{"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: Tesla PG503-216\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.2.r11.2/compiler.29618528_0\nGCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)\nPyTorch: 1.8.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.1\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_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.0.5\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -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, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.0, USE_CUDA=ON, USE_CUDNN=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: 0.9.0\nOpenCV: 4.5.5\nMMCV: 1.4.5\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMRotate: 0.1.0+", "config": "dataset_type = 'DOTADataset'\ndata_root = '/mnt/lustre/zhouyue.vendor/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 '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/annfiles/',\n img_prefix=\n '/mnt/lustre/zhouyue.vendor/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 '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/annfiles/',\n img_prefix=\n '/mnt/lustre/zhouyue.vendor/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='/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/test/images/',\n img_prefix='/mnt/lustre/zhouyue.vendor/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=0.0025, 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 = 'le135'\nmodel = dict(\n type='S2ANet',\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 fam_head=dict(\n type='RotatedRetinaHead',\n num_classes=15,\n in_channels=256,\n stacked_convs=2,\n feat_channels=256,\n assign_by_circumhbbox=None,\n anchor_generator=dict(\n type='RotatedAnchorGenerator',\n scales=[4],\n ratios=[1.0],\n strides=[8, 16, 32, 64, 128]),\n bbox_coder=dict(\n type='DeltaXYWHAOBBoxCoder',\n angle_range='le135',\n norm_factor=1,\n edge_swap=False,\n proj_xy=True,\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='SmoothL1Loss', beta=0.11, loss_weight=1.0)),\n align_cfgs=dict(\n type='AlignConv',\n kernel_size=3,\n channels=256,\n featmap_strides=[8, 16, 32, 64, 128]),\n odm_head=dict(\n type='ODMRefineHead',\n num_classes=15,\n in_channels=256,\n stacked_convs=2,\n feat_channels=256,\n assign_by_circumhbbox=None,\n anchor_generator=dict(\n type='PseudoAnchorGenerator', strides=[8, 16, 32, 64, 128]),\n bbox_coder=dict(\n type='DeltaXYWHAOBBoxCoder',\n angle_range='le135',\n norm_factor=1,\n edge_swap=False,\n proj_xy=True,\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='SmoothL1Loss', beta=0.11, loss_weight=1.0)),\n train_cfg=dict(\n fam_cfg=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.4,\n min_pos_iou=0,\n ignore_iof_thr=-1,\n iou_calculator=dict(type='RBboxOverlaps2D')),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n odm_cfg=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.4,\n min_pos_iou=0,\n ignore_iof_thr=-1,\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))\nfp16 = dict(loss_scale='dynamic')\nwork_dir = 'work_dirs/s2anet_r50_fpn_fp16_1x_dota_le135'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 1817041655, "exp_name": "s2anet_r50_fpn_fp16_1x_dota_le135.py"} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.001, "memory": 2219, "data_time": 2.63789, "fam.loss_cls": 1.13291, "fam.loss_bbox": 1.11037, "odm.loss_cls": 1.16416, "odm.loss_bbox": 1.01808, "loss": 4.42553, "grad_norm": Infinity, "time": 2.85448} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00116, "memory": 2219, "data_time": 0.00904, "fam.loss_cls": 1.10322, "fam.loss_bbox": 0.9966, "odm.loss_cls": 1.16911, "odm.loss_bbox": 0.96607, "loss": 4.235, "grad_norm": 8.47938, "time": 0.21122} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00133, "memory": 2219, "data_time": 0.00767, "fam.loss_cls": 1.01336, "fam.loss_bbox": 0.87887, "odm.loss_cls": 1.14269, "odm.loss_bbox": 0.79892, "loss": 3.83384, "grad_norm": 9.00548, "time": 0.2099} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0015, "memory": 2219, "data_time": 0.00849, "fam.loss_cls": 1.008, "fam.loss_bbox": 1.06177, "odm.loss_cls": 1.15407, "odm.loss_bbox": 0.99961, "loss": 4.22346, "grad_norm": 9.86382, "time": 0.20487} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00166, "memory": 2219, "data_time": 0.00797, "fam.loss_cls": 1.05075, "fam.loss_bbox": 0.98998, "odm.loss_cls": 1.13597, "odm.loss_bbox": 0.96486, "loss": 4.14156, "grad_norm": 10.11491, "time": 0.19717} {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00183, "memory": 2219, "data_time": 0.00742, "fam.loss_cls": 0.96338, "fam.loss_bbox": 0.93281, "odm.loss_cls": 1.05503, "odm.loss_bbox": 0.85863, "loss": 3.80985, "grad_norm": 8.93107, "time": 0.20187} {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.002, "memory": 2219, "data_time": 0.00761, "fam.loss_cls": 0.90231, "fam.loss_bbox": 0.84793, "odm.loss_cls": 1.10794, "odm.loss_bbox": 0.82932, "loss": 3.6875, "grad_norm": Infinity, "time": 0.20466} {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00216, "memory": 2219, "data_time": 0.01382, "fam.loss_cls": 1.04053, "fam.loss_bbox": 0.8796, "odm.loss_cls": 1.0543, "odm.loss_bbox": 0.87181, "loss": 3.84624, "grad_norm": 12.83633, "time": 0.2123} {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00233, "memory": 2219, "data_time": 0.00763, "fam.loss_cls": 0.94307, "fam.loss_bbox": 0.85683, "odm.loss_cls": 1.07902, "odm.loss_bbox": 0.83849, "loss": 3.71741, "grad_norm": 8.76189, "time": 0.20541} {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0025, "memory": 2219, "data_time": 0.00947, "fam.loss_cls": 1.03621, "fam.loss_bbox": 0.89379, "odm.loss_cls": 0.98454, "odm.loss_bbox": 0.78932, "loss": 3.70386, "grad_norm": Infinity, "time": 0.20489} {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0025, "memory": 2219, "data_time": 0.00758, "fam.loss_cls": 0.96879, "fam.loss_bbox": 0.80272, "odm.loss_cls": 1.02813, "odm.loss_bbox": 0.74611, "loss": 3.54575, "grad_norm": 9.99794, "time": 0.20256} {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0025, "memory": 2219, "data_time": 0.0077, "fam.loss_cls": 0.93315, "fam.loss_bbox": 0.84531, "odm.loss_cls": 0.98841, "odm.loss_bbox": 0.75749, "loss": 3.52436, "grad_norm": 10.44048, "time": 0.20091} {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0025, "memory": 2219, "data_time": 0.0075, "fam.loss_cls": 1.02208, "fam.loss_bbox": 0.9324, "odm.loss_cls": 1.08333, "odm.loss_bbox": 0.82086, "loss": 3.85867, "grad_norm": 8.67232, "time": 0.21101} {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0025, "memory": 2219, "data_time": 0.0086, "fam.loss_cls": 0.96822, "fam.loss_bbox": 0.83029, "odm.loss_cls": 0.96811, "odm.loss_bbox": 0.75194, "loss": 3.51855, "grad_norm": 7.47166, "time": 0.2063} {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0025, "memory": 2219, "data_time": 0.00695, "fam.loss_cls": 0.90945, "fam.loss_bbox": 0.86076, "odm.loss_cls": 0.97964, "odm.loss_bbox": 0.73706, "loss": 3.48691, "grad_norm": 8.02126, "time": 0.20096} {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0025, "memory": 2219, "data_time": 0.00831, "fam.loss_cls": 0.81353, "fam.loss_bbox": 0.78583, "odm.loss_cls": 0.82061, "odm.loss_bbox": 0.732, "loss": 3.15197, "grad_norm": 11.91105, "time": 0.20812} {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0025, "memory": 2219, "data_time": 0.01275, "fam.loss_cls": 0.82803, "fam.loss_bbox": 0.91541, "odm.loss_cls": 0.94107, "odm.loss_bbox": 0.72176, "loss": 3.40627, "grad_norm": 10.87362, "time": 0.20944} {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0025, "memory": 2219, "data_time": 0.00711, "fam.loss_cls": 0.87744, "fam.loss_bbox": 0.80766, "odm.loss_cls": 1.03323, "odm.loss_bbox": 0.72352, "loss": 3.44184, "grad_norm": 9.91417, "time": 0.21135} {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0025, "memory": 2219, "data_time": 0.00743, "fam.loss_cls": 0.8265, "fam.loss_bbox": 0.8277, "odm.loss_cls": 0.93966, "odm.loss_bbox": 0.69641, "loss": 3.29027, "grad_norm": 12.03483, "time": 0.19933} {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0025, "memory": 2219, "data_time": 0.00768, "fam.loss_cls": 0.82583, "fam.loss_bbox": 0.83425, "odm.loss_cls": 0.91795, "odm.loss_bbox": 0.66904, "loss": 3.24707, "grad_norm": 8.21048, "time": 0.2201} {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0025, "memory": 2219, "data_time": 0.00709, "fam.loss_cls": 0.82432, "fam.loss_bbox": 0.74717, "odm.loss_cls": 1.12125, "odm.loss_bbox": 0.59758, "loss": 3.29032, "grad_norm": Infinity, "time": 0.20098} {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0025, "memory": 2219, "data_time": 0.00781, "fam.loss_cls": 0.84778, "fam.loss_bbox": 0.64766, "odm.loss_cls": 0.85023, "odm.loss_bbox": 0.54391, "loss": 2.88957, "grad_norm": 8.3522, "time": 0.20354} {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0025, "memory": 2219, "data_time": 0.00722, "fam.loss_cls": 0.80238, "fam.loss_bbox": 0.69948, "odm.loss_cls": 0.79323, "odm.loss_bbox": 0.57911, "loss": 2.87419, "grad_norm": 9.09436, "time": 0.20041} {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0025, "memory": 2219, "data_time": 0.00721, "fam.loss_cls": 0.83823, "fam.loss_bbox": 0.71414, "odm.loss_cls": 0.77448, "odm.loss_bbox": 0.64167, "loss": 2.96851, "grad_norm": 7.59612, "time": 0.2053} {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0025, "memory": 2219, "data_time": 0.0073, "fam.loss_cls": 0.75098, "fam.loss_bbox": 0.80458, "odm.loss_cls": 0.7743, "odm.loss_bbox": 0.74001, "loss": 3.06988, "grad_norm": 9.16097, "time": 0.20588} {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0025, "memory": 2219, "data_time": 0.00738, "fam.loss_cls": 0.81589, "fam.loss_bbox": 0.69614, "odm.loss_cls": 0.80829, "odm.loss_bbox": 0.59093, "loss": 2.91125, "grad_norm": 8.46733, "time": 0.20553} {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0025, "memory": 2219, 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