We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Using the configuration file below:
_base_ = [ '../_base_/default_runtime.py', '../_base_/datasets/basicvsr_test_config.py' ] experiment_name = 'vrt-pp_c64n7_8xb1-600k_reds4' work_dir = f'./work_dirs/{experiment_name}' save_dir = './work_dirs' scale = 4 # model settings model = dict( type='NaiveVSR', generator=dict( type='VRTNet', spynet_path='cache/spynet_sintel_final-3d2a1287.pth'), pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'), train_cfg=dict(fix_iter=10000), data_preprocessor=dict( type='DataPreprocessor', mean=[0., 0., 0.], std=[255., 255., 255.], )) train_pipeline = [ dict(type='GenerateSegmentIndices', interval_list=[1]), dict(type='LoadImageFromFile', key='img', channel_order='rgb'), dict(type='LoadImageFromFile', key='gt', channel_order='rgb'), dict(type='SetValues', dictionary=dict(scale=scale)), dict(type='PairedRandomCrop', gt_patch_size=256), dict( type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='horizontal'), dict( type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'), dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5), dict(type='PackInputs') ] val_pipeline = [ dict(type='GenerateSegmentIndices', interval_list=[1]), dict(type='LoadImageFromFile', key='img', channel_order='rgb'), dict(type='LoadImageFromFile', key='gt', channel_order='rgb'), dict(type='SetValues', dictionary=dict(scale=scale)), dict(type='PairedRandomCrop', gt_patch_size=256), dict(type='PackInputs') ] data_root = '/workspace/mmagic/datasets/REDS' train_dataloader = dict( num_workers=15, batch_size=1, persistent_workers=False, sampler=dict(type='InfiniteSampler', shuffle=True), dataset=dict( type='BasicFramesDataset', metainfo=dict(dataset_type='reds_reds4', task_name='vsr'), data_root=data_root, data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'), ann_file='meta_info_reds4_train.txt', depth=1, num_input_frames=6, pipeline=train_pipeline)) val_dataloader = dict( num_workers=6, batch_size=1, persistent_workers=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='BasicFramesDataset', metainfo=dict(dataset_type='reds_reds4', task_name='vsr'), data_root=data_root, data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'), ann_file='meta_info_reds4_val.txt', depth=1, num_input_frames=6, fixed_seq_len=50, pipeline=val_pipeline)) val_evaluator = dict( type='Evaluator', metrics=[ dict(type='PSNR'), dict(type='SSIM'), ]) default_hooks = dict(checkpoint=dict(out_dir=save_dir)) train_cfg = dict( type='IterBasedTrainLoop', max_iters=300_000, val_interval=100) val_cfg = dict(type='MultiValLoop') # optimizer optim_wrapper = dict( constructor='DefaultOptimWrapperConstructor', type='OptimWrapper', optimizer=dict(type='Adam', lr=2e-4, betas=(0.9, 0.99)), paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})) # learning policy param_scheduler = dict( type='CosineRestartLR', by_epoch=False, periods=[300000], restart_weights=[1], eta_min=1e-7)
The text was updated successfully, but these errors were encountered:
Using the define input parameter
def __init__(self, upscale=4, in_chans=3, out_chans=3, img_size=[6, 64, 64], window_size=[6, 8, 8], depths=[8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4], indep_reconsts=[11, 12], embed_dims=[64, 64, 64, 64, 64, 64, 64, 96, 96, 96, 96, 96, 96], num_heads=[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], mul_attn_ratio=0.75, mlp_ratio=2., qkv_bias=True, qk_scale=None, drop_path_rate=0.2, norm_layer=nn.LayerNorm, spynet_path=None, pa_frames=2, deformable_groups=16, recal_all_flows=False, nonblind_denoising=False, use_checkpoint_attn=False, use_checkpoint_ffn=False, no_checkpoint_attn_blocks=[], no_checkpoint_ffn_blocks=[], ):
Sorry, something went wrong.
Hi @JingyunLiang,
I wonder, would the default setting acceptable for 4x VSR?
No branches or pull requests
Using the configuration file below:
The text was updated successfully, but these errors were encountered: