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resnet50d_8xb256-rsb-a2-300e_in1k.py
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resnet50d_8xb256-rsb-a2-300e_in1k.py
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_base_ = ['../_base_/default_runtime.py']
#
# from ../_base_/models/resnet50.py
#
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1d',
depth=50,
num_stages=4,
out_indices=(3, ),
norm_cfg=dict(type='SyncBN', requires_grad=True),
drop_path_rate=0.05,
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_sigmoid=True),
topk=(1, 5),
),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.1),
dict(type='CutMix', alpha=1.0)
]))
#
# from ../_base_/datasets/imagenet_bs256_rsb_a2.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,
)
bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=224,
backend='pillow',
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='timm_increasing',
num_policies=2,
total_level=10,
magnitude_level=7,
magnitude_std=0.5,
hparams=dict(
pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=236,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs')
]
train_dataloader = dict(
batch_size=256,
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='RepeatAugSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=256,
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_bs2048_rsb.py
#
# optimizer
optim_wrapper = dict(
optimizer=dict(type='Lamb', lr=0.005, weight_decay=0.02),
paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.))
# learning policy
param_scheduler = [
# warm up learning rate scheduler
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=5,
# update by iter
convert_to_iter_based=True),
# main learning rate scheduler
dict(
type='CosineAnnealingLR',
T_max=295,
eta_min=1.0e-6,
by_epoch=True,
begin=5,
end=300)
]
# train, val, test setting
train_cfg = dict(by_epoch=True, max_epochs=300, 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=2048)