diff --git "a/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.log.json" "b/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.log.json"
new file mode 100644--- /dev/null
+++ "b/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.log.json"
@@ -0,0 +1,821 @@
+{"env_info": "sys.platform: linux\nPython: 3.9.0 (default, Nov 15 2020, 14:28:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/petrelfs/share/cuda-11.1\nNVCC: Build cuda_11.1.TC455_06.29069683_0\nGCC: gcc (GCC) 5.4.0\nPyTorch: 1.11.0+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 v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\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 8.2\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-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=1.11.0, 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: 0.12.0+cu113\nOpenCV: 4.6.0\nMMCV: 1.4.8\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\nMMSegmentation: 0.27.0+", "seed": 176705155, "exp_name": "mask2former_dcnv3_huge_512_40k_cocostuff10k_with_pretrain_ss.py", "mmseg_version": "0.27.0+", "config": "num_things_classes = 80\nnum_stuff_classes = 91\nnum_classes = 171\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n    type='EncoderDecoderMask2Former',\n    pretrained=None,\n    backbone=dict(\n        type='DCNv3_5_new2',\n        deform_points=9,\n        embed_dim=320,\n        depths=[6, 6, 32, 6],\n        num_heads=[10, 20, 40, 80],\n        deform_padding=True,\n        dilation_rates=[1],\n        kernel_size=5,\n        mlp_ratio=4.0,\n        drop_path_rate=0.5,\n        res_post_norm=True,\n        level2_post_norm=True,\n        op_types=['D', 'D', 'D', 'D'],\n        with_cp=False,\n        dcnv3_core_type='MSDeformAttnGrid4_softmax_ab',\n        out_indices=(0, 1, 2, 3),\n        init_cfg=None),\n    decode_head=dict(\n        type='Mask2FormerHead',\n        in_channels=[320, 640, 1280, 2560],\n        feat_channels=1024,\n        out_channels=1024,\n        in_index=[0, 1, 2, 3],\n        num_things_classes=80,\n        num_stuff_classes=91,\n        num_queries=200,\n        num_transformer_feat_level=3,\n        pixel_decoder=dict(\n            type='MSDeformAttnPixelDecoder',\n            num_outs=3,\n            norm_cfg=dict(type='GN', num_groups=32),\n            act_cfg=dict(type='ReLU'),\n            encoder=dict(\n                type='DetrTransformerEncoder',\n                num_layers=6,\n                transformerlayers=dict(\n                    type='BaseTransformerLayer',\n                    attn_cfgs=dict(\n                        type='MultiScaleDeformableAttention',\n                        embed_dims=1024,\n                        num_heads=32,\n                        num_levels=3,\n                        num_points=4,\n                        im2col_step=64,\n                        dropout=0.0,\n                        batch_first=False,\n                        norm_cfg=None,\n                        init_cfg=None),\n                    ffn_cfgs=dict(\n                        type='FFN',\n                        embed_dims=1024,\n                        feedforward_channels=4096,\n                        num_fcs=2,\n                        ffn_drop=0.0,\n                        act_cfg=dict(type='ReLU', inplace=True),\n                        with_cp=False),\n                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),\n                init_cfg=None),\n            positional_encoding=dict(\n                type='SinePositionalEncoding', num_feats=512, normalize=True),\n            init_cfg=None),\n        enforce_decoder_input_project=False,\n        positional_encoding=dict(\n            type='SinePositionalEncoding', num_feats=512, normalize=True),\n        transformer_decoder=dict(\n            type='DetrTransformerDecoder',\n            return_intermediate=True,\n            num_layers=9,\n            transformerlayers=dict(\n                type='DetrTransformerDecoderLayer',\n                attn_cfgs=dict(\n                    type='MultiheadAttention',\n                    embed_dims=1024,\n                    num_heads=32,\n                    attn_drop=0.0,\n                    proj_drop=0.0,\n                    dropout_layer=None,\n                    batch_first=False),\n                ffn_cfgs=dict(\n                    embed_dims=1024,\n                    feedforward_channels=4096,\n                    num_fcs=2,\n                    act_cfg=dict(type='ReLU', inplace=True),\n                    ffn_drop=0.0,\n                    dropout_layer=None,\n                    add_identity=True,\n                    with_cp=False),\n                feedforward_channels=4096,\n                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',\n                                 'ffn', 'norm')),\n            init_cfg=None),\n        loss_cls=dict(\n            type='CrossEntropyLoss',\n            use_sigmoid=False,\n            loss_weight=2.0,\n            reduction='mean',\n            class_weight=[\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\n                1.0, 1.0, 1.0, 0.1\n            ]),\n        loss_mask=dict(\n            type='CrossEntropyLoss',\n            use_sigmoid=True,\n            reduction='mean',\n            loss_weight=5.0),\n        loss_dice=dict(\n            type='DiceLoss',\n            use_sigmoid=True,\n            activate=True,\n            reduction='mean',\n            naive_dice=True,\n            eps=1.0,\n            loss_weight=5.0),\n        train_cfg=dict(\n            num_points=12544,\n            oversample_ratio=3.0,\n            importance_sample_ratio=0.75,\n            assigner=dict(\n                type='MaskHungarianAssigner',\n                cls_cost=dict(type='ClassificationCost', weight=2.0),\n                mask_cost=dict(\n                    type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),\n                dice_cost=dict(\n                    type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),\n            sampler=dict(type='MaskPseudoSampler')),\n        test_cfg=dict(\n            panoptic_on=True,\n            semantic_on=False,\n            instance_on=True,\n            max_per_image=100,\n            iou_thr=0.8,\n            filter_low_score=True,\n            mode='whole')),\n    train_cfg=dict(\n        num_points=12544,\n        oversample_ratio=3.0,\n        importance_sample_ratio=0.75,\n        assigner=dict(\n            type='MaskHungarianAssigner',\n            cls_cost=dict(type='ClassificationCost', weight=2.0),\n            mask_cost=dict(\n                type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),\n            dice_cost=dict(\n                type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),\n        sampler=dict(type='MaskPseudoSampler')),\n    test_cfg=dict(\n        panoptic_on=True,\n        semantic_on=False,\n        instance_on=True,\n        max_per_image=100,\n        iou_thr=0.8,\n        filter_low_score=True,\n        mode='whole'),\n    init_cfg=None)\ndataset_type = 'COCOStuffDataset'\ndata_root = 'data/coco_stuff10k'\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='ToMask'),\n    dict(type='DefaultFormatBundle'),\n    dict(\n        type='Collect',\n        keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels'])\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='ResizeToMultiple', size_divisor=32),\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=1,\n    workers_per_gpu=4,\n    train=dict(\n        type='COCOStuffDataset',\n        data_root='data/coco_stuff10k',\n        reduce_zero_label=True,\n        img_dir='images/train2014',\n        ann_dir='annotations/train2014',\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='ToMask'),\n            dict(type='DefaultFormatBundle'),\n            dict(\n                type='Collect',\n                keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels'])\n        ]),\n    val=dict(\n        type='COCOStuffDataset',\n        data_root='data/coco_stuff10k',\n        reduce_zero_label=True,\n        img_dir='images/test2014',\n        ann_dir='annotations/test2014',\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='ResizeToMultiple', size_divisor=32),\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='COCOStuffDataset',\n        data_root='data/coco_stuff10k',\n        reduce_zero_label=True,\n        img_dir='images/test2014',\n        ann_dir='annotations/test2014',\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='ResizeToMultiple', size_divisor=32),\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, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'work_dirs/mask2former_dcnv3_huge_896_80k_cocostuff164k_lwlr095/iter_80000.pth'\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\noptimizer = dict(\n    type='AdamW',\n    lr=1e-05,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    constructor='InternLayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(\n        num_layers=50, layer_decay_rate=0.95, depths=[6, 6, 32, 6]))\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=40000)\ncheckpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=1)\nevaluation = dict(\n    interval=2000, metric='mIoU', pre_eval=True, save_best='mIoU')\nwork_dir = './work_dirs/mask2former_dcnv3_huge_512_40k_cocostuff10k_with_pretrain_ss'\ngpu_ids = range(0, 16)\nauto_resume = False\ndevice = 'cuda'\nseed = 176705155\n", "CLASSES": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "banner", "blanket", "branch", "bridge", "building-other", "bush", "cabinet", "cage", "cardboard", "carpet", "ceiling-other", "ceiling-tile", "cloth", "clothes", "clouds", "counter", "cupboard", "curtain", "desk-stuff", "dirt", "door-stuff", "fence", "floor-marble", "floor-other", "floor-stone", "floor-tile", "floor-wood", "flower", "fog", "food-other", "fruit", "furniture-other", "grass", "gravel", "ground-other", "hill", "house", "leaves", "light", "mat", "metal", "mirror-stuff", "moss", "mountain", "mud", "napkin", "net", "paper", "pavement", "pillow", "plant-other", "plastic", "platform", "playingfield", "railing", "railroad", "river", "road", "rock", "roof", "rug", "salad", "sand", "sea", "shelf", "sky-other", "skyscraper", "snow", "solid-other", "stairs", "stone", "straw", "structural-other", "table", "tent", "textile-other", "towel", "tree", "vegetable", "wall-brick", "wall-concrete", "wall-other", "wall-panel", "wall-stone", "wall-tile", "wall-wood", "water-other", "waterdrops", "window-blind", "window-other", "wood"], "PALETTE": [[0, 192, 64], [0, 192, 64], [0, 64, 96], [128, 192, 192], [0, 64, 64], [0, 192, 224], [0, 192, 192], [128, 192, 64], [0, 192, 96], [128, 192, 64], [128, 32, 192], [0, 0, 224], [0, 0, 64], [0, 160, 192], [128, 0, 96], [128, 0, 192], [0, 32, 192], [128, 128, 224], [0, 0, 192], [128, 160, 192], [128, 128, 0], [128, 0, 32], [128, 32, 0], [128, 0, 128], [64, 128, 32], [0, 160, 0], [0, 0, 0], [192, 128, 160], [0, 32, 0], [0, 128, 128], [64, 128, 160], [128, 160, 0], [0, 128, 0], [192, 128, 32], [128, 96, 128], [0, 0, 128], [64, 0, 32], [0, 224, 128], [128, 0, 0], [192, 0, 160], [0, 96, 128], [128, 128, 128], [64, 0, 160], [128, 224, 128], [128, 128, 64], [192, 0, 32], [128, 96, 0], [128, 0, 192], [0, 128, 32], [64, 224, 0], [0, 0, 64], [128, 128, 160], [64, 96, 0], [0, 128, 192], [0, 128, 160], [192, 224, 0], [0, 128, 64], [128, 128, 32], [192, 32, 128], [0, 64, 192], [0, 0, 32], [64, 160, 128], [128, 64, 64], [128, 0, 160], [64, 32, 128], [128, 192, 192], [0, 0, 160], [192, 160, 128], [128, 192, 0], [128, 0, 96], [192, 32, 0], [128, 64, 128], [64, 128, 96], [64, 160, 0], [0, 64, 0], [192, 128, 224], [64, 32, 0], [0, 192, 128], [64, 128, 224], [192, 160, 0], [0, 192, 0], [192, 128, 96], [192, 96, 128], [0, 64, 128], [64, 0, 96], [64, 224, 128], [128, 64, 0], [192, 0, 224], [64, 96, 128], [128, 192, 128], [64, 0, 224], [192, 224, 128], [128, 192, 64], [192, 0, 96], [192, 96, 0], [128, 64, 192], [0, 128, 96], [0, 224, 0], [64, 64, 64], [128, 128, 224], [0, 96, 0], [64, 192, 192], [0, 128, 224], [128, 224, 0], [64, 192, 64], [128, 128, 96], [128, 32, 128], [64, 0, 192], [0, 64, 96], [0, 160, 128], [192, 0, 64], [128, 64, 224], [0, 32, 128], [192, 128, 192], [0, 64, 224], [128, 160, 128], [192, 128, 0], [128, 64, 32], [128, 32, 64], [192, 0, 128], [64, 192, 32], [0, 160, 64], [64, 0, 0], [192, 192, 160], [0, 32, 64], [64, 128, 128], [64, 192, 160], [128, 160, 64], [64, 128, 0], [192, 192, 32], [128, 96, 192], [64, 0, 128], [64, 64, 32], [0, 224, 192], [192, 0, 0], [192, 64, 160], [0, 96, 192], [192, 128, 128], [64, 64, 160], [128, 224, 192], [192, 128, 64], [192, 64, 32], [128, 96, 64], [192, 0, 192], [0, 192, 32], [64, 224, 64], [64, 0, 64], [128, 192, 160], [64, 96, 64], [64, 128, 192], [0, 192, 160], [192, 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