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adabins_efnetb5ap_kitti_24e.txt
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{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0,1: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.1.TC455_06.29069683_0\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04) 9.4.0\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: 1.3.13\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nDepth: 0.0.0+f9dd5d9", "seed": null, "exp_name": "Adabins_efnetb5ap_kitti_24e.py", "depth_version": "0.0.0+f9dd5d9", "config": "model = dict(\n type='DepthEncoderDecoder',\n backbone=dict(type='EfficientNet'),\n decode_head=dict(\n type='AdabinsHead',\n in_channels=[24, 40, 64, 176, 2048],\n up_sample_channels=[128, 256, 512, 1024, 2048],\n channels=128,\n align_corners=True,\n loss_decode=dict(type='SigLoss', valid_mask=True, loss_weight=10),\n min_depth=0.001,\n max_depth=80,\n norm_cfg=dict(type='BN', requires_grad=True)),\n train_cfg=dict(),\n test_cfg=dict(mode='whole'))\ndataset_type = 'KITTIDataset'\ndata_root = 'data/kitti'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (352, 704)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='DepthLoadAnnotations'),\n dict(type='KBCrop', depth=True),\n dict(type='RandomRotate', prob=0.5, degree=2.5),\n dict(type='RandomFlip', prob=0.5),\n dict(type='RandomCrop', crop_size=(352, 704)),\n dict(\n type='ColorAug',\n prob=0.5,\n gamma_range=[0.9, 1.1],\n brightness_range=[0.9, 1.1],\n color_range=[0.9, 1.1]),\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='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'depth_gt'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='KBCrop', depth=False),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1216, 352),\n flip=True,\n flip_direction='horizontal',\n transforms=[\n dict(type='RandomFlip', direction='horizontal'),\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='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=8,\n workers_per_gpu=8,\n train=dict(\n type='KITTIDataset',\n data_root='data/kitti',\n img_dir='input',\n ann_dir='gt_depth',\n depth_scale=256,\n split='kitti_eigen_train.txt',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='DepthLoadAnnotations'),\n dict(type='KBCrop', depth=True),\n dict(type='RandomRotate', prob=0.5, degree=2.5),\n dict(type='RandomFlip', prob=0.5),\n dict(type='RandomCrop', crop_size=(352, 704)),\n dict(\n type='ColorAug',\n prob=0.5,\n gamma_range=[0.9, 1.1],\n brightness_range=[0.9, 1.1],\n color_range=[0.9, 1.1]),\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='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'depth_gt'])\n ],\n garg_crop=True,\n eigen_crop=False,\n min_depth=0.001,\n max_depth=80),\n val=dict(\n type='KITTIDataset',\n data_root='data/kitti',\n img_dir='input',\n ann_dir='gt_depth',\n depth_scale=256,\n split='kitti_eigen_test.txt',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='KBCrop', depth=False),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1216, 352),\n flip=True,\n flip_direction='horizontal',\n transforms=[\n dict(type='RandomFlip', direction='horizontal'),\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='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ],\n garg_crop=True,\n eigen_crop=False,\n min_depth=0.001,\n max_depth=80),\n test=dict(\n type='KITTIDataset',\n data_root='data/kitti',\n img_dir='input',\n ann_dir='gt_depth',\n depth_scale=256,\n split='kitti_eigen_test.txt',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='KBCrop', depth=False),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1216, 352),\n flip=True,\n flip_direction='horizontal',\n transforms=[\n dict(type='RandomFlip', direction='horizontal'),\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='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ],\n garg_crop=True,\n eigen_crop=False,\n min_depth=0.001,\n max_depth=80))\nlog_config = dict(\n interval=50,\n hooks=[\n dict(type='TextLoggerHook', by_epoch=True),\n dict(type='TensorboardLoggerHook')\n ])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\nmax_lr = 0.000357\noptimizer = dict(\n type='AdamW',\n lr=0.000357,\n betas=(0.95, 0.99),\n weight_decay=0.1,\n paramwise_cfg=dict(custom_keys=dict(decode_head=dict(lr_mult=10))))\nlr_config = dict(\n policy='OneCycle',\n max_lr=0.000357,\n div_factor=25,\n final_div_factor=100,\n by_epoch=False)\noptimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))\nrunner = dict(type='EpochBasedRunner', max_epochs=24)\ncheckpoint_config = dict(by_epoch=True, max_keep_ckpts=2, interval=1)\nevaluation = dict(by_epoch=True, interval=1, pre_eval=True)\nnorm_cfg = dict(type='BN', requires_grad=True)\nfind_unused_parameters = True\nSyncBN = True\nmomentum_config = dict(policy='OneCycle')\nwork_dir = 'nfs/saves/adabins/kitti'\ngpu_ids = range(0, 1)\nseed = None\n"}
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{"mode": "train", "epoch": 1, "iter": 100, "lr": 1e-05, "memory": 19642, "data_time": 0.00461, "decode.loss_depth": 2.89432, "decode.loss_chamfer": 1.60939, "loss": 4.5037, "grad_norm": 13.50468, "time": 0.67623}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 1e-05, "memory": 19642, "data_time": 0.00467, "decode.loss_depth": 2.36039, "decode.loss_chamfer": 1.2675, "loss": 3.62789, "grad_norm": 14.69388, "time": 0.67645}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 1e-05, "memory": 19642, "data_time": 0.00454, "decode.loss_depth": 2.08498, "decode.loss_chamfer": 1.10843, "loss": 3.1934, "grad_norm": 13.0293, "time": 0.67614}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 1e-05, "memory": 19642, "data_time": 0.00471, "decode.loss_depth": 1.99175, "decode.loss_chamfer": 1.44447, "loss": 3.43622, "grad_norm": 15.1327, "time": 0.67663}
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{"mode": "train", "epoch": 1, "iter": 350, "lr": 2e-05, "memory": 19642, "data_time": 0.00459, "decode.loss_depth": 1.82393, "decode.loss_chamfer": 1.26057, "loss": 3.08449, "grad_norm": 14.66179, "time": 0.67733}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 2e-05, "memory": 19642, "data_time": 0.00467, "decode.loss_depth": 1.75943, "decode.loss_chamfer": 1.02122, "loss": 2.78065, "grad_norm": 12.15571, "time": 0.67807}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 2e-05, "memory": 19642, "data_time": 0.00481, "decode.loss_depth": 1.73513, "decode.loss_chamfer": 0.90073, "loss": 2.63586, "grad_norm": 15.6703, "time": 0.6781}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 2e-05, "memory": 19642, "data_time": 0.00463, "decode.loss_depth": 1.67924, "decode.loss_chamfer": 0.70246, "loss": 2.3817, "grad_norm": 12.81545, "time": 0.67707}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 2e-05, "memory": 19642, "data_time": 0.00469, "decode.loss_depth": 1.63643, "decode.loss_chamfer": 1.02459, "loss": 2.66102, "grad_norm": 14.75464, "time": 0.67697}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 2e-05, "memory": 19642, "data_time": 0.00494, "decode.loss_depth": 1.64749, "decode.loss_chamfer": 0.70721, "loss": 2.3547, "grad_norm": 14.24629, "time": 0.67753}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 2e-05, "memory": 19642, "data_time": 0.00482, "decode.loss_depth": 1.60718, "decode.loss_chamfer": 0.71111, "loss": 2.31828, "grad_norm": 15.29228, "time": 0.67772}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 2e-05, "memory": 19642, "data_time": 0.00476, "decode.loss_depth": 1.56579, "decode.loss_chamfer": 0.70113, "loss": 2.26692, "grad_norm": 16.31527, "time": 0.67816}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 2e-05, "memory": 19642, "data_time": 0.00497, "decode.loss_depth": 1.53053, "decode.loss_chamfer": 0.55871, "loss": 2.08924, "grad_norm": 16.24379, "time": 0.67873}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 2e-05, "memory": 19642, "data_time": 0.00434, "decode.loss_depth": 1.48916, "decode.loss_chamfer": 0.56312, "loss": 2.05228, "grad_norm": 19.45884, "time": 0.67858}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 2e-05, "memory": 19642, "data_time": 0.00428, "decode.loss_depth": 1.46733, "decode.loss_chamfer": 0.5363, "loss": 2.00363, "grad_norm": 21.60186, "time": 0.67828}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 2e-05, "memory": 19642, "data_time": 0.00435, "decode.loss_depth": 1.49623, "decode.loss_chamfer": 0.41609, "loss": 1.91232, "grad_norm": 20.58207, "time": 0.67803}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 2e-05, "memory": 19642, "data_time": 0.0043, "decode.loss_depth": 1.48058, "decode.loss_chamfer": 0.36948, "loss": 1.85006, "grad_norm": 20.05328, "time": 0.67884}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 2e-05, "memory": 19642, "data_time": 0.00433, "decode.loss_depth": 1.40617, "decode.loss_chamfer": 0.41119, "loss": 1.81736, "grad_norm": 14.8887, "time": 0.67854}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 2e-05, "memory": 19642, "data_time": 0.00419, "decode.loss_depth": 1.41019, "decode.loss_chamfer": 0.31868, "loss": 1.72887, "grad_norm": 17.70461, "time": 0.67773}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 2e-05, "memory": 19642, "data_time": 0.00435, "decode.loss_depth": 1.37868, "decode.loss_chamfer": 0.42048, "loss": 1.79916, "grad_norm": 18.41924, "time": 0.67803}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 2e-05, "memory": 19642, "data_time": 0.00425, "decode.loss_depth": 1.3634, "decode.loss_chamfer": 0.28399, "loss": 1.64739, "grad_norm": 19.58696, "time": 0.67726}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 3e-05, "memory": 19642, "data_time": 0.00437, "decode.loss_depth": 1.37675, "decode.loss_chamfer": 0.3779, "loss": 1.75465, "grad_norm": 21.2705, "time": 0.67703}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 3e-05, "memory": 19642, "data_time": 0.00434, "decode.loss_depth": 1.40435, "decode.loss_chamfer": 0.34296, "loss": 1.74731, "grad_norm": 24.21924, "time": 0.67837}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 3e-05, "memory": 19642, "data_time": 0.00434, "decode.loss_depth": 1.3521, "decode.loss_chamfer": 0.34821, "loss": 1.70031, "grad_norm": 24.883, "time": 0.67769}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 3e-05, "memory": 19642, "data_time": 0.0045, "decode.loss_depth": 1.32013, "decode.loss_chamfer": 0.22522, "loss": 1.54535, "grad_norm": 21.32215, "time": 0.67811}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 3e-05, "memory": 19642, "data_time": 0.00435, "decode.loss_depth": 1.32246, "decode.loss_chamfer": 0.25513, "loss": 1.5776, "grad_norm": 25.58702, "time": 0.67769}
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