{"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='GlidingVertex',\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        version='le90',\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(\n            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),\n    roi_head=dict(\n        type='GVRatioRoIHead',\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='GVBBoxHead',\n            version='le90',\n            num_shared_fcs=2,\n            in_channels=256,\n            fc_out_channels=1024,\n            roi_feat_size=7,\n            num_classes=15,\n            ratio_thr=0.8,\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            fix_coder=dict(type='GVFixCoder', angle_range='le90'),\n            ratio_coder=dict(type='GVRatioCoder', angle_range='le90'),\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            loss_fix=dict(\n                type='SmoothL1Loss', beta=0.3333333333333333, loss_weight=1.0),\n            loss_ratio=dict(\n                type='SmoothL1Loss', beta=0.3333333333333333,\n                loss_weight=16.0))),\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/gliding_vertex_r50_fpn_1x_dota_le90'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 518, "exp_name": "gliding_vertex_r50_fpn_1x_dota_le90.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00199, "memory": 6485, "data_time": 0.42351, "loss_rpn_cls": 0.39916, "loss_rpn_bbox": 0.08092, "loss_cls": 0.72536, "acc": 90.72266, "loss_bbox": 0.0622, "loss_fix": 0.01446, "loss_ratio": 0.00221, "loss": 1.28431, "grad_norm": 8.60029, "time": 0.9043}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00233, "memory": 6485, "data_time": 0.00621, "loss_rpn_cls": 0.27744, "loss_rpn_bbox": 0.08285, "loss_cls": 0.4105, "acc": 92.90039, "loss_bbox": 0.109, "loss_fix": 0.01704, "loss_ratio": 0.00216, "loss": 0.899, "grad_norm": 5.00604, "time": 0.47419}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00266, "memory": 6485, "data_time": 0.00611, "loss_rpn_cls": 0.25556, "loss_rpn_bbox": 0.08339, "loss_cls": 0.51721, "acc": 90.6582, "loss_bbox": 0.18615, "loss_fix": 0.01851, "loss_ratio": 0.0021, "loss": 1.06292, "grad_norm": 5.84359, "time": 0.47447}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00299, "memory": 6485, "data_time": 0.00617, "loss_rpn_cls": 0.18523, "loss_rpn_bbox": 0.07037, "loss_cls": 0.44512, "acc": 90.78125, "loss_bbox": 0.19774, "loss_fix": 0.02172, "loss_ratio": 0.00249, "loss": 0.92267, "grad_norm": 3.96301, "time": 0.4623}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00333, "memory": 6485, "data_time": 0.00623, "loss_rpn_cls": 0.19305, "loss_rpn_bbox": 0.07607, "loss_cls": 0.41432, "acc": 92.5957, "loss_bbox": 0.15146, "loss_fix": 0.01575, "loss_ratio": 0.00184, "loss": 0.85249, "grad_norm": 4.15763, "time": 0.4699}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00366, "memory": 6485, "data_time": 0.00622, "loss_rpn_cls": 0.15604, "loss_rpn_bbox": 0.06351, "loss_cls": 0.41697, "acc": 91.61523, "loss_bbox": 0.17907, "loss_fix": 0.02022, "loss_ratio": 0.00265, "loss": 0.83845, "grad_norm": 4.42075, "time": 0.46849}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00399, "memory": 7306, "data_time": 0.00625, "loss_rpn_cls": 0.15579, "loss_rpn_bbox": 0.07935, "loss_cls": 0.52835, "acc": 88.52344, "loss_bbox": 0.22171, "loss_fix": 0.02202, "loss_ratio": 0.0025, "loss": 1.00971, "grad_norm": 3.86103, "time": 0.46981}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00433, "memory": 7306, "data_time": 0.00621, "loss_rpn_cls": 0.17897, "loss_rpn_bbox": 0.06882, "loss_cls": 0.45394, "acc": 90.49609, "loss_bbox": 0.19023, "loss_fix": 0.02373, "loss_ratio": 0.00291, "loss": 0.9186, "grad_norm": 4.50055, "time": 0.46463}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00466, "memory": 7306, "data_time": 0.00625, "loss_rpn_cls": 0.1763, "loss_rpn_bbox": 0.08175, "loss_cls": 0.52269, "acc": 89.16602, "loss_bbox": 0.19685, "loss_fix": 0.02604, "loss_ratio": 0.00311, "loss": 1.00674, "grad_norm": 4.45759, "time": 0.46818}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00499, "memory": 7306, "data_time": 0.00621, "loss_rpn_cls": 0.14508, "loss_rpn_bbox": 0.08382, "loss_cls": 0.44524, "acc": 89.47656, "loss_bbox": 0.19629, "loss_fix": 0.02194, "loss_ratio": 0.0027, "loss": 0.89506, "grad_norm": 4.08998, "time": 0.4743}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.005, "memory": 7306, "data_time": 0.00612, "loss_rpn_cls": 0.12385, "loss_rpn_bbox": 0.06066, "loss_cls": 0.43212, "acc": 89.48633, "loss_bbox": 0.18843, "loss_fix": 0.02504, "loss_ratio": 0.0031, "loss": 0.8332, "grad_norm": 4.23935, "time": 0.46739}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.005, "memory": 7306, "data_time": 0.0062, "loss_rpn_cls": 0.12885, "loss_rpn_bbox": 0.08678, "loss_cls": 0.45702, "acc": 88.21094, "loss_bbox": 0.19874, "loss_fix": 0.02465, "loss_ratio": 0.00284, "loss": 0.89888, "grad_norm": 4.50381, "time": 0.463}
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{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.005, "memory": 7306, "data_time": 0.0062, "loss_rpn_cls": 0.1084, "loss_rpn_bbox": 0.06036, "loss_cls": 0.42559, "acc": 88.52344, "loss_bbox": 0.18868, "loss_fix": 0.02645, "loss_ratio": 0.00259, "loss": 0.81207, "grad_norm": 4.54224, "time": 0.45409}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.005, "memory": 7306, "data_time": 0.00621, "loss_rpn_cls": 0.10737, "loss_rpn_bbox": 0.06134, "loss_cls": 0.37974, "acc": 89.75195, "loss_bbox": 0.1823, "loss_fix": 0.02319, "loss_ratio": 0.00272, "loss": 0.75666, "grad_norm": 4.48998, "time": 0.46662}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.005, "memory": 7306, "data_time": 0.00628, "loss_rpn_cls": 0.10642, "loss_rpn_bbox": 0.06652, "loss_cls": 0.4329, "acc": 87.65234, "loss_bbox": 0.19884, "loss_fix": 0.02599, "loss_ratio": 0.00251, "loss": 0.83318, "grad_norm": 4.7015, "time": 0.45814}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.005, "memory": 7306, "data_time": 0.00618, "loss_rpn_cls": 0.10362, "loss_rpn_bbox": 0.0653, "loss_cls": 0.39094, "acc": 88.67383, "loss_bbox": 0.18895, "loss_fix": 0.02691, "loss_ratio": 0.00261, "loss": 0.77832, "grad_norm": 4.38334, "time": 0.46652}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.005, "memory": 7306, "data_time": 0.00625, "loss_rpn_cls": 0.10307, "loss_rpn_bbox": 0.05951, "loss_cls": 0.3915, "acc": 89.16406, "loss_bbox": 0.1956, "loss_fix": 0.02769, "loss_ratio": 0.00272, "loss": 0.78008, "grad_norm": 4.75288, "time": 0.4656}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.005, "memory": 7306, "data_time": 0.00623, "loss_rpn_cls": 0.08075, "loss_rpn_bbox": 0.06227, "loss_cls": 0.33488, "acc": 89.82227, "loss_bbox": 0.17888, "loss_fix": 0.02559, "loss_ratio": 0.0022, "loss": 0.68457, "grad_norm": 3.6764, "time": 0.46639}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.005, "memory": 7306, "data_time": 0.00625, "loss_rpn_cls": 0.0962, "loss_rpn_bbox": 0.0744, "loss_cls": 0.36977, "acc": 88.19141, "loss_bbox": 0.17872, "loss_fix": 0.02834, "loss_ratio": 0.00284, "loss": 0.75027, "grad_norm": 4.30185, "time": 0.47201}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.005, "memory": 7306, "data_time": 0.0062, "loss_rpn_cls": 0.08554, "loss_rpn_bbox": 0.07821, "loss_cls": 0.3619, "acc": 88.78906, "loss_bbox": 0.17897, "loss_fix": 0.02508, "loss_ratio": 0.00276, "loss": 0.73246, "grad_norm": 4.35678, "time": 0.46617}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.005, "memory": 7306, "data_time": 0.00622, "loss_rpn_cls": 0.09446, "loss_rpn_bbox": 0.05521, "loss_cls": 0.3525, "acc": 89.0918, "loss_bbox": 0.1845, "loss_fix": 0.02747, "loss_ratio": 0.00261, "loss": 0.71675, "grad_norm": 4.10029, "time": 0.46775}
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