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vgg13bn_8xb32_in1k.py
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vgg13bn_8xb32_in1k.py
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_base_ = ['../_base_/default_runtime.py']
# from '../_base_/models/vgg13bn.py',
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='VGG', depth=13, norm_cfg=dict(type='BN'), num_classes=1000),
neck=None,
head=dict(
type='ClsHead',
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
# from '../_base_/datasets/imagenet_bs32_pil_resize.py',
# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', scale=224, backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=32,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet',
ann_file='meta/train.txt',
data_prefix='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=32,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet',
ann_file='meta/val.txt',
data_prefix='val',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
val_evaluator = dict(type='Accuracy', topk=(1, 5))
# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
# from '../_base_/schedules/imagenet_bs256.py',
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))
# learning policy
param_scheduler = dict(
type='MultiStepLR', by_epoch=True, milestones=[30, 60, 90], gamma=0.1)
# train, val, test setting
train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
val_cfg = dict()
test_cfg = dict()
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=256)