[04/14 19:06:59] fastreid INFO: Rank of current process: 0. World size: 4
[04/14 19:06:59] fastreid INFO: Environment info:
----------------------  ----------------------------------------------------------------------------------------------
sys.platform            linux
Python                  3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]
numpy                   1.25.2
fastreid                0.1.0 @/home/ma-user/work/Projects/ReIDNet_Finetune/FastReID/./fastreid
FASTREID_ENV_MODULE     <not set>
PyTorch                 1.12.1+cu113 @/home/ma-user/anaconda3/envs/Generate3D/lib/python3.10/site-packages/torch
PyTorch debug build     False
GPU available           True
GPU 0,1,2,3             Tesla V100S-PCIE-32GB
CUDA_HOME               /usr/local/cuda
Pillow                  10.1.0
torchvision             0.13.1+cu113 @/home/ma-user/anaconda3/envs/Generate3D/lib/python3.10/site-packages/torchvision
torchvision arch flags  sm_35, sm_50, sm_60, sm_70, sm_75, sm_80, sm_86
fvcore                  0.1.5.post20221221
cv2                     4.8.0
----------------------  ----------------------------------------------------------------------------------------------
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 8.5  (built against CUDA 11.7)
    - 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, 

[04/14 19:06:59] fastreid INFO: Command line arguments: Namespace(config_file='configs/CMDM/mgn_R50_lion.yml', finetune=False, resume=False, eval_only=False, num_gpus=4, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:34337', opts=[])
[04/14 19:06:59] fastreid INFO: Contents of args.config_file=configs/CMDM/mgn_R50_lion.yml:
_BASE_: "../Base-MGN.yml"

MODEL:
  BACKBONE:
    WITH_IBN: False
    EXTRA_BN: True
    PRETRAIN_PATH: "/home/ma-user/work/Projects/ReIDNets_Checkpoints_TransReID/ResNet50_ViTdefault_2468_406080100_bs120_originaldino_noreid.pth"
  PIXEL_MEAN: [123.675, 116.280, 103.530]
  PIXEL_STD: [58.395, 57.120, 57.375]

INPUT:
  REA:
    MEAN: [0.0, 0.0, 0.0]
  DO_AUTOAUG: False

SOLVER:
  HEADS_LR_FACTOR: 1.0
  BACKBONE_BN_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: -1

DATASETS:
  NAMES: ("CMDM",)
  TESTS: ("CMDM",)
  KWARGS: 'data_name:market+split_mode:id+split_ratio:1.0'
  ROOT: "/home/ma-user/work/Datasets/ImageReID_Datasets/mixreid"

TEST:
  EVAL_PERIOD: 60

OUTPUT_DIR: "logs/market/ResNet50_ViTdefault_LION_MGN"

[04/14 19:06:59] fastreid INFO: Running with full config:
CUDNN_BENCHMARK: True
DATALOADER:
  NAIVE_WAY: True
  NUM_INSTANCE: 16
  NUM_WORKERS: 8
  PK_SAMPLER: True
DATASETS:
  COMBINEALL: False
  IS_LMDB: False
  KWARGS: data_name:market+split_mode:id+split_ratio:1.0
  NAMES: ('CMDM',)
  ROOT: /home/ma-user/work/Datasets/ImageReID_Datasets/mixreid
  TESTS: ('CMDM',)
INPUT:
  CJ:
    BRIGHTNESS: 0.15
    CONTRAST: 0.15
    ENABLED: False
    HUE: 0.1
    PROB: 0.8
    SATURATION: 0.1
  DO_AUGMIX: False
  DO_AUTOAUG: False
  DO_FLIP: True
  DO_PAD: True
  FLIP_PROB: 0.5
  PADDING: 10
  PADDING_MODE: constant
  REA:
    ENABLED: True
    MEAN: [0.0, 0.0, 0.0]
    PROB: 0.5
  RPT:
    ENABLED: False
    PROB: 0.5
  SIZE_TEST: [384, 128]
  SIZE_TRAIN: [384, 128]
MODEL:
  BACKBONE:
    DEPTH: 50x
    EXTRA_BN: True
    FEAT_DIM: 2048
    LAST_STRIDE: 1
    NAME: build_resnet_backbone
    NORM: BN
    PRETRAIN: True
    PRETRAIN_PATH: /home/ma-user/work/Projects/ReIDNets_Checkpoints_TransReID/ResNet50_ViTdefault_2468_406080100_bs120_originaldino_noreid.pth
    WITH_IBN: False
    WITH_NL: False
    WITH_SE: False
  DEVICE: cuda
  FREEZE_LAYERS: ['backbone', 'b1', 'b2', 'b3']
  HEADS:
    CLS_LAYER: circleSoftmax
    EMBEDDING_DIM: 256
    MARGIN: 0.35
    NAME: EmbeddingHead
    NECK_FEAT: after
    NORM: BN
    NUM_CLASSES: 0
    POOL_LAYER: gempoolP
    SCALE: 64
    WITH_BNNECK: True
  LOSSES:
    CE:
      ALPHA: 0.2
      EPSILON: 0.1
      SCALE: 1.0
    CIRCLE:
      ALPHA: 128
      MARGIN: 0.25
      SCALE: 1.0
    FL:
      ALPHA: 0.25
      GAMMA: 2
      SCALE: 1.0
    NAME: ('CrossEntropyLoss', 'TripletLoss')
    TRI:
      HARD_MINING: True
      MARGIN: 0.0
      NORM_FEAT: False
      SCALE: 1.0
  META_ARCHITECTURE: MGN
  PIXEL_MEAN: [123.675, 116.28, 103.53]
  PIXEL_STD: [58.395, 57.12, 57.375]
  WEIGHTS: 
OUTPUT_DIR: logs/market/ResNet50_ViTdefault_LION_MGN
SOLVER:
  AMP_ENABLED: False
  BACKBONE_BN_LR_FACTOR: 1.0
  BASE_LR: 0.00035
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: -1
  DELAY_ITERS: 30
  ETA_MIN_LR: 7.7e-07
  FREEZE_ITERS: 10
  GAMMA: 0.1
  HEADS_LR_FACTOR: 1.0
  IMS_PER_BATCH: 64
  MAX_ITER: 60
  MOMENTUM: 0.9
  OPT: Adam
  SCHED: WarmupCosineAnnealingLR
  STEPS: [40, 90]
  SWA:
    ENABLED: False
    ETA_MIN_LR: 3.5e-06
    ITER: 10
    LR_FACTOR: 10.0
    LR_SCHED: False
    PERIOD: 2
  WARMUP_FACTOR: 0.01
  WARMUP_ITERS: 10
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0005
  WEIGHT_DECAY_BIAS: 0.0005
TEST:
  AQE:
    ALPHA: 3.0
    ENABLED: False
    QE_K: 5
    QE_TIME: 1
  EVAL_PERIOD: 60
  IMS_PER_BATCH: 128
  METRIC: cosine
  PRECISE_BN:
    DATASET: Market1501
    ENABLED: False
    NUM_ITER: 300
  RERANK:
    ENABLED: False
    K1: 20
    K2: 6
    LAMBDA: 0.3
  ROC_ENABLED: False
[04/14 19:06:59] fastreid INFO: Full config saved to /home/ma-user/work/Projects/ReIDNet_Finetune/FastReID/logs/market/ResNet50_ViTdefault_LION_MGN/config.yaml
[04/14 19:06:59] fastreid.utils.env INFO: Using a generated random seed 61108083
[04/14 19:06:59] fastreid.engine.defaults INFO: Prepare training set
[04/14 19:12:47] fastreid INFO: Rank of current process: 0. World size: 4
[04/14 19:12:47] fastreid INFO: Environment info:
----------------------  ----------------------------------------------------------------------------------------------
sys.platform            linux
Python                  3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]
numpy                   1.25.2
fastreid                0.1.0 @/home/ma-user/work/Projects/ReIDNet_Finetune/FastReID/./fastreid
FASTREID_ENV_MODULE     <not set>
PyTorch                 1.12.1+cu113 @/home/ma-user/anaconda3/envs/Generate3D/lib/python3.10/site-packages/torch
PyTorch debug build     False
GPU available           True
GPU 0,1,2,3             Tesla V100S-PCIE-32GB
CUDA_HOME               /usr/local/cuda
Pillow                  10.1.0
torchvision             0.13.1+cu113 @/home/ma-user/anaconda3/envs/Generate3D/lib/python3.10/site-packages/torchvision
torchvision arch flags  sm_35, sm_50, sm_60, sm_70, sm_75, sm_80, sm_86
fvcore                  0.1.5.post20221221
cv2                     4.8.0
----------------------  ----------------------------------------------------------------------------------------------
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 8.5  (built against CUDA 11.7)
    - 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, 

[04/14 19:12:47] fastreid INFO: Command line arguments: Namespace(config_file='configs/CMDM/mgn_R50_lion.yml', finetune=False, resume=False, eval_only=False, num_gpus=4, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:41480', opts=[])
[04/14 19:12:47] fastreid INFO: Contents of args.config_file=configs/CMDM/mgn_R50_lion.yml:
_BASE_: "../Base-MGN.yml"

MODEL:
  BACKBONE:
    WITH_IBN: False
    EXTRA_BN: True
    PRETRAIN_PATH: "/home/ma-user/work/Projects/ReIDNets_Checkpoints_TransReID/ResNet50_ViTdefault_2468_406080100_bs120_originaldino_noreid.pth"
  PIXEL_MEAN: [123.675, 116.280, 103.530]
  PIXEL_STD: [58.395, 57.120, 57.375]

INPUT:
  REA:
    MEAN: [0.0, 0.0, 0.0]
  DO_AUTOAUG: False

SOLVER:
  HEADS_LR_FACTOR: 1.0
  BACKBONE_BN_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: -1

DATASETS:
  NAMES: ("CMDM",)
  TESTS: ("CMDM",)
  KWARGS: 'data_name:market+split_mode:id+split_ratio:1.0'
  ROOT: "/home/ma-user/work/Datasets/ImageReID_Datasets/mixreid"

TEST:
  EVAL_PERIOD: 60

OUTPUT_DIR: "logs/market/ResNet50_ViTdefault_LION_MGN"

[04/14 19:12:47] fastreid INFO: Running with full config:
CUDNN_BENCHMARK: True
DATALOADER:
  NAIVE_WAY: True
  NUM_INSTANCE: 16
  NUM_WORKERS: 8
  PK_SAMPLER: True
DATASETS:
  COMBINEALL: False
  IS_LMDB: False
  KWARGS: data_name:market+split_mode:id+split_ratio:1.0
  NAMES: ('CMDM',)
  ROOT: /home/ma-user/work/Datasets/ImageReID_Datasets/mixreid
  TESTS: ('CMDM',)
INPUT:
  CJ:
    BRIGHTNESS: 0.15
    CONTRAST: 0.15
    ENABLED: False
    HUE: 0.1
    PROB: 0.8
    SATURATION: 0.1
  DO_AUGMIX: False
  DO_AUTOAUG: False
  DO_FLIP: True
  DO_PAD: True
  FLIP_PROB: 0.5
  PADDING: 10
  PADDING_MODE: constant
  REA:
    ENABLED: True
    MEAN: [0.0, 0.0, 0.0]
    PROB: 0.5
  RPT:
    ENABLED: False
    PROB: 0.5
  SIZE_TEST: [384, 128]
  SIZE_TRAIN: [384, 128]
MODEL:
  BACKBONE:
    DEPTH: 50x
    EXTRA_BN: True
    FEAT_DIM: 2048
    LAST_STRIDE: 1
    NAME: build_resnet_backbone
    NORM: BN
    PRETRAIN: True
    PRETRAIN_PATH: /home/ma-user/work/Projects/ReIDNets_Checkpoints_TransReID/ResNet50_ViTdefault_2468_406080100_bs120_originaldino_noreid.pth
    WITH_IBN: False
    WITH_NL: False
    WITH_SE: False
  DEVICE: cuda
  FREEZE_LAYERS: ['backbone', 'b1', 'b2', 'b3']
  HEADS:
    CLS_LAYER: circleSoftmax
    EMBEDDING_DIM: 256
    MARGIN: 0.35
    NAME: EmbeddingHead
    NECK_FEAT: after
    NORM: BN
    NUM_CLASSES: 0
    POOL_LAYER: gempoolP
    SCALE: 64
    WITH_BNNECK: True
  LOSSES:
    CE:
      ALPHA: 0.2
      EPSILON: 0.1
      SCALE: 1.0
    CIRCLE:
      ALPHA: 128
      MARGIN: 0.25
      SCALE: 1.0
    FL:
      ALPHA: 0.25
      GAMMA: 2
      SCALE: 1.0
    NAME: ('CrossEntropyLoss', 'TripletLoss')
    TRI:
      HARD_MINING: True
      MARGIN: 0.0
      NORM_FEAT: False
      SCALE: 1.0
  META_ARCHITECTURE: MGN
  PIXEL_MEAN: [123.675, 116.28, 103.53]
  PIXEL_STD: [58.395, 57.12, 57.375]
  WEIGHTS: 
OUTPUT_DIR: logs/market/ResNet50_ViTdefault_LION_MGN
SOLVER:
  AMP_ENABLED: False
  BACKBONE_BN_LR_FACTOR: 1.0
  BASE_LR: 0.00035
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: -1
  DELAY_ITERS: 30
  ETA_MIN_LR: 7.7e-07
  FREEZE_ITERS: 10
  GAMMA: 0.1
  HEADS_LR_FACTOR: 1.0
  IMS_PER_BATCH: 64
  MAX_ITER: 60
  MOMENTUM: 0.9
  OPT: Adam
  SCHED: WarmupCosineAnnealingLR
  STEPS: [40, 90]
  SWA:
    ENABLED: False
    ETA_MIN_LR: 3.5e-06
    ITER: 10
    LR_FACTOR: 10.0
    LR_SCHED: False
    PERIOD: 2
  WARMUP_FACTOR: 0.01
  WARMUP_ITERS: 10
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0005
  WEIGHT_DECAY_BIAS: 0.0005
TEST:
  AQE:
    ALPHA: 3.0
    ENABLED: False
    QE_K: 5
    QE_TIME: 1
  EVAL_PERIOD: 60
  IMS_PER_BATCH: 128
  METRIC: cosine
  PRECISE_BN:
    DATASET: Market1501
    ENABLED: False
    NUM_ITER: 300
  RERANK:
    ENABLED: False
    K1: 20
    K2: 6
    LAMBDA: 0.3
  ROC_ENABLED: False
[04/14 19:12:47] fastreid INFO: Full config saved to /home/ma-user/work/Projects/ReIDNet_Finetune/FastReID/logs/market/ResNet50_ViTdefault_LION_MGN/config.yaml
[04/14 19:12:47] fastreid.utils.env INFO: Using a generated random seed 49125039
[04/14 19:12:47] fastreid.engine.defaults INFO: Prepare training set
[04/14 19:12:48] fastreid.data.datasets.bases INFO: => Loaded CMDM in csv format: 
[36m| subset   | # ids   | # images   | # cameras   |
|:---------|:--------|:-----------|:------------|
| train    | 751     | 12936      | 6           |[0m
[04/14 19:12:48] fastreid.engine.defaults INFO: Auto-scaling the config to num_classes=751, max_Iter=12120, wamrup_Iter=2020, freeze_Iter=2020, delay_Iter=6060, step_Iter=[8080, 18180], ckpt_Iter=-203, eval_Iter=12200.
[04/14 19:12:49] fastreid.modeling.backbones.resnet INFO: Loading pretrained model from /home/ma-user/work/Projects/ReIDNets_Checkpoints_TransReID/ResNet50_ViTdefault_2468_406080100_bs120_originaldino_noreid.pth
[04/14 19:12:49] fastreid.modeling.backbones.resnet INFO: Some model parameters are not in the checkpoint:
  [34mconv1.weight[0m
  [34mbn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.0.conv1.weight[0m
  [34mlayer1.0.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.0.conv2.weight[0m
  [34mlayer1.0.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.0.conv3.weight[0m
  [34mlayer1.0.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.0.downsample.0.weight[0m
  [34mlayer1.0.downsample.1.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.1.conv1.weight[0m
  [34mlayer1.1.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.1.conv2.weight[0m
  [34mlayer1.1.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.1.conv3.weight[0m
  [34mlayer1.1.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.2.conv1.weight[0m
  [34mlayer1.2.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.2.conv2.weight[0m
  [34mlayer1.2.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer1.2.conv3.weight[0m
  [34mlayer1.2.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.0.conv1.weight[0m
  [34mlayer2.0.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.0.conv2.weight[0m
  [34mlayer2.0.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.0.conv3.weight[0m
  [34mlayer2.0.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.0.downsample.0.weight[0m
  [34mlayer2.0.downsample.1.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.1.conv1.weight[0m
  [34mlayer2.1.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.1.conv2.weight[0m
  [34mlayer2.1.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.1.conv3.weight[0m
  [34mlayer2.1.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.2.conv1.weight[0m
  [34mlayer2.2.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.2.conv2.weight[0m
  [34mlayer2.2.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.2.conv3.weight[0m
  [34mlayer2.2.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.3.conv1.weight[0m
  [34mlayer2.3.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.3.conv2.weight[0m
  [34mlayer2.3.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer2.3.conv3.weight[0m
  [34mlayer2.3.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.0.conv1.weight[0m
  [34mlayer3.0.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.0.conv2.weight[0m
  [34mlayer3.0.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.0.conv3.weight[0m
  [34mlayer3.0.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.0.downsample.0.weight[0m
  [34mlayer3.0.downsample.1.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.1.conv1.weight[0m
  [34mlayer3.1.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.1.conv2.weight[0m
  [34mlayer3.1.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.1.conv3.weight[0m
  [34mlayer3.1.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.2.conv1.weight[0m
  [34mlayer3.2.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.2.conv2.weight[0m
  [34mlayer3.2.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.2.conv3.weight[0m
  [34mlayer3.2.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.3.conv1.weight[0m
  [34mlayer3.3.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.3.conv2.weight[0m
  [34mlayer3.3.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.3.conv3.weight[0m
  [34mlayer3.3.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.4.conv1.weight[0m
  [34mlayer3.4.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.4.conv2.weight[0m
  [34mlayer3.4.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.4.conv3.weight[0m
  [34mlayer3.4.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.5.conv1.weight[0m
  [34mlayer3.5.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.5.conv2.weight[0m
  [34mlayer3.5.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer3.5.conv3.weight[0m
  [34mlayer3.5.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.0.conv1.weight[0m
  [34mlayer4.0.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.0.conv2.weight[0m
  [34mlayer4.0.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.0.conv3.weight[0m
  [34mlayer4.0.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.0.downsample.0.weight[0m
  [34mlayer4.0.downsample.1.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.1.conv1.weight[0m
  [34mlayer4.1.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.1.conv2.weight[0m
  [34mlayer4.1.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.1.conv3.weight[0m
  [34mlayer4.1.bn3.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.2.conv1.weight[0m
  [34mlayer4.2.bn1.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.2.conv2.weight[0m
  [34mlayer4.2.bn2.{weight, bias, running_mean, running_var}[0m
  [34mlayer4.2.conv3.weight[0m
  [34mlayer4.2.bn3.{weight, bias, running_mean, running_var}[0m
[04/14 19:12:49] fastreid.modeling.backbones.resnet INFO: The checkpoint contains parameters not used by the model:
  [35mstudent[0m
  [35mteacher[0m
  [35moptimizer[0m
  [35mepoch[0m
  [35margs[0m
  [35mfp16_scaler[0m
[04/14 19:18:39] fastreid INFO: Rank of current process: 0. World size: 4
[04/14 19:18:39] fastreid INFO: Environment info:
----------------------  ----------------------------------------------------------------------------------------------
sys.platform            linux
Python                  3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]
numpy                   1.25.2
fastreid                0.1.0 @/home/ma-user/work/Projects/ReIDNet_Finetune/FastReID/./fastreid
FASTREID_ENV_MODULE     <not set>
PyTorch                 1.12.1+cu113 @/home/ma-user/anaconda3/envs/Generate3D/lib/python3.10/site-packages/torch
PyTorch debug build     False
GPU available           True
GPU 0,1,2,3             Tesla V100S-PCIE-32GB
CUDA_HOME               /usr/local/cuda
Pillow                  10.1.0
torchvision             0.13.1+cu113 @/home/ma-user/anaconda3/envs/Generate3D/lib/python3.10/site-packages/torchvision
torchvision arch flags  sm_35, sm_50, sm_60, sm_70, sm_75, sm_80, sm_86
fvcore                  0.1.5.post20221221
cv2                     4.8.0
----------------------  ----------------------------------------------------------------------------------------------
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 8.5  (built against CUDA 11.7)
    - 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, 

[04/14 19:18:39] fastreid INFO: Command line arguments: Namespace(config_file='configs/CMDM/mgn_R50_lion.yml', finetune=False, resume=False, eval_only=False, num_gpus=4, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:36666', opts=[])
[04/14 19:18:39] fastreid INFO: Contents of args.config_file=configs/CMDM/mgn_R50_lion.yml:
_BASE_: "../Base-MGN.yml"

MODEL:
  BACKBONE:
    WITH_IBN: False
    EXTRA_BN: True
    PRETRAIN_PATH: "/home/ma-user/work/Projects/ReIDNets_Checkpoints_TransReID/ResNet50_ViTdefault_2468_406080100_bs120_originaldino_noreid.pth"
  PIXEL_MEAN: [123.675, 116.280, 103.530]
  PIXEL_STD: [58.395, 57.120, 57.375]

INPUT:
  REA:
    MEAN: [0.0, 0.0, 0.0]
  DO_AUTOAUG: False

SOLVER:
  HEADS_LR_FACTOR: 1.0
  BACKBONE_BN_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: -1

DATASETS:
  NAMES: ("CMDM",)
  TESTS: ("CMDM",)
  KWARGS: 'data_name:market+split_mode:id+split_ratio:1.0'
  ROOT: "/home/ma-user/work/Datasets/ImageReID_Datasets/mixreid"

TEST:
  EVAL_PERIOD: 60

OUTPUT_DIR: "logs/market/ResNet50_ViTdefault_LION_MGN"

[04/14 19:18:39] fastreid INFO: Running with full config:
CUDNN_BENCHMARK: True
DATALOADER:
  NAIVE_WAY: True
  NUM_INSTANCE: 16
  NUM_WORKERS: 8
  PK_SAMPLER: True
DATASETS:
  COMBINEALL: False
  IS_LMDB: False
  KWARGS: data_name:market+split_mode:id+split_ratio:1.0
  NAMES: ('CMDM',)
  ROOT: /home/ma-user/work/Datasets/ImageReID_Datasets/mixreid
  TESTS: ('CMDM',)
INPUT:
  CJ:
    BRIGHTNESS: 0.15
    CONTRAST: 0.15
    ENABLED: False
    HUE: 0.1
    PROB: 0.8
    SATURATION: 0.1
  DO_AUGMIX: False
  DO_AUTOAUG: False
  DO_FLIP: True
  DO_PAD: True
  FLIP_PROB: 0.5
  PADDING: 10
  PADDING_MODE: constant
  REA:
    ENABLED: True
    MEAN: [0.0, 0.0, 0.0]
    PROB: 0.5
  RPT:
    ENABLED: False
    PROB: 0.5
  SIZE_TEST: [384, 128]
  SIZE_TRAIN: [384, 128]
MODEL:
  BACKBONE:
    DEPTH: 50x
    EXTRA_BN: True
    FEAT_DIM: 2048
    LAST_STRIDE: 1
    NAME: build_resnet_backbone
    NORM: BN
    PRETRAIN: True
    PRETRAIN_PATH: /home/ma-user/work/Projects/ReIDNets_Checkpoints_TransReID/ResNet50_ViTdefault_2468_406080100_bs120_originaldino_noreid.pth
    WITH_IBN: False
    WITH_NL: False
    WITH_SE: False
  DEVICE: cuda
  FREEZE_LAYERS: ['backbone', 'b1', 'b2', 'b3']
  HEADS:
    CLS_LAYER: circleSoftmax
    EMBEDDING_DIM: 256
    MARGIN: 0.35
    NAME: EmbeddingHead
    NECK_FEAT: after
    NORM: BN
    NUM_CLASSES: 0
    POOL_LAYER: gempoolP
    SCALE: 64
    WITH_BNNECK: True
  LOSSES:
    CE:
      ALPHA: 0.2
      EPSILON: 0.1
      SCALE: 1.0
    CIRCLE:
      ALPHA: 128
      MARGIN: 0.25
      SCALE: 1.0
    FL:
      ALPHA: 0.25
      GAMMA: 2
      SCALE: 1.0
    NAME: ('CrossEntropyLoss', 'TripletLoss')
    TRI:
      HARD_MINING: True
      MARGIN: 0.0
      NORM_FEAT: False
      SCALE: 1.0
  META_ARCHITECTURE: MGN
  PIXEL_MEAN: [123.675, 116.28, 103.53]
  PIXEL_STD: [58.395, 57.12, 57.375]
  WEIGHTS: 
OUTPUT_DIR: logs/market/ResNet50_ViTdefault_LION_MGN
SOLVER:
  AMP_ENABLED: False
  BACKBONE_BN_LR_FACTOR: 1.0
  BASE_LR: 0.00035
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: -1
  DELAY_ITERS: 30
  ETA_MIN_LR: 7.7e-07
  FREEZE_ITERS: 10
  GAMMA: 0.1
  HEADS_LR_FACTOR: 1.0
  IMS_PER_BATCH: 64
  MAX_ITER: 60
  MOMENTUM: 0.9
  OPT: Adam
  SCHED: WarmupCosineAnnealingLR
  STEPS: [40, 90]
  SWA:
    ENABLED: False
    ETA_MIN_LR: 3.5e-06
    ITER: 10
    LR_FACTOR: 10.0
    LR_SCHED: False
    PERIOD: 2
  WARMUP_FACTOR: 0.01
  WARMUP_ITERS: 10
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0005
  WEIGHT_DECAY_BIAS: 0.0005
TEST:
  AQE:
    ALPHA: 3.0
    ENABLED: False
    QE_K: 5
    QE_TIME: 1
  EVAL_PERIOD: 60
  IMS_PER_BATCH: 128
  METRIC: cosine
  PRECISE_BN:
    DATASET: Market1501
    ENABLED: False
    NUM_ITER: 300
  RERANK:
    ENABLED: False
    K1: 20
    K2: 6
    LAMBDA: 0.3
  ROC_ENABLED: False
[04/14 19:18:39] fastreid INFO: Full config saved to /home/ma-user/work/Projects/ReIDNet_Finetune/FastReID/logs/market/ResNet50_ViTdefault_LION_MGN/config.yaml
[04/14 19:18:39] fastreid.utils.env INFO: Using a generated random seed 41048422
[04/14 19:18:39] fastreid.engine.defaults INFO: Prepare training set
[04/14 19:18:40] fastreid.data.datasets.bases INFO: => Loaded CMDM in csv format: 
[36m| subset   | # ids   | # images   | # cameras   |
|:---------|:--------|:-----------|:------------|
| train    | 751     | 12936      | 6           |[0m
[04/14 19:18:40] fastreid.engine.defaults INFO: Auto-scaling the config to num_classes=751, max_Iter=12120, wamrup_Iter=2020, freeze_Iter=2020, delay_Iter=6060, step_Iter=[8080, 18180], ckpt_Iter=-203, eval_Iter=12200.
[04/14 19:18:40] fastreid.modeling.backbones.resnet INFO: Loading pretrained model from /home/ma-user/work/Projects/ReIDNets_Checkpoints_TransReID/ResNet50_ViTdefault_2468_406080100_bs120_originaldino_noreid.pth
[04/14 19:18:58] fastreid.engine.defaults INFO: Freeze layer group "backbone,b1,b2,b3" training for 2020 iterations
[04/14 19:18:58] fastreid.utils.checkpoint INFO: No checkpoint found. Training model from scratch
[04/14 19:18:58] fastreid.engine.train_loop INFO: Starting training from iteration 0
[04/14 19:20:02] fastreid.utils.events INFO:  eta: 0:49:42  iter: 199  total_loss: 55.37  loss_cls_b1: 6.329  loss_cls_b2: 6.266  loss_cls_b21: 6.379  loss_cls_b22: 6.336  loss_cls_b3: 6.393  loss_cls_b31: 6.261  loss_cls_b32: 6.234  loss_cls_b33: 6.331  loss_triplet_b1: 0.7705  loss_triplet_b2: 0.7394  loss_triplet_b3: 0.764  loss_triplet_b22: 1.017  loss_triplet_b33: 1.27  time: 0.2509  data_time: 0.0006  lr: 3.76e-05  max_mem: 19406M
[04/14 19:21:02] fastreid.utils.events INFO:  eta: 0:48:59  iter: 399  total_loss: 54.07  loss_cls_b1: 6.258  loss_cls_b2: 6.239  loss_cls_b21: 6.362  loss_cls_b22: 6.252  loss_cls_b3: 6.188  loss_cls_b31: 6.288  loss_cls_b32: 6.177  loss_cls_b33: 6.228  loss_triplet_b1: 0.7038  loss_triplet_b2: 0.7164  loss_triplet_b3: 0.6643  loss_triplet_b22: 0.9545  loss_triplet_b33: 1.161  time: 0.2511  data_time: 0.0005  lr: 7.19e-05  max_mem: 19406M
[04/14 19:22:01] fastreid.utils.events INFO:  eta: 0:48:10  iter: 599  total_loss: 52.55  loss_cls_b1: 6.205  loss_cls_b2: 6.028  loss_cls_b21: 6.042  loss_cls_b22: 6.066  loss_cls_b3: 6.036  loss_cls_b31: 6.09  loss_cls_b32: 6.005  loss_cls_b33: 6.061  loss_triplet_b1: 0.6188  loss_triplet_b2: 0.6842  loss_triplet_b3: 0.6351  loss_triplet_b22: 0.828  loss_triplet_b33: 1.055  time: 0.2505  data_time: 0.0005  lr: 1.06e-04  max_mem: 19406M
[04/14 19:23:01] fastreid.utils.events INFO:  eta: 0:47:22  iter: 799  total_loss: 50.16  loss_cls_b1: 5.703  loss_cls_b2: 5.859  loss_cls_b21: 5.783  loss_cls_b22: 5.798  loss_cls_b3: 5.705  loss_cls_b31: 5.953  loss_cls_b32: 5.84  loss_cls_b33: 5.99  loss_triplet_b1: 0.5612  loss_triplet_b2: 0.5663  loss_triplet_b3: 0.5563  loss_triplet_b22: 0.7986  loss_triplet_b33: 0.9081  time: 0.2505  data_time: 0.0005  lr: 1.41e-04  max_mem: 19406M
[04/14 19:24:00] fastreid.utils.events INFO:  eta: 0:46:31  iter: 999  total_loss: 46.98  loss_cls_b1: 5.468  loss_cls_b2: 5.436  loss_cls_b21: 5.455  loss_cls_b22: 5.455  loss_cls_b3: 5.479  loss_cls_b31: 5.602  loss_cls_b32: 5.511  loss_cls_b33: 5.663  loss_triplet_b1: 0.513  loss_triplet_b2: 0.5091  loss_triplet_b3: 0.5202  loss_triplet_b22: 0.6757  loss_triplet_b33: 0.844  time: 0.2503  data_time: 0.0005  lr: 1.75e-04  max_mem: 19406M
[04/14 19:24:59] fastreid.utils.events INFO:  eta: 0:45:42  iter: 1199  total_loss: 44.05  loss_cls_b1: 5.054  loss_cls_b2: 5.021  loss_cls_b21: 5.22  loss_cls_b22: 5.204  loss_cls_b3: 5.075  loss_cls_b31: 5.328  loss_cls_b32: 5.305  loss_cls_b33: 5.243  loss_triplet_b1: 0.5077  loss_triplet_b2: 0.4798  loss_triplet_b3: 0.4389  loss_triplet_b22: 0.652  loss_triplet_b33: 0.7555  time: 0.2502  data_time: 0.0005  lr: 2.09e-04  max_mem: 19406M
[04/14 19:25:59] fastreid.utils.events INFO:  eta: 0:44:52  iter: 1399  total_loss: 40.87  loss_cls_b1: 4.755  loss_cls_b2: 4.574  loss_cls_b21: 4.862  loss_cls_b22: 4.764  loss_cls_b3: 4.7  loss_cls_b31: 4.846  loss_cls_b32: 4.82  loss_cls_b33: 4.873  loss_triplet_b1: 0.4835  loss_triplet_b2: 0.4767  loss_triplet_b3: 0.5015  loss_triplet_b22: 0.6124  loss_triplet_b33: 0.7844  time: 0.2500  data_time: 0.0005  lr: 2.43e-04  max_mem: 19406M
[04/14 19:26:58] fastreid.utils.events INFO:  eta: 0:44:01  iter: 1599  total_loss: 37.09  loss_cls_b1: 4.067  loss_cls_b2: 4.224  loss_cls_b21: 4.29  loss_cls_b22: 4.418  loss_cls_b3: 4.021  loss_cls_b31: 4.408  loss_cls_b32: 4.33  loss_cls_b33: 4.597  loss_triplet_b1: 0.4537  loss_triplet_b2: 0.4687  loss_triplet_b3: 0.4573  loss_triplet_b22: 0.5998  loss_triplet_b33: 0.7834  time: 0.2499  data_time: 0.0005  lr: 2.78e-04  max_mem: 19406M
[04/14 19:27:57] fastreid.utils.events INFO:  eta: 0:43:08  iter: 1799  total_loss: 33.34  loss_cls_b1: 3.764  loss_cls_b2: 3.754  loss_cls_b21: 4.074  loss_cls_b22: 3.986  loss_cls_b3: 3.719  loss_cls_b31: 3.972  loss_cls_b32: 4.006  loss_cls_b33: 4.055  loss_triplet_b1: 0.3423  loss_triplet_b2: 0.3336  loss_triplet_b3: 0.3669  loss_triplet_b22: 0.382  loss_triplet_b33: 0.4149  time: 0.2497  data_time: 0.0005  lr: 3.12e-04  max_mem: 19406M
[04/14 19:28:57] fastreid.utils.events INFO:  eta: 0:42:19  iter: 1999  total_loss: 31.05  loss_cls_b1: 3.41  loss_cls_b2: 3.176  loss_cls_b21: 3.517  loss_cls_b22: 3.728  loss_cls_b3: 3.357  loss_cls_b31: 3.704  loss_cls_b32: 3.638  loss_cls_b33: 3.92  loss_triplet_b1: 0.3361  loss_triplet_b2: 0.311  loss_triplet_b3: 0.3033  loss_triplet_b22: 0.3838  loss_triplet_b33: 0.4961  time: 0.2497  data_time: 0.0005  lr: 3.46e-04  max_mem: 19406M
[04/14 19:30:00] fastreid.utils.events INFO:  eta: 0:41:43  iter: 2199  total_loss: 29.44  loss_cls_b1: 3.464  loss_cls_b2: 3.275  loss_cls_b21: 3.66  loss_cls_b22: 3.611  loss_cls_b3: 3.346  loss_cls_b31: 3.858  loss_cls_b32: 3.628  loss_cls_b33: 3.804  loss_triplet_b1: 0.2421  loss_triplet_b2: 0.2303  loss_triplet_b3: 0.2115  loss_triplet_b22: 0.2817  loss_triplet_b33: 0.2857  time: 0.2553  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:31:04] fastreid.utils.events INFO:  eta: 0:41:19  iter: 2399  total_loss: 28.3  loss_cls_b1: 3.151  loss_cls_b2: 3.068  loss_cls_b21: 3.342  loss_cls_b22: 3.357  loss_cls_b3: 3.072  loss_cls_b31: 3.459  loss_cls_b32: 3.311  loss_cls_b33: 3.654  loss_triplet_b1: 0.1805  loss_triplet_b2: 0.1666  loss_triplet_b3: 0.168  loss_triplet_b22: 0.232  loss_triplet_b33: 0.3284  time: 0.2604  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:32:07] fastreid.utils.events INFO:  eta: 0:49:53  iter: 2599  total_loss: 22.56  loss_cls_b1: 2.478  loss_cls_b2: 2.481  loss_cls_b21: 2.905  loss_cls_b22: 2.866  loss_cls_b3: 2.495  loss_cls_b31: 3.117  loss_cls_b32: 2.81  loss_cls_b33: 3.104  loss_triplet_b1: 0.06398  loss_triplet_b2: 0.061  loss_triplet_b3: 0.06284  loss_triplet_b22: 0.08145  loss_triplet_b33: 0.09439  time: 0.2647  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:33:11] fastreid.utils.events INFO:  eta: 0:49:03  iter: 2799  total_loss: 21.8  loss_cls_b1: 2.371  loss_cls_b2: 2.345  loss_cls_b21: 2.745  loss_cls_b22: 2.661  loss_cls_b3: 2.349  loss_cls_b31: 2.945  loss_cls_b32: 2.642  loss_cls_b33: 2.967  loss_triplet_b1: 0.1067  loss_triplet_b2: 0.07881  loss_triplet_b3: 0.07493  loss_triplet_b22: 0.07552  loss_triplet_b33: 0.1162  time: 0.2685  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:34:15] fastreid.utils.events INFO:  eta: 0:48:07  iter: 2999  total_loss: 20.14  loss_cls_b1: 2.19  loss_cls_b2: 2.112  loss_cls_b21: 2.463  loss_cls_b22: 2.619  loss_cls_b3: 2.199  loss_cls_b31: 2.738  loss_cls_b32: 2.433  loss_cls_b33: 2.943  loss_triplet_b1: 0.07591  loss_triplet_b2: 0.07835  loss_triplet_b3: 0.0814  loss_triplet_b22: 0.08489  loss_triplet_b33: 0.1178  time: 0.2718  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:35:18] fastreid.utils.events INFO:  eta: 0:47:03  iter: 3199  total_loss: 18.66  loss_cls_b1: 2.03  loss_cls_b2: 1.941  loss_cls_b21: 2.392  loss_cls_b22: 2.388  loss_cls_b3: 2.011  loss_cls_b31: 2.69  loss_cls_b32: 2.347  loss_cls_b33: 2.823  loss_triplet_b1: 0.04552  loss_triplet_b2: 0.03512  loss_triplet_b3: 0.03616  loss_triplet_b22: 0.04045  loss_triplet_b33: 0.04632  time: 0.2746  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:36:22] fastreid.utils.events INFO:  eta: 0:45:59  iter: 3399  total_loss: 17.5  loss_cls_b1: 1.793  loss_cls_b2: 1.769  loss_cls_b21: 2.106  loss_cls_b22: 2.382  loss_cls_b3: 1.848  loss_cls_b31: 2.39  loss_cls_b32: 2.16  loss_cls_b33: 2.78  loss_triplet_b1: 0.04534  loss_triplet_b2: 0.03997  loss_triplet_b3: 0.04204  loss_triplet_b22: 0.03843  loss_triplet_b33: 0.03525  time: 0.2771  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:37:26] fastreid.utils.events INFO:  eta: 0:44:56  iter: 3599  total_loss: 17.93  loss_cls_b1: 1.893  loss_cls_b2: 1.825  loss_cls_b21: 2.076  loss_cls_b22: 2.286  loss_cls_b3: 1.902  loss_cls_b31: 2.388  loss_cls_b32: 2.18  loss_cls_b33: 2.736  loss_triplet_b1: 0.02878  loss_triplet_b2: 0.0262  loss_triplet_b3: 0.02864  loss_triplet_b22: 0.03163  loss_triplet_b33: 0.04973  time: 0.2793  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:38:29] fastreid.utils.events INFO:  eta: 0:43:52  iter: 3799  total_loss: 16.24  loss_cls_b1: 1.621  loss_cls_b2: 1.602  loss_cls_b21: 1.997  loss_cls_b22: 2.053  loss_cls_b3: 1.628  loss_cls_b31: 2.284  loss_cls_b32: 2.124  loss_cls_b33: 2.544  loss_triplet_b1: 0.03998  loss_triplet_b2: 0.03825  loss_triplet_b3: 0.03158  loss_triplet_b22: 0.03103  loss_triplet_b33: 0.03124  time: 0.2812  data_time: 0.0006  lr: 3.50e-04  max_mem: 19406M
[04/14 19:39:33] fastreid.utils.events INFO:  eta: 0:42:48  iter: 3999  total_loss: 17.04  loss_cls_b1: 1.766  loss_cls_b2: 1.686  loss_cls_b21: 2.147  loss_cls_b22: 2.103  loss_cls_b3: 1.72  loss_cls_b31: 2.432  loss_cls_b32: 2.141  loss_cls_b33: 2.667  loss_triplet_b1: 0.05157  loss_triplet_b2: 0.03575  loss_triplet_b3: 0.03854  loss_triplet_b22: 0.06302  loss_triplet_b33: 0.05627  time: 0.2830  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:40:36] fastreid.utils.events INFO:  eta: 0:41:44  iter: 4199  total_loss: 16.41  loss_cls_b1: 1.697  loss_cls_b2: 1.59  loss_cls_b21: 1.863  loss_cls_b22: 2.083  loss_cls_b3: 1.727  loss_cls_b31: 2.281  loss_cls_b32: 2.033  loss_cls_b33: 2.525  loss_triplet_b1: 0.06443  loss_triplet_b2: 0.06013  loss_triplet_b3: 0.05114  loss_triplet_b22: 0.07067  loss_triplet_b33: 0.08208  time: 0.2846  data_time: 0.0004  lr: 3.50e-04  max_mem: 19406M
[04/14 19:41:40] fastreid.utils.events INFO:  eta: 0:40:40  iter: 4399  total_loss: 15.27  loss_cls_b1: 1.519  loss_cls_b2: 1.476  loss_cls_b21: 1.983  loss_cls_b22: 1.902  loss_cls_b3: 1.569  loss_cls_b31: 2.309  loss_cls_b32: 1.974  loss_cls_b33: 2.368  loss_triplet_b1: 0.04227  loss_triplet_b2: 0.04539  loss_triplet_b3: 0.04275  loss_triplet_b22: 0.05051  loss_triplet_b33: 0.04623  time: 0.2860  data_time: 0.0004  lr: 3.50e-04  max_mem: 19406M
[04/14 19:42:43] fastreid.utils.events INFO:  eta: 0:39:37  iter: 4599  total_loss: 14.29  loss_cls_b1: 1.472  loss_cls_b2: 1.339  loss_cls_b21: 1.851  loss_cls_b22: 1.886  loss_cls_b3: 1.435  loss_cls_b31: 2.057  loss_cls_b32: 1.825  loss_cls_b33: 2.337  loss_triplet_b1: 0.02809  loss_triplet_b2: 0.03262  loss_triplet_b3: 0.02943  loss_triplet_b22: 0.03328  loss_triplet_b33: 0.03954  time: 0.2874  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:43:47] fastreid.utils.events INFO:  eta: 0:38:34  iter: 4799  total_loss: 14.25  loss_cls_b1: 1.456  loss_cls_b2: 1.343  loss_cls_b21: 1.807  loss_cls_b22: 1.932  loss_cls_b3: 1.305  loss_cls_b31: 2.106  loss_cls_b32: 1.805  loss_cls_b33: 2.346  loss_triplet_b1: 0.05195  loss_triplet_b2: 0.04456  loss_triplet_b3: 0.02895  loss_triplet_b22: 0.03805  loss_triplet_b33: 0.05098  time: 0.2886  data_time: 0.0004  lr: 3.50e-04  max_mem: 19406M
[04/14 19:44:51] fastreid.utils.events INFO:  eta: 0:37:30  iter: 4999  total_loss: 13.91  loss_cls_b1: 1.325  loss_cls_b2: 1.283  loss_cls_b21: 1.691  loss_cls_b22: 1.846  loss_cls_b3: 1.341  loss_cls_b31: 2.001  loss_cls_b32: 1.762  loss_cls_b33: 2.369  loss_triplet_b1: 0.03731  loss_triplet_b2: 0.03057  loss_triplet_b3: 0.02963  loss_triplet_b22: 0.03266  loss_triplet_b33: 0.03631  time: 0.2897  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:45:54] fastreid.utils.events INFO:  eta: 0:36:27  iter: 5199  total_loss: 13.07  loss_cls_b1: 1.309  loss_cls_b2: 1.198  loss_cls_b21: 1.754  loss_cls_b22: 1.684  loss_cls_b3: 1.204  loss_cls_b31: 2.103  loss_cls_b32: 1.594  loss_cls_b33: 2.257  loss_triplet_b1: 0.04147  loss_triplet_b2: 0.03128  loss_triplet_b3: 0.02877  loss_triplet_b22: 0.04203  loss_triplet_b33: 0.04252  time: 0.2908  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:46:58] fastreid.utils.events INFO:  eta: 0:35:24  iter: 5399  total_loss: 12.01  loss_cls_b1: 1.231  loss_cls_b2: 1.111  loss_cls_b21: 1.489  loss_cls_b22: 1.674  loss_cls_b3: 1.094  loss_cls_b31: 1.842  loss_cls_b32: 1.525  loss_cls_b33: 2.132  loss_triplet_b1: 0.02749  loss_triplet_b2: 0.02143  loss_triplet_b3: 0.02153  loss_triplet_b22: 0.02331  loss_triplet_b33: 0.0368  time: 0.2917  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:48:01] fastreid.utils.events INFO:  eta: 0:34:21  iter: 5599  total_loss: 12.5  loss_cls_b1: 1.23  loss_cls_b2: 1.113  loss_cls_b21: 1.482  loss_cls_b22: 1.555  loss_cls_b3: 1.138  loss_cls_b31: 1.74  loss_cls_b32: 1.501  loss_cls_b33: 2.124  loss_triplet_b1: 0.03112  loss_triplet_b2: 0.02222  loss_triplet_b3: 0.02392  loss_triplet_b22: 0.01971  loss_triplet_b33: 0.02837  time: 0.2926  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:49:05] fastreid.utils.events INFO:  eta: 0:33:18  iter: 5799  total_loss: 12.12  loss_cls_b1: 1.239  loss_cls_b2: 1.102  loss_cls_b21: 1.627  loss_cls_b22: 1.568  loss_cls_b3: 1.105  loss_cls_b31: 1.905  loss_cls_b32: 1.521  loss_cls_b33: 1.871  loss_triplet_b1: 0.03641  loss_triplet_b2: 0.03266  loss_triplet_b3: 0.03484  loss_triplet_b22: 0.04017  loss_triplet_b33: 0.04107  time: 0.2935  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:50:09] fastreid.utils.events INFO:  eta: 0:32:16  iter: 5999  total_loss: 11.13  loss_cls_b1: 1.063  loss_cls_b2: 0.9393  loss_cls_b21: 1.4  loss_cls_b22: 1.519  loss_cls_b3: 1.006  loss_cls_b31: 1.697  loss_cls_b32: 1.469  loss_cls_b33: 1.935  loss_triplet_b1: 0.02663  loss_triplet_b2: 0.01488  loss_triplet_b3: 0.02072  loss_triplet_b22: 0.0204  loss_triplet_b33: 0.0217  time: 0.2942  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:51:12] fastreid.utils.events INFO:  eta: 0:31:12  iter: 6199  total_loss: 11.44  loss_cls_b1: 1.061  loss_cls_b2: 1.039  loss_cls_b21: 1.493  loss_cls_b22: 1.42  loss_cls_b3: 1.019  loss_cls_b31: 1.83  loss_cls_b32: 1.393  loss_cls_b33: 1.943  loss_triplet_b1: 0.03906  loss_triplet_b2: 0.02814  loss_triplet_b3: 0.03239  loss_triplet_b22: 0.04167  loss_triplet_b33: 0.03208  time: 0.2949  data_time: 0.0005  lr: 3.50e-04  max_mem: 19406M
[04/14 19:52:16] fastreid.utils.events INFO:  eta: 0:30:09  iter: 6399  total_loss: 10.34  loss_cls_b1: 0.9746  loss_cls_b2: 0.8531  loss_cls_b21: 1.215  loss_cls_b22: 1.436  loss_cls_b3: 0.9041  loss_cls_b31: 1.598  loss_cls_b32: 1.245  loss_cls_b33: 1.946  loss_triplet_b1: 0.02614  loss_triplet_b2: 0.01859  loss_triplet_b3: 0.01698  loss_triplet_b22: 0.01786  loss_triplet_b33: 0.01847  time: 0.2956  data_time: 0.0005  lr: 3.47e-04  max_mem: 19406M
[04/14 19:53:19] fastreid.utils.events INFO:  eta: 0:29:05  iter: 6599  total_loss: 8.927  loss_cls_b1: 0.8107  loss_cls_b2: 0.723  loss_cls_b21: 1.126  loss_cls_b22: 1.188  loss_cls_b3: 0.7396  loss_cls_b31: 1.562  loss_cls_b32: 1.067  loss_cls_b33: 1.807  loss_triplet_b1: 0.02172  loss_triplet_b2: 0.0158  loss_triplet_b3: 0.01359  loss_triplet_b22: 0.01702  loss_triplet_b33: 0.0207  time: 0.2962  data_time: 0.0005  lr: 3.43e-04  max_mem: 19406M
[04/14 19:54:23] fastreid.utils.events INFO:  eta: 0:28:02  iter: 6799  total_loss: 8.668  loss_cls_b1: 0.7891  loss_cls_b2: 0.7249  loss_cls_b21: 1.112  loss_cls_b22: 1.06  loss_cls_b3: 0.7592  loss_cls_b31: 1.378  loss_cls_b32: 1.127  loss_cls_b33: 1.615  loss_triplet_b1: 0.01463  loss_triplet_b2: 0.01285  loss_triplet_b3: 0.01219  loss_triplet_b22: 0.01124  loss_triplet_b33: 0.007569  time: 0.2968  data_time: 0.0005  lr: 3.37e-04  max_mem: 19406M
[04/14 19:55:26] fastreid.utils.events INFO:  eta: 0:26:59  iter: 6999  total_loss: 9.458  loss_cls_b1: 0.9078  loss_cls_b2: 0.7944  loss_cls_b21: 1.19  loss_cls_b22: 1.212  loss_cls_b3: 0.7967  loss_cls_b31: 1.491  loss_cls_b32: 1.221  loss_cls_b33: 1.571  loss_triplet_b1: 0.02733  loss_triplet_b2: 0.01959  loss_triplet_b3: 0.02177  loss_triplet_b22: 0.01888  loss_triplet_b33: 0.01742  time: 0.2974  data_time: 0.0005  lr: 3.30e-04  max_mem: 19406M
[04/14 19:56:30] fastreid.utils.events INFO:  eta: 0:25:56  iter: 7199  total_loss: 8.543  loss_cls_b1: 0.8076  loss_cls_b2: 0.7506  loss_cls_b21: 1.13  loss_cls_b22: 1.156  loss_cls_b3: 0.7263  loss_cls_b31: 1.335  loss_cls_b32: 1.136  loss_cls_b33: 1.479  loss_triplet_b1: 0.02361  loss_triplet_b2: 0.01258  loss_triplet_b3: 0.01416  loss_triplet_b22: 0.01534  loss_triplet_b33: 0.01529  time: 0.2979  data_time: 0.0006  lr: 3.20e-04  max_mem: 19406M
[04/14 19:57:34] fastreid.utils.events INFO:  eta: 0:24:53  iter: 7399  total_loss: 8.75  loss_cls_b1: 0.8572  loss_cls_b2: 0.75  loss_cls_b21: 1.201  loss_cls_b22: 1.081  loss_cls_b3: 0.7211  loss_cls_b31: 1.458  loss_cls_b32: 1.166  loss_cls_b33: 1.512  loss_triplet_b1: 0.01472  loss_triplet_b2: 0.01098  loss_triplet_b3: 0.01448  loss_triplet_b22: 0.01033  loss_triplet_b33: 0.01198  time: 0.2984  data_time: 0.0005  lr: 3.10e-04  max_mem: 19406M
[04/14 19:58:37] fastreid.utils.events INFO:  eta: 0:23:50  iter: 7599  total_loss: 7.605  loss_cls_b1: 0.7152  loss_cls_b2: 0.623  loss_cls_b21: 0.9138  loss_cls_b22: 1.051  loss_cls_b3: 0.6142  loss_cls_b31: 1.119  loss_cls_b32: 1.007  loss_cls_b33: 1.44  loss_triplet_b1: 0.0127  loss_triplet_b2: 0.008827  loss_triplet_b3: 0.008509  loss_triplet_b22: 0.01257  loss_triplet_b33: 0.009279  time: 0.2989  data_time: 0.0005  lr: 2.97e-04  max_mem: 19406M
[04/14 19:59:41] fastreid.utils.events INFO:  eta: 0:22:46  iter: 7799  total_loss: 8.622  loss_cls_b1: 0.8367  loss_cls_b2: 0.7461  loss_cls_b21: 1.019  loss_cls_b22: 1.167  loss_cls_b3: 0.7463  loss_cls_b31: 1.244  loss_cls_b32: 1.099  loss_cls_b33: 1.587  loss_triplet_b1: 0.0311  loss_triplet_b2: 0.01812  loss_triplet_b3: 0.01612  loss_triplet_b22: 0.01906  loss_triplet_b33: 0.01885  time: 0.2993  data_time: 0.0005  lr: 2.84e-04  max_mem: 19406M
[04/14 20:00:44] fastreid.utils.events INFO:  eta: 0:21:43  iter: 7999  total_loss: 7.367  loss_cls_b1: 0.7418  loss_cls_b2: 0.6441  loss_cls_b21: 0.8857  loss_cls_b22: 1.053  loss_cls_b3: 0.6589  loss_cls_b31: 1.197  loss_cls_b32: 1.027  loss_cls_b33: 1.401  loss_triplet_b1: 0.0189  loss_triplet_b2: 0.01054  loss_triplet_b3: 0.01047  loss_triplet_b22: 0.0105  loss_triplet_b33: 0.008893  time: 0.2998  data_time: 0.0005  lr: 2.69e-04  max_mem: 19406M
[04/14 20:01:48] fastreid.utils.events INFO:  eta: 0:20:39  iter: 8199  total_loss: 7.627  loss_cls_b1: 0.6805  loss_cls_b2: 0.6242  loss_cls_b21: 0.9264  loss_cls_b22: 1.028  loss_cls_b3: 0.6125  loss_cls_b31: 1.257  loss_cls_b32: 0.984  loss_cls_b33: 1.452  loss_triplet_b1: 0.01659  loss_triplet_b2: 0.013  loss_triplet_b3: 0.01093  loss_triplet_b22: 0.01411  loss_triplet_b33: 0.006601  time: 0.3002  data_time: 0.0004  lr: 2.53e-04  max_mem: 19406M
[04/14 20:02:51] fastreid.utils.events INFO:  eta: 0:19:36  iter: 8399  total_loss: 7.214  loss_cls_b1: 0.6769  loss_cls_b2: 0.5392  loss_cls_b21: 0.9435  loss_cls_b22: 0.9138  loss_cls_b3: 0.5615  loss_cls_b31: 1.183  loss_cls_b32: 0.9198  loss_cls_b33: 1.336  loss_triplet_b1: 0.01387  loss_triplet_b2: 0.00974  loss_triplet_b3: 0.01005  loss_triplet_b22: 0.007464  loss_triplet_b33: 0.01185  time: 0.3005  data_time: 0.0005  lr: 2.37e-04  max_mem: 19406M
[04/14 20:03:55] fastreid.utils.events INFO:  eta: 0:18:33  iter: 8599  total_loss: 7.377  loss_cls_b1: 0.6754  loss_cls_b2: 0.5707  loss_cls_b21: 0.9405  loss_cls_b22: 0.9609  loss_cls_b3: 0.6063  loss_cls_b31: 1.115  loss_cls_b32: 0.9533  loss_cls_b33: 1.296  loss_triplet_b1: 0.0172  loss_triplet_b2: 0.0111  loss_triplet_b3: 0.01015  loss_triplet_b22: 0.01085  loss_triplet_b33: 0.01206  time: 0.3009  data_time: 0.0005  lr: 2.19e-04  max_mem: 19406M
[04/14 20:04:58] fastreid.utils.events INFO:  eta: 0:17:29  iter: 8799  total_loss: 6.062  loss_cls_b1: 0.568  loss_cls_b2: 0.4415  loss_cls_b21: 0.7577  loss_cls_b22: 0.7319  loss_cls_b3: 0.4537  loss_cls_b31: 1.011  loss_cls_b32: 0.7975  loss_cls_b33: 1.145  loss_triplet_b1: 0.009691  loss_triplet_b2: 0.006959  loss_triplet_b3: 0.005677  loss_triplet_b22: 0.005301  loss_triplet_b33: 0.004817  time: 0.3013  data_time: 0.0005  lr: 2.02e-04  max_mem: 19406M
[04/14 20:06:02] fastreid.utils.events INFO:  eta: 0:16:26  iter: 8999  total_loss: 6.571  loss_cls_b1: 0.6281  loss_cls_b2: 0.5199  loss_cls_b21: 0.8446  loss_cls_b22: 0.8473  loss_cls_b3: 0.5053  loss_cls_b31: 1.067  loss_cls_b32: 0.8524  loss_cls_b33: 1.186  loss_triplet_b1: 0.01261  loss_triplet_b2: 0.00916  loss_triplet_b3: 0.007329  loss_triplet_b22: 0.006966  loss_triplet_b33: 0.008333  time: 0.3016  data_time: 0.0005  lr: 1.84e-04  max_mem: 19406M
[04/14 20:07:05] fastreid.utils.events INFO:  eta: 0:15:23  iter: 9199  total_loss: 5.81  loss_cls_b1: 0.5188  loss_cls_b2: 0.4114  loss_cls_b21: 0.6823  loss_cls_b22: 0.8013  loss_cls_b3: 0.4376  loss_cls_b31: 0.9296  loss_cls_b32: 0.793  loss_cls_b33: 1.128  loss_triplet_b1: 0.00968  loss_triplet_b2: 0.005632  loss_triplet_b3: 0.007628  loss_triplet_b22: 0.006487  loss_triplet_b33: 0.00591  time: 0.3019  data_time: 0.0005  lr: 1.66e-04  max_mem: 19406M
[04/14 20:08:09] fastreid.utils.events INFO:  eta: 0:14:20  iter: 9399  total_loss: 5.721  loss_cls_b1: 0.5459  loss_cls_b2: 0.4695  loss_cls_b21: 0.682  loss_cls_b22: 0.7558  loss_cls_b3: 0.438  loss_cls_b31: 0.9426  loss_cls_b32: 0.7908  loss_cls_b33: 1.134  loss_triplet_b1: 0.009902  loss_triplet_b2: 0.005067  loss_triplet_b3: 0.006112  loss_triplet_b22: 0.003329  loss_triplet_b33: 0.002969  time: 0.3022  data_time: 0.0005  lr: 1.48e-04  max_mem: 19406M
[04/14 20:09:13] fastreid.utils.events INFO:  eta: 0:13:17  iter: 9599  total_loss: 5.352  loss_cls_b1: 0.4787  loss_cls_b2: 0.4168  loss_cls_b21: 0.6446  loss_cls_b22: 0.6664  loss_cls_b3: 0.4387  loss_cls_b31: 0.8656  loss_cls_b32: 0.7072  loss_cls_b33: 0.9909  loss_triplet_b1: 0.01027  loss_triplet_b2: 0.008074  loss_triplet_b3: 0.007853  loss_triplet_b22: 0.007789  loss_triplet_b33: 0.00347  time: 0.3025  data_time: 0.0005  lr: 1.30e-04  max_mem: 19406M
[04/14 20:10:16] fastreid.utils.events INFO:  eta: 0:12:14  iter: 9799  total_loss: 5.123  loss_cls_b1: 0.4657  loss_cls_b2: 0.376  loss_cls_b21: 0.6296  loss_cls_b22: 0.6479  loss_cls_b3: 0.3733  loss_cls_b31: 0.8038  loss_cls_b32: 0.641  loss_cls_b33: 1.015  loss_triplet_b1: 0.008934  loss_triplet_b2: 0.005902  loss_triplet_b3: 0.006628  loss_triplet_b22: 0.004025  loss_triplet_b33: 0.003218  time: 0.3028  data_time: 0.0005  lr: 1.13e-04  max_mem: 19406M
[04/14 20:11:20] fastreid.utils.events INFO:  eta: 0:11:11  iter: 9999  total_loss: 4.747  loss_cls_b1: 0.3889  loss_cls_b2: 0.3182  loss_cls_b21: 0.6079  loss_cls_b22: 0.5649  loss_cls_b3: 0.3928  loss_cls_b31: 0.8784  loss_cls_b32: 0.6453  loss_cls_b33: 0.9019  loss_triplet_b1: 0.006522  loss_triplet_b2: 0.004016  loss_triplet_b3: 0.005222  loss_triplet_b22: 0.003699  loss_triplet_b33: 0.002872  time: 0.3031  data_time: 0.0005  lr: 9.61e-05  max_mem: 19406M
[04/14 20:12:23] fastreid.utils.events INFO:  eta: 0:10:07  iter: 10199  total_loss: 4.144  loss_cls_b1: 0.3527  loss_cls_b2: 0.2848  loss_cls_b21: 0.5333  loss_cls_b22: 0.5053  loss_cls_b3: 0.3  loss_cls_b31: 0.7294  loss_cls_b32: 0.5345  loss_cls_b33: 0.8256  loss_triplet_b1: 0.004896  loss_triplet_b2: 0.003367  loss_triplet_b3: 0.002841  loss_triplet_b22: 0.003128  loss_triplet_b33: 0.002634  time: 0.3034  data_time: 0.0005  lr: 8.04e-05  max_mem: 19406M
[04/14 20:13:27] fastreid.utils.events INFO:  eta: 0:09:04  iter: 10399  total_loss: 4.095  loss_cls_b1: 0.3414  loss_cls_b2: 0.2961  loss_cls_b21: 0.5073  loss_cls_b22: 0.5187  loss_cls_b3: 0.3039  loss_cls_b31: 0.6892  loss_cls_b32: 0.5799  loss_cls_b33: 0.8377  loss_triplet_b1: 0.004156  loss_triplet_b2: 0.002547  loss_triplet_b3: 0.002935  loss_triplet_b22: 0.001999  loss_triplet_b33: 0.002349  time: 0.3036  data_time: 0.0005  lr: 6.58e-05  max_mem: 19406M
[04/14 20:14:30] fastreid.utils.events INFO:  eta: 0:08:00  iter: 10599  total_loss: 4.851  loss_cls_b1: 0.3896  loss_cls_b2: 0.3579  loss_cls_b21: 0.5992  loss_cls_b22: 0.5795  loss_cls_b3: 0.3558  loss_cls_b31: 0.7562  loss_cls_b32: 0.6276  loss_cls_b33: 0.9383  loss_triplet_b1: 0.004341  loss_triplet_b2: 0.004024  loss_triplet_b3: 0.004599  loss_triplet_b22: 0.004082  loss_triplet_b33: 0.003232  time: 0.3038  data_time: 0.0005  lr: 5.23e-05  max_mem: 19406M
[04/14 20:15:34] fastreid.utils.events INFO:  eta: 0:06:57  iter: 10799  total_loss: 4.254  loss_cls_b1: 0.3679  loss_cls_b2: 0.3414  loss_cls_b21: 0.557  loss_cls_b22: 0.5748  loss_cls_b3: 0.3358  loss_cls_b31: 0.7007  loss_cls_b32: 0.5804  loss_cls_b33: 0.8697  loss_triplet_b1: 0.006352  loss_triplet_b2: 0.004437  loss_triplet_b3: 0.006062  loss_triplet_b22: 0.002621  loss_triplet_b33: 0.002167  time: 0.3041  data_time: 0.0005  lr: 4.01e-05  max_mem: 19406M
[04/14 20:16:38] fastreid.utils.events INFO:  eta: 0:05:54  iter: 10999  total_loss: 4.073  loss_cls_b1: 0.3344  loss_cls_b2: 0.2969  loss_cls_b21: 0.5588  loss_cls_b22: 0.4725  loss_cls_b3: 0.3158  loss_cls_b31: 0.6975  loss_cls_b32: 0.5352  loss_cls_b33: 0.7451  loss_triplet_b1: 0.003816  loss_triplet_b2: 0.003168  loss_triplet_b3: 0.002742  loss_triplet_b22: 0.001761  loss_triplet_b33: 0.001387  time: 0.3043  data_time: 0.0005  lr: 2.94e-05  max_mem: 19406M
[04/14 20:17:41] fastreid.utils.events INFO:  eta: 0:04:50  iter: 11199  total_loss: 3.891  loss_cls_b1: 0.3122  loss_cls_b2: 0.2683  loss_cls_b21: 0.5046  loss_cls_b22: 0.5189  loss_cls_b3: 0.2891  loss_cls_b31: 0.6578  loss_cls_b32: 0.5252  loss_cls_b33: 0.7755  loss_triplet_b1: 0.003394  loss_triplet_b2: 0.00271  loss_triplet_b3: 0.003532  loss_triplet_b22: 0.001693  loss_triplet_b33: 0.0016  time: 0.3045  data_time: 0.0005  lr: 2.03e-05  max_mem: 19406M
[04/14 20:18:45] fastreid.utils.events INFO:  eta: 0:03:47  iter: 11399  total_loss: 3.704  loss_cls_b1: 0.2999  loss_cls_b2: 0.2573  loss_cls_b21: 0.4497  loss_cls_b22: 0.4857  loss_cls_b3: 0.2794  loss_cls_b31: 0.6077  loss_cls_b32: 0.5438  loss_cls_b33: 0.7537  loss_triplet_b1: 0.002876  loss_triplet_b2: 0.002584  loss_triplet_b3: 0.002436  loss_triplet_b22: 0.002124  loss_triplet_b33: 0.0008497  time: 0.3047  data_time: 0.0005  lr: 1.28e-05  max_mem: 19406M
[04/14 20:19:48] fastreid.utils.events INFO:  eta: 0:02:44  iter: 11599  total_loss: 3.638  loss_cls_b1: 0.298  loss_cls_b2: 0.2668  loss_cls_b21: 0.3899  loss_cls_b22: 0.4913  loss_cls_b3: 0.2742  loss_cls_b31: 0.5971  loss_cls_b32: 0.444  loss_cls_b33: 0.723  loss_triplet_b1: 0.00383  loss_triplet_b2: 0.002053  loss_triplet_b3: 0.002951  loss_triplet_b22: 0.001388  loss_triplet_b33: 0.001  time: 0.3049  data_time: 0.0006  lr: 7.10e-06  max_mem: 19406M
[04/14 20:20:52] fastreid.utils.events INFO:  eta: 0:01:41  iter: 11799  total_loss: 3.617  loss_cls_b1: 0.2976  loss_cls_b2: 0.2665  loss_cls_b21: 0.4375  loss_cls_b22: 0.4924  loss_cls_b3: 0.2772  loss_cls_b31: 0.6193  loss_cls_b32: 0.4839  loss_cls_b33: 0.7325  loss_triplet_b1: 0.003819  loss_triplet_b2: 0.003014  loss_triplet_b3: 0.003642  loss_triplet_b22: 0.002868  loss_triplet_b33: 0.001735  time: 0.3051  data_time: 0.0005  lr: 3.18e-06  max_mem: 19406M
[04/14 20:21:55] fastreid.utils.events INFO:  eta: 0:00:38  iter: 11999  total_loss: 3.389  loss_cls_b1: 0.2815  loss_cls_b2: 0.2467  loss_cls_b21: 0.4218  loss_cls_b22: 0.4392  loss_cls_b3: 0.2536  loss_cls_b31: 0.5735  loss_cls_b32: 0.4444  loss_cls_b33: 0.6375  loss_triplet_b1: 0.003738  loss_triplet_b2: 0.002418  loss_triplet_b3: 0.002711  loss_triplet_b22: 0.00166  loss_triplet_b33: 0.0009898  time: 0.3053  data_time: 0.0004  lr: 1.11e-06  max_mem: 19406M
[04/14 20:22:33] fastreid.engine.defaults INFO: Prepare testing set
[04/14 20:22:34] fastreid.data.datasets.bases INFO: => Loaded CMDM in csv format: 
[36m| subset   | # ids   | # images   | # cameras   |
|:---------|:--------|:-----------|:------------|
| query    | 750     | 3368       | 6           |
| gallery  | 751     | 15913      | 6           |[0m
[04/14 20:22:34] fastreid.evaluation.evaluator INFO: Start inference on 19281 images
[04/14 20:22:44] fastreid.evaluation.evaluator INFO: Inference done 11/151. 0.0336 s / batch. ETA=0:00:10
[04/14 20:22:59] fastreid.evaluation.evaluator INFO: Total inference time: 0:00:15.360481 (0.105209 s / batch per device)
[04/14 20:22:59] fastreid.evaluation.evaluator INFO: Total inference pure compute time: 0:00:08 (0.056120 s / batch per device)
[04/14 20:24:23] fastreid.evaluation.testing INFO: Evaluation results in csv format: 
[36m| Datasets   | Rank-1   | Rank-5   | Rank-10   | mAP    | mINP   |
|:-----------|:---------|:---------|:----------|:-------|:-------|
| CMDM       | 97.03%   | 99.23%   | 99.58%    | 92.24% | 73.97% |[0m
[04/14 20:24:23] fastreid.utils.events INFO:  eta: 0:00:00  iter: 12119  total_loss: 3.741  loss_cls_b1: 0.301  loss_cls_b2: 0.2763  loss_cls_b21: 0.4568  loss_cls_b22: 0.4877  loss_cls_b3: 0.2799  loss_cls_b31: 0.6474  loss_cls_b32: 0.5089  loss_cls_b33: 0.7248  loss_triplet_b1: 0.004861  loss_triplet_b2: 0.002886  loss_triplet_b3: 0.002744  loss_triplet_b22: 0.001526  loss_triplet_b33: 0.001053  time: 0.3054  data_time: 0.0005  lr: 7.70e-07  max_mem: 19406M
[04/14 20:24:23] fastreid.engine.hooks INFO: Overall training speed: 12117 iterations in 1:01:40 (0.3054 s / it)
[04/14 20:24:23] fastreid.engine.hooks INFO: Total training time: 1:05:19 (0:03:38 on hooks)
