更换内存条之后，深度学习的训练速度下降

2023-06-13 17:46:24 +08:00
 saberQi

当我将 16G 内存更换为 32G 内存之后，基于 mmorotate 的训练时间反而增强了。 这是我的训练日志：

2023-06-08 19:36:31,696 - mmrotate - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.15 (default, Nov 24 2022, 21:12:53) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3060 Laptop GPU
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0
PyTorch: 1.10.1
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 11.3
  - 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
  - CuDNN 8.2
  - Magma 2.5.2
  - 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-variable -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=1.10.1, 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, 

TorchVision: 0.11.2
OpenCV: 4.6.0
MMCV: 1.7.0
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.3
MMRotate: 0.3.4+794a319
------------------------------------------------------------

2023-06-08 19:36:31,954 - mmrotate - INFO - Distributed training: False
2023-06-08 19:36:32,192 - mmrotate - INFO - Config:
dataset_type = 'HRSCDataset'
data_root = 'data/hrsc/'
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='RResize', img_scale=(1333, 800)),
    dict(type='RRandomFlip', 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='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(800, 800),
        flip=False,
        transforms=[
            dict(type='RResize'),
            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='DefaultFormatBundle'),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='HRSCDataset',
        classwise=False,
        ann_file='data/hrsc/ImageSets/trainval.txt',
        ann_subdir='data/hrsc/FullDataSet/Annotations/',
        img_subdir='data/hrsc/FullDataSet/AllImages/',
        img_prefix='data/hrsc/FullDataSet/AllImages/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='RResize', img_scale=(1333, 800)),
            dict(type='RRandomFlip', 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='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ]),
    val=dict(
        type='HRSCDataset',
        classwise=False,
        ann_file='data/hrsc/ImageSets/test.txt',
        ann_subdir='data/hrsc/FullDataSet/Annotations/',
        img_subdir='data/hrsc/FullDataSet/AllImages/',
        img_prefix='data/hrsc/FullDataSet/AllImages/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(800, 800),
                flip=False,
                transforms=[
                    dict(type='RResize'),
                    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='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='HRSCDataset',
        classwise=False,
        ann_file='data/hrsc/ImageSets/test.txt',
        ann_subdir='data/hrsc/FullDataSet/Annotations/',
        img_subdir='data/hrsc/FullDataSet/AllImages/',
        img_prefix='data/hrsc/FullDataSet/AllImages/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(800, 800),
                flip=False,
                transforms=[
                    dict(type='RResize'),
                    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='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
evaluation = dict(interval=1, metric='mAP')
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=0.3333333333333333,
    step=[24, 33])
runner = dict(type='EpochBasedRunner', max_epochs=36)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
angle_version = 'le90'
model = dict(
    type='OrientedRCNN',
    backbone=dict(
        type='ResNet',
        depth=50,
        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',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='OrientedRPNHead',
        in_channels=256,
        feat_channels=256,
        version='le90',
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='MidpointOffsetCoder',
            angle_range='le90',
            target_means=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(
            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
    roi_head=dict(
        type='OrientedStandardRoIHead',
        bbox_roi_extractor=dict(
            type='RotatedSingleRoIExtractor',
            roi_layer=dict(
                type='RiRoIAlignRotated',
                out_size=7,
                num_samples=2,
                num_orientations=8,
                clockwise=True),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='RotatedShared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=1,
            bbox_coder=dict(
                type='DeltaXYWHAOBBoxCoder',
                angle_range='le90',
                norm_factor=None,
                edge_swap=True,
                proj_xy=True,
                target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
                target_stds=(0.1, 0.1, 0.2, 0.2, 0.1)),
            reg_class_agnostic=True,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.8),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                iou_calculator=dict(type='RBboxOverlaps2D'),
                ignore_iof_thr=-1),
            sampler=dict(
                type='RRandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.8),
            min_bbox_size=0),
        rcnn=dict(
            nms_pre=2000,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(iou_thr=0.1),
            max_per_img=2000)))
work_dir = './work_dirs/oriented_rcnn_r50_fpn_3x_hrsc_le90_no'
auto_resume = False
gpu_ids = range(0, 1)

这是我没有更换内存所需要的训练时间：

2023-04-10 19:13:11,617 - mmrotate - INFO - Epoch [1][50/309]   lr: 3.987e-03, eta: 3:40:53, time: 1.197, data_time: 0.050, memory: 4030, loss_rpn_cls: 0.2154, loss_rpn_bbox: 0.0664, loss_cls: 0.0827, acc: 98.8281, loss_bbox: 0.0113, loss: 0.3759, grad_norm: 3.0720

这是我进行内存更换后的训练时间：

23-06-10 13:47:24,479 - mmrotate - INFO - Epoch [1][50/309]    lr: 3.987e-03, eta: 4 days, 14:54:31, time: 36.055, data_time: 0.055, memory: 5111, loss_rpn_cls: 0.2287, loss_rpn_bbox: 0.1007, loss_cls: 0.1085, acc: 97.1152, loss_bbox: 0.0055, loss: 0.4434, grad_norm: 3.3885

请问有人碰到过这种情况吗？ 怎么进行解决？

1413 次点击
所在节点    程序员
6 条回复
DigitalFarmer
2023-06-13 20:09:53 +08:00
没遇到过。。。去 pytorch 的 GitHub 问问？
lloovve
2023-06-13 20:13:17 +08:00
如果内存不一样，注意双通道插法，具体可以百度
laqow
2023-06-14 10:37:26 +08:00
如果主板自己焊了半条内存，可能只能插和它一样的内存，不然通道数减半
NetLauu
2023-06-14 11:42:46 +08:00
内存没有用双通道插法吧，或者内存频率低了
saberQi
2023-06-14 13:33:18 +08:00
我的电脑是笔记本 用的是戴尔 G15 然后使用的内存时三星 DDR4 3200 *2 应该是双通道吧..
@NetLauu #4
@lloovve #2
saberQi
2023-06-14 13:33:40 +08:00
@laqow #3 之前也是三星的...

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