2023/06/12 00:03:57 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.9.0 (default, Nov 15 2020, 14:28:56) [GCC 7.3.0] CUDA available: True numpy_random_seed: 630791900 GPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 PyTorch: 1.12.1+cu113 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.2 - Built with CuDNN 8.3.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.13.1+cu102 OpenCV: 4.7.0 MMEngine: 0.7.3 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 630791900 diff_rank_seed: False deterministic: False Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/06/12 00:03:57 - mmengine - INFO - Config: default_scope = 'mmaction' default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=20, ignore_last=False), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1, save_best=None), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffers=dict(type='SyncBuffersHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')]) log_level = 'INFO' load_from = None resume = False model = dict( type='CLIPSimilarity', clip_arch='ViT-B/32', to_float32=True, frozen_layers=0, data_preprocessor=dict( type='MultiModalDataPreprocessor', preprocessors=dict( imgs=dict( type='ActionDataPreprocessor', mean=[122.771, 116.746, 104.093], std=[68.5, 66.632, 70.323], format_shape='NCHW'), text=dict(type='ActionDataPreprocessor', to_float32=False))), adapter=dict(type='SimpleMeanAdapter')) dataset_type = 'VideoTextDataset' data_root = 'data/video_retrieval/msrvtt' file_client_args = dict(io_backend='disk') train_pipeline = [ dict(type='DecordInit', io_backend='disk'), dict(type='UniformSample', clip_len=12, num_clips=1), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize'), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ] val_pipeline = [ dict(type='DecordInit', io_backend='disk'), dict(type='UniformSample', clip_len=12, num_clips=1, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize'), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ] test_pipeline = [ dict(type='DecordInit', io_backend='disk'), dict(type='UniformSample', clip_len=12, num_clips=1, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize'), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ] train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='VideoTextDataset', ann_file='train_9k.json', data_root='data/video_retrieval/msrvtt', data_prefix=dict(video='videos'), pipeline=[ dict(type='DecordInit', io_backend='disk'), dict(type='UniformSample', clip_len=12, num_clips=1), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize'), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ])) val_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='VideoTextDataset', ann_file='test_JSFUSION.json', data_root='data/video_retrieval/msrvtt', data_prefix=dict(video='videos'), pipeline=[ dict(type='DecordInit', io_backend='disk'), dict( type='UniformSample', clip_len=12, num_clips=1, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize'), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ], test_mode=True)) test_dataloader = dict( batch_size=1, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='VideoTextDataset', ann_file='test_JSFUSION.json', data_root='data/video_retrieval/msrvtt', data_prefix=dict(video='videos'), pipeline=[ dict(type='DecordInit', io_backend='disk'), dict( type='UniformSample', clip_len=12, num_clips=1, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='CLIPTokenize'), dict(type='PackActionInputs', collect_keys=('imgs', 'text')) ], test_mode=True)) val_evaluator = dict(type='RetrievalMetric') test_evaluator = dict(type='RetrievalMetric') train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=5, 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=0.5, convert_to_iter_based=True), dict( type='CosineAnnealingLR', T_max=4.5, eta_min=0, by_epoch=True, begin=0.5, end=5, convert_to_iter_based=True) ] optim_wrapper = dict( optimizer=dict( type='AdamW', lr=1e-05, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.05), paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0)) auto_scale_lr = dict(enable=False, base_batch_size=128) launcher = 'pytorch' work_dir = './work_dirs/clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb' randomness = dict(seed=630791900, diff_rank_seed=False, deterministic=False) 2023/06/12 00:04:00 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train: (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.0.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.0.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.0.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.0.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.0.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.0.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.0.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.1.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.1.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.1.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.1.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.1.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.1.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.1.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.2.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.2.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.2.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.2.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.2.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.2.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.2.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.3.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.3.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.3.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.3.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.3.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.3.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.3.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.4.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.4.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.4.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.4.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.4.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.4.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.4.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.5.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.5.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - 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mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.6.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.6.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.6.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.6.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.7.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.7.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.7.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.7.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.7.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.7.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.7.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.8.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.8.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.8.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.8.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.8.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.8.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.8.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.9.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.9.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.9.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.9.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.9.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - 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00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.10.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.11.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.11.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.11.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.11.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.11.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.11.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.transformer.resblocks.11.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.ln_post.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.visual.ln_post.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.0.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.0.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.0.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.0.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.0.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.0.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.0.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.1.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.1.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.1.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.1.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.1.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.1.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.1.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.2.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.2.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.2.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.2.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.2.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.2.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.2.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.3.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.3.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.3.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.3.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.3.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.3.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.3.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.4.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.4.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.4.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.4.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.4.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.4.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.4.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.5.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.5.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.5.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.5.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.5.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.5.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.5.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.6.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.6.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.6.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.6.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.6.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.6.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.6.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.7.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.7.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.7.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.7.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.7.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.7.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.7.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.8.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.8.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.8.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.8.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.8.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.8.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.8.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.9.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.9.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.9.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.9.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.9.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.9.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.9.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.10.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.10.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.10.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.10.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.10.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.10.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.10.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.11.attn.out_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.11.ln_1.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.11.ln_1.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.11.mlp.c_fc.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.11.mlp.c_proj.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.11.ln_2.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.transformer.resblocks.11.ln_2.bias:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.ln_final.weight:weight_decay=0.0 2023/06/12 00:04:01 - mmengine - INFO - paramwise_options -- clip.ln_final.bias:weight_decay=0.0 Name of parameter - Initialization information clip.positional_embedding - torch.Size([77, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.text_projection - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.logit_scale - torch.Size([]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.class_embedding - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.positional_embedding - torch.Size([50, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.proj - torch.Size([768, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.conv1.weight - torch.Size([768, 3, 32, 32]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.ln_pre.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.ln_pre.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.0.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.1.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.2.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.3.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.4.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.5.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.6.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.7.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.8.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.9.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.10.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.attn.in_proj_weight - torch.Size([2304, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.attn.in_proj_bias - torch.Size([2304]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.attn.out_proj.weight - torch.Size([768, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.attn.out_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.ln_1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.ln_1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.mlp.c_fc.weight - torch.Size([3072, 768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.mlp.c_fc.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.mlp.c_proj.weight - torch.Size([768, 3072]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.mlp.c_proj.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.ln_2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.transformer.resblocks.11.ln_2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.ln_post.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.visual.ln_post.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.0.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.1.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.2.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.3.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.4.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.5.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.6.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.7.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.8.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.9.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.10.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.ln_1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.ln_1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.mlp.c_fc.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.mlp.c_fc.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.mlp.c_proj.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.mlp.c_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.ln_2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.transformer.resblocks.11.ln_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.token_embedding.weight - torch.Size([49408, 512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.ln_final.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity clip.ln_final.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of CLIPSimilarity 2023/06/12 00:04:02 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/06/12 00:04:02 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/06/12 00:04:02 - mmengine - INFO - Checkpoints will be saved to /mnt/workspace/daiwenxun/mmlab/mmaction2/work_dirs/clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb. 2023/06/12 00:04:18 - mmengine - INFO - Epoch(train) [1][ 20/1407] lr: 7.5712e-07 eta: 1:36:58 time: 0.8295 data_time: 0.1366 memory: 10497 loss: 1.6929 sim_loss_v2t: 0.8590 sim_loss_t2v: 0.8339 2023/06/12 00:04:28 - mmengine - INFO - Epoch(train) [1][ 40/1407] lr: 1.0278e-06 eta: 1:16:39 time: 0.4856 data_time: 0.0170 memory: 10497 loss: 1.5703 sim_loss_v2t: 0.7759 sim_loss_t2v: 0.7944 2023/06/12 00:04:38 - mmengine - INFO - Epoch(train) [1][ 60/1407] lr: 1.2984e-06 eta: 1:09:44 time: 0.4848 data_time: 0.0174 memory: 10497 loss: 1.3682 sim_loss_v2t: 0.6729 sim_loss_t2v: 0.6953 2023/06/12 00:04:48 - mmengine - INFO - Epoch(train) [1][ 80/1407] lr: 1.5691e-06 eta: 1:06:16 time: 0.4873 data_time: 0.0185 memory: 10497 loss: 1.2988 sim_loss_v2t: 0.6356 sim_loss_t2v: 0.6632 2023/06/12 00:04:57 - mmengine - INFO - Epoch(train) [1][ 100/1407] lr: 1.8397e-06 eta: 1:04:10 time: 0.4887 data_time: 0.0182 memory: 10497 loss: 1.2562 sim_loss_v2t: 0.6141 sim_loss_t2v: 0.6421 2023/06/12 00:05:07 - mmengine - INFO - Epoch(train) [1][ 120/1407] lr: 2.1104e-06 eta: 1:02:44 time: 0.4903 data_time: 0.0182 memory: 10497 loss: 1.2323 sim_loss_v2t: 0.6042 sim_loss_t2v: 0.6281 2023/06/12 00:05:17 - mmengine - INFO - Epoch(train) [1][ 140/1407] lr: 2.3811e-06 eta: 1:01:39 time: 0.4892 data_time: 0.0176 memory: 10497 loss: 1.2815 sim_loss_v2t: 0.6301 sim_loss_t2v: 0.6514 2023/06/12 00:05:27 - mmengine - INFO - Epoch(train) [1][ 160/1407] lr: 2.6517e-06 eta: 1:00:46 time: 0.4880 data_time: 0.0169 memory: 10497 loss: 1.1447 sim_loss_v2t: 0.5606 sim_loss_t2v: 0.5841 2023/06/12 00:05:36 - mmengine - INFO - Epoch(train) [1][ 180/1407] lr: 2.9224e-06 eta: 1:00:05 time: 0.4898 data_time: 0.0168 memory: 10497 loss: 1.0934 sim_loss_v2t: 0.5371 sim_loss_t2v: 0.5563 2023/06/12 00:05:46 - mmengine - INFO - Epoch(train) [1][ 200/1407] lr: 3.1930e-06 eta: 0:59:32 time: 0.4931 data_time: 0.0173 memory: 10497 loss: 1.1830 sim_loss_v2t: 0.5815 sim_loss_t2v: 0.6015 2023/06/12 00:05:56 - mmengine - INFO - Epoch(train) [1][ 220/1407] lr: 3.4637e-06 eta: 0:59:01 time: 0.4895 data_time: 0.0172 memory: 10497 loss: 1.1203 sim_loss_v2t: 0.5468 sim_loss_t2v: 0.5734 2023/06/12 00:06:06 - mmengine - INFO - Epoch(train) [1][ 240/1407] lr: 3.7343e-06 eta: 0:58:34 time: 0.4902 data_time: 0.0169 memory: 10497 loss: 1.0842 sim_loss_v2t: 0.5344 sim_loss_t2v: 0.5498 2023/06/12 00:06:16 - mmengine - INFO - Epoch(train) [1][ 260/1407] lr: 4.0050e-06 eta: 0:58:09 time: 0.4893 data_time: 0.0175 memory: 10497 loss: 1.0947 sim_loss_v2t: 0.5383 sim_loss_t2v: 0.5563 2023/06/12 00:06:26 - mmengine - INFO - Epoch(train) [1][ 280/1407] lr: 4.2756e-06 eta: 0:57:47 time: 0.4905 data_time: 0.0178 memory: 10497 loss: 1.0453 sim_loss_v2t: 0.5123 sim_loss_t2v: 0.5330 2023/06/12 00:06:35 - mmengine - INFO - Epoch(train) [1][ 300/1407] lr: 4.5463e-06 eta: 0:57:26 time: 0.4898 data_time: 0.0178 memory: 10497 loss: 1.0726 sim_loss_v2t: 0.5307 sim_loss_t2v: 0.5420 2023/06/12 00:06:45 - mmengine - INFO - Epoch(train) [1][ 320/1407] lr: 4.8170e-06 eta: 0:57:07 time: 0.4919 data_time: 0.0167 memory: 10497 loss: 0.9917 sim_loss_v2t: 0.4832 sim_loss_t2v: 0.5084 2023/06/12 00:06:55 - mmengine - INFO - Epoch(train) [1][ 340/1407] lr: 5.0876e-06 eta: 0:56:49 time: 0.4892 data_time: 0.0168 memory: 10497 loss: 1.0356 sim_loss_v2t: 0.5103 sim_loss_t2v: 0.5253 2023/06/12 00:07:05 - mmengine - INFO - Epoch(train) [1][ 360/1407] lr: 5.3583e-06 eta: 0:56:31 time: 0.4896 data_time: 0.0178 memory: 10497 loss: 1.0215 sim_loss_v2t: 0.5027 sim_loss_t2v: 0.5188 2023/06/12 00:07:15 - mmengine - INFO - Epoch(train) [1][ 380/1407] lr: 5.6289e-06 eta: 0:56:15 time: 0.4915 data_time: 0.0179 memory: 10497 loss: 0.9990 sim_loss_v2t: 0.4889 sim_loss_t2v: 0.5101 2023/06/12 00:07:24 - mmengine - INFO - Epoch(train) [1][ 400/1407] lr: 5.8996e-06 eta: 0:55:59 time: 0.4893 data_time: 0.0175 memory: 10497 loss: 0.9769 sim_loss_v2t: 0.4768 sim_loss_t2v: 0.5001 2023/06/12 00:07:34 - mmengine - INFO - Epoch(train) [1][ 420/1407] lr: 6.1702e-06 eta: 0:55:44 time: 0.4908 data_time: 0.0189 memory: 10497 loss: 0.9810 sim_loss_v2t: 0.4805 sim_loss_t2v: 0.5005 2023/06/12 00:07:44 - mmengine - INFO - Epoch(train) [1][ 440/1407] lr: 6.4409e-06 eta: 0:55:30 time: 0.4911 data_time: 0.0172 memory: 10497 loss: 0.9815 sim_loss_v2t: 0.4860 sim_loss_t2v: 0.4954 2023/06/12 00:07:54 - mmengine - INFO - Epoch(train) [1][ 460/1407] lr: 6.7115e-06 eta: 0:55:16 time: 0.4912 data_time: 0.0178 memory: 10497 loss: 1.0055 sim_loss_v2t: 0.4915 sim_loss_t2v: 0.5141 2023/06/12 00:08:04 - mmengine - INFO - Epoch(train) [1][ 480/1407] lr: 6.9822e-06 eta: 0:55:02 time: 0.4926 data_time: 0.0181 memory: 10497 loss: 0.9642 sim_loss_v2t: 0.4728 sim_loss_t2v: 0.4914 2023/06/12 00:08:13 - mmengine - INFO - Epoch(train) [1][ 500/1407] lr: 7.2528e-06 eta: 0:54:48 time: 0.4884 data_time: 0.0174 memory: 10497 loss: 0.9812 sim_loss_v2t: 0.4783 sim_loss_t2v: 0.5028 2023/06/12 00:08:23 - mmengine - INFO - Epoch(train) [1][ 520/1407] lr: 7.5235e-06 eta: 0:54:35 time: 0.4890 data_time: 0.0187 memory: 10497 loss: 0.9704 sim_loss_v2t: 0.4778 sim_loss_t2v: 0.4926 2023/06/12 00:08:33 - mmengine - INFO - Epoch(train) [1][ 540/1407] lr: 7.7942e-06 eta: 0:54:22 time: 0.4909 data_time: 0.0181 memory: 10497 loss: 1.0100 sim_loss_v2t: 0.4926 sim_loss_t2v: 0.5174 2023/06/12 00:08:43 - mmengine - INFO - Epoch(train) [1][ 560/1407] lr: 8.0648e-06 eta: 0:54:09 time: 0.4920 data_time: 0.0170 memory: 10497 loss: 0.9938 sim_loss_v2t: 0.4886 sim_loss_t2v: 0.5052 2023/06/12 00:08:53 - mmengine - INFO - Epoch(train) [1][ 580/1407] lr: 8.3355e-06 eta: 0:53:57 time: 0.4905 data_time: 0.0166 memory: 10497 loss: 0.9720 sim_loss_v2t: 0.4746 sim_loss_t2v: 0.4975 2023/06/12 00:09:02 - mmengine - INFO - Epoch(train) [1][ 600/1407] lr: 8.6061e-06 eta: 0:53:44 time: 0.4904 data_time: 0.0182 memory: 10497 loss: 0.9322 sim_loss_v2t: 0.4567 sim_loss_t2v: 0.4755 2023/06/12 00:09:12 - mmengine - INFO - Epoch(train) [1][ 620/1407] lr: 8.8768e-06 eta: 0:53:32 time: 0.4887 data_time: 0.0188 memory: 10497 loss: 0.9549 sim_loss_v2t: 0.4678 sim_loss_t2v: 0.4871 2023/06/12 00:09:22 - mmengine - INFO - Epoch(train) [1][ 640/1407] lr: 9.1474e-06 eta: 0:53:20 time: 0.4901 data_time: 0.0179 memory: 10497 loss: 0.9931 sim_loss_v2t: 0.4865 sim_loss_t2v: 0.5066 2023/06/12 00:09:32 - mmengine - INFO - Epoch(train) [1][ 660/1407] lr: 9.4181e-06 eta: 0:53:07 time: 0.4894 data_time: 0.0180 memory: 10497 loss: 0.9282 sim_loss_v2t: 0.4524 sim_loss_t2v: 0.4758 2023/06/12 00:09:42 - mmengine - INFO - Epoch(train) [1][ 680/1407] lr: 9.6887e-06 eta: 0:52:56 time: 0.4925 data_time: 0.0183 memory: 10497 loss: 0.9104 sim_loss_v2t: 0.4463 sim_loss_t2v: 0.4642 2023/06/12 00:09:52 - mmengine - INFO - Epoch(train) [1][ 700/1407] lr: 9.9594e-06 eta: 0:52:44 time: 0.4917 data_time: 0.0173 memory: 10497 loss: 0.9263 sim_loss_v2t: 0.4536 sim_loss_t2v: 0.4727 2023/06/12 00:10:01 - mmengine - INFO - Epoch(train) [1][ 720/1407] lr: 9.9998e-06 eta: 0:52:34 time: 0.4940 data_time: 0.0174 memory: 10497 loss: 0.9443 sim_loss_v2t: 0.4549 sim_loss_t2v: 0.4894 2023/06/12 00:10:11 - mmengine - INFO - Epoch(train) [1][ 740/1407] lr: 9.9992e-06 eta: 0:52:22 time: 0.4892 data_time: 0.0160 memory: 10497 loss: 0.9135 sim_loss_v2t: 0.4471 sim_loss_t2v: 0.4663 2023/06/12 00:10:21 - mmengine - INFO - Epoch(train) [1][ 760/1407] lr: 9.9981e-06 eta: 0:52:11 time: 0.4916 data_time: 0.0171 memory: 10497 loss: 0.8764 sim_loss_v2t: 0.4270 sim_loss_t2v: 0.4494 2023/06/12 00:10:31 - mmengine - INFO - Epoch(train) [1][ 780/1407] lr: 9.9964e-06 eta: 0:51:59 time: 0.4917 data_time: 0.0167 memory: 10497 loss: 0.9018 sim_loss_v2t: 0.4414 sim_loss_t2v: 0.4604 2023/06/12 00:10:41 - mmengine - INFO - Epoch(train) [1][ 800/1407] lr: 9.9943e-06 eta: 0:51:48 time: 0.4924 data_time: 0.0172 memory: 10497 loss: 0.9136 sim_loss_v2t: 0.4489 sim_loss_t2v: 0.4647 2023/06/12 00:10:51 - mmengine - INFO - Epoch(train) [1][ 820/1407] lr: 9.9917e-06 eta: 0:51:38 time: 0.4925 data_time: 0.0174 memory: 10497 loss: 0.8975 sim_loss_v2t: 0.4383 sim_loss_t2v: 0.4592 2023/06/12 00:11:00 - mmengine - INFO - Epoch(train) [1][ 840/1407] lr: 9.9886e-06 eta: 0:51:27 time: 0.4913 data_time: 0.0170 memory: 10497 loss: 0.8975 sim_loss_v2t: 0.4400 sim_loss_t2v: 0.4574 2023/06/12 00:11:10 - mmengine - INFO - Epoch(train) [1][ 860/1407] lr: 9.9850e-06 eta: 0:51:16 time: 0.4949 data_time: 0.0172 memory: 10497 loss: 0.9094 sim_loss_v2t: 0.4426 sim_loss_t2v: 0.4669 2023/06/12 00:11:20 - mmengine - INFO - Epoch(train) [1][ 880/1407] lr: 9.9809e-06 eta: 0:51:05 time: 0.4907 data_time: 0.0180 memory: 10497 loss: 0.8628 sim_loss_v2t: 0.4195 sim_loss_t2v: 0.4433 2023/06/12 00:11:30 - mmengine - INFO - Epoch(train) [1][ 900/1407] lr: 9.9764e-06 eta: 0:50:54 time: 0.4901 data_time: 0.0169 memory: 10497 loss: 0.8882 sim_loss_v2t: 0.4384 sim_loss_t2v: 0.4498 2023/06/12 00:11:40 - mmengine - INFO - Epoch(train) [1][ 920/1407] lr: 9.9713e-06 eta: 0:50:43 time: 0.4908 data_time: 0.0172 memory: 10497 loss: 0.8632 sim_loss_v2t: 0.4203 sim_loss_t2v: 0.4429 2023/06/12 00:11:50 - mmengine - INFO - Epoch(train) [1][ 940/1407] lr: 9.9658e-06 eta: 0:50:33 time: 0.4928 data_time: 0.0173 memory: 10497 loss: 0.8700 sim_loss_v2t: 0.4257 sim_loss_t2v: 0.4442 2023/06/12 00:11:59 - mmengine - INFO - Epoch(train) [1][ 960/1407] lr: 9.9597e-06 eta: 0:50:22 time: 0.4901 data_time: 0.0166 memory: 10497 loss: 0.8645 sim_loss_v2t: 0.4236 sim_loss_t2v: 0.4409 2023/06/12 00:12:09 - mmengine - INFO - Epoch(train) [1][ 980/1407] lr: 9.9532e-06 eta: 0:50:11 time: 0.4921 data_time: 0.0176 memory: 10497 loss: 0.8214 sim_loss_v2t: 0.4032 sim_loss_t2v: 0.4182 2023/06/12 00:12:19 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:12:19 - mmengine - INFO - Epoch(train) [1][1000/1407] lr: 9.9462e-06 eta: 0:50:00 time: 0.4919 data_time: 0.0176 memory: 10497 loss: 0.8783 sim_loss_v2t: 0.4337 sim_loss_t2v: 0.4446 2023/06/12 00:12:29 - mmengine - INFO - Epoch(train) [1][1020/1407] lr: 9.9387e-06 eta: 0:49:50 time: 0.4917 data_time: 0.0175 memory: 10497 loss: 0.8902 sim_loss_v2t: 0.4302 sim_loss_t2v: 0.4599 2023/06/12 00:12:39 - mmengine - INFO - Epoch(train) [1][1040/1407] lr: 9.9307e-06 eta: 0:49:39 time: 0.4896 data_time: 0.0174 memory: 10497 loss: 0.8675 sim_loss_v2t: 0.4209 sim_loss_t2v: 0.4466 2023/06/12 00:12:49 - mmengine - INFO - Epoch(train) [1][1060/1407] lr: 9.9222e-06 eta: 0:49:28 time: 0.4913 data_time: 0.0170 memory: 10497 loss: 0.8661 sim_loss_v2t: 0.4234 sim_loss_t2v: 0.4427 2023/06/12 00:12:58 - mmengine - INFO - Epoch(train) [1][1080/1407] lr: 9.9132e-06 eta: 0:49:18 time: 0.4901 data_time: 0.0172 memory: 10497 loss: 0.8328 sim_loss_v2t: 0.4114 sim_loss_t2v: 0.4214 2023/06/12 00:13:08 - mmengine - INFO - Epoch(train) [1][1100/1407] lr: 9.9038e-06 eta: 0:49:07 time: 0.4904 data_time: 0.0181 memory: 10497 loss: 0.8180 sim_loss_v2t: 0.3976 sim_loss_t2v: 0.4204 2023/06/12 00:13:18 - mmengine - INFO - Epoch(train) [1][1120/1407] lr: 9.8939e-06 eta: 0:48:57 time: 0.4914 data_time: 0.0178 memory: 10497 loss: 0.8187 sim_loss_v2t: 0.3981 sim_loss_t2v: 0.4206 2023/06/12 00:13:28 - mmengine - INFO - Epoch(train) [1][1140/1407] lr: 9.8835e-06 eta: 0:48:46 time: 0.4896 data_time: 0.0172 memory: 10497 loss: 0.8015 sim_loss_v2t: 0.3869 sim_loss_t2v: 0.4147 2023/06/12 00:13:38 - mmengine - INFO - Epoch(train) [1][1160/1407] lr: 9.8726e-06 eta: 0:48:35 time: 0.4891 data_time: 0.0182 memory: 10497 loss: 0.7839 sim_loss_v2t: 0.3842 sim_loss_t2v: 0.3997 2023/06/12 00:13:47 - mmengine - INFO - Epoch(train) [1][1180/1407] lr: 9.8612e-06 eta: 0:48:25 time: 0.4918 data_time: 0.0177 memory: 10497 loss: 0.8276 sim_loss_v2t: 0.4025 sim_loss_t2v: 0.4251 2023/06/12 00:13:57 - mmengine - INFO - Epoch(train) [1][1200/1407] lr: 9.8493e-06 eta: 0:48:15 time: 0.4932 data_time: 0.0175 memory: 10497 loss: 0.8852 sim_loss_v2t: 0.4311 sim_loss_t2v: 0.4541 2023/06/12 00:14:07 - mmengine - INFO - Epoch(train) [1][1220/1407] lr: 9.8370e-06 eta: 0:48:04 time: 0.4906 data_time: 0.0177 memory: 10497 loss: 0.8534 sim_loss_v2t: 0.4179 sim_loss_t2v: 0.4355 2023/06/12 00:14:17 - mmengine - INFO - Epoch(train) [1][1240/1407] lr: 9.8242e-06 eta: 0:47:54 time: 0.4913 data_time: 0.0185 memory: 10497 loss: 0.8407 sim_loss_v2t: 0.4155 sim_loss_t2v: 0.4252 2023/06/12 00:14:27 - mmengine - INFO - Epoch(train) [1][1260/1407] lr: 9.8109e-06 eta: 0:47:43 time: 0.4901 data_time: 0.0187 memory: 10497 loss: 0.7773 sim_loss_v2t: 0.3803 sim_loss_t2v: 0.3970 2023/06/12 00:14:36 - mmengine - INFO - Epoch(train) [1][1280/1407] lr: 9.7972e-06 eta: 0:47:33 time: 0.4907 data_time: 0.0176 memory: 10497 loss: 0.8536 sim_loss_v2t: 0.4190 sim_loss_t2v: 0.4346 2023/06/12 00:14:46 - mmengine - INFO - Epoch(train) [1][1300/1407] lr: 9.7830e-06 eta: 0:47:23 time: 0.4909 data_time: 0.0178 memory: 10497 loss: 0.7940 sim_loss_v2t: 0.3831 sim_loss_t2v: 0.4108 2023/06/12 00:14:56 - mmengine - INFO - Epoch(train) [1][1320/1407] lr: 9.7683e-06 eta: 0:47:12 time: 0.4895 data_time: 0.0180 memory: 10497 loss: 0.8122 sim_loss_v2t: 0.3971 sim_loss_t2v: 0.4151 2023/06/12 00:15:06 - mmengine - INFO - Epoch(train) [1][1340/1407] lr: 9.7531e-06 eta: 0:47:02 time: 0.4910 data_time: 0.0187 memory: 10497 loss: 0.8126 sim_loss_v2t: 0.3959 sim_loss_t2v: 0.4168 2023/06/12 00:15:16 - mmengine - INFO - Epoch(train) [1][1360/1407] lr: 9.7375e-06 eta: 0:46:52 time: 0.4907 data_time: 0.0187 memory: 10497 loss: 0.7717 sim_loss_v2t: 0.3740 sim_loss_t2v: 0.3977 2023/06/12 00:15:26 - mmengine - INFO - Epoch(train) [1][1380/1407] lr: 9.7214e-06 eta: 0:46:41 time: 0.4899 data_time: 0.0169 memory: 10497 loss: 0.7646 sim_loss_v2t: 0.3729 sim_loss_t2v: 0.3917 2023/06/12 00:15:35 - mmengine - INFO - Epoch(train) [1][1400/1407] lr: 9.7048e-06 eta: 0:46:31 time: 0.4876 data_time: 0.0177 memory: 10497 loss: 0.7336 sim_loss_v2t: 0.3577 sim_loss_t2v: 0.3759 2023/06/12 00:15:39 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:15:39 - mmengine - INFO - Epoch(train) [1][1407/1407] lr: 9.6989e-06 eta: 0:46:26 time: 0.4772 data_time: 0.0175 memory: 10497 loss: 0.6972 sim_loss_v2t: 0.3346 sim_loss_t2v: 0.3625 2023/06/12 00:15:39 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/06/12 00:16:37 - mmengine - INFO - Epoch(val) [1][8/8] retrieval/R1: 41.6000 retrieval/R5: 68.4000 retrieval/R10: 79.7000 retrieval/MdR: 2.0000 retrieval/MnR: 15.9020 data_time: 0.2848 time: 0.4228 2023/06/12 00:16:50 - mmengine - INFO - Epoch(train) [2][ 20/1407] lr: 9.6817e-06 eta: 0:46:28 time: 0.6473 data_time: 0.1447 memory: 10497 loss: 0.6386 sim_loss_v2t: 0.3062 sim_loss_t2v: 0.3324 2023/06/12 00:16:59 - mmengine - INFO - Epoch(train) [2][ 40/1407] lr: 9.6640e-06 eta: 0:46:18 time: 0.4876 data_time: 0.0170 memory: 10497 loss: 0.6015 sim_loss_v2t: 0.2939 sim_loss_t2v: 0.3076 2023/06/12 00:17:09 - mmengine - INFO - Epoch(train) [2][ 60/1407] lr: 9.6459e-06 eta: 0:46:07 time: 0.4885 data_time: 0.0180 memory: 10497 loss: 0.6665 sim_loss_v2t: 0.3190 sim_loss_t2v: 0.3475 2023/06/12 00:17:19 - mmengine - INFO - Epoch(train) [2][ 80/1407] lr: 9.6274e-06 eta: 0:45:56 time: 0.4883 data_time: 0.0175 memory: 10497 loss: 0.6015 sim_loss_v2t: 0.2868 sim_loss_t2v: 0.3147 2023/06/12 00:17:29 - mmengine - INFO - Epoch(train) [2][ 100/1407] lr: 9.6083e-06 eta: 0:45:46 time: 0.4892 data_time: 0.0173 memory: 10497 loss: 0.6023 sim_loss_v2t: 0.2909 sim_loss_t2v: 0.3114 2023/06/12 00:17:38 - mmengine - INFO - Epoch(train) [2][ 120/1407] lr: 9.5889e-06 eta: 0:45:36 time: 0.4913 data_time: 0.0174 memory: 10497 loss: 0.6323 sim_loss_v2t: 0.3028 sim_loss_t2v: 0.3296 2023/06/12 00:17:48 - mmengine - INFO - Epoch(train) [2][ 140/1407] lr: 9.5689e-06 eta: 0:45:25 time: 0.4903 data_time: 0.0186 memory: 10497 loss: 0.6030 sim_loss_v2t: 0.2912 sim_loss_t2v: 0.3117 2023/06/12 00:17:58 - mmengine - INFO - Epoch(train) [2][ 160/1407] lr: 9.5486e-06 eta: 0:45:15 time: 0.4908 data_time: 0.0179 memory: 10497 loss: 0.6299 sim_loss_v2t: 0.3001 sim_loss_t2v: 0.3298 2023/06/12 00:18:08 - mmengine - INFO - Epoch(train) [2][ 180/1407] lr: 9.5277e-06 eta: 0:45:05 time: 0.4921 data_time: 0.0180 memory: 10497 loss: 0.6017 sim_loss_v2t: 0.2896 sim_loss_t2v: 0.3122 2023/06/12 00:18:18 - mmengine - INFO - Epoch(train) [2][ 200/1407] lr: 9.5065e-06 eta: 0:44:54 time: 0.4914 data_time: 0.0164 memory: 10497 loss: 0.6223 sim_loss_v2t: 0.2972 sim_loss_t2v: 0.3251 2023/06/12 00:18:28 - mmengine - INFO - Epoch(train) [2][ 220/1407] lr: 9.4847e-06 eta: 0:44:44 time: 0.4904 data_time: 0.0181 memory: 10497 loss: 0.5733 sim_loss_v2t: 0.2720 sim_loss_t2v: 0.3013 2023/06/12 00:18:37 - mmengine - INFO - Epoch(train) [2][ 240/1407] lr: 9.4626e-06 eta: 0:44:34 time: 0.4922 data_time: 0.0175 memory: 10497 loss: 0.6047 sim_loss_v2t: 0.2879 sim_loss_t2v: 0.3168 2023/06/12 00:18:47 - mmengine - INFO - Epoch(train) [2][ 260/1407] lr: 9.4400e-06 eta: 0:44:24 time: 0.4924 data_time: 0.0174 memory: 10497 loss: 0.6206 sim_loss_v2t: 0.2993 sim_loss_t2v: 0.3213 2023/06/12 00:18:57 - mmengine - INFO - Epoch(train) [2][ 280/1407] lr: 9.4169e-06 eta: 0:44:13 time: 0.4924 data_time: 0.0178 memory: 10497 loss: 0.5949 sim_loss_v2t: 0.2888 sim_loss_t2v: 0.3061 2023/06/12 00:19:07 - mmengine - INFO - Epoch(train) [2][ 300/1407] lr: 9.3935e-06 eta: 0:44:03 time: 0.4902 data_time: 0.0186 memory: 10497 loss: 0.5997 sim_loss_v2t: 0.2884 sim_loss_t2v: 0.3113 2023/06/12 00:19:17 - mmengine - INFO - Epoch(train) [2][ 320/1407] lr: 9.3696e-06 eta: 0:43:53 time: 0.4931 data_time: 0.0170 memory: 10497 loss: 0.6129 sim_loss_v2t: 0.3001 sim_loss_t2v: 0.3128 2023/06/12 00:19:27 - mmengine - INFO - Epoch(train) [2][ 340/1407] lr: 9.3452e-06 eta: 0:43:43 time: 0.4912 data_time: 0.0171 memory: 10497 loss: 0.6298 sim_loss_v2t: 0.3009 sim_loss_t2v: 0.3290 2023/06/12 00:19:36 - mmengine - INFO - Epoch(train) [2][ 360/1407] lr: 9.3205e-06 eta: 0:43:32 time: 0.4883 data_time: 0.0179 memory: 10497 loss: 0.6353 sim_loss_v2t: 0.3088 sim_loss_t2v: 0.3265 2023/06/12 00:19:46 - mmengine - INFO - Epoch(train) [2][ 380/1407] lr: 9.2953e-06 eta: 0:43:22 time: 0.4930 data_time: 0.0179 memory: 10497 loss: 0.6154 sim_loss_v2t: 0.2928 sim_loss_t2v: 0.3226 2023/06/12 00:19:56 - mmengine - INFO - Epoch(train) [2][ 400/1407] lr: 9.2697e-06 eta: 0:43:12 time: 0.4894 data_time: 0.0175 memory: 10497 loss: 0.6307 sim_loss_v2t: 0.2991 sim_loss_t2v: 0.3316 2023/06/12 00:20:06 - mmengine - INFO - Epoch(train) [2][ 420/1407] lr: 9.2437e-06 eta: 0:43:02 time: 0.4896 data_time: 0.0172 memory: 10497 loss: 0.6710 sim_loss_v2t: 0.3195 sim_loss_t2v: 0.3515 2023/06/12 00:20:16 - mmengine - INFO - Epoch(train) [2][ 440/1407] lr: 9.2172e-06 eta: 0:42:52 time: 0.4903 data_time: 0.0176 memory: 10497 loss: 0.5541 sim_loss_v2t: 0.2697 sim_loss_t2v: 0.2844 2023/06/12 00:20:25 - mmengine - INFO - Epoch(train) [2][ 460/1407] lr: 9.1903e-06 eta: 0:42:41 time: 0.4888 data_time: 0.0176 memory: 10497 loss: 0.5937 sim_loss_v2t: 0.2856 sim_loss_t2v: 0.3081 2023/06/12 00:20:35 - mmengine - INFO - Epoch(train) [2][ 480/1407] lr: 9.1631e-06 eta: 0:42:31 time: 0.4886 data_time: 0.0174 memory: 10497 loss: 0.6169 sim_loss_v2t: 0.2920 sim_loss_t2v: 0.3249 2023/06/12 00:20:45 - mmengine - INFO - Epoch(train) [2][ 500/1407] lr: 9.1354e-06 eta: 0:42:21 time: 0.4906 data_time: 0.0166 memory: 10497 loss: 0.6110 sim_loss_v2t: 0.2920 sim_loss_t2v: 0.3190 2023/06/12 00:20:55 - mmengine - INFO - Epoch(train) [2][ 520/1407] lr: 9.1073e-06 eta: 0:42:11 time: 0.4901 data_time: 0.0176 memory: 10497 loss: 0.6455 sim_loss_v2t: 0.3111 sim_loss_t2v: 0.3344 2023/06/12 00:21:05 - mmengine - INFO - Epoch(train) [2][ 540/1407] lr: 9.0788e-06 eta: 0:42:00 time: 0.4920 data_time: 0.0173 memory: 10497 loss: 0.6102 sim_loss_v2t: 0.2917 sim_loss_t2v: 0.3185 2023/06/12 00:21:14 - mmengine - INFO - Epoch(train) [2][ 560/1407] lr: 9.0499e-06 eta: 0:41:50 time: 0.4896 data_time: 0.0172 memory: 10497 loss: 0.6021 sim_loss_v2t: 0.2957 sim_loss_t2v: 0.3064 2023/06/12 00:21:24 - mmengine - INFO - Epoch(train) [2][ 580/1407] lr: 9.0206e-06 eta: 0:41:40 time: 0.4925 data_time: 0.0165 memory: 10497 loss: 0.6398 sim_loss_v2t: 0.3102 sim_loss_t2v: 0.3296 2023/06/12 00:21:31 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:21:34 - mmengine - INFO - Epoch(train) [2][ 600/1407] lr: 8.9909e-06 eta: 0:41:30 time: 0.4923 data_time: 0.0174 memory: 10497 loss: 0.6101 sim_loss_v2t: 0.2979 sim_loss_t2v: 0.3122 2023/06/12 00:21:44 - mmengine - INFO - Epoch(train) [2][ 620/1407] lr: 8.9608e-06 eta: 0:41:20 time: 0.4910 data_time: 0.0177 memory: 10497 loss: 0.6670 sim_loss_v2t: 0.3178 sim_loss_t2v: 0.3492 2023/06/12 00:21:54 - mmengine - INFO - Epoch(train) [2][ 640/1407] lr: 8.9303e-06 eta: 0:41:10 time: 0.4902 data_time: 0.0176 memory: 10497 loss: 0.5968 sim_loss_v2t: 0.2860 sim_loss_t2v: 0.3108 2023/06/12 00:22:04 - mmengine - INFO - Epoch(train) [2][ 660/1407] lr: 8.8995e-06 eta: 0:41:00 time: 0.4895 data_time: 0.0179 memory: 10497 loss: 0.6423 sim_loss_v2t: 0.3062 sim_loss_t2v: 0.3360 2023/06/12 00:22:13 - mmengine - INFO - Epoch(train) [2][ 680/1407] lr: 8.8682e-06 eta: 0:40:50 time: 0.4896 data_time: 0.0177 memory: 10497 loss: 0.5965 sim_loss_v2t: 0.2885 sim_loss_t2v: 0.3081 2023/06/12 00:22:23 - mmengine - INFO - Epoch(train) [2][ 700/1407] lr: 8.8366e-06 eta: 0:40:39 time: 0.4907 data_time: 0.0172 memory: 10497 loss: 0.6295 sim_loss_v2t: 0.2986 sim_loss_t2v: 0.3309 2023/06/12 00:22:33 - mmengine - INFO - Epoch(train) [2][ 720/1407] lr: 8.8046e-06 eta: 0:40:29 time: 0.4913 data_time: 0.0176 memory: 10497 loss: 0.6066 sim_loss_v2t: 0.2930 sim_loss_t2v: 0.3136 2023/06/12 00:22:43 - mmengine - INFO - Epoch(train) [2][ 740/1407] lr: 8.7722e-06 eta: 0:40:19 time: 0.4886 data_time: 0.0170 memory: 10497 loss: 0.6193 sim_loss_v2t: 0.3010 sim_loss_t2v: 0.3183 2023/06/12 00:22:53 - mmengine - INFO - Epoch(train) [2][ 760/1407] lr: 8.7395e-06 eta: 0:40:09 time: 0.4922 data_time: 0.0177 memory: 10497 loss: 0.5981 sim_loss_v2t: 0.2906 sim_loss_t2v: 0.3075 2023/06/12 00:23:02 - mmengine - INFO - Epoch(train) [2][ 780/1407] lr: 8.7063e-06 eta: 0:39:59 time: 0.4895 data_time: 0.0174 memory: 10497 loss: 0.6236 sim_loss_v2t: 0.3018 sim_loss_t2v: 0.3218 2023/06/12 00:23:12 - mmengine - INFO - Epoch(train) [2][ 800/1407] lr: 8.6728e-06 eta: 0:39:49 time: 0.4900 data_time: 0.0186 memory: 10497 loss: 0.6128 sim_loss_v2t: 0.3020 sim_loss_t2v: 0.3108 2023/06/12 00:23:22 - mmengine - INFO - Epoch(train) [2][ 820/1407] lr: 8.6390e-06 eta: 0:39:39 time: 0.4889 data_time: 0.0178 memory: 10497 loss: 0.6161 sim_loss_v2t: 0.2978 sim_loss_t2v: 0.3183 2023/06/12 00:23:32 - mmengine - INFO - Epoch(train) [2][ 840/1407] lr: 8.6048e-06 eta: 0:39:29 time: 0.4905 data_time: 0.0173 memory: 10497 loss: 0.5869 sim_loss_v2t: 0.2827 sim_loss_t2v: 0.3042 2023/06/12 00:23:42 - mmengine - INFO - Epoch(train) [2][ 860/1407] lr: 8.5702e-06 eta: 0:39:19 time: 0.4902 data_time: 0.0176 memory: 10497 loss: 0.6128 sim_loss_v2t: 0.2997 sim_loss_t2v: 0.3131 2023/06/12 00:23:51 - mmengine - INFO - Epoch(train) [2][ 880/1407] lr: 8.5353e-06 eta: 0:39:08 time: 0.4894 data_time: 0.0179 memory: 10497 loss: 0.5625 sim_loss_v2t: 0.2727 sim_loss_t2v: 0.2898 2023/06/12 00:24:01 - mmengine - INFO - Epoch(train) [2][ 900/1407] lr: 8.5001e-06 eta: 0:38:58 time: 0.4936 data_time: 0.0167 memory: 10497 loss: 0.6342 sim_loss_v2t: 0.3054 sim_loss_t2v: 0.3288 2023/06/12 00:24:11 - mmengine - INFO - Epoch(train) [2][ 920/1407] lr: 8.4644e-06 eta: 0:38:48 time: 0.4901 data_time: 0.0184 memory: 10497 loss: 0.5805 sim_loss_v2t: 0.2824 sim_loss_t2v: 0.2981 2023/06/12 00:24:21 - mmengine - INFO - Epoch(train) [2][ 940/1407] lr: 8.4285e-06 eta: 0:38:38 time: 0.4892 data_time: 0.0181 memory: 10497 loss: 0.5964 sim_loss_v2t: 0.2838 sim_loss_t2v: 0.3126 2023/06/12 00:24:31 - mmengine - INFO - Epoch(train) [2][ 960/1407] lr: 8.3922e-06 eta: 0:38:28 time: 0.4893 data_time: 0.0172 memory: 10497 loss: 0.5730 sim_loss_v2t: 0.2764 sim_loss_t2v: 0.2966 2023/06/12 00:24:40 - mmengine - INFO - Epoch(train) [2][ 980/1407] lr: 8.3556e-06 eta: 0:38:18 time: 0.4897 data_time: 0.0178 memory: 10497 loss: 0.5715 sim_loss_v2t: 0.2793 sim_loss_t2v: 0.2923 2023/06/12 00:24:50 - mmengine - INFO - Epoch(train) [2][1000/1407] lr: 8.3186e-06 eta: 0:38:08 time: 0.4899 data_time: 0.0179 memory: 10497 loss: 0.5506 sim_loss_v2t: 0.2638 sim_loss_t2v: 0.2868 2023/06/12 00:25:00 - mmengine - INFO - Epoch(train) [2][1020/1407] lr: 8.2814e-06 eta: 0:37:58 time: 0.4906 data_time: 0.0176 memory: 10497 loss: 0.5896 sim_loss_v2t: 0.2852 sim_loss_t2v: 0.3044 2023/06/12 00:25:10 - mmengine - INFO - Epoch(train) [2][1040/1407] lr: 8.2438e-06 eta: 0:37:48 time: 0.4909 data_time: 0.0181 memory: 10497 loss: 0.6147 sim_loss_v2t: 0.2930 sim_loss_t2v: 0.3217 2023/06/12 00:25:20 - mmengine - INFO - Epoch(train) [2][1060/1407] lr: 8.2059e-06 eta: 0:37:38 time: 0.4925 data_time: 0.0174 memory: 10497 loss: 0.5543 sim_loss_v2t: 0.2640 sim_loss_t2v: 0.2904 2023/06/12 00:25:29 - mmengine - INFO - Epoch(train) [2][1080/1407] lr: 8.1676e-06 eta: 0:37:28 time: 0.4903 data_time: 0.0170 memory: 10497 loss: 0.6102 sim_loss_v2t: 0.2963 sim_loss_t2v: 0.3139 2023/06/12 00:25:39 - mmengine - INFO - Epoch(train) [2][1100/1407] lr: 8.1291e-06 eta: 0:37:18 time: 0.4904 data_time: 0.0178 memory: 10497 loss: 0.5886 sim_loss_v2t: 0.2800 sim_loss_t2v: 0.3086 2023/06/12 00:25:49 - mmengine - INFO - Epoch(train) [2][1120/1407] lr: 8.0902e-06 eta: 0:37:08 time: 0.4939 data_time: 0.0176 memory: 10497 loss: 0.6056 sim_loss_v2t: 0.2940 sim_loss_t2v: 0.3117 2023/06/12 00:25:59 - mmengine - INFO - Epoch(train) [2][1140/1407] lr: 8.0511e-06 eta: 0:36:58 time: 0.4937 data_time: 0.0172 memory: 10497 loss: 0.6194 sim_loss_v2t: 0.2967 sim_loss_t2v: 0.3226 2023/06/12 00:26:09 - mmengine - INFO - Epoch(train) [2][1160/1407] lr: 8.0116e-06 eta: 0:36:48 time: 0.4908 data_time: 0.0168 memory: 10497 loss: 0.5453 sim_loss_v2t: 0.2622 sim_loss_t2v: 0.2832 2023/06/12 00:26:19 - mmengine - INFO - Epoch(train) [2][1180/1407] lr: 7.9718e-06 eta: 0:36:38 time: 0.4884 data_time: 0.0166 memory: 10497 loss: 0.5814 sim_loss_v2t: 0.2816 sim_loss_t2v: 0.2998 2023/06/12 00:26:28 - mmengine - INFO - Epoch(train) [2][1200/1407] lr: 7.9318e-06 eta: 0:36:28 time: 0.4913 data_time: 0.0177 memory: 10497 loss: 0.5547 sim_loss_v2t: 0.2665 sim_loss_t2v: 0.2882 2023/06/12 00:26:38 - mmengine - INFO - Epoch(train) [2][1220/1407] lr: 7.8915e-06 eta: 0:36:18 time: 0.4893 data_time: 0.0177 memory: 10497 loss: 0.5940 sim_loss_v2t: 0.2886 sim_loss_t2v: 0.3053 2023/06/12 00:26:48 - mmengine - INFO - Epoch(train) [2][1240/1407] lr: 7.8508e-06 eta: 0:36:08 time: 0.4904 data_time: 0.0175 memory: 10497 loss: 0.5617 sim_loss_v2t: 0.2685 sim_loss_t2v: 0.2932 2023/06/12 00:26:58 - mmengine - INFO - Epoch(train) [2][1260/1407] lr: 7.8099e-06 eta: 0:35:58 time: 0.4908 data_time: 0.0180 memory: 10497 loss: 0.5975 sim_loss_v2t: 0.2890 sim_loss_t2v: 0.3085 2023/06/12 00:27:08 - mmengine - INFO - Epoch(train) [2][1280/1407] lr: 7.7688e-06 eta: 0:35:48 time: 0.4908 data_time: 0.0189 memory: 10497 loss: 0.5666 sim_loss_v2t: 0.2708 sim_loss_t2v: 0.2958 2023/06/12 00:27:18 - mmengine - INFO - Epoch(train) [2][1300/1407] lr: 7.7273e-06 eta: 0:35:38 time: 0.4933 data_time: 0.0185 memory: 10497 loss: 0.5465 sim_loss_v2t: 0.2618 sim_loss_t2v: 0.2847 2023/06/12 00:27:27 - mmengine - INFO - Epoch(train) [2][1320/1407] lr: 7.6856e-06 eta: 0:35:28 time: 0.4906 data_time: 0.0187 memory: 10497 loss: 0.5707 sim_loss_v2t: 0.2743 sim_loss_t2v: 0.2964 2023/06/12 00:27:37 - mmengine - INFO - Epoch(train) [2][1340/1407] lr: 7.6436e-06 eta: 0:35:18 time: 0.4895 data_time: 0.0181 memory: 10497 loss: 0.5279 sim_loss_v2t: 0.2503 sim_loss_t2v: 0.2776 2023/06/12 00:27:47 - mmengine - INFO - Epoch(train) [2][1360/1407] lr: 7.6014e-06 eta: 0:35:08 time: 0.4922 data_time: 0.0174 memory: 10497 loss: 0.5457 sim_loss_v2t: 0.2583 sim_loss_t2v: 0.2875 2023/06/12 00:27:57 - mmengine - INFO - Epoch(train) [2][1380/1407] lr: 7.5589e-06 eta: 0:34:58 time: 0.4912 data_time: 0.0178 memory: 10497 loss: 0.5544 sim_loss_v2t: 0.2612 sim_loss_t2v: 0.2933 2023/06/12 00:28:07 - mmengine - INFO - Epoch(train) [2][1400/1407] lr: 7.5161e-06 eta: 0:34:48 time: 0.4860 data_time: 0.0177 memory: 10497 loss: 0.5310 sim_loss_v2t: 0.2501 sim_loss_t2v: 0.2809 2023/06/12 00:28:10 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:28:10 - mmengine - INFO - Epoch(train) [2][1407/1407] lr: 7.5011e-06 eta: 0:34:44 time: 0.4710 data_time: 0.0161 memory: 10497 loss: 0.5393 sim_loss_v2t: 0.2548 sim_loss_t2v: 0.2845 2023/06/12 00:28:10 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/06/12 00:28:18 - mmengine - INFO - Epoch(val) [2][8/8] retrieval/R1: 42.3000 retrieval/R5: 70.4000 retrieval/R10: 79.4000 retrieval/MdR: 2.0000 retrieval/MnR: 15.9840 data_time: 0.2002 time: 0.3339 2023/06/12 00:28:31 - mmengine - INFO - Epoch(train) [3][ 20/1407] lr: 7.4580e-06 eta: 0:34:39 time: 0.6452 data_time: 0.1232 memory: 10497 loss: 0.4041 sim_loss_v2t: 0.1875 sim_loss_t2v: 0.2165 2023/06/12 00:28:41 - mmengine - INFO - Epoch(train) [3][ 40/1407] lr: 7.4147e-06 eta: 0:34:29 time: 0.4884 data_time: 0.0171 memory: 10497 loss: 0.4115 sim_loss_v2t: 0.1933 sim_loss_t2v: 0.2182 2023/06/12 00:28:50 - mmengine - INFO - Epoch(train) [3][ 60/1407] lr: 7.3711e-06 eta: 0:34:19 time: 0.4905 data_time: 0.0164 memory: 10497 loss: 0.3901 sim_loss_v2t: 0.1826 sim_loss_t2v: 0.2074 2023/06/12 00:29:00 - mmengine - INFO - Epoch(train) [3][ 80/1407] lr: 7.3273e-06 eta: 0:34:08 time: 0.4880 data_time: 0.0181 memory: 10497 loss: 0.4164 sim_loss_v2t: 0.1937 sim_loss_t2v: 0.2226 2023/06/12 00:29:10 - mmengine - INFO - Epoch(train) [3][ 100/1407] lr: 7.2833e-06 eta: 0:33:58 time: 0.4912 data_time: 0.0171 memory: 10497 loss: 0.4174 sim_loss_v2t: 0.1987 sim_loss_t2v: 0.2187 2023/06/12 00:29:20 - mmengine - INFO - Epoch(train) [3][ 120/1407] lr: 7.2390e-06 eta: 0:33:48 time: 0.4903 data_time: 0.0180 memory: 10497 loss: 0.4309 sim_loss_v2t: 0.2014 sim_loss_t2v: 0.2295 2023/06/12 00:29:30 - mmengine - INFO - Epoch(train) [3][ 140/1407] lr: 7.1945e-06 eta: 0:33:38 time: 0.4915 data_time: 0.0182 memory: 10497 loss: 0.4235 sim_loss_v2t: 0.2000 sim_loss_t2v: 0.2235 2023/06/12 00:29:39 - mmengine - INFO - Epoch(train) [3][ 160/1407] lr: 7.1498e-06 eta: 0:33:29 time: 0.4916 data_time: 0.0170 memory: 10497 loss: 0.4027 sim_loss_v2t: 0.1903 sim_loss_t2v: 0.2124 2023/06/12 00:29:49 - mmengine - INFO - Epoch(train) [3][ 180/1407] lr: 7.1049e-06 eta: 0:33:18 time: 0.4903 data_time: 0.0179 memory: 10497 loss: 0.3858 sim_loss_v2t: 0.1788 sim_loss_t2v: 0.2070 2023/06/12 00:29:52 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:29:59 - mmengine - INFO - Epoch(train) [3][ 200/1407] lr: 7.0598e-06 eta: 0:33:08 time: 0.4903 data_time: 0.0183 memory: 10497 loss: 0.4198 sim_loss_v2t: 0.1998 sim_loss_t2v: 0.2200 2023/06/12 00:30:09 - mmengine - INFO - Epoch(train) [3][ 220/1407] lr: 7.0145e-06 eta: 0:32:59 time: 0.4938 data_time: 0.0182 memory: 10497 loss: 0.4351 sim_loss_v2t: 0.2104 sim_loss_t2v: 0.2247 2023/06/12 00:30:19 - mmengine - INFO - Epoch(train) [3][ 240/1407] lr: 6.9690e-06 eta: 0:32:49 time: 0.4923 data_time: 0.0173 memory: 10497 loss: 0.4330 sim_loss_v2t: 0.2042 sim_loss_t2v: 0.2288 2023/06/12 00:30:29 - mmengine - INFO - Epoch(train) [3][ 260/1407] lr: 6.9233e-06 eta: 0:32:39 time: 0.4918 data_time: 0.0180 memory: 10497 loss: 0.4203 sim_loss_v2t: 0.2010 sim_loss_t2v: 0.2193 2023/06/12 00:30:38 - mmengine - INFO - Epoch(train) [3][ 280/1407] lr: 6.8774e-06 eta: 0:32:29 time: 0.4905 data_time: 0.0174 memory: 10497 loss: 0.3874 sim_loss_v2t: 0.1776 sim_loss_t2v: 0.2099 2023/06/12 00:30:48 - mmengine - INFO - Epoch(train) [3][ 300/1407] lr: 6.8313e-06 eta: 0:32:19 time: 0.4894 data_time: 0.0177 memory: 10497 loss: 0.3721 sim_loss_v2t: 0.1704 sim_loss_t2v: 0.2017 2023/06/12 00:30:58 - mmengine - INFO - Epoch(train) [3][ 320/1407] lr: 6.7851e-06 eta: 0:32:09 time: 0.4906 data_time: 0.0182 memory: 10497 loss: 0.4135 sim_loss_v2t: 0.1909 sim_loss_t2v: 0.2226 2023/06/12 00:31:08 - mmengine - INFO - Epoch(train) [3][ 340/1407] lr: 6.7386e-06 eta: 0:31:59 time: 0.4890 data_time: 0.0168 memory: 10497 loss: 0.4184 sim_loss_v2t: 0.1971 sim_loss_t2v: 0.2213 2023/06/12 00:31:18 - mmengine - INFO - Epoch(train) [3][ 360/1407] lr: 6.6920e-06 eta: 0:31:49 time: 0.4897 data_time: 0.0176 memory: 10497 loss: 0.4116 sim_loss_v2t: 0.1912 sim_loss_t2v: 0.2204 2023/06/12 00:31:27 - mmengine - INFO - Epoch(train) [3][ 380/1407] lr: 6.6452e-06 eta: 0:31:39 time: 0.4908 data_time: 0.0168 memory: 10497 loss: 0.4011 sim_loss_v2t: 0.1823 sim_loss_t2v: 0.2188 2023/06/12 00:31:37 - mmengine - INFO - Epoch(train) [3][ 400/1407] lr: 6.5983e-06 eta: 0:31:29 time: 0.4897 data_time: 0.0182 memory: 10497 loss: 0.4155 sim_loss_v2t: 0.1970 sim_loss_t2v: 0.2186 2023/06/12 00:31:47 - mmengine - INFO - Epoch(train) [3][ 420/1407] lr: 6.5512e-06 eta: 0:31:19 time: 0.4904 data_time: 0.0175 memory: 10497 loss: 0.3717 sim_loss_v2t: 0.1748 sim_loss_t2v: 0.1969 2023/06/12 00:31:57 - mmengine - INFO - Epoch(train) [3][ 440/1407] lr: 6.5040e-06 eta: 0:31:09 time: 0.4890 data_time: 0.0180 memory: 10497 loss: 0.3851 sim_loss_v2t: 0.1796 sim_loss_t2v: 0.2055 2023/06/12 00:32:07 - mmengine - INFO - Epoch(train) [3][ 460/1407] lr: 6.4566e-06 eta: 0:30:59 time: 0.4919 data_time: 0.0179 memory: 10497 loss: 0.4164 sim_loss_v2t: 0.1958 sim_loss_t2v: 0.2206 2023/06/12 00:32:16 - mmengine - INFO - Epoch(train) [3][ 480/1407] lr: 6.4090e-06 eta: 0:30:49 time: 0.4903 data_time: 0.0175 memory: 10497 loss: 0.3943 sim_loss_v2t: 0.1857 sim_loss_t2v: 0.2087 2023/06/12 00:32:26 - mmengine - INFO - Epoch(train) [3][ 500/1407] lr: 6.3614e-06 eta: 0:30:39 time: 0.4923 data_time: 0.0175 memory: 10497 loss: 0.4117 sim_loss_v2t: 0.1902 sim_loss_t2v: 0.2215 2023/06/12 00:32:36 - mmengine - INFO - Epoch(train) [3][ 520/1407] lr: 6.3136e-06 eta: 0:30:29 time: 0.4928 data_time: 0.0185 memory: 10497 loss: 0.4073 sim_loss_v2t: 0.1901 sim_loss_t2v: 0.2172 2023/06/12 00:32:46 - mmengine - INFO - Epoch(train) [3][ 540/1407] lr: 6.2656e-06 eta: 0:30:19 time: 0.4896 data_time: 0.0175 memory: 10497 loss: 0.4096 sim_loss_v2t: 0.1908 sim_loss_t2v: 0.2188 2023/06/12 00:32:56 - mmengine - INFO - Epoch(train) [3][ 560/1407] lr: 6.2176e-06 eta: 0:30:09 time: 0.4935 data_time: 0.0185 memory: 10497 loss: 0.4231 sim_loss_v2t: 0.1986 sim_loss_t2v: 0.2245 2023/06/12 00:33:06 - mmengine - INFO - Epoch(train) [3][ 580/1407] lr: 6.1694e-06 eta: 0:29:59 time: 0.4929 data_time: 0.0183 memory: 10497 loss: 0.4215 sim_loss_v2t: 0.1955 sim_loss_t2v: 0.2260 2023/06/12 00:33:15 - mmengine - INFO - Epoch(train) [3][ 600/1407] lr: 6.1211e-06 eta: 0:29:49 time: 0.4917 data_time: 0.0185 memory: 10497 loss: 0.3961 sim_loss_v2t: 0.1820 sim_loss_t2v: 0.2141 2023/06/12 00:33:25 - mmengine - INFO - Epoch(train) [3][ 620/1407] lr: 6.0727e-06 eta: 0:29:39 time: 0.4907 data_time: 0.0193 memory: 10497 loss: 0.4076 sim_loss_v2t: 0.1943 sim_loss_t2v: 0.2133 2023/06/12 00:33:35 - mmengine - INFO - Epoch(train) [3][ 640/1407] lr: 6.0241e-06 eta: 0:29:29 time: 0.4922 data_time: 0.0175 memory: 10497 loss: 0.4259 sim_loss_v2t: 0.1999 sim_loss_t2v: 0.2260 2023/06/12 00:33:45 - mmengine - INFO - Epoch(train) [3][ 660/1407] lr: 5.9755e-06 eta: 0:29:19 time: 0.4906 data_time: 0.0182 memory: 10497 loss: 0.4189 sim_loss_v2t: 0.1963 sim_loss_t2v: 0.2226 2023/06/12 00:33:55 - mmengine - INFO - Epoch(train) [3][ 680/1407] lr: 5.9268e-06 eta: 0:29:09 time: 0.4919 data_time: 0.0177 memory: 10497 loss: 0.4230 sim_loss_v2t: 0.1981 sim_loss_t2v: 0.2250 2023/06/12 00:34:05 - mmengine - INFO - Epoch(train) [3][ 700/1407] lr: 5.8780e-06 eta: 0:28:59 time: 0.4906 data_time: 0.0178 memory: 10497 loss: 0.3928 sim_loss_v2t: 0.1866 sim_loss_t2v: 0.2062 2023/06/12 00:34:14 - mmengine - INFO - Epoch(train) [3][ 720/1407] lr: 5.8291e-06 eta: 0:28:49 time: 0.4888 data_time: 0.0171 memory: 10497 loss: 0.4169 sim_loss_v2t: 0.1997 sim_loss_t2v: 0.2172 2023/06/12 00:34:24 - mmengine - INFO - Epoch(train) [3][ 740/1407] lr: 5.7802e-06 eta: 0:28:39 time: 0.4895 data_time: 0.0183 memory: 10497 loss: 0.4029 sim_loss_v2t: 0.1890 sim_loss_t2v: 0.2139 2023/06/12 00:34:34 - mmengine - INFO - Epoch(train) [3][ 760/1407] lr: 5.7311e-06 eta: 0:28:29 time: 0.4891 data_time: 0.0181 memory: 10497 loss: 0.4010 sim_loss_v2t: 0.1906 sim_loss_t2v: 0.2103 2023/06/12 00:34:44 - mmengine - INFO - Epoch(train) [3][ 780/1407] lr: 5.6820e-06 eta: 0:28:20 time: 0.4928 data_time: 0.0173 memory: 10497 loss: 0.4006 sim_loss_v2t: 0.1851 sim_loss_t2v: 0.2155 2023/06/12 00:34:54 - mmengine - INFO - Epoch(train) [3][ 800/1407] lr: 5.6328e-06 eta: 0:28:10 time: 0.4898 data_time: 0.0167 memory: 10497 loss: 0.3905 sim_loss_v2t: 0.1852 sim_loss_t2v: 0.2053 2023/06/12 00:35:03 - mmengine - INFO - Epoch(train) [3][ 820/1407] lr: 5.5835e-06 eta: 0:28:00 time: 0.4908 data_time: 0.0172 memory: 10497 loss: 0.4117 sim_loss_v2t: 0.1876 sim_loss_t2v: 0.2240 2023/06/12 00:35:13 - mmengine - INFO - Epoch(train) [3][ 840/1407] lr: 5.5342e-06 eta: 0:27:50 time: 0.4936 data_time: 0.0182 memory: 10497 loss: 0.4475 sim_loss_v2t: 0.2122 sim_loss_t2v: 0.2353 2023/06/12 00:35:23 - mmengine - INFO - Epoch(train) [3][ 860/1407] lr: 5.4849e-06 eta: 0:27:40 time: 0.4921 data_time: 0.0177 memory: 10497 loss: 0.3982 sim_loss_v2t: 0.1892 sim_loss_t2v: 0.2090 2023/06/12 00:35:33 - mmengine - INFO - Epoch(train) [3][ 880/1407] lr: 5.4355e-06 eta: 0:27:30 time: 0.4935 data_time: 0.0179 memory: 10497 loss: 0.3981 sim_loss_v2t: 0.1866 sim_loss_t2v: 0.2115 2023/06/12 00:35:43 - mmengine - INFO - Epoch(train) [3][ 900/1407] lr: 5.3860e-06 eta: 0:27:20 time: 0.4894 data_time: 0.0182 memory: 10497 loss: 0.3992 sim_loss_v2t: 0.1863 sim_loss_t2v: 0.2129 2023/06/12 00:35:53 - mmengine - INFO - Epoch(train) [3][ 920/1407] lr: 5.3365e-06 eta: 0:27:10 time: 0.4893 data_time: 0.0173 memory: 10497 loss: 0.3982 sim_loss_v2t: 0.1920 sim_loss_t2v: 0.2062 2023/06/12 00:36:02 - mmengine - INFO - Epoch(train) [3][ 940/1407] lr: 5.2870e-06 eta: 0:27:00 time: 0.4895 data_time: 0.0181 memory: 10497 loss: 0.3868 sim_loss_v2t: 0.1798 sim_loss_t2v: 0.2070 2023/06/12 00:36:12 - mmengine - INFO - Epoch(train) [3][ 960/1407] lr: 5.2375e-06 eta: 0:26:50 time: 0.4904 data_time: 0.0177 memory: 10497 loss: 0.4164 sim_loss_v2t: 0.1935 sim_loss_t2v: 0.2229 2023/06/12 00:36:22 - mmengine - INFO - Epoch(train) [3][ 980/1407] lr: 5.1879e-06 eta: 0:26:40 time: 0.4888 data_time: 0.0174 memory: 10497 loss: 0.3820 sim_loss_v2t: 0.1805 sim_loss_t2v: 0.2015 2023/06/12 00:36:32 - mmengine - INFO - Epoch(train) [3][1000/1407] lr: 5.1383e-06 eta: 0:26:30 time: 0.4934 data_time: 0.0186 memory: 10497 loss: 0.4256 sim_loss_v2t: 0.1980 sim_loss_t2v: 0.2276 2023/06/12 00:36:42 - mmengine - INFO - Epoch(train) [3][1020/1407] lr: 5.0887e-06 eta: 0:26:20 time: 0.4911 data_time: 0.0175 memory: 10497 loss: 0.3584 sim_loss_v2t: 0.1664 sim_loss_t2v: 0.1920 2023/06/12 00:36:51 - mmengine - INFO - Epoch(train) [3][1040/1407] lr: 5.0391e-06 eta: 0:26:10 time: 0.4909 data_time: 0.0176 memory: 10497 loss: 0.4472 sim_loss_v2t: 0.2123 sim_loss_t2v: 0.2350 2023/06/12 00:37:01 - mmengine - INFO - Epoch(train) [3][1060/1407] lr: 4.9895e-06 eta: 0:26:00 time: 0.4901 data_time: 0.0173 memory: 10497 loss: 0.4011 sim_loss_v2t: 0.1921 sim_loss_t2v: 0.2090 2023/06/12 00:37:11 - mmengine - INFO - Epoch(train) [3][1080/1407] lr: 4.9398e-06 eta: 0:25:51 time: 0.4913 data_time: 0.0186 memory: 10497 loss: 0.4259 sim_loss_v2t: 0.1991 sim_loss_t2v: 0.2268 2023/06/12 00:37:21 - mmengine - INFO - Epoch(train) [3][1100/1407] lr: 4.8902e-06 eta: 0:25:41 time: 0.4913 data_time: 0.0179 memory: 10497 loss: 0.3935 sim_loss_v2t: 0.1858 sim_loss_t2v: 0.2077 2023/06/12 00:37:31 - mmengine - INFO - Epoch(train) [3][1120/1407] lr: 4.8406e-06 eta: 0:25:31 time: 0.4912 data_time: 0.0174 memory: 10497 loss: 0.3791 sim_loss_v2t: 0.1777 sim_loss_t2v: 0.2014 2023/06/12 00:37:41 - mmengine - INFO - Epoch(train) [3][1140/1407] lr: 4.7910e-06 eta: 0:25:21 time: 0.4916 data_time: 0.0175 memory: 10497 loss: 0.4010 sim_loss_v2t: 0.1891 sim_loss_t2v: 0.2119 2023/06/12 00:37:50 - mmengine - INFO - Epoch(train) [3][1160/1407] lr: 4.7415e-06 eta: 0:25:11 time: 0.4891 data_time: 0.0175 memory: 10497 loss: 0.4096 sim_loss_v2t: 0.1905 sim_loss_t2v: 0.2191 2023/06/12 00:38:00 - mmengine - INFO - Epoch(train) [3][1180/1407] lr: 4.6919e-06 eta: 0:25:01 time: 0.4899 data_time: 0.0175 memory: 10497 loss: 0.3992 sim_loss_v2t: 0.1824 sim_loss_t2v: 0.2168 2023/06/12 00:38:03 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:38:10 - mmengine - INFO - Epoch(train) [3][1200/1407] lr: 4.6424e-06 eta: 0:24:51 time: 0.4909 data_time: 0.0178 memory: 10497 loss: 0.4111 sim_loss_v2t: 0.1918 sim_loss_t2v: 0.2193 2023/06/12 00:38:20 - mmengine - INFO - Epoch(train) [3][1220/1407] lr: 4.5930e-06 eta: 0:24:41 time: 0.4903 data_time: 0.0186 memory: 10497 loss: 0.3883 sim_loss_v2t: 0.1828 sim_loss_t2v: 0.2055 2023/06/12 00:38:30 - mmengine - INFO - Epoch(train) [3][1240/1407] lr: 4.5435e-06 eta: 0:24:31 time: 0.4903 data_time: 0.0183 memory: 10497 loss: 0.4240 sim_loss_v2t: 0.2012 sim_loss_t2v: 0.2228 2023/06/12 00:38:39 - mmengine - INFO - Epoch(train) [3][1260/1407] lr: 4.4941e-06 eta: 0:24:21 time: 0.4938 data_time: 0.0168 memory: 10497 loss: 0.4126 sim_loss_v2t: 0.1901 sim_loss_t2v: 0.2225 2023/06/12 00:38:49 - mmengine - INFO - Epoch(train) [3][1280/1407] lr: 4.4448e-06 eta: 0:24:11 time: 0.4918 data_time: 0.0175 memory: 10497 loss: 0.3995 sim_loss_v2t: 0.1851 sim_loss_t2v: 0.2144 2023/06/12 00:38:59 - mmengine - INFO - Epoch(train) [3][1300/1407] lr: 4.3955e-06 eta: 0:24:01 time: 0.4908 data_time: 0.0168 memory: 10497 loss: 0.3782 sim_loss_v2t: 0.1743 sim_loss_t2v: 0.2039 2023/06/12 00:39:09 - mmengine - INFO - Epoch(train) [3][1320/1407] lr: 4.3463e-06 eta: 0:23:52 time: 0.4911 data_time: 0.0168 memory: 10497 loss: 0.4118 sim_loss_v2t: 0.1911 sim_loss_t2v: 0.2207 2023/06/12 00:39:19 - mmengine - INFO - Epoch(train) [3][1340/1407] lr: 4.2971e-06 eta: 0:23:42 time: 0.4918 data_time: 0.0179 memory: 10497 loss: 0.4059 sim_loss_v2t: 0.1924 sim_loss_t2v: 0.2135 2023/06/12 00:39:29 - mmengine - INFO - Epoch(train) [3][1360/1407] lr: 4.2480e-06 eta: 0:23:32 time: 0.4928 data_time: 0.0172 memory: 10497 loss: 0.3647 sim_loss_v2t: 0.1699 sim_loss_t2v: 0.1948 2023/06/12 00:39:38 - mmengine - INFO - Epoch(train) [3][1380/1407] lr: 4.1990e-06 eta: 0:23:22 time: 0.4922 data_time: 0.0174 memory: 10497 loss: 0.3770 sim_loss_v2t: 0.1723 sim_loss_t2v: 0.2047 2023/06/12 00:39:48 - mmengine - INFO - Epoch(train) [3][1400/1407] lr: 4.1501e-06 eta: 0:23:12 time: 0.4869 data_time: 0.0168 memory: 10497 loss: 0.4205 sim_loss_v2t: 0.2024 sim_loss_t2v: 0.2180 2023/06/12 00:39:51 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:39:51 - mmengine - INFO - Epoch(train) [3][1407/1407] lr: 4.1330e-06 eta: 0:23:08 time: 0.4721 data_time: 0.0162 memory: 10497 loss: 0.4174 sim_loss_v2t: 0.1989 sim_loss_t2v: 0.2185 2023/06/12 00:39:51 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/06/12 00:39:59 - mmengine - INFO - Epoch(val) [3][8/8] retrieval/R1: 43.1000 retrieval/R5: 69.4000 retrieval/R10: 78.9000 retrieval/MdR: 2.0000 retrieval/MnR: 16.8050 data_time: 0.1999 time: 0.3335 2023/06/12 00:40:12 - mmengine - INFO - Epoch(train) [4][ 20/1407] lr: 4.0842e-06 eta: 0:23:00 time: 0.6412 data_time: 0.1301 memory: 10497 loss: 0.3086 sim_loss_v2t: 0.1434 sim_loss_t2v: 0.1651 2023/06/12 00:40:22 - mmengine - INFO - Epoch(train) [4][ 40/1407] lr: 4.0354e-06 eta: 0:22:50 time: 0.4889 data_time: 0.0173 memory: 10497 loss: 0.3001 sim_loss_v2t: 0.1389 sim_loss_t2v: 0.1612 2023/06/12 00:40:32 - mmengine - INFO - Epoch(train) [4][ 60/1407] lr: 3.9868e-06 eta: 0:22:40 time: 0.4893 data_time: 0.0166 memory: 10497 loss: 0.2853 sim_loss_v2t: 0.1317 sim_loss_t2v: 0.1536 2023/06/12 00:40:41 - mmengine - INFO - Epoch(train) [4][ 80/1407] lr: 3.9382e-06 eta: 0:22:31 time: 0.4919 data_time: 0.0168 memory: 10497 loss: 0.2701 sim_loss_v2t: 0.1248 sim_loss_t2v: 0.1453 2023/06/12 00:40:51 - mmengine - INFO - Epoch(train) [4][ 100/1407] lr: 3.8898e-06 eta: 0:22:21 time: 0.4927 data_time: 0.0168 memory: 10497 loss: 0.2936 sim_loss_v2t: 0.1353 sim_loss_t2v: 0.1584 2023/06/12 00:41:01 - mmengine - INFO - Epoch(train) [4][ 120/1407] lr: 3.8415e-06 eta: 0:22:11 time: 0.4914 data_time: 0.0175 memory: 10497 loss: 0.2838 sim_loss_v2t: 0.1310 sim_loss_t2v: 0.1529 2023/06/12 00:41:11 - mmengine - INFO - Epoch(train) [4][ 140/1407] lr: 3.7933e-06 eta: 0:22:01 time: 0.4929 data_time: 0.0170 memory: 10497 loss: 0.2916 sim_loss_v2t: 0.1356 sim_loss_t2v: 0.1559 2023/06/12 00:41:21 - mmengine - INFO - Epoch(train) [4][ 160/1407] lr: 3.7452e-06 eta: 0:21:51 time: 0.4900 data_time: 0.0171 memory: 10497 loss: 0.2721 sim_loss_v2t: 0.1271 sim_loss_t2v: 0.1449 2023/06/12 00:41:31 - mmengine - INFO - Epoch(train) [4][ 180/1407] lr: 3.6972e-06 eta: 0:21:41 time: 0.4898 data_time: 0.0169 memory: 10497 loss: 0.2755 sim_loss_v2t: 0.1246 sim_loss_t2v: 0.1509 2023/06/12 00:41:40 - mmengine - INFO - Epoch(train) [4][ 200/1407] lr: 3.6494e-06 eta: 0:21:31 time: 0.4908 data_time: 0.0171 memory: 10497 loss: 0.2985 sim_loss_v2t: 0.1423 sim_loss_t2v: 0.1562 2023/06/12 00:41:50 - mmengine - INFO - Epoch(train) [4][ 220/1407] lr: 3.6017e-06 eta: 0:21:21 time: 0.4930 data_time: 0.0174 memory: 10497 loss: 0.2825 sim_loss_v2t: 0.1267 sim_loss_t2v: 0.1559 2023/06/12 00:42:00 - mmengine - INFO - Epoch(train) [4][ 240/1407] lr: 3.5541e-06 eta: 0:21:11 time: 0.4900 data_time: 0.0167 memory: 10497 loss: 0.2745 sim_loss_v2t: 0.1252 sim_loss_t2v: 0.1492 2023/06/12 00:42:10 - mmengine - INFO - Epoch(train) [4][ 260/1407] lr: 3.5067e-06 eta: 0:21:01 time: 0.4894 data_time: 0.0175 memory: 10497 loss: 0.2827 sim_loss_v2t: 0.1356 sim_loss_t2v: 0.1470 2023/06/12 00:42:20 - mmengine - INFO - Epoch(train) [4][ 280/1407] lr: 3.4594e-06 eta: 0:20:51 time: 0.4903 data_time: 0.0168 memory: 10497 loss: 0.2635 sim_loss_v2t: 0.1211 sim_loss_t2v: 0.1424 2023/06/12 00:42:29 - mmengine - INFO - Epoch(train) [4][ 300/1407] lr: 3.4123e-06 eta: 0:20:41 time: 0.4907 data_time: 0.0177 memory: 10497 loss: 0.2745 sim_loss_v2t: 0.1235 sim_loss_t2v: 0.1510 2023/06/12 00:42:39 - mmengine - INFO - Epoch(train) [4][ 320/1407] lr: 3.3653e-06 eta: 0:20:32 time: 0.4897 data_time: 0.0179 memory: 10497 loss: 0.2969 sim_loss_v2t: 0.1404 sim_loss_t2v: 0.1565 2023/06/12 00:42:49 - mmengine - INFO - Epoch(train) [4][ 340/1407] lr: 3.3185e-06 eta: 0:20:22 time: 0.4907 data_time: 0.0183 memory: 10497 loss: 0.2925 sim_loss_v2t: 0.1369 sim_loss_t2v: 0.1556 2023/06/12 00:42:59 - mmengine - INFO - Epoch(train) [4][ 360/1407] lr: 3.2718e-06 eta: 0:20:12 time: 0.4914 data_time: 0.0176 memory: 10497 loss: 0.3033 sim_loss_v2t: 0.1455 sim_loss_t2v: 0.1578 2023/06/12 00:43:09 - mmengine - INFO - Epoch(train) [4][ 380/1407] lr: 3.2254e-06 eta: 0:20:02 time: 0.4902 data_time: 0.0188 memory: 10497 loss: 0.2757 sim_loss_v2t: 0.1265 sim_loss_t2v: 0.1492 2023/06/12 00:43:19 - mmengine - INFO - Epoch(train) [4][ 400/1407] lr: 3.1791e-06 eta: 0:19:52 time: 0.4916 data_time: 0.0168 memory: 10497 loss: 0.3029 sim_loss_v2t: 0.1405 sim_loss_t2v: 0.1624 2023/06/12 00:43:28 - mmengine - INFO - Epoch(train) [4][ 420/1407] lr: 3.1329e-06 eta: 0:19:42 time: 0.4899 data_time: 0.0173 memory: 10497 loss: 0.3092 sim_loss_v2t: 0.1480 sim_loss_t2v: 0.1611 2023/06/12 00:43:38 - mmengine - INFO - Epoch(train) [4][ 440/1407] lr: 3.0870e-06 eta: 0:19:32 time: 0.4915 data_time: 0.0176 memory: 10497 loss: 0.2758 sim_loss_v2t: 0.1296 sim_loss_t2v: 0.1463 2023/06/12 00:43:48 - mmengine - INFO - Epoch(train) [4][ 460/1407] lr: 3.0413e-06 eta: 0:19:22 time: 0.4927 data_time: 0.0177 memory: 10497 loss: 0.2782 sim_loss_v2t: 0.1301 sim_loss_t2v: 0.1482 2023/06/12 00:43:58 - mmengine - INFO - Epoch(train) [4][ 480/1407] lr: 2.9957e-06 eta: 0:19:12 time: 0.4904 data_time: 0.0178 memory: 10497 loss: 0.2808 sim_loss_v2t: 0.1257 sim_loss_t2v: 0.1551 2023/06/12 00:44:08 - mmengine - INFO - Epoch(train) [4][ 500/1407] lr: 2.9504e-06 eta: 0:19:02 time: 0.4914 data_time: 0.0174 memory: 10497 loss: 0.2909 sim_loss_v2t: 0.1359 sim_loss_t2v: 0.1550 2023/06/12 00:44:17 - mmengine - INFO - Epoch(train) [4][ 520/1407] lr: 2.9052e-06 eta: 0:18:52 time: 0.4888 data_time: 0.0175 memory: 10497 loss: 0.2571 sim_loss_v2t: 0.1167 sim_loss_t2v: 0.1404 2023/06/12 00:44:27 - mmengine - INFO - Epoch(train) [4][ 540/1407] lr: 2.8602e-06 eta: 0:18:42 time: 0.4884 data_time: 0.0175 memory: 10497 loss: 0.2777 sim_loss_v2t: 0.1274 sim_loss_t2v: 0.1504 2023/06/12 00:44:37 - mmengine - INFO - Epoch(train) [4][ 560/1407] lr: 2.8155e-06 eta: 0:18:33 time: 0.4903 data_time: 0.0171 memory: 10497 loss: 0.2790 sim_loss_v2t: 0.1324 sim_loss_t2v: 0.1466 2023/06/12 00:44:47 - mmengine - INFO - Epoch(train) [4][ 580/1407] lr: 2.7710e-06 eta: 0:18:23 time: 0.4979 data_time: 0.0169 memory: 10497 loss: 0.2808 sim_loss_v2t: 0.1261 sim_loss_t2v: 0.1547 2023/06/12 00:44:57 - mmengine - INFO - Epoch(train) [4][ 600/1407] lr: 2.7267e-06 eta: 0:18:13 time: 0.4955 data_time: 0.0163 memory: 10497 loss: 0.2757 sim_loss_v2t: 0.1270 sim_loss_t2v: 0.1487 2023/06/12 00:45:07 - mmengine - INFO - Epoch(train) [4][ 620/1407] lr: 2.6826e-06 eta: 0:18:03 time: 0.4906 data_time: 0.0167 memory: 10497 loss: 0.2828 sim_loss_v2t: 0.1320 sim_loss_t2v: 0.1508 2023/06/12 00:45:16 - mmengine - INFO - Epoch(train) [4][ 640/1407] lr: 2.6387e-06 eta: 0:17:53 time: 0.4888 data_time: 0.0181 memory: 10497 loss: 0.2691 sim_loss_v2t: 0.1237 sim_loss_t2v: 0.1454 2023/06/12 00:45:26 - mmengine - INFO - Epoch(train) [4][ 660/1407] lr: 2.5951e-06 eta: 0:17:43 time: 0.4915 data_time: 0.0177 memory: 10497 loss: 0.2836 sim_loss_v2t: 0.1293 sim_loss_t2v: 0.1542 2023/06/12 00:45:36 - mmengine - INFO - Epoch(train) [4][ 680/1407] lr: 2.5517e-06 eta: 0:17:33 time: 0.4904 data_time: 0.0175 memory: 10497 loss: 0.2827 sim_loss_v2t: 0.1283 sim_loss_t2v: 0.1544 2023/06/12 00:45:46 - mmengine - INFO - Epoch(train) [4][ 700/1407] lr: 2.5086e-06 eta: 0:17:23 time: 0.4911 data_time: 0.0182 memory: 10497 loss: 0.2751 sim_loss_v2t: 0.1297 sim_loss_t2v: 0.1454 2023/06/12 00:45:56 - mmengine - INFO - Epoch(train) [4][ 720/1407] lr: 2.4657e-06 eta: 0:17:13 time: 0.4897 data_time: 0.0182 memory: 10497 loss: 0.2563 sim_loss_v2t: 0.1144 sim_loss_t2v: 0.1419 2023/06/12 00:46:06 - mmengine - INFO - Epoch(train) [4][ 740/1407] lr: 2.4231e-06 eta: 0:17:04 time: 0.4932 data_time: 0.0179 memory: 10497 loss: 0.2674 sim_loss_v2t: 0.1198 sim_loss_t2v: 0.1476 2023/06/12 00:46:15 - mmengine - INFO - Epoch(train) [4][ 760/1407] lr: 2.3807e-06 eta: 0:16:54 time: 0.4902 data_time: 0.0186 memory: 10497 loss: 0.2503 sim_loss_v2t: 0.1117 sim_loss_t2v: 0.1386 2023/06/12 00:46:25 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:46:25 - mmengine - INFO - Epoch(train) [4][ 780/1407] lr: 2.3385e-06 eta: 0:16:44 time: 0.4928 data_time: 0.0181 memory: 10497 loss: 0.3073 sim_loss_v2t: 0.1424 sim_loss_t2v: 0.1650 2023/06/12 00:46:35 - mmengine - INFO - Epoch(train) [4][ 800/1407] lr: 2.2967e-06 eta: 0:16:34 time: 0.4898 data_time: 0.0178 memory: 10497 loss: 0.3104 sim_loss_v2t: 0.1441 sim_loss_t2v: 0.1663 2023/06/12 00:46:45 - mmengine - INFO - Epoch(train) [4][ 820/1407] lr: 2.2550e-06 eta: 0:16:24 time: 0.4928 data_time: 0.0178 memory: 10497 loss: 0.2871 sim_loss_v2t: 0.1297 sim_loss_t2v: 0.1574 2023/06/12 00:46:55 - mmengine - INFO - Epoch(train) [4][ 840/1407] lr: 2.2137e-06 eta: 0:16:14 time: 0.4908 data_time: 0.0181 memory: 10497 loss: 0.2761 sim_loss_v2t: 0.1289 sim_loss_t2v: 0.1472 2023/06/12 00:47:05 - mmengine - INFO - Epoch(train) [4][ 860/1407] lr: 2.1726e-06 eta: 0:16:04 time: 0.4917 data_time: 0.0181 memory: 10497 loss: 0.2823 sim_loss_v2t: 0.1283 sim_loss_t2v: 0.1540 2023/06/12 00:47:14 - mmengine - INFO - Epoch(train) [4][ 880/1407] lr: 2.1319e-06 eta: 0:15:54 time: 0.4905 data_time: 0.0173 memory: 10497 loss: 0.2793 sim_loss_v2t: 0.1271 sim_loss_t2v: 0.1522 2023/06/12 00:47:24 - mmengine - INFO - Epoch(train) [4][ 900/1407] lr: 2.0914e-06 eta: 0:15:44 time: 0.4905 data_time: 0.0180 memory: 10497 loss: 0.2760 sim_loss_v2t: 0.1268 sim_loss_t2v: 0.1493 2023/06/12 00:47:34 - mmengine - INFO - Epoch(train) [4][ 920/1407] lr: 2.0511e-06 eta: 0:15:34 time: 0.4888 data_time: 0.0182 memory: 10497 loss: 0.3036 sim_loss_v2t: 0.1416 sim_loss_t2v: 0.1621 2023/06/12 00:47:44 - mmengine - INFO - Epoch(train) [4][ 940/1407] lr: 2.0112e-06 eta: 0:15:25 time: 0.4895 data_time: 0.0180 memory: 10497 loss: 0.2904 sim_loss_v2t: 0.1320 sim_loss_t2v: 0.1584 2023/06/12 00:47:54 - mmengine - INFO - Epoch(train) [4][ 960/1407] lr: 1.9716e-06 eta: 0:15:15 time: 0.4894 data_time: 0.0178 memory: 10497 loss: 0.2864 sim_loss_v2t: 0.1291 sim_loss_t2v: 0.1573 2023/06/12 00:48:03 - mmengine - INFO - Epoch(train) [4][ 980/1407] lr: 1.9323e-06 eta: 0:15:05 time: 0.4893 data_time: 0.0178 memory: 10497 loss: 0.2912 sim_loss_v2t: 0.1330 sim_loss_t2v: 0.1582 2023/06/12 00:48:13 - mmengine - INFO - Epoch(train) [4][1000/1407] lr: 1.8932e-06 eta: 0:14:55 time: 0.4927 data_time: 0.0175 memory: 10497 loss: 0.2692 sim_loss_v2t: 0.1271 sim_loss_t2v: 0.1422 2023/06/12 00:48:23 - mmengine - INFO - Epoch(train) [4][1020/1407] lr: 1.8545e-06 eta: 0:14:45 time: 0.4902 data_time: 0.0175 memory: 10497 loss: 0.2758 sim_loss_v2t: 0.1239 sim_loss_t2v: 0.1520 2023/06/12 00:48:33 - mmengine - INFO - Epoch(train) [4][1040/1407] lr: 1.8161e-06 eta: 0:14:35 time: 0.4893 data_time: 0.0165 memory: 10497 loss: 0.2727 sim_loss_v2t: 0.1255 sim_loss_t2v: 0.1472 2023/06/12 00:48:43 - mmengine - INFO - Epoch(train) [4][1060/1407] lr: 1.7780e-06 eta: 0:14:25 time: 0.4895 data_time: 0.0178 memory: 10497 loss: 0.2814 sim_loss_v2t: 0.1234 sim_loss_t2v: 0.1580 2023/06/12 00:48:52 - mmengine - INFO - Epoch(train) [4][1080/1407] lr: 1.7402e-06 eta: 0:14:15 time: 0.4906 data_time: 0.0173 memory: 10497 loss: 0.2724 sim_loss_v2t: 0.1249 sim_loss_t2v: 0.1475 2023/06/12 00:49:02 - mmengine - INFO - Epoch(train) [4][1100/1407] lr: 1.7027e-06 eta: 0:14:05 time: 0.4899 data_time: 0.0180 memory: 10497 loss: 0.2747 sim_loss_v2t: 0.1253 sim_loss_t2v: 0.1493 2023/06/12 00:49:12 - mmengine - INFO - Epoch(train) [4][1120/1407] lr: 1.6656e-06 eta: 0:13:56 time: 0.4903 data_time: 0.0182 memory: 10497 loss: 0.2913 sim_loss_v2t: 0.1363 sim_loss_t2v: 0.1550 2023/06/12 00:49:22 - mmengine - INFO - Epoch(train) [4][1140/1407] lr: 1.6288e-06 eta: 0:13:46 time: 0.4904 data_time: 0.0183 memory: 10497 loss: 0.3020 sim_loss_v2t: 0.1443 sim_loss_t2v: 0.1577 2023/06/12 00:49:32 - mmengine - INFO - Epoch(train) [4][1160/1407] lr: 1.5923e-06 eta: 0:13:36 time: 0.4896 data_time: 0.0179 memory: 10497 loss: 0.2979 sim_loss_v2t: 0.1344 sim_loss_t2v: 0.1635 2023/06/12 00:49:41 - mmengine - INFO - Epoch(train) [4][1180/1407] lr: 1.5562e-06 eta: 0:13:26 time: 0.4910 data_time: 0.0181 memory: 10497 loss: 0.2462 sim_loss_v2t: 0.1123 sim_loss_t2v: 0.1339 2023/06/12 00:49:51 - mmengine - INFO - Epoch(train) [4][1200/1407] lr: 1.5204e-06 eta: 0:13:16 time: 0.4902 data_time: 0.0184 memory: 10497 loss: 0.2648 sim_loss_v2t: 0.1226 sim_loss_t2v: 0.1423 2023/06/12 00:50:01 - mmengine - INFO - Epoch(train) [4][1220/1407] lr: 1.4849e-06 eta: 0:13:06 time: 0.4897 data_time: 0.0180 memory: 10497 loss: 0.2822 sim_loss_v2t: 0.1350 sim_loss_t2v: 0.1472 2023/06/12 00:50:11 - mmengine - INFO - Epoch(train) [4][1240/1407] lr: 1.4498e-06 eta: 0:12:56 time: 0.4893 data_time: 0.0178 memory: 10497 loss: 0.2632 sim_loss_v2t: 0.1220 sim_loss_t2v: 0.1411 2023/06/12 00:50:21 - mmengine - INFO - Epoch(train) [4][1260/1407] lr: 1.4150e-06 eta: 0:12:46 time: 0.4913 data_time: 0.0192 memory: 10497 loss: 0.2947 sim_loss_v2t: 0.1393 sim_loss_t2v: 0.1554 2023/06/12 00:50:30 - mmengine - INFO - Epoch(train) [4][1280/1407] lr: 1.3806e-06 eta: 0:12:36 time: 0.4894 data_time: 0.0175 memory: 10497 loss: 0.2825 sim_loss_v2t: 0.1275 sim_loss_t2v: 0.1549 2023/06/12 00:50:40 - mmengine - INFO - Epoch(train) [4][1300/1407] lr: 1.3466e-06 eta: 0:12:27 time: 0.4905 data_time: 0.0181 memory: 10497 loss: 0.3181 sim_loss_v2t: 0.1495 sim_loss_t2v: 0.1686 2023/06/12 00:50:50 - mmengine - INFO - Epoch(train) [4][1320/1407] lr: 1.3129e-06 eta: 0:12:17 time: 0.4909 data_time: 0.0177 memory: 10497 loss: 0.2781 sim_loss_v2t: 0.1270 sim_loss_t2v: 0.1511 2023/06/12 00:51:00 - mmengine - INFO - Epoch(train) [4][1340/1407] lr: 1.2795e-06 eta: 0:12:07 time: 0.4909 data_time: 0.0180 memory: 10497 loss: 0.2920 sim_loss_v2t: 0.1288 sim_loss_t2v: 0.1631 2023/06/12 00:51:10 - mmengine - INFO - Epoch(train) [4][1360/1407] lr: 1.2466e-06 eta: 0:11:57 time: 0.4908 data_time: 0.0174 memory: 10497 loss: 0.2810 sim_loss_v2t: 0.1287 sim_loss_t2v: 0.1523 2023/06/12 00:51:19 - mmengine - INFO - Epoch(train) [4][1380/1407] lr: 1.2140e-06 eta: 0:11:47 time: 0.4907 data_time: 0.0177 memory: 10497 loss: 0.2871 sim_loss_v2t: 0.1324 sim_loss_t2v: 0.1548 2023/06/12 00:51:29 - mmengine - INFO - Epoch(train) [4][1400/1407] lr: 1.1818e-06 eta: 0:11:37 time: 0.4844 data_time: 0.0176 memory: 10497 loss: 0.2643 sim_loss_v2t: 0.1184 sim_loss_t2v: 0.1459 2023/06/12 00:51:32 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:51:32 - mmengine - INFO - Epoch(train) [4][1407/1407] lr: 1.1706e-06 eta: 0:11:34 time: 0.4699 data_time: 0.0167 memory: 10497 loss: 0.2579 sim_loss_v2t: 0.1173 sim_loss_t2v: 0.1407 2023/06/12 00:51:32 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/06/12 00:51:40 - mmengine - INFO - Epoch(val) [4][8/8] retrieval/R1: 42.5000 retrieval/R5: 69.1000 retrieval/R10: 79.5000 retrieval/MdR: 2.0000 retrieval/MnR: 16.7000 data_time: 0.1998 time: 0.3338 2023/06/12 00:51:53 - mmengine - INFO - Epoch(train) [5][ 20/1407] lr: 1.1389e-06 eta: 0:11:24 time: 0.6455 data_time: 0.1153 memory: 10497 loss: 0.2163 sim_loss_v2t: 0.1004 sim_loss_t2v: 0.1159 2023/06/12 00:52:03 - mmengine - INFO - Epoch(train) [5][ 40/1407] lr: 1.1075e-06 eta: 0:11:15 time: 0.4898 data_time: 0.0176 memory: 10497 loss: 0.2168 sim_loss_v2t: 0.0962 sim_loss_t2v: 0.1206 2023/06/12 00:52:13 - mmengine - INFO - Epoch(train) [5][ 60/1407] lr: 1.0766e-06 eta: 0:11:05 time: 0.4914 data_time: 0.0177 memory: 10497 loss: 0.1993 sim_loss_v2t: 0.0916 sim_loss_t2v: 0.1078 2023/06/12 00:52:23 - mmengine - INFO - Epoch(train) [5][ 80/1407] lr: 1.0460e-06 eta: 0:10:55 time: 0.4919 data_time: 0.0167 memory: 10497 loss: 0.2335 sim_loss_v2t: 0.1061 sim_loss_t2v: 0.1274 2023/06/12 00:52:32 - mmengine - INFO - Epoch(train) [5][ 100/1407] lr: 1.0158e-06 eta: 0:10:45 time: 0.4895 data_time: 0.0178 memory: 10497 loss: 0.2278 sim_loss_v2t: 0.1044 sim_loss_t2v: 0.1234 2023/06/12 00:52:42 - mmengine - INFO - Epoch(train) [5][ 120/1407] lr: 9.8605e-07 eta: 0:10:35 time: 0.4906 data_time: 0.0186 memory: 10497 loss: 0.2295 sim_loss_v2t: 0.1094 sim_loss_t2v: 0.1202 2023/06/12 00:52:52 - mmengine - INFO - Epoch(train) [5][ 140/1407] lr: 9.5667e-07 eta: 0:10:25 time: 0.4899 data_time: 0.0187 memory: 10497 loss: 0.2190 sim_loss_v2t: 0.1049 sim_loss_t2v: 0.1141 2023/06/12 00:53:02 - mmengine - INFO - Epoch(train) [5][ 160/1407] lr: 9.2768e-07 eta: 0:10:15 time: 0.4913 data_time: 0.0171 memory: 10497 loss: 0.2539 sim_loss_v2t: 0.1194 sim_loss_t2v: 0.1345 2023/06/12 00:53:12 - mmengine - INFO - Epoch(train) [5][ 180/1407] lr: 8.9909e-07 eta: 0:10:05 time: 0.4902 data_time: 0.0173 memory: 10497 loss: 0.2184 sim_loss_v2t: 0.0989 sim_loss_t2v: 0.1195 2023/06/12 00:53:21 - mmengine - INFO - Epoch(train) [5][ 200/1407] lr: 8.7090e-07 eta: 0:09:55 time: 0.4928 data_time: 0.0185 memory: 10497 loss: 0.2320 sim_loss_v2t: 0.1041 sim_loss_t2v: 0.1279 2023/06/12 00:53:31 - mmengine - INFO - Epoch(train) [5][ 220/1407] lr: 8.4313e-07 eta: 0:09:46 time: 0.4943 data_time: 0.0175 memory: 10497 loss: 0.2349 sim_loss_v2t: 0.1053 sim_loss_t2v: 0.1297 2023/06/12 00:53:41 - mmengine - INFO - Epoch(train) [5][ 240/1407] lr: 8.1576e-07 eta: 0:09:36 time: 0.4889 data_time: 0.0177 memory: 10497 loss: 0.2015 sim_loss_v2t: 0.0923 sim_loss_t2v: 0.1092 2023/06/12 00:53:51 - mmengine - INFO - Epoch(train) [5][ 260/1407] lr: 7.8880e-07 eta: 0:09:26 time: 0.4923 data_time: 0.0169 memory: 10497 loss: 0.2265 sim_loss_v2t: 0.1087 sim_loss_t2v: 0.1179 2023/06/12 00:54:01 - mmengine - INFO - Epoch(train) [5][ 280/1407] lr: 7.6226e-07 eta: 0:09:16 time: 0.4890 data_time: 0.0174 memory: 10497 loss: 0.2183 sim_loss_v2t: 0.0993 sim_loss_t2v: 0.1190 2023/06/12 00:54:10 - mmengine - INFO - Epoch(train) [5][ 300/1407] lr: 7.3613e-07 eta: 0:09:06 time: 0.4885 data_time: 0.0177 memory: 10497 loss: 0.2154 sim_loss_v2t: 0.0951 sim_loss_t2v: 0.1202 2023/06/12 00:54:20 - mmengine - INFO - Epoch(train) [5][ 320/1407] lr: 7.1043e-07 eta: 0:08:56 time: 0.4888 data_time: 0.0176 memory: 10497 loss: 0.2360 sim_loss_v2t: 0.1067 sim_loss_t2v: 0.1293 2023/06/12 00:54:30 - mmengine - INFO - Epoch(train) [5][ 340/1407] lr: 6.8515e-07 eta: 0:08:46 time: 0.4909 data_time: 0.0177 memory: 10497 loss: 0.2327 sim_loss_v2t: 0.1020 sim_loss_t2v: 0.1307 2023/06/12 00:54:40 - mmengine - INFO - Epoch(train) [5][ 360/1407] lr: 6.6029e-07 eta: 0:08:36 time: 0.4906 data_time: 0.0182 memory: 10497 loss: 0.2130 sim_loss_v2t: 0.0956 sim_loss_t2v: 0.1174 2023/06/12 00:54:46 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 00:54:50 - mmengine - INFO - Epoch(train) [5][ 380/1407] lr: 6.3586e-07 eta: 0:08:26 time: 0.4923 data_time: 0.0185 memory: 10497 loss: 0.2098 sim_loss_v2t: 0.0952 sim_loss_t2v: 0.1145 2023/06/12 00:55:00 - mmengine - INFO - Epoch(train) [5][ 400/1407] lr: 6.1186e-07 eta: 0:08:17 time: 0.4911 data_time: 0.0173 memory: 10497 loss: 0.2160 sim_loss_v2t: 0.0998 sim_loss_t2v: 0.1163 2023/06/12 00:55:09 - mmengine - INFO - Epoch(train) [5][ 420/1407] lr: 5.8829e-07 eta: 0:08:07 time: 0.4903 data_time: 0.0172 memory: 10497 loss: 0.2100 sim_loss_v2t: 0.0977 sim_loss_t2v: 0.1123 2023/06/12 00:55:19 - mmengine - INFO - Epoch(train) [5][ 440/1407] lr: 5.6516e-07 eta: 0:07:57 time: 0.4902 data_time: 0.0183 memory: 10497 loss: 0.2126 sim_loss_v2t: 0.0973 sim_loss_t2v: 0.1153 2023/06/12 00:55:29 - mmengine - INFO - Epoch(train) [5][ 460/1407] lr: 5.4246e-07 eta: 0:07:47 time: 0.4904 data_time: 0.0180 memory: 10497 loss: 0.2198 sim_loss_v2t: 0.1016 sim_loss_t2v: 0.1182 2023/06/12 00:55:39 - mmengine - INFO - Epoch(train) [5][ 480/1407] lr: 5.2021e-07 eta: 0:07:37 time: 0.4916 data_time: 0.0179 memory: 10497 loss: 0.2351 sim_loss_v2t: 0.1087 sim_loss_t2v: 0.1264 2023/06/12 00:55:49 - mmengine - INFO - Epoch(train) [5][ 500/1407] lr: 4.9839e-07 eta: 0:07:27 time: 0.4916 data_time: 0.0195 memory: 10497 loss: 0.2251 sim_loss_v2t: 0.1018 sim_loss_t2v: 0.1233 2023/06/12 00:55:58 - mmengine - INFO - Epoch(train) [5][ 520/1407] lr: 4.7702e-07 eta: 0:07:17 time: 0.4917 data_time: 0.0173 memory: 10497 loss: 0.2076 sim_loss_v2t: 0.0951 sim_loss_t2v: 0.1125 2023/06/12 00:56:08 - mmengine - INFO - Epoch(train) [5][ 540/1407] lr: 4.5609e-07 eta: 0:07:07 time: 0.4890 data_time: 0.0175 memory: 10497 loss: 0.2336 sim_loss_v2t: 0.1078 sim_loss_t2v: 0.1257 2023/06/12 00:56:18 - mmengine - INFO - Epoch(train) [5][ 560/1407] lr: 4.3561e-07 eta: 0:06:58 time: 0.4897 data_time: 0.0171 memory: 10497 loss: 0.2448 sim_loss_v2t: 0.1138 sim_loss_t2v: 0.1310 2023/06/12 00:56:28 - mmengine - INFO - Epoch(train) [5][ 580/1407] lr: 4.1558e-07 eta: 0:06:48 time: 0.4893 data_time: 0.0180 memory: 10497 loss: 0.2404 sim_loss_v2t: 0.1144 sim_loss_t2v: 0.1260 2023/06/12 00:56:38 - mmengine - INFO - Epoch(train) [5][ 600/1407] lr: 3.9600e-07 eta: 0:06:38 time: 0.4916 data_time: 0.0179 memory: 10497 loss: 0.2261 sim_loss_v2t: 0.1056 sim_loss_t2v: 0.1205 2023/06/12 00:56:47 - mmengine - INFO - Epoch(train) [5][ 620/1407] lr: 3.7687e-07 eta: 0:06:28 time: 0.4893 data_time: 0.0180 memory: 10497 loss: 0.2267 sim_loss_v2t: 0.1002 sim_loss_t2v: 0.1264 2023/06/12 00:56:57 - mmengine - INFO - Epoch(train) [5][ 640/1407] lr: 3.5820e-07 eta: 0:06:18 time: 0.4900 data_time: 0.0172 memory: 10497 loss: 0.2282 sim_loss_v2t: 0.1045 sim_loss_t2v: 0.1237 2023/06/12 00:57:07 - mmengine - INFO - Epoch(train) [5][ 660/1407] lr: 3.3999e-07 eta: 0:06:08 time: 0.4919 data_time: 0.0167 memory: 10497 loss: 0.2158 sim_loss_v2t: 0.0975 sim_loss_t2v: 0.1183 2023/06/12 00:57:17 - mmengine - INFO - Epoch(train) [5][ 680/1407] lr: 3.2223e-07 eta: 0:05:58 time: 0.4937 data_time: 0.0172 memory: 10497 loss: 0.2115 sim_loss_v2t: 0.0926 sim_loss_t2v: 0.1189 2023/06/12 00:57:27 - mmengine - INFO - Epoch(train) [5][ 700/1407] lr: 3.0494e-07 eta: 0:05:48 time: 0.4922 data_time: 0.0178 memory: 10497 loss: 0.2138 sim_loss_v2t: 0.0953 sim_loss_t2v: 0.1185 2023/06/12 00:57:37 - mmengine - INFO - Epoch(train) [5][ 720/1407] lr: 2.8811e-07 eta: 0:05:39 time: 0.4892 data_time: 0.0182 memory: 10497 loss: 0.2329 sim_loss_v2t: 0.1092 sim_loss_t2v: 0.1236 2023/06/12 00:57:46 - mmengine - INFO - Epoch(train) [5][ 740/1407] lr: 2.7174e-07 eta: 0:05:29 time: 0.4907 data_time: 0.0182 memory: 10497 loss: 0.2128 sim_loss_v2t: 0.0944 sim_loss_t2v: 0.1185 2023/06/12 00:57:56 - mmengine - INFO - Epoch(train) [5][ 760/1407] lr: 2.5584e-07 eta: 0:05:19 time: 0.4959 data_time: 0.0169 memory: 10497 loss: 0.2146 sim_loss_v2t: 0.0987 sim_loss_t2v: 0.1159 2023/06/12 00:58:06 - mmengine - INFO - Epoch(train) [5][ 780/1407] lr: 2.4040e-07 eta: 0:05:09 time: 0.4912 data_time: 0.0172 memory: 10497 loss: 0.2244 sim_loss_v2t: 0.0994 sim_loss_t2v: 0.1249 2023/06/12 00:58:16 - mmengine - INFO - Epoch(train) [5][ 800/1407] lr: 2.2544e-07 eta: 0:04:59 time: 0.4896 data_time: 0.0186 memory: 10497 loss: 0.2065 sim_loss_v2t: 0.0945 sim_loss_t2v: 0.1120 2023/06/12 00:58:26 - mmengine - INFO - Epoch(train) [5][ 820/1407] lr: 2.1094e-07 eta: 0:04:49 time: 0.4897 data_time: 0.0175 memory: 10497 loss: 0.2270 sim_loss_v2t: 0.1054 sim_loss_t2v: 0.1215 2023/06/12 00:58:36 - mmengine - INFO - Epoch(train) [5][ 840/1407] lr: 1.9692e-07 eta: 0:04:39 time: 0.4894 data_time: 0.0180 memory: 10497 loss: 0.2094 sim_loss_v2t: 0.0910 sim_loss_t2v: 0.1184 2023/06/12 00:58:45 - mmengine - INFO - Epoch(train) [5][ 860/1407] lr: 1.8337e-07 eta: 0:04:29 time: 0.4909 data_time: 0.0181 memory: 10497 loss: 0.2367 sim_loss_v2t: 0.1045 sim_loss_t2v: 0.1322 2023/06/12 00:58:55 - mmengine - INFO - Epoch(train) [5][ 880/1407] lr: 1.7029e-07 eta: 0:04:20 time: 0.4908 data_time: 0.0180 memory: 10497 loss: 0.2177 sim_loss_v2t: 0.0982 sim_loss_t2v: 0.1195 2023/06/12 00:59:05 - mmengine - INFO - Epoch(train) [5][ 900/1407] lr: 1.5769e-07 eta: 0:04:10 time: 0.4899 data_time: 0.0179 memory: 10497 loss: 0.1875 sim_loss_v2t: 0.0862 sim_loss_t2v: 0.1012 2023/06/12 00:59:15 - mmengine - INFO - Epoch(train) [5][ 920/1407] lr: 1.4557e-07 eta: 0:04:00 time: 0.4893 data_time: 0.0171 memory: 10497 loss: 0.2153 sim_loss_v2t: 0.0996 sim_loss_t2v: 0.1156 2023/06/12 00:59:25 - mmengine - INFO - Epoch(train) [5][ 940/1407] lr: 1.3392e-07 eta: 0:03:50 time: 0.4892 data_time: 0.0170 memory: 10497 loss: 0.2232 sim_loss_v2t: 0.1021 sim_loss_t2v: 0.1211 2023/06/12 00:59:34 - mmengine - INFO - Epoch(train) [5][ 960/1407] lr: 1.2275e-07 eta: 0:03:40 time: 0.4900 data_time: 0.0177 memory: 10497 loss: 0.2043 sim_loss_v2t: 0.0933 sim_loss_t2v: 0.1109 2023/06/12 00:59:44 - mmengine - INFO - Epoch(train) [5][ 980/1407] lr: 1.1206e-07 eta: 0:03:30 time: 0.4926 data_time: 0.0181 memory: 10497 loss: 0.2204 sim_loss_v2t: 0.1019 sim_loss_t2v: 0.1185 2023/06/12 00:59:54 - mmengine - INFO - Epoch(train) [5][1000/1407] lr: 1.0186e-07 eta: 0:03:20 time: 0.4921 data_time: 0.0173 memory: 10497 loss: 0.2123 sim_loss_v2t: 0.0948 sim_loss_t2v: 0.1175 2023/06/12 01:00:04 - mmengine - INFO - Epoch(train) [5][1020/1407] lr: 9.2136e-08 eta: 0:03:10 time: 0.4933 data_time: 0.0173 memory: 10497 loss: 0.2021 sim_loss_v2t: 0.0920 sim_loss_t2v: 0.1102 2023/06/12 01:00:14 - mmengine - INFO - Epoch(train) [5][1040/1407] lr: 8.2897e-08 eta: 0:03:01 time: 0.4925 data_time: 0.0173 memory: 10497 loss: 0.1866 sim_loss_v2t: 0.0801 sim_loss_t2v: 0.1065 2023/06/12 01:00:24 - mmengine - INFO - Epoch(train) [5][1060/1407] lr: 7.4141e-08 eta: 0:02:51 time: 0.4919 data_time: 0.0182 memory: 10497 loss: 0.2192 sim_loss_v2t: 0.0959 sim_loss_t2v: 0.1233 2023/06/12 01:00:33 - mmengine - INFO - Epoch(train) [5][1080/1407] lr: 6.5871e-08 eta: 0:02:41 time: 0.4944 data_time: 0.0171 memory: 10497 loss: 0.2134 sim_loss_v2t: 0.0969 sim_loss_t2v: 0.1166 2023/06/12 01:00:43 - mmengine - INFO - Epoch(train) [5][1100/1407] lr: 5.8086e-08 eta: 0:02:31 time: 0.4923 data_time: 0.0172 memory: 10497 loss: 0.2361 sim_loss_v2t: 0.1096 sim_loss_t2v: 0.1265 2023/06/12 01:00:53 - mmengine - INFO - Epoch(train) [5][1120/1407] lr: 5.0789e-08 eta: 0:02:21 time: 0.4920 data_time: 0.0175 memory: 10497 loss: 0.2622 sim_loss_v2t: 0.1197 sim_loss_t2v: 0.1425 2023/06/12 01:01:03 - mmengine - INFO - Epoch(train) [5][1140/1407] lr: 4.3978e-08 eta: 0:02:11 time: 0.4919 data_time: 0.0184 memory: 10497 loss: 0.2246 sim_loss_v2t: 0.1029 sim_loss_t2v: 0.1217 2023/06/12 01:01:13 - mmengine - INFO - Epoch(train) [5][1160/1407] lr: 3.7656e-08 eta: 0:02:01 time: 0.4908 data_time: 0.0179 memory: 10497 loss: 0.1977 sim_loss_v2t: 0.0932 sim_loss_t2v: 0.1046 2023/06/12 01:01:23 - mmengine - INFO - Epoch(train) [5][1180/1407] lr: 3.1822e-08 eta: 0:01:51 time: 0.4914 data_time: 0.0185 memory: 10497 loss: 0.2146 sim_loss_v2t: 0.0951 sim_loss_t2v: 0.1195 2023/06/12 01:01:32 - mmengine - INFO - Epoch(train) [5][1200/1407] lr: 2.6478e-08 eta: 0:01:42 time: 0.4898 data_time: 0.0179 memory: 10497 loss: 0.2151 sim_loss_v2t: 0.0966 sim_loss_t2v: 0.1186 2023/06/12 01:01:42 - mmengine - INFO - Epoch(train) [5][1220/1407] lr: 2.1623e-08 eta: 0:01:32 time: 0.4929 data_time: 0.0171 memory: 10497 loss: 0.2215 sim_loss_v2t: 0.0992 sim_loss_t2v: 0.1223 2023/06/12 01:01:52 - mmengine - INFO - Epoch(train) [5][1240/1407] lr: 1.7259e-08 eta: 0:01:22 time: 0.4918 data_time: 0.0178 memory: 10497 loss: 0.2214 sim_loss_v2t: 0.0995 sim_loss_t2v: 0.1219 2023/06/12 01:02:02 - mmengine - INFO - Epoch(train) [5][1260/1407] lr: 1.3385e-08 eta: 0:01:12 time: 0.4951 data_time: 0.0181 memory: 10497 loss: 0.2135 sim_loss_v2t: 0.1007 sim_loss_t2v: 0.1128 2023/06/12 01:02:12 - mmengine - INFO - Epoch(train) [5][1280/1407] lr: 1.0002e-08 eta: 0:01:02 time: 0.4923 data_time: 0.0177 memory: 10497 loss: 0.2150 sim_loss_v2t: 0.1002 sim_loss_t2v: 0.1148 2023/06/12 01:02:22 - mmengine - INFO - Epoch(train) [5][1300/1407] lr: 7.1112e-09 eta: 0:00:52 time: 0.4917 data_time: 0.0186 memory: 10497 loss: 0.2048 sim_loss_v2t: 0.0949 sim_loss_t2v: 0.1100 2023/06/12 01:02:32 - mmengine - INFO - Epoch(train) [5][1320/1407] lr: 4.7117e-09 eta: 0:00:42 time: 0.4913 data_time: 0.0179 memory: 10497 loss: 0.2081 sim_loss_v2t: 0.0946 sim_loss_t2v: 0.1135 2023/06/12 01:02:41 - mmengine - INFO - Epoch(train) [5][1340/1407] lr: 2.8041e-09 eta: 0:00:33 time: 0.4912 data_time: 0.0178 memory: 10497 loss: 0.2322 sim_loss_v2t: 0.1086 sim_loss_t2v: 0.1236 2023/06/12 01:02:51 - mmengine - INFO - Epoch(train) [5][1360/1407] lr: 1.3887e-09 eta: 0:00:23 time: 0.4900 data_time: 0.0185 memory: 10497 loss: 0.2307 sim_loss_v2t: 0.1017 sim_loss_t2v: 0.1289 2023/06/12 01:02:57 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 01:03:01 - mmengine - INFO - Epoch(train) [5][1380/1407] lr: 4.6546e-10 eta: 0:00:13 time: 0.4931 data_time: 0.0184 memory: 10497 loss: 0.2185 sim_loss_v2t: 0.0968 sim_loss_t2v: 0.1217 2023/06/12 01:03:11 - mmengine - INFO - Epoch(train) [5][1400/1407] lr: 3.4622e-11 eta: 0:00:03 time: 0.4864 data_time: 0.0172 memory: 10497 loss: 0.2066 sim_loss_v2t: 0.0953 sim_loss_t2v: 0.1114 2023/06/12 01:03:14 - mmengine - INFO - Exp name: clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb_20230612_000311 2023/06/12 01:03:14 - mmengine - INFO - Epoch(train) [5][1407/1407] lr: 1.5387e-13 eta: 0:00:00 time: 0.4708 data_time: 0.0163 memory: 10497 loss: 0.2031 sim_loss_v2t: 0.0939 sim_loss_t2v: 0.1092 2023/06/12 01:03:14 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/06/12 01:03:22 - mmengine - INFO - Epoch(val) [5][8/8] retrieval/R1: 42.3000 retrieval/R5: 69.3000 retrieval/R10: 79.1000 retrieval/MdR: 2.0000 retrieval/MnR: 16.2220 data_time: 0.1987 time: 0.3325