diff --git "a/segmentation/upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.log.json" "b/segmentation/upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.log.json" new file mode 100644--- /dev/null +++ "b/segmentation/upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.log.json" @@ -0,0 +1,1681 @@ +{"env_info": "sys.platform: linux\nPython: 3.9.19 (main, May 6 2024, 19:43:03) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/petrelfs/share/cuda-11.7/\nNVCC: Cuda compilation tools, release 11.7, V11.7.99\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.12.0+cu113\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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: AVX2\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 8.3.2 (built against CUDA 11.5)\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/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-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.13.0+cu113\nOpenCV: 4.9.0\nMMCV: 1.7.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.7\nMMSegmentation: 0.27.0+6d3ca17", "seed": 942084185, "exp_name": "upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py", "mmseg_version": "0.27.0+6d3ca17", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n type='EncoderDecoder',\n pretrained=None,\n backbone=dict(\n type='PIIPTwoBranch',\n n_points=4,\n deform_num_heads=16,\n cffn_ratio=0.25,\n deform_ratio=0.5,\n with_cffn=True,\n interact_attn_type='deform',\n interaction_drop_path_rate=0.4,\n interaction_proj=False,\n norm_layer='none',\n branch1=dict(\n img_size=256,\n pretrain_img_size=224,\n patch_size=16,\n pretrain_patch_size=14,\n depth=48,\n embed_dim=3200,\n num_heads=25,\n mlp_ratio=4,\n qkv_bias=False,\n init_values=0.1,\n with_cp=True,\n use_flash_attn=True,\n qk_normalization=True,\n layerscale_force_fp32=False,\n with_fpn=False,\n drop_path_rate=0.4,\n pretrained='pretrained/intern_vit_6b_224px.pth',\n norm_layer_type='RMSNorm',\n mlp_type='fused_mlp'),\n branch2=dict(\n img_size=512,\n pretrain_img_size=224,\n patch_size=16,\n pretrain_patch_size=14,\n depth=32,\n embed_dim=1280,\n num_heads=16,\n mlp_ratio=4,\n qkv_bias=True,\n init_values=1.0,\n with_cp=True,\n use_flash_attn=True,\n qk_normalization=False,\n layerscale_force_fp32=False,\n with_fpn=False,\n drop_path_rate=0.4,\n pretrained='pretrained/mae_pretrain_vit_huge.pth',\n norm_layer_type='LayerNorm',\n mlp_type='fused_mlp')),\n decode_head=dict(\n type='UPerHead',\n in_channels=[3200, 3200, 3200, 3200],\n in_index=[0, 1, 2, 3],\n pool_scales=(1, 2, 3, 6),\n channels=1536,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n auxiliary_head=dict(\n type='FCNHead',\n in_channels=3200,\n in_index=2,\n channels=1536,\n num_convs=1,\n concat_input=False,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),\n train_cfg=dict(),\n test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)))\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (512, 512)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\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=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(\n type='SETR_Resize',\n keep_ratio=True,\n crop_size=(512, 512),\n setr_multi_scale=True),\n dict(type='RandomFlip'),\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=2,\n workers_per_gpu=4,\n train=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/training',\n ann_dir='annotations/training',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\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=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ]),\n val=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(\n type='SETR_Resize',\n keep_ratio=True,\n crop_size=(512, 512),\n setr_multi_scale=True),\n dict(type='RandomFlip'),\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 test=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(\n type='SETR_Resize',\n keep_ratio=True,\n crop_size=(512, 512),\n setr_multi_scale=True),\n dict(type='RandomFlip'),\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 ]))\nlog_config = dict(\n interval=50,\n hooks=[\n dict(type='TextLoggerHook', by_epoch=False),\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\noptimizer = dict(\n type='AdamW',\n lr=4e-05,\n betas=(0.9, 0.999),\n weight_decay=0.05,\n constructor='CustomLayerDecayOptimizerConstructor',\n paramwise_cfg=dict(num_layers=48, layer_decay_rate=0.95, skip_stride=1.5))\noptimizer_config = dict()\nlr_config = dict(\n policy='poly',\n warmup='linear',\n warmup_iters=1500,\n warmup_ratio=1e-06,\n power=1.0,\n min_lr=0.0,\n by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=80000)\ncheckpoint_config = dict(\n by_epoch=False, interval=2000, deepspeed=True, max_keep_ckpts=1)\nevaluation = dict(interval=1000, metric='mIoU', pre_eval=True, save_best=None)\ndeepspeed = True\ndeepspeed_config = 'zero_configs/adam_zero1_bf16.json'\npretrained = None\ncustom_hooks = [dict(type='ToBFloat16Hook', priority=49)]\nwork_dir = './work_dirs/upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5'\ngpu_ids = range(0, 8)\nauto_resume = False\ndevice = 'cuda'\nseed = 942084185\n", "CLASSES": ["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", "awning", "streetlight", "booth", "television receiver", "airplane", "dirt track", "apparel", "pole", "land", "bannister", "escalator", "ottoman", "bottle", "buffet", "poster", "stage", "van", "ship", "fountain", "conveyer belt", "canopy", "washer", "plaything", "swimming pool", "stool", "barrel", "basket", "waterfall", "tent", "bag", "minibike", "cradle", "oven", "ball", "food", "step", "tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket", "sculpture", "hood", "sconce", "vase", "traffic light", "tray", "ashcan", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass", "clock", "flag"], "PALETTE": [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0], [255, 224, 0], [153, 255, 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