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Published 20 hours ago • verified publisher icon Scalsol

loss nan

Open Hiwyl opened this issue 3 years ago • 3 comments

image

Hiwyl avatar Sep 12 '20 11:09 Hiwyl

作者你好，这是一个惊艳的工作，单阶段anchor free的算法的精度竟然可以达到52.1.我对您的工作十分感兴趣。我想试一下算法的效果。我使用了自己制作的数据集，数据集只有一类。使用了你的默认配文件。我一共尝试了reppointv2-x101-dcn，和reppointv2-x101.在这些配置文件中我只是更改了学习率为0.00125，图像分辨率为1024*1024.我还尝试了不改变图像分辨率，不改变学习率，在你的coco的与训练模型上finetune。等等的尝试。但是这些配置文件更改后训练loss均是nan。我不知道是哪里出了问题。 具体配置文件如下所示： dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1024, 768), (1024, 1024)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='LoadRPDV2Annotations'), dict(type='RPDV2FormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_sem_map', 'gt_sem_weights']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(768, 768), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=1, workers_per_gpu=4, train=dict( type='CocoDataset', ann_file= '/media/zf/D/Dataset/bridge_768_add5/annotations/instances_train2017.json', img_prefix='/media/zf/D/Dataset/bridge_768_add5/train2017/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1024, 768), (1024, 1024)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='LoadRPDV2Annotations'), dict(type='RPDV2FormatBundle'), dict( type='Collect', keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_sem_map', 'gt_sem_weights' ]) ]), val=dict( type='CocoDataset', ann_file= '/media/zf/D/Dataset/bridge_768_add5/annotations/instances_val2017.json', img_prefix='/media/zf/D/Dataset/bridge_768_add5/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(768, 768), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='CocoDataset', ann_file= '/media/zf/D/Dataset/bridge_768_add5/annotations/instances_val2017.json', img_prefix='/media/zf/D/Dataset/bridge_768_add5/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(768, 768), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) evaluation = dict(interval=1, metric='bbox') optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[16, 22]) total_epochs = 24 checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = '/home/zf/RepPointsV2/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_coco-3d418239.pth' resume_from = None workflow = [('train', 1)] norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( type='RepPointsV2Detector', pretrained= '/home/zf/RepPointsV2/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_coco-3d418239.pth', backbone=dict( type='ResNeXt', depth=101, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', groups=64, base_width=4, dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False), stage_with_dcn=(False, True, True, True), with_cp=True), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs='on_input', num_outs=5, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)), bbox_head=dict( type='RepPointsV2Head', num_classes=80, in_channels=256, feat_channels=256, point_feat_channels=256, stacked_convs=3, shared_stacked_convs=1, first_kernel_size=3, kernel_size=1, corner_dim=64, num_points=9, gradient_mul=0.1, point_strides=[8, 16, 32, 64, 128], point_base_scale=4, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox_init=dict(type='GIoULoss', loss_weight=1.0), loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0), loss_heatmap=dict( type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=0.25), loss_offset=dict( type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0), loss_sem=dict( type='SEPFocalLoss', gamma=2.0, alpha=0.25, loss_weight=0.1), transform_method='exact_minmax')) train_cfg = dict( init=dict( assigner=dict(type='PointAssignerV2', scale=4, pos_num=1), allowed_border=-1, pos_weight=-1, debug=False), heatmap=dict( assigner=dict( type='PointHMAssigner', gaussian_bump=True, gaussian_iou=0.7), allowed_border=-1, pos_weight=-1, debug=False), refine=dict( assigner=dict(type='ATSSAssigner', topk=9), allowed_border=-1, pos_weight=-1, debug=False)) test_cfg = dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_thr=0.6), max_per_img=100) work_dir = './work_dirs/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_bridge' gpu_ids = range(0, 1)

非常愿意接受您的指导。

zf020114 avatar Oct 01 '20 23:10 zf020114

训练日志如下： 2020-09-27 14:53:46,457 - mmdet - INFO - Environment info:

sys.platform: linux Python: 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 02:25:08) [GCC 7.5.0] CUDA available: True CUDA_HOME: /usr/local/cuda-10.0 NVCC: Cuda compilation tools, release 10.0, V10.0.130 GPU 0: GeForce GTX 1060 GCC: gcc (Ubuntu 6.5.0-2ubuntu1~18.04) 6.5.0 20181026 PyTorch: 1.4.0 PyTorch compiling details: PyTorch built with:

  • GCC 7.3
  • Intel(R) Math Kernel Library Version 2020.0.1 Product Build 20200208 for Intel(R) 64 architecture applications
  • Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
  • OpenMP 201511 (a.k.a. OpenMP 4.5)
  • NNPACK is enabled
  • CUDA Runtime 10.0
  • 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_37,code=compute_37
  • CuDNN 7.6.3
  • Magma 2.5.1
  • Build settings: BLAS=MKL, BUILD_NAMEDTENSOR=OFF, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -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 -Wno-stringop-overflow, DISABLE_NUMA=1, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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_STATIC_DISPATCH=OFF,

TorchVision: 0.5.0 OpenCV: 4.4.0 MMCV: 0.6.2 MMDetection: 2.2.0+unknown MMDetection Compiler: GCC 6.5 MMDetection CUDA Compiler: 10.0

2020-09-27 14:53:46,457 - mmdet - INFO - Distributed training: False 2020-09-27 14:53:47,792 - mmdet - INFO - Config: dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1024, 768), (1024, 1024)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='LoadRPDV2Annotations'), dict(type='RPDV2FormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_sem_map', 'gt_sem_weights']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(768, 768), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=1, workers_per_gpu=4, train=dict( type='CocoDataset', ann_file= '/media/zf/D/Dataset/bridge_768_add5/annotations/instances_train2017.json', img_prefix='/media/zf/D/Dataset/bridge_768_add5/train2017/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1024, 768), (1024, 1024)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='LoadRPDV2Annotations'), dict(type='RPDV2FormatBundle'), dict( type='Collect', keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_sem_map', 'gt_sem_weights' ]) ]), val=dict( type='CocoDataset', ann_file= '/media/zf/D/Dataset/bridge_768_add5/annotations/instances_val2017.json', img_prefix='/media/zf/D/Dataset/bridge_768_add5/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(768, 768), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='CocoDataset', ann_file= '/media/zf/D/Dataset/bridge_768_add5/annotations/instances_val2017.json', img_prefix='/media/zf/D/Dataset/bridge_768_add5/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(768, 768), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) evaluation = dict(interval=1, metric='bbox') optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[16, 22]) total_epochs = 24 checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = '/home/zf/RepPointsV2/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_coco-3d418239.pth' resume_from = None workflow = [('train', 1)] norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( type='RepPointsV2Detector', pretrained= '/home/zf/RepPointsV2/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_coco-3d418239.pth', backbone=dict( type='ResNeXt', depth=101, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', groups=64, base_width=4, dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False), stage_with_dcn=(False, True, True, True), with_cp=True), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs='on_input', num_outs=5, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)), bbox_head=dict( type='RepPointsV2Head', num_classes=80, in_channels=256, feat_channels=256, point_feat_channels=256, stacked_convs=3, shared_stacked_convs=1, first_kernel_size=3, kernel_size=1, corner_dim=64, num_points=9, gradient_mul=0.1, point_strides=[8, 16, 32, 64, 128], point_base_scale=4, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox_init=dict(type='GIoULoss', loss_weight=1.0), loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0), loss_heatmap=dict( type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=0.25), loss_offset=dict( type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0), loss_sem=dict( type='SEPFocalLoss', gamma=2.0, alpha=0.25, loss_weight=0.1), transform_method='exact_minmax')) train_cfg = dict( init=dict( assigner=dict(type='PointAssignerV2', scale=4, pos_num=1), allowed_border=-1, pos_weight=-1, debug=False), heatmap=dict( assigner=dict( type='PointHMAssigner', gaussian_bump=True, gaussian_iou=0.7), allowed_border=-1, pos_weight=-1, debug=False), refine=dict( assigner=dict(type='ATSSAssigner', topk=9), allowed_border=-1, pos_weight=-1, debug=False)) test_cfg = dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_thr=0.6), max_per_img=100) work_dir = './work_dirs/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_bridge' gpu_ids = range(0, 1)

2020-09-27 14:53:49,775 - mmdet - INFO - load model from: /home/zf/RepPointsV2/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_coco-3d418239.pth 2020-09-27 14:53:50,068 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.conv1.weight, backbone.bn1.weight, backbone.bn1.bias, backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.num_batches_tracked, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.num_batches_tracked, backbone.layer1.0.conv3.weight, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.num_batches_tracked, backbone.layer1.0.downsample.0.weight, backbone.layer1.0.downsample.1.weight, backbone.layer1.0.downsample.1.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.0.downsample.1.num_batches_tracked, backbone.layer1.1.conv1.weight, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.num_batches_tracked, backbone.layer1.1.conv2.weight, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.num_batches_tracked, backbone.layer1.1.conv3.weight, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.num_batches_tracked, backbone.layer1.2.conv1.weight, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.num_batches_tracked, backbone.layer1.2.conv2.weight, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.num_batches_tracked, backbone.layer1.2.conv3.weight, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.num_batches_tracked, backbone.layer2.0.conv1.weight, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.num_batches_tracked, backbone.layer2.0.conv2.weight, backbone.layer2.0.conv2.conv_offset.weight, backbone.layer2.0.conv2.conv_offset.bias, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.num_batches_tracked, backbone.layer2.0.conv3.weight, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, 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2020-09-27 14:53:54,673 - mmdet - INFO - load checkpoint from /home/zf/RepPointsV2/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_coco-3d418239.pth 2020-09-27 14:53:55,050 - mmdet - INFO - Start running, host: zf@HP, work_dir: /home/zf/RepPointsV2/work_dirs/reppoints_v2_x101_fpn_dconv_c3-c5_giou_mstrain_2x_bridge 2020-09-27 14:53:55,050 - mmdet - INFO - workflow: [('train', 1)], max: 24 epochs 2020-09-27 14:56:54,715 - mmdet - INFO - Epoch [1][50/25903] lr: 9.890e-04, eta: 25 days, 20:23:08, time: 3.593, data_time: 0.050, memory: 3506, loss_cls: 1119.4495, loss_pts_init: nan, loss_pts_refine: nan, loss_heatmap: nan, loss_offset: nan, loss_sem: nan, loss: nan, grad_norm: nan 2020-09-27 15:00:05,128 - mmdet - INFO - Epoch [1][100/25903] lr: 1.988e-03, eta: 26 days, 14:56:07, time: 3.808, data_time: 0.005, memory: 3506, loss_cls: 0.0181, loss_pts_init: nan, loss_pts_refine: nan, loss_heatmap: nan, loss_offset: nan, loss_sem: nan, loss: nan, grad_norm: nan

zf020114 avatar Oct 01 '20 23:10 zf020114

Hi, guys you must change mmdet/datasets/coco.py CLASSES = ('your's label here'), then it solved

azuredsky avatar Jan 21 '21 07:01 azuredsky