2023-10-24 11:40:00,350 transreid INFO: Saving model in the path :/media/zzr/My_Passport/Clip_reid/output
2023-10-24 11:40:00,350 transreid INFO: Namespace(config_file='configs/veri/cnn_prom.yml', local_rank=0, opts=[])
2023-10-24 11:40:00,350 transreid INFO: Loaded configuration file configs/veri/cnn_prom.yml
2023-10-24 11:40:00,350 transreid INFO: 
MODEL:
  PRETRAIN_CHOICE: 'imagenet'
  METRIC_LOSS_TYPE: 'triplet'
  IF_LABELSMOOTH: 'on'
  IF_WITH_CENTER: 'no'
  NAME: 'RN50'
  ID_LOSS_WEIGHT : 1.0
  TRIPLET_LOSS_WEIGHT : 1.0

INPUT:
  SIZE_TRAIN: [256, 256]
  SIZE_TEST: [256, 256]
  PROB: 0.5 # random horizontal flip
  RE_PROB: 0.5 # random erasing
  PADDING: 10
  PIXEL_MEAN: [0.485, 0.456, 0.406]
  PIXEL_STD: [0.229, 0.224, 0.225]

DATALOADER:
  SAMPLER: 'softmax_triplet'
  NUM_INSTANCE: 4
  NUM_WORKERS: 8

SOLVER:
  STAGE1:
    IMS_PER_BATCH: 64
    OPTIMIZER_NAME: "Adam"
    BASE_LR: 0.00035
    WARMUP_LR_INIT: 0.00001
    LR_MIN: 1e-6
    WARMUP_METHOD: 'linear'
    WEIGHT_DECAY:  1e-4
    WEIGHT_DECAY_BIAS: 1e-4
    MAX_EPOCHS: 60
    CHECKPOINT_PERIOD: 60
    LOG_PERIOD: 50
    WARMUP_EPOCHS: 5

  STAGE2:
    IMS_PER_BATCH: 64
    OPTIMIZER_NAME: "Adam"
    BASE_LR: 0.00035
    WARMUP_METHOD: 'linear'
    WARMUP_ITERS: 10
    WARMUP_FACTOR: 0.01
    WEIGHT_DECAY:  0.0005
    WEIGHT_DECAY_BIAS: 0.0005
    LARGE_FC_LR: False
    MAX_EPOCHS: 120
    CHECKPOINT_PERIOD: 120
    LOG_PERIOD: 50
    EVAL_PERIOD: 120
    BIAS_LR_FACTOR: 2
    
    STEPS: [40, 70]
    GAMMA: 0.1
  
TEST:
  EVAL: True
  IMS_PER_BATCH: 64
  RE_RANKING: False
  WEIGHT: ''
  NECK_FEAT: 'before'
  FEAT_NORM: 'yes'

DATASETS:
   NAMES: ('veri')
   ROOT_DIR: ('/media/zzr/My_Passport/Clip_reid')
OUTPUT_DIR: '/media/zzr/My_Passport/Clip_reid/output'

# CUDA_VISIBLE_DEVICES=2 python train_clipreid.py --config_file configs/veri/res_clipreid.yml

2023-10-24 11:40:00,350 transreid INFO: Running with config:
DATALOADER:
  NUM_INSTANCE: 4
  NUM_WORKERS: 8
  SAMPLER: softmax_triplet
DATASETS:
  NAMES: veri
  ROOT_DIR: /media/zzr/My_Passport/Clip_reid
INPUT:
  PADDING: 10
  PIXEL_MEAN: [0.485, 0.456, 0.406]
  PIXEL_STD: [0.229, 0.224, 0.225]
  PROB: 0.5
  RE_PROB: 0.5
  SIZE_TEST: [256, 256]
  SIZE_TRAIN: [256, 256]
MODEL:
  ATT_DROP_RATE: 0.0
  COS_LAYER: False
  DEVICE: cuda
  DEVICE_ID: 0
  DIST_TRAIN: False
  DROP_OUT: 0.0
  DROP_PATH: 0.1
  I2T_LOSS_WEIGHT: 1.0
  ID_LOSS_TYPE: softmax
  ID_LOSS_WEIGHT: 1.0
  IF_LABELSMOOTH: on
  IF_WITH_CENTER: no
  LAST_STRIDE: 1
  METRIC_LOSS_TYPE: triplet
  NAME: RN50
  NECK: bnneck
  NO_MARGIN: False
  PRETRAIN_CHOICE: imagenet
  PRETRAIN_PATH: 
  SIE_CAMERA: False
  SIE_COE: 3.0
  SIE_VIEW: False
  STRIDE_SIZE: [16, 16]
  TRANSFORMER_TYPE: None
  TRIPLET_LOSS_WEIGHT: 1.0
OUTPUT_DIR: /media/zzr/My_Passport/Clip_reid/output
SOLVER:
  MARGIN: 0.3
  SEED: 1234
  STAGE1:
    BASE_LR: 0.00035
    CHECKPOINT_PERIOD: 60
    COSINE_MARGIN: 0.5
    COSINE_SCALE: 30
    EVAL_PERIOD: 10
    IMS_PER_BATCH: 64
    LOG_PERIOD: 50
    LR_MIN: 1e-06
    MAX_EPOCHS: 60
    MOMENTUM: 0.9
    OPTIMIZER_NAME: Adam
    WARMUP_EPOCHS: 5
    WARMUP_FACTOR: 0.01
    WARMUP_ITERS: 500
    WARMUP_LR_INIT: 1e-05
    WARMUP_METHOD: linear
    WEIGHT_DECAY: 0.0001
    WEIGHT_DECAY_BIAS: 0.0001
  STAGE2:
    BASE_LR: 0.00035
    BIAS_LR_FACTOR: 2
    CENTER_LOSS_WEIGHT: 0.0005
    CENTER_LR: 0.5
    CHECKPOINT_PERIOD: 120
    COSINE_MARGIN: 0.5
    COSINE_SCALE: 30
    EVAL_PERIOD: 120
    GAMMA: 0.1
    IMS_PER_BATCH: 64
    LARGE_FC_LR: False
    LOG_PERIOD: 50
    LR_MIN: 1.6e-05
    MAX_EPOCHS: 120
    MOMENTUM: 0.9
    OPTIMIZER_NAME: Adam
    STEPS: (40, 70)
    WARMUP_EPOCHS: 5
    WARMUP_FACTOR: 0.01
    WARMUP_ITERS: 10
    WARMUP_LR_INIT: 0.01
    WARMUP_METHOD: linear
    WEIGHT_DECAY: 0.0005
    WEIGHT_DECAY_BIAS: 0.0005
TEST:
  DIST_MAT: dist_mat.npy
  EVAL: True
  FEAT_NORM: yes
  IMS_PER_BATCH: 64
  NECK_FEAT: before
  RE_RANKING: False
  WEIGHT: 
2023-10-24 11:40:10,631 transreid.train INFO: start training
2023-10-24 11:40:10,645 transreid.train INFO: model: build_transformer(
  (classifier): Linear(in_features=2048, out_features=576, bias=False)
  (classifier_proj): Linear(in_features=1024, out_features=576, bias=False)
  (bottleneck): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bottleneck_proj): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (image_encoder): ModifiedResNet(
    (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (avgpool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    (relu): ReLU(inplace=True)
    (layer1): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (-1): AvgPool2d(kernel_size=1, stride=1, padding=0)
          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (layer2): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): AvgPool2d(kernel_size=2, stride=2, padding=0)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (-1): AvgPool2d(kernel_size=2, stride=2, padding=0)
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (3): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (layer3): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): AvgPool2d(kernel_size=2, stride=2, padding=0)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (-1): AvgPool2d(kernel_size=2, stride=2, padding=0)
          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (3): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (4): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (5): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (layer4): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (-1): AvgPool2d(kernel_size=1, stride=1, padding=0)
          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (avgpool): Identity()
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (attnpool): AttentionPool2d(
      (k_proj): Linear(in_features=2048, out_features=2048, bias=True)
      (q_proj): Linear(in_features=2048, out_features=2048, bias=True)
      (v_proj): Linear(in_features=2048, out_features=2048, bias=True)
      (c_proj): Linear(in_features=2048, out_features=1024, bias=True)
    )
  )
  (prompt_learner): PromptLearner()
  (text_encoder): TextEncoder(
    (transformer): Transformer(
      (resblocks): Sequential(
        (0): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (1): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (2): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (3): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (4): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (5): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (6): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (7): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (8): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (9): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (10): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
        (11): ResidualAttentionBlock(
          (attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)
          )
          (ln_1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
          (mlp): Sequential(
            (c_fc): Linear(in_features=512, out_features=2048, bias=True)
            (gelu): QuickGELU()
            (c_proj): Linear(in_features=2048, out_features=512, bias=True)
          )
          (ln_2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
      )
    )
    (ln_final): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
  )
)
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2023-10-24 20:12:08,481 transreid.train INFO: Epoch[117] Iteration[350/577] Loss: 3.066, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:12:27,921 transreid.train INFO: Epoch[117] Iteration[400/577] Loss: 3.063, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:12:46,659 transreid.train INFO: Epoch[117] Iteration[450/577] Loss: 3.060, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:13:06,174 transreid.train INFO: Epoch[117] Iteration[500/577] Loss: 3.057, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:13:25,496 transreid.train INFO: Epoch[117] Iteration[550/577] Loss: 3.053, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:13:32,166 transreid.train INFO: Epoch 117 done. Time per batch: 0.405[s] Speed: 158.2[samples/s]
2023-10-24 20:13:53,489 transreid.train INFO: Epoch[118] Iteration[50/577] Loss: 3.094, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:14:12,771 transreid.train INFO: Epoch[118] Iteration[100/577] Loss: 3.082, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:14:32,447 transreid.train INFO: Epoch[118] Iteration[150/577] Loss: 3.080, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:14:52,620 transreid.train INFO: Epoch[118] Iteration[200/577] Loss: 3.077, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:15:13,273 transreid.train INFO: Epoch[118] Iteration[250/577] Loss: 3.076, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:15:34,302 transreid.train INFO: Epoch[118] Iteration[300/577] Loss: 3.072, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:15:55,205 transreid.train INFO: Epoch[118] Iteration[350/577] Loss: 3.068, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:16:15,038 transreid.train INFO: Epoch[118] Iteration[400/577] Loss: 3.066, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:16:34,265 transreid.train INFO: Epoch[118] Iteration[450/577] Loss: 3.064, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:16:53,475 transreid.train INFO: Epoch[118] Iteration[500/577] Loss: 3.060, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:17:12,397 transreid.train INFO: Epoch[118] Iteration[550/577] Loss: 3.056, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:17:19,003 transreid.train INFO: Epoch 118 done. Time per batch: 0.400[s] Speed: 160.0[samples/s]
2023-10-24 20:17:39,628 transreid.train INFO: Epoch[119] Iteration[50/577] Loss: 3.103, Acc: 0.997, Base Lr: 3.50e-06
2023-10-24 20:17:57,971 transreid.train INFO: Epoch[119] Iteration[100/577] Loss: 3.092, Acc: 0.997, Base Lr: 3.50e-06
2023-10-24 20:18:16,252 transreid.train INFO: Epoch[119] Iteration[150/577] Loss: 3.084, Acc: 0.997, Base Lr: 3.50e-06
2023-10-24 20:18:35,784 transreid.train INFO: Epoch[119] Iteration[200/577] Loss: 3.082, Acc: 0.997, Base Lr: 3.50e-06
2023-10-24 20:18:55,978 transreid.train INFO: Epoch[119] Iteration[250/577] Loss: 3.077, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:19:16,171 transreid.train INFO: Epoch[119] Iteration[300/577] Loss: 3.073, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:19:36,732 transreid.train INFO: Epoch[119] Iteration[350/577] Loss: 3.069, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:19:57,352 transreid.train INFO: Epoch[119] Iteration[400/577] Loss: 3.067, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:20:15,715 transreid.train INFO: Epoch[119] Iteration[450/577] Loss: 3.064, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:20:34,931 transreid.train INFO: Epoch[119] Iteration[500/577] Loss: 3.060, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:20:54,110 transreid.train INFO: Epoch[119] Iteration[550/577] Loss: 3.056, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:21:00,537 transreid.train INFO: Epoch 119 done. Time per batch: 0.391[s] Speed: 163.5[samples/s]
2023-10-24 20:21:21,366 transreid.train INFO: Epoch[120] Iteration[50/577] Loss: 3.088, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:21:40,830 transreid.train INFO: Epoch[120] Iteration[100/577] Loss: 3.086, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:22:00,770 transreid.train INFO: Epoch[120] Iteration[150/577] Loss: 3.081, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:22:21,132 transreid.train INFO: Epoch[120] Iteration[200/577] Loss: 3.081, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:22:42,271 transreid.train INFO: Epoch[120] Iteration[250/577] Loss: 3.077, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:23:02,856 transreid.train INFO: Epoch[120] Iteration[300/577] Loss: 3.076, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:23:23,388 transreid.train INFO: Epoch[120] Iteration[350/577] Loss: 3.072, Acc: 0.998, Base Lr: 3.50e-06
2023-10-24 20:23:42,523 transreid.train INFO: Epoch[120] Iteration[400/577] Loss: 3.068, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:24:01,954 transreid.train INFO: Epoch[120] Iteration[450/577] Loss: 3.064, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:24:21,195 transreid.train INFO: Epoch[120] Iteration[500/577] Loss: 3.060, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:24:40,539 transreid.train INFO: Epoch[120] Iteration[550/577] Loss: 3.056, Acc: 0.999, Base Lr: 3.50e-06
2023-10-24 20:24:47,060 transreid.train INFO: Epoch 120 done. Time per batch: 0.400[s] Speed: 159.9[samples/s]
2023-10-24 20:33:26,275 transreid.train INFO: Validation Results - Epoch: 120
2023-10-24 20:33:26,295 transreid.train INFO: mAP: 75.5%
2023-10-24 20:33:26,296 transreid.train INFO: CMC curve, Rank-1  :92.0%
2023-10-24 20:33:26,296 transreid.train INFO: CMC curve, Rank-5  :94.4%
2023-10-24 20:33:26,296 transreid.train INFO: CMC curve, Rank-10 :95.9%
2023-10-24 20:33:26,318 transreid.train INFO: Total running time: 7:48:58.723651