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waymo_kitti_24e.txt
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waymo_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+a5e9011", "seed": null, "exp_name": "SimIPU_r50_waymo_50e_kitti.py", "depth_version": "0.0.0+a5e9011", "config": "norm_cfg = dict(type='BN', requires_grad=True)\nmodel = dict(\n type='DepthEncoderDecoder',\n backbone=dict(\n type='ResNet',\n depth=50,\n num_stages=4,\n out_indices=(0, 1, 2, 3, 4),\n style='pytorch',\n norm_cfg=dict(type='BN', requires_grad=True),\n init_cfg=dict(\n type='Pretrained',\n checkpoint='nfs/saves/SimIPU/checkpoints/SimIPU_waymo_50e.pth')),\n decode_head=dict(\n type='DenseDepthHead',\n in_channels=[64, 256, 512, 1024, 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=1.0),\n scale_up=True,\n min_depth=0.001,\n max_depth=80),\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 = 0.0001\noptimizer = dict(\n type='AdamW', lr=0.0001, betas=(0.95, 0.99), weight_decay=0.01)\nlr_config = dict(\n policy='OneCycle',\n max_lr=0.0001,\n div_factor=25,\n final_div_factor=100,\n by_epoch=False)\noptimizer_config = dict(grad_clip=dict(max_norm=35, 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=6, pre_eval=True)\nwork_dir = 'nfs/saves/SimIPU/waymo_50e_kitti'\ngpu_ids = range(0, 1)\nseed = None\n"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 14129, "data_time": 0.40016, "decode.loss_depth": 0.40064, "loss": 0.40064, "grad_norm": 4.14915, "time": 0.96259}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 14129, "data_time": 0.00463, "decode.loss_depth": 0.27093, "loss": 0.27093, "grad_norm": 3.49718, "time": 0.39992}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0, "memory": 14129, "data_time": 0.00439, "decode.loss_depth": 0.24655, "loss": 0.24655, "grad_norm": 2.74776, "time": 0.40002}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0, "memory": 14129, "data_time": 0.00437, "decode.loss_depth": 0.22604, "loss": 0.22604, "grad_norm": 3.16628, "time": 0.40053}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0, "memory": 14129, "data_time": 0.00455, "decode.loss_depth": 0.21811, "loss": 0.21811, "grad_norm": 3.31699, "time": 0.40153}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.0, "memory": 14129, "data_time": 0.00444, "decode.loss_depth": 0.20954, "loss": 0.20954, "grad_norm": 2.72526, "time": 0.40191}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.0, "memory": 14129, "data_time": 0.00451, "decode.loss_depth": 0.20452, "loss": 0.20452, "grad_norm": 3.2926, "time": 0.40181}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.0, "memory": 14129, "data_time": 0.00454, "decode.loss_depth": 0.19599, "loss": 0.19599, "grad_norm": 3.24451, "time": 0.40193}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.0, "memory": 14129, "data_time": 0.0044, "decode.loss_depth": 0.19082, "loss": 0.19082, "grad_norm": 3.57362, "time": 0.40188}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0, "memory": 14129, "data_time": 0.00448, "decode.loss_depth": 0.18778, "loss": 0.18778, "grad_norm": 3.20175, "time": 0.4017}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0, "memory": 14129, "data_time": 0.00459, "decode.loss_depth": 0.18115, "loss": 0.18115, "grad_norm": 3.09784, "time": 0.4016}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0, "memory": 14129, "data_time": 0.00443, "decode.loss_depth": 0.18246, "loss": 0.18246, "grad_norm": 3.14646, "time": 0.40197}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0, "memory": 14129, "data_time": 0.00462, "decode.loss_depth": 0.18074, "loss": 0.18074, "grad_norm": 2.87989, "time": 0.40234}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 1e-05, "memory": 14129, "data_time": 0.00447, "decode.loss_depth": 0.17369, "loss": 0.17369, "grad_norm": 3.46757, "time": 0.40185}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 1e-05, "memory": 14129, "data_time": 0.0046, "decode.loss_depth": 0.17127, "loss": 0.17127, "grad_norm": 2.85144, "time": 0.40196}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 1e-05, "memory": 14129, "data_time": 0.00464, "decode.loss_depth": 0.16918, "loss": 0.16918, "grad_norm": 2.48748, "time": 0.40173}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 1e-05, "memory": 14129, "data_time": 0.00451, "decode.loss_depth": 0.16621, "loss": 0.16621, "grad_norm": 2.8841, "time": 0.40179}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 1e-05, "memory": 14129, "data_time": 0.00469, "decode.loss_depth": 0.1666, "loss": 0.1666, "grad_norm": 2.75614, "time": 0.40224}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 1e-05, "memory": 14129, "data_time": 0.00467, "decode.loss_depth": 0.16155, "loss": 0.16155, "grad_norm": 2.91467, "time": 0.40206}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 1e-05, "memory": 14129, "data_time": 0.00464, "decode.loss_depth": 0.16589, "loss": 0.16589, "grad_norm": 4.10793, "time": 0.40191}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 1e-05, "memory": 14129, "data_time": 0.00458, "decode.loss_depth": 0.15962, "loss": 0.15962, "grad_norm": 2.37562, "time": 0.40159}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 1e-05, "memory": 14129, "data_time": 0.00472, "decode.loss_depth": 0.15662, "loss": 0.15662, "grad_norm": 3.20545, "time": 0.4019}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 1e-05, "memory": 14129, "data_time": 0.00461, "decode.loss_depth": 0.15467, "loss": 0.15467, "grad_norm": 2.64243, "time": 0.40195}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 1e-05, "memory": 14129, "data_time": 0.0046, "decode.loss_depth": 0.15316, "loss": 0.15316, "grad_norm": 2.83323, "time": 0.40203}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 1e-05, "memory": 14129, "data_time": 0.00476, "decode.loss_depth": 0.15557, "loss": 0.15557, "grad_norm": 3.70046, "time": 0.40214}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 1e-05, "memory": 14129, "data_time": 0.00467, "decode.loss_depth": 0.15386, "loss": 0.15386, "grad_norm": 3.11696, "time": 0.40151}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 1e-05, "memory": 14129, "data_time": 0.00477, "decode.loss_depth": 0.15116, "loss": 0.15116, "grad_norm": 2.92341, "time": 0.40212}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 1e-05, "memory": 14129, "data_time": 0.00466, "decode.loss_depth": 0.1501, "loss": 0.1501, "grad_norm": 3.99336, "time": 0.40162}
{"mode": "train", "epoch": 2, "iter": 50, "lr": 1e-05, "memory": 14129, "data_time": 0.05128, "decode.loss_depth": 0.14851, "loss": 0.14851, "grad_norm": 3.07158, "time": 0.44725}
{"mode": "train", "epoch": 2, "iter": 100, "lr": 1e-05, "memory": 14129, "data_time": 0.00449, "decode.loss_depth": 0.14627, "loss": 0.14627, "grad_norm": 3.97194, "time": 0.40092}
{"mode": "train", "epoch": 2, "iter": 150, "lr": 1e-05, "memory": 14129, "data_time": 0.00438, "decode.loss_depth": 0.14373, "loss": 0.14373, "grad_norm": 3.08664, "time": 0.40135}
{"mode": "train", "epoch": 2, "iter": 200, "lr": 1e-05, "memory": 14129, "data_time": 0.00453, "decode.loss_depth": 0.14451, "loss": 0.14451, "grad_norm": 3.30292, "time": 0.40141}
{"mode": "train", "epoch": 2, "iter": 250, "lr": 1e-05, "memory": 14129, "data_time": 0.00469, "decode.loss_depth": 0.14423, "loss": 0.14423, "grad_norm": 4.1012, "time": 0.40235}
{"mode": "train", "epoch": 2, "iter": 300, "lr": 1e-05, "memory": 14129, "data_time": 0.00468, "decode.loss_depth": 0.14357, "loss": 0.14357, "grad_norm": 4.48313, "time": 0.4022}
{"mode": "train", "epoch": 2, "iter": 350, "lr": 1e-05, "memory": 14129, "data_time": 0.00449, "decode.loss_depth": 0.14083, "loss": 0.14083, "grad_norm": 3.38885, "time": 0.4018}
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