{"env_info": "sys.platform: linux\nPython: 3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]\nCUDA available: True\nGPU 0: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.3.r11.3/compiler.29920130_0\nGCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0\nPyTorch: 1.10.0+cu113\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX512\n - CUDA Runtime 11.3\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;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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 -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -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=1.10.0, 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: 0.11.1+cu113\nOpenCV: 4.5.5\nMMCV: 1.4.8\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMRotate: 0.2.0+", "config": "dataset_type = 'DOTADataset'\ndata_root = '/root/autodl-tmp/dataset/dota_split/'\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='/root/autodl-tmp/dataset/dota_split/trainval/annfiles/',\n img_prefix='/root/autodl-tmp/dataset/dota_split/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='/root/autodl-tmp/dataset/dota_split/trainval/annfiles/',\n img_prefix='/root/autodl-tmp/dataset/dota_split/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='/root/autodl-tmp/dataset/dota_split/test/images/',\n img_prefix='/root/autodl-tmp/dataset/dota_split/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=0.0025, 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(\n interval=50,\n hooks=[dict(type='TextLoggerHook'),\n dict(type='TensorboardLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nangle_version = 'le90'\nmodel = dict(\n type='RotatedFCOS',\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_output',\n num_outs=5,\n relu_before_extra_convs=True),\n bbox_head=dict(\n type='RotatedFCOSHead',\n num_classes=15,\n in_channels=256,\n stacked_convs=4,\n feat_channels=256,\n strides=[8, 16, 32, 64, 128],\n center_sampling=True,\n center_sample_radius=1.5,\n norm_on_bbox=True,\n centerness_on_reg=True,\n separate_angle=False,\n scale_angle=True,\n bbox_coder=dict(type='DistanceAnglePointCoder', angle_version='le90'),\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='PolyIoULoss', loss_weight=1.0),\n loss_centerness=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),\n train_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 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 = '/root/tf-logs/fcos_iou'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 688540654, "exp_name": "rotated_fcos_r50_fpn_1x_dota_le90.py"} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.001, "memory": 3442, "data_time": 0.05929, "loss_cls": 1.15194, "loss_bbox": 1.50337, "loss_centerness": 0.70608, "loss": 3.36139, "grad_norm": 35.40922, "time": 0.255} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00116, "memory": 3442, "data_time": 0.00673, "loss_cls": 1.10868, "loss_bbox": 1.06917, "loss_centerness": 0.70768, "loss": 2.88552, "grad_norm": 13.80554, "time": 0.20087} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00133, "memory": 3797, "data_time": 0.00731, "loss_cls": 1.12053, "loss_bbox": 1.10912, "loss_centerness": 0.69728, "loss": 2.92694, "grad_norm": 12.06153, "time": 0.20505} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0015, "memory": 3797, "data_time": 0.00666, "loss_cls": 0.93856, "loss_bbox": 1.04535, "loss_centerness": 0.69847, "loss": 2.68239, "grad_norm": 11.26152, "time": 0.19942} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00166, "memory": 3797, "data_time": 0.00806, "loss_cls": 0.98514, "loss_bbox": 0.95995, "loss_centerness": 0.70188, "loss": 2.64697, "grad_norm": 7.43586, "time": 0.19855} {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00183, "memory": 3797, "data_time": 0.00721, "loss_cls": 0.88778, "loss_bbox": 1.06397, "loss_centerness": 0.69781, "loss": 2.64955, "grad_norm": 7.61028, "time": 0.19864} {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.002, "memory": 3797, "data_time": 0.00727, "loss_cls": 0.83203, "loss_bbox": 1.0163, "loss_centerness": 0.6992, "loss": 2.54753, "grad_norm": 7.84349, "time": 0.20189} {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00216, "memory": 3797, "data_time": 0.00762, "loss_cls": 0.83598, "loss_bbox": 1.00028, "loss_centerness": 0.69631, "loss": 2.53258, "grad_norm": 6.57458, "time": 0.20005} {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00233, "memory": 3797, "data_time": 0.007, "loss_cls": 0.8172, "loss_bbox": 1.02489, "loss_centerness": 0.70153, "loss": 2.54362, "grad_norm": 8.21121, "time": 0.1968} {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0025, "memory": 3807, "data_time": 0.00743, "loss_cls": 0.84636, "loss_bbox": 1.0927, "loss_centerness": 0.7017, "loss": 2.64076, "grad_norm": 6.2998, "time": 0.20191} {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0025, "memory": 3807, "data_time": 0.00762, "loss_cls": 0.81418, "loss_bbox": 1.03564, "loss_centerness": 0.6956, "loss": 2.54542, "grad_norm": 8.67459, "time": 0.20107} {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0025, "memory": 3807, "data_time": 0.00689, "loss_cls": 0.83673, "loss_bbox": 1.0414, "loss_centerness": 0.70631, "loss": 2.58444, "grad_norm": 6.85303, "time": 0.19904} {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0025, "memory": 3807, "data_time": 0.00692, "loss_cls": 0.7834, "loss_bbox": 1.03239, "loss_centerness": 0.68919, "loss": 2.50499, "grad_norm": 6.51989, "time": 0.19514} {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0025, "memory": 3807, "data_time": 0.00754, "loss_cls": 0.79563, "loss_bbox": 1.01756, "loss_centerness": 0.68593, "loss": 2.49913, "grad_norm": 6.74763, "time": 0.20138} {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0025, "memory": 3807, "data_time": 0.00734, "loss_cls": 0.81095, "loss_bbox": 0.93734, "loss_centerness": 0.67948, "loss": 2.42777, "grad_norm": 5.90643, "time": 0.20089} {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0025, "memory": 3807, "data_time": 0.00687, "loss_cls": 0.76896, "loss_bbox": 0.94891, "loss_centerness": 0.68455, "loss": 2.40243, "grad_norm": 5.70553, "time": 0.19903} {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0025, "memory": 3807, "data_time": 0.00727, "loss_cls": 0.76546, "loss_bbox": 0.96432, "loss_centerness": 0.68799, "loss": 2.41777, "grad_norm": 6.69514, "time": 0.20056} {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0025, "memory": 3870, "data_time": 0.00683, "loss_cls": 0.78226, "loss_bbox": 1.02517, "loss_centerness": 0.68989, "loss": 2.49733, "grad_norm": 5.10384, "time": 0.1999} {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0025, "memory": 3945, "data_time": 0.00687, "loss_cls": 0.83917, "loss_bbox": 0.94322, "loss_centerness": 0.68122, "loss": 2.46361, "grad_norm": 7.10259, "time": 0.20213} {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0025, "memory": 3945, "data_time": 0.00664, "loss_cls": 0.67963, "loss_bbox": 0.88663, "loss_centerness": 0.69542, "loss": 2.26167, "grad_norm": 6.00451, "time": 0.19981} {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0025, "memory": 3945, "data_time": 0.0065, "loss_cls": 0.73277, "loss_bbox": 0.93953, "loss_centerness": 0.68502, "loss": 2.35731, "grad_norm": 6.13132, "time": 0.19998} {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0025, "memory": 3945, "data_time": 0.00672, "loss_cls": 0.89031, "loss_bbox": 0.95108, "loss_centerness": 0.68811, "loss": 2.5295, "grad_norm": 5.20385, "time": 0.19798} {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0025, "memory": 3945, "data_time": 0.00705, "loss_cls": 0.78137, "loss_bbox": 0.92831, "loss_centerness": 0.6903, "loss": 2.39998, "grad_norm": 7.8508, "time": 0.19971} {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0025, "memory": 3945, "data_time": 0.00712, "loss_cls": 0.74515, "loss_bbox": 0.95544, "loss_centerness": 0.68814, "loss": 2.38873, "grad_norm": 6.04776, "time": 0.20231} {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0025, "memory": 3945, "data_time": 0.00706, "loss_cls": 0.67531, "loss_bbox": 0.9888, "loss_centerness": 0.71197, "loss": 2.37608, "grad_norm": 6.58396, "time": 0.19991} {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0025, "memory": 3945, "data_time": 0.00703, "loss_cls": 0.71354, "loss_bbox": 0.95557, "loss_centerness": 0.69325, "loss": 2.36235, "grad_norm": 5.12995, "time": 0.20023} {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0025, "memory": 3945, "data_time": 0.00674, "loss_cls": 0.64647, "loss_bbox": 0.91119, "loss_centerness": 0.68316, "loss": 2.24083, "grad_norm": 5.64523, "time": 0.19999} {"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0025, "memory": 3945, "data_time": 0.00663, "loss_cls": 0.6817, "loss_bbox": 0.93487, "loss_centerness": 0.69039, "loss": 2.30696, "grad_norm": 6.0847, "time": 0.1996} {"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0025, "memory": 3945, "data_time": 0.00658, "loss_cls": 0.7316, "loss_bbox": 0.95254, "loss_centerness": 0.67755, "loss": 2.36169, "grad_norm": 6.10689, "time": 0.20043} {"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0025, "memory": 3945, "data_time": 0.00665, "loss_cls": 0.69285, "loss_bbox": 0.86113, "loss_centerness": 0.67768, "loss": 2.23165, "grad_norm": 6.05971, "time": 0.19828} {"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.0025, "memory": 3945, "data_time": 0.0064, "loss_cls": 0.67399, "loss_bbox": 0.88797, "loss_centerness": 0.6779, "loss": 2.23987, "grad_norm": 5.60833, "time": 0.20138} {"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.0025, "memory": 3945, "data_time": 0.00716, "loss_cls": 0.61038, "loss_bbox": 0.878, "loss_centerness": 0.6895, "loss": 2.17787, "grad_norm": 4.84467, "time": 0.19954} {"mode": "train", "epoch": 1, "iter": 1650, "lr": 0.0025, "memory": 3945, "data_time": 0.00727, "loss_cls": 0.65693, "loss_bbox": 0.88467, "loss_centerness": 0.67849, "loss": 2.22008, "grad_norm": 5.86457, "time": 0.2005} {"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.0025, "memory": 3945, "data_time": 0.00749, "loss_cls": 0.61682, "loss_bbox": 0.91233, "loss_centerness": 0.67858, "loss": 2.20774, "grad_norm": 4.79846, "time": 0.2001} {"mode": "train", "epoch": 1, "iter": 1750, "lr": 0.0025, "memory": 3945, "data_time": 0.00707, "loss_cls": 0.64743, "loss_bbox": 0.92717, "loss_centerness": 0.68633, "loss": 2.26094, "grad_norm": 5.35652, "time": 0.20009} {"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.0025, "memory": 3945, "data_time": 0.00681, "loss_cls": 0.61239, "loss_bbox": 0.86357, "loss_centerness": 0.67876, "loss": 2.15473, "grad_norm": 5.81026, "time": 0.20003} {"mode": "train", "epoch": 1, "iter": 1850, "lr": 0.0025, "memory": 3945, "data_time": 0.00698, "loss_cls": 0.65574, "loss_bbox": 0.9542, "loss_centerness": 0.68573, "loss": 2.29567, "grad_norm": 6.55205, "time": 0.19999} {"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.0025, "memory": 3945, "data_time": 0.00643, "loss_cls": 0.63623, "loss_bbox": 0.87851, "loss_centerness": 0.68796, "loss": 2.2027, "grad_norm": 5.98452, "time": 0.19997} {"mode": "train", "epoch": 1, "iter": 1950, "lr": 0.0025, "memory": 3988, "data_time": 0.00659, "loss_cls": 0.63264, "loss_bbox": 0.89237, "loss_centerness": 0.67334, "loss": 2.19835, "grad_norm": 5.44626, "time": 0.20002} {"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.0025, "memory": 3988, "data_time": 0.00643, "loss_cls": 0.65173, "loss_bbox": 0.88678, 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