diff --git "a/detection/mask_rcnn_deit_vit_b_fpn_1x_coco_bs16.log.json" "b/detection/mask_rcnn_deit_vit_b_fpn_1x_coco_bs16.log.json" new file mode 100644--- /dev/null +++ "b/detection/mask_rcnn_deit_vit_b_fpn_1x_coco_bs16.log.json" @@ -0,0 +1,1765 @@ +{"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\nMMDetection: 2.25.3+d30b7c1", "config": "model = dict(\n type='MaskRCNN',\n backbone=dict(\n type='vit_models',\n pretrain_img_size=224,\n pretrain_patch_size=16,\n patch_size=16,\n embed_dim=768,\n depth=12,\n num_heads=12,\n mlp_ratio=4.0,\n qkv_bias=True,\n drop_path_rate=0.15,\n init_scale=1,\n with_fpn=True,\n out_indices=[11],\n output_dtype='float32',\n window_attn=[\n True, True, False, True, True, False, True, True, False, True,\n True, False\n ],\n window_size=[14, 14, -1, 14, 14, -1, 14, 14, -1, 14, 14, -1],\n use_flash_attn=True,\n pretrained='./pretrained/deit_3_base_224_21k.pth'),\n neck=dict(\n type='FPN',\n in_channels=[768, 768, 768, 768],\n out_channels=256,\n num_outs=5),\n rpn_head=dict(\n type='RPNHead',\n in_channels=256,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8],\n ratios=[0.5, 1.0, 2.0],\n strides=[4, 8, 16, 32, 64]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n roi_head=dict(\n type='StandardRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n bbox_head=dict(\n type='Shared2FCBBoxHead',\n in_channels=256,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=80,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.1, 0.1, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n mask_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n mask_head=dict(\n type='FCNMaskHead',\n num_convs=4,\n in_channels=256,\n conv_out_channels=256,\n num_classes=80,\n loss_mask=dict(\n type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=2000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=512,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n mask_size=28,\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=1000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100,\n mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1024, 614), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n to_rgb=True),\n dict(type='Pad', size_divisor=64),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1024, 614),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n to_rgb=True),\n dict(type='Pad', size_divisor=64),\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, with_mask=True),\n dict(type='Resize', img_scale=(1024, 614), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n to_rgb=True),\n dict(type='Pad', size_divisor=64),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\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=(1024, 614),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n to_rgb=True),\n dict(type='Pad', size_divisor=64),\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=(1024, 614),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n to_rgb=True),\n dict(type='Pad', size_divisor=64),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(metric=['bbox', 'segm'], interval=1, save_best=None)\noptimizer = dict(\n type='AdamW',\n lr=0.0001,\n betas=(0.9, 0.999),\n weight_decay=0.05,\n constructor='CustomLayerDecayOptimizerConstructorMMDet',\n paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.95))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=0.001,\n step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1, deepspeed=True, max_keep_ckpts=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='ToBFloat16HookMMDet', priority=49)]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\ndeepspeed = True\ndeepspeed_config = 'zero_configs/adam_zero1_bf16.json'\npretrained = './pretrained/deit_3_base_224_21k.pth'\ncustom_imports = dict(\n imports=['mmdet.mmcv_custom'], allow_failed_imports=False)\nwork_dir = './work_dirs/mask_rcnn_deit_vit_b_fpn_1x_coco_bs16'\nauto_resume = True\ngpu_ids = range(0, 8)\n", "seed": 1260420511, "exp_name": "mask_rcnn_deit_vit_b_fpn_1x_coco_bs16.py"} +{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 3519, "data_time": 0.08536, "loss_rpn_cls": 0.60193, "loss_rpn_bbox": 0.57976, "loss_cls": 7.91173, "acc": 54.18091, "loss_bbox": 0.06247, "loss_mask": 10.16269, "loss": 19.31858, "time": 0.37214} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 1e-05, "memory": 3519, "data_time": 0.01728, "loss_rpn_cls": 0.3419, "loss_rpn_bbox": 0.38738, "loss_cls": 0.71384, "acc": 95.86353, "loss_bbox": 0.07193, "loss_mask": 4.18092, "loss": 5.69598, "time": 0.23803} +{"mode": "train", "epoch": 1, "iter": 150, "lr": 2e-05, "memory": 3695, "data_time": 0.01638, "loss_rpn_cls": 0.31459, "loss_rpn_bbox": 0.32257, "loss_cls": 0.63142, "acc": 94.05298, "loss_bbox": 0.1252, "loss_mask": 1.99084, "loss": 3.38462, "time": 0.23932} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 2e-05, "memory": 3833, "data_time": 0.01579, "loss_rpn_cls": 0.29571, "loss_rpn_bbox": 0.24929, "loss_cls": 0.58823, "acc": 93.47021, "loss_bbox": 0.16801, "loss_mask": 1.11914, "loss": 2.42039, "time": 0.24701} +{"mode": "train", "epoch": 1, "iter": 250, "lr": 3e-05, "memory": 3833, "data_time": 0.01381, "loss_rpn_cls": 0.25699, "loss_rpn_bbox": 0.16238, "loss_cls": 0.54419, "acc": 93.63086, "loss_bbox": 0.19147, "loss_mask": 0.78838, "loss": 1.94342, "time": 0.2441} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 3e-05, "memory": 3833, "data_time": 0.0149, "loss_rpn_cls": 0.26392, "loss_rpn_bbox": 0.13658, "loss_cls": 0.51112, "acc": 93.63525, "loss_bbox": 0.20478, "loss_mask": 0.71277, "loss": 1.82918, "time": 0.2432} +{"mode": "train", "epoch": 1, "iter": 350, "lr": 4e-05, "memory": 3833, "data_time": 0.01542, "loss_rpn_cls": 0.24073, "loss_rpn_bbox": 0.10976, "loss_cls": 0.46315, "acc": 93.97705, "loss_bbox": 0.20231, "loss_mask": 0.69659, "loss": 1.71254, "time": 0.24273} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 4e-05, "memory": 3833, "data_time": 0.01461, "loss_rpn_cls": 0.22757, "loss_rpn_bbox": 0.09814, "loss_cls": 0.44444, "acc": 94.02979, "loss_bbox": 0.20398, "loss_mask": 0.68946, "loss": 1.6636, "time": 0.24671} +{"mode": "train", "epoch": 1, "iter": 450, "lr": 5e-05, "memory": 3833, "data_time": 0.01268, "loss_rpn_cls": 0.20902, "loss_rpn_bbox": 0.08973, "loss_cls": 0.42825, "acc": 94.16089, "loss_bbox": 0.20383, "loss_mask": 0.68738, "loss": 1.6182, "time": 0.24322} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 5e-05, "memory": 3833, "data_time": 0.01501, "loss_rpn_cls": 0.19473, "loss_rpn_bbox": 0.0836, "loss_cls": 0.43974, "acc": 93.8335, "loss_bbox": 0.21977, "loss_mask": 0.68329, "loss": 1.62113, "time": 0.24565} +{"mode": "train", "epoch": 1, "iter": 550, "lr": 5e-05, "memory": 3934, "data_time": 0.01363, "loss_rpn_cls": 0.18992, "loss_rpn_bbox": 0.08262, "loss_cls": 0.43305, "acc": 93.69458, "loss_bbox": 0.22106, "loss_mask": 0.67913, "loss": 1.60577, "time": 0.24465} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 5e-05, "memory": 3934, "data_time": 0.01517, "loss_rpn_cls": 0.17495, "loss_rpn_bbox": 0.07737, "loss_cls": 0.43307, "acc": 93.55811, "loss_bbox": 0.23069, "loss_mask": 0.6745, "loss": 1.59057, "time": 0.24434} +{"mode": "train", "epoch": 1, "iter": 650, "lr": 5e-05, "memory": 3934, "data_time": 0.01494, "loss_rpn_cls": 0.16266, "loss_rpn_bbox": 0.07729, "loss_cls": 0.41393, "acc": 93.86011, "loss_bbox": 0.21908, "loss_mask": 0.67277, "loss": 1.54573, "time": 0.24299} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 5e-05, "memory": 3934, "data_time": 0.01486, "loss_rpn_cls": 0.1571, "loss_rpn_bbox": 0.07438, "loss_cls": 0.44147, "acc": 93.29248, "loss_bbox": 0.24349, "loss_mask": 0.66352, "loss": 1.57997, "time": 0.24391} +{"mode": "train", "epoch": 1, "iter": 750, "lr": 5e-05, "memory": 3934, "data_time": 0.01382, "loss_rpn_cls": 0.15813, "loss_rpn_bbox": 0.08078, "loss_cls": 0.43205, "acc": 93.18433, "loss_bbox": 0.2444, "loss_mask": 0.65143, "loss": 1.56679, "time": 0.24375} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 5e-05, "memory": 3934, "data_time": 0.01326, "loss_rpn_cls": 0.15222, "loss_rpn_bbox": 0.07547, "loss_cls": 0.44045, "acc": 92.93188, "loss_bbox": 0.25608, "loss_mask": 0.64486, "loss": 1.56907, "time": 0.24711} +{"mode": "train", "epoch": 1, "iter": 850, "lr": 5e-05, "memory": 3934, "data_time": 0.0134, "loss_rpn_cls": 0.15384, "loss_rpn_bbox": 0.07578, "loss_cls": 0.42538, "acc": 93.03687, "loss_bbox": 0.2508, "loss_mask": 0.63742, "loss": 1.54322, "time": 0.24665} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 5e-05, "memory": 3934, "data_time": 0.01382, "loss_rpn_cls": 0.14801, "loss_rpn_bbox": 0.07977, "loss_cls": 0.45946, "acc": 92.38672, "loss_bbox": 0.27592, "loss_mask": 0.63731, "loss": 1.60047, "time": 0.24991} +{"mode": "train", "epoch": 1, "iter": 950, "lr": 5e-05, "memory": 3934, "data_time": 0.01491, "loss_rpn_cls": 0.14301, "loss_rpn_bbox": 0.0754, "loss_cls": 0.43161, "acc": 92.59839, "loss_bbox": 0.26811, "loss_mask": 0.63151, "loss": 1.54966, "time": 0.24843} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 5e-05, "memory": 4008, "data_time": 0.01429, "loss_rpn_cls": 0.13699, "loss_rpn_bbox": 0.0728, "loss_cls": 0.43078, "acc": 92.38477, "loss_bbox": 0.27738, "loss_mask": 0.61826, "loss": 1.5362, "time": 0.24813} +{"mode": "train", "epoch": 1, "iter": 1050, "lr": 5e-05, "memory": 4008, "data_time": 0.01343, "loss_rpn_cls": 0.13991, "loss_rpn_bbox": 0.07869, "loss_cls": 0.43957, "acc": 92.07666, "loss_bbox": 0.28616, "loss_mask": 0.61651, "loss": 1.56084, "time": 0.24925} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 5e-05, "memory": 4008, "data_time": 0.0143, "loss_rpn_cls": 0.12697, "loss_rpn_bbox": 0.06818, "loss_cls": 0.42941, "acc": 92.41113, 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