2023/09/05 00:24:45 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.9.17 (main, Jul 5 2023, 20:41:20) [GCC 11.2.0] CUDA available: True numpy_random_seed: 308696377 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+cu113 OpenCV: 4.8.0 MMEngine: 0.8.4 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 308696377 diff_rank_seed: False deterministic: False Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/09/05 00:24:45 - mmengine - INFO - Config: anno_file_test = 'data/msrvtt/anno_downstream/msrvtt_ret_test1k.json' anno_file_train = 'data/msrvtt/anno_downstream/msrvtt_ret_train9k.json' auto_scale_lr = dict(base_batch_size=128, enable=True) custom_hooks = [ dict(after_epoch=True, type='EmptyCacheHook'), ] dataset_type = 'MSRVTT_Ret' default_hooks = dict( checkpoint=dict( interval=1, rule='greater', save_best='t2i/retrieval/Recall@1', type='CheckpointHook'), logger=dict(ignore_last=False, interval=20, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), runtime_info=dict(type='RuntimeInfoHook'), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffers=dict(type='SyncBuffersHook'), timer=dict(type='IterTimerHook')) default_scope = 'mmaction' env_cfg = dict( cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) file_client_args = dict(io_backend='disk') find_unused_parameters = True launcher = 'pytorch' load_from = None log_level = 'INFO' log_processor = dict(by_epoch=True, type='LogProcessor', window_size=20) model = dict( data_preprocessor=dict( format_shape='NCTHW', mean=[ 128, ], std=[ 128, ], type='ActionDataPreprocessor'), gradient_checkpointing=True, init_cfg=dict(checkpoint='checkpoints/5M-pretrain.pth', type='Pretrained'), max_txt_len=32, proj_dim=256, temperature=0.07, text_encoder=dict( add_pooling_layer=False, encoder_width=768, fusion_layer=9, pretrained_model_name_or_path='bert-base-uncased', type='XBertModel'), tokenizer=dict( pretrained_model_name_or_path='bert-base-uncased', type='VindLUTokenizer'), topk=128, type='VindLURetrieval', vision_encoder=dict( add_ln=True, config='microsoft/beit-base-patch16-224-pt22k-ft22k', encoder_width=768, tem_config=dict( num_frames=12, temporal_model_block='timesformer', temporal_model_config=dict(input_dim=768), temporal_model_position='last', use_temporal_position_embedding=True), type='BeitModel3D')) model_wrapper_cfg = dict(static_graph=True, type='MMDistributedDataParallel') optim_wrapper = dict( clip_grad=dict(max_norm=50, norm_type=2), optimizer=dict(lr=1e-05, type='AdamW', weight_decay=0.02), paramwise_cfg=dict( bias_decay_mult=0.0, bypass_duplicate=True, norm_decay_mult=0.0), type='AmpOptimWrapper') param_scheduler = [ dict( T_max=5, begin=0, by_epoch=True, convert_to_iter_based=True, end=5, eta_min_ratio=0.01, type='CosineAnnealingLR'), ] pretrained_ckpt_path = 'checkpoints/5M-pretrain.pth' randomness = dict(deterministic=False, diff_rank_seed=False, seed=None) resume = False test_cfg = dict(type='RetrievalTestLoop') test_dataloader = dict( batch_size=8, dataset=dict( ann_file='data/msrvtt/anno_downstream/msrvtt_ret_test1k.json', data_prefix=dict(video='data/msrvtt/msrvtt_2fps_224'), pipeline=[ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=12, out_of_bound_opt='repeat_last', test_mode=True, type='SampleFrames'), dict(type='DecordDecode'), dict( interpolation='bicubic', keep_ratio=False, scale=( 224, 224, ), type='Resize'), dict(input_format='NCHW', type='FormatShape'), dict( algorithm_keys=( 'text', 'gt_video_id', 'gt_text_id', ), type='PackActionInputs'), ], type='MSRVTT_Ret'), num_workers=8, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = dict( topk=( 1, 5, 10, ), type='RetrievalRecall') test_pipeline = [ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=12, out_of_bound_opt='repeat_last', test_mode=True, type='SampleFrames'), dict(type='DecordDecode'), dict( interpolation='bicubic', keep_ratio=False, scale=( 224, 224, ), type='Resize'), dict(input_format='NCHW', type='FormatShape'), dict( algorithm_keys=( 'text', 'gt_video_id', 'gt_text_id', ), type='PackActionInputs'), ] train_cfg = dict( max_epochs=5, type='EpochBasedTrainLoop', val_begin=1, val_interval=1) train_dataloader = dict( batch_size=16, dataset=dict( ann_file='data/msrvtt/anno_downstream/msrvtt_ret_train9k.json', data_prefix=dict(video='data/msrvtt/msrvtt_2fps_224'), pipeline=[ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=12, out_of_bound_opt='repeat_last', type='SampleFrames'), dict(type='DecordDecode'), dict(area_range=( 0.5, 1.0, ), type='RandomResizedCrop'), dict( interpolation='bicubic', keep_ratio=False, scale=( 224, 224, ), type='Resize'), dict(flip_ratio=0.5, type='Flip'), dict(input_format='NCHW', type='FormatShape'), dict( algorithm_keys=( 'text', 'gt_video_id', 'gt_text_id', ), type='PackActionInputs'), ], type='MSRVTT_Ret'), num_workers=8, persistent_workers=True, sampler=dict(shuffle=True, type='DefaultSampler')) train_pipeline = [ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=12, out_of_bound_opt='repeat_last', type='SampleFrames'), dict(type='DecordDecode'), dict(area_range=( 0.5, 1.0, ), type='RandomResizedCrop'), dict( interpolation='bicubic', keep_ratio=False, scale=( 224, 224, ), type='Resize'), dict(flip_ratio=0.5, type='Flip'), dict(input_format='NCHW', type='FormatShape'), dict( algorithm_keys=( 'text', 'gt_video_id', 'gt_text_id', ), type='PackActionInputs'), ] val_cfg = dict(type='RetrievalValLoop') val_dataloader = dict( batch_size=8, dataset=dict( ann_file='data/msrvtt/anno_downstream/msrvtt_ret_test1k.json', data_prefix=dict(video='data/msrvtt/msrvtt_2fps_224'), pipeline=[ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=12, out_of_bound_opt='repeat_last', test_mode=True, type='SampleFrames'), dict(type='DecordDecode'), dict( interpolation='bicubic', keep_ratio=False, scale=( 224, 224, ), type='Resize'), dict(input_format='NCHW', type='FormatShape'), dict( algorithm_keys=( 'text', 'gt_video_id', 'gt_text_id', ), type='PackActionInputs'), ], type='MSRVTT_Ret'), num_workers=8, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = dict( topk=( 1, 5, 10, ), type='RetrievalRecall') val_pipeline = [ dict(io_backend='disk', type='DecordInit'), dict( clip_len=1, frame_interval=1, num_clips=12, out_of_bound_opt='repeat_last', test_mode=True, type='SampleFrames'), dict(type='DecordDecode'), dict( interpolation='bicubic', keep_ratio=False, scale=( 224, 224, ), type='Resize'), dict(input_format='NCHW', type='FormatShape'), dict( algorithm_keys=( 'text', 'gt_video_id', 'gt_text_id', ), type='PackActionInputs'), ] video_root = 'data/msrvtt/msrvtt_2fps_224' vis_backends = [ dict(type='LocalVisBackend'), ] visualizer = dict( type='ActionVisualizer', vis_backends=[ dict(type='LocalVisBackend'), ]) work_dir = 'work_dirs/vindlu_9_4/msrvtt_retrieval_train_8x16' 2023/09/05 00:24:54 - mmengine - INFO - build bert with cross_module: ca 2023/09/05 00:25:14 - 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 (NORMAL ) EmptyCacheHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook (NORMAL ) EmptyCacheHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook (NORMAL ) EmptyCacheHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_val: (VERY_HIGH ) RuntimeInfoHook -------------------- after_train: (VERY_HIGH ) RuntimeInfoHook (VERY_LOW ) CheckpointHook -------------------- before_test: (VERY_HIGH ) RuntimeInfoHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook -------------------- after_test: (VERY_HIGH ) RuntimeInfoHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_layernorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_layernorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.embeddings.patch_embeddings.projection.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.attention.attention.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.attention.attention.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.layernorm_before.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.layernorm_before.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.layernorm_after.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.layernorm_after.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.temp_model.temporal_attn.out_proj.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.temp_model.norm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.temp_model.norm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.0.temp_model.linear.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.attention.attention.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.attention.attention.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.layernorm_before.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.layernorm_before.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.layernorm_after.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.layernorm_after.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.temp_model.temporal_attn.out_proj.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.temp_model.norm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.temp_model.norm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.1.temp_model.linear.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.attention.attention.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.attention.attention.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.layernorm_before.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.layernorm_before.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.layernorm_after.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.layernorm_after.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.temp_model.temporal_attn.out_proj.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.temp_model.norm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.temp_model.norm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.2.temp_model.linear.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.attention.attention.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.attention.attention.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.layernorm_before.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.layernorm_before.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.layernorm_after.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.layernorm_after.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.temp_model.temporal_attn.out_proj.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.temp_model.norm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.temp_model.norm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.3.temp_model.linear.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.attention.attention.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.attention.attention.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.layernorm_before.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.layernorm_before.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.layernorm_after.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.layernorm_after.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.temp_model.temporal_attn.out_proj.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.temp_model.norm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.temp_model.norm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.4.temp_model.linear.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.attention.attention.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.attention.attention.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.layernorm_before.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.layernorm_before.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.layernorm_after.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.layernorm_after.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.temp_model.temporal_attn.out_proj.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.temp_model.norm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.temp_model.norm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.5.temp_model.linear.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.6.attention.attention.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.6.attention.attention.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- 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vision_encoder.encoder.layer.11.attention.attention.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.11.attention.attention.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.11.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.11.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.11.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.11.layernorm_before.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_encoder.encoder.layer.11.layernorm_before.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- 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- mmengine - INFO - paramwise_options -- text_encoder.embeddings.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.embeddings.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.0.attention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.0.attention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.0.attention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.0.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.0.attention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- 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2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.1.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.1.attention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.1.attention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.1.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.1.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.1.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.1.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- 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2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.3.attention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.3.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.3.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.3.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.3.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.4.attention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.4.attention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- 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text_encoder.encoder.layer.5.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.5.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.5.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.5.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.attention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.attention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.attention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.attention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.attention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.6.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.attention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.attention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.attention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.attention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.attention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.7.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.attention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.attention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.attention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.attention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.attention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.8.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.attention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.attention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.attention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.attention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.attention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.crossattention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.crossattention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.crossattention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.crossattention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.crossattention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.crossattention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.9.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.attention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.attention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.attention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.attention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.attention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.crossattention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.crossattention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.crossattention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.crossattention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.crossattention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.crossattention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.10.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.attention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.attention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.attention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.attention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.attention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.attention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.crossattention.self.query.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.crossattention.self.key.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.crossattention.self.value.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.crossattention.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.crossattention.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.crossattention.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.intermediate.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.output.dense.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.output.LayerNorm.weight:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_encoder.encoder.layer.11.output.LayerNorm.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- vision_proj.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- text_proj.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - paramwise_options -- itm_head.bias:weight_decay=0.0 2023/09/05 00:25:17 - mmengine - INFO - LR is set based on batch size of 128 and the current batch size is 128. Scaling the original LR by 1.0. 2023/09/05 00:25:19 - mmengine - INFO - interpolate temporal positional embeddings: vision_encoder.embeddings.temporal_position_embeddings 2023/09/05 00:25:19 - mmengine - INFO - Load temporal_embeddings, lengths: 4-->12 2023/09/05 00:25:19 - mmengine - INFO - _IncompatibleKeys(missing_keys=[], unexpected_keys=['temporal_embeddings', 'text_encoder.cls.predictions.bias', 'text_encoder.cls.predictions.transform.dense.weight', 'text_encoder.cls.predictions.transform.dense.bias', 'text_encoder.cls.predictions.transform.LayerNorm.weight', 'text_encoder.cls.predictions.transform.LayerNorm.bias', 'text_encoder.cls.predictions.decoder.weight', 'text_encoder.cls.predictions.decoder.bias']) 2023/09/05 00:25:19 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/09/05 00:25:19 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/09/05 00:25:19 - mmengine - INFO - Checkpoints will be saved to /mnt/workspace/lilin/Repos/mmaction2/work_dirs/vindlu_9_4/msrvtt_retrieval_train_8x16. 2023/09/05 00:25:53 - mmengine - INFO - Epoch(train) [1][ 20/1407] base_lr: 9.9998e-06 lr: 9.9998e-06 eta: 3:21:22 time: 1.7224 data_time: 0.0687 memory: 8640 grad_norm: nan loss: 3.4325 itc_loss: 2.7425 itm_loss: 0.6900 2023/09/05 00:26:23 - mmengine - INFO - Epoch(train) [1][ 40/1407] base_lr: 9.9992e-06 lr: 9.9992e-06 eta: 3:05:33 time: 1.4608 data_time: 0.0137 memory: 8640 grad_norm: 37.0943 loss: 2.6114 itc_loss: 2.1622 itm_loss: 0.4492 2023/09/05 00:26:52 - mmengine - INFO - Epoch(train) [1][ 60/1407] base_lr: 9.9983e-06 lr: 9.9983e-06 eta: 3:00:01 time: 1.4625 data_time: 0.0135 memory: 8640 grad_norm: 23.1736 loss: 2.2631 itc_loss: 1.8734 itm_loss: 0.3896 2023/09/05 00:27:21 - mmengine - INFO - Epoch(train) [1][ 80/1407] base_lr: 9.9969e-06 lr: 9.9969e-06 eta: 2:57:01 time: 1.4629 data_time: 0.0132 memory: 8640 grad_norm: 12.6602 loss: 2.1507 itc_loss: 1.7796 itm_loss: 0.3711 2023/09/05 00:27:50 - mmengine - INFO - Epoch(train) [1][ 100/1407] base_lr: 9.9952e-06 lr: 9.9952e-06 eta: 2:55:01 time: 1.4629 data_time: 0.0132 memory: 8640 grad_norm: 14.9057 loss: 2.3166 itc_loss: 1.9161 itm_loss: 0.4005 2023/09/05 00:28:20 - mmengine - INFO - Epoch(train) [1][ 120/1407] base_lr: 9.9930e-06 lr: 9.9930e-06 eta: 2:53:32 time: 1.4634 data_time: 0.0133 memory: 8640 grad_norm: 10.0477 loss: 2.1264 itc_loss: 1.7236 itm_loss: 0.4028 2023/09/05 00:28:49 - mmengine - INFO - Epoch(train) [1][ 140/1407] base_lr: 9.9905e-06 lr: 9.9905e-06 eta: 2:52:19 time: 1.4617 data_time: 0.0130 memory: 8640 grad_norm: 10.6860 loss: 1.9383 itc_loss: 1.5689 itm_loss: 0.3694 2023/09/05 00:29:18 - mmengine - INFO - Epoch(train) [1][ 160/1407] base_lr: 9.9875e-06 lr: 9.9875e-06 eta: 2:51:19 time: 1.4648 data_time: 0.0129 memory: 8640 grad_norm: 11.2137 loss: 2.1559 itc_loss: 1.7670 itm_loss: 0.3890 2023/09/05 00:29:48 - mmengine - INFO - Epoch(train) [1][ 180/1407] base_lr: 9.9842e-06 lr: 9.9842e-06 eta: 2:50:25 time: 1.4635 data_time: 0.0132 memory: 8640 grad_norm: 12.7235 loss: 2.0340 itc_loss: 1.6840 itm_loss: 0.3500 2023/09/05 00:30:17 - mmengine - INFO - Epoch(train) [1][ 200/1407] base_lr: 9.9805e-06 lr: 9.9805e-06 eta: 2:49:36 time: 1.4631 data_time: 0.0135 memory: 8640 grad_norm: 9.8782 loss: 2.0309 itc_loss: 1.6629 itm_loss: 0.3681 2023/09/05 00:30:46 - mmengine - INFO - Epoch(train) [1][ 220/1407] base_lr: 9.9763e-06 lr: 9.9763e-06 eta: 2:48:51 time: 1.4646 data_time: 0.0129 memory: 8640 grad_norm: 7.8263 loss: 1.9001 itc_loss: 1.5526 itm_loss: 0.3474 2023/09/05 00:31:15 - mmengine - INFO - Epoch(train) [1][ 240/1407] base_lr: 9.9718e-06 lr: 9.9718e-06 eta: 2:48:09 time: 1.4647 data_time: 0.0128 memory: 8640 grad_norm: 8.3941 loss: 1.7814 itc_loss: 1.4563 itm_loss: 0.3251 2023/09/05 00:31:45 - mmengine - INFO - Epoch(train) [1][ 260/1407] base_lr: 9.9669e-06 lr: 9.9669e-06 eta: 2:47:28 time: 1.4642 data_time: 0.0131 memory: 8640 grad_norm: 8.9847 loss: 1.8654 itc_loss: 1.5336 itm_loss: 0.3317 2023/09/05 00:32:14 - mmengine - INFO - Epoch(train) [1][ 280/1407] base_lr: 9.9616e-06 lr: 9.9616e-06 eta: 2:46:49 time: 1.4637 data_time: 0.0134 memory: 8640 grad_norm: 10.6579 loss: 1.8826 itc_loss: 1.5408 itm_loss: 0.3418 2023/09/05 00:32:43 - mmengine - INFO - Epoch(train) [1][ 300/1407] base_lr: 9.9559e-06 lr: 9.9559e-06 eta: 2:46:12 time: 1.4642 data_time: 0.0138 memory: 8640 grad_norm: 7.3858 loss: 1.9217 itc_loss: 1.5541 itm_loss: 0.3676 2023/09/05 00:33:13 - mmengine - INFO - Epoch(train) [1][ 320/1407] base_lr: 9.9499e-06 lr: 9.9499e-06 eta: 2:45:35 time: 1.4650 data_time: 0.0130 memory: 8640 grad_norm: 7.7745 loss: 1.8746 itc_loss: 1.5224 itm_loss: 0.3523 2023/09/05 00:33:42 - mmengine - INFO - Epoch(train) [1][ 340/1407] base_lr: 9.9434e-06 lr: 9.9434e-06 eta: 2:44:59 time: 1.4632 data_time: 0.0130 memory: 8640 grad_norm: 7.9282 loss: 1.9392 itc_loss: 1.5817 itm_loss: 0.3576 2023/09/05 00:34:11 - mmengine - INFO - Epoch(train) [1][ 360/1407] base_lr: 9.9365e-06 lr: 9.9365e-06 eta: 2:44:25 time: 1.4654 data_time: 0.0130 memory: 8640 grad_norm: 8.6019 loss: 1.8729 itc_loss: 1.5208 itm_loss: 0.3521 2023/09/05 00:34:40 - mmengine - INFO - Epoch(train) [1][ 380/1407] base_lr: 9.9293e-06 lr: 9.9293e-06 eta: 2:43:50 time: 1.4643 data_time: 0.0131 memory: 8640 grad_norm: 7.5581 loss: 2.0198 itc_loss: 1.6591 itm_loss: 0.3606 2023/09/05 00:35:10 - mmengine - INFO - Epoch(train) [1][ 400/1407] base_lr: 9.9216e-06 lr: 9.9216e-06 eta: 2:43:17 time: 1.4642 data_time: 0.0128 memory: 8640 grad_norm: 8.9614 loss: 1.8483 itc_loss: 1.5199 itm_loss: 0.3284 2023/09/05 00:35:39 - mmengine - INFO - Epoch(train) [1][ 420/1407] base_lr: 9.9136e-06 lr: 9.9136e-06 eta: 2:42:43 time: 1.4652 data_time: 0.0131 memory: 8640 grad_norm: 7.4153 loss: 1.8322 itc_loss: 1.5111 itm_loss: 0.3211 2023/09/05 00:36:08 - mmengine - INFO - Epoch(train) [1][ 440/1407] base_lr: 9.9052e-06 lr: 9.9052e-06 eta: 2:42:11 time: 1.4646 data_time: 0.0130 memory: 8640 grad_norm: 10.0158 loss: 1.9019 itc_loss: 1.5650 itm_loss: 0.3369 2023/09/05 00:36:38 - mmengine - INFO - Epoch(train) [1][ 460/1407] base_lr: 9.8964e-06 lr: 9.8964e-06 eta: 2:41:38 time: 1.4652 data_time: 0.0131 memory: 8640 grad_norm: 6.9487 loss: 1.6647 itc_loss: 1.3409 itm_loss: 0.3239 2023/09/05 00:37:07 - mmengine - INFO - Epoch(train) [1][ 480/1407] base_lr: 9.8872e-06 lr: 9.8872e-06 eta: 2:41:06 time: 1.4648 data_time: 0.0129 memory: 8640 grad_norm: 8.7527 loss: 1.8188 itc_loss: 1.4771 itm_loss: 0.3416 2023/09/05 00:37:36 - mmengine - INFO - Epoch(train) [1][ 500/1407] base_lr: 9.8776e-06 lr: 9.8776e-06 eta: 2:40:34 time: 1.4648 data_time: 0.0129 memory: 8640 grad_norm: 7.5348 loss: 1.7662 itc_loss: 1.4277 itm_loss: 0.3385 2023/09/05 00:38:05 - mmengine - INFO - Epoch(train) [1][ 520/1407] base_lr: 9.8676e-06 lr: 9.8676e-06 eta: 2:40:02 time: 1.4655 data_time: 0.0130 memory: 8640 grad_norm: 7.8544 loss: 1.8218 itc_loss: 1.4745 itm_loss: 0.3473 2023/09/05 00:38:35 - mmengine - INFO - Epoch(train) [1][ 540/1407] base_lr: 9.8573e-06 lr: 9.8573e-06 eta: 2:39:30 time: 1.4637 data_time: 0.0134 memory: 8640 grad_norm: 7.9700 loss: 1.7195 itc_loss: 1.4141 itm_loss: 0.3054 2023/09/05 00:39:04 - mmengine - INFO - Epoch(train) [1][ 560/1407] base_lr: 9.8466e-06 lr: 9.8466e-06 eta: 2:38:59 time: 1.4653 data_time: 0.0130 memory: 8640 grad_norm: 8.4346 loss: 1.8291 itc_loss: 1.4962 itm_loss: 0.3329 2023/09/05 00:39:33 - mmengine - INFO - Epoch(train) [1][ 580/1407] base_lr: 9.8355e-06 lr: 9.8355e-06 eta: 2:38:28 time: 1.4670 data_time: 0.0136 memory: 8640 grad_norm: 6.9725 loss: 1.5752 itc_loss: 1.2609 itm_loss: 0.3143 2023/09/05 00:40:03 - mmengine - INFO - Epoch(train) [1][ 600/1407] base_lr: 9.8240e-06 lr: 9.8240e-06 eta: 2:37:57 time: 1.4662 data_time: 0.0132 memory: 8640 grad_norm: 7.8430 loss: 1.5831 itc_loss: 1.3004 itm_loss: 0.2828 2023/09/05 00:40:32 - mmengine - INFO - Epoch(train) [1][ 620/1407] base_lr: 9.8121e-06 lr: 9.8121e-06 eta: 2:37:26 time: 1.4637 data_time: 0.0133 memory: 8640 grad_norm: 8.4390 loss: 1.9402 itc_loss: 1.5849 itm_loss: 0.3552 2023/09/05 00:41:01 - mmengine - INFO - Epoch(train) [1][ 640/1407] base_lr: 9.7998e-06 lr: 9.7998e-06 eta: 2:36:54 time: 1.4640 data_time: 0.0132 memory: 8640 grad_norm: 8.7752 loss: 1.7078 itc_loss: 1.3803 itm_loss: 0.3274 2023/09/05 00:41:31 - mmengine - INFO - Epoch(train) [1][ 660/1407] base_lr: 9.7872e-06 lr: 9.7872e-06 eta: 2:36:23 time: 1.4634 data_time: 0.0134 memory: 8640 grad_norm: 7.4475 loss: 1.7387 itc_loss: 1.4125 itm_loss: 0.3262 2023/09/05 00:42:00 - mmengine - INFO - Epoch(train) [1][ 680/1407] base_lr: 9.7742e-06 lr: 9.7742e-06 eta: 2:35:52 time: 1.4646 data_time: 0.0130 memory: 8640 grad_norm: 6.4308 loss: 1.7089 itc_loss: 1.3596 itm_loss: 0.3493 2023/09/05 00:42:29 - mmengine - INFO - Epoch(train) [1][ 700/1407] base_lr: 9.7608e-06 lr: 9.7608e-06 eta: 2:35:22 time: 1.4650 data_time: 0.0131 memory: 8640 grad_norm: 8.5300 loss: 1.7511 itc_loss: 1.4234 itm_loss: 0.3276 2023/09/05 00:42:58 - mmengine - INFO - Epoch(train) [1][ 720/1407] base_lr: 9.7470e-06 lr: 9.7470e-06 eta: 2:34:52 time: 1.4665 data_time: 0.0128 memory: 8640 grad_norm: 7.5135 loss: 1.6742 itc_loss: 1.3375 itm_loss: 0.3367 2023/09/05 00:43:28 - mmengine - INFO - Epoch(train) [1][ 740/1407] base_lr: 9.7329e-06 lr: 9.7329e-06 eta: 2:34:21 time: 1.4643 data_time: 0.0133 memory: 8640 grad_norm: 7.2822 loss: 1.7521 itc_loss: 1.4097 itm_loss: 0.3424 2023/09/05 00:43:57 - mmengine - INFO - Epoch(train) [1][ 760/1407] base_lr: 9.7184e-06 lr: 9.7184e-06 eta: 2:33:50 time: 1.4635 data_time: 0.0134 memory: 8640 grad_norm: 7.6954 loss: 1.9600 itc_loss: 1.5908 itm_loss: 0.3692 2023/09/05 00:44:26 - mmengine - INFO - Epoch(train) [1][ 780/1407] base_lr: 9.7035e-06 lr: 9.7035e-06 eta: 2:33:20 time: 1.4648 data_time: 0.0131 memory: 8640 grad_norm: 8.4049 loss: 1.7583 itc_loss: 1.4343 itm_loss: 0.3240 2023/09/05 00:44:56 - mmengine - INFO - Epoch(train) [1][ 800/1407] base_lr: 9.6882e-06 lr: 9.6882e-06 eta: 2:32:51 time: 1.4755 data_time: 0.0128 memory: 8640 grad_norm: 8.5436 loss: 1.8439 itc_loss: 1.5096 itm_loss: 0.3343 2023/09/05 00:45:25 - mmengine - INFO - Epoch(train) [1][ 820/1407] base_lr: 9.6726e-06 lr: 9.6726e-06 eta: 2:32:22 time: 1.4767 data_time: 0.0130 memory: 8640 grad_norm: 7.5522 loss: 1.8188 itc_loss: 1.4469 itm_loss: 0.3719 2023/09/05 00:45:55 - mmengine - INFO - Epoch(train) [1][ 840/1407] base_lr: 9.6566e-06 lr: 9.6566e-06 eta: 2:31:52 time: 1.4637 data_time: 0.0131 memory: 8640 grad_norm: 7.8723 loss: 1.5114 itc_loss: 1.2185 itm_loss: 0.2929 2023/09/05 00:46:24 - mmengine - INFO - Epoch(train) [1][ 860/1407] base_lr: 9.6402e-06 lr: 9.6402e-06 eta: 2:31:22 time: 1.4648 data_time: 0.0131 memory: 8640 grad_norm: 6.3431 loss: 1.8276 itc_loss: 1.4736 itm_loss: 0.3539 2023/09/05 00:46:53 - mmengine - INFO - Epoch(train) [1][ 880/1407] base_lr: 9.6235e-06 lr: 9.6235e-06 eta: 2:30:52 time: 1.4653 data_time: 0.0131 memory: 8640 grad_norm: 7.5613 loss: 1.6114 itc_loss: 1.2859 itm_loss: 0.3254 2023/09/05 00:47:23 - mmengine - INFO - Epoch(train) [1][ 900/1407] base_lr: 9.6064e-06 lr: 9.6064e-06 eta: 2:30:21 time: 1.4638 data_time: 0.0130 memory: 8640 grad_norm: 8.2936 loss: 1.5784 itc_loss: 1.2642 itm_loss: 0.3142 2023/09/05 00:47:52 - mmengine - INFO - Epoch(train) [1][ 920/1407] base_lr: 9.5890e-06 lr: 9.5890e-06 eta: 2:29:51 time: 1.4647 data_time: 0.0130 memory: 8640 grad_norm: 7.6299 loss: 1.6752 itc_loss: 1.3098 itm_loss: 0.3654 2023/09/05 00:48:21 - mmengine - INFO - Epoch(train) [1][ 940/1407] base_lr: 9.5712e-06 lr: 9.5712e-06 eta: 2:29:21 time: 1.4649 data_time: 0.0130 memory: 8640 grad_norm: 7.2673 loss: 1.7066 itc_loss: 1.3774 itm_loss: 0.3292 2023/09/05 00:48:50 - mmengine - INFO - Epoch(train) [1][ 960/1407] base_lr: 9.5530e-06 lr: 9.5530e-06 eta: 2:28:51 time: 1.4659 data_time: 0.0131 memory: 8640 grad_norm: 8.9251 loss: 1.7730 itc_loss: 1.4433 itm_loss: 0.3298 2023/09/05 00:49:20 - mmengine - INFO - Epoch(train) [1][ 980/1407] base_lr: 9.5344e-06 lr: 9.5344e-06 eta: 2:28:21 time: 1.4661 data_time: 0.0131 memory: 8640 grad_norm: 6.3312 loss: 1.7842 itc_loss: 1.4471 itm_loss: 0.3371 2023/09/05 00:49:49 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 00:49:49 - mmengine - INFO - Epoch(train) [1][1000/1407] base_lr: 9.5155e-06 lr: 9.5155e-06 eta: 2:27:51 time: 1.4645 data_time: 0.0132 memory: 8640 grad_norm: 6.0145 loss: 1.7152 itc_loss: 1.3804 itm_loss: 0.3347 2023/09/05 00:50:18 - mmengine - INFO - Epoch(train) [1][1020/1407] base_lr: 9.4963e-06 lr: 9.4963e-06 eta: 2:27:21 time: 1.4636 data_time: 0.0134 memory: 8640 grad_norm: 6.9802 loss: 1.7788 itc_loss: 1.4423 itm_loss: 0.3365 2023/09/05 00:50:48 - mmengine - INFO - Epoch(train) [1][1040/1407] base_lr: 9.4767e-06 lr: 9.4767e-06 eta: 2:26:51 time: 1.4638 data_time: 0.0134 memory: 8640 grad_norm: 6.7588 loss: 1.6116 itc_loss: 1.2827 itm_loss: 0.3289 2023/09/05 00:51:17 - mmengine - INFO - Epoch(train) [1][1060/1407] base_lr: 9.4567e-06 lr: 9.4567e-06 eta: 2:26:21 time: 1.4635 data_time: 0.0133 memory: 8640 grad_norm: 6.0103 loss: 1.5105 itc_loss: 1.2223 itm_loss: 0.2882 2023/09/05 00:51:46 - mmengine - INFO - Epoch(train) [1][1080/1407] base_lr: 9.4364e-06 lr: 9.4364e-06 eta: 2:25:51 time: 1.4643 data_time: 0.0130 memory: 8640 grad_norm: 8.0310 loss: 1.7237 itc_loss: 1.3874 itm_loss: 0.3363 2023/09/05 00:52:15 - mmengine - INFO - Epoch(train) [1][1100/1407] base_lr: 9.4157e-06 lr: 9.4157e-06 eta: 2:25:21 time: 1.4650 data_time: 0.0140 memory: 8640 grad_norm: 7.9236 loss: 1.7674 itc_loss: 1.3781 itm_loss: 0.3893 2023/09/05 00:52:45 - mmengine - INFO - Epoch(train) [1][1120/1407] base_lr: 9.3947e-06 lr: 9.3947e-06 eta: 2:24:51 time: 1.4648 data_time: 0.0133 memory: 8640 grad_norm: 7.0778 loss: 1.6684 itc_loss: 1.3476 itm_loss: 0.3208 2023/09/05 00:53:14 - mmengine - INFO - Epoch(train) [1][1140/1407] base_lr: 9.3734e-06 lr: 9.3734e-06 eta: 2:24:21 time: 1.4641 data_time: 0.0133 memory: 8640 grad_norm: 9.6405 loss: 1.6155 itc_loss: 1.3091 itm_loss: 0.3063 2023/09/05 00:53:43 - mmengine - INFO - Epoch(train) [1][1160/1407] base_lr: 9.3517e-06 lr: 9.3517e-06 eta: 2:23:51 time: 1.4655 data_time: 0.0133 memory: 8640 grad_norm: 8.3737 loss: 1.7487 itc_loss: 1.4225 itm_loss: 0.3262 2023/09/05 00:54:13 - mmengine - INFO - Epoch(train) [1][1180/1407] base_lr: 9.3296e-06 lr: 9.3296e-06 eta: 2:23:21 time: 1.4656 data_time: 0.0132 memory: 8640 grad_norm: 6.7039 loss: 1.6990 itc_loss: 1.3806 itm_loss: 0.3183 2023/09/05 00:54:42 - mmengine - INFO - Epoch(train) [1][1200/1407] base_lr: 9.3072e-06 lr: 9.3072e-06 eta: 2:22:52 time: 1.4661 data_time: 0.0132 memory: 8640 grad_norm: 6.7408 loss: 1.5474 itc_loss: 1.2445 itm_loss: 0.3030 2023/09/05 00:55:11 - mmengine - INFO - Epoch(train) [1][1220/1407] base_lr: 9.2845e-06 lr: 9.2845e-06 eta: 2:22:22 time: 1.4637 data_time: 0.0134 memory: 8640 grad_norm: 7.3685 loss: 1.6624 itc_loss: 1.3202 itm_loss: 0.3422 2023/09/05 00:55:41 - mmengine - INFO - Epoch(train) [1][1240/1407] base_lr: 9.2614e-06 lr: 9.2614e-06 eta: 2:21:52 time: 1.4645 data_time: 0.0132 memory: 8640 grad_norm: 8.4571 loss: 1.6121 itc_loss: 1.3066 itm_loss: 0.3054 2023/09/05 00:56:10 - mmengine - INFO - Epoch(train) [1][1260/1407] base_lr: 9.2380e-06 lr: 9.2380e-06 eta: 2:21:22 time: 1.4638 data_time: 0.0132 memory: 8640 grad_norm: 8.3623 loss: 1.6624 itc_loss: 1.3322 itm_loss: 0.3302 2023/09/05 00:56:39 - mmengine - INFO - Epoch(train) [1][1280/1407] base_lr: 9.2143e-06 lr: 9.2143e-06 eta: 2:20:52 time: 1.4651 data_time: 0.0133 memory: 8640 grad_norm: 7.1599 loss: 1.5233 itc_loss: 1.2195 itm_loss: 0.3038 2023/09/05 00:57:08 - mmengine - INFO - Epoch(train) [1][1300/1407] base_lr: 9.1902e-06 lr: 9.1902e-06 eta: 2:20:23 time: 1.4636 data_time: 0.0132 memory: 8640 grad_norm: 6.7200 loss: 1.4576 itc_loss: 1.1744 itm_loss: 0.2832 2023/09/05 00:57:38 - mmengine - INFO - Epoch(train) [1][1320/1407] base_lr: 9.1659e-06 lr: 9.1659e-06 eta: 2:19:53 time: 1.4651 data_time: 0.0131 memory: 8640 grad_norm: 8.5082 loss: 1.4301 itc_loss: 1.1135 itm_loss: 0.3166 2023/09/05 00:58:07 - mmengine - INFO - Epoch(train) [1][1340/1407] base_lr: 9.1411e-06 lr: 9.1411e-06 eta: 2:19:23 time: 1.4647 data_time: 0.0134 memory: 8640 grad_norm: 7.0793 loss: 1.6606 itc_loss: 1.3345 itm_loss: 0.3261 2023/09/05 00:58:36 - mmengine - INFO - Epoch(train) [1][1360/1407] base_lr: 9.1161e-06 lr: 9.1161e-06 eta: 2:18:53 time: 1.4647 data_time: 0.0132 memory: 8640 grad_norm: 6.8038 loss: 1.6338 itc_loss: 1.3306 itm_loss: 0.3032 2023/09/05 00:59:06 - mmengine - INFO - Epoch(train) [1][1380/1407] base_lr: 9.0907e-06 lr: 9.0907e-06 eta: 2:18:24 time: 1.4665 data_time: 0.0136 memory: 8640 grad_norm: 7.0243 loss: 1.7315 itc_loss: 1.3713 itm_loss: 0.3601 2023/09/05 00:59:35 - mmengine - INFO - Epoch(train) [1][1400/1407] base_lr: 9.0650e-06 lr: 9.0650e-06 eta: 2:17:54 time: 1.4640 data_time: 0.0135 memory: 8640 grad_norm: 8.8468 loss: 1.7569 itc_loss: 1.4327 itm_loss: 0.3242 2023/09/05 00:59:44 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 00:59:44 - mmengine - INFO - Epoch(train) [1][1407/1407] base_lr: 9.0559e-06 lr: 9.0559e-06 eta: 2:17:40 time: 1.4203 data_time: 0.0135 memory: 8640 grad_norm: nan loss: 1.6379 itc_loss: 1.3440 itm_loss: 0.2939 2023/09/05 00:59:44 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/09/05 01:00:49 - mmengine - INFO - Epoch(val) [1][16/16] i2t/retrieval/Recall@1: 41.1000 i2t/retrieval/Recall@5: 69.0000 i2t/retrieval/Recall@10: 78.4000 t2i/retrieval/Recall@1: 41.9000 t2i/retrieval/Recall@5: 68.2000 t2i/retrieval/Recall@10: 78.5000 data_time: 0.0503 time: 0.3502 2023/09/05 01:00:52 - mmengine - INFO - The best checkpoint with 41.9000 t2i/retrieval/Recall@1 at 1 epoch is saved to best_t2i_retrieval_Recall@1_epoch_1.pth. 2023/09/05 01:01:29 - mmengine - INFO - Epoch(train) [2][ 20/1407] base_lr: 9.0298e-06 lr: 9.0298e-06 eta: 2:17:13 time: 1.4868 data_time: 0.0354 memory: 19276 grad_norm: 7.6692 loss: 1.4803 itc_loss: 1.1497 itm_loss: 0.3305 2023/09/05 01:01:58 - mmengine - INFO - Epoch(train) [2][ 40/1407] base_lr: 9.0034e-06 lr: 9.0034e-06 eta: 2:16:43 time: 1.4623 data_time: 0.0129 memory: 8642 grad_norm: 7.2382 loss: 1.4134 itc_loss: 1.0820 itm_loss: 0.3314 2023/09/05 01:02:27 - mmengine - INFO - Epoch(train) [2][ 60/1407] base_lr: 8.9766e-06 lr: 8.9766e-06 eta: 2:16:13 time: 1.4632 data_time: 0.0129 memory: 8642 grad_norm: 7.8841 loss: 1.3844 itc_loss: 1.0851 itm_loss: 0.2992 2023/09/05 01:02:56 - mmengine - INFO - Epoch(train) [2][ 80/1407] base_lr: 8.9495e-06 lr: 8.9495e-06 eta: 2:15:43 time: 1.4642 data_time: 0.0130 memory: 8642 grad_norm: 7.6404 loss: 1.4137 itc_loss: 1.1339 itm_loss: 0.2798 2023/09/05 01:03:26 - mmengine - INFO - Epoch(train) [2][ 100/1407] base_lr: 8.9221e-06 lr: 8.9221e-06 eta: 2:15:14 time: 1.4632 data_time: 0.0130 memory: 8642 grad_norm: 7.3801 loss: 1.4268 itc_loss: 1.1393 itm_loss: 0.2875 2023/09/05 01:03:55 - mmengine - INFO - Epoch(train) [2][ 120/1407] base_lr: 8.8944e-06 lr: 8.8944e-06 eta: 2:14:44 time: 1.4634 data_time: 0.0130 memory: 8642 grad_norm: 5.8763 loss: 1.5567 itc_loss: 1.2533 itm_loss: 0.3034 2023/09/05 01:04:24 - mmengine - INFO - Epoch(train) [2][ 140/1407] base_lr: 8.8664e-06 lr: 8.8664e-06 eta: 2:14:14 time: 1.4630 data_time: 0.0128 memory: 8642 grad_norm: 7.7431 loss: 1.4071 itc_loss: 1.1240 itm_loss: 0.2831 2023/09/05 01:04:53 - mmengine - INFO - Epoch(train) [2][ 160/1407] base_lr: 8.8381e-06 lr: 8.8381e-06 eta: 2:13:45 time: 1.4630 data_time: 0.0129 memory: 8642 grad_norm: 7.7952 loss: 1.5289 itc_loss: 1.2353 itm_loss: 0.2936 2023/09/05 01:05:23 - mmengine - INFO - Epoch(train) [2][ 180/1407] base_lr: 8.8095e-06 lr: 8.8095e-06 eta: 2:13:15 time: 1.4637 data_time: 0.0130 memory: 8642 grad_norm: 6.8449 loss: 1.5189 itc_loss: 1.2352 itm_loss: 0.2838 2023/09/05 01:05:52 - mmengine - INFO - Epoch(train) [2][ 200/1407] base_lr: 8.7806e-06 lr: 8.7806e-06 eta: 2:12:45 time: 1.4640 data_time: 0.0137 memory: 8642 grad_norm: 7.4410 loss: 1.6431 itc_loss: 1.3222 itm_loss: 0.3209 2023/09/05 01:06:21 - mmengine - INFO - Epoch(train) [2][ 220/1407] base_lr: 8.7514e-06 lr: 8.7514e-06 eta: 2:12:16 time: 1.4645 data_time: 0.0129 memory: 8642 grad_norm: 7.4323 loss: 1.6077 itc_loss: 1.2750 itm_loss: 0.3326 2023/09/05 01:06:51 - mmengine - INFO - Epoch(train) [2][ 240/1407] base_lr: 8.7219e-06 lr: 8.7219e-06 eta: 2:11:46 time: 1.4644 data_time: 0.0127 memory: 8642 grad_norm: 6.5226 loss: 1.5162 itc_loss: 1.1908 itm_loss: 0.3253 2023/09/05 01:07:20 - mmengine - INFO - Epoch(train) [2][ 260/1407] base_lr: 8.6921e-06 lr: 8.6921e-06 eta: 2:11:17 time: 1.4641 data_time: 0.0129 memory: 8642 grad_norm: 7.6005 loss: 1.4311 itc_loss: 1.1308 itm_loss: 0.3004 2023/09/05 01:07:49 - mmengine - INFO - Epoch(train) [2][ 280/1407] base_lr: 8.6620e-06 lr: 8.6620e-06 eta: 2:10:47 time: 1.4639 data_time: 0.0128 memory: 8642 grad_norm: 7.4854 loss: 1.3973 itc_loss: 1.1225 itm_loss: 0.2748 2023/09/05 01:08:18 - mmengine - INFO - Epoch(train) [2][ 300/1407] base_lr: 8.6317e-06 lr: 8.6317e-06 eta: 2:10:18 time: 1.4638 data_time: 0.0129 memory: 8642 grad_norm: 8.5491 loss: 1.4032 itc_loss: 1.1108 itm_loss: 0.2924 2023/09/05 01:08:48 - mmengine - INFO - Epoch(train) [2][ 320/1407] base_lr: 8.6010e-06 lr: 8.6010e-06 eta: 2:09:48 time: 1.4639 data_time: 0.0129 memory: 8642 grad_norm: 7.0265 loss: 1.3136 itc_loss: 1.0474 itm_loss: 0.2662 2023/09/05 01:09:17 - mmengine - INFO - Epoch(train) [2][ 340/1407] base_lr: 8.5701e-06 lr: 8.5701e-06 eta: 2:09:19 time: 1.4669 data_time: 0.0127 memory: 8642 grad_norm: 7.1747 loss: 1.3506 itc_loss: 1.0788 itm_loss: 0.2718 2023/09/05 01:09:46 - mmengine - INFO - Epoch(train) [2][ 360/1407] base_lr: 8.5388e-06 lr: 8.5388e-06 eta: 2:08:49 time: 1.4644 data_time: 0.0129 memory: 8642 grad_norm: 6.9336 loss: 1.3623 itc_loss: 1.0810 itm_loss: 0.2814 2023/09/05 01:10:16 - mmengine - INFO - Epoch(train) [2][ 380/1407] base_lr: 8.5073e-06 lr: 8.5073e-06 eta: 2:08:20 time: 1.4669 data_time: 0.0130 memory: 8642 grad_norm: 6.3900 loss: 1.4669 itc_loss: 1.1752 itm_loss: 0.2917 2023/09/05 01:10:45 - mmengine - INFO - Epoch(train) [2][ 400/1407] base_lr: 8.4756e-06 lr: 8.4756e-06 eta: 2:07:51 time: 1.4838 data_time: 0.0127 memory: 8642 grad_norm: 7.6231 loss: 1.3692 itc_loss: 1.0976 itm_loss: 0.2716 2023/09/05 01:11:15 - mmengine - INFO - Epoch(train) [2][ 420/1407] base_lr: 8.4435e-06 lr: 8.4435e-06 eta: 2:07:22 time: 1.4729 data_time: 0.0129 memory: 8642 grad_norm: 7.7012 loss: 1.3859 itc_loss: 1.1025 itm_loss: 0.2834 2023/09/05 01:11:44 - mmengine - INFO - Epoch(train) [2][ 440/1407] base_lr: 8.4112e-06 lr: 8.4112e-06 eta: 2:06:53 time: 1.4625 data_time: 0.0130 memory: 8642 grad_norm: 8.1818 loss: 1.3543 itc_loss: 1.0737 itm_loss: 0.2806 2023/09/05 01:12:13 - mmengine - INFO - Epoch(train) [2][ 460/1407] base_lr: 8.3786e-06 lr: 8.3786e-06 eta: 2:06:23 time: 1.4628 data_time: 0.0132 memory: 8642 grad_norm: 6.8427 loss: 1.5570 itc_loss: 1.2650 itm_loss: 0.2920 2023/09/05 01:12:43 - mmengine - INFO - Epoch(train) [2][ 480/1407] base_lr: 8.3457e-06 lr: 8.3457e-06 eta: 2:05:53 time: 1.4630 data_time: 0.0130 memory: 8642 grad_norm: 6.3590 loss: 1.3547 itc_loss: 1.0670 itm_loss: 0.2877 2023/09/05 01:13:12 - mmengine - INFO - Epoch(train) [2][ 500/1407] base_lr: 8.3126e-06 lr: 8.3126e-06 eta: 2:05:24 time: 1.4637 data_time: 0.0130 memory: 8642 grad_norm: 6.3624 loss: 1.5095 itc_loss: 1.2025 itm_loss: 0.3070 2023/09/05 01:13:41 - mmengine - INFO - Epoch(train) [2][ 520/1407] base_lr: 8.2792e-06 lr: 8.2792e-06 eta: 2:04:55 time: 1.4658 data_time: 0.0130 memory: 8642 grad_norm: 6.6204 loss: 1.4760 itc_loss: 1.1864 itm_loss: 0.2896 2023/09/05 01:14:10 - mmengine - INFO - Epoch(train) [2][ 540/1407] base_lr: 8.2456e-06 lr: 8.2456e-06 eta: 2:04:25 time: 1.4653 data_time: 0.0132 memory: 8642 grad_norm: 8.0010 loss: 1.4979 itc_loss: 1.2106 itm_loss: 0.2873 2023/09/05 01:14:40 - mmengine - INFO - Epoch(train) [2][ 560/1407] base_lr: 8.2117e-06 lr: 8.2117e-06 eta: 2:03:56 time: 1.4639 data_time: 0.0132 memory: 8642 grad_norm: 5.9043 loss: 1.4444 itc_loss: 1.1152 itm_loss: 0.3292 2023/09/05 01:15:09 - mmengine - INFO - Epoch(train) [2][ 580/1407] base_lr: 8.1776e-06 lr: 8.1776e-06 eta: 2:03:26 time: 1.4646 data_time: 0.0134 memory: 8642 grad_norm: 6.8777 loss: 1.5487 itc_loss: 1.2473 itm_loss: 0.3014 2023/09/05 01:15:28 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 01:15:38 - mmengine - INFO - Epoch(train) [2][ 600/1407] base_lr: 8.1432e-06 lr: 8.1432e-06 eta: 2:02:57 time: 1.4631 data_time: 0.0132 memory: 8642 grad_norm: 6.9108 loss: 1.2562 itc_loss: 0.9884 itm_loss: 0.2677 2023/09/05 01:16:08 - mmengine - INFO - Epoch(train) [2][ 620/1407] base_lr: 8.1085e-06 lr: 8.1085e-06 eta: 2:02:27 time: 1.4647 data_time: 0.0132 memory: 8642 grad_norm: 5.8892 loss: 1.4693 itc_loss: 1.1752 itm_loss: 0.2941 2023/09/05 01:16:37 - mmengine - INFO - Epoch(train) [2][ 640/1407] base_lr: 8.0737e-06 lr: 8.0737e-06 eta: 2:01:58 time: 1.4659 data_time: 0.0134 memory: 8642 grad_norm: 6.8838 loss: 1.4791 itc_loss: 1.2017 itm_loss: 0.2775 2023/09/05 01:17:06 - mmengine - INFO - Epoch(train) [2][ 660/1407] base_lr: 8.0385e-06 lr: 8.0385e-06 eta: 2:01:28 time: 1.4638 data_time: 0.0133 memory: 8642 grad_norm: 7.8173 loss: 1.3755 itc_loss: 1.1119 itm_loss: 0.2636 2023/09/05 01:17:35 - mmengine - INFO - Epoch(train) [2][ 680/1407] base_lr: 8.0032e-06 lr: 8.0032e-06 eta: 2:00:59 time: 1.4635 data_time: 0.0133 memory: 8642 grad_norm: 6.5800 loss: 1.3279 itc_loss: 1.0595 itm_loss: 0.2683 2023/09/05 01:18:05 - mmengine - INFO - Epoch(train) [2][ 700/1407] base_lr: 7.9676e-06 lr: 7.9676e-06 eta: 2:00:29 time: 1.4642 data_time: 0.0133 memory: 8642 grad_norm: 6.6985 loss: 1.5382 itc_loss: 1.2414 itm_loss: 0.2968 2023/09/05 01:18:34 - mmengine - INFO - Epoch(train) [2][ 720/1407] base_lr: 7.9317e-06 lr: 7.9317e-06 eta: 2:00:00 time: 1.4641 data_time: 0.0134 memory: 8642 grad_norm: 7.5396 loss: 1.3212 itc_loss: 1.0700 itm_loss: 0.2512 2023/09/05 01:19:03 - mmengine - INFO - Epoch(train) [2][ 740/1407] base_lr: 7.8957e-06 lr: 7.8957e-06 eta: 1:59:30 time: 1.4660 data_time: 0.0139 memory: 8642 grad_norm: 7.7069 loss: 1.4522 itc_loss: 1.1626 itm_loss: 0.2897 2023/09/05 01:19:33 - mmengine - INFO - Epoch(train) [2][ 760/1407] base_lr: 7.8594e-06 lr: 7.8594e-06 eta: 1:59:01 time: 1.4628 data_time: 0.0134 memory: 8642 grad_norm: 6.8054 loss: 1.2995 itc_loss: 1.0191 itm_loss: 0.2804 2023/09/05 01:20:02 - mmengine - INFO - Epoch(train) [2][ 780/1407] base_lr: 7.8229e-06 lr: 7.8229e-06 eta: 1:58:31 time: 1.4656 data_time: 0.0134 memory: 8642 grad_norm: 6.2065 loss: 1.2322 itc_loss: 0.9641 itm_loss: 0.2681 2023/09/05 01:20:31 - mmengine - INFO - Epoch(train) [2][ 800/1407] base_lr: 7.7862e-06 lr: 7.7862e-06 eta: 1:58:02 time: 1.4666 data_time: 0.0133 memory: 8642 grad_norm: 6.6951 loss: 1.4859 itc_loss: 1.1997 itm_loss: 0.2862 2023/09/05 01:21:01 - mmengine - INFO - Epoch(train) [2][ 820/1407] base_lr: 7.7492e-06 lr: 7.7492e-06 eta: 1:57:33 time: 1.4649 data_time: 0.0134 memory: 8642 grad_norm: 6.4945 loss: 1.2332 itc_loss: 0.9729 itm_loss: 0.2603 2023/09/05 01:21:30 - mmengine - INFO - Epoch(train) [2][ 840/1407] base_lr: 7.7120e-06 lr: 7.7120e-06 eta: 1:57:03 time: 1.4638 data_time: 0.0135 memory: 8642 grad_norm: 7.3285 loss: 1.3048 itc_loss: 1.0395 itm_loss: 0.2653 2023/09/05 01:21:59 - mmengine - INFO - Epoch(train) [2][ 860/1407] base_lr: 7.6747e-06 lr: 7.6747e-06 eta: 1:56:34 time: 1.4640 data_time: 0.0136 memory: 8642 grad_norm: 7.0824 loss: 1.3969 itc_loss: 1.0866 itm_loss: 0.3103 2023/09/05 01:22:28 - mmengine - INFO - Epoch(train) [2][ 880/1407] base_lr: 7.6371e-06 lr: 7.6371e-06 eta: 1:56:04 time: 1.4643 data_time: 0.0134 memory: 8642 grad_norm: 6.2191 loss: 1.5314 itc_loss: 1.2396 itm_loss: 0.2918 2023/09/05 01:22:58 - mmengine - INFO - Epoch(train) [2][ 900/1407] base_lr: 7.5993e-06 lr: 7.5993e-06 eta: 1:55:35 time: 1.4625 data_time: 0.0132 memory: 8642 grad_norm: 7.1948 loss: 1.4414 itc_loss: 1.1648 itm_loss: 0.2767 2023/09/05 01:23:27 - mmengine - INFO - Epoch(train) [2][ 920/1407] base_lr: 7.5613e-06 lr: 7.5613e-06 eta: 1:55:05 time: 1.4637 data_time: 0.0136 memory: 8642 grad_norm: 8.0215 loss: 1.4054 itc_loss: 1.0954 itm_loss: 0.3100 2023/09/05 01:23:56 - mmengine - INFO - Epoch(train) [2][ 940/1407] base_lr: 7.5231e-06 lr: 7.5231e-06 eta: 1:54:36 time: 1.4644 data_time: 0.0135 memory: 8642 grad_norm: 8.1771 loss: 1.5782 itc_loss: 1.2731 itm_loss: 0.3050 2023/09/05 01:24:25 - mmengine - INFO - Epoch(train) [2][ 960/1407] base_lr: 7.4847e-06 lr: 7.4847e-06 eta: 1:54:06 time: 1.4639 data_time: 0.0135 memory: 8642 grad_norm: 7.3231 loss: 1.4530 itc_loss: 1.1432 itm_loss: 0.3097 2023/09/05 01:24:55 - mmengine - INFO - Epoch(train) [2][ 980/1407] base_lr: 7.4461e-06 lr: 7.4461e-06 eta: 1:53:37 time: 1.4622 data_time: 0.0133 memory: 8642 grad_norm: 6.9200 loss: 1.3799 itc_loss: 1.1169 itm_loss: 0.2630 2023/09/05 01:25:24 - mmengine - INFO - Epoch(train) [2][1000/1407] base_lr: 7.4073e-06 lr: 7.4073e-06 eta: 1:53:07 time: 1.4638 data_time: 0.0133 memory: 8642 grad_norm: 6.8909 loss: 1.4509 itc_loss: 1.1497 itm_loss: 0.3012 2023/09/05 01:25:53 - mmengine - INFO - Epoch(train) [2][1020/1407] base_lr: 7.3684e-06 lr: 7.3684e-06 eta: 1:52:38 time: 1.4624 data_time: 0.0133 memory: 8642 grad_norm: 6.4231 loss: 1.3445 itc_loss: 1.0672 itm_loss: 0.2774 2023/09/05 01:26:23 - mmengine - INFO - Epoch(train) [2][1040/1407] base_lr: 7.3292e-06 lr: 7.3292e-06 eta: 1:52:08 time: 1.4652 data_time: 0.0134 memory: 8642 grad_norm: 6.2511 loss: 1.3075 itc_loss: 1.0446 itm_loss: 0.2629 2023/09/05 01:26:52 - mmengine - INFO - Epoch(train) [2][1060/1407] base_lr: 7.2899e-06 lr: 7.2899e-06 eta: 1:51:39 time: 1.4640 data_time: 0.0133 memory: 8642 grad_norm: 5.8983 loss: 1.1977 itc_loss: 0.9359 itm_loss: 0.2618 2023/09/05 01:27:21 - mmengine - INFO - Epoch(train) [2][1080/1407] base_lr: 7.2504e-06 lr: 7.2504e-06 eta: 1:51:10 time: 1.4654 data_time: 0.0133 memory: 8642 grad_norm: 6.9526 loss: 1.3740 itc_loss: 1.0860 itm_loss: 0.2879 2023/09/05 01:27:50 - mmengine - INFO - Epoch(train) [2][1100/1407] base_lr: 7.2107e-06 lr: 7.2107e-06 eta: 1:50:40 time: 1.4643 data_time: 0.0134 memory: 8642 grad_norm: 6.8464 loss: 1.3759 itc_loss: 1.0685 itm_loss: 0.3074 2023/09/05 01:28:20 - mmengine - INFO - Epoch(train) [2][1120/1407] base_lr: 7.1708e-06 lr: 7.1708e-06 eta: 1:50:11 time: 1.4632 data_time: 0.0132 memory: 8642 grad_norm: 7.8565 loss: 1.0973 itc_loss: 0.8507 itm_loss: 0.2466 2023/09/05 01:28:49 - mmengine - INFO - Epoch(train) [2][1140/1407] base_lr: 7.1308e-06 lr: 7.1308e-06 eta: 1:49:41 time: 1.4642 data_time: 0.0134 memory: 8642 grad_norm: 7.1178 loss: 1.3809 itc_loss: 1.0936 itm_loss: 0.2873 2023/09/05 01:29:18 - mmengine - INFO - Epoch(train) [2][1160/1407] base_lr: 7.0906e-06 lr: 7.0906e-06 eta: 1:49:12 time: 1.4631 data_time: 0.0133 memory: 8642 grad_norm: 6.3890 loss: 1.2405 itc_loss: 0.9719 itm_loss: 0.2685 2023/09/05 01:29:48 - mmengine - INFO - Epoch(train) [2][1180/1407] base_lr: 7.0502e-06 lr: 7.0502e-06 eta: 1:48:43 time: 1.4649 data_time: 0.0134 memory: 8642 grad_norm: 7.0735 loss: 1.3094 itc_loss: 1.0451 itm_loss: 0.2643 2023/09/05 01:30:17 - mmengine - INFO - Epoch(train) [2][1200/1407] base_lr: 7.0097e-06 lr: 7.0097e-06 eta: 1:48:13 time: 1.4629 data_time: 0.0134 memory: 8642 grad_norm: 6.0971 loss: 1.2696 itc_loss: 1.0109 itm_loss: 0.2588 2023/09/05 01:30:46 - mmengine - INFO - Epoch(train) [2][1220/1407] base_lr: 6.9690e-06 lr: 6.9690e-06 eta: 1:47:44 time: 1.4659 data_time: 0.0134 memory: 8642 grad_norm: 7.7234 loss: 1.3587 itc_loss: 1.0754 itm_loss: 0.2833 2023/09/05 01:31:15 - mmengine - INFO - Epoch(train) [2][1240/1407] base_lr: 6.9282e-06 lr: 6.9282e-06 eta: 1:47:14 time: 1.4638 data_time: 0.0134 memory: 8642 grad_norm: 6.5769 loss: 1.3920 itc_loss: 1.1104 itm_loss: 0.2816 2023/09/05 01:31:45 - mmengine - INFO - Epoch(train) [2][1260/1407] base_lr: 6.8872e-06 lr: 6.8872e-06 eta: 1:46:45 time: 1.4631 data_time: 0.0134 memory: 8642 grad_norm: 7.3285 loss: 1.3003 itc_loss: 1.0469 itm_loss: 0.2534 2023/09/05 01:32:14 - mmengine - INFO - Epoch(train) [2][1280/1407] base_lr: 6.8461e-06 lr: 6.8461e-06 eta: 1:46:16 time: 1.4645 data_time: 0.0141 memory: 8642 grad_norm: 6.4567 loss: 1.3657 itc_loss: 1.0655 itm_loss: 0.3002 2023/09/05 01:32:43 - mmengine - INFO - Epoch(train) [2][1300/1407] base_lr: 6.8048e-06 lr: 6.8048e-06 eta: 1:45:46 time: 1.4647 data_time: 0.0134 memory: 8642 grad_norm: 6.5550 loss: 1.2675 itc_loss: 1.0110 itm_loss: 0.2565 2023/09/05 01:33:13 - mmengine - INFO - Epoch(train) [2][1320/1407] base_lr: 6.7634e-06 lr: 6.7634e-06 eta: 1:45:17 time: 1.4653 data_time: 0.0133 memory: 8642 grad_norm: 6.9983 loss: 1.4048 itc_loss: 1.1022 itm_loss: 0.3026 2023/09/05 01:33:42 - mmengine - INFO - Epoch(train) [2][1340/1407] base_lr: 6.7219e-06 lr: 6.7219e-06 eta: 1:44:47 time: 1.4645 data_time: 0.0135 memory: 8642 grad_norm: 6.9644 loss: 1.2920 itc_loss: 1.0272 itm_loss: 0.2648 2023/09/05 01:34:11 - mmengine - INFO - Epoch(train) [2][1360/1407] base_lr: 6.6802e-06 lr: 6.6802e-06 eta: 1:44:18 time: 1.4644 data_time: 0.0134 memory: 8642 grad_norm: 6.8137 loss: 1.4090 itc_loss: 1.0906 itm_loss: 0.3184 2023/09/05 01:34:40 - mmengine - INFO - Epoch(train) [2][1380/1407] base_lr: 6.6384e-06 lr: 6.6384e-06 eta: 1:43:49 time: 1.4632 data_time: 0.0132 memory: 8642 grad_norm: 6.6757 loss: 1.3539 itc_loss: 1.0680 itm_loss: 0.2859 2023/09/05 01:35:10 - mmengine - INFO - Epoch(train) [2][1400/1407] base_lr: 6.5964e-06 lr: 6.5964e-06 eta: 1:43:19 time: 1.4633 data_time: 0.0134 memory: 8642 grad_norm: 7.3738 loss: 1.0912 itc_loss: 0.8455 itm_loss: 0.2457 2023/09/05 01:35:19 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 01:35:19 - mmengine - INFO - Epoch(train) [2][1407/1407] base_lr: 6.5817e-06 lr: 6.5817e-06 eta: 1:43:08 time: 1.4214 data_time: 0.0134 memory: 8642 grad_norm: 7.1837 loss: 1.1242 itc_loss: 0.8745 itm_loss: 0.2497 2023/09/05 01:35:19 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/09/05 01:36:23 - mmengine - INFO - Epoch(val) [2][16/16] i2t/retrieval/Recall@1: 43.0000 i2t/retrieval/Recall@5: 70.6000 i2t/retrieval/Recall@10: 80.5000 t2i/retrieval/Recall@1: 42.7000 t2i/retrieval/Recall@5: 69.7000 t2i/retrieval/Recall@10: 79.5000 data_time: 0.0165 time: 0.3174 2023/09/05 01:36:23 - mmengine - INFO - The previous best checkpoint /mnt/workspace/lilin/Repos/mmaction2/work_dirs/vindlu_9_4/msrvtt_retrieval_train_8x16/best_t2i_retrieval_Recall@1_epoch_1.pth is removed 2023/09/05 01:36:25 - mmengine - INFO - The best checkpoint with 42.7000 t2i/retrieval/Recall@1 at 2 epoch is saved to best_t2i_retrieval_Recall@1_epoch_2.pth. 2023/09/05 01:37:02 - mmengine - INFO - Epoch(train) [3][ 20/1407] base_lr: 6.5396e-06 lr: 6.5396e-06 eta: 1:42:39 time: 1.4965 data_time: 0.0392 memory: 19276 grad_norm: 6.7720 loss: 1.2095 itc_loss: 0.9513 itm_loss: 0.2583 2023/09/05 01:37:31 - mmengine - INFO - Epoch(train) [3][ 40/1407] base_lr: 6.4974e-06 lr: 6.4974e-06 eta: 1:42:10 time: 1.4681 data_time: 0.0131 memory: 8642 grad_norm: 7.6191 loss: 1.0465 itc_loss: 0.8038 itm_loss: 0.2428 2023/09/05 01:38:01 - mmengine - INFO - Epoch(train) [3][ 60/1407] base_lr: 6.4551e-06 lr: 6.4551e-06 eta: 1:41:41 time: 1.4724 data_time: 0.0132 memory: 8642 grad_norm: 6.2098 loss: 1.2711 itc_loss: 0.9971 itm_loss: 0.2740 2023/09/05 01:38:30 - mmengine - INFO - Epoch(train) [3][ 80/1407] base_lr: 6.4126e-06 lr: 6.4126e-06 eta: 1:41:11 time: 1.4642 data_time: 0.0132 memory: 8642 grad_norm: 7.4510 loss: 1.1163 itc_loss: 0.8575 itm_loss: 0.2588 2023/09/05 01:38:59 - mmengine - INFO - Epoch(train) [3][ 100/1407] base_lr: 6.3701e-06 lr: 6.3701e-06 eta: 1:40:42 time: 1.4637 data_time: 0.0132 memory: 8642 grad_norm: 6.4433 loss: 1.1729 itc_loss: 0.9175 itm_loss: 0.2554 2023/09/05 01:39:29 - mmengine - INFO - Epoch(train) [3][ 120/1407] base_lr: 6.3274e-06 lr: 6.3274e-06 eta: 1:40:13 time: 1.4631 data_time: 0.0132 memory: 8642 grad_norm: 6.2545 loss: 1.3163 itc_loss: 1.0393 itm_loss: 0.2770 2023/09/05 01:39:58 - mmengine - INFO - Epoch(train) [3][ 140/1407] base_lr: 6.2847e-06 lr: 6.2847e-06 eta: 1:39:43 time: 1.4635 data_time: 0.0134 memory: 8642 grad_norm: 6.7422 loss: 1.3013 itc_loss: 1.0366 itm_loss: 0.2647 2023/09/05 01:40:27 - mmengine - INFO - Epoch(train) [3][ 160/1407] base_lr: 6.2418e-06 lr: 6.2418e-06 eta: 1:39:14 time: 1.4643 data_time: 0.0134 memory: 8642 grad_norm: 5.7478 loss: 1.2795 itc_loss: 1.0304 itm_loss: 0.2491 2023/09/05 01:40:56 - mmengine - INFO - Epoch(train) [3][ 180/1407] base_lr: 6.1988e-06 lr: 6.1988e-06 eta: 1:38:44 time: 1.4661 data_time: 0.0135 memory: 8642 grad_norm: 6.3749 loss: 1.2871 itc_loss: 1.0077 itm_loss: 0.2794 2023/09/05 01:41:05 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 01:41:26 - mmengine - INFO - Epoch(train) [3][ 200/1407] base_lr: 6.1558e-06 lr: 6.1558e-06 eta: 1:38:15 time: 1.4650 data_time: 0.0133 memory: 8642 grad_norm: 7.6903 loss: 1.2604 itc_loss: 0.9919 itm_loss: 0.2686 2023/09/05 01:41:55 - mmengine - INFO - Epoch(train) [3][ 220/1407] base_lr: 6.1127e-06 lr: 6.1127e-06 eta: 1:37:46 time: 1.4637 data_time: 0.0140 memory: 8642 grad_norm: 6.6780 loss: 1.3185 itc_loss: 1.0150 itm_loss: 0.3035 2023/09/05 01:42:24 - mmengine - INFO - Epoch(train) [3][ 240/1407] base_lr: 6.0694e-06 lr: 6.0694e-06 eta: 1:37:16 time: 1.4636 data_time: 0.0133 memory: 8642 grad_norm: 6.9992 loss: 1.1659 itc_loss: 0.9128 itm_loss: 0.2531 2023/09/05 01:42:54 - mmengine - INFO - Epoch(train) [3][ 260/1407] base_lr: 6.0261e-06 lr: 6.0261e-06 eta: 1:36:47 time: 1.4630 data_time: 0.0134 memory: 8642 grad_norm: 8.1106 loss: 1.2123 itc_loss: 0.9377 itm_loss: 0.2746 2023/09/05 01:43:23 - mmengine - INFO - Epoch(train) [3][ 280/1407] base_lr: 5.9827e-06 lr: 5.9827e-06 eta: 1:36:18 time: 1.4630 data_time: 0.0135 memory: 8642 grad_norm: 6.1834 loss: 1.3289 itc_loss: 1.0481 itm_loss: 0.2809 2023/09/05 01:43:52 - mmengine - INFO - Epoch(train) [3][ 300/1407] base_lr: 5.9393e-06 lr: 5.9393e-06 eta: 1:35:48 time: 1.4624 data_time: 0.0132 memory: 8642 grad_norm: 6.6198 loss: 1.2450 itc_loss: 1.0100 itm_loss: 0.2350 2023/09/05 01:44:21 - mmengine - INFO - Epoch(train) [3][ 320/1407] base_lr: 5.8958e-06 lr: 5.8958e-06 eta: 1:35:19 time: 1.4636 data_time: 0.0134 memory: 8642 grad_norm: 7.2902 loss: 1.3500 itc_loss: 1.0336 itm_loss: 0.3164 2023/09/05 01:44:51 - mmengine - INFO - Epoch(train) [3][ 340/1407] base_lr: 5.8522e-06 lr: 5.8522e-06 eta: 1:34:49 time: 1.4642 data_time: 0.0133 memory: 8642 grad_norm: 7.1876 loss: 1.0525 itc_loss: 0.8157 itm_loss: 0.2368 2023/09/05 01:45:20 - mmengine - INFO - Epoch(train) [3][ 360/1407] base_lr: 5.8085e-06 lr: 5.8085e-06 eta: 1:34:20 time: 1.4635 data_time: 0.0134 memory: 8642 grad_norm: 5.6827 loss: 1.2415 itc_loss: 0.9734 itm_loss: 0.2681 2023/09/05 01:45:49 - mmengine - INFO - Epoch(train) [3][ 380/1407] base_lr: 5.7648e-06 lr: 5.7648e-06 eta: 1:33:51 time: 1.4647 data_time: 0.0139 memory: 8642 grad_norm: 7.8980 loss: 1.3097 itc_loss: 1.0364 itm_loss: 0.2733 2023/09/05 01:46:18 - mmengine - INFO - Epoch(train) [3][ 400/1407] base_lr: 5.7210e-06 lr: 5.7210e-06 eta: 1:33:21 time: 1.4636 data_time: 0.0131 memory: 8642 grad_norm: 7.1695 loss: 1.2836 itc_loss: 1.0319 itm_loss: 0.2518 2023/09/05 01:46:48 - mmengine - INFO - Epoch(train) [3][ 420/1407] base_lr: 5.6772e-06 lr: 5.6772e-06 eta: 1:32:52 time: 1.4640 data_time: 0.0130 memory: 8642 grad_norm: 7.0912 loss: 1.1703 itc_loss: 0.9493 itm_loss: 0.2210 2023/09/05 01:47:17 - mmengine - INFO - Epoch(train) [3][ 440/1407] base_lr: 5.6333e-06 lr: 5.6333e-06 eta: 1:32:22 time: 1.4641 data_time: 0.0131 memory: 8642 grad_norm: 7.7766 loss: 1.3728 itc_loss: 1.0827 itm_loss: 0.2901 2023/09/05 01:47:46 - mmengine - INFO - Epoch(train) [3][ 460/1407] base_lr: 5.5894e-06 lr: 5.5894e-06 eta: 1:31:53 time: 1.4638 data_time: 0.0133 memory: 8642 grad_norm: 5.8938 loss: 1.3588 itc_loss: 1.0887 itm_loss: 0.2701 2023/09/05 01:48:16 - mmengine - INFO - Epoch(train) [3][ 480/1407] base_lr: 5.5454e-06 lr: 5.5454e-06 eta: 1:31:24 time: 1.4640 data_time: 0.0133 memory: 8642 grad_norm: 7.9944 loss: 1.2190 itc_loss: 0.9557 itm_loss: 0.2632 2023/09/05 01:48:45 - mmengine - INFO - Epoch(train) [3][ 500/1407] base_lr: 5.5014e-06 lr: 5.5014e-06 eta: 1:30:54 time: 1.4642 data_time: 0.0132 memory: 8642 grad_norm: 6.6019 loss: 1.0666 itc_loss: 0.8405 itm_loss: 0.2261 2023/09/05 01:49:14 - mmengine - INFO - Epoch(train) [3][ 520/1407] base_lr: 5.4574e-06 lr: 5.4574e-06 eta: 1:30:25 time: 1.4636 data_time: 0.0133 memory: 8642 grad_norm: 6.5909 loss: 1.2528 itc_loss: 1.0026 itm_loss: 0.2501 2023/09/05 01:49:43 - mmengine - INFO - Epoch(train) [3][ 540/1407] base_lr: 5.4133e-06 lr: 5.4133e-06 eta: 1:29:56 time: 1.4640 data_time: 0.0130 memory: 8642 grad_norm: 6.9561 loss: 1.2741 itc_loss: 1.0112 itm_loss: 0.2629 2023/09/05 01:50:13 - mmengine - INFO - Epoch(train) [3][ 560/1407] base_lr: 5.3692e-06 lr: 5.3692e-06 eta: 1:29:26 time: 1.4645 data_time: 0.0132 memory: 8642 grad_norm: 5.9061 loss: 1.2392 itc_loss: 0.9713 itm_loss: 0.2679 2023/09/05 01:50:42 - mmengine - INFO - Epoch(train) [3][ 580/1407] base_lr: 5.3251e-06 lr: 5.3251e-06 eta: 1:28:57 time: 1.4663 data_time: 0.0136 memory: 8642 grad_norm: 5.8994 loss: 1.1188 itc_loss: 0.8842 itm_loss: 0.2346 2023/09/05 01:51:11 - mmengine - INFO - Epoch(train) [3][ 600/1407] base_lr: 5.2809e-06 lr: 5.2809e-06 eta: 1:28:28 time: 1.4646 data_time: 0.0134 memory: 8642 grad_norm: 7.7096 loss: 1.2860 itc_loss: 1.0112 itm_loss: 0.2747 2023/09/05 01:51:41 - mmengine - INFO - Epoch(train) [3][ 620/1407] base_lr: 5.2367e-06 lr: 5.2367e-06 eta: 1:27:58 time: 1.4664 data_time: 0.0136 memory: 8642 grad_norm: 6.5593 loss: 1.2077 itc_loss: 0.9384 itm_loss: 0.2692 2023/09/05 01:52:10 - mmengine - INFO - Epoch(train) [3][ 640/1407] base_lr: 5.1926e-06 lr: 5.1926e-06 eta: 1:27:29 time: 1.4657 data_time: 0.0135 memory: 8642 grad_norm: 6.5190 loss: 1.1956 itc_loss: 0.9357 itm_loss: 0.2599 2023/09/05 01:52:39 - mmengine - INFO - Epoch(train) [3][ 660/1407] base_lr: 5.1484e-06 lr: 5.1484e-06 eta: 1:27:00 time: 1.4645 data_time: 0.0134 memory: 8642 grad_norm: 7.0776 loss: 1.1917 itc_loss: 0.9187 itm_loss: 0.2730 2023/09/05 01:53:09 - mmengine - INFO - Epoch(train) [3][ 680/1407] base_lr: 5.1042e-06 lr: 5.1042e-06 eta: 1:26:30 time: 1.4631 data_time: 0.0134 memory: 8642 grad_norm: 8.1325 loss: 1.1625 itc_loss: 0.9178 itm_loss: 0.2448 2023/09/05 01:53:38 - mmengine - INFO - Epoch(train) [3][ 700/1407] base_lr: 5.0599e-06 lr: 5.0599e-06 eta: 1:26:01 time: 1.4638 data_time: 0.0136 memory: 8642 grad_norm: 7.4234 loss: 1.1255 itc_loss: 0.8830 itm_loss: 0.2425 2023/09/05 01:54:07 - mmengine - INFO - Epoch(train) [3][ 720/1407] base_lr: 5.0157e-06 lr: 5.0157e-06 eta: 1:25:32 time: 1.4659 data_time: 0.0134 memory: 8642 grad_norm: 6.6623 loss: 1.2934 itc_loss: 1.0169 itm_loss: 0.2765 2023/09/05 01:54:36 - mmengine - INFO - Epoch(train) [3][ 740/1407] base_lr: 4.9715e-06 lr: 4.9715e-06 eta: 1:25:02 time: 1.4648 data_time: 0.0136 memory: 8642 grad_norm: 7.1082 loss: 1.3341 itc_loss: 1.0474 itm_loss: 0.2868 2023/09/05 01:55:06 - mmengine - INFO - Epoch(train) [3][ 760/1407] base_lr: 4.9273e-06 lr: 4.9273e-06 eta: 1:24:33 time: 1.4641 data_time: 0.0135 memory: 8642 grad_norm: 6.3153 loss: 1.4809 itc_loss: 1.1861 itm_loss: 0.2948 2023/09/05 01:55:35 - mmengine - INFO - Epoch(train) [3][ 780/1407] base_lr: 4.8831e-06 lr: 4.8831e-06 eta: 1:24:04 time: 1.4657 data_time: 0.0134 memory: 8642 grad_norm: 6.4661 loss: 1.1862 itc_loss: 0.9452 itm_loss: 0.2409 2023/09/05 01:56:04 - mmengine - INFO - Epoch(train) [3][ 800/1407] base_lr: 4.8390e-06 lr: 4.8390e-06 eta: 1:23:34 time: 1.4647 data_time: 0.0133 memory: 8642 grad_norm: 6.7617 loss: 1.2424 itc_loss: 0.9837 itm_loss: 0.2587 2023/09/05 01:56:34 - mmengine - INFO - Epoch(train) [3][ 820/1407] base_lr: 4.7948e-06 lr: 4.7948e-06 eta: 1:23:05 time: 1.4648 data_time: 0.0133 memory: 8642 grad_norm: 7.3674 loss: 1.1486 itc_loss: 0.8962 itm_loss: 0.2524 2023/09/05 01:57:03 - mmengine - INFO - Epoch(train) [3][ 840/1407] base_lr: 4.7507e-06 lr: 4.7507e-06 eta: 1:22:36 time: 1.4639 data_time: 0.0134 memory: 8642 grad_norm: 6.4264 loss: 1.2171 itc_loss: 0.9524 itm_loss: 0.2646 2023/09/05 01:57:32 - mmengine - INFO - Epoch(train) [3][ 860/1407] base_lr: 4.7065e-06 lr: 4.7065e-06 eta: 1:22:06 time: 1.4652 data_time: 0.0140 memory: 8642 grad_norm: 7.1204 loss: 1.1826 itc_loss: 0.9371 itm_loss: 0.2455 2023/09/05 01:58:01 - mmengine - INFO - Epoch(train) [3][ 880/1407] base_lr: 4.6625e-06 lr: 4.6625e-06 eta: 1:21:37 time: 1.4637 data_time: 0.0133 memory: 8642 grad_norm: 6.5267 loss: 1.1070 itc_loss: 0.8882 itm_loss: 0.2188 2023/09/05 01:58:31 - mmengine - INFO - Epoch(train) [3][ 900/1407] base_lr: 4.6184e-06 lr: 4.6184e-06 eta: 1:21:08 time: 1.4647 data_time: 0.0141 memory: 8642 grad_norm: 8.0445 loss: 1.2579 itc_loss: 0.9900 itm_loss: 0.2678 2023/09/05 01:59:00 - mmengine - INFO - Epoch(train) [3][ 920/1407] base_lr: 4.5744e-06 lr: 4.5744e-06 eta: 1:20:38 time: 1.4639 data_time: 0.0134 memory: 8642 grad_norm: 6.7371 loss: 1.2760 itc_loss: 0.9706 itm_loss: 0.3054 2023/09/05 01:59:29 - mmengine - INFO - Epoch(train) [3][ 940/1407] base_lr: 4.5304e-06 lr: 4.5304e-06 eta: 1:20:09 time: 1.4681 data_time: 0.0133 memory: 8642 grad_norm: 6.0762 loss: 1.1689 itc_loss: 0.8920 itm_loss: 0.2769 2023/09/05 01:59:59 - mmengine - INFO - Epoch(train) [3][ 960/1407] base_lr: 4.4864e-06 lr: 4.4864e-06 eta: 1:19:40 time: 1.4636 data_time: 0.0135 memory: 8642 grad_norm: 5.4623 loss: 1.0994 itc_loss: 0.8513 itm_loss: 0.2481 2023/09/05 02:00:28 - mmengine - INFO - Epoch(train) [3][ 980/1407] base_lr: 4.4425e-06 lr: 4.4425e-06 eta: 1:19:10 time: 1.4648 data_time: 0.0135 memory: 8642 grad_norm: 6.1514 loss: 1.3589 itc_loss: 1.0611 itm_loss: 0.2978 2023/09/05 02:00:57 - mmengine - INFO - Epoch(train) [3][1000/1407] base_lr: 4.3987e-06 lr: 4.3987e-06 eta: 1:18:41 time: 1.4649 data_time: 0.0135 memory: 8642 grad_norm: 6.5169 loss: 1.2678 itc_loss: 1.0058 itm_loss: 0.2621 2023/09/05 02:01:27 - mmengine - INFO - Epoch(train) [3][1020/1407] base_lr: 4.3549e-06 lr: 4.3549e-06 eta: 1:18:12 time: 1.4633 data_time: 0.0134 memory: 8642 grad_norm: 7.1650 loss: 1.2350 itc_loss: 0.9888 itm_loss: 0.2462 2023/09/05 02:01:56 - mmengine - INFO - Epoch(train) [3][1040/1407] base_lr: 4.3111e-06 lr: 4.3111e-06 eta: 1:17:42 time: 1.4636 data_time: 0.0134 memory: 8642 grad_norm: 7.0037 loss: 1.2747 itc_loss: 1.0074 itm_loss: 0.2673 2023/09/05 02:02:25 - mmengine - INFO - Epoch(train) [3][1060/1407] base_lr: 4.2675e-06 lr: 4.2675e-06 eta: 1:17:13 time: 1.4642 data_time: 0.0134 memory: 8642 grad_norm: 7.3751 loss: 1.2316 itc_loss: 0.9572 itm_loss: 0.2744 2023/09/05 02:02:54 - mmengine - INFO - Epoch(train) [3][1080/1407] base_lr: 4.2238e-06 lr: 4.2238e-06 eta: 1:16:43 time: 1.4638 data_time: 0.0135 memory: 8642 grad_norm: 6.2470 loss: 1.1473 itc_loss: 0.8956 itm_loss: 0.2517 2023/09/05 02:03:24 - mmengine - INFO - Epoch(train) [3][1100/1407] base_lr: 4.1803e-06 lr: 4.1803e-06 eta: 1:16:14 time: 1.4686 data_time: 0.0134 memory: 8642 grad_norm: 7.0073 loss: 1.2885 itc_loss: 1.0217 itm_loss: 0.2668 2023/09/05 02:03:53 - mmengine - INFO - Epoch(train) [3][1120/1407] base_lr: 4.1368e-06 lr: 4.1368e-06 eta: 1:15:45 time: 1.4673 data_time: 0.0135 memory: 8642 grad_norm: 7.7409 loss: 1.1046 itc_loss: 0.8542 itm_loss: 0.2504 2023/09/05 02:04:23 - mmengine - INFO - Epoch(train) [3][1140/1407] base_lr: 4.0934e-06 lr: 4.0934e-06 eta: 1:15:16 time: 1.4720 data_time: 0.0133 memory: 8642 grad_norm: 7.1363 loss: 1.0937 itc_loss: 0.8559 itm_loss: 0.2378 2023/09/05 02:04:52 - mmengine - INFO - Epoch(train) [3][1160/1407] base_lr: 4.0500e-06 lr: 4.0500e-06 eta: 1:14:47 time: 1.4724 data_time: 0.0133 memory: 8642 grad_norm: 8.1391 loss: 1.1836 itc_loss: 0.9165 itm_loss: 0.2671 2023/09/05 02:05:21 - mmengine - INFO - Epoch(train) [3][1180/1407] base_lr: 4.0068e-06 lr: 4.0068e-06 eta: 1:14:17 time: 1.4647 data_time: 0.0133 memory: 8642 grad_norm: 7.0410 loss: 1.0993 itc_loss: 0.8743 itm_loss: 0.2251 2023/09/05 02:05:30 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 02:05:51 - mmengine - INFO - Epoch(train) [3][1200/1407] base_lr: 3.9636e-06 lr: 3.9636e-06 eta: 1:13:48 time: 1.4647 data_time: 0.0135 memory: 8642 grad_norm: 7.0056 loss: 1.2601 itc_loss: 0.9721 itm_loss: 0.2881 2023/09/05 02:06:20 - mmengine - INFO - Epoch(train) [3][1220/1407] base_lr: 3.9205e-06 lr: 3.9205e-06 eta: 1:13:18 time: 1.4641 data_time: 0.0134 memory: 8642 grad_norm: 6.2612 loss: 1.4914 itc_loss: 1.1909 itm_loss: 0.3004 2023/09/05 02:06:49 - mmengine - INFO - Epoch(train) [3][1240/1407] base_lr: 3.8775e-06 lr: 3.8775e-06 eta: 1:12:49 time: 1.4659 data_time: 0.0134 memory: 8642 grad_norm: 6.6339 loss: 1.1390 itc_loss: 0.8767 itm_loss: 0.2623 2023/09/05 02:07:18 - mmengine - INFO - Epoch(train) [3][1260/1407] base_lr: 3.8346e-06 lr: 3.8346e-06 eta: 1:12:20 time: 1.4650 data_time: 0.0136 memory: 8642 grad_norm: 6.2837 loss: 1.2750 itc_loss: 1.0124 itm_loss: 0.2626 2023/09/05 02:07:48 - mmengine - INFO - Epoch(train) [3][1280/1407] base_lr: 3.7918e-06 lr: 3.7918e-06 eta: 1:11:51 time: 1.4643 data_time: 0.0136 memory: 8642 grad_norm: 6.7469 loss: 1.2980 itc_loss: 1.0207 itm_loss: 0.2773 2023/09/05 02:08:17 - mmengine - INFO - Epoch(train) [3][1300/1407] base_lr: 3.7491e-06 lr: 3.7491e-06 eta: 1:11:21 time: 1.4653 data_time: 0.0135 memory: 8642 grad_norm: 7.8072 loss: 1.2312 itc_loss: 0.9715 itm_loss: 0.2597 2023/09/05 02:08:46 - mmengine - INFO - Epoch(train) [3][1320/1407] base_lr: 3.7065e-06 lr: 3.7065e-06 eta: 1:10:52 time: 1.4653 data_time: 0.0135 memory: 8642 grad_norm: 6.2394 loss: 1.2818 itc_loss: 1.0277 itm_loss: 0.2542 2023/09/05 02:09:16 - mmengine - INFO - Epoch(train) [3][1340/1407] base_lr: 3.6640e-06 lr: 3.6640e-06 eta: 1:10:23 time: 1.4641 data_time: 0.0136 memory: 8642 grad_norm: 6.2493 loss: 1.0608 itc_loss: 0.8274 itm_loss: 0.2334 2023/09/05 02:09:45 - mmengine - INFO - Epoch(train) [3][1360/1407] base_lr: 3.6216e-06 lr: 3.6216e-06 eta: 1:09:53 time: 1.4658 data_time: 0.0135 memory: 8642 grad_norm: 6.7672 loss: 1.2317 itc_loss: 0.9566 itm_loss: 0.2751 2023/09/05 02:10:14 - mmengine - INFO - Epoch(train) [3][1380/1407] base_lr: 3.5793e-06 lr: 3.5793e-06 eta: 1:09:24 time: 1.4676 data_time: 0.0140 memory: 8642 grad_norm: 6.6557 loss: 1.1418 itc_loss: 0.8996 itm_loss: 0.2422 2023/09/05 02:10:44 - mmengine - INFO - Epoch(train) [3][1400/1407] base_lr: 3.5372e-06 lr: 3.5372e-06 eta: 1:08:55 time: 1.4641 data_time: 0.0137 memory: 8642 grad_norm: 6.8089 loss: 1.1521 itc_loss: 0.9039 itm_loss: 0.2482 2023/09/05 02:10:53 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 02:10:53 - mmengine - INFO - Epoch(train) [3][1407/1407] base_lr: 3.5225e-06 lr: 3.5225e-06 eta: 1:08:44 time: 1.4202 data_time: 0.0136 memory: 8642 grad_norm: inf loss: 1.1754 itc_loss: 0.9440 itm_loss: 0.2314 2023/09/05 02:10:53 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/09/05 02:11:57 - mmengine - INFO - Epoch(val) [3][16/16] i2t/retrieval/Recall@1: 43.1000 i2t/retrieval/Recall@5: 70.0000 i2t/retrieval/Recall@10: 80.2000 t2i/retrieval/Recall@1: 43.7000 t2i/retrieval/Recall@5: 69.8000 t2i/retrieval/Recall@10: 80.0000 data_time: 0.0163 time: 0.3173 2023/09/05 02:11:57 - mmengine - INFO - The previous best checkpoint /mnt/workspace/lilin/Repos/mmaction2/work_dirs/vindlu_9_4/msrvtt_retrieval_train_8x16/best_t2i_retrieval_Recall@1_epoch_2.pth is removed 2023/09/05 02:11:59 - mmengine - INFO - The best checkpoint with 43.7000 t2i/retrieval/Recall@1 at 3 epoch is saved to best_t2i_retrieval_Recall@1_epoch_3.pth. 2023/09/05 02:12:36 - mmengine - INFO - Epoch(train) [4][ 20/1407] base_lr: 3.4805e-06 lr: 3.4805e-06 eta: 1:08:15 time: 1.4822 data_time: 0.0317 memory: 19276 grad_norm: 6.0988 loss: 1.0003 itc_loss: 0.7867 itm_loss: 0.2136 2023/09/05 02:13:05 - mmengine - INFO - Epoch(train) [4][ 40/1407] base_lr: 3.4386e-06 lr: 3.4386e-06 eta: 1:07:45 time: 1.4619 data_time: 0.0130 memory: 8642 grad_norm: 6.4998 loss: 1.2603 itc_loss: 0.9719 itm_loss: 0.2884 2023/09/05 02:13:34 - mmengine - INFO - Epoch(train) [4][ 60/1407] base_lr: 3.3969e-06 lr: 3.3969e-06 eta: 1:07:16 time: 1.4638 data_time: 0.0132 memory: 8642 grad_norm: 6.4080 loss: 1.1578 itc_loss: 0.8924 itm_loss: 0.2654 2023/09/05 02:14:03 - mmengine - INFO - Epoch(train) [4][ 80/1407] base_lr: 3.3553e-06 lr: 3.3553e-06 eta: 1:06:47 time: 1.4642 data_time: 0.0133 memory: 8642 grad_norm: 6.4322 loss: 1.2106 itc_loss: 0.9725 itm_loss: 0.2380 2023/09/05 02:14:33 - mmengine - INFO - Epoch(train) [4][ 100/1407] base_lr: 3.3138e-06 lr: 3.3138e-06 eta: 1:06:17 time: 1.4651 data_time: 0.0131 memory: 8642 grad_norm: 6.6690 loss: 1.1598 itc_loss: 0.9096 itm_loss: 0.2501 2023/09/05 02:15:02 - mmengine - INFO - Epoch(train) [4][ 120/1407] base_lr: 3.2725e-06 lr: 3.2725e-06 eta: 1:05:48 time: 1.4636 data_time: 0.0135 memory: 8642 grad_norm: 6.2698 loss: 1.0994 itc_loss: 0.8685 itm_loss: 0.2310 2023/09/05 02:15:31 - mmengine - INFO - Epoch(train) [4][ 140/1407] base_lr: 3.2313e-06 lr: 3.2313e-06 eta: 1:05:19 time: 1.4628 data_time: 0.0133 memory: 8642 grad_norm: 6.1412 loss: 1.0034 itc_loss: 0.7876 itm_loss: 0.2158 2023/09/05 02:16:01 - mmengine - INFO - Epoch(train) [4][ 160/1407] base_lr: 3.1902e-06 lr: 3.1902e-06 eta: 1:04:49 time: 1.4649 data_time: 0.0135 memory: 8642 grad_norm: 6.4679 loss: 0.9957 itc_loss: 0.7831 itm_loss: 0.2126 2023/09/05 02:16:30 - mmengine - INFO - Epoch(train) [4][ 180/1407] base_lr: 3.1493e-06 lr: 3.1493e-06 eta: 1:04:20 time: 1.4652 data_time: 0.0130 memory: 8642 grad_norm: 6.0180 loss: 1.1580 itc_loss: 0.9028 itm_loss: 0.2552 2023/09/05 02:16:59 - mmengine - INFO - Epoch(train) [4][ 200/1407] base_lr: 3.1086e-06 lr: 3.1086e-06 eta: 1:03:51 time: 1.4636 data_time: 0.0131 memory: 8642 grad_norm: 6.7633 loss: 1.1266 itc_loss: 0.9087 itm_loss: 0.2180 2023/09/05 02:17:28 - mmengine - INFO - Epoch(train) [4][ 220/1407] base_lr: 3.0680e-06 lr: 3.0680e-06 eta: 1:03:21 time: 1.4640 data_time: 0.0131 memory: 8642 grad_norm: 6.2284 loss: 1.1116 itc_loss: 0.8767 itm_loss: 0.2350 2023/09/05 02:17:58 - mmengine - INFO - Epoch(train) [4][ 240/1407] base_lr: 3.0276e-06 lr: 3.0276e-06 eta: 1:02:52 time: 1.4646 data_time: 0.0132 memory: 8642 grad_norm: 7.0100 loss: 1.1767 itc_loss: 0.8886 itm_loss: 0.2880 2023/09/05 02:18:27 - mmengine - INFO - Epoch(train) [4][ 260/1407] base_lr: 2.9873e-06 lr: 2.9873e-06 eta: 1:02:23 time: 1.4649 data_time: 0.0131 memory: 8642 grad_norm: 6.3874 loss: 1.0188 itc_loss: 0.7828 itm_loss: 0.2360 2023/09/05 02:18:56 - mmengine - INFO - Epoch(train) [4][ 280/1407] base_lr: 2.9472e-06 lr: 2.9472e-06 eta: 1:01:53 time: 1.4638 data_time: 0.0134 memory: 8642 grad_norm: 7.2515 loss: 1.0723 itc_loss: 0.8487 itm_loss: 0.2235 2023/09/05 02:19:26 - mmengine - INFO - Epoch(train) [4][ 300/1407] base_lr: 2.9073e-06 lr: 2.9073e-06 eta: 1:01:24 time: 1.4644 data_time: 0.0134 memory: 8642 grad_norm: 6.4463 loss: 1.2101 itc_loss: 0.9252 itm_loss: 0.2849 2023/09/05 02:19:55 - mmengine - INFO - Epoch(train) [4][ 320/1407] base_lr: 2.8675e-06 lr: 2.8675e-06 eta: 1:00:55 time: 1.4642 data_time: 0.0133 memory: 8642 grad_norm: 8.1693 loss: 1.1979 itc_loss: 0.9347 itm_loss: 0.2632 2023/09/05 02:20:24 - mmengine - INFO - Epoch(train) [4][ 340/1407] base_lr: 2.8279e-06 lr: 2.8279e-06 eta: 1:00:25 time: 1.4655 data_time: 0.0133 memory: 8642 grad_norm: 6.3188 loss: 1.3035 itc_loss: 1.0296 itm_loss: 0.2739 2023/09/05 02:20:53 - mmengine - INFO - Epoch(train) [4][ 360/1407] base_lr: 2.7885e-06 lr: 2.7885e-06 eta: 0:59:56 time: 1.4635 data_time: 0.0134 memory: 8642 grad_norm: 7.0146 loss: 1.1541 itc_loss: 0.9090 itm_loss: 0.2451 2023/09/05 02:21:23 - mmengine - INFO - Epoch(train) [4][ 380/1407] base_lr: 2.7492e-06 lr: 2.7492e-06 eta: 0:59:27 time: 1.4640 data_time: 0.0134 memory: 8642 grad_norm: 6.6845 loss: 1.1060 itc_loss: 0.8533 itm_loss: 0.2528 2023/09/05 02:21:52 - mmengine - INFO - Epoch(train) [4][ 400/1407] base_lr: 2.7102e-06 lr: 2.7102e-06 eta: 0:58:57 time: 1.4645 data_time: 0.0133 memory: 8642 grad_norm: 8.0490 loss: 1.0908 itc_loss: 0.8341 itm_loss: 0.2568 2023/09/05 02:22:21 - mmengine - INFO - Epoch(train) [4][ 420/1407] base_lr: 2.6713e-06 lr: 2.6713e-06 eta: 0:58:28 time: 1.4637 data_time: 0.0133 memory: 8642 grad_norm: 7.0763 loss: 1.2246 itc_loss: 0.9654 itm_loss: 0.2593 2023/09/05 02:22:51 - mmengine - INFO - Epoch(train) [4][ 440/1407] base_lr: 2.6327e-06 lr: 2.6327e-06 eta: 0:57:59 time: 1.4640 data_time: 0.0133 memory: 8642 grad_norm: 6.3775 loss: 1.1768 itc_loss: 0.9330 itm_loss: 0.2438 2023/09/05 02:23:20 - mmengine - INFO - Epoch(train) [4][ 460/1407] base_lr: 2.5942e-06 lr: 2.5942e-06 eta: 0:57:29 time: 1.4679 data_time: 0.0133 memory: 8642 grad_norm: 6.7028 loss: 1.2832 itc_loss: 1.0362 itm_loss: 0.2469 2023/09/05 02:23:49 - mmengine - INFO - Epoch(train) [4][ 480/1407] base_lr: 2.5559e-06 lr: 2.5559e-06 eta: 0:57:00 time: 1.4630 data_time: 0.0134 memory: 8642 grad_norm: 5.9086 loss: 1.1389 itc_loss: 0.8657 itm_loss: 0.2732 2023/09/05 02:24:18 - mmengine - INFO - Epoch(train) [4][ 500/1407] base_lr: 2.5178e-06 lr: 2.5178e-06 eta: 0:56:31 time: 1.4641 data_time: 0.0134 memory: 8642 grad_norm: 6.6140 loss: 1.2261 itc_loss: 0.9731 itm_loss: 0.2530 2023/09/05 02:24:48 - mmengine - INFO - Epoch(train) [4][ 520/1407] base_lr: 2.4799e-06 lr: 2.4799e-06 eta: 0:56:01 time: 1.4638 data_time: 0.0133 memory: 8642 grad_norm: 6.5810 loss: 1.1071 itc_loss: 0.8713 itm_loss: 0.2358 2023/09/05 02:25:17 - mmengine - INFO - Epoch(train) [4][ 540/1407] base_lr: 2.4422e-06 lr: 2.4422e-06 eta: 0:55:32 time: 1.4653 data_time: 0.0140 memory: 8642 grad_norm: 6.1226 loss: 1.1376 itc_loss: 0.8824 itm_loss: 0.2552 2023/09/05 02:25:46 - mmengine - INFO - Epoch(train) [4][ 560/1407] base_lr: 2.4048e-06 lr: 2.4048e-06 eta: 0:55:03 time: 1.4633 data_time: 0.0132 memory: 8642 grad_norm: 6.8525 loss: 1.1049 itc_loss: 0.8602 itm_loss: 0.2446 2023/09/05 02:26:16 - mmengine - INFO - Epoch(train) [4][ 580/1407] base_lr: 2.3675e-06 lr: 2.3675e-06 eta: 0:54:33 time: 1.4638 data_time: 0.0133 memory: 8642 grad_norm: 6.1200 loss: 1.2823 itc_loss: 1.0321 itm_loss: 0.2502 2023/09/05 02:26:45 - mmengine - INFO - Epoch(train) [4][ 600/1407] base_lr: 2.3304e-06 lr: 2.3304e-06 eta: 0:54:04 time: 1.4655 data_time: 0.0134 memory: 8642 grad_norm: 7.0966 loss: 1.0448 itc_loss: 0.8186 itm_loss: 0.2262 2023/09/05 02:27:14 - mmengine - INFO - Epoch(train) [4][ 620/1407] base_lr: 2.2936e-06 lr: 2.2936e-06 eta: 0:53:35 time: 1.4631 data_time: 0.0134 memory: 8642 grad_norm: 7.6286 loss: 1.1012 itc_loss: 0.8744 itm_loss: 0.2267 2023/09/05 02:27:43 - mmengine - INFO - Epoch(train) [4][ 640/1407] base_lr: 2.2570e-06 lr: 2.2570e-06 eta: 0:53:05 time: 1.4642 data_time: 0.0131 memory: 8642 grad_norm: 5.9655 loss: 1.2384 itc_loss: 0.9962 itm_loss: 0.2421 2023/09/05 02:28:13 - mmengine - INFO - Epoch(train) [4][ 660/1407] base_lr: 2.2206e-06 lr: 2.2206e-06 eta: 0:52:36 time: 1.4628 data_time: 0.0131 memory: 8642 grad_norm: 6.4100 loss: 1.1528 itc_loss: 0.8814 itm_loss: 0.2713 2023/09/05 02:28:42 - mmengine - INFO - Epoch(train) [4][ 680/1407] base_lr: 2.1845e-06 lr: 2.1845e-06 eta: 0:52:07 time: 1.4662 data_time: 0.0130 memory: 8642 grad_norm: 6.6510 loss: 1.1894 itc_loss: 0.9656 itm_loss: 0.2238 2023/09/05 02:29:11 - mmengine - INFO - Epoch(train) [4][ 700/1407] base_lr: 2.1485e-06 lr: 2.1485e-06 eta: 0:51:38 time: 1.4646 data_time: 0.0134 memory: 8642 grad_norm: 6.7751 loss: 1.0444 itc_loss: 0.8064 itm_loss: 0.2380 2023/09/05 02:29:41 - mmengine - INFO - Epoch(train) [4][ 720/1407] base_lr: 2.1128e-06 lr: 2.1128e-06 eta: 0:51:08 time: 1.4677 data_time: 0.0132 memory: 8642 grad_norm: 6.7613 loss: 0.9954 itc_loss: 0.8055 itm_loss: 0.1898 2023/09/05 02:30:10 - mmengine - INFO - Epoch(train) [4][ 740/1407] base_lr: 2.0773e-06 lr: 2.0773e-06 eta: 0:50:39 time: 1.4670 data_time: 0.0131 memory: 8642 grad_norm: 7.3686 loss: 1.1099 itc_loss: 0.8377 itm_loss: 0.2721 2023/09/05 02:30:39 - mmengine - INFO - Epoch(train) [4][ 760/1407] base_lr: 2.0421e-06 lr: 2.0421e-06 eta: 0:50:10 time: 1.4764 data_time: 0.0133 memory: 8642 grad_norm: 7.0531 loss: 0.8829 itc_loss: 0.6983 itm_loss: 0.1846 2023/09/05 02:31:07 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 02:31:09 - mmengine - INFO - Epoch(train) [4][ 780/1407] base_lr: 2.0071e-06 lr: 2.0071e-06 eta: 0:49:40 time: 1.4673 data_time: 0.0133 memory: 8642 grad_norm: 6.1955 loss: 1.2604 itc_loss: 0.9979 itm_loss: 0.2624 2023/09/05 02:31:38 - mmengine - INFO - Epoch(train) [4][ 800/1407] base_lr: 1.9724e-06 lr: 1.9724e-06 eta: 0:49:11 time: 1.4655 data_time: 0.0133 memory: 8642 grad_norm: 6.1005 loss: 1.1139 itc_loss: 0.8782 itm_loss: 0.2357 2023/09/05 02:32:07 - mmengine - INFO - Epoch(train) [4][ 820/1407] base_lr: 1.9379e-06 lr: 1.9379e-06 eta: 0:48:42 time: 1.4652 data_time: 0.0133 memory: 8642 grad_norm: 6.6123 loss: 1.1012 itc_loss: 0.8538 itm_loss: 0.2475 2023/09/05 02:32:37 - mmengine - INFO - Epoch(train) [4][ 840/1407] base_lr: 1.9036e-06 lr: 1.9036e-06 eta: 0:48:12 time: 1.4646 data_time: 0.0132 memory: 8642 grad_norm: 6.4626 loss: 1.1906 itc_loss: 0.9306 itm_loss: 0.2600 2023/09/05 02:33:06 - mmengine - INFO - Epoch(train) [4][ 860/1407] base_lr: 1.8696e-06 lr: 1.8696e-06 eta: 0:47:43 time: 1.4629 data_time: 0.0133 memory: 8642 grad_norm: 6.6653 loss: 1.1410 itc_loss: 0.8932 itm_loss: 0.2478 2023/09/05 02:33:35 - mmengine - INFO - Epoch(train) [4][ 880/1407] base_lr: 1.8359e-06 lr: 1.8359e-06 eta: 0:47:14 time: 1.4643 data_time: 0.0134 memory: 8642 grad_norm: 6.5474 loss: 1.3697 itc_loss: 1.0938 itm_loss: 0.2759 2023/09/05 02:34:05 - mmengine - INFO - Epoch(train) [4][ 900/1407] base_lr: 1.8024e-06 lr: 1.8024e-06 eta: 0:46:45 time: 1.4645 data_time: 0.0134 memory: 8642 grad_norm: 6.7433 loss: 1.1728 itc_loss: 0.9217 itm_loss: 0.2512 2023/09/05 02:34:34 - mmengine - INFO - Epoch(train) [4][ 920/1407] base_lr: 1.7691e-06 lr: 1.7691e-06 eta: 0:46:15 time: 1.4649 data_time: 0.0134 memory: 8642 grad_norm: 6.5109 loss: 1.1586 itc_loss: 0.9033 itm_loss: 0.2553 2023/09/05 02:35:03 - mmengine - INFO - Epoch(train) [4][ 940/1407] base_lr: 1.7362e-06 lr: 1.7362e-06 eta: 0:45:46 time: 1.4630 data_time: 0.0133 memory: 8642 grad_norm: 6.9757 loss: 1.1044 itc_loss: 0.8531 itm_loss: 0.2513 2023/09/05 02:35:32 - mmengine - INFO - Epoch(train) [4][ 960/1407] base_lr: 1.7035e-06 lr: 1.7035e-06 eta: 0:45:17 time: 1.4642 data_time: 0.0132 memory: 8642 grad_norm: 5.9227 loss: 1.1547 itc_loss: 0.9020 itm_loss: 0.2527 2023/09/05 02:36:02 - mmengine - INFO - Epoch(train) [4][ 980/1407] base_lr: 1.6710e-06 lr: 1.6710e-06 eta: 0:44:47 time: 1.4640 data_time: 0.0134 memory: 8642 grad_norm: 6.4429 loss: 1.1091 itc_loss: 0.8703 itm_loss: 0.2388 2023/09/05 02:36:31 - mmengine - INFO - Epoch(train) [4][1000/1407] base_lr: 1.6388e-06 lr: 1.6388e-06 eta: 0:44:18 time: 1.4635 data_time: 0.0133 memory: 8642 grad_norm: 7.2524 loss: 1.1921 itc_loss: 0.9621 itm_loss: 0.2300 2023/09/05 02:37:00 - mmengine - INFO - Epoch(train) [4][1020/1407] base_lr: 1.6069e-06 lr: 1.6069e-06 eta: 0:43:49 time: 1.4628 data_time: 0.0133 memory: 8642 grad_norm: 7.0461 loss: 1.1664 itc_loss: 0.9303 itm_loss: 0.2361 2023/09/05 02:37:29 - mmengine - INFO - Epoch(train) [4][1040/1407] base_lr: 1.5753e-06 lr: 1.5753e-06 eta: 0:43:19 time: 1.4638 data_time: 0.0132 memory: 8642 grad_norm: 7.1847 loss: 1.0748 itc_loss: 0.8329 itm_loss: 0.2419 2023/09/05 02:37:59 - mmengine - INFO - Epoch(train) [4][1060/1407] base_lr: 1.5440e-06 lr: 1.5440e-06 eta: 0:42:50 time: 1.4665 data_time: 0.0143 memory: 8642 grad_norm: 6.8877 loss: 1.1470 itc_loss: 0.8932 itm_loss: 0.2538 2023/09/05 02:38:28 - mmengine - INFO - Epoch(train) [4][1080/1407] base_lr: 1.5129e-06 lr: 1.5129e-06 eta: 0:42:21 time: 1.4630 data_time: 0.0134 memory: 8642 grad_norm: 8.1124 loss: 1.2489 itc_loss: 0.9836 itm_loss: 0.2653 2023/09/05 02:38:57 - mmengine - INFO - Epoch(train) [4][1100/1407] base_lr: 1.4821e-06 lr: 1.4821e-06 eta: 0:41:51 time: 1.4636 data_time: 0.0132 memory: 8642 grad_norm: 6.0848 loss: 1.0240 itc_loss: 0.7704 itm_loss: 0.2536 2023/09/05 02:39:27 - mmengine - INFO - Epoch(train) [4][1120/1407] base_lr: 1.4516e-06 lr: 1.4516e-06 eta: 0:41:22 time: 1.4647 data_time: 0.0133 memory: 8642 grad_norm: 5.9149 loss: 1.2976 itc_loss: 1.0310 itm_loss: 0.2666 2023/09/05 02:39:56 - mmengine - INFO - Epoch(train) [4][1140/1407] base_lr: 1.4214e-06 lr: 1.4214e-06 eta: 0:40:53 time: 1.4624 data_time: 0.0135 memory: 8642 grad_norm: 6.8970 loss: 1.1306 itc_loss: 0.8679 itm_loss: 0.2627 2023/09/05 02:40:25 - mmengine - INFO - Epoch(train) [4][1160/1407] base_lr: 1.3915e-06 lr: 1.3915e-06 eta: 0:40:23 time: 1.4647 data_time: 0.0134 memory: 8642 grad_norm: 7.2346 loss: 1.1774 itc_loss: 0.9298 itm_loss: 0.2476 2023/09/05 02:40:54 - mmengine - INFO - Epoch(train) [4][1180/1407] base_lr: 1.3618e-06 lr: 1.3618e-06 eta: 0:39:54 time: 1.4645 data_time: 0.0133 memory: 8642 grad_norm: 6.8149 loss: 1.2373 itc_loss: 0.9600 itm_loss: 0.2774 2023/09/05 02:41:24 - mmengine - INFO - Epoch(train) [4][1200/1407] base_lr: 1.3325e-06 lr: 1.3325e-06 eta: 0:39:25 time: 1.4637 data_time: 0.0133 memory: 8642 grad_norm: 6.8790 loss: 1.1071 itc_loss: 0.8577 itm_loss: 0.2495 2023/09/05 02:41:53 - mmengine - INFO - Epoch(train) [4][1220/1407] base_lr: 1.3035e-06 lr: 1.3035e-06 eta: 0:38:55 time: 1.4633 data_time: 0.0132 memory: 8642 grad_norm: 6.8967 loss: 1.2297 itc_loss: 0.9785 itm_loss: 0.2513 2023/09/05 02:42:22 - mmengine - INFO - Epoch(train) [4][1240/1407] base_lr: 1.2747e-06 lr: 1.2747e-06 eta: 0:38:26 time: 1.4647 data_time: 0.0134 memory: 8642 grad_norm: 7.8865 loss: 1.1226 itc_loss: 0.8923 itm_loss: 0.2304 2023/09/05 02:42:52 - mmengine - INFO - Epoch(train) [4][1260/1407] base_lr: 1.2463e-06 lr: 1.2463e-06 eta: 0:37:57 time: 1.4631 data_time: 0.0133 memory: 8642 grad_norm: 6.9251 loss: 1.1468 itc_loss: 0.8942 itm_loss: 0.2526 2023/09/05 02:43:21 - mmengine - INFO - Epoch(train) [4][1280/1407] base_lr: 1.2181e-06 lr: 1.2181e-06 eta: 0:37:27 time: 1.4633 data_time: 0.0134 memory: 8642 grad_norm: 6.9634 loss: 1.1700 itc_loss: 0.9236 itm_loss: 0.2464 2023/09/05 02:43:50 - mmengine - INFO - Epoch(train) [4][1300/1407] base_lr: 1.1903e-06 lr: 1.1903e-06 eta: 0:36:58 time: 1.4654 data_time: 0.0133 memory: 8642 grad_norm: 6.7081 loss: 1.1390 itc_loss: 0.8971 itm_loss: 0.2420 2023/09/05 02:44:19 - mmengine - INFO - Epoch(train) [4][1320/1407] base_lr: 1.1628e-06 lr: 1.1628e-06 eta: 0:36:29 time: 1.4647 data_time: 0.0133 memory: 8642 grad_norm: 6.1369 loss: 1.3222 itc_loss: 1.0366 itm_loss: 0.2855 2023/09/05 02:44:49 - mmengine - INFO - Epoch(train) [4][1340/1407] base_lr: 1.1356e-06 lr: 1.1356e-06 eta: 0:36:00 time: 1.4645 data_time: 0.0133 memory: 8642 grad_norm: 6.5147 loss: 1.1100 itc_loss: 0.8500 itm_loss: 0.2600 2023/09/05 02:45:18 - mmengine - INFO - Epoch(train) [4][1360/1407] base_lr: 1.1086e-06 lr: 1.1086e-06 eta: 0:35:30 time: 1.4660 data_time: 0.0132 memory: 8642 grad_norm: 6.8735 loss: 1.2368 itc_loss: 0.9741 itm_loss: 0.2627 2023/09/05 02:45:47 - mmengine - INFO - Epoch(train) [4][1380/1407] base_lr: 1.0821e-06 lr: 1.0821e-06 eta: 0:35:01 time: 1.4636 data_time: 0.0135 memory: 8642 grad_norm: 6.8625 loss: 1.2301 itc_loss: 0.9699 itm_loss: 0.2602 2023/09/05 02:46:17 - mmengine - INFO - Epoch(train) [4][1400/1407] base_lr: 1.0558e-06 lr: 1.0558e-06 eta: 0:34:32 time: 1.4632 data_time: 0.0134 memory: 8642 grad_norm: 6.4674 loss: 1.1412 itc_loss: 0.8998 itm_loss: 0.2413 2023/09/05 02:46:26 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 02:46:26 - mmengine - INFO - Epoch(train) [4][1407/1407] base_lr: 1.0467e-06 lr: 1.0467e-06 eta: 0:34:21 time: 1.4206 data_time: 0.0134 memory: 8642 grad_norm: 6.3728 loss: 1.0115 itc_loss: 0.7941 itm_loss: 0.2174 2023/09/05 02:46:26 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/09/05 02:47:30 - mmengine - INFO - Epoch(val) [4][16/16] i2t/retrieval/Recall@1: 42.2000 i2t/retrieval/Recall@5: 70.3000 i2t/retrieval/Recall@10: 80.4000 t2i/retrieval/Recall@1: 44.0000 t2i/retrieval/Recall@5: 70.6000 t2i/retrieval/Recall@10: 80.0000 data_time: 0.0162 time: 0.3173 2023/09/05 02:47:30 - mmengine - INFO - The previous best checkpoint /mnt/workspace/lilin/Repos/mmaction2/work_dirs/vindlu_9_4/msrvtt_retrieval_train_8x16/best_t2i_retrieval_Recall@1_epoch_3.pth is removed 2023/09/05 02:47:32 - mmengine - INFO - The best checkpoint with 44.0000 t2i/retrieval/Recall@1 at 4 epoch is saved to best_t2i_retrieval_Recall@1_epoch_4.pth. 2023/09/05 02:48:08 - mmengine - INFO - Epoch(train) [5][ 20/1407] base_lr: 1.0208e-06 lr: 1.0208e-06 eta: 0:33:52 time: 1.4857 data_time: 0.0356 memory: 19276 grad_norm: 6.3270 loss: 1.0402 itc_loss: 0.8149 itm_loss: 0.2253 2023/09/05 02:48:38 - mmengine - INFO - Epoch(train) [5][ 40/1407] base_lr: 9.9530e-07 lr: 9.9530e-07 eta: 0:33:23 time: 1.4674 data_time: 0.0130 memory: 8642 grad_norm: 6.0813 loss: 1.1127 itc_loss: 0.8773 itm_loss: 0.2355 2023/09/05 02:49:07 - mmengine - INFO - Epoch(train) [5][ 60/1407] base_lr: 9.7011e-07 lr: 9.7011e-07 eta: 0:32:53 time: 1.4620 data_time: 0.0132 memory: 8642 grad_norm: 6.9259 loss: 1.1821 itc_loss: 0.9433 itm_loss: 0.2387 2023/09/05 02:49:36 - mmengine - INFO - Epoch(train) [5][ 80/1407] base_lr: 9.4523e-07 lr: 9.4523e-07 eta: 0:32:24 time: 1.4634 data_time: 0.0131 memory: 8642 grad_norm: 7.2575 loss: 1.1680 itc_loss: 0.9056 itm_loss: 0.2624 2023/09/05 02:50:06 - mmengine - INFO - Epoch(train) [5][ 100/1407] base_lr: 9.2069e-07 lr: 9.2069e-07 eta: 0:31:55 time: 1.4639 data_time: 0.0133 memory: 8642 grad_norm: 7.4309 loss: 1.0695 itc_loss: 0.8318 itm_loss: 0.2378 2023/09/05 02:50:35 - mmengine - INFO - Epoch(train) [5][ 120/1407] base_lr: 8.9648e-07 lr: 8.9648e-07 eta: 0:31:25 time: 1.4640 data_time: 0.0134 memory: 8642 grad_norm: 5.8491 loss: 1.1044 itc_loss: 0.8550 itm_loss: 0.2494 2023/09/05 02:51:04 - mmengine - INFO - Epoch(train) [5][ 140/1407] base_lr: 8.7259e-07 lr: 8.7259e-07 eta: 0:30:56 time: 1.4646 data_time: 0.0136 memory: 8642 grad_norm: 6.6386 loss: 1.1544 itc_loss: 0.9267 itm_loss: 0.2277 2023/09/05 02:51:33 - mmengine - INFO - Epoch(train) [5][ 160/1407] base_lr: 8.4904e-07 lr: 8.4904e-07 eta: 0:30:27 time: 1.4642 data_time: 0.0140 memory: 8642 grad_norm: 6.7829 loss: 1.2326 itc_loss: 0.9930 itm_loss: 0.2395 2023/09/05 02:52:03 - mmengine - INFO - Epoch(train) [5][ 180/1407] base_lr: 8.2583e-07 lr: 8.2583e-07 eta: 0:29:57 time: 1.4644 data_time: 0.0131 memory: 8642 grad_norm: 6.6347 loss: 1.1995 itc_loss: 0.9382 itm_loss: 0.2613 2023/09/05 02:52:32 - mmengine - INFO - Epoch(train) [5][ 200/1407] base_lr: 8.0295e-07 lr: 8.0295e-07 eta: 0:29:28 time: 1.4626 data_time: 0.0130 memory: 8642 grad_norm: 6.4874 loss: 1.0280 itc_loss: 0.7980 itm_loss: 0.2300 2023/09/05 02:53:01 - mmengine - INFO - Epoch(train) [5][ 220/1407] base_lr: 7.8041e-07 lr: 7.8041e-07 eta: 0:28:59 time: 1.4632 data_time: 0.0130 memory: 8642 grad_norm: 6.4985 loss: 1.0428 itc_loss: 0.8324 itm_loss: 0.2104 2023/09/05 02:53:31 - mmengine - INFO - Epoch(train) [5][ 240/1407] base_lr: 7.5821e-07 lr: 7.5821e-07 eta: 0:28:29 time: 1.4642 data_time: 0.0132 memory: 8642 grad_norm: 6.7676 loss: 1.2160 itc_loss: 0.9631 itm_loss: 0.2528 2023/09/05 02:54:00 - mmengine - INFO - Epoch(train) [5][ 260/1407] base_lr: 7.3635e-07 lr: 7.3635e-07 eta: 0:28:00 time: 1.4653 data_time: 0.0130 memory: 8642 grad_norm: 7.3668 loss: 1.0819 itc_loss: 0.8402 itm_loss: 0.2417 2023/09/05 02:54:29 - mmengine - INFO - Epoch(train) [5][ 280/1407] base_lr: 7.1484e-07 lr: 7.1484e-07 eta: 0:27:31 time: 1.4647 data_time: 0.0131 memory: 8642 grad_norm: 6.2987 loss: 1.1308 itc_loss: 0.9012 itm_loss: 0.2296 2023/09/05 02:54:58 - mmengine - INFO - Epoch(train) [5][ 300/1407] base_lr: 6.9367e-07 lr: 6.9367e-07 eta: 0:27:02 time: 1.4641 data_time: 0.0134 memory: 8642 grad_norm: 6.3292 loss: 1.1657 itc_loss: 0.9246 itm_loss: 0.2412 2023/09/05 02:55:28 - mmengine - INFO - Epoch(train) [5][ 320/1407] base_lr: 6.7285e-07 lr: 6.7285e-07 eta: 0:26:32 time: 1.4643 data_time: 0.0129 memory: 8642 grad_norm: 6.1949 loss: 1.1823 itc_loss: 0.9472 itm_loss: 0.2351 2023/09/05 02:55:57 - mmengine - INFO - Epoch(train) [5][ 340/1407] base_lr: 6.5238e-07 lr: 6.5238e-07 eta: 0:26:03 time: 1.4637 data_time: 0.0131 memory: 8642 grad_norm: 5.8414 loss: 1.1542 itc_loss: 0.8996 itm_loss: 0.2546 2023/09/05 02:56:26 - mmengine - INFO - Epoch(train) [5][ 360/1407] base_lr: 6.3227e-07 lr: 6.3227e-07 eta: 0:25:34 time: 1.4682 data_time: 0.0130 memory: 8642 grad_norm: 7.3542 loss: 1.0742 itc_loss: 0.8330 itm_loss: 0.2412 2023/09/05 02:56:44 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 02:56:56 - mmengine - INFO - Epoch(train) [5][ 380/1407] base_lr: 6.1250e-07 lr: 6.1250e-07 eta: 0:25:04 time: 1.4802 data_time: 0.0132 memory: 8642 grad_norm: 7.4240 loss: 1.1691 itc_loss: 0.9160 itm_loss: 0.2532 2023/09/05 02:57:25 - mmengine - INFO - Epoch(train) [5][ 400/1407] base_lr: 5.9309e-07 lr: 5.9309e-07 eta: 0:24:35 time: 1.4690 data_time: 0.0132 memory: 8642 grad_norm: 6.1813 loss: 1.0573 itc_loss: 0.8245 itm_loss: 0.2328 2023/09/05 02:57:55 - mmengine - INFO - Epoch(train) [5][ 420/1407] base_lr: 5.7403e-07 lr: 5.7403e-07 eta: 0:24:06 time: 1.4663 data_time: 0.0133 memory: 8642 grad_norm: 5.8413 loss: 1.0483 itc_loss: 0.8286 itm_loss: 0.2196 2023/09/05 02:58:24 - mmengine - INFO - Epoch(train) [5][ 440/1407] base_lr: 5.5533e-07 lr: 5.5533e-07 eta: 0:23:36 time: 1.4634 data_time: 0.0134 memory: 8642 grad_norm: 6.8495 loss: 1.0044 itc_loss: 0.7787 itm_loss: 0.2257 2023/09/05 02:58:53 - mmengine - INFO - Epoch(train) [5][ 460/1407] base_lr: 5.3699e-07 lr: 5.3699e-07 eta: 0:23:07 time: 1.4654 data_time: 0.0135 memory: 8642 grad_norm: 6.7145 loss: 1.0902 itc_loss: 0.8488 itm_loss: 0.2414 2023/09/05 02:59:23 - mmengine - INFO - Epoch(train) [5][ 480/1407] base_lr: 5.1900e-07 lr: 5.1900e-07 eta: 0:22:38 time: 1.4658 data_time: 0.0134 memory: 8642 grad_norm: 7.8402 loss: 1.1612 itc_loss: 0.9052 itm_loss: 0.2560 2023/09/05 02:59:52 - mmengine - INFO - Epoch(train) [5][ 500/1407] base_lr: 5.0138e-07 lr: 5.0138e-07 eta: 0:22:09 time: 1.4652 data_time: 0.0136 memory: 8642 grad_norm: 6.4012 loss: 1.3358 itc_loss: 1.0684 itm_loss: 0.2674 2023/09/05 03:00:21 - mmengine - INFO - Epoch(train) [5][ 520/1407] base_lr: 4.8413e-07 lr: 4.8413e-07 eta: 0:21:39 time: 1.4656 data_time: 0.0134 memory: 8642 grad_norm: 7.2601 loss: 1.0211 itc_loss: 0.7863 itm_loss: 0.2348 2023/09/05 03:00:50 - mmengine - INFO - Epoch(train) [5][ 540/1407] base_lr: 4.6723e-07 lr: 4.6723e-07 eta: 0:21:10 time: 1.4643 data_time: 0.0133 memory: 8642 grad_norm: 6.6502 loss: 1.0584 itc_loss: 0.8311 itm_loss: 0.2273 2023/09/05 03:01:20 - mmengine - INFO - Epoch(train) [5][ 560/1407] base_lr: 4.5071e-07 lr: 4.5071e-07 eta: 0:20:41 time: 1.4644 data_time: 0.0134 memory: 8642 grad_norm: 7.6383 loss: 1.2057 itc_loss: 0.9538 itm_loss: 0.2520 2023/09/05 03:01:49 - mmengine - INFO - Epoch(train) [5][ 580/1407] base_lr: 4.3454e-07 lr: 4.3454e-07 eta: 0:20:11 time: 1.4655 data_time: 0.0134 memory: 8642 grad_norm: 7.0941 loss: 1.0628 itc_loss: 0.8042 itm_loss: 0.2587 2023/09/05 03:02:18 - mmengine - INFO - Epoch(train) [5][ 600/1407] base_lr: 4.1875e-07 lr: 4.1875e-07 eta: 0:19:42 time: 1.4636 data_time: 0.0135 memory: 8642 grad_norm: 6.4536 loss: 1.1483 itc_loss: 0.8944 itm_loss: 0.2539 2023/09/05 03:02:48 - mmengine - INFO - Epoch(train) [5][ 620/1407] base_lr: 4.0333e-07 lr: 4.0333e-07 eta: 0:19:13 time: 1.4650 data_time: 0.0136 memory: 8642 grad_norm: 6.5475 loss: 1.2718 itc_loss: 1.0133 itm_loss: 0.2586 2023/09/05 03:03:17 - mmengine - INFO - Epoch(train) [5][ 640/1407] base_lr: 3.8828e-07 lr: 3.8828e-07 eta: 0:18:43 time: 1.4657 data_time: 0.0136 memory: 8642 grad_norm: 7.1654 loss: 1.1853 itc_loss: 0.9324 itm_loss: 0.2528 2023/09/05 03:03:46 - mmengine - INFO - Epoch(train) [5][ 660/1407] base_lr: 3.7359e-07 lr: 3.7359e-07 eta: 0:18:14 time: 1.4659 data_time: 0.0135 memory: 8642 grad_norm: 7.4481 loss: 1.1700 itc_loss: 0.9376 itm_loss: 0.2323 2023/09/05 03:04:16 - mmengine - INFO - Epoch(train) [5][ 680/1407] base_lr: 3.5929e-07 lr: 3.5929e-07 eta: 0:17:45 time: 1.4653 data_time: 0.0132 memory: 8642 grad_norm: 5.4770 loss: 1.1668 itc_loss: 0.9139 itm_loss: 0.2529 2023/09/05 03:04:45 - mmengine - INFO - Epoch(train) [5][ 700/1407] base_lr: 3.4535e-07 lr: 3.4535e-07 eta: 0:17:15 time: 1.4655 data_time: 0.0136 memory: 8642 grad_norm: 6.5802 loss: 1.0914 itc_loss: 0.8524 itm_loss: 0.2390 2023/09/05 03:05:14 - mmengine - INFO - Epoch(train) [5][ 720/1407] base_lr: 3.3180e-07 lr: 3.3180e-07 eta: 0:16:46 time: 1.4654 data_time: 0.0140 memory: 8642 grad_norm: 6.6269 loss: 1.2131 itc_loss: 0.9274 itm_loss: 0.2857 2023/09/05 03:05:44 - mmengine - INFO - Epoch(train) [5][ 740/1407] base_lr: 3.1861e-07 lr: 3.1861e-07 eta: 0:16:17 time: 1.4669 data_time: 0.0137 memory: 8642 grad_norm: 7.1291 loss: 1.1927 itc_loss: 0.9480 itm_loss: 0.2447 2023/09/05 03:06:13 - mmengine - INFO - Epoch(train) [5][ 760/1407] base_lr: 3.0581e-07 lr: 3.0581e-07 eta: 0:15:48 time: 1.4644 data_time: 0.0136 memory: 8642 grad_norm: 6.4045 loss: 1.1093 itc_loss: 0.8852 itm_loss: 0.2241 2023/09/05 03:06:42 - mmengine - INFO - Epoch(train) [5][ 780/1407] base_lr: 2.9338e-07 lr: 2.9338e-07 eta: 0:15:18 time: 1.4660 data_time: 0.0137 memory: 8642 grad_norm: 6.8657 loss: 1.0460 itc_loss: 0.7928 itm_loss: 0.2532 2023/09/05 03:07:11 - mmengine - INFO - Epoch(train) [5][ 800/1407] base_lr: 2.8134e-07 lr: 2.8134e-07 eta: 0:14:49 time: 1.4650 data_time: 0.0134 memory: 8642 grad_norm: 5.7805 loss: 1.1194 itc_loss: 0.8671 itm_loss: 0.2523 2023/09/05 03:07:41 - mmengine - INFO - Epoch(train) [5][ 820/1407] base_lr: 2.6967e-07 lr: 2.6967e-07 eta: 0:14:20 time: 1.4635 data_time: 0.0143 memory: 8642 grad_norm: 6.9890 loss: 1.2462 itc_loss: 0.9583 itm_loss: 0.2879 2023/09/05 03:08:10 - mmengine - INFO - Epoch(train) [5][ 840/1407] base_lr: 2.5838e-07 lr: 2.5838e-07 eta: 0:13:50 time: 1.4652 data_time: 0.0132 memory: 8642 grad_norm: 7.0647 loss: 1.1966 itc_loss: 0.9426 itm_loss: 0.2540 2023/09/05 03:08:39 - mmengine - INFO - Epoch(train) [5][ 860/1407] base_lr: 2.4748e-07 lr: 2.4748e-07 eta: 0:13:21 time: 1.4654 data_time: 0.0133 memory: 8642 grad_norm: 7.4418 loss: 1.0639 itc_loss: 0.8491 itm_loss: 0.2148 2023/09/05 03:09:09 - mmengine - INFO - Epoch(train) [5][ 880/1407] base_lr: 2.3696e-07 lr: 2.3696e-07 eta: 0:12:52 time: 1.4659 data_time: 0.0130 memory: 8642 grad_norm: 6.5965 loss: 1.0556 itc_loss: 0.8423 itm_loss: 0.2133 2023/09/05 03:09:38 - mmengine - INFO - Epoch(train) [5][ 900/1407] base_lr: 2.2683e-07 lr: 2.2683e-07 eta: 0:12:22 time: 1.4649 data_time: 0.0134 memory: 8642 grad_norm: 6.2255 loss: 1.0695 itc_loss: 0.8494 itm_loss: 0.2201 2023/09/05 03:10:07 - mmengine - INFO - Epoch(train) [5][ 920/1407] base_lr: 2.1708e-07 lr: 2.1708e-07 eta: 0:11:53 time: 1.4652 data_time: 0.0134 memory: 8642 grad_norm: 7.1949 loss: 1.1308 itc_loss: 0.8659 itm_loss: 0.2650 2023/09/05 03:10:37 - mmengine - INFO - Epoch(train) [5][ 940/1407] base_lr: 2.0771e-07 lr: 2.0771e-07 eta: 0:11:24 time: 1.4648 data_time: 0.0133 memory: 8642 grad_norm: 6.1949 loss: 1.0276 itc_loss: 0.7685 itm_loss: 0.2591 2023/09/05 03:11:06 - mmengine - INFO - Epoch(train) [5][ 960/1407] base_lr: 1.9873e-07 lr: 1.9873e-07 eta: 0:10:54 time: 1.4646 data_time: 0.0135 memory: 8642 grad_norm: 7.0379 loss: 1.1724 itc_loss: 0.9168 itm_loss: 0.2555 2023/09/05 03:11:35 - mmengine - INFO - Epoch(train) [5][ 980/1407] base_lr: 1.9014e-07 lr: 1.9014e-07 eta: 0:10:25 time: 1.4642 data_time: 0.0134 memory: 8642 grad_norm: 7.1009 loss: 1.1303 itc_loss: 0.8879 itm_loss: 0.2424 2023/09/05 03:12:04 - mmengine - INFO - Epoch(train) [5][1000/1407] base_lr: 1.8193e-07 lr: 1.8193e-07 eta: 0:09:56 time: 1.4646 data_time: 0.0135 memory: 8642 grad_norm: 6.1643 loss: 1.2245 itc_loss: 0.9459 itm_loss: 0.2786 2023/09/05 03:12:34 - mmengine - INFO - Epoch(train) [5][1020/1407] base_lr: 1.7412e-07 lr: 1.7412e-07 eta: 0:09:27 time: 1.4635 data_time: 0.0134 memory: 8642 grad_norm: 7.3027 loss: 0.9836 itc_loss: 0.7856 itm_loss: 0.1980 2023/09/05 03:13:03 - mmengine - INFO - Epoch(train) [5][1040/1407] base_lr: 1.6669e-07 lr: 1.6669e-07 eta: 0:08:57 time: 1.4642 data_time: 0.0137 memory: 8642 grad_norm: 6.9627 loss: 1.1674 itc_loss: 0.9238 itm_loss: 0.2436 2023/09/05 03:13:32 - mmengine - INFO - Epoch(train) [5][1060/1407] base_lr: 1.5965e-07 lr: 1.5965e-07 eta: 0:08:28 time: 1.4651 data_time: 0.0136 memory: 8642 grad_norm: 7.3772 loss: 1.0657 itc_loss: 0.8466 itm_loss: 0.2190 2023/09/05 03:14:02 - mmengine - INFO - Epoch(train) [5][1080/1407] base_lr: 1.5301e-07 lr: 1.5301e-07 eta: 0:07:59 time: 1.4665 data_time: 0.0133 memory: 8642 grad_norm: 7.2858 loss: 1.0979 itc_loss: 0.8716 itm_loss: 0.2263 2023/09/05 03:14:31 - mmengine - INFO - Epoch(train) [5][1100/1407] base_lr: 1.4675e-07 lr: 1.4675e-07 eta: 0:07:29 time: 1.4639 data_time: 0.0132 memory: 8642 grad_norm: 6.7012 loss: 1.0585 itc_loss: 0.8337 itm_loss: 0.2248 2023/09/05 03:15:00 - mmengine - INFO - Epoch(train) [5][1120/1407] base_lr: 1.4088e-07 lr: 1.4088e-07 eta: 0:07:00 time: 1.4670 data_time: 0.0132 memory: 8642 grad_norm: 5.8476 loss: 1.0680 itc_loss: 0.8382 itm_loss: 0.2298 2023/09/05 03:15:30 - mmengine - INFO - Epoch(train) [5][1140/1407] base_lr: 1.3541e-07 lr: 1.3541e-07 eta: 0:06:31 time: 1.4660 data_time: 0.0135 memory: 8642 grad_norm: 7.5586 loss: 1.0031 itc_loss: 0.7767 itm_loss: 0.2264 2023/09/05 03:15:59 - mmengine - INFO - Epoch(train) [5][1160/1407] base_lr: 1.3033e-07 lr: 1.3033e-07 eta: 0:06:01 time: 1.4663 data_time: 0.0135 memory: 8642 grad_norm: 6.8332 loss: 1.2065 itc_loss: 0.9356 itm_loss: 0.2709 2023/09/05 03:16:28 - mmengine - INFO - Epoch(train) [5][1180/1407] base_lr: 1.2564e-07 lr: 1.2564e-07 eta: 0:05:32 time: 1.4647 data_time: 0.0135 memory: 8642 grad_norm: 7.3907 loss: 1.0893 itc_loss: 0.8404 itm_loss: 0.2489 2023/09/05 03:16:57 - mmengine - INFO - Epoch(train) [5][1200/1407] base_lr: 1.2134e-07 lr: 1.2134e-07 eta: 0:05:03 time: 1.4646 data_time: 0.0134 memory: 8642 grad_norm: 6.1411 loss: 1.0595 itc_loss: 0.8454 itm_loss: 0.2140 2023/09/05 03:17:27 - mmengine - INFO - Epoch(train) [5][1220/1407] base_lr: 1.1743e-07 lr: 1.1743e-07 eta: 0:04:34 time: 1.4663 data_time: 0.0133 memory: 8642 grad_norm: 6.5343 loss: 1.0415 itc_loss: 0.8151 itm_loss: 0.2264 2023/09/05 03:17:56 - mmengine - INFO - Epoch(train) [5][1240/1407] base_lr: 1.1392e-07 lr: 1.1392e-07 eta: 0:04:04 time: 1.4651 data_time: 0.0133 memory: 8642 grad_norm: 6.2188 loss: 1.0872 itc_loss: 0.8431 itm_loss: 0.2441 2023/09/05 03:18:25 - mmengine - INFO - Epoch(train) [5][1260/1407] base_lr: 1.1081e-07 lr: 1.1081e-07 eta: 0:03:35 time: 1.4650 data_time: 0.0135 memory: 8642 grad_norm: 7.0341 loss: 1.1913 itc_loss: 0.9520 itm_loss: 0.2393 2023/09/05 03:18:55 - mmengine - INFO - Epoch(train) [5][1280/1407] base_lr: 1.0808e-07 lr: 1.0808e-07 eta: 0:03:06 time: 1.4647 data_time: 0.0135 memory: 8642 grad_norm: 6.0690 loss: 0.9696 itc_loss: 0.7773 itm_loss: 0.1923 2023/09/05 03:19:24 - mmengine - INFO - Epoch(train) [5][1300/1407] base_lr: 1.0576e-07 lr: 1.0576e-07 eta: 0:02:36 time: 1.4644 data_time: 0.0134 memory: 8642 grad_norm: 6.1589 loss: 1.1653 itc_loss: 0.9267 itm_loss: 0.2386 2023/09/05 03:19:53 - mmengine - INFO - Epoch(train) [5][1320/1407] base_lr: 1.0382e-07 lr: 1.0382e-07 eta: 0:02:07 time: 1.4646 data_time: 0.0133 memory: 8642 grad_norm: 6.6970 loss: 1.2666 itc_loss: 0.9589 itm_loss: 0.3077 2023/09/05 03:20:23 - mmengine - INFO - Epoch(train) [5][1340/1407] base_lr: 1.0228e-07 lr: 1.0228e-07 eta: 0:01:38 time: 1.4657 data_time: 0.0133 memory: 8642 grad_norm: 6.1220 loss: 1.0224 itc_loss: 0.8128 itm_loss: 0.2096 2023/09/05 03:20:52 - mmengine - INFO - Epoch(train) [5][1360/1407] base_lr: 1.0114e-07 lr: 1.0114e-07 eta: 0:01:08 time: 1.4645 data_time: 0.0134 memory: 8642 grad_norm: 5.7250 loss: 1.2365 itc_loss: 0.9433 itm_loss: 0.2932 2023/09/05 03:21:09 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 03:21:21 - mmengine - INFO - Epoch(train) [5][1380/1407] base_lr: 1.0039e-07 lr: 1.0039e-07 eta: 0:00:39 time: 1.4644 data_time: 0.0134 memory: 8642 grad_norm: 6.3278 loss: 1.0038 itc_loss: 0.7808 itm_loss: 0.2230 2023/09/05 03:21:50 - mmengine - INFO - Epoch(train) [5][1400/1407] base_lr: 1.0003e-07 lr: 1.0003e-07 eta: 0:00:10 time: 1.4631 data_time: 0.0133 memory: 8642 grad_norm: 7.5380 loss: 1.0574 itc_loss: 0.8409 itm_loss: 0.2166 2023/09/05 03:22:00 - mmengine - INFO - Exp name: vindlu_ret_train_20230905_002440 2023/09/05 03:22:00 - mmengine - INFO - Epoch(train) [5][1407/1407] base_lr: 1.0000e-07 lr: 1.0000e-07 eta: 0:00:00 time: 1.4217 data_time: 0.0133 memory: 8642 grad_norm: 7.6684 loss: 1.0274 itc_loss: 0.8187 itm_loss: 0.2087 2023/09/05 03:22:00 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/09/05 03:23:04 - mmengine - INFO - Epoch(val) [5][16/16] i2t/retrieval/Recall@1: 42.4000 i2t/retrieval/Recall@5: 70.5000 i2t/retrieval/Recall@10: 80.3000 t2i/retrieval/Recall@1: 43.5000 t2i/retrieval/Recall@5: 70.8000 t2i/retrieval/Recall@10: 80.2000 data_time: 0.0167 time: 0.3179