{"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='RotatedFasterRCNN',\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        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        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='RotatedStandardRoIHead',\n        version='le90',\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='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, 0.1)),\n            reg_class_agnostic=True,\n            loss_cls=dict(\n                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, 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=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=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            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    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/rotated_faster_rcnn_r50_fpn_1x_dota_le90'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 518, "exp_name": "rotated_faster_rcnn_r50_fpn_1x_dota_le90.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00199, "memory": 6486, "data_time": 0.43613, "loss_rpn_cls": 0.49059, "loss_rpn_bbox": 0.06531, "loss_cls": 0.5188, "acc": 91.23438, "loss_bbox": 0.20507, "loss": 1.27978, "grad_norm": 9.82756, "time": 0.88713}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00233, "memory": 6486, "data_time": 0.00623, "loss_rpn_cls": 0.28362, "loss_rpn_bbox": 0.0736, "loss_cls": 0.38322, "acc": 92.75, "loss_bbox": 0.23848, "loss": 0.97893, "grad_norm": 4.94182, "time": 0.44253}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00266, "memory": 6486, "data_time": 0.00624, "loss_rpn_cls": 0.26106, "loss_rpn_bbox": 0.07937, "loss_cls": 0.48674, "acc": 89.7207, "loss_bbox": 0.39221, "loss": 1.21938, "grad_norm": 5.12528, "time": 0.43654}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00299, "memory": 6486, "data_time": 0.00622, "loss_rpn_cls": 0.20786, "loss_rpn_bbox": 0.06751, "loss_cls": 0.42783, "acc": 90.51758, "loss_bbox": 0.31594, "loss": 1.01914, "grad_norm": 5.07601, "time": 0.43541}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00333, "memory": 6486, "data_time": 0.00622, "loss_rpn_cls": 0.19774, "loss_rpn_bbox": 0.0669, "loss_cls": 0.37383, "acc": 92.29297, "loss_bbox": 0.25894, "loss": 0.89741, "grad_norm": 5.17941, "time": 0.43326}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00366, "memory": 6486, "data_time": 0.00621, "loss_rpn_cls": 0.1703, "loss_rpn_bbox": 0.05697, "loss_cls": 0.41269, "acc": 91.08594, "loss_bbox": 0.31667, "loss": 0.95662, "grad_norm": 5.0102, "time": 0.43684}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00399, "memory": 7307, "data_time": 0.00623, "loss_rpn_cls": 0.17432, "loss_rpn_bbox": 0.07429, "loss_cls": 0.47693, "acc": 88.98633, "loss_bbox": 0.35332, "loss": 1.07886, "grad_norm": 5.80096, "time": 0.4447}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00433, "memory": 7307, "data_time": 0.00621, "loss_rpn_cls": 0.18224, "loss_rpn_bbox": 0.06054, "loss_cls": 0.42664, "acc": 90.79688, "loss_bbox": 0.29013, "loss": 0.95954, "grad_norm": 6.18261, "time": 0.43569}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00466, "memory": 7307, "data_time": 0.00626, "loss_rpn_cls": 0.16323, "loss_rpn_bbox": 0.07119, "loss_cls": 0.468, "acc": 88.72266, "loss_bbox": 0.28701, "loss": 0.98944, "grad_norm": 4.91777, "time": 0.43614}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00499, "memory": 7307, "data_time": 0.00622, "loss_rpn_cls": 0.14538, "loss_rpn_bbox": 0.06616, "loss_cls": 0.41581, "acc": 89.4707, "loss_bbox": 0.31196, "loss": 0.93931, "grad_norm": 5.02501, "time": 0.44244}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.005, "memory": 7307, "data_time": 0.00623, "loss_rpn_cls": 0.12692, "loss_rpn_bbox": 0.05947, "loss_cls": 0.38509, "acc": 90.26562, "loss_bbox": 0.27677, "loss": 0.84826, "grad_norm": 4.72574, "time": 0.43496}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.005, "memory": 7307, "data_time": 0.0062, "loss_rpn_cls": 0.13595, "loss_rpn_bbox": 0.0793, "loss_cls": 0.41916, "acc": 89.15234, "loss_bbox": 0.3391, "loss": 0.97352, "grad_norm": 4.89745, "time": 0.43452}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.005, "memory": 7307, "data_time": 0.0063, "loss_rpn_cls": 0.11116, "loss_rpn_bbox": 0.06753, "loss_cls": 0.39116, "acc": 90.25, "loss_bbox": 0.28996, "loss": 0.8598, "grad_norm": 4.70115, "time": 0.44446}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.005, "memory": 7307, "data_time": 0.00619, "loss_rpn_cls": 0.12155, "loss_rpn_bbox": 0.07187, "loss_cls": 0.42718, "acc": 87.19336, "loss_bbox": 0.3789, "loss": 0.9995, "grad_norm": 5.2454, "time": 0.44587}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.005, "memory": 7307, "data_time": 0.00629, "loss_rpn_cls": 0.09164, "loss_rpn_bbox": 0.05626, "loss_cls": 0.38466, "acc": 90.88281, "loss_bbox": 0.25304, "loss": 0.7856, "grad_norm": 4.34716, "time": 0.44178}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.005, "memory": 7307, "data_time": 0.00626, "loss_rpn_cls": 0.10531, "loss_rpn_bbox": 0.06019, "loss_cls": 0.36972, "acc": 89.91992, "loss_bbox": 0.28405, "loss": 0.81927, "grad_norm": 4.22904, "time": 0.43712}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.005, "memory": 7307, "data_time": 0.00631, "loss_rpn_cls": 0.0994, "loss_rpn_bbox": 0.06504, "loss_cls": 0.3893, "acc": 89.06445, "loss_bbox": 0.30858, "loss": 0.86232, "grad_norm": 4.64679, "time": 0.44209}
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{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.005, "memory": 7307, "data_time": 0.00643, "loss_rpn_cls": 0.10538, "loss_rpn_bbox": 0.05692, "loss_cls": 0.36326, "acc": 89.99609, "loss_bbox": 0.28375, "loss": 0.80932, "grad_norm": 4.57408, "time": 0.44034}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.005, "memory": 7307, "data_time": 0.00645, "loss_rpn_cls": 0.10855, "loss_rpn_bbox": 0.06348, "loss_cls": 0.38966, "acc": 88.66602, "loss_bbox": 0.29293, "loss": 0.85461, "grad_norm": 4.42875, "time": 0.44912}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.005, "memory": 7307, "data_time": 0.00642, "loss_rpn_cls": 0.10314, "loss_rpn_bbox": 0.06455, "loss_cls": 0.35619, "acc": 89.50195, "loss_bbox": 0.31328, "loss": 0.83717, "grad_norm": 4.66624, "time": 0.44064}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.005, "memory": 7307, "data_time": 0.00634, "loss_rpn_cls": 0.10262, "loss_rpn_bbox": 0.05832, "loss_cls": 0.33681, "acc": 89.67578, "loss_bbox": 0.30075, "loss": 0.7985, "grad_norm": 4.29851, "time": 0.43184}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.005, "memory": 7307, "data_time": 0.00644, "loss_rpn_cls": 0.08178, "loss_rpn_bbox": 0.05036, "loss_cls": 0.32078, "acc": 90.26953, "loss_bbox": 0.35442, "loss": 0.80734, "grad_norm": 4.35418, "time": 0.44839}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.005, "memory": 7307, "data_time": 0.00642, "loss_rpn_cls": 0.09473, "loss_rpn_bbox": 0.06765, "loss_cls": 0.35644, "acc": 88.74219, "loss_bbox": 0.29717, "loss": 0.81599, "grad_norm": 4.46882, "time": 0.44764}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.005, "memory": 7307, "data_time": 0.00645, "loss_rpn_cls": 0.08511, "loss_rpn_bbox": 0.06467, "loss_cls": 0.33704, "acc": 89.37109, "loss_bbox": 0.28853, "loss": 0.77535, "grad_norm": 4.33352, "time": 0.45407}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.005, "memory": 7307, "data_time": 0.00632, "loss_rpn_cls": 0.10586, "loss_rpn_bbox": 0.05252, "loss_cls": 0.3694, "acc": 89.12891, "loss_bbox": 0.35452, "loss": 0.88231, "grad_norm": 4.91047, "time": 0.44631}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.005, "memory": 7307, "data_time": 0.00655, "loss_rpn_cls": 0.0875, "loss_rpn_bbox": 0.05205, "loss_cls": 0.31817, "acc": 90.33789, "loss_bbox": 0.27862, "loss": 0.73633, "grad_norm": 4.42386, "time": 0.43827}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.005, "memory": 7307, "data_time": 0.00637, "loss_rpn_cls": 0.10587, "loss_rpn_bbox": 0.06213, "loss_cls": 0.35284, "acc": 89.01367, "loss_bbox": 0.3301, "loss": 0.85095, "grad_norm": 4.66901, "time": 0.4475}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.005, "memory": 8387, "data_time": 0.00639, "loss_rpn_cls": 0.07083, "loss_rpn_bbox": 0.04371, "loss_cls": 0.29416, "acc": 91.22461, "loss_bbox": 0.23321, "loss": 0.64191, "grad_norm": 3.4939, "time": 0.44843}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.005, "memory": 8387, "data_time": 0.00641, "loss_rpn_cls": 0.0918, "loss_rpn_bbox": 0.05475, "loss_cls": 0.30923, "acc": 90.42383, "loss_bbox": 0.25184, "loss": 0.70762, "grad_norm": 3.8822, "time": 0.44365}
{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.005, "memory": 8387, "data_time": 0.00649, "loss_rpn_cls": 0.07462, "loss_rpn_bbox": 0.06282, "loss_cls": 0.28957, "acc": 90.55859, "loss_bbox": 0.24578, "loss": 0.67279, "grad_norm": 3.96387, "time": 0.43644}
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