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https://github.com/open-mmlab/mmdeploy.git
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840adcfb43
* mmcls -> mmpretrain * add constraints.txt * fix lint * fix lint * remove constraints.txt * fix windows ci * modify requirements * fix mdlink and mmpretrain version * fix dead link * modify codebase cmakelist * fix rename
109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='ResNet',
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depth=18,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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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=512,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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topk=(1, 5)))
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dataset_type = 'ImageNet'
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data_preprocessor = dict(
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num_classes=1000,
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', scale=224),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(type='PackInputs')
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='ResizeEdge', scale=256, edge='short'),
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dict(type='CenterCrop', crop_size=224),
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dict(type='PackInputs')
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]
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train_dataloader = dict(
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batch_size=2,
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num_workers=1,
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dataset=dict(
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type='ImageNet',
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data_root='tests/test_codebase/test_mmpretrain/data/imgs',
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ann_file='ann.txt',
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data_prefix='train',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', scale=224),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(type='PackInputs')
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]),
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sampler=dict(type='DefaultSampler', shuffle=True))
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val_dataloader = dict(
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batch_size=2,
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num_workers=1,
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dataset=dict(
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type='ImageNet',
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data_root='tests/test_codebase/test_mmpretrain/data/imgs',
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ann_file='ann.txt',
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data_prefix='val',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(type='ResizeEdge', scale=256, edge='short'),
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dict(type='CenterCrop', crop_size=224),
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dict(type='PackInputs')
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]),
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sampler=dict(type='DefaultSampler', shuffle=False))
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val_evaluator = dict(type='Accuracy', topk=(1, 5))
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test_dataloader = dict(
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batch_size=2,
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num_workers=1,
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dataset=dict(
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type='ImageNet',
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data_root='tests/test_codebase/test_mmpretrain/data/imgs',
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ann_file='ann.txt',
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data_prefix='val',
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pipeline=[
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dict(type='LoadImageFromFile'),
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dict(type='ResizeEdge', scale=256, edge='short'),
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dict(type='CenterCrop', crop_size=224),
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dict(type='PackInputs')
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]),
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sampler=dict(type='DefaultSampler', shuffle=False))
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test_evaluator = dict(type='Accuracy', topk=(1, 5))
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optim_wrapper = dict(
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optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))
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param_scheduler = dict(
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type='MultiStepLR', by_epoch=True, milestones=[30, 60, 90], gamma=0.1)
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train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
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val_cfg = dict()
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test_cfg = dict()
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auto_scale_lr = dict(base_batch_size=256)
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default_scope = 'mmpretrain'
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default_hooks = dict(
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timer=dict(type='IterTimerHook'),
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logger=dict(type='LoggerHook', interval=100),
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param_scheduler=dict(type='ParamSchedulerHook'),
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checkpoint=dict(type='CheckpointHook', interval=1),
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sampler_seed=dict(type='DistSamplerSeedHook'),
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visualization=dict(type='VisualizationHook', enable=False))
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env_cfg = dict(
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cudnn_benchmark=False,
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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dist_cfg=dict(backend='nccl'))
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vis_backends = [dict(type='LocalVisBackend')]
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visualizer = dict(
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type='UniversalVisualizer', vis_backends=[dict(type='LocalVisBackend')])
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log_level = 'INFO'
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load_from = None
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resume = False
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randomness = dict(seed=None, deterministic=False)
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