{"env_info": "sys.platform: linux\nPython: 3.7.7 (default, May 7 2020, 21:25:33) [GCC 7.3.0]\nCUDA available: True\nGPU 0: GeForce RTX 2080 Ti\nCUDA_HOME: /usr/local/cuda/\nNVCC: Cuda compilation tools, release 10.1, V10.1.243\nGCC: gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0\nPyTorch: 1.7.1+cu101\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.1 Product Build 20200208 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_70,code=sm_70;-gencode;arch=compute_75,code=sm_75\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: 0.8.2+cu101\nOpenCV: 4.5.2\nMMCV: 1.4.6\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 10.1\nMMRotate: 0.2.0+", "config": "dataset_type = 'DOTADataset'\ndata_root = '/data/home/yangxue/dataset/split_ss_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(type='RRandomFlip', flip_ratio=0.5, version='oc'),\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 '/data/home/yangxue/dataset/split_ss_dota1_0/trainval/annfiles/',\n img_prefix=\n '/data/home/yangxue/dataset/split_ss_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(type='RRandomFlip', flip_ratio=0.5, version='oc'),\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='oc'),\n val=dict(\n type='DOTADataset',\n ann_file=\n '/data/home/yangxue/dataset/split_ss_dota1_0/trainval/annfiles/',\n img_prefix=\n '/data/home/yangxue/dataset/split_ss_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='oc'),\n test=dict(\n type='DOTADataset',\n ann_file='/data/home/yangxue/dataset/split_ss_dota1_0/test/images/',\n img_prefix='/data/home/yangxue/dataset/split_ss_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='oc'))\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 = 'oc'\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='RotatedATSSHead',\n num_classes=15,\n in_channels=256,\n stacked_convs=4,\n feat_channels=256,\n assign_by_circumhbbox='oc',\n anchor_generator=dict(\n type='RotatedAnchorGenerator',\n octave_base_scale=4,\n scales_per_octave=1,\n ratios=[1.0],\n strides=[8, 16, 32, 64, 128]),\n bbox_coder=dict(\n type='DeltaXYWHAOBBoxCoder',\n angle_range='oc',\n norm_factor=None,\n edge_swap=False,\n proj_xy=False,\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='ATSSObbAssigner',\n topk=9,\n angle_version='oc',\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_atss_hbb_r50_fpn_1x_dota_oc'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 0, "exp_name": "rotated_atss_hbb_r50_fpn_1x_dota_oc.py"} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.001, "memory": 3036, "data_time": 0.50281, "loss_cls": 1.13274, "loss_bbox": 1.96947, "loss": 3.10221, "grad_norm": 6.79869, "time": 0.80353} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00116, "memory": 3038, "data_time": 0.01015, "loss_cls": 1.12437, "loss_bbox": 1.75299, "loss": 2.87736, "grad_norm": 5.45932, "time": 0.27067} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00133, "memory": 3064, "data_time": 0.00659, "loss_cls": 1.08369, "loss_bbox": 1.80695, "loss": 2.89063, "grad_norm": 7.63561, "time": 0.25672} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0015, "memory": 3116, "data_time": 0.00934, "loss_cls": 1.02393, "loss_bbox": 1.70606, "loss": 2.73, "grad_norm": 5.11354, "time": 0.27483} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00166, "memory": 3116, "data_time": 0.0104, "loss_cls": 0.89329, "loss_bbox": 1.65361, "loss": 2.5469, "grad_norm": 5.79386, "time": 0.28097} {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00183, "memory": 3116, "data_time": 0.01093, "loss_cls": 0.89341, "loss_bbox": 1.82684, "loss": 2.72025, "grad_norm": 4.80687, "time": 0.28555} {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.002, "memory": 3116, "data_time": 0.00882, "loss_cls": 0.87663, "loss_bbox": 1.62342, "loss": 2.50004, "grad_norm": 5.53739, "time": 0.28333} {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00216, "memory": 3116, "data_time": 0.00987, "loss_cls": 0.94684, "loss_bbox": 1.74303, "loss": 2.68987, "grad_norm": 7.77225, "time": 0.28551} {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00233, "memory": 3116, "data_time": 0.00959, "loss_cls": 0.98506, "loss_bbox": 1.87816, "loss": 2.86322, "grad_norm": 8.05138, "time": 0.2852} {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0025, "memory": 3116, "data_time": 0.00975, "loss_cls": 1.03407, "loss_bbox": 1.80165, "loss": 2.83572, "grad_norm": 7.96905, "time": 0.28415} {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0025, "memory": 3116, "data_time": 0.0109, "loss_cls": 1.00706, "loss_bbox": 1.72932, "loss": 2.73638, "grad_norm": 2.88948, "time": 0.28066} {"mode": "train", "epoch": 1, 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0.01163, "loss_cls": 0.93579, "loss_bbox": 1.7806, "loss": 2.71638, "grad_norm": 1.40585, "time": 0.29254} {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0025, "memory": 3119, "data_time": 0.00944, "loss_cls": 0.91427, "loss_bbox": 1.75112, "loss": 2.66539, "grad_norm": 1.66956, "time": 0.28559} {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0025, "memory": 3119, "data_time": 0.00992, "loss_cls": 0.87746, "loss_bbox": 1.73387, "loss": 2.61133, "grad_norm": 2.13765, "time": 0.29146} {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0025, "memory": 3119, "data_time": 0.00897, "loss_cls": 0.88122, "loss_bbox": 1.81095, "loss": 2.69217, "grad_norm": 1.99513, "time": 0.28921} {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0025, "memory": 3119, "data_time": 0.00863, "loss_cls": 0.81379, "loss_bbox": 1.67878, "loss": 2.49256, "grad_norm": 2.18453, "time": 0.29572} {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0025, "memory": 3119, "data_time": 0.0093, "loss_cls": 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