diff --git "a/dino_4scale_internimage_l_1x_coco_layer_wise_lr.log.json" "b/dino_4scale_internimage_l_1x_coco_layer_wise_lr.log.json" new file mode 100644--- /dev/null +++ "b/dino_4scale_internimage_l_1x_coco_layer_wise_lr.log.json" @@ -0,0 +1,1765 @@ +{"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/lustre/share/cuda-11.3\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: gcc (GCC) 7.3.0\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: 4.7.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.28.1+216ec8a", "config": "dataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='AutoAugment',\n policies=[[{\n 'type':\n 'Resize',\n 'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),\n (608, 1333), (640, 1333), (672, 1333), (704, 1333),\n (736, 1333), (768, 1333), (800, 1333)],\n 'multiscale_mode':\n 'value',\n 'keep_ratio':\n True\n }],\n [{\n 'type': 'Resize',\n 'img_scale': [(400, 4200), (500, 4200), (600, 4200)],\n 'multiscale_mode': 'value',\n 'keep_ratio': True\n }, {\n 'type': 'RandomCrop',\n 'crop_type': 'absolute_range',\n 'crop_size': (384, 600),\n 'allow_negative_crop': False\n }, {\n 'type':\n 'Resize',\n 'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n (576, 1333), (608, 1333), (640, 1333),\n (672, 1333), (704, 1333), (736, 1333),\n (768, 1333), (800, 1333)],\n 'multiscale_mode':\n 'value',\n 'override':\n True,\n 'keep_ratio':\n True\n }]]),\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=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\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=32),\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=2,\n train=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_train2017.json',\n img_prefix='data/coco/train2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='AutoAugment',\n policies=[[{\n 'type':\n 'Resize',\n 'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n (576, 1333), (608, 1333), (640, 1333),\n (672, 1333), (704, 1333), (736, 1333),\n (768, 1333), (800, 1333)],\n 'multiscale_mode':\n 'value',\n 'keep_ratio':\n True\n }],\n [{\n 'type': 'Resize',\n 'img_scale': [(400, 4200), (500, 4200),\n (600, 4200)],\n 'multiscale_mode': 'value',\n 'keep_ratio': True\n }, {\n 'type': 'RandomCrop',\n 'crop_type': 'absolute_range',\n 'crop_size': (384, 600),\n 'allow_negative_crop': False\n }, {\n 'type':\n 'Resize',\n 'img_scale': [(480, 1333), (512, 1333),\n (544, 1333), (576, 1333),\n (608, 1333), (640, 1333),\n (672, 1333), (704, 1333),\n (736, 1333), (768, 1333),\n (800, 1333)],\n 'multiscale_mode':\n 'value',\n 'override':\n True,\n 'keep_ratio':\n True\n }]]),\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=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n ]),\n val=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\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=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\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=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(interval=1, metric='bbox', classwise=True, save_best='auto')\ncheckpoint_config = dict(interval=1, max_keep_ckpts=3, save_last=True)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\noptimizer = dict(\n type='AdamW',\n lr=0.0001,\n weight_decay=0.0001,\n constructor='CustomLayerDecayOptimizerConstructor',\n paramwise_cfg=dict(\n num_layers=37, layer_decay_rate=0.9, depths=[5, 5, 22, 5]))\noptimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=0.001,\n step=[11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\npretrained = 'https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth'\nmodel = dict(\n type='DINO',\n backbone=dict(\n type='InternImage',\n core_op='DCNv3',\n channels=160,\n depths=[5, 5, 22, 5],\n groups=[10, 20, 40, 80],\n mlp_ratio=4.0,\n drop_path_rate=0.4,\n norm_layer='LN',\n layer_scale=1.0,\n offset_scale=2.0,\n post_norm=True,\n with_cp=False,\n out_indices=(1, 2, 3),\n init_cfg=dict(\n type='Pretrained',\n checkpoint=\n 'https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth'\n )),\n neck=dict(\n type='ChannelMapper',\n in_channels=[320, 640, 1280],\n kernel_size=1,\n out_channels=256,\n act_cfg=None,\n norm_cfg=dict(type='GN', num_groups=32),\n num_outs=4),\n bbox_head=dict(\n type='DINOHead',\n num_query=900,\n num_classes=80,\n in_channels=2048,\n sync_cls_avg_factor=True,\n as_two_stage=True,\n with_box_refine=True,\n dn_cfg=dict(\n type='CdnQueryGenerator',\n noise_scale=dict(label=0.5, box=1.0),\n group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=100)),\n transformer=dict(\n type='DinoTransformer',\n two_stage_num_proposals=900,\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=256,\n dropout=0.0),\n feedforward_channels=2048,\n ffn_dropout=0.0,\n operation_order=('self_attn', 'norm', 'ffn', 'norm'))),\n decoder=dict(\n type='DinoTransformerDecoder',\n num_layers=6,\n return_intermediate=True,\n transformerlayers=dict(\n type='DetrTransformerDecoderLayer',\n attn_cfgs=[\n dict(\n type='MultiheadAttention',\n embed_dims=256,\n num_heads=8,\n dropout=0.0),\n dict(\n type='MultiScaleDeformableAttention',\n embed_dims=256,\n dropout=0.0)\n ],\n feedforward_channels=2048,\n ffn_dropout=0.0,\n operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n 'ffn', 'norm')))),\n positional_encoding=dict(\n type='SinePositionalEncoding',\n num_feats=128,\n temperature=20,\n normalize=True),\n loss_cls=dict(\n type='FocalLoss',\n use_sigmoid=True,\n gamma=2.0,\n alpha=0.25,\n loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=5.0),\n loss_iou=dict(type='GIoULoss', loss_weight=2.0)),\n train_cfg=dict(\n assigner=dict(\n type='HungarianAssigner',\n cls_cost=dict(type='FocalLossCost', weight=2.0),\n reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),\n iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),\n test_cfg=dict(max_per_img=300))\nwork_dir = './work_dirs/dino_4scale_internimage_l_1x_coco_try2'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 1716822696, "exp_name": "dino_4scale_internimage_l_1x_coco_try2.py", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 35185, "data_time": 0.06513, "interm_loss_cls": 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99.0453, "time": 0.80243} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 39008, "data_time": 0.00425, "interm_loss_cls": 1.00839, "interm_loss_bbox": 1.58681, "interm_loss_iou": 1.78616, "loss_cls": 0.9594, "loss_bbox": 1.53853, "loss_iou": 1.75533, "d0.loss_cls": 0.94271, "d0.loss_bbox": 1.60857, "d0.loss_iou": 1.80661, "d1.loss_cls": 0.94182, "d1.loss_bbox": 1.58138, "d1.loss_iou": 1.7835, "d2.loss_cls": 0.95333, "d2.loss_bbox": 1.56509, "d2.loss_iou": 1.77262, "d3.loss_cls": 0.96307, "d3.loss_bbox": 1.55256, "d3.loss_iou": 1.76643, "d4.loss_cls": 0.96611, "d4.loss_bbox": 1.54543, "d4.loss_iou": 1.75988, "dn_loss_cls": 0.83818, "dn_loss_bbox": 1.2966, "dn_loss_iou": 1.28868, "d0.dn_loss_cls": 0.85136, "d0.dn_loss_bbox": 1.29034, "d0.dn_loss_iou": 1.29011, "d1.dn_loss_cls": 0.81628, "d1.dn_loss_bbox": 1.29021, "d1.dn_loss_iou": 1.28913, "d2.dn_loss_cls": 0.82262, "d2.dn_loss_bbox": 1.29099, "d2.dn_loss_iou": 1.2885, "d3.dn_loss_cls": 0.81988, "d3.dn_loss_bbox": 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