diff --git "a/ablation/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/20230304_211228.log.json" "b/ablation/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/20230304_211228.log.json" new file mode 100644--- /dev/null +++ "b/ablation/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits/20230304_211228.log.json" @@ -0,0 +1,1452 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.16 (default, Jan 17 2023, 22:20:44) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/petrelfs/laizeqiang/miniconda3/envs/torch\nNVCC: Cuda compilation tools, release 11.6, V11.6.124\nGCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)\nPyTorch: 1.13.1\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 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 11.6\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 8.3.2 (built against CUDA 11.5)\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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 -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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=1.13.1, 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, USE_ROCM=OFF, \n\nTorchVision: 0.14.1\nOpenCV: 4.7.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.6\nMMSegmentation: 0.30.0+6749699", "seed": 1340171616, "exp_name": "ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits.py", "mmseg_version": "0.30.0+6749699", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\ncheckpoint = 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth'\nmodel = dict(\n type='EncoderDecoderFreeze',\n freeze_parameters=['backbone', 'decode_head'],\n pretrained=\n 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth',\n backbone=dict(\n type='MixVisionTransformerCustomInitWeights',\n in_channels=3,\n embed_dims=64,\n num_stages=4,\n num_layers=[3, 4, 6, 3],\n num_heads=[1, 2, 5, 8],\n patch_sizes=[7, 3, 3, 3],\n sr_ratios=[8, 4, 2, 1],\n out_indices=(0, 1, 2, 3),\n mlp_ratio=4,\n qkv_bias=True,\n drop_rate=0.0,\n attn_drop_rate=0.0,\n drop_path_rate=0.1,\n pretrained=\n 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth'\n ),\n decode_head=dict(\n type='SegformerHeadUnetFCHeadSingleStepLogits',\n pretrained=\n 'pretrained/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth',\n dim=128,\n out_dim=256,\n unet_channels=166,\n dim_mults=[1, 1, 1],\n cat_embedding_dim=16,\n in_channels=[64, 128, 320, 512],\n in_index=[0, 1, 2, 3],\n channels=256,\n dropout_ratio=0.1,\n num_classes=151,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n ignore_index=0,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n train_cfg=dict(),\n test_cfg=dict(mode='whole'))\ndataset_type = 'ADE20K151Dataset'\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=False),\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=0),\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(type='Resize', keep_ratio=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='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=4,\n workers_per_gpu=4,\n train=dict(\n type='ADE20K151Dataset',\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=False),\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=0),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ]),\n val=dict(\n type='ADE20K151Dataset',\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(type='Resize', keep_ratio=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(\n type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='ADE20K151Dataset',\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(type='Resize', keep_ratio=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(\n type='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nlog_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\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', lr=0.00015, betas=[0.9, 0.96], weight_decay=0.045)\noptimizer_config = dict()\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=1000,\n warmup_ratio=1e-06,\n step=10000,\n gamma=0.5,\n min_lr=1e-06,\n by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=80000)\ncheckpoint_config = dict(by_epoch=False, interval=8000)\nevaluation = dict(\n interval=8000, metric='mIoU', pre_eval=True, save_best='mIoU')\nwork_dir = './work_dirs2/ablation_segformer_mit_b2_segformer_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151_logits'\ngpu_ids = range(0, 8)\nauto_resume = True\ndevice = 'cuda'\nseed = 1340171616\n", "CLASSES": ["background", "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": [[0, 0, 0], [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, 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0], [82, 0, 255], [163, 255, 0], [255, 235, 0], [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], [102, 255, 0], [92, 0, 255]], "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 8000, "lr": 0.00015, "memory": 19833, "data_time": 0.47485, "decode.loss_ce": 0.39765, "decode.acc_seg": 85.8395, "loss": 0.39765, "time": 6.53204} +{"mode": "val", "epoch": 1, "iter": 250, "lr": 0.00015, "aAcc": 0.8016, "mIoU": 0.4262, "mAcc": 0.5519, "IoU.background": NaN, "IoU.wall": 0.7483, "IoU.building": 0.7777, "IoU.sky": 0.9363, "IoU.floor": 0.7977, "IoU.tree": 0.6749, "IoU.ceiling": 0.812, "IoU.road": 0.792, "IoU.bed ": 0.8561, "IoU.windowpane": 0.5848, "IoU.grass": 0.6125, "IoU.cabinet": 0.5595, "IoU.sidewalk": 0.5807, "IoU.person": 0.735, "IoU.earth": 0.3475, "IoU.door": 0.3973, "IoU.table": 0.5265, "IoU.mountain": 0.5507, "IoU.plant": 0.4789, "IoU.curtain": 0.7105, "IoU.chair": 0.5016, "IoU.car": 0.7535, "IoU.water": 0.5594, "IoU.painting": 0.6736, "IoU.sofa": 0.5965, "IoU.shelf": 0.3974, "IoU.house": 0.4192, "IoU.sea": 0.594, "IoU.mirror": 0.6083, "IoU.rug": 0.608, "IoU.field": 0.273, "IoU.armchair": 0.36, "IoU.seat": 0.6278, "IoU.fence": 0.2438, "IoU.desk": 0.4204, "IoU.rock": 0.358, "IoU.wardrobe": 0.523, "IoU.lamp": 0.4904, "IoU.bathtub": 0.6196, "IoU.railing": 0.2339, "IoU.cushion": 0.5035, "IoU.base": 0.1898, "IoU.box": 0.1672, "IoU.column": 0.4236, "IoU.signboard": 0.3295, "IoU.chest of drawers": 0.362, "IoU.counter": 0.2266, "IoU.sand": 0.3919, "IoU.sink": 0.6089, "IoU.skyscraper": 0.4741, "IoU.fireplace": 0.6971, "IoU.refrigerator": 0.7005, "IoU.grandstand": 0.47, "IoU.path": 0.1719, "IoU.stairs": 0.1419, "IoU.runway": 0.6641, "IoU.case": 0.5199, "IoU.pool table": 0.8881, "IoU.pillow": 0.5119, "IoU.screen door": 0.5377, "IoU.stairway": 0.2169, "IoU.river": 0.1033, "IoU.bridge": 0.2452, "IoU.bookcase": 0.3778, "IoU.blind": 0.3438, "IoU.coffee table": 0.5125, "IoU.toilet": 0.7606, "IoU.flower": 0.3278, "IoU.book": 0.4313, "IoU.hill": 0.1346, "IoU.bench": 0.3833, "IoU.countertop": 0.4916, "IoU.stove": 0.6762, "IoU.palm": 0.4742, "IoU.kitchen island": 0.34, "IoU.computer": 0.5635, "IoU.swivel chair": 0.3606, "IoU.boat": 0.628, "IoU.bar": 0.2361, "IoU.arcade machine": 0.6506, "IoU.hovel": 0.2042, "IoU.bus": 0.7567, "IoU.towel": 0.5828, "IoU.light": 0.3798, "IoU.truck": 0.1438, "IoU.tower": 0.1004, "IoU.chandelier": 0.5522, "IoU.awning": 0.1379, "IoU.streetlight": 0.1804, "IoU.booth": 0.4396, "IoU.television receiver": 0.6241, "IoU.airplane": 0.548, "IoU.dirt track": 0.1099, "IoU.apparel": 0.2812, "IoU.pole": 0.1195, "IoU.land": 0.0281, "IoU.bannister": 0.0418, "IoU.escalator": 0.2023, "IoU.ottoman": 0.3575, "IoU.bottle": 0.2473, "IoU.buffet": 0.4351, "IoU.poster": 0.2096, "IoU.stage": 0.1326, "IoU.van": 0.3071, "IoU.ship": 0.7613, "IoU.fountain": 0.0664, "IoU.conveyer belt": 0.8165, "IoU.canopy": 0.2237, "IoU.washer": 0.8141, "IoU.plaything": 0.1851, "IoU.swimming pool": 0.7165, "IoU.stool": 0.3255, "IoU.barrel": 0.2459, "IoU.basket": 0.2024, "IoU.waterfall": 0.501, "IoU.tent": 0.9409, "IoU.bag": 0.089, "IoU.minibike": 0.5459, "IoU.cradle": 0.7996, "IoU.oven": 0.4096, "IoU.ball": 0.3852, "IoU.food": 0.3686, "IoU.step": 0.0494, "IoU.tank": 0.4927, "IoU.trade name": 0.1639, "IoU.microwave": 0.7189, "IoU.pot": 0.3055, "IoU.animal": 0.4784, "IoU.bicycle": 0.369, "IoU.lake": 0.5628, "IoU.dishwasher": 0.6081, "IoU.screen": 0.5849, 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