{"env_info": "sys.platform: linux\nPython: 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 06:08:21) [GCC 9.4.0]\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) 5.4.0\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: 1.4.3\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='le135'),\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='le135'),\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='le135'),\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='le135'),\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='le135'))\nevaluation = dict(interval=12, metric='mAP')\noptimizer = dict(type='SGD', lr=0.008, 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 = 'le135'\nnorm_cfg = dict(type='GN', num_groups=32, requires_grad=True)\nmodel = dict(\n type='RotatedRepPoints',\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 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),\n bbox_head=dict(\n type='RotatedRepPointsHead',\n num_classes=15,\n in_channels=256,\n feat_channels=256,\n point_feat_channels=256,\n stacked_convs=3,\n num_points=9,\n gradient_mul=0.3,\n point_strides=[8, 16, 32, 64, 128],\n point_base_scale=2,\n norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),\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_init=dict(type='ConvexGIoULoss', loss_weight=0.375),\n loss_bbox_refine=dict(type='ConvexGIoULoss', loss_weight=1.0),\n transform_method='rotrect',\n use_reassign=True,\n topk=6,\n anti_factor=0.75),\n train_cfg=dict(\n init=dict(\n assigner=dict(type='ConvexAssigner', scale=4, pos_num=1),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n refine=dict(\n assigner=dict(\n type='MaxConvexIoUAssigner',\n pos_iou_thr=0.1,\n neg_iou_thr=0.1,\n min_pos_iou=0,\n ignore_iof_thr=-1),\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=0.4),\n max_per_img=2000))\nwork_dir = './work_dirs/cfa_r50_fpn_1x_dota_le135'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 518, "exp_name": "cfa_r50_fpn_1x_dota_le135.py"} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00319, "memory": 3532, "data_time": 0.45978, "loss_cls": 1.1232, "loss_pts_init": 0.93099, "loss_pts_refine": 0.84585, "loss": 2.90003, "grad_norm": 11.32081, "time": 1.18921} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00372, "memory": 3532, "data_time": 0.00598, "loss_cls": 1.01375, "loss_pts_init": 0.70445, "loss_pts_refine": 0.68622, "loss": 2.40443, "grad_norm": 4.00928, "time": 0.81177} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00426, "memory": 3532, "data_time": 0.00599, "loss_cls": 1.05478, "loss_pts_init": 0.59278, "loss_pts_refine": 0.66645, "loss": 2.31401, "grad_norm": 4.23592, "time": 0.72494} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00479, "memory": 3532, "data_time": 0.00597, "loss_cls": 1.07696, "loss_pts_init": 0.61468, "loss_pts_refine": 0.63699, "loss": 2.32862, "grad_norm": 2.8029, "time": 0.83909} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00532, "memory": 3532, "data_time": 0.006, "loss_cls": 1.00427, "loss_pts_init": 0.60138, "loss_pts_refine": 0.66298, "loss": 2.26864, "grad_norm": 3.57786, "time": 0.81431} {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00586, "memory": 3532, "data_time": 0.00595, "loss_cls": 1.06973, "loss_pts_init": 0.58987, "loss_pts_refine": 0.62507, "loss": 2.28467, "grad_norm": 2.77108, "time": 0.84941} {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00639, "memory": 3532, "data_time": 0.00589, "loss_cls": 1.06858, "loss_pts_init": 0.57091, "loss_pts_refine": 0.61618, "loss": 2.25567, "grad_norm": 2.68864, "time": 0.79659} {"mode": "train", "epoch": 1, 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