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1.Add a backbone: deitiii. 2.Add an optimizer: lamb. 3.Add a sampler: RASampler. 4.Add a lr update hook: CosineAnnealingWarmupByEpochLrUpdaterHook. 5.In easycv/models/classification/classification.py, I remove the default mixup_cfg to keep the classification.py clean.
144 lines
3.7 KiB
Python
144 lines
3.7 KiB
Python
# from PIL import Image
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_base_ = 'configs/base.py'
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log_config = dict(
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interval=10,
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hooks=[dict(type='TextLoggerHook'),
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dict(type='TensorboardLoggerHook')])
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# model settings
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model = dict(
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type='Classification',
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train_preprocess=['mixUp'],
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pretrained=False,
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mixup_cfg=dict(
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mixup_alpha=0.8,
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cutmix_alpha=1.0,
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cutmix_minmax=None,
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prob=1.0,
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switch_prob=0.5,
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mode='batch',
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label_smoothing=0.0,
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num_classes=1000),
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backbone=dict(
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type='VisionTransformer',
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img_size=[192],
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num_classes=1000,
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patch_size=16,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4,
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qkv_bias=True,
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drop_rate=0.,
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drop_path_rate=0.2,
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use_layer_scale=True),
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head=dict(
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type='ClsHead',
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loss_config=dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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loss_weight=1.0,
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label_ceil=True),
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with_fc=False,
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use_num_classes=False))
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data_train_list = 'data/imagenet1k/train.txt'
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data_train_root = 'data/imagenet1k/train/'
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data_test_list = 'data/imagenet1k/val.txt'
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data_test_root = 'data/imagenet1k/val/'
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dataset_type = 'ClsDataset'
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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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three_augment_policies = [[
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dict(type='PILGaussianBlur', prob=1.0, radius_min=0.1, radius_max=2.0),
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], [
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dict(type='Solarization', threshold=128),
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], [
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dict(type='Grayscale', num_output_channels=3),
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]]
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train_pipeline = [
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dict(
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type='RandomResizedCrop', size=192, scale=(0.08, 1.0),
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interpolation=3), # interpolation='bicubic'
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dict(type='RandomHorizontalFlip'),
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dict(type='MMAutoAugment', policies=three_augment_policies),
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dict(type='ColorJitter', brightness=0.3, contrast=0.3, saturation=0.3),
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Collect', keys=['img', 'gt_labels'])
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]
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size = int((256 / 224) * 192)
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test_pipeline = [
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dict(type='Resize', size=size, interpolation=3),
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dict(type='CenterCrop', size=192),
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Collect', keys=['img', 'gt_labels'])
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]
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data = dict(
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imgs_per_gpu=256,
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workers_per_gpu=8,
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use_repeated_augment_sampler=True,
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train=dict(
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type=dataset_type,
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data_source=dict(
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list_file=data_train_list,
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root=data_train_root,
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type='ClsSourceImageList'),
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_source=dict(
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list_file=data_test_list,
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root=data_test_root,
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type='ClsSourceImageList'),
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pipeline=test_pipeline))
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eval_config = dict(initial=True, interval=1, gpu_collect=True)
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eval_pipelines = [
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dict(
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mode='test',
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data=data['val'],
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dist_eval=True,
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evaluators=[dict(type='ClsEvaluator', topk=(1, 5))],
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)
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]
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# additional hooks
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custom_hooks = []
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# optimizer
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optimizer = dict(
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type='Lamb',
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lr=0.003,
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weight_decay=0.05,
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eps=1e-8,
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paramwise_options={
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'cls_token': dict(weight_decay=0.),
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'pos_embed': dict(weight_decay=0.),
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'bias': dict(weight_decay=0.),
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'norm': dict(weight_decay=0.),
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'gamma_1': dict(weight_decay=0.),
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'gamma_2': dict(weight_decay=0.),
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})
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optimizer_config = dict(grad_clip=None, update_interval=1)
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lr_config = dict(
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policy='CosineAnnealingWarmupByEpoch',
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by_epoch=True,
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min_lr_ratio=0.00001 / 0.003,
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warmup='linear',
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warmup_by_epoch=True,
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warmup_iters=5,
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warmup_ratio=0.000001 / 0.003,
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)
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checkpoint_config = dict(interval=10)
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# runtime settings
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total_epochs = 800
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ema = dict(decay=0.99996)
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