67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
_base_ = [
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'../_base_/datasets/imagenet_bs32_pil_resize.py',
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'../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
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]
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# model settings
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model = dict(
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type='ImageClassifier',
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backbone=dict(type='CSPResNet', depth=50),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearClsHead',
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num_classes=1000,
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in_channels=1024,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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topk=(1, 5),
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))
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# dataset settings
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dataset_type = 'ImageNet'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='RandomResizedCrop',
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size=224,
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='Resize',
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size=(288, -1),
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backend='pillow',
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interpolation='bicubic'),
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dict(type='CenterCrop', crop_size=256),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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]
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data = dict(
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samples_per_gpu=32,
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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data_prefix='data/imagenet/train',
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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_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline),
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test=dict(
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# replace `data/val` with `data/test` for standard test
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline))
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evaluation = dict(interval=1, metric='accuracy')
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