Can you share the training configs?

[leexinhao]

No description provided.

[farewellthree]

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)