mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
5c87dd9565
* mmcv.Config -> mmengine Config * support mmedit part * add rewriter for BaseEditModels * fix visualizer * mmedit visualization * remove unused code * fix realesrgan * fix trt * support MultiTestLoop; rewriter fix mmediting bugs; fix ut * fix uts * fix mmedit sdk * fix regression test(part) * fix torchscript * part of fix regression test * fix checkenv.py * fix test.py for mmedit2.0 * support for mmedit * fix regression_test * fix check copyright ci * fix isort * fix docformatter * fix yapf * fix tests * fix sdk after 1040 * add a file for ut * fix docformatter * fix export info * fix super_resolution * fix test.py * stage configs * remove unused code * remove rewriter of multitestloop * fix yapf
255 lines
7.2 KiB
Python
255 lines
7.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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default_scope = 'mmedit'
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save_dir = './work_dirs'
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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(
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type='CheckpointHook',
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interval=5000,
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out_dir=save_dir,
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by_epoch=False,
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max_keep_ckpts=10,
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save_best='PSNR',
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rule='greater',
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),
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sampler_seed=dict(type='DistSamplerSeedHook'),
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)
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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=4),
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dist_cfg=dict(backend='nccl'),
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)
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log_level = 'INFO'
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log_processor = dict(type='LogProcessor', window_size=100, by_epoch=False)
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load_from = None
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resume = False
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experiment_name = 'srcnn_x4k915_1xb16-1000k_div2k'
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work_dir = f'./work_dirs/{experiment_name}'
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save_dir = './work_dirs/'
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scale = 4
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# model settings
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model = dict(
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type='BaseEditModel',
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generator=dict(
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type='SRCNNNet',
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channels=(3, 64, 32, 3),
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kernel_sizes=(9, 1, 5),
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upscale_factor=scale),
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pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
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train_cfg=dict(),
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test_cfg=dict(metrics=['PSNR'], crop_border=scale),
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data_preprocessor=dict(
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type='EditDataPreprocessor',
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mean=[0., 0., 0.],
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std=[255., 255., 255.],
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))
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train_pipeline = [
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dict(
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type='LoadImageFromFile',
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key='img',
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color_type='color',
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channel_order='rgb',
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imdecode_backend='cv2'),
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dict(
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type='LoadImageFromFile',
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key='gt',
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color_type='color',
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channel_order='rgb',
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imdecode_backend='cv2'),
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dict(type='SetValues', dictionary=dict(scale=scale)),
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dict(type='PairedRandomCrop', gt_patch_size=128),
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dict(
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type='Flip',
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keys=['img', 'gt'],
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flip_ratio=0.5,
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direction='horizontal'),
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dict(
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type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),
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dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),
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dict(type='ToTensor', keys=['img', 'gt']),
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dict(type='PackEditInputs')
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]
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val_pipeline = [
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dict(
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type='LoadImageFromFile',
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key='img',
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color_type='color',
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channel_order='rgb',
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imdecode_backend='cv2'),
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dict(
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type='LoadImageFromFile',
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key='gt',
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color_type='color',
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channel_order='rgb',
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imdecode_backend='cv2'),
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dict(type='ToTensor', keys=['img', 'gt']),
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dict(type='PackEditInputs')
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]
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# dataset settings
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dataset_type = 'BasicImageDataset'
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data_root = 'data'
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train_dataloader = dict(
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num_workers=4,
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persistent_workers=False,
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sampler=dict(type='InfiniteSampler', shuffle=True),
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dataset=dict(
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type=dataset_type,
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ann_file='meta_info_DIV2K800sub_GT.txt',
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metainfo=dict(dataset_type='div2k', task_name='sisr'),
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data_root=data_root + '/DIV2K',
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data_prefix=dict(
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img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),
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filename_tmpl=dict(img='{}', gt='{}'),
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pipeline=train_pipeline))
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val_dataloader = dict(
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num_workers=4,
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persistent_workers=False,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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metainfo=dict(dataset_type='set5', task_name='sisr'),
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data_root=data_root + '/Set5',
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data_prefix=dict(img='LRbicx4', gt='GTmod12'),
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pipeline=val_pipeline))
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val_evaluator = [
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dict(type='MAE'),
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dict(type='PSNR', crop_border=scale),
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dict(type='SSIM', crop_border=scale),
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]
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train_cfg = dict(
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type='IterBasedTrainLoop', max_iters=1000000, val_interval=5000)
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val_cfg = dict(type='ValLoop')
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# optimizer
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optim_wrapper = dict(
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constructor='DefaultOptimWrapperConstructor',
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type='OptimWrapper',
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optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.99)))
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# learning policy
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param_scheduler = dict(
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type='CosineRestartLR',
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by_epoch=False,
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periods=[250000, 250000, 250000, 250000],
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restart_weights=[1, 1, 1, 1],
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eta_min=1e-7)
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default_hooks = dict(
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checkpoint=dict(
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type='CheckpointHook',
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interval=5000,
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save_optimizer=True,
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by_epoch=False,
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out_dir=save_dir,
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),
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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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sampler_seed=dict(type='DistSamplerSeedHook'),
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)
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test_pipeline = [
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dict(
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type='LoadImageFromFile',
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key='img',
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color_type='color',
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channel_order='rgb',
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imdecode_backend='cv2'),
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dict(
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type='LoadImageFromFile',
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key='gt',
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color_type='color',
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channel_order='rgb',
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imdecode_backend='cv2'),
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dict(type='ToTensor', keys=['img', 'gt']),
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dict(type='PackEditInputs')
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]
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# test config for Set5
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set5_data_root = 'data/Set5'
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set5_dataloader = dict(
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num_workers=4,
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persistent_workers=False,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='BasicImageDataset',
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metainfo=dict(dataset_type='set5', task_name='sisr'),
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data_root=set5_data_root,
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data_prefix=dict(img='LRbicx4', gt='GTmod12'),
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pipeline=test_pipeline))
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set5_evaluator = [
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dict(type='PSNR', crop_border=2, prefix='Set5'),
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dict(type='SSIM', crop_border=2, prefix='Set5'),
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]
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set14_data_root = 'data/Set14'
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set14_dataloader = dict(
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num_workers=4,
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persistent_workers=False,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='BasicImageDataset',
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metainfo=dict(dataset_type='set14', task_name='sisr'),
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data_root=set5_data_root,
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data_prefix=dict(img='LRbicx4', gt='GTmod12'),
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pipeline=test_pipeline))
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set14_evaluator = [
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dict(type='PSNR', crop_border=2, prefix='Set14'),
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dict(type='SSIM', crop_border=2, prefix='Set14'),
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]
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ut_data_root = 'tests/test_codebase/test_mmedit/data'
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ut_dataloader = dict(
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num_workers=4,
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persistent_workers=False,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='BasicImageDataset',
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metainfo=dict(dataset_type='set14', task_name='sisr'),
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data_root=ut_data_root,
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data_prefix=dict(img='imgs', gt='imgs'),
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pipeline=test_pipeline))
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# test config for DIV2K
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div2k_data_root = 'data/DIV2K'
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div2k_dataloader = dict(
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num_workers=4,
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persistent_workers=False,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='BasicImageDataset',
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ann_file='meta_info_DIV2K100sub_GT.txt',
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metainfo=dict(dataset_type='div2k', task_name='sisr'),
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data_root=div2k_data_root,
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data_prefix=dict(
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img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),
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# filename_tmpl=dict(img='{}_x4', gt='{}'),
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pipeline=test_pipeline))
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div2k_evaluator = [
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dict(type='PSNR', crop_border=2, prefix='DIV2K'),
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dict(type='SSIM', crop_border=2, prefix='DIV2K'),
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]
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# test config
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test_cfg = dict(type='MultiTestLoop')
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test_dataloader = [ut_dataloader, ut_dataloader]
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test_evaluator = [set5_evaluator, set14_evaluator]
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