{"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='RoITransformer',\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='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='RoIAlignRotated',\n                    out_size=7,\n                    sample_num=2,\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(type='le90', iou_thr=0.1),\n            max_per_img=2000)))\nwork_dir = './work_dirs/roi_trans_r50_fpn_1x_dota_le90'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 518, "exp_name": "roi_trans_r50_fpn_1x_dota_le90.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00199, "memory": 6699, "data_time": 0.43829, "loss_rpn_cls": 0.46548, "loss_rpn_bbox": 0.0648, "s0.loss_cls": 0.51409, "s0.acc": 90.62695, "s0.loss_bbox": 0.2028, "s1.loss_cls": 0.37314, "s1.acc": 93.76953, "s1.loss_bbox": 0.07594, "loss": 1.69625, "grad_norm": 14.01644, "time": 0.98334}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00233, "memory": 6699, "data_time": 0.00619, "loss_rpn_cls": 0.29149, "loss_rpn_bbox": 0.07511, "s0.loss_cls": 0.38135, "s0.acc": 93.0625, "s0.loss_bbox": 0.21647, "s1.loss_cls": 0.26013, "s1.acc": 95.46484, "s1.loss_bbox": 0.09044, "loss": 1.315, "grad_norm": 6.36982, "time": 0.54744}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00266, "memory": 6699, "data_time": 0.00616, "loss_rpn_cls": 0.27265, "loss_rpn_bbox": 0.07943, "s0.loss_cls": 0.49646, "s0.acc": 90.13281, "s0.loss_bbox": 0.36935, "s1.loss_cls": 0.3068, "s1.acc": 94.375, "s1.loss_bbox": 0.17892, "loss": 1.70362, "grad_norm": 6.93557, "time": 0.53055}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00299, "memory": 6699, "data_time": 0.00608, "loss_rpn_cls": 0.21299, "loss_rpn_bbox": 0.06795, "s0.loss_cls": 0.40754, "s0.acc": 90.83203, "s0.loss_bbox": 0.29797, "s1.loss_cls": 0.30096, "s1.acc": 93.23301, "s1.loss_bbox": 0.26872, "loss": 1.55613, "grad_norm": 5.76454, "time": 0.53151}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00333, "memory": 6699, "data_time": 0.00605, "loss_rpn_cls": 0.19412, "loss_rpn_bbox": 0.06757, "s0.loss_cls": 0.36979, "s0.acc": 92.14258, "s0.loss_bbox": 0.2526, "s1.loss_cls": 0.22717, "s1.acc": 95.19113, "s1.loss_bbox": 0.17019, "loss": 1.28144, "grad_norm": 5.90161, "time": 0.52294}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00366, "memory": 6699, "data_time": 0.00607, "loss_rpn_cls": 0.16744, "loss_rpn_bbox": 0.05753, "s0.loss_cls": 0.41927, "s0.acc": 90.85938, "s0.loss_bbox": 0.28546, "s1.loss_cls": 0.26658, "s1.acc": 94.06166, "s1.loss_bbox": 0.2339, "loss": 1.43019, "grad_norm": 6.07417, "time": 0.53129}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00399, "memory": 7523, "data_time": 0.00607, "loss_rpn_cls": 0.1768, "loss_rpn_bbox": 0.07118, "s0.loss_cls": 0.43788, "s0.acc": 89.2168, "s0.loss_bbox": 0.35267, "s1.loss_cls": 0.30811, "s1.acc": 92.17901, "s1.loss_bbox": 0.27736, "loss": 1.62401, "grad_norm": 7.21649, "time": 0.53334}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00433, "memory": 7523, "data_time": 0.00606, "loss_rpn_cls": 0.17287, "loss_rpn_bbox": 0.06022, "s0.loss_cls": 0.38915, "s0.acc": 90.97852, "s0.loss_bbox": 0.26973, "s1.loss_cls": 0.27667, "s1.acc": 93.6804, "s1.loss_bbox": 0.26246, "loss": 1.4311, "grad_norm": 6.38993, "time": 0.52007}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00466, "memory": 7523, "data_time": 0.00612, "loss_rpn_cls": 0.1792, "loss_rpn_bbox": 0.07219, "s0.loss_cls": 0.45217, "s0.acc": 88.80469, "s0.loss_bbox": 0.26703, "s1.loss_cls": 0.35416, "s1.acc": 91.511, "s1.loss_bbox": 0.30219, "loss": 1.62692, "grad_norm": 6.41705, "time": 0.52756}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00499, "memory": 7523, "data_time": 0.00611, "loss_rpn_cls": 0.15023, "loss_rpn_bbox": 0.06609, "s0.loss_cls": 0.41876, "s0.acc": 89.23828, "s0.loss_bbox": 0.34398, "s1.loss_cls": 0.23796, "s1.acc": 93.43058, "s1.loss_bbox": 0.24528, "loss": 1.46231, "grad_norm": 6.14568, "time": 0.51771}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.005, "memory": 7523, "data_time": 0.00606, "loss_rpn_cls": 0.14204, "loss_rpn_bbox": 0.0564, "s0.loss_cls": 0.3927, "s0.acc": 90.36719, "s0.loss_bbox": 0.24763, "s1.loss_cls": 0.29766, "s1.acc": 92.70467, "s1.loss_bbox": 0.29351, "loss": 1.42994, "grad_norm": 6.05337, "time": 0.50868}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.005, "memory": 7523, "data_time": 0.00607, "loss_rpn_cls": 0.14249, "loss_rpn_bbox": 0.07209, "s0.loss_cls": 0.40502, "s0.acc": 89.31641, "s0.loss_bbox": 0.33075, "s1.loss_cls": 0.24055, "s1.acc": 93.7276, "s1.loss_bbox": 0.24366, "loss": 1.43456, "grad_norm": 5.73793, "time": 0.51259}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.005, "memory": 7523, "data_time": 0.00611, "loss_rpn_cls": 0.12283, "loss_rpn_bbox": 0.07174, "s0.loss_cls": 0.39329, "s0.acc": 90.2832, "s0.loss_bbox": 0.25974, "s1.loss_cls": 0.24438, "s1.acc": 94.04969, "s1.loss_bbox": 0.24773, "loss": 1.33969, "grad_norm": 5.68346, "time": 0.50838}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.005, "memory": 7523, "data_time": 0.00608, "loss_rpn_cls": 0.13558, "loss_rpn_bbox": 0.07009, "s0.loss_cls": 0.42784, "s0.acc": 87.83594, "s0.loss_bbox": 0.34273, "s1.loss_cls": 0.26695, "s1.acc": 92.49335, "s1.loss_bbox": 0.35892, "loss": 1.60212, "grad_norm": 5.55302, "time": 0.50571}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.005, "memory": 7523, "data_time": 0.00608, "loss_rpn_cls": 0.09687, "loss_rpn_bbox": 0.05163, "s0.loss_cls": 0.36318, "s0.acc": 90.86719, "s0.loss_bbox": 0.25719, "s1.loss_cls": 0.23379, "s1.acc": 93.85028, "s1.loss_bbox": 0.2739, "loss": 1.27656, "grad_norm": 5.43022, "time": 0.52172}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.005, "memory": 7523, "data_time": 0.00609, "loss_rpn_cls": 0.11743, "loss_rpn_bbox": 0.06214, "s0.loss_cls": 0.37481, "s0.acc": 89.72852, "s0.loss_bbox": 0.27927, "s1.loss_cls": 0.23981, "s1.acc": 93.20283, "s1.loss_bbox": 0.3167, "loss": 1.39016, "grad_norm": 5.63957, "time": 0.51302}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.005, "memory": 7523, "data_time": 0.00611, "loss_rpn_cls": 0.12269, "loss_rpn_bbox": 0.0657, "s0.loss_cls": 0.36816, "s0.acc": 89.89648, "s0.loss_bbox": 0.29099, "s1.loss_cls": 0.21294, "s1.acc": 94.31873, "s1.loss_bbox": 0.25784, "loss": 1.31832, "grad_norm": 5.33692, "time": 0.51662}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.005, "memory": 7523, "data_time": 0.00612, "loss_rpn_cls": 0.10759, "loss_rpn_bbox": 0.06572, "s0.loss_cls": 0.3336, "s0.acc": 90.3125, "s0.loss_bbox": 0.2591, "s1.loss_cls": 0.20797, "s1.acc": 94.15242, "s1.loss_bbox": 0.28455, "loss": 1.25853, "grad_norm": 5.48901, "time": 0.50488}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.005, "memory": 7523, "data_time": 0.00608, "loss_rpn_cls": 0.11271, "loss_rpn_bbox": 0.05884, "s0.loss_cls": 0.38681, "s0.acc": 89.01562, "s0.loss_bbox": 0.2641, "s1.loss_cls": 0.23171, "s1.acc": 93.73078, "s1.loss_bbox": 0.28781, "loss": 1.34198, "grad_norm": 5.50592, "time": 0.50319}
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