Open leexinhao opened 1 year ago
Sorry, for the late reply. I have reimplemented the STAN using mmcv2.0. Here is the training config on msrvtt.
base = '../../base/default_runtime.py' model = dict( type='CLIPSimilarity_split', visual_encoder=dict(type='VITCLIPPretrained_STAN', depth=4, clip_weight="ckpt/clip/B32"), text_encoder=dict(type='CLIPTextPretrained', clip_weight="ckpt/clip/B32"), to_float32=True, frozen_layers=-1, data_preprocessor=dict( type='MultiModalDataPreprocessor', preprocessors=dict( imgs=dict( type='ActionDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.3751], format_shape='NCHW'), text=dict(type='ActionDataPreprocessor', to_float32=False))), tau = 0.01, adapter=None)
dataset_type = 'MsrvttDataset' data_root = 'data/video_retrieval/msrvtt' file_client_args = dict(io_backend='disk') train_pipeline = [ dict(type='DecordInit', file_client_args), dict(type='UniformSample', clip_len=12, num_clips=1), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='RandomResizedCrop'), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize', length=32), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ] val_pipeline = [ dict(type='DecordInit', file_client_args), dict(type='UniformSample', clip_len=12, num_clips=1, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 224)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize', length=32), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ] test_pipeline = val_pipeline
train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type=dataset_type, ann_file='train_9k.json', data_root=data_root, data_prefix=dict(video='videos'), pipeline=train_pipeline)) val_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type=dataset_type, ann_file='test_JSFUSION.json', data_root=data_root, data_prefix=dict(video='videos'), pipeline=val_pipeline, test_mode=True)) test_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type=dataset_type, ann_file='test_JSFUSION.json', data_root=data_root, data_prefix=dict(video='videos'), pipeline=test_pipeline, test_mode=True))
val_evaluator = dict(type='RetrievalMetric') test_evaluator = val_evaluator
train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=20, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop')
param_scheduler = [ dict( type='LinearLR', start_factor=0.05, by_epoch=True, begin=0, end=10, convert_to_iter_based=True), dict( type='CosineAnnealingLR', T_max=4.5, eta_min=0, by_epoch=True, begin=10, end=100, convert_to_iter_based=True) ]
optim_wrapper = dict( type='AmpOptimWrapper', optimizer=dict( type='AdamW', lr=2e-06, betas=(0.9, 0.98), eps=1e-08, weight_decay=0.02), paramwise_cfg=dict( norm_decay_mult=0., bias_decay_mult=0., custom_keys={ 'STAN': dict(lr_mult=10.), }), clip_grad=dict(max_norm=5, norm_type=2) )
default_hooks = dict(checkpoint=dict(type='printBest_CheckpointHook', interval=-1, save_best='auto', rule='greater'))
auto_scale_lr = dict(enable=True, base_batch_size=128)
Sorry, for the late reply. I have reimplemented the STAN using mmcv2.0. Here is the training config on msrvtt.
base = '../../base/default_runtime.py' model = dict( type='CLIPSimilarity_split', visual_encoder=dict(type='VITCLIPPretrained_STAN', depth=4, clip_weight="ckpt/clip/B32"), text_encoder=dict(type='CLIPTextPretrained', clip_weight="ckpt/clip/B32"), to_float32=True, frozen_layers=-1, data_preprocessor=dict( type='MultiModalDataPreprocessor', preprocessors=dict( imgs=dict( type='ActionDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.3751], format_shape='NCHW'), text=dict(type='ActionDataPreprocessor', to_float32=False))), tau = 0.01, adapter=None)
dataset_type = 'MsrvttDataset' data_root = 'data/video_retrieval/msrvtt' file_client_args = dict(io_backend='disk') train_pipeline = [ dict(type='DecordInit', file_client_args), dict(type='UniformSample', clip_len=12, num_clips=1), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='RandomResizedCrop'), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize', length=32), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ] val_pipeline = [ dict(type='DecordInit', file_client_args), dict(type='UniformSample', clip_len=12, num_clips=1, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 224)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize', length=32), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ] test_pipeline = val_pipeline
train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type=dataset_type, ann_file='train_9k.json', data_root=data_root, data_prefix=dict(video='videos'), pipeline=train_pipeline)) val_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type=dataset_type, ann_file='test_JSFUSION.json', data_root=data_root, data_prefix=dict(video='videos'), pipeline=val_pipeline, test_mode=True)) test_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type=dataset_type, ann_file='test_JSFUSION.json', data_root=data_root, data_prefix=dict(video='videos'), pipeline=test_pipeline, test_mode=True))
val_evaluator = dict(type='RetrievalMetric') test_evaluator = val_evaluator
train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=20, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop')
param_scheduler = [ dict( type='LinearLR', start_factor=0.05, by_epoch=True, begin=0, end=10, convert_to_iter_based=True), dict( type='CosineAnnealingLR', T_max=4.5, eta_min=0, by_epoch=True, begin=10, end=100, convert_to_iter_based=True) ]
optim_wrapper = dict( type='AmpOptimWrapper', optimizer=dict( type='AdamW', lr=2e-06, betas=(0.9, 0.98), eps=1e-08, weight_decay=0.02), paramwise_cfg=dict( norm_decay_mult=0., bias_decay_mult=0., custom_keys={ 'STAN': dict(lr_mult=10.), }), clip_grad=dict(max_norm=5, norm_type=2) )
default_hooks = dict(checkpoint=dict(type='printBest_CheckpointHook', interval=-1, save_best='auto', rule='greater'))
auto_scale_lr = dict(enable=True, base_batch_size=128)