OpenMMLab(1) configuration file

File List

configs configuration file

resnet18_8xb16_cifar10

18 layers, 8 cards b16: batch (8×16) cifar10: dataset mixup: data enhancement coslr: learning rate decay

_base__ Network structure models, dataset definition datasets, learning strategy schedules, conventional training methods

config--_base_--models--resnet18.py network model

model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='ResNet',
        depth=18,
        num_stages=4,
        out_indices=(3, ),
        style='pytorch'),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=1000,
        in_channels=512,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
        topk=(1, 5),
    ))

type：mmcls--models--classifiers--image.py--ImageClassifier(forward train)

backbone: mmcls--models--backbones--resnet.py feature extraction

• depth: the number of layers
• num_stages: how many stages have passed
• out_indices: Which layer to output, can be multiple layers (3--the third layer [0, 1, 2, 3])
• style: pytorch--3×3 convolution step size s=2 caffe--1×1 convolution s=2

neck: fusion strategy GlobalAveragePooling equal in size

• head: output
• type: classification task, using FC full connection
• num_classes: number of categories, modify by yourself
• in_channels: according to the neck
• loss: loss mmcls--models--losses to choose by yourself
• topk: evaluation criteria, top1 accuracy, top5 accuracy

configs--_base_--datasets--imagenet_bs32.py data set loading and dataload processing

# dataset settings
dataset_type = 'ImageNet'
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='RandomResizedCrop', size=224),
    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='ToTensor', keys=['gt_label']),
    dict(type='Collect', keys=['img', 'gt_label'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', size=(256, -1)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img'])
]
data = dict(
    samples_per_gpu=32,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        data_prefix='data/imagenet/train',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        data_prefix='data/imagenet/val',
        ann_file='data/imagenet/meta/val.txt',
        pipeline=test_pipeline),
    test=dict(
        # replace `data/val` with `data/test` for standard test
        type=dataset_type,
        data_prefix='data/imagenet/val',
        ann_file='data/imagenet/meta/val.txt',
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='accuracy')

dataset_type：mmcls--datasets--imagenet.py

img_norm_cfg: normalization, mean, standard deviation x-μ/σ

train_pipeline, test_pipeline: data preprocessing

type: is to call the method in --datasets--pipelines in mmcls

• dict(type='LoadImageFromFile'), read image data, the method is in dataset_type
• dict(type='RandomResizedCrop', size=224), random size cropping
• dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),  翻转
• dict(type='Normalize', **img_norm_cfg), normalized
• dict(type='ImageToTensor', keys=['img']), convert to Tensor
• dict(type='ToTensor', keys=['gt_label']), the label is converted to Tensor
• dict(type='Collect', keys=['img', 'gt_label']) returns images and labels

data = dict(
    samples_per_gpu=32, the batchsize of a single card, or the batchsize of each card for multiple cards
    workers_per_gpu=2,
    train=dict( training set
        type=dataset_type,
        data_prefix='data/imagenet/train', read path, no Specify the label, use the label folder as the label
        pipeline=train_pipeline),
    val=dict( validation set
        type=dataset_type,
        data_prefix='data/imagenet/val',    
        ann_file='data/imagenet/meta/val.txt', specify the label File path name, then use the label in the file
        pipeline=test_pipeline),
    test=dict( test set
        # replace `data/val` with `data/test` for standard test
        type=dataset_type,
        data_prefix='data/imagenet/val',
        ann_file='data/imagenet/meta/val.txt',
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='accuracy') evaluation, the interval goes through the validation set after 1epoch, which metrics should be printed by the metric

configs--_base_--schedules--imagenet_bs256.py optimizer and number of iterations

# optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[30, 60, 90])
runner = dict(type='EpochBasedRunner', max_epochs=100)

optimizer: optimizer learning rate momentum learning rate decay

lr_config: iterate 30, 60, 90 decays

runner: how many final iterations

configs--_base_--default_runtime log

# checkpoint saving
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=100,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable

dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]

checkpoint_config: save after how many

log_config: save the log, how many iterations to save the information