norm_factor=2,\n                    edge_swap=True,\n                    target_means=[0.0, 0.0, 0.0, 0.0, 0.0],\n                    target_stds=[0.1, 0.1, 0.2, 0.2, 1]),\n                reg_class_agnostic=True,\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,\n                               loss_weight=1.0)),\n            dict(\n                type='RotatedShared2FCBBoxHead',\n                in_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=15,\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=[0.05, 0.05, 0.1, 0.1, 0.5]),\n                reg_class_agnostic=False,\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))\n        ]),\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=0,\n            pos_weight=-1,\n            debug=False),\n        rpn_proposal=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=[\n            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=False,\n                    ignore_iof_thr=-1,\n                    iou_calculator=dict(type='BboxOverlaps2D')),\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                pos_weight=-1,\n                debug=False),\n            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=False,\n                    ignore_iof_thr=-1,\n                    iou_calculator=dict(type='RBboxOverlaps2D')),\n                sampler=dict(\n                    type='RRandomSampler',\n                    num=512,\n                    pos_fraction=0.25,\n                    neg_pos_ub=-1,\n                    add_gt_as_proposals=True),\n                pos_weight=-1,\n                debug=False)\n        ]),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=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/redet_re50_fpn_1x_dota_le90'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 518, "exp_name": "redet_re50_fpn_1x_dota_le90.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00199, "memory": 7371, "data_time": 0.39636, "loss_rpn_cls": 0.46217, "loss_rpn_bbox": 0.0677, "s0.loss_cls": 0.48913, "s0.acc": 92.55469, "s0.loss_bbox": 0.23721, "s1.loss_cls": 0.43332, "s1.acc": 93.24219, "s1.loss_bbox": 0.12742, "loss": 1.81695, "grad_norm": 11.85262, "time": 1.14868}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00233, "memory": 7371, "data_time": 0.00659, "loss_rpn_cls": 0.29048, "loss_rpn_bbox": 0.07587, "s0.loss_cls": 0.41992, "s0.acc": 92.39258, "s0.loss_bbox": 0.26998, "s1.loss_cls": 0.30001, "s1.acc": 94.87695, "s1.loss_bbox": 0.14179, "loss": 1.49806, "grad_norm": 6.0743, "time": 0.75778}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00266, "memory": 7371, "data_time": 0.00659, "loss_rpn_cls": 0.25651, "loss_rpn_bbox": 0.07915, "s0.loss_cls": 0.44634, "s0.acc": 90.17188, "s0.loss_bbox": 0.3355, "s1.loss_cls": 0.27246, "s1.acc": 94.0957, "s1.loss_bbox": 0.2091, "loss": 1.59907, "grad_norm": 5.22448, "time": 0.75719}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00299, "memory": 7371, "data_time": 0.00655, "loss_rpn_cls": 0.2083, "loss_rpn_bbox": 0.06681, "s0.loss_cls": 0.4019, "s0.acc": 90.90039, "s0.loss_bbox": 0.27404, "s1.loss_cls": 0.27463, "s1.acc": 93.58029, "s1.loss_bbox": 0.24623, "loss": 1.47192, "grad_norm": 5.64394, "time": 0.75945}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00333, "memory": 7371, "data_time": 0.00663, "loss_rpn_cls": 0.18381, "loss_rpn_bbox": 0.06796, "s0.loss_cls": 0.34604, "s0.acc": 92.31836, "s0.loss_bbox": 0.22626, "s1.loss_cls": 0.19558, "s1.acc": 95.69727, "s1.loss_bbox": 0.16891, "loss": 1.18856, "grad_norm": 4.87598, "time": 0.76268}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00366, "memory": 7371, "data_time": 0.00659, "loss_rpn_cls": 0.14611, "loss_rpn_bbox": 0.05699, "s0.loss_cls": 0.39408, "s0.acc": 90.75, "s0.loss_bbox": 0.27921, "s1.loss_cls": 0.2568, "s1.acc": 94.07853, "s1.loss_bbox": 0.25625, "loss": 1.38944, "grad_norm": 5.5332, "time": 0.76341}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00399, "memory": 8192, "data_time": 0.00659, "loss_rpn_cls": 0.1711, "loss_rpn_bbox": 0.07269, "s0.loss_cls": 0.4454, "s0.acc": 88.89062, "s0.loss_bbox": 0.30941, "s1.loss_cls": 0.29636, "s1.acc": 92.54094, "s1.loss_bbox": 0.2858, "loss": 1.58077, "grad_norm": 6.16338, "time": 0.77164}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00433, "memory": 8192, "data_time": 0.0066, "loss_rpn_cls": 0.15507, "loss_rpn_bbox": 0.06028, "s0.loss_cls": 0.39721, "s0.acc": 90.42773, "s0.loss_bbox": 0.26357, "s1.loss_cls": 0.25769, "s1.acc": 93.9572, "s1.loss_bbox": 0.27358, "loss": 1.4074, "grad_norm": 5.55374, "time": 0.75546}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00466, "memory": 8192, "data_time": 0.00657, "loss_rpn_cls": 0.12414, "loss_rpn_bbox": 0.07892, "s0.loss_cls": 0.45123, "s0.acc": 88.71484, "s0.loss_bbox": 0.274, "s1.loss_cls": 0.30969, "s1.acc": 91.97779, "s1.loss_bbox": 0.31886, "loss": 1.55685, "grad_norm": 5.6236, "time": 0.76282}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00499, "memory": 8192, "data_time": 0.00662, "loss_rpn_cls": 0.12878, "loss_rpn_bbox": 0.06717, "s0.loss_cls": 0.40492, "s0.acc": 89.38086, "s0.loss_bbox": 0.28076, "s1.loss_cls": 0.23162, "s1.acc": 93.66152, "s1.loss_bbox": 0.27395, "loss": 1.3872, "grad_norm": 5.40058, "time": 0.7593}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.005, "memory": 8192, "data_time": 0.00657, "loss_rpn_cls": 0.11263, "loss_rpn_bbox": 0.05266, "s0.loss_cls": 0.36713, "s0.acc": 90.43359, "s0.loss_bbox": 0.23268, "s1.loss_cls": 0.26523, "s1.acc": 93.21549, "s1.loss_bbox": 0.30391, "loss": 1.33424, "grad_norm": 5.56983, "time": 0.76778}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.005, "memory": 8192, "data_time": 0.00658, "loss_rpn_cls": 0.10727, "loss_rpn_bbox": 0.07475, "s0.loss_cls": 0.38347, "s0.acc": 89.29297, "s0.loss_bbox": 0.27798, "s1.loss_cls": 0.21917, "s1.acc": 94.0192, "s1.loss_bbox": 0.2593, "loss": 1.32194, "grad_norm": 4.86018, "time": 0.75962}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.005, "memory": 8192, "data_time": 0.00661, "loss_rpn_cls": 0.08696, "loss_rpn_bbox": 0.06452, "s0.loss_cls": 0.4073, "s0.acc": 89.25195, "s0.loss_bbox": 0.26382, "s1.loss_cls": 0.22506, "s1.acc": 93.83511, "s1.loss_bbox": 0.27647, "loss": 1.32412, "grad_norm": 5.42565, "time": 0.756}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.005, "memory": 8192, "data_time": 0.00662, "loss_rpn_cls": 0.10887, "loss_rpn_bbox": 0.07225, "s0.loss_cls": 0.44327, "s0.acc": 86.76367, "s0.loss_bbox": 0.33698, "s1.loss_cls": 0.2309, "s1.acc": 93.08216, "s1.loss_bbox": 0.33055, "loss": 1.52283, "grad_norm": 5.50938, "time": 0.76159}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.005, "memory": 8192, "data_time": 0.00661, "loss_rpn_cls": 0.07072, "loss_rpn_bbox": 0.04655, "s0.loss_cls": 0.34023, "s0.acc": 90.87109, "s0.loss_bbox": 0.22689, "s1.loss_cls": 0.20328, "s1.acc": 94.65542, "s1.loss_bbox": 0.28762, "loss": 1.17529, "grad_norm": 4.42643, "time": 0.76128}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.005, "memory": 8192, "data_time": 0.0066, "loss_rpn_cls": 0.09086, "loss_rpn_bbox": 0.0573, "s0.loss_cls": 0.32323, "s0.acc": 90.64844, "s0.loss_bbox": 0.23607, "s1.loss_cls": 0.20742, "s1.acc": 94.17555, "s1.loss_bbox": 0.31229, "loss": 1.22716, "grad_norm": 4.47289, "time": 0.75863}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.005, "memory": 8192, "data_time": 0.00663, "loss_rpn_cls": 0.096, "loss_rpn_bbox": 0.06859, "s0.loss_cls": 0.34656, "s0.acc": 90.05664, "s0.loss_bbox": 0.24698, "s1.loss_cls": 0.20207, "s1.acc": 94.39554, "s1.loss_bbox": 0.26968, "loss": 1.22988, "grad_norm": 4.78459, "time": 0.75648}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.005, "memory": 8192, "data_time": 0.00661, "loss_rpn_cls": 0.0918, "loss_rpn_bbox": 0.06761, "s0.loss_cls": 0.30176, "s0.acc": 90.74805, "s0.loss_bbox": 0.23217, "s1.loss_cls": 0.19289, "s1.acc": 94.4868, "s1.loss_bbox": 0.29507, "loss": 1.1813, "grad_norm": 4.68372, "time": 0.75562}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.005, "memory": 8192, "data_time": 0.0066, "loss_rpn_cls": 0.09173, "loss_rpn_bbox": 0.05582, "s0.loss_cls": 0.33305, "s0.acc": 89.6875, "s0.loss_bbox": 0.23672, "s1.loss_cls": 0.19851, "s1.acc": 94.22508, "s1.loss_bbox": 0.25827, "loss": 1.17409, "grad_norm": 4.56873, "time": 0.75749}
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