nohup.out

2022-09-07 11:49:09 [INFO]	
------------Environment Information-------------
platform: Linux-4.15.0-140-generic-x86_64-with-debian-stretch-sid
Python: 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0]
Paddle compiled with cuda: True
NVCC: Build cuda_11.2.r11.2/compiler.29618528_0
cudnn: 8.2
GPUs used: 1
CUDA_VISIBLE_DEVICES: None
GPU: ['GPU 0: Tesla V100-SXM2-32GB']
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0
PaddlePaddle: 2.3.2
------------------------------------------------
Traceback (most recent call last):
  File "train.py", line 204, in <module>
    main(args)
  File "train.py", line 149, in main
    batch_size=args.batch_size)
  File "/home/aistudio/MedicalSeg/medicalseg/cvlibs/config.py", line 80, in __init__
    raise FileNotFoundError('File {} does not exist'.format(path))
FileNotFoundError: File MedicalSeg/configs/trans_unet/trans_unet_synapse.yml does not exist
2022-09-07 11:49:50 [INFO]	
------------Environment Information-------------
platform: Linux-4.15.0-140-generic-x86_64-with-debian-stretch-sid
Python: 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0]
Paddle compiled with cuda: True
NVCC: Build cuda_11.2.r11.2/compiler.29618528_0
cudnn: 8.2
GPUs used: 1
CUDA_VISIBLE_DEVICES: None
GPU: ['GPU 0: Tesla V100-SXM2-32GB']
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0
PaddlePaddle: 2.3.2
------------------------------------------------
/home/aistudio/MedicalSeg/medicalseg/cvlibs/config.py:451: UserWarning: Warning: The data dir now is /home/aistudio/MedicalSeg/data/, you should change the data_root in the global.yml if this directory didn't have enough space
  .format(absolute_data_dir))
2022-09-07 11:49:50 [INFO]	
---------------Config Information---------------
batch_size: 24
data_root: data/
export:
  inference_helper:
    type: TransUNetInferenceHelper
  transforms:
  - size:
    - 1
    - 224
    - 224
    type: Resize3D
iters: 13950
loss:
  coef:
  - 1
  types:
  - coef:
    - 1
    - 1
    losses:
    - type: CrossEntropyLoss
      weight: null
    - type: DiceLoss
    type: MixedLoss
lr_scheduler:
  decay_steps: 13950
  end_lr: 0
  learning_rate: 0.01
  power: 0.9
  type: PolynomialDecay
model:
  attention_dropout_rate: 0.0
  backbone:
    block_units:
    - 3
    - 4
    - 9
    type: ResNet
    width_factor: 1
  classifier: seg
  decoder_channels:
  - 256
  - 128
  - 64
  - 16
  dropout_rate: 0.1
  hidden_size: 768
  img_size: 224
  mlp_dim: 3072
  n_skip: 3
  num_classes: 9
  num_heads: 12
  num_layers: 12
  patches_grid:
  - 14
  - 14
  pretrained_path: https://paddleseg.bj.bcebos.com/paddleseg3d/synapse/transunet_synapse_1_224_224_14k_1e-2/pretrain_model.pdparams
  skip_channels:
  - 512
  - 256
  - 64
  - 16
  type: TransUNet
optimizer:
  momentum: 0.9
  type: sgd
  weight_decay: 0.0001
test_dataset:
  dataset_root: ./Synapse_npy
  mode: test
  num_classes: 9
  result_dir: ./output
  transforms:
  - size:
    - 1
    - 224
    - 224
    type: Resize3D
  type: Synapse
train_dataset:
  dataset_root: ./Synapse_npy
  mode: train
  num_classes: 9
  result_dir: ./output
  transforms:
  - flip_axis:
    - 1
    - 2
    rotate_planes:
    - - 1
      - 2
    type: RandomFlipRotation3D
  - degrees: 20
    prob: 0.5
    rotate_planes:
    - - 1
      - 2
    type: RandomRotation3D
  - size:
    - 1
    - 224
    - 224
    type: Resize3D
  type: Synapse
val_dataset:
  dataset_root: ./Synapse_npy
  mode: test
  num_classes: 9
  result_dir: ./output
  transforms:
  - size:
    - 1
    - 224
    - 224
    type: Resize3D
  type: Synapse
------------------------------------------------
W0907 11:49:50.831183  5286 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2
W0907 11:49:50.831221  5286 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
2022-09-07 11:49:51 [INFO]	Loading pretrained model from https://paddleseg.bj.bcebos.com/paddleseg3d/synapse/transunet_synapse_1_224_224_14k_1e-2/pretrain_model.pdparams
Connecting to https://paddleseg.bj.bcebos.com/paddleseg3d/synapse/transunet_synapse_1_224_224_14k_1e-2/pretrain_model.pdparams
Downloading pretrain_model.pdparams

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2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.0.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.0.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.0.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.0.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.0.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.0.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.0.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.0.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.1.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.1.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.1.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.1.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.1.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.1.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.1.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.1.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.2.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.2.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.2.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.2.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.2.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.2.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.2.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.2.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.3.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.3.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.3.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.3.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.3.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.3.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.3.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.3.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.4.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.4.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.4.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.4.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.4.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.4.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.4.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.4.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.5.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.5.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.5.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.5.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.5.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.5.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.5.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.5.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.6.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.6.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.6.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.6.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.6.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.6.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.6.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.6.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.7.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.7.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.7.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.7.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.7.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.7.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.7.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.7.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.8.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.8.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.8.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.8.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.8.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.8.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.8.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.8.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.9.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.9.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.9.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.9.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.9.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.9.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.9.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.9.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.10.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.10.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.10.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.10.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.10.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.10.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.10.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.10.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.11.attn.query.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.11.attn.query.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.11.attn.key.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.11.attn.key.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.11.attn.value.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.11.attn.value.bias is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.11.attn.out.weight is not in pretrained model
2022-09-07 11:50:01 [WARNING]	transformer.encoder.layer.11.attn.out.bias is not in pretrained model
2022-09-07 11:50:01 [INFO]	There are 304/400 variables loaded into TransUNet.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:278: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.bool, the right dtype will convert to paddle.float32
  format(lhs_dtype, rhs_dtype, lhs_dtype))
2022-09-07 11:50:09 [INFO]	[TRAIN] epoch: 0, iter: 10/13950, loss: 2.4945, DSC: 11.4800, lr: 0.009994, batch_cost: 0.7757, reader_cost: 0.09192, ips: 30.9378 samples/sec | ETA 03:00:13


--------------------------------------
C++ Traceback (most recent call last):
--------------------------------------
0   paddle::platform::is_mlu_place(phi::Place const&)

----------------------
Error Message Summary:
----------------------
FatalError: `Termination signal` is detected by the operating system.
  [TimeInfo: *** Aborted at 1662522612 (unix time) try "date -d @1662522612" if you are using GNU date ***]
  [SignalInfo: *** SIGTERM (@0x3e800001524) received by PID 5286 (TID 0x7f05d47e0700) from PID 5412 ***]

2022-09-07 11:50:33 [INFO]	
------------Environment Information-------------
platform: Linux-4.15.0-140-generic-x86_64-with-debian-stretch-sid
Python: 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0]
Paddle compiled with cuda: True
NVCC: Build cuda_11.2.r11.2/compiler.29618528_0
cudnn: 8.2
GPUs used: 1
CUDA_VISIBLE_DEVICES: None
GPU: ['GPU 0: Tesla V100-SXM2-32GB']
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0
PaddlePaddle: 2.3.2
------------------------------------------------
/home/aistudio/MedicalSeg/medicalseg/cvlibs/config.py:451: UserWarning: Warning: The data dir now is /home/aistudio/MedicalSeg/data/, you should change the data_root in the global.yml if this directory didn't have enough space
  .format(absolute_data_dir))
2022-09-07 11:50:33 [INFO]	
---------------Config Information---------------
batch_size: 24
data_root: data/
export:
  inference_helper:
    type: TransUNetInferenceHelper
  transforms:
  - size:
    - 1
    - 224
    - 224
    type: Resize3D
iters: 13950
loss:
  coef:
  - 1
  types:
  - coef:
    - 1
    - 1
    losses:
    - type: CrossEntropyLoss
      weight: null
    - type: DiceLoss
    type: MixedLoss
lr_scheduler:
  decay_steps: 13950
  end_lr: 0
  learning_rate: 0.01
  power: 0.9
  type: PolynomialDecay
model:
  attention_dropout_rate: 0.0
  backbone:
    block_units:
    - 3
    - 4
    - 9
    type: ResNet
    width_factor: 1
  classifier: seg
  decoder_channels:
  - 256
  - 128
  - 64
  - 16
  dropout_rate: 0.1
  hidden_size: 768
  img_size: 224
  mlp_dim: 3072
  n_skip: 3
  num_classes: 9
  num_heads: 12
  num_layers: 12
  patches_grid:
  - 14
  - 14
  pretrained_path: https://paddleseg.bj.bcebos.com/paddleseg3d/synapse/transunet_synapse_1_224_224_14k_1e-2/pretrain_model.pdparams
  skip_channels:
  - 512
  - 256
  - 64
  - 16
  type: TransUNet
optimizer:
  momentum: 0.9
  type: sgd
  weight_decay: 0.0001
test_dataset:
  dataset_root: ./Synapse_npy
  mode: test
  num_classes: 9
  result_dir: ./output
  transforms:
  - size:
    - 1
    - 224
    - 224
    type: Resize3D
  type: Synapse
train_dataset:
  dataset_root: ./Synapse_npy
  mode: train
  num_classes: 9
  result_dir: ./output
  transforms:
  - flip_axis:
    - 1
    - 2
    rotate_planes:
    - - 1
      - 2
    type: RandomFlipRotation3D
  - degrees: 20
    prob: 0.5
    rotate_planes:
    - - 1
      - 2
    type: RandomRotation3D
  - size:
    - 1
    - 224
    - 224
    type: Resize3D
  type: Synapse
val_dataset:
  dataset_root: ./Synapse_npy
  mode: test
  num_classes: 9
  result_dir: ./output
  transforms:
  - size:
    - 1
    - 224
    - 224
    type: Resize3D
  type: Synapse
------------------------------------------------
W0907 11:50:33.258643  5477 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2
W0907 11:50:33.258687  5477 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
2022-09-07 11:50:34 [INFO]	Loading pretrained model from https://paddleseg.bj.bcebos.com/paddleseg3d/synapse/transunet_synapse_1_224_224_14k_1e-2/pretrain_model.pdparams
Connecting to https://paddleseg.bj.bcebos.com/paddleseg3d/synapse/transunet_synapse_1_224_224_14k_1e-2/pretrain_model.pdparams
Downloading pretrain_model.pdparams

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2022-09-07 11:50:49 [INFO]	There are 400/400 variables loaded into TransUNet.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:278: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.bool, the right dtype will convert to paddle.float32
  format(lhs_dtype, rhs_dtype, lhs_dtype))
2022-09-07 11:50:57 [INFO]	[TRAIN] epoch: 0, iter: 10/13950, loss: 2.5018, DSC: 11.4480, lr: 0.009994, batch_cost: 0.8110, reader_cost: 0.12933, ips: 29.5933 samples/sec | ETA 03:08:25
2022-09-07 11:51:02 [INFO]	[TRAIN] epoch: 0, iter: 20/13950, loss: 0.9948, DSC: 12.8828, lr: 0.009988, batch_cost: 0.4928, reader_cost: 0.00027, ips: 48.7046 samples/sec | ETA 01:54:24
2022-09-07 11:51:07 [INFO]	[TRAIN] epoch: 0, iter: 30/13950, loss: 0.9925, DSC: 13.7872, lr: 0.009981, batch_cost: 0.4940, reader_cost: 0.00026, ips: 48.5790 samples/sec | ETA 01:54:37
2022-09-07 11:51:11 [INFO]	[TRAIN] epoch: 0, iter: 40/13950, loss: 0.9541, DSC: 14.6018, lr: 0.009975, batch_cost: 0.4946, reader_cost: 0.00027, ips: 48.5214 samples/sec | ETA 01:54:40
2022-09-07 11:51:16 [INFO]	[TRAIN] epoch: 0, iter: 50/13950, loss: 0.9311, DSC: 14.8355, lr: 0.009968, batch_cost: 0.4947, reader_cost: 0.00028, ips: 48.5188 samples/sec | ETA 01:54:35
2022-09-07 11:51:21 [INFO]	[TRAIN] epoch: 0, iter: 60/13950, loss: 0.9165, DSC: 15.6598, lr: 0.009962, batch_cost: 0.4951, reader_cost: 0.00026, ips: 48.4786 samples/sec | ETA 01:54:36
2022-09-07 11:51:26 [INFO]	[TRAIN] epoch: 0, iter: 70/13950, loss: 0.9027, DSC: 16.5057, lr: 0.009955, batch_cost: 0.4959, reader_cost: 0.00026, ips: 48.3959 samples/sec | ETA 01:54:43
2022-09-07 11:51:31 [INFO]	[TRAIN] epoch: 0, iter: 80/13950, loss: 0.8899, DSC: 18.0665, lr: 0.009949, batch_cost: 0.4963, reader_cost: 0.00025, ips: 48.3613 samples/sec | ETA 01:54:43
2022-09-07 11:51:36 [INFO]	[TRAIN] epoch: 0, iter: 90/13950, loss: 0.8772, DSC: 19.1904, lr: 0.009943, batch_cost: 0.4948, reader_cost: 0.00028, ips: 48.5020 samples/sec | ETA 01:54:18
2022-09-07 11:51:42 [INFO]	[TRAIN] epoch: 1, iter: 100/13950, loss: 0.8661, DSC: 19.1731, lr: 0.009936, batch_cost: 0.5992, reader_cost: 0.12098, ips: 40.0554 samples/sec | ETA 02:18:18
2022-09-07 11:51:47 [INFO]	[TRAIN] epoch: 1, iter: 110/13950, loss: 0.8621, DSC: 20.1777, lr: 0.009930, batch_cost: 0.4977, reader_cost: 0.00027, ips: 48.2258 samples/sec | ETA 01:54:47
2022-09-07 11:51:52 [INFO]	[TRAIN] epoch: 1, iter: 120/13950, loss: 0.8506, DSC: 21.8082, lr: 0.009923, batch_cost: 0.4965, reader_cost: 0.00027, ips: 48.3390 samples/sec | ETA 01:54:26
2022-09-07 11:51:57 [INFO]	[TRAIN] epoch: 1, iter: 130/13950, loss: 0.8363, DSC: 22.1735, lr: 0.009917, batch_cost: 0.5004, reader_cost: 0.00030, ips: 47.9573 samples/sec | ETA 01:55:16
2022-09-07 11:52:02 [INFO]	[TRAIN] epoch: 1, iter: 140/13950, loss: 0.8356, DSC: 22.6151, lr: 0.009910, batch_cost: 0.4982, reader_cost: 0.00031, ips: 48.1728 samples/sec | ETA 01:54:40
2022-09-07 11:52:07 [INFO]	[TRAIN] epoch: 1, iter: 150/13950, loss: 0.8285, DSC: 24.3006, lr: 0.009904, batch_cost: 0.5015, reader_cost: 0.00030, ips: 47.8522 samples/sec | ETA 01:55:21
2022-09-07 11:52:12 [INFO]	[TRAIN] epoch: 1, iter: 160/13950, loss: 0.8174, DSC: 24.4328, lr: 0.009897, batch_cost: 0.5004, reader_cost: 0.00029, ips: 47.9643 samples/sec | ETA 01:55:00
2022-09-07 11:52:17 [INFO]	[TRAIN] epoch: 1, iter: 170/13950, loss: 0.8120, DSC: 25.3727, lr: 0.009891, batch_cost: 0.5036, reader_cost: 0.00029, ips: 47.6584 samples/sec | ETA 01:55:39
2022-09-07 11:52:22 [INFO]	[TRAIN] epoch: 1, iter: 180/13950, loss: 0.8018, DSC: 26.2308, lr: 0.009884, batch_cost: 0.4994, reader_cost: 0.00027, ips: 48.0544 samples/sec | ETA 01:54:37
2022-09-07 11:52:28 [INFO]	[TRAIN] epoch: 2, iter: 190/13950, loss: 0.8127, DSC: 24.6344, lr: 0.009878, batch_cost: 0.6110, reader_cost: 0.12442, ips: 39.2809 samples/sec | ETA 02:20:07
2022-09-07 11:52:33 [INFO]	[TRAIN] epoch: 2, iter: 200/13950, loss: 0.7944, DSC: 26.5479, lr: 0.009872, batch_cost: 0.5016, reader_cost: 0.00026, ips: 47.8492 samples/sec | ETA 01:54:56
2022-09-07 11:52:38 [INFO]	[TRAIN] epoch: 2, iter: 210/13950, loss: 0.7806, DSC: 27.6409, lr: 0.009865, batch_cost: 0.4997, reader_cost: 0.00032, ips: 48.0288 samples/sec | ETA 01:54:25
2022-09-07 11:52:43 [INFO]	[TRAIN] epoch: 2, iter: 220/13950, loss: 0.7756, DSC: 28.6790, lr: 0.009859, batch_cost: 0.4985, reader_cost: 0.00027, ips: 48.1402 samples/sec | ETA 01:54:05
2022-09-07 11:52:48 [INFO]	[TRAIN] epoch: 2, iter: 230/13950, loss: 0.7756, DSC: 29.0652, lr: 0.009852, batch_cost: 0.5003, reader_cost: 0.00026, ips: 47.9732 samples/sec | ETA 01:54:23
2022-09-07 11:52:53 [INFO]	[TRAIN] epoch: 2, iter: 240/13950, loss: 0.7692, DSC: 29.3559, lr: 0.009846, batch_cost: 0.5018, reader_cost: 0.00027, ips: 47.8235 samples/sec | ETA 01:54:40
2022-09-07 11:52:58 [INFO]	[TRAIN] epoch: 2, iter: 250/13950, loss: 0.7572, DSC: 30.2228, lr: 0.009839, batch_cost: 0.5037, reader_cost: 0.00033, ips: 47.6495 samples/sec | ETA 01:55:00
2022-09-07 11:53:03 [INFO]	[TRAIN] epoch: 2, iter: 260/13950, loss: 0.7452, DSC: 30.4972, lr: 0.009833, batch_cost: 0.5024, reader_cost: 0.00027, ips: 47.7722 samples/sec | ETA 01:54:37
2022-09-07 11:53:08 [INFO]	[TRAIN] epoch: 2, iter: 270/13950, loss: 0.7513, DSC: 30.5025, lr: 0.009826, batch_cost: 0.5018, reader_cost: 0.00026, ips: 47.8289 samples/sec | ETA 01:54:24
2022-09-07 11:53:14 [INFO]	[TRAIN] epoch: 3, iter: 280/13950, loss: 0.7416, DSC: 32.8919, lr: 0.009820, batch_cost: 0.5974, reader_cost: 0.11801, ips: 40.1720 samples/sec | ETA 02:16:06
2022-09-07 11:53:19 [INFO]	[TRAIN] epoch: 3, iter: 290/13950, loss: 0.7263, DSC: 33.9826, lr: 0.009813, batch_cost: 0.5103, reader_cost: 0.00038, ips: 47.0357 samples/sec | ETA 01:56:10
2022-09-07 11:53:25 [INFO]	[TRAIN] epoch: 3, iter: 300/13950, loss: 0.7042, DSC: 36.3885, lr: 0.009807, batch_cost: 0.5031, reader_cost: 0.00029, ips: 47.7034 samples/sec | ETA 01:54:27
2022-09-07 11:53:30 [INFO]	[TRAIN] epoch: 3, iter: 310/13950, loss: 0.6932, DSC: 36.4719, lr: 0.009800, batch_cost: 0.5041, reader_cost: 0.00034, ips: 47.6077 samples/sec | ETA 01:54:36
2022-09-07 11:53:35 [INFO]	[TRAIN] epoch: 3, iter: 320/13950, loss: 0.6791, DSC: 37.8334, lr: 0.009794, batch_cost: 0.5043, reader_cost: 0.00039, ips: 47.5940 samples/sec | ETA 01:54:33
2022-09-07 11:53:40 [INFO]	[TRAIN] epoch: 3, iter: 330/13950, loss: 0.6575, DSC: 39.7633, lr: 0.009787, batch_cost: 0.5042, reader_cost: 0.00027, ips: 47.6030 samples/sec | ETA 01:54:26
2022-09-07 11:53:45 [INFO]	[TRAIN] epoch: 3, iter: 340/13950, loss: 0.6361, DSC: 41.6647, lr: 0.009781, batch_cost: 0.5046, reader_cost: 0.00028, ips: 47.5645 samples/sec | ETA 01:54:27
2022-09-07 11:53:50 [INFO]	[TRAIN] epoch: 3, iter: 350/13950, loss: 0.6290, DSC: 43.0757, lr: 0.009775, batch_cost: 0.5069, reader_cost: 0.00029, ips: 47.3472 samples/sec | ETA 01:54:53
2022-09-07 11:53:55 [INFO]	[TRAIN] epoch: 3, iter: 360/13950, loss: 0.6129, DSC: 43.0695, lr: 0.009768, batch_cost: 0.5058, reader_cost: 0.00028, ips: 47.4473 samples/sec | ETA 01:54:34
2022-09-07 11:54:00 [INFO]	[TRAIN] epoch: 3, iter: 370/13950, loss: 0.5984, DSC: 45.5799, lr: 0.009762, batch_cost: 0.5028, reader_cost: 0.00032, ips: 47.7315 samples/sec | ETA 01:53:48
2022-09-07 11:54:06 [INFO]	[TRAIN] epoch: 4, iter: 380/13950, loss: 0.6024, DSC: 43.9555, lr: 0.009755, batch_cost: 0.6012, reader_cost: 0.11265, ips: 39.9200 samples/sec | ETA 02:15:58
2022-09-07 11:54:11 [INFO]	[TRAIN] epoch: 4, iter: 390/13950, loss: 0.6332, DSC: 42.3259, lr: 0.009749, batch_cost: 0.5082, reader_cost: 0.00030, ips: 47.2270 samples/sec | ETA 01:54:50
2022-09-07 11:54:16 [INFO]	[TRAIN] epoch: 4, iter: 400/13950, loss: 0.5888, DSC: 45.7674, lr: 0.009742, batch_cost: 0.5057, reader_cost: 0.00034, ips: 47.4583 samples/sec | ETA 01:54:12
2022-09-07 11:54:21 [INFO]	[TRAIN] epoch: 4, iter: 410/13950, loss: 0.5623, DSC: 48.9231, lr: 0.009736, batch_cost: 0.5056, reader_cost: 0.00028, ips: 47.4698 samples/sec | ETA 01:54:05
2022-09-07 11:54:26 [INFO]	[TRAIN] epoch: 4, iter: 420/13950, loss: 0.5327, DSC: 51.2247, lr: 0.009729, batch_cost: 0.5057, reader_cost: 0.00029, ips: 47.4571 samples/sec | ETA 01:54:02
2022-09-07 11:54:31 [INFO]	[TRAIN] epoch: 4, iter: 430/13950, loss: 0.4935, DSC: 56.0343, lr: 0.009723, batch_cost: 0.5048, reader_cost: 0.00027, ips: 47.5480 samples/sec | ETA 01:53:44
2022-09-07 11:54:36 [INFO]	[TRAIN] epoch: 4, iter: 440/13950, loss: 0.4978, DSC: 55.0798, lr: 0.009716, batch_cost: 0.5047, reader_cost: 0.00038, ips: 47.5557 samples/sec | ETA 01:53:38
2022-09-07 11:54:41 [INFO]	[TRAIN] epoch: 4, iter: 450/13950, loss: 0.4899, DSC: 55.5863, lr: 0.009710, batch_cost: 0.5049, reader_cost: 0.00027, ips: 47.5385 samples/sec | ETA 01:53:35
2022-09-07 11:54:46 [INFO]	[TRAIN] epoch: 4, iter: 460/13950, loss: 0.4765, DSC: 56.5683, lr: 0.009703, batch_cost: 0.5031, reader_cost: 0.00025, ips: 47.7059 samples/sec | ETA 01:53:06
2022-09-07 11:54:52 [INFO]	[TRAIN] epoch: 5, iter: 470/13950, loss: 0.4964, DSC: 54.3620, lr: 0.009697, batch_cost: 0.6102, reader_cost: 0.11932, ips: 39.3322 samples/sec | ETA 02:17:05
2022-09-07 11:54:57 [INFO]	[TRAIN] epoch: 5, iter: 480/13950, loss: 0.4719, DSC: 57.1715, lr: 0.009690, batch_cost: 0.5041, reader_cost: 0.00030, ips: 47.6056 samples/sec | ETA 01:53:10
2022-09-07 11:55:02 [INFO]	[TRAIN] epoch: 5, iter: 490/13950, loss: 0.4662, DSC: 57.5773, lr: 0.009684, batch_cost: 0.5024, reader_cost: 0.00028, ips: 47.7705 samples/sec | ETA 01:52:42
2022-09-07 11:55:07 [INFO]	[TRAIN] epoch: 5, iter: 500/13950, loss: 0.4592, DSC: 58.1426, lr: 0.009677, batch_cost: 0.5026, reader_cost: 0.00028, ips: 47.7543 samples/sec | ETA 01:52:39
2022-09-07 11:55:13 [INFO]	[TRAIN] epoch: 5, iter: 510/13950, loss: 0.4359, DSC: 59.7246, lr: 0.009671, batch_cost: 0.5065, reader_cost: 0.00028, ips: 47.3806 samples/sec | ETA 01:53:27
2022-09-07 11:55:18 [INFO]	[TRAIN] epoch: 5, iter: 520/13950, loss: 0.4453, DSC: 59.4985, lr: 0.009665, batch_cost: 0.5032, reader_cost: 0.00034, ips: 47.6958 samples/sec | ETA 01:52:37
2022-09-07 11:55:23 [INFO]	[TRAIN] epoch: 5, iter: 530/13950, loss: 0.4241, DSC: 61.3035, lr: 0.009658, batch_cost: 0.5037, reader_cost: 0.00029, ips: 47.6459 samples/sec | ETA 01:52:39
2022-09-07 11:55:28 [INFO]	[TRAIN] epoch: 5, iter: 540/13950, loss: 0.4214, DSC: 61.0743, lr: 0.009652, batch_cost: 0.5048, reader_cost: 0.00034, ips: 47.5481 samples/sec | ETA 01:52:48
2022-09-07 11:55:33 [INFO]	[TRAIN] epoch: 5, iter: 550/13950, loss: 0.4103, DSC: 62.6077, lr: 0.009645, batch_cost: 0.5059, reader_cost: 0.00027, ips: 47.4395 samples/sec | ETA 01:52:59
2022-09-07 11:55:39 [INFO]	[TRAIN] epoch: 6, iter: 560/13950, loss: 0.3965, DSC: 63.9007, lr: 0.009639, batch_cost: 0.6055, reader_cost: 0.12321, ips: 39.6369 samples/sec | ETA 02:15:07
2022-09-07 11:55:44 [INFO]	[TRAIN] epoch: 6, iter: 570/13950, loss: 0.3997, DSC: 63.5642, lr: 0.009632, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5463 samples/sec | ETA 01:52:33
2022-09-07 11:55:49 [INFO]	[TRAIN] epoch: 6, iter: 580/13950, loss: 0.3646, DSC: 66.6703, lr: 0.009626, batch_cost: 0.5025, reader_cost: 0.00028, ips: 47.7610 samples/sec | ETA 01:51:58
2022-09-07 11:55:54 [INFO]	[TRAIN] epoch: 6, iter: 590/13950, loss: 0.3491, DSC: 68.3966, lr: 0.009619, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5677 samples/sec | ETA 01:52:20
2022-09-07 11:55:59 [INFO]	[TRAIN] epoch: 6, iter: 600/13950, loss: 0.3637, DSC: 66.8093, lr: 0.009613, batch_cost: 0.5047, reader_cost: 0.00029, ips: 47.5573 samples/sec | ETA 01:52:17
2022-09-07 11:56:04 [INFO]	[TRAIN] epoch: 6, iter: 610/13950, loss: 0.3393, DSC: 69.3414, lr: 0.009606, batch_cost: 0.5035, reader_cost: 0.00028, ips: 47.6626 samples/sec | ETA 01:51:57
2022-09-07 11:56:09 [INFO]	[TRAIN] epoch: 6, iter: 620/13950, loss: 0.3549, DSC: 67.6056, lr: 0.009600, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6543 samples/sec | ETA 01:51:53
2022-09-07 11:56:14 [INFO]	[TRAIN] epoch: 6, iter: 630/13950, loss: 0.3577, DSC: 67.4019, lr: 0.009593, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5699 samples/sec | ETA 01:52:00
2022-09-07 11:56:19 [INFO]	[TRAIN] epoch: 6, iter: 640/13950, loss: 0.3578, DSC: 67.4579, lr: 0.009587, batch_cost: 0.5033, reader_cost: 0.00026, ips: 47.6839 samples/sec | ETA 01:51:39
2022-09-07 11:56:24 [INFO]	[TRAIN] epoch: 6, iter: 650/13950, loss: 0.3532, DSC: 67.7578, lr: 0.009580, batch_cost: 0.5036, reader_cost: 0.00023, ips: 47.6575 samples/sec | ETA 01:51:37
2022-09-07 11:56:30 [INFO]	[TRAIN] epoch: 7, iter: 660/13950, loss: 0.3729, DSC: 66.7252, lr: 0.009574, batch_cost: 0.6133, reader_cost: 0.11197, ips: 39.1339 samples/sec | ETA 02:15:50
2022-09-07 11:56:35 [INFO]	[TRAIN] epoch: 7, iter: 670/13950, loss: 0.3429, DSC: 69.2355, lr: 0.009567, batch_cost: 0.5046, reader_cost: 0.00028, ips: 47.5598 samples/sec | ETA 01:51:41
2022-09-07 11:56:40 [INFO]	[TRAIN] epoch: 7, iter: 680/13950, loss: 0.3201, DSC: 70.8619, lr: 0.009561, batch_cost: 0.5034, reader_cost: 0.00027, ips: 47.6754 samples/sec | ETA 01:51:20
2022-09-07 11:56:45 [INFO]	[TRAIN] epoch: 7, iter: 690/13950, loss: 0.3136, DSC: 71.1949, lr: 0.009554, batch_cost: 0.5037, reader_cost: 0.00029, ips: 47.6479 samples/sec | ETA 01:51:18
2022-09-07 11:56:50 [INFO]	[TRAIN] epoch: 7, iter: 700/13950, loss: 0.3369, DSC: 68.7318, lr: 0.009548, batch_cost: 0.5047, reader_cost: 0.00028, ips: 47.5532 samples/sec | ETA 01:51:27
2022-09-07 11:56:55 [INFO]	[TRAIN] epoch: 7, iter: 710/13950, loss: 0.3254, DSC: 69.8315, lr: 0.009541, batch_cost: 0.5050, reader_cost: 0.00029, ips: 47.5288 samples/sec | ETA 01:51:25
2022-09-07 11:57:01 [INFO]	[TRAIN] epoch: 7, iter: 720/13950, loss: 0.3022, DSC: 72.4038, lr: 0.009535, batch_cost: 0.5068, reader_cost: 0.00031, ips: 47.3552 samples/sec | ETA 01:51:45
2022-09-07 11:57:06 [INFO]	[TRAIN] epoch: 7, iter: 730/13950, loss: 0.2863, DSC: 73.9581, lr: 0.009528, batch_cost: 0.5052, reader_cost: 0.00030, ips: 47.5097 samples/sec | ETA 01:51:18
2022-09-07 11:57:11 [INFO]	[TRAIN] epoch: 7, iter: 740/13950, loss: 0.2776, DSC: 74.7168, lr: 0.009522, batch_cost: 0.5039, reader_cost: 0.00027, ips: 47.6266 samples/sec | ETA 01:50:56
2022-09-07 11:57:17 [INFO]	[TRAIN] epoch: 8, iter: 750/13950, loss: 0.3190, DSC: 70.6327, lr: 0.009515, batch_cost: 0.6291, reader_cost: 0.13135, ips: 38.1473 samples/sec | ETA 02:18:24
2022-09-07 11:57:22 [INFO]	[TRAIN] epoch: 8, iter: 760/13950, loss: 0.2688, DSC: 75.6918, lr: 0.009509, batch_cost: 0.5068, reader_cost: 0.00030, ips: 47.3546 samples/sec | ETA 01:51:24
2022-09-07 11:57:27 [INFO]	[TRAIN] epoch: 8, iter: 770/13950, loss: 0.2706, DSC: 75.2449, lr: 0.009502, batch_cost: 0.5063, reader_cost: 0.00030, ips: 47.4026 samples/sec | ETA 01:51:13
2022-09-07 11:57:32 [INFO]	[TRAIN] epoch: 8, iter: 780/13950, loss: 0.2925, DSC: 73.1948, lr: 0.009496, batch_cost: 0.5070, reader_cost: 0.00030, ips: 47.3392 samples/sec | ETA 01:51:16
2022-09-07 11:57:37 [INFO]	[TRAIN] epoch: 8, iter: 790/13950, loss: 0.2564, DSC: 76.3509, lr: 0.009489, batch_cost: 0.5078, reader_cost: 0.00038, ips: 47.2582 samples/sec | ETA 01:51:23
2022-09-07 11:57:42 [INFO]	[TRAIN] epoch: 8, iter: 800/13950, loss: 0.2898, DSC: 73.3192, lr: 0.009483, batch_cost: 0.5051, reader_cost: 0.00029, ips: 47.5121 samples/sec | ETA 01:50:42
2022-09-07 11:57:47 [INFO]	[TRAIN] epoch: 8, iter: 810/13950, loss: 0.2769, DSC: 74.8726, lr: 0.009477, batch_cost: 0.5055, reader_cost: 0.00029, ips: 47.4819 samples/sec | ETA 01:50:41
2022-09-07 11:57:52 [INFO]	[TRAIN] epoch: 8, iter: 820/13950, loss: 0.2858, DSC: 73.7668, lr: 0.009470, batch_cost: 0.5057, reader_cost: 0.00036, ips: 47.4604 samples/sec | ETA 01:50:39
2022-09-07 11:57:57 [INFO]	[TRAIN] epoch: 8, iter: 830/13950, loss: 0.2615, DSC: 76.3177, lr: 0.009464, batch_cost: 0.5071, reader_cost: 0.00039, ips: 47.3289 samples/sec | ETA 01:50:53
2022-09-07 11:58:04 [INFO]	[TRAIN] epoch: 9, iter: 840/13950, loss: 0.2913, DSC: 73.4651, lr: 0.009457, batch_cost: 0.6148, reader_cost: 0.12114, ips: 39.0399 samples/sec | ETA 02:14:19
2022-09-07 11:58:09 [INFO]	[TRAIN] epoch: 9, iter: 850/13950, loss: 0.2906, DSC: 73.4650, lr: 0.009451, batch_cost: 0.5108, reader_cost: 0.00036, ips: 46.9823 samples/sec | ETA 01:51:31
2022-09-07 11:58:14 [INFO]	[TRAIN] epoch: 9, iter: 860/13950, loss: 0.2599, DSC: 76.0388, lr: 0.009444, batch_cost: 0.5029, reader_cost: 0.00028, ips: 47.7221 samples/sec | ETA 01:49:43
2022-09-07 11:58:19 [INFO]	[TRAIN] epoch: 9, iter: 870/13950, loss: 0.2638, DSC: 76.1200, lr: 0.009438, batch_cost: 0.5062, reader_cost: 0.00029, ips: 47.4122 samples/sec | ETA 01:50:21
2022-09-07 11:58:24 [INFO]	[TRAIN] epoch: 9, iter: 880/13950, loss: 0.2577, DSC: 76.6586, lr: 0.009431, batch_cost: 0.5022, reader_cost: 0.00029, ips: 47.7938 samples/sec | ETA 01:49:23
2022-09-07 11:58:29 [INFO]	[TRAIN] epoch: 9, iter: 890/13950, loss: 0.2296, DSC: 79.1241, lr: 0.009425, batch_cost: 0.5041, reader_cost: 0.00028, ips: 47.6051 samples/sec | ETA 01:49:44
2022-09-07 11:58:34 [INFO]	[TRAIN] epoch: 9, iter: 900/13950, loss: 0.2560, DSC: 76.2506, lr: 0.009418, batch_cost: 0.5044, reader_cost: 0.00029, ips: 47.5770 samples/sec | ETA 01:49:43
2022-09-07 11:58:39 [INFO]	[TRAIN] epoch: 9, iter: 910/13950, loss: 0.2559, DSC: 76.5617, lr: 0.009412, batch_cost: 0.5073, reader_cost: 0.00031, ips: 47.3107 samples/sec | ETA 01:50:14
2022-09-07 11:58:44 [INFO]	[TRAIN] epoch: 9, iter: 920/13950, loss: 0.2678, DSC: 75.3316, lr: 0.009405, batch_cost: 0.5050, reader_cost: 0.00034, ips: 47.5248 samples/sec | ETA 01:49:40
2022-09-07 11:58:49 [INFO]	[TRAIN] epoch: 10, iter: 930/13950, loss: 0.2660, DSC: 75.4568, lr: 0.009399, batch_cost: 0.4765, reader_cost: 0.00025, ips: 50.3663 samples/sec | ETA 01:43:24
2022-09-07 11:58:55 [INFO]	[TRAIN] epoch: 10, iter: 940/13950, loss: 0.2424, DSC: 77.8399, lr: 0.009392, batch_cost: 0.6463, reader_cost: 0.12780, ips: 37.1362 samples/sec | ETA 02:20:07
2022-09-07 11:59:00 [INFO]	[TRAIN] epoch: 10, iter: 950/13950, loss: 0.2381, DSC: 78.5076, lr: 0.009386, batch_cost: 0.5068, reader_cost: 0.00029, ips: 47.3597 samples/sec | ETA 01:49:47
2022-09-07 11:59:05 [INFO]	[TRAIN] epoch: 10, iter: 960/13950, loss: 0.2459, DSC: 77.1455, lr: 0.009379, batch_cost: 0.5061, reader_cost: 0.00036, ips: 47.4199 samples/sec | ETA 01:49:34
2022-09-07 11:59:10 [INFO]	[TRAIN] epoch: 10, iter: 970/13950, loss: 0.2588, DSC: 76.1731, lr: 0.009373, batch_cost: 0.5074, reader_cost: 0.00031, ips: 47.2980 samples/sec | ETA 01:49:46
2022-09-07 11:59:16 [INFO]	[TRAIN] epoch: 10, iter: 980/13950, loss: 0.2349, DSC: 78.8821, lr: 0.009366, batch_cost: 0.5047, reader_cost: 0.00036, ips: 47.5523 samples/sec | ETA 01:49:06
2022-09-07 11:59:21 [INFO]	[TRAIN] epoch: 10, iter: 990/13950, loss: 0.2063, DSC: 81.0452, lr: 0.009360, batch_cost: 0.5074, reader_cost: 0.00029, ips: 47.3003 samples/sec | ETA 01:49:35
2022-09-07 11:59:26 [INFO]	[TRAIN] epoch: 10, iter: 1000/13950, loss: 0.2312, DSC: 78.9193, lr: 0.009353, batch_cost: 0.5043, reader_cost: 0.00029, ips: 47.5892 samples/sec | ETA 01:48:50
2022-09-07 11:59:26 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0030 - reader cost: 1.1533
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2683 - reader cost: 0.5794
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0467 - reader cost: 0.3940 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.1963 - reader cost: 0.4463
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1284 - reader cost: 0.3614
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0809 - reader cost: 0.3101
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0122 - reader cost: 0.2686
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1133 - reader cost: 0.3180
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0875 - reader cost: 0.2849
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0625 - reader cost: 0.2595
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0119 - reader cost: 0.2374
12/12 [==============================] - 12s 975ms/step - batch_cost: 0.9750 - reader cost: 0.2194
2022-09-07 11:59:37 [INFO]	[EVAL] #Images: 12, Dice: 0.7425, Loss: 0.287339
2022-09-07 11:59:37 [INFO]	[EVAL] Class dice: 
[0.9898 0.8298 0.3434 0.7943 0.7939 0.8981 0.5808 0.7938 0.6587]
2022-09-07 11:59:40 [INFO]	[EVAL] The model with the best validation mDice (0.7425) was saved at iter 1000.
2022-09-07 11:59:46 [INFO]	[TRAIN] epoch: 10, iter: 1010/13950, loss: 0.2385, DSC: 78.1162, lr: 0.009347, batch_cost: 0.5057, reader_cost: 0.00048, ips: 47.4633 samples/sec | ETA 01:49:03
2022-09-07 11:59:51 [INFO]	[TRAIN] epoch: 10, iter: 1020/13950, loss: 0.2252, DSC: 79.2737, lr: 0.009340, batch_cost: 0.5048, reader_cost: 0.00035, ips: 47.5398 samples/sec | ETA 01:48:47
2022-09-07 11:59:57 [INFO]	[TRAIN] epoch: 11, iter: 1030/13950, loss: 0.2500, DSC: 76.7184, lr: 0.009334, batch_cost: 0.6009, reader_cost: 0.11016, ips: 39.9423 samples/sec | ETA 02:09:23
2022-09-07 12:00:02 [INFO]	[TRAIN] epoch: 11, iter: 1040/13950, loss: 0.2334, DSC: 78.5381, lr: 0.009327, batch_cost: 0.5027, reader_cost: 0.00030, ips: 47.7469 samples/sec | ETA 01:48:09
2022-09-07 12:00:07 [INFO]	[TRAIN] epoch: 11, iter: 1050/13950, loss: 0.2418, DSC: 77.7909, lr: 0.009321, batch_cost: 0.5029, reader_cost: 0.00035, ips: 47.7262 samples/sec | ETA 01:48:07
2022-09-07 12:00:12 [INFO]	[TRAIN] epoch: 11, iter: 1060/13950, loss: 0.2381, DSC: 77.9033, lr: 0.009314, batch_cost: 0.5032, reader_cost: 0.00036, ips: 47.6992 samples/sec | ETA 01:48:05
2022-09-07 12:00:17 [INFO]	[TRAIN] epoch: 11, iter: 1070/13950, loss: 0.2337, DSC: 78.8707, lr: 0.009308, batch_cost: 0.5040, reader_cost: 0.00034, ips: 47.6213 samples/sec | ETA 01:48:11
2022-09-07 12:00:22 [INFO]	[TRAIN] epoch: 11, iter: 1080/13950, loss: 0.2250, DSC: 79.3062, lr: 0.009301, batch_cost: 0.5083, reader_cost: 0.00038, ips: 47.2189 samples/sec | ETA 01:49:01
2022-09-07 12:00:27 [INFO]	[TRAIN] epoch: 11, iter: 1090/13950, loss: 0.2347, DSC: 78.4494, lr: 0.009295, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5440 samples/sec | ETA 01:48:11
2022-09-07 12:00:32 [INFO]	[TRAIN] epoch: 11, iter: 1100/13950, loss: 0.2154, DSC: 80.1309, lr: 0.009288, batch_cost: 0.5041, reader_cost: 0.00030, ips: 47.6057 samples/sec | ETA 01:47:58
2022-09-07 12:00:37 [INFO]	[TRAIN] epoch: 11, iter: 1110/13950, loss: 0.1977, DSC: 81.9830, lr: 0.009282, batch_cost: 0.5043, reader_cost: 0.00028, ips: 47.5885 samples/sec | ETA 01:47:55
2022-09-07 12:00:44 [INFO]	[TRAIN] epoch: 12, iter: 1120/13950, loss: 0.2231, DSC: 79.5187, lr: 0.009275, batch_cost: 0.6628, reader_cost: 0.16849, ips: 36.2090 samples/sec | ETA 02:21:43
2022-09-07 12:00:49 [INFO]	[TRAIN] epoch: 12, iter: 1130/13950, loss: 0.2180, DSC: 79.9861, lr: 0.009269, batch_cost: 0.5073, reader_cost: 0.00036, ips: 47.3079 samples/sec | ETA 01:48:23
2022-09-07 12:00:54 [INFO]	[TRAIN] epoch: 12, iter: 1140/13950, loss: 0.2168, DSC: 79.7567, lr: 0.009262, batch_cost: 0.5074, reader_cost: 0.00030, ips: 47.2974 samples/sec | ETA 01:48:20
2022-09-07 12:00:59 [INFO]	[TRAIN] epoch: 12, iter: 1150/13950, loss: 0.2443, DSC: 77.5324, lr: 0.009256, batch_cost: 0.5068, reader_cost: 0.00036, ips: 47.3591 samples/sec | ETA 01:48:06
2022-09-07 12:01:04 [INFO]	[TRAIN] epoch: 12, iter: 1160/13950, loss: 0.2120, DSC: 80.5988, lr: 0.009249, batch_cost: 0.5068, reader_cost: 0.00036, ips: 47.3516 samples/sec | ETA 01:48:02
2022-09-07 12:01:09 [INFO]	[TRAIN] epoch: 12, iter: 1170/13950, loss: 0.1986, DSC: 81.8853, lr: 0.009243, batch_cost: 0.5063, reader_cost: 0.00036, ips: 47.4016 samples/sec | ETA 01:47:50
2022-09-07 12:01:14 [INFO]	[TRAIN] epoch: 12, iter: 1180/13950, loss: 0.1942, DSC: 82.2847, lr: 0.009236, batch_cost: 0.5074, reader_cost: 0.00030, ips: 47.2979 samples/sec | ETA 01:47:59
2022-09-07 12:01:19 [INFO]	[TRAIN] epoch: 12, iter: 1190/13950, loss: 0.2066, DSC: 80.9038, lr: 0.009230, batch_cost: 0.5059, reader_cost: 0.00029, ips: 47.4413 samples/sec | ETA 01:47:35
2022-09-07 12:01:24 [INFO]	[TRAIN] epoch: 12, iter: 1200/13950, loss: 0.1839, DSC: 83.3554, lr: 0.009223, batch_cost: 0.5073, reader_cost: 0.00030, ips: 47.3102 samples/sec | ETA 01:47:47
2022-09-07 12:01:30 [INFO]	[TRAIN] epoch: 13, iter: 1210/13950, loss: 0.2102, DSC: 80.7673, lr: 0.009217, batch_cost: 0.5937, reader_cost: 0.11213, ips: 40.4243 samples/sec | ETA 02:06:03
2022-09-07 12:01:36 [INFO]	[TRAIN] epoch: 13, iter: 1220/13950, loss: 0.1940, DSC: 82.3094, lr: 0.009210, batch_cost: 0.5223, reader_cost: 0.00037, ips: 45.9479 samples/sec | ETA 01:50:49
2022-09-07 12:01:41 [INFO]	[TRAIN] epoch: 13, iter: 1230/13950, loss: 0.1995, DSC: 81.4175, lr: 0.009203, batch_cost: 0.5058, reader_cost: 0.00035, ips: 47.4462 samples/sec | ETA 01:47:14
2022-09-07 12:01:46 [INFO]	[TRAIN] epoch: 13, iter: 1240/13950, loss: 0.2250, DSC: 78.9576, lr: 0.009197, batch_cost: 0.5147, reader_cost: 0.00029, ips: 46.6309 samples/sec | ETA 01:49:01
2022-09-07 12:01:51 [INFO]	[TRAIN] epoch: 13, iter: 1250/13950, loss: 0.2119, DSC: 80.3331, lr: 0.009190, batch_cost: 0.5055, reader_cost: 0.00027, ips: 47.4731 samples/sec | ETA 01:47:00
2022-09-07 12:01:56 [INFO]	[TRAIN] epoch: 13, iter: 1260/13950, loss: 0.2131, DSC: 80.6082, lr: 0.009184, batch_cost: 0.5065, reader_cost: 0.00028, ips: 47.3853 samples/sec | ETA 01:47:07
2022-09-07 12:02:01 [INFO]	[TRAIN] epoch: 13, iter: 1270/13950, loss: 0.1979, DSC: 81.8700, lr: 0.009177, batch_cost: 0.5061, reader_cost: 0.00034, ips: 47.4228 samples/sec | ETA 01:46:57
2022-09-07 12:02:06 [INFO]	[TRAIN] epoch: 13, iter: 1280/13950, loss: 0.1919, DSC: 82.4028, lr: 0.009171, batch_cost: 0.5055, reader_cost: 0.00029, ips: 47.4803 samples/sec | ETA 01:46:44
2022-09-07 12:02:11 [INFO]	[TRAIN] epoch: 13, iter: 1290/13950, loss: 0.2073, DSC: 81.2893, lr: 0.009164, batch_cost: 0.5060, reader_cost: 0.00035, ips: 47.4323 samples/sec | ETA 01:46:45
2022-09-07 12:02:16 [INFO]	[TRAIN] epoch: 13, iter: 1300/13950, loss: 0.2041, DSC: 81.0908, lr: 0.009158, batch_cost: 0.5041, reader_cost: 0.00026, ips: 47.6134 samples/sec | ETA 01:46:16
2022-09-07 12:02:22 [INFO]	[TRAIN] epoch: 14, iter: 1310/13950, loss: 0.2179, DSC: 80.0927, lr: 0.009151, batch_cost: 0.6115, reader_cost: 0.12248, ips: 39.2473 samples/sec | ETA 02:08:49
2022-09-07 12:02:27 [INFO]	[TRAIN] epoch: 14, iter: 1320/13950, loss: 0.1906, DSC: 82.6380, lr: 0.009145, batch_cost: 0.5058, reader_cost: 0.00029, ips: 47.4513 samples/sec | ETA 01:46:28
2022-09-07 12:02:32 [INFO]	[TRAIN] epoch: 14, iter: 1330/13950, loss: 0.1989, DSC: 81.8766, lr: 0.009138, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6124 samples/sec | ETA 01:46:01
2022-09-07 12:02:37 [INFO]	[TRAIN] epoch: 14, iter: 1340/13950, loss: 0.2205, DSC: 79.6664, lr: 0.009132, batch_cost: 0.5049, reader_cost: 0.00028, ips: 47.5348 samples/sec | ETA 01:46:06
2022-09-07 12:02:42 [INFO]	[TRAIN] epoch: 14, iter: 1350/13950, loss: 0.2090, DSC: 80.7240, lr: 0.009125, batch_cost: 0.5056, reader_cost: 0.00027, ips: 47.4644 samples/sec | ETA 01:46:11
2022-09-07 12:02:48 [INFO]	[TRAIN] epoch: 14, iter: 1360/13950, loss: 0.2356, DSC: 78.2563, lr: 0.009119, batch_cost: 0.5097, reader_cost: 0.00035, ips: 47.0831 samples/sec | ETA 01:46:57
2022-09-07 12:02:53 [INFO]	[TRAIN] epoch: 14, iter: 1370/13950, loss: 0.2003, DSC: 81.4887, lr: 0.009112, batch_cost: 0.5036, reader_cost: 0.00028, ips: 47.6600 samples/sec | ETA 01:45:34
2022-09-07 12:02:58 [INFO]	[TRAIN] epoch: 14, iter: 1380/13950, loss: 0.2150, DSC: 80.1202, lr: 0.009106, batch_cost: 0.5048, reader_cost: 0.00028, ips: 47.5417 samples/sec | ETA 01:45:45
2022-09-07 12:03:03 [INFO]	[TRAIN] epoch: 14, iter: 1390/13950, loss: 0.1863, DSC: 82.8213, lr: 0.009099, batch_cost: 0.5029, reader_cost: 0.00025, ips: 47.7225 samples/sec | ETA 01:45:16
2022-09-07 12:03:09 [INFO]	[TRAIN] epoch: 15, iter: 1400/13950, loss: 0.1879, DSC: 82.8924, lr: 0.009093, batch_cost: 0.6103, reader_cost: 0.12758, ips: 39.3261 samples/sec | ETA 02:07:39
2022-09-07 12:03:14 [INFO]	[TRAIN] epoch: 15, iter: 1410/13950, loss: 0.1895, DSC: 82.4572, lr: 0.009086, batch_cost: 0.5030, reader_cost: 0.00027, ips: 47.7137 samples/sec | ETA 01:45:07
2022-09-07 12:03:19 [INFO]	[TRAIN] epoch: 15, iter: 1420/13950, loss: 0.1620, DSC: 85.1083, lr: 0.009080, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5778 samples/sec | ETA 01:45:20
2022-09-07 12:03:24 [INFO]	[TRAIN] epoch: 15, iter: 1430/13950, loss: 0.1834, DSC: 83.0272, lr: 0.009073, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5890 samples/sec | ETA 01:45:14
2022-09-07 12:03:29 [INFO]	[TRAIN] epoch: 15, iter: 1440/13950, loss: 0.1712, DSC: 84.2611, lr: 0.009067, batch_cost: 0.5046, reader_cost: 0.00028, ips: 47.5620 samples/sec | ETA 01:45:12
2022-09-07 12:03:34 [INFO]	[TRAIN] epoch: 15, iter: 1450/13950, loss: 0.1842, DSC: 82.9889, lr: 0.009060, batch_cost: 0.5040, reader_cost: 0.00028, ips: 47.6165 samples/sec | ETA 01:45:00
2022-09-07 12:03:39 [INFO]	[TRAIN] epoch: 15, iter: 1460/13950, loss: 0.1967, DSC: 81.8346, lr: 0.009054, batch_cost: 0.5029, reader_cost: 0.00026, ips: 47.7237 samples/sec | ETA 01:44:41
2022-09-07 12:03:44 [INFO]	[TRAIN] epoch: 15, iter: 1470/13950, loss: 0.1998, DSC: 81.7178, lr: 0.009047, batch_cost: 0.5052, reader_cost: 0.00033, ips: 47.5103 samples/sec | ETA 01:45:04
2022-09-07 12:03:49 [INFO]	[TRAIN] epoch: 15, iter: 1480/13950, loss: 0.1905, DSC: 82.4661, lr: 0.009041, batch_cost: 0.5069, reader_cost: 0.00029, ips: 47.3506 samples/sec | ETA 01:45:20
2022-09-07 12:03:55 [INFO]	[TRAIN] epoch: 16, iter: 1490/13950, loss: 0.2219, DSC: 79.1673, lr: 0.009034, batch_cost: 0.6010, reader_cost: 0.11720, ips: 39.9359 samples/sec | ETA 02:04:48
2022-09-07 12:04:00 [INFO]	[TRAIN] epoch: 16, iter: 1500/13950, loss: 0.1853, DSC: 83.0110, lr: 0.009027, batch_cost: 0.5062, reader_cost: 0.00028, ips: 47.4152 samples/sec | ETA 01:45:01
2022-09-07 12:04:05 [INFO]	[TRAIN] epoch: 16, iter: 1510/13950, loss: 0.1968, DSC: 81.7384, lr: 0.009021, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5583 samples/sec | ETA 01:44:37
2022-09-07 12:04:10 [INFO]	[TRAIN] epoch: 16, iter: 1520/13950, loss: 0.1881, DSC: 82.5975, lr: 0.009014, batch_cost: 0.5044, reader_cost: 0.00033, ips: 47.5856 samples/sec | ETA 01:44:29
2022-09-07 12:04:15 [INFO]	[TRAIN] epoch: 16, iter: 1530/13950, loss: 0.2123, DSC: 80.2369, lr: 0.009008, batch_cost: 0.5053, reader_cost: 0.00035, ips: 47.5007 samples/sec | ETA 01:44:35
2022-09-07 12:04:20 [INFO]	[TRAIN] epoch: 16, iter: 1540/13950, loss: 0.1800, DSC: 83.5255, lr: 0.009001, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5529 samples/sec | ETA 01:44:23
2022-09-07 12:04:25 [INFO]	[TRAIN] epoch: 16, iter: 1550/13950, loss: 0.1715, DSC: 84.2498, lr: 0.008995, batch_cost: 0.5055, reader_cost: 0.00027, ips: 47.4792 samples/sec | ETA 01:44:28
2022-09-07 12:04:30 [INFO]	[TRAIN] epoch: 16, iter: 1560/13950, loss: 0.1742, DSC: 83.9684, lr: 0.008988, batch_cost: 0.5052, reader_cost: 0.00034, ips: 47.5102 samples/sec | ETA 01:44:18
2022-09-07 12:04:36 [INFO]	[TRAIN] epoch: 16, iter: 1570/13950, loss: 0.1682, DSC: 84.7002, lr: 0.008982, batch_cost: 0.5053, reader_cost: 0.00029, ips: 47.4953 samples/sec | ETA 01:44:15
2022-09-07 12:04:41 [INFO]	[TRAIN] epoch: 16, iter: 1580/13950, loss: 0.1785, DSC: 83.3585, lr: 0.008975, batch_cost: 0.5030, reader_cost: 0.00023, ips: 47.7090 samples/sec | ETA 01:43:42
2022-09-07 12:04:47 [INFO]	[TRAIN] epoch: 17, iter: 1590/13950, loss: 0.2170, DSC: 79.9662, lr: 0.008969, batch_cost: 0.6041, reader_cost: 0.11398, ips: 39.7271 samples/sec | ETA 02:04:26
2022-09-07 12:04:52 [INFO]	[TRAIN] epoch: 17, iter: 1600/13950, loss: 0.1733, DSC: 83.9981, lr: 0.008962, batch_cost: 0.5066, reader_cost: 0.00035, ips: 47.3722 samples/sec | ETA 01:44:16
2022-09-07 12:04:57 [INFO]	[TRAIN] epoch: 17, iter: 1610/13950, loss: 0.1635, DSC: 85.1041, lr: 0.008956, batch_cost: 0.5059, reader_cost: 0.00030, ips: 47.4377 samples/sec | ETA 01:44:03
2022-09-07 12:05:02 [INFO]	[TRAIN] epoch: 17, iter: 1620/13950, loss: 0.1664, DSC: 84.6847, lr: 0.008949, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6593 samples/sec | ETA 01:43:29
2022-09-07 12:05:07 [INFO]	[TRAIN] epoch: 17, iter: 1630/13950, loss: 0.1752, DSC: 83.7667, lr: 0.008943, batch_cost: 0.5036, reader_cost: 0.00035, ips: 47.6558 samples/sec | ETA 01:43:24
2022-09-07 12:05:12 [INFO]	[TRAIN] epoch: 17, iter: 1640/13950, loss: 0.1686, DSC: 84.5641, lr: 0.008936, batch_cost: 0.5030, reader_cost: 0.00027, ips: 47.7115 samples/sec | ETA 01:43:12
2022-09-07 12:05:17 [INFO]	[TRAIN] epoch: 17, iter: 1650/13950, loss: 0.1762, DSC: 83.7494, lr: 0.008930, batch_cost: 0.5035, reader_cost: 0.00027, ips: 47.6634 samples/sec | ETA 01:43:13
2022-09-07 12:05:22 [INFO]	[TRAIN] epoch: 17, iter: 1660/13950, loss: 0.1741, DSC: 83.7095, lr: 0.008923, batch_cost: 0.5037, reader_cost: 0.00026, ips: 47.6514 samples/sec | ETA 01:43:09
2022-09-07 12:05:27 [INFO]	[TRAIN] epoch: 17, iter: 1670/13950, loss: 0.1798, DSC: 83.3763, lr: 0.008916, batch_cost: 0.5046, reader_cost: 0.00033, ips: 47.5648 samples/sec | ETA 01:43:16
2022-09-07 12:05:33 [INFO]	[TRAIN] epoch: 18, iter: 1680/13950, loss: 0.1748, DSC: 83.9362, lr: 0.008910, batch_cost: 0.6277, reader_cost: 0.13410, ips: 38.2321 samples/sec | ETA 02:08:22
2022-09-07 12:05:38 [INFO]	[TRAIN] epoch: 18, iter: 1690/13950, loss: 0.1585, DSC: 85.2929, lr: 0.008903, batch_cost: 0.5028, reader_cost: 0.00027, ips: 47.7303 samples/sec | ETA 01:42:44
2022-09-07 12:05:43 [INFO]	[TRAIN] epoch: 18, iter: 1700/13950, loss: 0.1591, DSC: 85.5016, lr: 0.008897, batch_cost: 0.5033, reader_cost: 0.00027, ips: 47.6842 samples/sec | ETA 01:42:45
2022-09-07 12:05:48 [INFO]	[TRAIN] epoch: 18, iter: 1710/13950, loss: 0.1710, DSC: 84.2760, lr: 0.008890, batch_cost: 0.5035, reader_cost: 0.00027, ips: 47.6675 samples/sec | ETA 01:42:42
2022-09-07 12:05:53 [INFO]	[TRAIN] epoch: 18, iter: 1720/13950, loss: 0.1959, DSC: 81.7081, lr: 0.008884, batch_cost: 0.5069, reader_cost: 0.00029, ips: 47.3428 samples/sec | ETA 01:43:19
2022-09-07 12:05:58 [INFO]	[TRAIN] epoch: 18, iter: 1730/13950, loss: 0.1781, DSC: 83.6974, lr: 0.008877, batch_cost: 0.5061, reader_cost: 0.00029, ips: 47.4200 samples/sec | ETA 01:43:04
2022-09-07 12:06:03 [INFO]	[TRAIN] epoch: 18, iter: 1740/13950, loss: 0.1580, DSC: 85.6699, lr: 0.008871, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5857 samples/sec | ETA 01:42:38
2022-09-07 12:06:09 [INFO]	[TRAIN] epoch: 18, iter: 1750/13950, loss: 0.1753, DSC: 83.7500, lr: 0.008864, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6394 samples/sec | ETA 01:42:26
2022-09-07 12:06:14 [INFO]	[TRAIN] epoch: 18, iter: 1760/13950, loss: 0.1595, DSC: 85.3658, lr: 0.008858, batch_cost: 0.5045, reader_cost: 0.00034, ips: 47.5740 samples/sec | ETA 01:42:29
2022-09-07 12:06:20 [INFO]	[TRAIN] epoch: 19, iter: 1770/13950, loss: 0.2445, DSC: 76.8091, lr: 0.008851, batch_cost: 0.6108, reader_cost: 0.12892, ips: 39.2956 samples/sec | ETA 02:03:58
2022-09-07 12:06:25 [INFO]	[TRAIN] epoch: 19, iter: 1780/13950, loss: 0.1626, DSC: 85.0759, lr: 0.008845, batch_cost: 0.5074, reader_cost: 0.00028, ips: 47.2976 samples/sec | ETA 01:42:55
2022-09-07 12:06:30 [INFO]	[TRAIN] epoch: 19, iter: 1790/13950, loss: 0.1555, DSC: 85.7636, lr: 0.008838, batch_cost: 0.5062, reader_cost: 0.00030, ips: 47.4097 samples/sec | ETA 01:42:35
2022-09-07 12:06:35 [INFO]	[TRAIN] epoch: 19, iter: 1800/13950, loss: 0.1816, DSC: 82.9081, lr: 0.008831, batch_cost: 0.5059, reader_cost: 0.00034, ips: 47.4385 samples/sec | ETA 01:42:26
2022-09-07 12:06:40 [INFO]	[TRAIN] epoch: 19, iter: 1810/13950, loss: 0.1667, DSC: 84.4696, lr: 0.008825, batch_cost: 0.5043, reader_cost: 0.00028, ips: 47.5940 samples/sec | ETA 01:42:01
2022-09-07 12:06:45 [INFO]	[TRAIN] epoch: 19, iter: 1820/13950, loss: 0.1569, DSC: 85.5634, lr: 0.008818, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5280 samples/sec | ETA 01:42:05
2022-09-07 12:06:50 [INFO]	[TRAIN] epoch: 19, iter: 1830/13950, loss: 0.1673, DSC: 84.4710, lr: 0.008812, batch_cost: 0.5043, reader_cost: 0.00028, ips: 47.5874 samples/sec | ETA 01:41:52
2022-09-07 12:06:55 [INFO]	[TRAIN] epoch: 19, iter: 1840/13950, loss: 0.1685, DSC: 84.4526, lr: 0.008805, batch_cost: 0.5061, reader_cost: 0.00030, ips: 47.4208 samples/sec | ETA 01:42:08
2022-09-07 12:07:00 [INFO]	[TRAIN] epoch: 19, iter: 1850/13950, loss: 0.1583, DSC: 85.4784, lr: 0.008799, batch_cost: 0.5052, reader_cost: 0.00030, ips: 47.5043 samples/sec | ETA 01:41:53
2022-09-07 12:07:05 [INFO]	[TRAIN] epoch: 20, iter: 1860/13950, loss: 0.1870, DSC: 82.6687, lr: 0.008792, batch_cost: 0.4754, reader_cost: 0.00024, ips: 50.4859 samples/sec | ETA 01:35:47
2022-09-07 12:07:11 [INFO]	[TRAIN] epoch: 20, iter: 1870/13950, loss: 0.1553, DSC: 85.8015, lr: 0.008786, batch_cost: 0.6382, reader_cost: 0.12248, ips: 37.6058 samples/sec | ETA 02:08:29
2022-09-07 12:07:16 [INFO]	[TRAIN] epoch: 20, iter: 1880/13950, loss: 0.1566, DSC: 85.6268, lr: 0.008779, batch_cost: 0.5030, reader_cost: 0.00035, ips: 47.7129 samples/sec | ETA 01:41:11
2022-09-07 12:07:21 [INFO]	[TRAIN] epoch: 20, iter: 1890/13950, loss: 0.1525, DSC: 85.8346, lr: 0.008773, batch_cost: 0.5026, reader_cost: 0.00027, ips: 47.7538 samples/sec | ETA 01:41:01
2022-09-07 12:07:26 [INFO]	[TRAIN] epoch: 20, iter: 1900/13950, loss: 0.1690, DSC: 84.3711, lr: 0.008766, batch_cost: 0.5030, reader_cost: 0.00029, ips: 47.7135 samples/sec | ETA 01:41:01
2022-09-07 12:07:31 [INFO]	[TRAIN] epoch: 20, iter: 1910/13950, loss: 0.1567, DSC: 85.3516, lr: 0.008760, batch_cost: 0.5049, reader_cost: 0.00028, ips: 47.5358 samples/sec | ETA 01:41:18
2022-09-07 12:07:36 [INFO]	[TRAIN] epoch: 20, iter: 1920/13950, loss: 0.1623, DSC: 85.1140, lr: 0.008753, batch_cost: 0.5066, reader_cost: 0.00028, ips: 47.3717 samples/sec | ETA 01:41:34
2022-09-07 12:07:42 [INFO]	[TRAIN] epoch: 20, iter: 1930/13950, loss: 0.1722, DSC: 83.9030, lr: 0.008746, batch_cost: 0.5035, reader_cost: 0.00029, ips: 47.6676 samples/sec | ETA 01:40:51
2022-09-07 12:07:47 [INFO]	[TRAIN] epoch: 20, iter: 1940/13950, loss: 0.1584, DSC: 85.3160, lr: 0.008740, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5669 samples/sec | ETA 01:40:59
2022-09-07 12:07:52 [INFO]	[TRAIN] epoch: 20, iter: 1950/13950, loss: 0.1418, DSC: 87.0691, lr: 0.008733, batch_cost: 0.5041, reader_cost: 0.00025, ips: 47.6091 samples/sec | ETA 01:40:49
2022-09-07 12:07:58 [INFO]	[TRAIN] epoch: 21, iter: 1960/13950, loss: 0.1850, DSC: 82.6861, lr: 0.008727, batch_cost: 0.6106, reader_cost: 0.12456, ips: 39.3041 samples/sec | ETA 02:02:01
2022-09-07 12:08:03 [INFO]	[TRAIN] epoch: 21, iter: 1970/13950, loss: 0.1535, DSC: 85.6943, lr: 0.008720, batch_cost: 0.5029, reader_cost: 0.00029, ips: 47.7240 samples/sec | ETA 01:40:24
2022-09-07 12:08:08 [INFO]	[TRAIN] epoch: 21, iter: 1980/13950, loss: 0.1441, DSC: 86.5824, lr: 0.008714, batch_cost: 0.5038, reader_cost: 0.00028, ips: 47.6357 samples/sec | ETA 01:40:30
2022-09-07 12:08:13 [INFO]	[TRAIN] epoch: 21, iter: 1990/13950, loss: 0.1676, DSC: 84.3701, lr: 0.008707, batch_cost: 0.5041, reader_cost: 0.00028, ips: 47.6128 samples/sec | ETA 01:40:28
2022-09-07 12:08:18 [INFO]	[TRAIN] epoch: 21, iter: 2000/13950, loss: 0.1421, DSC: 87.0533, lr: 0.008701, batch_cost: 0.5041, reader_cost: 0.00033, ips: 47.6053 samples/sec | ETA 01:40:24
2022-09-07 12:08:18 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 21s - batch_cost: 1.9883 - reader cost: 1.1412
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2618 - reader cost: 0.5730
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0423 - reader cost: 0.3896 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.1949 - reader cost: 0.4420
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1269 - reader cost: 0.3580
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0779 - reader cost: 0.3052
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0097 - reader cost: 0.2644
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1076 - reader cost: 0.3112
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0825 - reader cost: 0.2790
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0580 - reader cost: 0.2539
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0079 - reader cost: 0.2322
12/12 [==============================] - 12s 972ms/step - batch_cost: 0.9714 - reader cost: 0.2147
2022-09-07 12:08:30 [INFO]	[EVAL] #Images: 12, Dice: 0.7935, Loss: 0.227099
2022-09-07 12:08:30 [INFO]	[EVAL] Class dice: 
[0.9939 0.8587 0.4614 0.8229 0.8268 0.9409 0.6371 0.8615 0.7386]
2022-09-07 12:08:35 [INFO]	[EVAL] The model with the best validation mDice (0.7935) was saved at iter 2000.
2022-09-07 12:08:40 [INFO]	[TRAIN] epoch: 21, iter: 2010/13950, loss: 0.1554, DSC: 85.8826, lr: 0.008694, batch_cost: 0.5031, reader_cost: 0.00044, ips: 47.7005 samples/sec | ETA 01:40:07
2022-09-07 12:08:45 [INFO]	[TRAIN] epoch: 21, iter: 2020/13950, loss: 0.1454, DSC: 86.6078, lr: 0.008687, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6489 samples/sec | ETA 01:40:08
2022-09-07 12:08:50 [INFO]	[TRAIN] epoch: 21, iter: 2030/13950, loss: 0.1398, DSC: 87.2151, lr: 0.008681, batch_cost: 0.5039, reader_cost: 0.00026, ips: 47.6267 samples/sec | ETA 01:40:06
2022-09-07 12:08:55 [INFO]	[TRAIN] epoch: 21, iter: 2040/13950, loss: 0.1585, DSC: 85.3721, lr: 0.008674, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5851 samples/sec | ETA 01:40:06
2022-09-07 12:09:02 [INFO]	[TRAIN] epoch: 22, iter: 2050/13950, loss: 0.2123, DSC: 79.9810, lr: 0.008668, batch_cost: 0.6289, reader_cost: 0.14031, ips: 38.1635 samples/sec | ETA 02:04:43
2022-09-07 12:09:07 [INFO]	[TRAIN] epoch: 22, iter: 2060/13950, loss: 0.1374, DSC: 87.3488, lr: 0.008661, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5132 samples/sec | ETA 01:40:05
2022-09-07 12:09:12 [INFO]	[TRAIN] epoch: 22, iter: 2070/13950, loss: 0.1566, DSC: 85.4578, lr: 0.008655, batch_cost: 0.5024, reader_cost: 0.00028, ips: 47.7749 samples/sec | ETA 01:39:27
2022-09-07 12:09:17 [INFO]	[TRAIN] epoch: 22, iter: 2080/13950, loss: 0.1477, DSC: 86.4653, lr: 0.008648, batch_cost: 0.5042, reader_cost: 0.00040, ips: 47.6041 samples/sec | ETA 01:39:44
2022-09-07 12:09:22 [INFO]	[TRAIN] epoch: 22, iter: 2090/13950, loss: 0.1588, DSC: 85.2573, lr: 0.008642, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6563 samples/sec | ETA 01:39:32
2022-09-07 12:09:27 [INFO]	[TRAIN] epoch: 22, iter: 2100/13950, loss: 0.1537, DSC: 85.8739, lr: 0.008635, batch_cost: 0.5030, reader_cost: 0.00034, ips: 47.7159 samples/sec | ETA 01:39:20
2022-09-07 12:09:32 [INFO]	[TRAIN] epoch: 22, iter: 2110/13950, loss: 0.1419, DSC: 86.9741, lr: 0.008628, batch_cost: 0.5039, reader_cost: 0.00027, ips: 47.6306 samples/sec | ETA 01:39:25
2022-09-07 12:09:37 [INFO]	[TRAIN] epoch: 22, iter: 2120/13950, loss: 0.1556, DSC: 85.5703, lr: 0.008622, batch_cost: 0.5039, reader_cost: 0.00026, ips: 47.6330 samples/sec | ETA 01:39:20
2022-09-07 12:09:42 [INFO]	[TRAIN] epoch: 22, iter: 2130/13950, loss: 0.1572, DSC: 85.2898, lr: 0.008615, batch_cost: 0.5050, reader_cost: 0.00028, ips: 47.5251 samples/sec | ETA 01:39:29
2022-09-07 12:09:48 [INFO]	[TRAIN] epoch: 23, iter: 2140/13950, loss: 0.1454, DSC: 86.6636, lr: 0.008609, batch_cost: 0.6017, reader_cost: 0.11939, ips: 39.8856 samples/sec | ETA 01:58:26
2022-09-07 12:09:53 [INFO]	[TRAIN] epoch: 23, iter: 2150/13950, loss: 0.1492, DSC: 86.2342, lr: 0.008602, batch_cost: 0.5121, reader_cost: 0.00028, ips: 46.8663 samples/sec | ETA 01:40:42
2022-09-07 12:09:58 [INFO]	[TRAIN] epoch: 23, iter: 2160/13950, loss: 0.1572, DSC: 85.4084, lr: 0.008596, batch_cost: 0.5040, reader_cost: 0.00033, ips: 47.6184 samples/sec | ETA 01:39:02
2022-09-07 12:10:03 [INFO]	[TRAIN] epoch: 23, iter: 2170/13950, loss: 0.1629, DSC: 85.0747, lr: 0.008589, batch_cost: 0.5053, reader_cost: 0.00029, ips: 47.4982 samples/sec | ETA 01:39:12
2022-09-07 12:10:08 [INFO]	[TRAIN] epoch: 23, iter: 2180/13950, loss: 0.1537, DSC: 85.7878, lr: 0.008583, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5884 samples/sec | ETA 01:38:55
2022-09-07 12:10:13 [INFO]	[TRAIN] epoch: 23, iter: 2190/13950, loss: 0.1529, DSC: 85.7550, lr: 0.008576, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5424 samples/sec | ETA 01:38:56
2022-09-07 12:10:18 [INFO]	[TRAIN] epoch: 23, iter: 2200/13950, loss: 0.1639, DSC: 84.7797, lr: 0.008569, batch_cost: 0.5032, reader_cost: 0.00033, ips: 47.6991 samples/sec | ETA 01:38:32
2022-09-07 12:10:23 [INFO]	[TRAIN] epoch: 23, iter: 2210/13950, loss: 0.1561, DSC: 85.5003, lr: 0.008563, batch_cost: 0.5040, reader_cost: 0.00027, ips: 47.6236 samples/sec | ETA 01:38:36
2022-09-07 12:10:28 [INFO]	[TRAIN] epoch: 23, iter: 2220/13950, loss: 0.1607, DSC: 85.1827, lr: 0.008556, batch_cost: 0.5038, reader_cost: 0.00033, ips: 47.6399 samples/sec | ETA 01:38:29
2022-09-07 12:10:33 [INFO]	[TRAIN] epoch: 23, iter: 2230/13950, loss: 0.1445, DSC: 86.5443, lr: 0.008550, batch_cost: 0.5032, reader_cost: 0.00024, ips: 47.6922 samples/sec | ETA 01:38:17
2022-09-07 12:10:39 [INFO]	[TRAIN] epoch: 24, iter: 2240/13950, loss: 0.1743, DSC: 83.8006, lr: 0.008543, batch_cost: 0.6109, reader_cost: 0.13184, ips: 39.2875 samples/sec | ETA 01:59:13
2022-09-07 12:10:45 [INFO]	[TRAIN] epoch: 24, iter: 2250/13950, loss: 0.1541, DSC: 85.7246, lr: 0.008537, batch_cost: 0.5052, reader_cost: 0.00028, ips: 47.5072 samples/sec | ETA 01:38:30
2022-09-07 12:10:50 [INFO]	[TRAIN] epoch: 24, iter: 2260/13950, loss: 0.1346, DSC: 87.7316, lr: 0.008530, batch_cost: 0.5021, reader_cost: 0.00033, ips: 47.7967 samples/sec | ETA 01:37:49
2022-09-07 12:10:55 [INFO]	[TRAIN] epoch: 24, iter: 2270/13950, loss: 0.1514, DSC: 86.0331, lr: 0.008523, batch_cost: 0.5043, reader_cost: 0.00035, ips: 47.5942 samples/sec | ETA 01:38:09
2022-09-07 12:11:00 [INFO]	[TRAIN] epoch: 24, iter: 2280/13950, loss: 0.1485, DSC: 86.1612, lr: 0.008517, batch_cost: 0.5048, reader_cost: 0.00036, ips: 47.5438 samples/sec | ETA 01:38:10
2022-09-07 12:11:05 [INFO]	[TRAIN] epoch: 24, iter: 2290/13950, loss: 0.1531, DSC: 85.7852, lr: 0.008510, batch_cost: 0.5074, reader_cost: 0.00035, ips: 47.2988 samples/sec | ETA 01:38:36
2022-09-07 12:11:10 [INFO]	[TRAIN] epoch: 24, iter: 2300/13950, loss: 0.1436, DSC: 86.8408, lr: 0.008504, batch_cost: 0.5047, reader_cost: 0.00029, ips: 47.5576 samples/sec | ETA 01:37:59
2022-09-07 12:11:15 [INFO]	[TRAIN] epoch: 24, iter: 2310/13950, loss: 0.1424, DSC: 86.8928, lr: 0.008497, batch_cost: 0.5041, reader_cost: 0.00029, ips: 47.6073 samples/sec | ETA 01:37:48
2022-09-07 12:11:20 [INFO]	[TRAIN] epoch: 24, iter: 2320/13950, loss: 0.1482, DSC: 86.2103, lr: 0.008491, batch_cost: 0.5041, reader_cost: 0.00025, ips: 47.6103 samples/sec | ETA 01:37:42
2022-09-07 12:11:26 [INFO]	[TRAIN] epoch: 25, iter: 2330/13950, loss: 0.1804, DSC: 82.9783, lr: 0.008484, batch_cost: 0.6074, reader_cost: 0.12333, ips: 39.5144 samples/sec | ETA 01:57:37
2022-09-07 12:11:31 [INFO]	[TRAIN] epoch: 25, iter: 2340/13950, loss: 0.1502, DSC: 86.1308, lr: 0.008477, batch_cost: 0.5029, reader_cost: 0.00028, ips: 47.7196 samples/sec | ETA 01:37:19
2022-09-07 12:11:36 [INFO]	[TRAIN] epoch: 25, iter: 2350/13950, loss: 0.1172, DSC: 89.3383, lr: 0.008471, batch_cost: 0.5037, reader_cost: 0.00035, ips: 47.6437 samples/sec | ETA 01:37:23
2022-09-07 12:11:41 [INFO]	[TRAIN] epoch: 25, iter: 2360/13950, loss: 0.1344, DSC: 87.5934, lr: 0.008464, batch_cost: 0.5030, reader_cost: 0.00028, ips: 47.7127 samples/sec | ETA 01:37:09
2022-09-07 12:11:46 [INFO]	[TRAIN] epoch: 25, iter: 2370/13950, loss: 0.1666, DSC: 84.4388, lr: 0.008458, batch_cost: 0.5053, reader_cost: 0.00028, ips: 47.4922 samples/sec | ETA 01:37:31
2022-09-07 12:11:51 [INFO]	[TRAIN] epoch: 25, iter: 2380/13950, loss: 0.1531, DSC: 85.7306, lr: 0.008451, batch_cost: 0.5033, reader_cost: 0.00027, ips: 47.6877 samples/sec | ETA 01:37:02
2022-09-07 12:11:56 [INFO]	[TRAIN] epoch: 25, iter: 2390/13950, loss: 0.1448, DSC: 86.4483, lr: 0.008445, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5449 samples/sec | ETA 01:37:15
2022-09-07 12:12:01 [INFO]	[TRAIN] epoch: 25, iter: 2400/13950, loss: 0.1431, DSC: 86.9469, lr: 0.008438, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5404 samples/sec | ETA 01:37:10
2022-09-07 12:12:06 [INFO]	[TRAIN] epoch: 25, iter: 2410/13950, loss: 0.1566, DSC: 85.4696, lr: 0.008431, batch_cost: 0.5063, reader_cost: 0.00036, ips: 47.3993 samples/sec | ETA 01:37:23
2022-09-07 12:12:12 [INFO]	[TRAIN] epoch: 26, iter: 2420/13950, loss: 0.1667, DSC: 84.6373, lr: 0.008425, batch_cost: 0.6171, reader_cost: 0.13350, ips: 38.8927 samples/sec | ETA 01:58:34
2022-09-07 12:12:18 [INFO]	[TRAIN] epoch: 26, iter: 2430/13950, loss: 0.1388, DSC: 87.3085, lr: 0.008418, batch_cost: 0.5161, reader_cost: 0.00029, ips: 46.5065 samples/sec | ETA 01:39:04
2022-09-07 12:12:23 [INFO]	[TRAIN] epoch: 26, iter: 2440/13950, loss: 0.1438, DSC: 86.5831, lr: 0.008412, batch_cost: 0.5036, reader_cost: 0.00029, ips: 47.6547 samples/sec | ETA 01:36:36
2022-09-07 12:12:28 [INFO]	[TRAIN] epoch: 26, iter: 2450/13950, loss: 0.1337, DSC: 87.6924, lr: 0.008405, batch_cost: 0.5044, reader_cost: 0.00041, ips: 47.5845 samples/sec | ETA 01:36:40
2022-09-07 12:12:33 [INFO]	[TRAIN] epoch: 26, iter: 2460/13950, loss: 0.1523, DSC: 85.9706, lr: 0.008399, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5464 samples/sec | ETA 01:36:39
2022-09-07 12:12:38 [INFO]	[TRAIN] epoch: 26, iter: 2470/13950, loss: 0.1457, DSC: 86.5148, lr: 0.008392, batch_cost: 0.5032, reader_cost: 0.00027, ips: 47.6962 samples/sec | ETA 01:36:16
2022-09-07 12:12:43 [INFO]	[TRAIN] epoch: 26, iter: 2480/13950, loss: 0.1308, DSC: 87.9530, lr: 0.008385, batch_cost: 0.5039, reader_cost: 0.00033, ips: 47.6323 samples/sec | ETA 01:36:19
2022-09-07 12:12:48 [INFO]	[TRAIN] epoch: 26, iter: 2490/13950, loss: 0.1420, DSC: 86.8987, lr: 0.008379, batch_cost: 0.5103, reader_cost: 0.00028, ips: 47.0294 samples/sec | ETA 01:37:28
2022-09-07 12:12:53 [INFO]	[TRAIN] epoch: 26, iter: 2500/13950, loss: 0.1517, DSC: 85.8258, lr: 0.008372, batch_cost: 0.5030, reader_cost: 0.00028, ips: 47.7095 samples/sec | ETA 01:35:59
2022-09-07 12:12:58 [INFO]	[TRAIN] epoch: 26, iter: 2510/13950, loss: 0.1851, DSC: 82.6325, lr: 0.008366, batch_cost: 0.5028, reader_cost: 0.00024, ips: 47.7370 samples/sec | ETA 01:35:51
2022-09-07 12:13:04 [INFO]	[TRAIN] epoch: 27, iter: 2520/13950, loss: 0.1922, DSC: 81.9012, lr: 0.008359, batch_cost: 0.6121, reader_cost: 0.12760, ips: 39.2068 samples/sec | ETA 01:56:36
2022-09-07 12:13:09 [INFO]	[TRAIN] epoch: 27, iter: 2530/13950, loss: 0.1710, DSC: 83.9677, lr: 0.008353, batch_cost: 0.5075, reader_cost: 0.00029, ips: 47.2933 samples/sec | ETA 01:36:35
2022-09-07 12:13:14 [INFO]	[TRAIN] epoch: 27, iter: 2540/13950, loss: 0.1431, DSC: 86.8020, lr: 0.008346, batch_cost: 0.5067, reader_cost: 0.00031, ips: 47.3652 samples/sec | ETA 01:36:21
2022-09-07 12:13:19 [INFO]	[TRAIN] epoch: 27, iter: 2550/13950, loss: 0.1410, DSC: 86.9284, lr: 0.008339, batch_cost: 0.5057, reader_cost: 0.00029, ips: 47.4594 samples/sec | ETA 01:36:04
2022-09-07 12:13:24 [INFO]	[TRAIN] epoch: 27, iter: 2560/13950, loss: 0.1421, DSC: 86.8264, lr: 0.008333, batch_cost: 0.5048, reader_cost: 0.00027, ips: 47.5479 samples/sec | ETA 01:35:49
2022-09-07 12:13:29 [INFO]	[TRAIN] epoch: 27, iter: 2570/13950, loss: 0.1404, DSC: 87.1344, lr: 0.008326, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5218 samples/sec | ETA 01:35:47
2022-09-07 12:13:34 [INFO]	[TRAIN] epoch: 27, iter: 2580/13950, loss: 0.1689, DSC: 83.9349, lr: 0.008320, batch_cost: 0.5047, reader_cost: 0.00028, ips: 47.5492 samples/sec | ETA 01:35:38
2022-09-07 12:13:39 [INFO]	[TRAIN] epoch: 27, iter: 2590/13950, loss: 0.1452, DSC: 86.6265, lr: 0.008313, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6337 samples/sec | ETA 01:35:23
2022-09-07 12:13:45 [INFO]	[TRAIN] epoch: 27, iter: 2600/13950, loss: 0.1494, DSC: 86.1689, lr: 0.008306, batch_cost: 0.5044, reader_cost: 0.00033, ips: 47.5860 samples/sec | ETA 01:35:24
2022-09-07 12:13:51 [INFO]	[TRAIN] epoch: 28, iter: 2610/13950, loss: 0.1786, DSC: 83.2164, lr: 0.008300, batch_cost: 0.6641, reader_cost: 0.13183, ips: 36.1377 samples/sec | ETA 02:05:31
2022-09-07 12:13:56 [INFO]	[TRAIN] epoch: 28, iter: 2620/13950, loss: 0.1501, DSC: 86.1046, lr: 0.008293, batch_cost: 0.5064, reader_cost: 0.00028, ips: 47.3941 samples/sec | ETA 01:35:37
2022-09-07 12:14:01 [INFO]	[TRAIN] epoch: 28, iter: 2630/13950, loss: 0.1283, DSC: 88.2573, lr: 0.008287, batch_cost: 0.5050, reader_cost: 0.00034, ips: 47.5254 samples/sec | ETA 01:35:16
2022-09-07 12:14:06 [INFO]	[TRAIN] epoch: 28, iter: 2640/13950, loss: 0.1390, DSC: 87.1317, lr: 0.008280, batch_cost: 0.5060, reader_cost: 0.00034, ips: 47.4270 samples/sec | ETA 01:35:23
2022-09-07 12:14:11 [INFO]	[TRAIN] epoch: 28, iter: 2650/13950, loss: 0.1289, DSC: 88.0000, lr: 0.008273, batch_cost: 0.5051, reader_cost: 0.00028, ips: 47.5119 samples/sec | ETA 01:35:08
2022-09-07 12:14:16 [INFO]	[TRAIN] epoch: 28, iter: 2660/13950, loss: 0.1381, DSC: 87.2978, lr: 0.008267, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4882 samples/sec | ETA 01:35:05
2022-09-07 12:14:21 [INFO]	[TRAIN] epoch: 28, iter: 2670/13950, loss: 0.1388, DSC: 87.2264, lr: 0.008260, batch_cost: 0.5043, reader_cost: 0.00028, ips: 47.5878 samples/sec | ETA 01:34:48
2022-09-07 12:14:27 [INFO]	[TRAIN] epoch: 28, iter: 2680/13950, loss: 0.1391, DSC: 87.1488, lr: 0.008254, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4904 samples/sec | ETA 01:34:55
2022-09-07 12:14:32 [INFO]	[TRAIN] epoch: 28, iter: 2690/13950, loss: 0.1251, DSC: 88.4782, lr: 0.008247, batch_cost: 0.5051, reader_cost: 0.00028, ips: 47.5196 samples/sec | ETA 01:34:46
2022-09-07 12:14:38 [INFO]	[TRAIN] epoch: 29, iter: 2700/13950, loss: 0.1533, DSC: 85.6732, lr: 0.008241, batch_cost: 0.6210, reader_cost: 0.13655, ips: 38.6497 samples/sec | ETA 01:56:25
2022-09-07 12:14:43 [INFO]	[TRAIN] epoch: 29, iter: 2710/13950, loss: 0.1502, DSC: 85.9448, lr: 0.008234, batch_cost: 0.5061, reader_cost: 0.00028, ips: 47.4194 samples/sec | ETA 01:34:48
2022-09-07 12:14:48 [INFO]	[TRAIN] epoch: 29, iter: 2720/13950, loss: 0.1595, DSC: 85.1229, lr: 0.008227, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6127 samples/sec | ETA 01:34:20
2022-09-07 12:14:53 [INFO]	[TRAIN] epoch: 29, iter: 2730/13950, loss: 0.1436, DSC: 86.8274, lr: 0.008221, batch_cost: 0.5090, reader_cost: 0.00029, ips: 47.1512 samples/sec | ETA 01:35:10
2022-09-07 12:14:58 [INFO]	[TRAIN] epoch: 29, iter: 2740/13950, loss: 0.1484, DSC: 86.2855, lr: 0.008214, batch_cost: 0.5063, reader_cost: 0.00029, ips: 47.4045 samples/sec | ETA 01:34:35
2022-09-07 12:15:03 [INFO]	[TRAIN] epoch: 29, iter: 2750/13950, loss: 0.1303, DSC: 88.0207, lr: 0.008208, batch_cost: 0.5028, reader_cost: 0.00029, ips: 47.7281 samples/sec | ETA 01:33:51
2022-09-07 12:15:08 [INFO]	[TRAIN] epoch: 29, iter: 2760/13950, loss: 0.1403, DSC: 87.0503, lr: 0.008201, batch_cost: 0.5022, reader_cost: 0.00027, ips: 47.7878 samples/sec | ETA 01:33:39
2022-09-07 12:15:13 [INFO]	[TRAIN] epoch: 29, iter: 2770/13950, loss: 0.1419, DSC: 86.9279, lr: 0.008194, batch_cost: 0.5035, reader_cost: 0.00035, ips: 47.6661 samples/sec | ETA 01:33:49
2022-09-07 12:15:18 [INFO]	[TRAIN] epoch: 29, iter: 2780/13950, loss: 0.1198, DSC: 88.9465, lr: 0.008188, batch_cost: 0.5030, reader_cost: 0.00028, ips: 47.7174 samples/sec | ETA 01:33:38
2022-09-07 12:15:23 [INFO]	[TRAIN] epoch: 30, iter: 2790/13950, loss: 0.1820, DSC: 82.8579, lr: 0.008181, batch_cost: 0.4745, reader_cost: 0.00023, ips: 50.5813 samples/sec | ETA 01:28:15
2022-09-07 12:15:29 [INFO]	[TRAIN] epoch: 30, iter: 2800/13950, loss: 0.1496, DSC: 86.1379, lr: 0.008175, batch_cost: 0.6558, reader_cost: 0.12881, ips: 36.5949 samples/sec | ETA 02:01:52
2022-09-07 12:15:35 [INFO]	[TRAIN] epoch: 30, iter: 2810/13950, loss: 0.1358, DSC: 87.5042, lr: 0.008168, batch_cost: 0.5031, reader_cost: 0.00027, ips: 47.7051 samples/sec | ETA 01:33:24
2022-09-07 12:15:40 [INFO]	[TRAIN] epoch: 30, iter: 2820/13950, loss: 0.1305, DSC: 88.0295, lr: 0.008161, batch_cost: 0.5027, reader_cost: 0.00026, ips: 47.7407 samples/sec | ETA 01:33:15
2022-09-07 12:15:45 [INFO]	[TRAIN] epoch: 30, iter: 2830/13950, loss: 0.1480, DSC: 85.9663, lr: 0.008155, batch_cost: 0.5040, reader_cost: 0.00028, ips: 47.6180 samples/sec | ETA 01:33:24
2022-09-07 12:15:50 [INFO]	[TRAIN] epoch: 30, iter: 2840/13950, loss: 0.1281, DSC: 88.3727, lr: 0.008148, batch_cost: 0.5042, reader_cost: 0.00028, ips: 47.6006 samples/sec | ETA 01:33:21
2022-09-07 12:15:55 [INFO]	[TRAIN] epoch: 30, iter: 2850/13950, loss: 0.1353, DSC: 87.4632, lr: 0.008142, batch_cost: 0.5065, reader_cost: 0.00029, ips: 47.3795 samples/sec | ETA 01:33:42
2022-09-07 12:16:00 [INFO]	[TRAIN] epoch: 30, iter: 2860/13950, loss: 0.1394, DSC: 87.0343, lr: 0.008135, batch_cost: 0.5049, reader_cost: 0.00027, ips: 47.5335 samples/sec | ETA 01:33:19
2022-09-07 12:16:05 [INFO]	[TRAIN] epoch: 30, iter: 2870/13950, loss: 0.1190, DSC: 89.1528, lr: 0.008128, batch_cost: 0.5037, reader_cost: 0.00034, ips: 47.6507 samples/sec | ETA 01:33:00
2022-09-07 12:16:10 [INFO]	[TRAIN] epoch: 30, iter: 2880/13950, loss: 0.1559, DSC: 85.3720, lr: 0.008122, batch_cost: 0.5031, reader_cost: 0.00024, ips: 47.7058 samples/sec | ETA 01:32:49
2022-09-07 12:16:16 [INFO]	[TRAIN] epoch: 31, iter: 2890/13950, loss: 0.1875, DSC: 82.2724, lr: 0.008115, batch_cost: 0.6141, reader_cost: 0.12073, ips: 39.0803 samples/sec | ETA 01:53:12
2022-09-07 12:16:21 [INFO]	[TRAIN] epoch: 31, iter: 2900/13950, loss: 0.1282, DSC: 88.0527, lr: 0.008109, batch_cost: 0.5011, reader_cost: 0.00027, ips: 47.8904 samples/sec | ETA 01:32:17
2022-09-07 12:16:26 [INFO]	[TRAIN] epoch: 31, iter: 2910/13950, loss: 0.1499, DSC: 86.1183, lr: 0.008102, batch_cost: 0.5031, reader_cost: 0.00034, ips: 47.7032 samples/sec | ETA 01:32:34
2022-09-07 12:16:31 [INFO]	[TRAIN] epoch: 31, iter: 2920/13950, loss: 0.1434, DSC: 86.6635, lr: 0.008095, batch_cost: 0.5035, reader_cost: 0.00026, ips: 47.6668 samples/sec | ETA 01:32:33
2022-09-07 12:16:36 [INFO]	[TRAIN] epoch: 31, iter: 2930/13950, loss: 0.1385, DSC: 87.1268, lr: 0.008089, batch_cost: 0.5044, reader_cost: 0.00029, ips: 47.5839 samples/sec | ETA 01:32:38
2022-09-07 12:16:41 [INFO]	[TRAIN] epoch: 31, iter: 2940/13950, loss: 0.1290, DSC: 88.1863, lr: 0.008082, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5916 samples/sec | ETA 01:32:32
2022-09-07 12:16:46 [INFO]	[TRAIN] epoch: 31, iter: 2950/13950, loss: 0.1375, DSC: 87.3392, lr: 0.008076, batch_cost: 0.5053, reader_cost: 0.00036, ips: 47.4978 samples/sec | ETA 01:32:38
2022-09-07 12:16:51 [INFO]	[TRAIN] epoch: 31, iter: 2960/13950, loss: 0.1430, DSC: 86.5295, lr: 0.008069, batch_cost: 0.5042, reader_cost: 0.00027, ips: 47.5991 samples/sec | ETA 01:32:21
2022-09-07 12:16:56 [INFO]	[TRAIN] epoch: 31, iter: 2970/13950, loss: 0.1370, DSC: 87.3413, lr: 0.008062, batch_cost: 0.5065, reader_cost: 0.00029, ips: 47.3813 samples/sec | ETA 01:32:41
2022-09-07 12:17:03 [INFO]	[TRAIN] epoch: 32, iter: 2980/13950, loss: 0.1282, DSC: 88.1284, lr: 0.008056, batch_cost: 0.6521, reader_cost: 0.16062, ips: 36.8016 samples/sec | ETA 01:59:14
2022-09-07 12:17:08 [INFO]	[TRAIN] epoch: 32, iter: 2990/13950, loss: 0.1461, DSC: 86.2977, lr: 0.008049, batch_cost: 0.5085, reader_cost: 0.00030, ips: 47.1988 samples/sec | ETA 01:32:53
2022-09-07 12:17:13 [INFO]	[TRAIN] epoch: 32, iter: 3000/13950, loss: 0.1296, DSC: 88.0542, lr: 0.008043, batch_cost: 0.5065, reader_cost: 0.00031, ips: 47.3841 samples/sec | ETA 01:32:26
2022-09-07 12:17:13 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0035 - reader cost: 1.1571
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2708 - reader cost: 0.5814
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0485 - reader cost: 0.3955 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.2042 - reader cost: 0.4514
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1344 - reader cost: 0.3654
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0872 - reader cost: 0.3143
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0175 - reader cost: 0.2722
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1194 - reader cost: 0.3230
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0925 - reader cost: 0.2894
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0670 - reader cost: 0.2635
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0160 - reader cost: 0.2410
12/12 [==============================] - 12s 979ms/step - batch_cost: 0.9787 - reader cost: 0.2227
2022-09-07 12:17:25 [INFO]	[EVAL] #Images: 12, Dice: 0.8123, Loss: 0.207095
2022-09-07 12:17:25 [INFO]	[EVAL] Class dice: 
[0.9942 0.8936 0.5192 0.8764 0.8409 0.9417 0.6097 0.8726 0.7625]
2022-09-07 12:17:30 [INFO]	[EVAL] The model with the best validation mDice (0.8123) was saved at iter 3000.
2022-09-07 12:17:35 [INFO]	[TRAIN] epoch: 32, iter: 3010/13950, loss: 0.1403, DSC: 87.0535, lr: 0.008036, batch_cost: 0.5053, reader_cost: 0.00053, ips: 47.4971 samples/sec | ETA 01:32:07
2022-09-07 12:17:40 [INFO]	[TRAIN] epoch: 32, iter: 3020/13950, loss: 0.1387, DSC: 87.1131, lr: 0.008029, batch_cost: 0.5047, reader_cost: 0.00040, ips: 47.5573 samples/sec | ETA 01:31:55
2022-09-07 12:17:45 [INFO]	[TRAIN] epoch: 32, iter: 3030/13950, loss: 0.1529, DSC: 85.7226, lr: 0.008023, batch_cost: 0.5039, reader_cost: 0.00025, ips: 47.6278 samples/sec | ETA 01:31:42
2022-09-07 12:17:51 [INFO]	[TRAIN] epoch: 32, iter: 3040/13950, loss: 0.1397, DSC: 86.9959, lr: 0.008016, batch_cost: 0.5048, reader_cost: 0.00033, ips: 47.5391 samples/sec | ETA 01:31:47
2022-09-07 12:17:56 [INFO]	[TRAIN] epoch: 32, iter: 3050/13950, loss: 0.1339, DSC: 87.6137, lr: 0.008009, batch_cost: 0.5063, reader_cost: 0.00043, ips: 47.3981 samples/sec | ETA 01:31:59
2022-09-07 12:18:01 [INFO]	[TRAIN] epoch: 32, iter: 3060/13950, loss: 0.1381, DSC: 87.2564, lr: 0.008003, batch_cost: 0.5069, reader_cost: 0.00027, ips: 47.3423 samples/sec | ETA 01:32:00
2022-09-07 12:18:07 [INFO]	[TRAIN] epoch: 33, iter: 3070/13950, loss: 0.1955, DSC: 81.5692, lr: 0.007996, batch_cost: 0.6047, reader_cost: 0.12382, ips: 39.6894 samples/sec | ETA 01:49:39
2022-09-07 12:18:12 [INFO]	[TRAIN] epoch: 33, iter: 3080/13950, loss: 0.1578, DSC: 85.2546, lr: 0.007990, batch_cost: 0.5074, reader_cost: 0.00030, ips: 47.3017 samples/sec | ETA 01:31:55
2022-09-07 12:18:17 [INFO]	[TRAIN] epoch: 33, iter: 3090/13950, loss: 0.1488, DSC: 86.2819, lr: 0.007983, batch_cost: 0.5035, reader_cost: 0.00028, ips: 47.6628 samples/sec | ETA 01:31:08
2022-09-07 12:18:22 [INFO]	[TRAIN] epoch: 33, iter: 3100/13950, loss: 0.1397, DSC: 87.2511, lr: 0.007976, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6464 samples/sec | ETA 01:31:05
2022-09-07 12:18:27 [INFO]	[TRAIN] epoch: 33, iter: 3110/13950, loss: 0.1363, DSC: 87.3665, lr: 0.007970, batch_cost: 0.5032, reader_cost: 0.00027, ips: 47.6984 samples/sec | ETA 01:30:54
2022-09-07 12:18:32 [INFO]	[TRAIN] epoch: 33, iter: 3120/13950, loss: 0.1288, DSC: 88.1687, lr: 0.007963, batch_cost: 0.5037, reader_cost: 0.00026, ips: 47.6481 samples/sec | ETA 01:30:54
2022-09-07 12:18:37 [INFO]	[TRAIN] epoch: 33, iter: 3130/13950, loss: 0.1412, DSC: 86.8485, lr: 0.007957, batch_cost: 0.5067, reader_cost: 0.00029, ips: 47.3639 samples/sec | ETA 01:31:22
2022-09-07 12:18:42 [INFO]	[TRAIN] epoch: 33, iter: 3140/13950, loss: 0.1285, DSC: 88.1268, lr: 0.007950, batch_cost: 0.5035, reader_cost: 0.00033, ips: 47.6695 samples/sec | ETA 01:30:42
2022-09-07 12:18:47 [INFO]	[TRAIN] epoch: 33, iter: 3150/13950, loss: 0.1246, DSC: 88.5695, lr: 0.007943, batch_cost: 0.5039, reader_cost: 0.00027, ips: 47.6313 samples/sec | ETA 01:30:41
2022-09-07 12:18:52 [INFO]	[TRAIN] epoch: 33, iter: 3160/13950, loss: 0.1658, DSC: 84.3633, lr: 0.007937, batch_cost: 0.5041, reader_cost: 0.00024, ips: 47.6055 samples/sec | ETA 01:30:39
2022-09-07 12:18:59 [INFO]	[TRAIN] epoch: 34, iter: 3170/13950, loss: 0.1453, DSC: 86.6210, lr: 0.007930, batch_cost: 0.6125, reader_cost: 0.12720, ips: 39.1823 samples/sec | ETA 01:50:02
2022-09-07 12:19:04 [INFO]	[TRAIN] epoch: 34, iter: 3180/13950, loss: 0.1534, DSC: 85.7002, lr: 0.007923, batch_cost: 0.5062, reader_cost: 0.00028, ips: 47.4110 samples/sec | ETA 01:30:51
2022-09-07 12:19:09 [INFO]	[TRAIN] epoch: 34, iter: 3190/13950, loss: 0.1489, DSC: 86.1758, lr: 0.007917, batch_cost: 0.5031, reader_cost: 0.00027, ips: 47.7061 samples/sec | ETA 01:30:13
2022-09-07 12:19:14 [INFO]	[TRAIN] epoch: 34, iter: 3200/13950, loss: 0.1332, DSC: 87.6341, lr: 0.007910, batch_cost: 0.5040, reader_cost: 0.00027, ips: 47.6217 samples/sec | ETA 01:30:17
2022-09-07 12:19:19 [INFO]	[TRAIN] epoch: 34, iter: 3210/13950, loss: 0.1419, DSC: 86.8114, lr: 0.007904, batch_cost: 0.5029, reader_cost: 0.00026, ips: 47.7264 samples/sec | ETA 01:30:00
2022-09-07 12:19:24 [INFO]	[TRAIN] epoch: 34, iter: 3220/13950, loss: 0.1433, DSC: 86.5978, lr: 0.007897, batch_cost: 0.5046, reader_cost: 0.00028, ips: 47.5650 samples/sec | ETA 01:30:14
2022-09-07 12:19:29 [INFO]	[TRAIN] epoch: 34, iter: 3230/13950, loss: 0.1364, DSC: 87.2912, lr: 0.007890, batch_cost: 0.5041, reader_cost: 0.00026, ips: 47.6069 samples/sec | ETA 01:30:04
2022-09-07 12:19:34 [INFO]	[TRAIN] epoch: 34, iter: 3240/13950, loss: 0.1151, DSC: 89.4121, lr: 0.007884, batch_cost: 0.5038, reader_cost: 0.00034, ips: 47.6357 samples/sec | ETA 01:29:55
2022-09-07 12:19:39 [INFO]	[TRAIN] epoch: 34, iter: 3250/13950, loss: 0.1371, DSC: 87.2941, lr: 0.007877, batch_cost: 0.5043, reader_cost: 0.00026, ips: 47.5895 samples/sec | ETA 01:29:56
2022-09-07 12:19:45 [INFO]	[TRAIN] epoch: 35, iter: 3260/13950, loss: 0.1522, DSC: 85.8338, lr: 0.007870, batch_cost: 0.6003, reader_cost: 0.11724, ips: 39.9824 samples/sec | ETA 01:46:56
2022-09-07 12:19:50 [INFO]	[TRAIN] epoch: 35, iter: 3270/13950, loss: 0.1292, DSC: 88.0563, lr: 0.007864, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6108 samples/sec | ETA 01:29:43
2022-09-07 12:19:55 [INFO]	[TRAIN] epoch: 35, iter: 3280/13950, loss: 0.1416, DSC: 86.9864, lr: 0.007857, batch_cost: 0.5060, reader_cost: 0.00030, ips: 47.4327 samples/sec | ETA 01:29:58
2022-09-07 12:20:00 [INFO]	[TRAIN] epoch: 35, iter: 3290/13950, loss: 0.1247, DSC: 88.4195, lr: 0.007851, batch_cost: 0.5039, reader_cost: 0.00026, ips: 47.6266 samples/sec | ETA 01:29:31
2022-09-07 12:20:05 [INFO]	[TRAIN] epoch: 35, iter: 3300/13950, loss: 0.1286, DSC: 88.1092, lr: 0.007844, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5702 samples/sec | ETA 01:29:33
2022-09-07 12:20:10 [INFO]	[TRAIN] epoch: 35, iter: 3310/13950, loss: 0.1352, DSC: 87.6128, lr: 0.007837, batch_cost: 0.5008, reader_cost: 0.00027, ips: 47.9218 samples/sec | ETA 01:28:48
2022-09-07 12:20:15 [INFO]	[TRAIN] epoch: 35, iter: 3320/13950, loss: 0.1358, DSC: 87.2320, lr: 0.007831, batch_cost: 0.5022, reader_cost: 0.00027, ips: 47.7930 samples/sec | ETA 01:28:58
2022-09-07 12:20:20 [INFO]	[TRAIN] epoch: 35, iter: 3330/13950, loss: 0.1438, DSC: 86.4121, lr: 0.007824, batch_cost: 0.5021, reader_cost: 0.00027, ips: 47.8012 samples/sec | ETA 01:28:52
2022-09-07 12:20:25 [INFO]	[TRAIN] epoch: 35, iter: 3340/13950, loss: 0.1440, DSC: 86.5960, lr: 0.007817, batch_cost: 0.5041, reader_cost: 0.00029, ips: 47.6065 samples/sec | ETA 01:29:08
2022-09-07 12:20:31 [INFO]	[TRAIN] epoch: 36, iter: 3350/13950, loss: 0.1536, DSC: 86.0051, lr: 0.007811, batch_cost: 0.5922, reader_cost: 0.11379, ips: 40.5290 samples/sec | ETA 01:44:36
2022-09-07 12:20:36 [INFO]	[TRAIN] epoch: 36, iter: 3360/13950, loss: 0.1259, DSC: 88.6372, lr: 0.007804, batch_cost: 0.5109, reader_cost: 0.00027, ips: 46.9778 samples/sec | ETA 01:30:10
2022-09-07 12:20:41 [INFO]	[TRAIN] epoch: 36, iter: 3370/13950, loss: 0.1266, DSC: 88.2761, lr: 0.007798, batch_cost: 0.5046, reader_cost: 0.00034, ips: 47.5642 samples/sec | ETA 01:28:58
2022-09-07 12:20:46 [INFO]	[TRAIN] epoch: 36, iter: 3380/13950, loss: 0.1284, DSC: 88.2826, lr: 0.007791, batch_cost: 0.5056, reader_cost: 0.00034, ips: 47.4683 samples/sec | ETA 01:29:04
2022-09-07 12:20:51 [INFO]	[TRAIN] epoch: 36, iter: 3390/13950, loss: 0.1422, DSC: 86.7621, lr: 0.007784, batch_cost: 0.5041, reader_cost: 0.00028, ips: 47.6053 samples/sec | ETA 01:28:43
2022-09-07 12:20:56 [INFO]	[TRAIN] epoch: 36, iter: 3400/13950, loss: 0.1284, DSC: 88.0442, lr: 0.007778, batch_cost: 0.5056, reader_cost: 0.00029, ips: 47.4672 samples/sec | ETA 01:28:54
2022-09-07 12:21:01 [INFO]	[TRAIN] epoch: 36, iter: 3410/13950, loss: 0.1662, DSC: 84.2350, lr: 0.007771, batch_cost: 0.5051, reader_cost: 0.00028, ips: 47.5134 samples/sec | ETA 01:28:43
2022-09-07 12:21:06 [INFO]	[TRAIN] epoch: 36, iter: 3420/13950, loss: 0.1310, DSC: 87.7421, lr: 0.007764, batch_cost: 0.5056, reader_cost: 0.00028, ips: 47.4694 samples/sec | ETA 01:28:43
2022-09-07 12:21:12 [INFO]	[TRAIN] epoch: 36, iter: 3430/13950, loss: 0.1407, DSC: 86.8432, lr: 0.007758, batch_cost: 0.5050, reader_cost: 0.00032, ips: 47.5231 samples/sec | ETA 01:28:32
2022-09-07 12:21:17 [INFO]	[TRAIN] epoch: 36, iter: 3440/13950, loss: 0.1328, DSC: 87.6284, lr: 0.007751, batch_cost: 0.5051, reader_cost: 0.00025, ips: 47.5189 samples/sec | ETA 01:28:28
2022-09-07 12:21:23 [INFO]	[TRAIN] epoch: 37, iter: 3450/13950, loss: 0.1743, DSC: 83.4346, lr: 0.007744, batch_cost: 0.5931, reader_cost: 0.10734, ips: 40.4634 samples/sec | ETA 01:43:47
2022-09-07 12:21:28 [INFO]	[TRAIN] epoch: 37, iter: 3460/13950, loss: 0.1567, DSC: 85.4130, lr: 0.007738, batch_cost: 0.5043, reader_cost: 0.00028, ips: 47.5924 samples/sec | ETA 01:28:09
2022-09-07 12:21:33 [INFO]	[TRAIN] epoch: 37, iter: 3470/13950, loss: 0.1304, DSC: 88.0447, lr: 0.007731, batch_cost: 0.5024, reader_cost: 0.00034, ips: 47.7731 samples/sec | ETA 01:27:44
2022-09-07 12:21:38 [INFO]	[TRAIN] epoch: 37, iter: 3480/13950, loss: 0.1389, DSC: 87.2156, lr: 0.007725, batch_cost: 0.5019, reader_cost: 0.00028, ips: 47.8154 samples/sec | ETA 01:27:35
2022-09-07 12:21:43 [INFO]	[TRAIN] epoch: 37, iter: 3490/13950, loss: 0.1471, DSC: 86.4487, lr: 0.007718, batch_cost: 0.5022, reader_cost: 0.00034, ips: 47.7884 samples/sec | ETA 01:27:33
2022-09-07 12:21:48 [INFO]	[TRAIN] epoch: 37, iter: 3500/13950, loss: 0.1239, DSC: 88.6734, lr: 0.007711, batch_cost: 0.5036, reader_cost: 0.00028, ips: 47.6592 samples/sec | ETA 01:27:42
2022-09-07 12:21:53 [INFO]	[TRAIN] epoch: 37, iter: 3510/13950, loss: 0.1259, DSC: 88.3997, lr: 0.007705, batch_cost: 0.5036, reader_cost: 0.00036, ips: 47.6549 samples/sec | ETA 01:27:37
2022-09-07 12:21:58 [INFO]	[TRAIN] epoch: 37, iter: 3520/13950, loss: 0.1402, DSC: 86.8814, lr: 0.007698, batch_cost: 0.5074, reader_cost: 0.00030, ips: 47.2966 samples/sec | ETA 01:28:12
2022-09-07 12:22:03 [INFO]	[TRAIN] epoch: 37, iter: 3530/13950, loss: 0.1504, DSC: 85.8354, lr: 0.007691, batch_cost: 0.5037, reader_cost: 0.00026, ips: 47.6484 samples/sec | ETA 01:27:28
2022-09-07 12:22:09 [INFO]	[TRAIN] epoch: 38, iter: 3540/13950, loss: 0.1420, DSC: 86.8827, lr: 0.007685, batch_cost: 0.6258, reader_cost: 0.12851, ips: 38.3524 samples/sec | ETA 01:48:34
2022-09-07 12:22:14 [INFO]	[TRAIN] epoch: 38, iter: 3550/13950, loss: 0.1201, DSC: 88.8715, lr: 0.007678, batch_cost: 0.5025, reader_cost: 0.00030, ips: 47.7651 samples/sec | ETA 01:27:05
2022-09-07 12:22:19 [INFO]	[TRAIN] epoch: 38, iter: 3560/13950, loss: 0.1289, DSC: 88.1819, lr: 0.007671, batch_cost: 0.5035, reader_cost: 0.00028, ips: 47.6703 samples/sec | ETA 01:27:10
2022-09-07 12:22:24 [INFO]	[TRAIN] epoch: 38, iter: 3570/13950, loss: 0.1316, DSC: 87.8257, lr: 0.007665, batch_cost: 0.5042, reader_cost: 0.00027, ips: 47.6039 samples/sec | ETA 01:27:13
2022-09-07 12:22:29 [INFO]	[TRAIN] epoch: 38, iter: 3580/13950, loss: 0.1390, DSC: 87.0657, lr: 0.007658, batch_cost: 0.5053, reader_cost: 0.00027, ips: 47.4957 samples/sec | ETA 01:27:20
2022-09-07 12:22:34 [INFO]	[TRAIN] epoch: 38, iter: 3590/13950, loss: 0.1122, DSC: 89.7861, lr: 0.007651, batch_cost: 0.5045, reader_cost: 0.00027, ips: 47.5678 samples/sec | ETA 01:27:07
2022-09-07 12:22:39 [INFO]	[TRAIN] epoch: 38, iter: 3600/13950, loss: 0.1260, DSC: 88.3074, lr: 0.007645, batch_cost: 0.5040, reader_cost: 0.00028, ips: 47.6181 samples/sec | ETA 01:26:56
2022-09-07 12:22:44 [INFO]	[TRAIN] epoch: 38, iter: 3610/13950, loss: 0.1284, DSC: 87.9939, lr: 0.007638, batch_cost: 0.5050, reader_cost: 0.00030, ips: 47.5239 samples/sec | ETA 01:27:01
2022-09-07 12:22:49 [INFO]	[TRAIN] epoch: 38, iter: 3620/13950, loss: 0.1402, DSC: 86.9184, lr: 0.007632, batch_cost: 0.5051, reader_cost: 0.00035, ips: 47.5118 samples/sec | ETA 01:26:58
2022-09-07 12:22:55 [INFO]	[TRAIN] epoch: 39, iter: 3630/13950, loss: 0.1650, DSC: 84.4849, lr: 0.007625, batch_cost: 0.6056, reader_cost: 0.11891, ips: 39.6282 samples/sec | ETA 01:44:10
2022-09-07 12:23:01 [INFO]	[TRAIN] epoch: 39, iter: 3640/13950, loss: 0.1335, DSC: 87.5376, lr: 0.007618, batch_cost: 0.5062, reader_cost: 0.00028, ips: 47.4101 samples/sec | ETA 01:26:59
2022-09-07 12:23:06 [INFO]	[TRAIN] epoch: 39, iter: 3650/13950, loss: 0.1316, DSC: 88.0625, lr: 0.007612, batch_cost: 0.5042, reader_cost: 0.00036, ips: 47.6032 samples/sec | ETA 01:26:32
2022-09-07 12:23:11 [INFO]	[TRAIN] epoch: 39, iter: 3660/13950, loss: 0.1263, DSC: 88.4754, lr: 0.007605, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5748 samples/sec | ETA 01:26:30
2022-09-07 12:23:16 [INFO]	[TRAIN] epoch: 39, iter: 3670/13950, loss: 0.1491, DSC: 86.1324, lr: 0.007598, batch_cost: 0.5049, reader_cost: 0.00028, ips: 47.5364 samples/sec | ETA 01:26:30
2022-09-07 12:23:21 [INFO]	[TRAIN] epoch: 39, iter: 3680/13950, loss: 0.1284, DSC: 88.0699, lr: 0.007592, batch_cost: 0.5080, reader_cost: 0.00027, ips: 47.2434 samples/sec | ETA 01:26:57
2022-09-07 12:23:26 [INFO]	[TRAIN] epoch: 39, iter: 3690/13950, loss: 0.1147, DSC: 89.3730, lr: 0.007585, batch_cost: 0.5052, reader_cost: 0.00028, ips: 47.5030 samples/sec | ETA 01:26:23
2022-09-07 12:23:31 [INFO]	[TRAIN] epoch: 39, iter: 3700/13950, loss: 0.1310, DSC: 87.8991, lr: 0.007578, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5494 samples/sec | ETA 01:26:13
2022-09-07 12:23:36 [INFO]	[TRAIN] epoch: 39, iter: 3710/13950, loss: 0.1445, DSC: 86.2567, lr: 0.007572, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5731 samples/sec | ETA 01:26:05
2022-09-07 12:23:41 [INFO]	[TRAIN] epoch: 40, iter: 3720/13950, loss: 0.2152, DSC: 79.4081, lr: 0.007565, batch_cost: 0.4745, reader_cost: 0.00022, ips: 50.5769 samples/sec | ETA 01:20:54
2022-09-07 12:23:47 [INFO]	[TRAIN] epoch: 40, iter: 3730/13950, loss: 0.1345, DSC: 87.5014, lr: 0.007558, batch_cost: 0.6219, reader_cost: 0.11020, ips: 38.5928 samples/sec | ETA 01:45:55
2022-09-07 12:23:52 [INFO]	[TRAIN] epoch: 40, iter: 3740/13950, loss: 0.1199, DSC: 88.6988, lr: 0.007552, batch_cost: 0.5052, reader_cost: 0.00028, ips: 47.5099 samples/sec | ETA 01:25:57
2022-09-07 12:23:57 [INFO]	[TRAIN] epoch: 40, iter: 3750/13950, loss: 0.1338, DSC: 87.5482, lr: 0.007545, batch_cost: 0.5030, reader_cost: 0.00029, ips: 47.7133 samples/sec | ETA 01:25:30
2022-09-07 12:24:02 [INFO]	[TRAIN] epoch: 40, iter: 3760/13950, loss: 0.1433, DSC: 86.5885, lr: 0.007538, batch_cost: 0.5027, reader_cost: 0.00027, ips: 47.7436 samples/sec | ETA 01:25:22
2022-09-07 12:24:07 [INFO]	[TRAIN] epoch: 40, iter: 3770/13950, loss: 0.1200, DSC: 88.8413, lr: 0.007532, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5827 samples/sec | ETA 01:25:34
2022-09-07 12:24:12 [INFO]	[TRAIN] epoch: 40, iter: 3780/13950, loss: 0.1367, DSC: 87.2067, lr: 0.007525, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5838 samples/sec | ETA 01:25:29
2022-09-07 12:24:17 [INFO]	[TRAIN] epoch: 40, iter: 3790/13950, loss: 0.1178, DSC: 89.2767, lr: 0.007518, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5790 samples/sec | ETA 01:25:24
2022-09-07 12:24:22 [INFO]	[TRAIN] epoch: 40, iter: 3800/13950, loss: 0.1212, DSC: 88.8948, lr: 0.007512, batch_cost: 0.5049, reader_cost: 0.00028, ips: 47.5329 samples/sec | ETA 01:25:24
2022-09-07 12:24:27 [INFO]	[TRAIN] epoch: 40, iter: 3810/13950, loss: 0.1272, DSC: 88.3099, lr: 0.007505, batch_cost: 0.5032, reader_cost: 0.00025, ips: 47.6922 samples/sec | ETA 01:25:02
2022-09-07 12:24:33 [INFO]	[TRAIN] epoch: 41, iter: 3820/13950, loss: 0.2038, DSC: 80.4990, lr: 0.007498, batch_cost: 0.6063, reader_cost: 0.10685, ips: 39.5819 samples/sec | ETA 01:42:22
2022-09-07 12:24:38 [INFO]	[TRAIN] epoch: 41, iter: 3830/13950, loss: 0.1395, DSC: 86.8363, lr: 0.007492, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6569 samples/sec | ETA 01:24:56
2022-09-07 12:24:43 [INFO]	[TRAIN] epoch: 41, iter: 3840/13950, loss: 0.1301, DSC: 87.8598, lr: 0.007485, batch_cost: 0.5032, reader_cost: 0.00027, ips: 47.6951 samples/sec | ETA 01:24:47
2022-09-07 12:24:48 [INFO]	[TRAIN] epoch: 41, iter: 3850/13950, loss: 0.1195, DSC: 89.0183, lr: 0.007478, batch_cost: 0.5035, reader_cost: 0.00027, ips: 47.6663 samples/sec | ETA 01:24:45
2022-09-07 12:24:53 [INFO]	[TRAIN] epoch: 41, iter: 3860/13950, loss: 0.1357, DSC: 87.3055, lr: 0.007472, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5880 samples/sec | ETA 01:24:48
2022-09-07 12:24:58 [INFO]	[TRAIN] epoch: 41, iter: 3870/13950, loss: 0.1133, DSC: 89.5216, lr: 0.007465, batch_cost: 0.5053, reader_cost: 0.00029, ips: 47.4961 samples/sec | ETA 01:24:53
2022-09-07 12:25:03 [INFO]	[TRAIN] epoch: 41, iter: 3880/13950, loss: 0.1252, DSC: 88.3457, lr: 0.007458, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5834 samples/sec | ETA 01:24:39
2022-09-07 12:25:09 [INFO]	[TRAIN] epoch: 41, iter: 3890/13950, loss: 0.1152, DSC: 89.3941, lr: 0.007452, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6472 samples/sec | ETA 01:24:27
2022-09-07 12:25:14 [INFO]	[TRAIN] epoch: 41, iter: 3900/13950, loss: 0.1422, DSC: 86.7845, lr: 0.007445, batch_cost: 0.5046, reader_cost: 0.00035, ips: 47.5643 samples/sec | ETA 01:24:31
2022-09-07 12:25:20 [INFO]	[TRAIN] epoch: 42, iter: 3910/13950, loss: 0.1530, DSC: 85.7339, lr: 0.007438, batch_cost: 0.6058, reader_cost: 0.12122, ips: 39.6180 samples/sec | ETA 01:41:22
2022-09-07 12:25:25 [INFO]	[TRAIN] epoch: 42, iter: 3920/13950, loss: 0.1288, DSC: 88.3802, lr: 0.007432, batch_cost: 0.5047, reader_cost: 0.00034, ips: 47.5489 samples/sec | ETA 01:24:22
2022-09-07 12:25:30 [INFO]	[TRAIN] epoch: 42, iter: 3930/13950, loss: 0.1426, DSC: 86.8197, lr: 0.007425, batch_cost: 0.5045, reader_cost: 0.00027, ips: 47.5676 samples/sec | ETA 01:24:15
2022-09-07 12:25:35 [INFO]	[TRAIN] epoch: 42, iter: 3940/13950, loss: 0.1273, DSC: 88.2469, lr: 0.007418, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5414 samples/sec | ETA 01:24:13
2022-09-07 12:25:40 [INFO]	[TRAIN] epoch: 42, iter: 3950/13950, loss: 0.1384, DSC: 87.1660, lr: 0.007412, batch_cost: 0.5043, reader_cost: 0.00026, ips: 47.5871 samples/sec | ETA 01:24:03
2022-09-07 12:25:45 [INFO]	[TRAIN] epoch: 42, iter: 3960/13950, loss: 0.1429, DSC: 86.6883, lr: 0.007405, batch_cost: 0.5035, reader_cost: 0.00025, ips: 47.6691 samples/sec | ETA 01:23:49
2022-09-07 12:25:50 [INFO]	[TRAIN] epoch: 42, iter: 3970/13950, loss: 0.1293, DSC: 87.9553, lr: 0.007398, batch_cost: 0.5027, reader_cost: 0.00031, ips: 47.7393 samples/sec | ETA 01:23:37
2022-09-07 12:25:55 [INFO]	[TRAIN] epoch: 42, iter: 3980/13950, loss: 0.1442, DSC: 86.5218, lr: 0.007392, batch_cost: 0.5050, reader_cost: 0.00028, ips: 47.5226 samples/sec | ETA 01:23:55
2022-09-07 12:26:00 [INFO]	[TRAIN] epoch: 42, iter: 3990/13950, loss: 0.1158, DSC: 89.2511, lr: 0.007385, batch_cost: 0.5034, reader_cost: 0.00033, ips: 47.6736 samples/sec | ETA 01:23:34
2022-09-07 12:26:06 [INFO]	[TRAIN] epoch: 43, iter: 4000/13950, loss: 0.1246, DSC: 88.3765, lr: 0.007378, batch_cost: 0.5826, reader_cost: 0.10494, ips: 41.1919 samples/sec | ETA 01:36:37
2022-09-07 12:26:06 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 23s - batch_cost: 2.1063 - reader cost: 1.2598
 2/12 [====>.........................] - ETA: 13s - batch_cost: 1.3213 - reader cost: 0.6326
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0817 - reader cost: 0.4293 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.2235 - reader cost: 0.4711
 5/12 [===========>..................] - ETA: 8s - batch_cost: 1.1498 - reader cost: 0.3811
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10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0688 - reader cost: 0.2658
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0178 - reader cost: 0.2432
12/12 [==============================] - 12s 981ms/step - batch_cost: 0.9805 - reader cost: 0.2247
2022-09-07 12:26:18 [INFO]	[EVAL] #Images: 12, Dice: 0.8107, Loss: 0.210965
2022-09-07 12:26:18 [INFO]	[EVAL] Class dice: 
[0.9948 0.8865 0.5197 0.8683 0.8489 0.9411 0.6215 0.8716 0.7436]
2022-09-07 12:26:20 [INFO]	[EVAL] The model with the best validation mDice (0.8123) was saved at iter 3000.
2022-09-07 12:26:25 [INFO]	[TRAIN] epoch: 43, iter: 4010/13950, loss: 0.1461, DSC: 86.3217, lr: 0.007372, batch_cost: 0.5049, reader_cost: 0.00043, ips: 47.5350 samples/sec | ETA 01:23:38
2022-09-07 12:26:30 [INFO]	[TRAIN] epoch: 43, iter: 4020/13950, loss: 0.1243, DSC: 88.4145, lr: 0.007365, batch_cost: 0.5053, reader_cost: 0.00026, ips: 47.4981 samples/sec | ETA 01:23:37
2022-09-07 12:26:35 [INFO]	[TRAIN] epoch: 43, iter: 4030/13950, loss: 0.1239, DSC: 88.5173, lr: 0.007358, batch_cost: 0.5047, reader_cost: 0.00031, ips: 47.5540 samples/sec | ETA 01:23:26
2022-09-07 12:26:40 [INFO]	[TRAIN] epoch: 43, iter: 4040/13950, loss: 0.1511, DSC: 85.7445, lr: 0.007352, batch_cost: 0.5048, reader_cost: 0.00026, ips: 47.5456 samples/sec | ETA 01:23:22
2022-09-07 12:26:45 [INFO]	[TRAIN] epoch: 43, iter: 4050/13950, loss: 0.1269, DSC: 88.1912, lr: 0.007345, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6188 samples/sec | ETA 01:23:09
2022-09-07 12:26:50 [INFO]	[TRAIN] epoch: 43, iter: 4060/13950, loss: 0.1094, DSC: 90.0750, lr: 0.007338, batch_cost: 0.5002, reader_cost: 0.00024, ips: 47.9820 samples/sec | ETA 01:22:26
2022-09-07 12:26:55 [INFO]	[TRAIN] epoch: 43, iter: 4070/13950, loss: 0.1176, DSC: 89.1939, lr: 0.007332, batch_cost: 0.5059, reader_cost: 0.00029, ips: 47.4432 samples/sec | ETA 01:23:17
2022-09-07 12:27:00 [INFO]	[TRAIN] epoch: 43, iter: 4080/13950, loss: 0.1148, DSC: 89.3949, lr: 0.007325, batch_cost: 0.5057, reader_cost: 0.00029, ips: 47.4628 samples/sec | ETA 01:23:10
2022-09-07 12:27:05 [INFO]	[TRAIN] epoch: 43, iter: 4090/13950, loss: 0.1373, DSC: 87.2374, lr: 0.007318, batch_cost: 0.5035, reader_cost: 0.00024, ips: 47.6618 samples/sec | ETA 01:22:44
2022-09-07 12:27:11 [INFO]	[TRAIN] epoch: 44, iter: 4100/13950, loss: 0.1959, DSC: 81.1754, lr: 0.007312, batch_cost: 0.6066, reader_cost: 0.10794, ips: 39.5635 samples/sec | ETA 01:39:35
2022-09-07 12:27:16 [INFO]	[TRAIN] epoch: 44, iter: 4110/13950, loss: 0.1170, DSC: 89.3320, lr: 0.007305, batch_cost: 0.5060, reader_cost: 0.00029, ips: 47.4275 samples/sec | ETA 01:22:59
2022-09-07 12:27:22 [INFO]	[TRAIN] epoch: 44, iter: 4120/13950, loss: 0.1394, DSC: 86.8942, lr: 0.007298, batch_cost: 0.5062, reader_cost: 0.00029, ips: 47.4145 samples/sec | ETA 01:22:55
2022-09-07 12:27:27 [INFO]	[TRAIN] epoch: 44, iter: 4130/13950, loss: 0.1136, DSC: 89.5773, lr: 0.007292, batch_cost: 0.5068, reader_cost: 0.00029, ips: 47.3602 samples/sec | ETA 01:22:56
2022-09-07 12:27:32 [INFO]	[TRAIN] epoch: 44, iter: 4140/13950, loss: 0.1664, DSC: 84.1648, lr: 0.007285, batch_cost: 0.5046, reader_cost: 0.00029, ips: 47.5593 samples/sec | ETA 01:22:30
2022-09-07 12:27:37 [INFO]	[TRAIN] epoch: 44, iter: 4150/13950, loss: 0.1332, DSC: 87.5292, lr: 0.007278, batch_cost: 0.5041, reader_cost: 0.00028, ips: 47.6060 samples/sec | ETA 01:22:20
2022-09-07 12:27:42 [INFO]	[TRAIN] epoch: 44, iter: 4160/13950, loss: 0.1211, DSC: 88.9002, lr: 0.007272, batch_cost: 0.5039, reader_cost: 0.00028, ips: 47.6251 samples/sec | ETA 01:22:13
2022-09-07 12:27:47 [INFO]	[TRAIN] epoch: 44, iter: 4170/13950, loss: 0.1131, DSC: 89.5980, lr: 0.007265, batch_cost: 0.5047, reader_cost: 0.00030, ips: 47.5484 samples/sec | ETA 01:22:16
2022-09-07 12:27:52 [INFO]	[TRAIN] epoch: 44, iter: 4180/13950, loss: 0.1186, DSC: 89.0463, lr: 0.007258, batch_cost: 0.5048, reader_cost: 0.00026, ips: 47.5412 samples/sec | ETA 01:22:12
2022-09-07 12:27:58 [INFO]	[TRAIN] epoch: 45, iter: 4190/13950, loss: 0.1221, DSC: 88.6422, lr: 0.007252, batch_cost: 0.6176, reader_cost: 0.12917, ips: 38.8584 samples/sec | ETA 01:40:28
2022-09-07 12:28:03 [INFO]	[TRAIN] epoch: 45, iter: 4200/13950, loss: 0.1113, DSC: 89.6305, lr: 0.007245, batch_cost: 0.5076, reader_cost: 0.00043, ips: 47.2782 samples/sec | ETA 01:22:29
2022-09-07 12:28:08 [INFO]	[TRAIN] epoch: 45, iter: 4210/13950, loss: 0.1243, DSC: 88.4573, lr: 0.007238, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6547 samples/sec | ETA 01:21:45
2022-09-07 12:28:13 [INFO]	[TRAIN] epoch: 45, iter: 4220/13950, loss: 0.1311, DSC: 87.8782, lr: 0.007231, batch_cost: 0.5041, reader_cost: 0.00028, ips: 47.6050 samples/sec | ETA 01:21:45
2022-09-07 12:28:18 [INFO]	[TRAIN] epoch: 45, iter: 4230/13950, loss: 0.1300, DSC: 87.9472, lr: 0.007225, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5268 samples/sec | ETA 01:21:48
2022-09-07 12:28:23 [INFO]	[TRAIN] epoch: 45, iter: 4240/13950, loss: 0.1099, DSC: 89.9218, lr: 0.007218, batch_cost: 0.5042, reader_cost: 0.00027, ips: 47.6019 samples/sec | ETA 01:21:35
2022-09-07 12:28:28 [INFO]	[TRAIN] epoch: 45, iter: 4250/13950, loss: 0.1141, DSC: 89.4043, lr: 0.007211, batch_cost: 0.5058, reader_cost: 0.00034, ips: 47.4489 samples/sec | ETA 01:21:46
2022-09-07 12:28:33 [INFO]	[TRAIN] epoch: 45, iter: 4260/13950, loss: 0.1210, DSC: 88.8627, lr: 0.007205, batch_cost: 0.5042, reader_cost: 0.00029, ips: 47.5964 samples/sec | ETA 01:21:26
2022-09-07 12:28:38 [INFO]	[TRAIN] epoch: 45, iter: 4270/13950, loss: 0.1107, DSC: 89.6437, lr: 0.007198, batch_cost: 0.5045, reader_cost: 0.00031, ips: 47.5728 samples/sec | ETA 01:21:23
2022-09-07 12:28:44 [INFO]	[TRAIN] epoch: 46, iter: 4280/13950, loss: 0.1673, DSC: 84.1623, lr: 0.007191, batch_cost: 0.5958, reader_cost: 0.11203, ips: 40.2850 samples/sec | ETA 01:36:00
2022-09-07 12:28:49 [INFO]	[TRAIN] epoch: 46, iter: 4290/13950, loss: 0.1215, DSC: 88.7615, lr: 0.007185, batch_cost: 0.5071, reader_cost: 0.00034, ips: 47.3282 samples/sec | ETA 01:21:38
2022-09-07 12:28:54 [INFO]	[TRAIN] epoch: 46, iter: 4300/13950, loss: 0.1591, DSC: 84.9578, lr: 0.007178, batch_cost: 0.5054, reader_cost: 0.00028, ips: 47.4886 samples/sec | ETA 01:21:16
2022-09-07 12:29:00 [INFO]	[TRAIN] epoch: 46, iter: 4310/13950, loss: 0.1199, DSC: 89.0403, lr: 0.007171, batch_cost: 0.5026, reader_cost: 0.00027, ips: 47.7470 samples/sec | ETA 01:20:45
2022-09-07 12:29:05 [INFO]	[TRAIN] epoch: 46, iter: 4320/13950, loss: 0.1032, DSC: 90.5272, lr: 0.007165, batch_cost: 0.5029, reader_cost: 0.00027, ips: 47.7252 samples/sec | ETA 01:20:42
2022-09-07 12:29:10 [INFO]	[TRAIN] epoch: 46, iter: 4330/13950, loss: 0.1448, DSC: 86.4272, lr: 0.007158, batch_cost: 0.5035, reader_cost: 0.00027, ips: 47.6672 samples/sec | ETA 01:20:43
2022-09-07 12:29:15 [INFO]	[TRAIN] epoch: 46, iter: 4340/13950, loss: 0.1417, DSC: 86.7031, lr: 0.007151, batch_cost: 0.5047, reader_cost: 0.00028, ips: 47.5523 samples/sec | ETA 01:20:50
2022-09-07 12:29:20 [INFO]	[TRAIN] epoch: 46, iter: 4350/13950, loss: 0.1243, DSC: 88.4877, lr: 0.007144, batch_cost: 0.5043, reader_cost: 0.00028, ips: 47.5921 samples/sec | ETA 01:20:41
2022-09-07 12:29:25 [INFO]	[TRAIN] epoch: 46, iter: 4360/13950, loss: 0.1214, DSC: 88.7326, lr: 0.007138, batch_cost: 0.5054, reader_cost: 0.00033, ips: 47.4841 samples/sec | ETA 01:20:47
2022-09-07 12:29:30 [INFO]	[TRAIN] epoch: 46, iter: 4370/13950, loss: 0.1310, DSC: 87.7526, lr: 0.007131, batch_cost: 0.5010, reader_cost: 0.00020, ips: 47.9068 samples/sec | ETA 01:19:59
2022-09-07 12:29:36 [INFO]	[TRAIN] epoch: 47, iter: 4380/13950, loss: 0.1883, DSC: 81.9245, lr: 0.007124, batch_cost: 0.6059, reader_cost: 0.12166, ips: 39.6081 samples/sec | ETA 01:36:38
2022-09-07 12:29:41 [INFO]	[TRAIN] epoch: 47, iter: 4390/13950, loss: 0.1149, DSC: 89.3426, lr: 0.007118, batch_cost: 0.5043, reader_cost: 0.00025, ips: 47.5940 samples/sec | ETA 01:20:20
2022-09-07 12:29:46 [INFO]	[TRAIN] epoch: 47, iter: 4400/13950, loss: 0.1360, DSC: 87.2650, lr: 0.007111, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5198 samples/sec | ETA 01:20:23
2022-09-07 12:29:51 [INFO]	[TRAIN] epoch: 47, iter: 4410/13950, loss: 0.1110, DSC: 89.8332, lr: 0.007104, batch_cost: 0.5050, reader_cost: 0.00026, ips: 47.5260 samples/sec | ETA 01:20:17
2022-09-07 12:29:56 [INFO]	[TRAIN] epoch: 47, iter: 4420/13950, loss: 0.1240, DSC: 88.5654, lr: 0.007098, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5845 samples/sec | ETA 01:20:06
2022-09-07 12:30:01 [INFO]	[TRAIN] epoch: 47, iter: 4430/13950, loss: 0.1126, DSC: 89.5667, lr: 0.007091, batch_cost: 0.5040, reader_cost: 0.00027, ips: 47.6177 samples/sec | ETA 01:19:58
2022-09-07 12:30:06 [INFO]	[TRAIN] epoch: 47, iter: 4440/13950, loss: 0.1162, DSC: 89.0970, lr: 0.007084, batch_cost: 0.5052, reader_cost: 0.00026, ips: 47.5086 samples/sec | ETA 01:20:04
2022-09-07 12:30:11 [INFO]	[TRAIN] epoch: 47, iter: 4450/13950, loss: 0.1368, DSC: 87.0963, lr: 0.007077, batch_cost: 0.5068, reader_cost: 0.00028, ips: 47.3520 samples/sec | ETA 01:20:15
2022-09-07 12:30:16 [INFO]	[TRAIN] epoch: 47, iter: 4460/13950, loss: 0.1258, DSC: 88.4272, lr: 0.007071, batch_cost: 0.5045, reader_cost: 0.00025, ips: 47.5715 samples/sec | ETA 01:19:47
2022-09-07 12:30:22 [INFO]	[TRAIN] epoch: 48, iter: 4470/13950, loss: 0.1635, DSC: 84.5438, lr: 0.007064, batch_cost: 0.5910, reader_cost: 0.10472, ips: 40.6058 samples/sec | ETA 01:33:23
2022-09-07 12:30:27 [INFO]	[TRAIN] epoch: 48, iter: 4480/13950, loss: 0.1332, DSC: 87.6298, lr: 0.007057, batch_cost: 0.5045, reader_cost: 0.00027, ips: 47.5733 samples/sec | ETA 01:19:37
2022-09-07 12:30:32 [INFO]	[TRAIN] epoch: 48, iter: 4490/13950, loss: 0.1215, DSC: 88.7341, lr: 0.007051, batch_cost: 0.5029, reader_cost: 0.00025, ips: 47.7190 samples/sec | ETA 01:19:17
2022-09-07 12:30:37 [INFO]	[TRAIN] epoch: 48, iter: 4500/13950, loss: 0.1229, DSC: 88.6565, lr: 0.007044, batch_cost: 0.5032, reader_cost: 0.00025, ips: 47.6909 samples/sec | ETA 01:19:15
2022-09-07 12:30:42 [INFO]	[TRAIN] epoch: 48, iter: 4510/13950, loss: 0.1161, DSC: 89.3767, lr: 0.007037, batch_cost: 0.5041, reader_cost: 0.00026, ips: 47.6080 samples/sec | ETA 01:19:18
2022-09-07 12:30:47 [INFO]	[TRAIN] epoch: 48, iter: 4520/13950, loss: 0.1193, DSC: 88.8772, lr: 0.007030, batch_cost: 0.5061, reader_cost: 0.00029, ips: 47.4186 samples/sec | ETA 01:19:32
2022-09-07 12:30:52 [INFO]	[TRAIN] epoch: 48, iter: 4530/13950, loss: 0.1346, DSC: 87.2583, lr: 0.007024, batch_cost: 0.5044, reader_cost: 0.00026, ips: 47.5835 samples/sec | ETA 01:19:11
2022-09-07 12:30:57 [INFO]	[TRAIN] epoch: 48, iter: 4540/13950, loss: 0.1342, DSC: 87.3509, lr: 0.007017, batch_cost: 0.5050, reader_cost: 0.00028, ips: 47.5259 samples/sec | ETA 01:19:11
2022-09-07 12:31:02 [INFO]	[TRAIN] epoch: 48, iter: 4550/13950, loss: 0.1347, DSC: 87.3839, lr: 0.007010, batch_cost: 0.5050, reader_cost: 0.00026, ips: 47.5234 samples/sec | ETA 01:19:07
2022-09-07 12:31:08 [INFO]	[TRAIN] epoch: 49, iter: 4560/13950, loss: 0.2043, DSC: 80.4261, lr: 0.007004, batch_cost: 0.5840, reader_cost: 0.10032, ips: 41.0945 samples/sec | ETA 01:31:23
2022-09-07 12:31:13 [INFO]	[TRAIN] epoch: 49, iter: 4570/13950, loss: 0.1252, DSC: 88.4057, lr: 0.006997, batch_cost: 0.5071, reader_cost: 0.00027, ips: 47.3312 samples/sec | ETA 01:19:16
2022-09-07 12:31:18 [INFO]	[TRAIN] epoch: 49, iter: 4580/13950, loss: 0.1184, DSC: 88.9744, lr: 0.006990, batch_cost: 0.5024, reader_cost: 0.00026, ips: 47.7736 samples/sec | ETA 01:18:27
2022-09-07 12:31:23 [INFO]	[TRAIN] epoch: 49, iter: 4590/13950, loss: 0.1095, DSC: 89.9615, lr: 0.006984, batch_cost: 0.5005, reader_cost: 0.00026, ips: 47.9496 samples/sec | ETA 01:18:04
2022-09-07 12:31:28 [INFO]	[TRAIN] epoch: 49, iter: 4600/13950, loss: 0.1372, DSC: 87.2129, lr: 0.006977, batch_cost: 0.5024, reader_cost: 0.00027, ips: 47.7679 samples/sec | ETA 01:18:17
2022-09-07 12:31:33 [INFO]	[TRAIN] epoch: 49, iter: 4610/13950, loss: 0.1361, DSC: 87.3437, lr: 0.006970, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6485 samples/sec | ETA 01:18:24
2022-09-07 12:31:38 [INFO]	[TRAIN] epoch: 49, iter: 4620/13950, loss: 0.1259, DSC: 88.3175, lr: 0.006963, batch_cost: 0.5023, reader_cost: 0.00026, ips: 47.7800 samples/sec | ETA 01:18:06
2022-09-07 12:31:44 [INFO]	[TRAIN] epoch: 49, iter: 4630/13950, loss: 0.1331, DSC: 87.5747, lr: 0.006957, batch_cost: 0.5024, reader_cost: 0.00026, ips: 47.7719 samples/sec | ETA 01:18:02
2022-09-07 12:31:49 [INFO]	[TRAIN] epoch: 49, iter: 4640/13950, loss: 0.1080, DSC: 90.0096, lr: 0.006950, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5588 samples/sec | ETA 01:18:18
2022-09-07 12:31:53 [INFO]	[TRAIN] epoch: 50, iter: 4650/13950, loss: 0.1900, DSC: 81.6874, lr: 0.006943, batch_cost: 0.4741, reader_cost: 0.00031, ips: 50.6226 samples/sec | ETA 01:13:29
2022-09-07 12:32:00 [INFO]	[TRAIN] epoch: 50, iter: 4660/13950, loss: 0.1304, DSC: 87.8143, lr: 0.006936, batch_cost: 0.6410, reader_cost: 0.12859, ips: 37.4433 samples/sec | ETA 01:39:14
2022-09-07 12:32:05 [INFO]	[TRAIN] epoch: 50, iter: 4670/13950, loss: 0.1122, DSC: 89.6044, lr: 0.006930, batch_cost: 0.5011, reader_cost: 0.00027, ips: 47.8939 samples/sec | ETA 01:17:30
2022-09-07 12:32:10 [INFO]	[TRAIN] epoch: 50, iter: 4680/13950, loss: 0.1135, DSC: 89.4361, lr: 0.006923, batch_cost: 0.5030, reader_cost: 0.00028, ips: 47.7125 samples/sec | ETA 01:17:42
2022-09-07 12:32:15 [INFO]	[TRAIN] epoch: 50, iter: 4690/13950, loss: 0.1174, DSC: 89.1839, lr: 0.006916, batch_cost: 0.5051, reader_cost: 0.00029, ips: 47.5152 samples/sec | ETA 01:17:57
2022-09-07 12:32:20 [INFO]	[TRAIN] epoch: 50, iter: 4700/13950, loss: 0.1394, DSC: 86.9053, lr: 0.006910, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6606 samples/sec | ETA 01:17:37
2022-09-07 12:32:25 [INFO]	[TRAIN] epoch: 50, iter: 4710/13950, loss: 0.1168, DSC: 89.0578, lr: 0.006903, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5802 samples/sec | ETA 01:17:40
2022-09-07 12:32:30 [INFO]	[TRAIN] epoch: 50, iter: 4720/13950, loss: 0.1301, DSC: 87.8764, lr: 0.006896, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6367 samples/sec | ETA 01:17:30
2022-09-07 12:32:35 [INFO]	[TRAIN] epoch: 50, iter: 4730/13950, loss: 0.1287, DSC: 88.0521, lr: 0.006889, batch_cost: 0.5034, reader_cost: 0.00026, ips: 47.6797 samples/sec | ETA 01:17:20
2022-09-07 12:32:40 [INFO]	[TRAIN] epoch: 50, iter: 4740/13950, loss: 0.1109, DSC: 89.8262, lr: 0.006883, batch_cost: 0.5017, reader_cost: 0.00023, ips: 47.8327 samples/sec | ETA 01:17:01
2022-09-07 12:32:46 [INFO]	[TRAIN] epoch: 51, iter: 4750/13950, loss: 0.1816, DSC: 82.5200, lr: 0.006876, batch_cost: 0.6009, reader_cost: 0.11520, ips: 39.9383 samples/sec | ETA 01:32:08
2022-09-07 12:32:51 [INFO]	[TRAIN] epoch: 51, iter: 4760/13950, loss: 0.1155, DSC: 89.3543, lr: 0.006869, batch_cost: 0.5078, reader_cost: 0.00029, ips: 47.2668 samples/sec | ETA 01:17:46
2022-09-07 12:32:56 [INFO]	[TRAIN] epoch: 51, iter: 4770/13950, loss: 0.1195, DSC: 88.9154, lr: 0.006863, batch_cost: 0.5071, reader_cost: 0.00029, ips: 47.3274 samples/sec | ETA 01:17:35
2022-09-07 12:33:01 [INFO]	[TRAIN] epoch: 51, iter: 4780/13950, loss: 0.1189, DSC: 88.9809, lr: 0.006856, batch_cost: 0.5058, reader_cost: 0.00028, ips: 47.4481 samples/sec | ETA 01:17:18
2022-09-07 12:33:06 [INFO]	[TRAIN] epoch: 51, iter: 4790/13950, loss: 0.1432, DSC: 86.4785, lr: 0.006849, batch_cost: 0.5055, reader_cost: 0.00035, ips: 47.4812 samples/sec | ETA 01:17:10
2022-09-07 12:33:11 [INFO]	[TRAIN] epoch: 51, iter: 4800/13950, loss: 0.1303, DSC: 87.7911, lr: 0.006842, batch_cost: 0.5027, reader_cost: 0.00025, ips: 47.7390 samples/sec | ETA 01:16:40
2022-09-07 12:33:16 [INFO]	[TRAIN] epoch: 51, iter: 4810/13950, loss: 0.1259, DSC: 88.2198, lr: 0.006836, batch_cost: 0.5053, reader_cost: 0.00032, ips: 47.5008 samples/sec | ETA 01:16:58
2022-09-07 12:33:21 [INFO]	[TRAIN] epoch: 51, iter: 4820/13950, loss: 0.1208, DSC: 89.0904, lr: 0.006829, batch_cost: 0.5043, reader_cost: 0.00025, ips: 47.5924 samples/sec | ETA 01:16:44
2022-09-07 12:33:26 [INFO]	[TRAIN] epoch: 51, iter: 4830/13950, loss: 0.1339, DSC: 87.6279, lr: 0.006822, batch_cost: 0.5051, reader_cost: 0.00026, ips: 47.5152 samples/sec | ETA 01:16:46
2022-09-07 12:33:33 [INFO]	[TRAIN] epoch: 52, iter: 4840/13950, loss: 0.1233, DSC: 88.6135, lr: 0.006815, batch_cost: 0.6222, reader_cost: 0.13101, ips: 38.5741 samples/sec | ETA 01:34:28
2022-09-07 12:33:38 [INFO]	[TRAIN] epoch: 52, iter: 4850/13950, loss: 0.1326, DSC: 87.6379, lr: 0.006809, batch_cost: 0.5049, reader_cost: 0.00033, ips: 47.5349 samples/sec | ETA 01:16:34
2022-09-07 12:33:43 [INFO]	[TRAIN] epoch: 52, iter: 4860/13950, loss: 0.1232, DSC: 88.6267, lr: 0.006802, batch_cost: 0.5037, reader_cost: 0.00026, ips: 47.6459 samples/sec | ETA 01:16:18
2022-09-07 12:33:48 [INFO]	[TRAIN] epoch: 52, iter: 4870/13950, loss: 0.0959, DSC: 91.2821, lr: 0.006795, batch_cost: 0.5073, reader_cost: 0.00029, ips: 47.3054 samples/sec | ETA 01:16:46
2022-09-07 12:33:53 [INFO]	[TRAIN] epoch: 52, iter: 4880/13950, loss: 0.1328, DSC: 87.5282, lr: 0.006788, batch_cost: 0.5086, reader_cost: 0.00034, ips: 47.1859 samples/sec | ETA 01:16:53
2022-09-07 12:33:58 [INFO]	[TRAIN] epoch: 52, iter: 4890/13950, loss: 0.1320, DSC: 87.5808, lr: 0.006782, batch_cost: 0.5031, reader_cost: 0.00027, ips: 47.7025 samples/sec | ETA 01:15:58
2022-09-07 12:34:03 [INFO]	[TRAIN] epoch: 52, iter: 4900/13950, loss: 0.1120, DSC: 89.6906, lr: 0.006775, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6392 samples/sec | ETA 01:15:59
2022-09-07 12:34:08 [INFO]	[TRAIN] epoch: 52, iter: 4910/13950, loss: 0.1122, DSC: 89.7176, lr: 0.006768, batch_cost: 0.5054, reader_cost: 0.00035, ips: 47.4912 samples/sec | ETA 01:16:08
2022-09-07 12:34:13 [INFO]	[TRAIN] epoch: 52, iter: 4920/13950, loss: 0.1252, DSC: 88.3525, lr: 0.006762, batch_cost: 0.5043, reader_cost: 0.00029, ips: 47.5905 samples/sec | ETA 01:15:53
2022-09-07 12:34:19 [INFO]	[TRAIN] epoch: 53, iter: 4930/13950, loss: 0.2004, DSC: 80.6856, lr: 0.006755, batch_cost: 0.5902, reader_cost: 0.11231, ips: 40.6666 samples/sec | ETA 01:28:43
2022-09-07 12:34:24 [INFO]	[TRAIN] epoch: 53, iter: 4940/13950, loss: 0.1213, DSC: 88.7102, lr: 0.006748, batch_cost: 0.5207, reader_cost: 0.00035, ips: 46.0935 samples/sec | ETA 01:18:11
2022-09-07 12:34:29 [INFO]	[TRAIN] epoch: 53, iter: 4950/13950, loss: 0.1081, DSC: 89.9496, lr: 0.006741, batch_cost: 0.5018, reader_cost: 0.00026, ips: 47.8322 samples/sec | ETA 01:15:15
2022-09-07 12:34:34 [INFO]	[TRAIN] epoch: 53, iter: 4960/13950, loss: 0.1312, DSC: 87.8612, lr: 0.006735, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6423 samples/sec | ETA 01:15:28
2022-09-07 12:34:39 [INFO]	[TRAIN] epoch: 53, iter: 4970/13950, loss: 0.1160, DSC: 89.1131, lr: 0.006728, batch_cost: 0.5026, reader_cost: 0.00025, ips: 47.7491 samples/sec | ETA 01:15:13
2022-09-07 12:34:44 [INFO]	[TRAIN] epoch: 53, iter: 4980/13950, loss: 0.1146, DSC: 89.4022, lr: 0.006721, batch_cost: 0.5033, reader_cost: 0.00026, ips: 47.6867 samples/sec | ETA 01:15:14
2022-09-07 12:34:49 [INFO]	[TRAIN] epoch: 53, iter: 4990/13950, loss: 0.1219, DSC: 88.6015, lr: 0.006714, batch_cost: 0.5023, reader_cost: 0.00026, ips: 47.7830 samples/sec | ETA 01:15:00
2022-09-07 12:34:54 [INFO]	[TRAIN] epoch: 53, iter: 5000/13950, loss: 0.1193, DSC: 88.9187, lr: 0.006708, batch_cost: 0.5129, reader_cost: 0.00029, ips: 46.7904 samples/sec | ETA 01:16:30
2022-09-07 12:34:54 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0131 - reader cost: 1.1657
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2735 - reader cost: 0.5856
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0494 - reader cost: 0.3981 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.2057 - reader cost: 0.4552
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1361 - reader cost: 0.3684
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0849 - reader cost: 0.3135
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0158 - reader cost: 0.2714
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1157 - reader cost: 0.3197
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0894 - reader cost: 0.2864
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0639 - reader cost: 0.2608
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0132 - reader cost: 0.2386
12/12 [==============================] - 12s 976ms/step - batch_cost: 0.9758 - reader cost: 0.2204
2022-09-07 12:35:06 [INFO]	[EVAL] #Images: 12, Dice: 0.8185, Loss: 0.200956
2022-09-07 12:35:06 [INFO]	[EVAL] Class dice: 
[0.995  0.8946 0.527  0.881  0.8472 0.946  0.6372 0.8768 0.7618]
2022-09-07 12:35:12 [INFO]	[EVAL] The model with the best validation mDice (0.8185) was saved at iter 5000.
2022-09-07 12:35:17 [INFO]	[TRAIN] epoch: 53, iter: 5010/13950, loss: 0.1111, DSC: 89.7339, lr: 0.006701, batch_cost: 0.5013, reader_cost: 0.00037, ips: 47.8743 samples/sec | ETA 01:14:41
2022-09-07 12:35:22 [INFO]	[TRAIN] epoch: 53, iter: 5020/13950, loss: 0.1329, DSC: 87.6178, lr: 0.006694, batch_cost: 0.4980, reader_cost: 0.00021, ips: 48.1929 samples/sec | ETA 01:14:07
2022-09-07 12:35:28 [INFO]	[TRAIN] epoch: 54, iter: 5030/13950, loss: 0.1732, DSC: 83.5166, lr: 0.006687, batch_cost: 0.6419, reader_cost: 0.15545, ips: 37.3914 samples/sec | ETA 01:35:25
2022-09-07 12:35:33 [INFO]	[TRAIN] epoch: 54, iter: 5040/13950, loss: 0.1171, DSC: 89.1591, lr: 0.006681, batch_cost: 0.5033, reader_cost: 0.00033, ips: 47.6830 samples/sec | ETA 01:14:44
2022-09-07 12:35:38 [INFO]	[TRAIN] epoch: 54, iter: 5050/13950, loss: 0.1153, DSC: 89.1735, lr: 0.006674, batch_cost: 0.5027, reader_cost: 0.00026, ips: 47.7386 samples/sec | ETA 01:14:34
2022-09-07 12:35:43 [INFO]	[TRAIN] epoch: 54, iter: 5060/13950, loss: 0.1255, DSC: 88.1794, lr: 0.006667, batch_cost: 0.5033, reader_cost: 0.00025, ips: 47.6892 samples/sec | ETA 01:14:33
2022-09-07 12:35:49 [INFO]	[TRAIN] epoch: 54, iter: 5070/13950, loss: 0.1241, DSC: 88.5502, lr: 0.006660, batch_cost: 0.5041, reader_cost: 0.00033, ips: 47.6057 samples/sec | ETA 01:14:36
2022-09-07 12:35:54 [INFO]	[TRAIN] epoch: 54, iter: 5080/13950, loss: 0.1265, DSC: 88.2408, lr: 0.006654, batch_cost: 0.5033, reader_cost: 0.00028, ips: 47.6865 samples/sec | ETA 01:14:24
2022-09-07 12:35:59 [INFO]	[TRAIN] epoch: 54, iter: 5090/13950, loss: 0.1244, DSC: 88.4840, lr: 0.006647, batch_cost: 0.5363, reader_cost: 0.00029, ips: 44.7487 samples/sec | ETA 01:19:11
2022-09-07 12:36:04 [INFO]	[TRAIN] epoch: 54, iter: 5100/13950, loss: 0.1368, DSC: 87.2265, lr: 0.006640, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4915 samples/sec | ETA 01:14:32
2022-09-07 12:36:09 [INFO]	[TRAIN] epoch: 54, iter: 5110/13950, loss: 0.1348, DSC: 87.3528, lr: 0.006633, batch_cost: 0.5035, reader_cost: 0.00025, ips: 47.6662 samples/sec | ETA 01:14:10
2022-09-07 12:36:15 [INFO]	[TRAIN] epoch: 55, iter: 5120/13950, loss: 0.1364, DSC: 87.2225, lr: 0.006627, batch_cost: 0.5835, reader_cost: 0.09667, ips: 41.1288 samples/sec | ETA 01:25:52
2022-09-07 12:36:20 [INFO]	[TRAIN] epoch: 55, iter: 5130/13950, loss: 0.1190, DSC: 88.9011, lr: 0.006620, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6572 samples/sec | ETA 01:14:01
2022-09-07 12:36:25 [INFO]	[TRAIN] epoch: 55, iter: 5140/13950, loss: 0.1356, DSC: 87.4000, lr: 0.006613, batch_cost: 0.5052, reader_cost: 0.00034, ips: 47.5015 samples/sec | ETA 01:14:11
2022-09-07 12:36:30 [INFO]	[TRAIN] epoch: 55, iter: 5150/13950, loss: 0.1094, DSC: 89.9522, lr: 0.006606, batch_cost: 0.5041, reader_cost: 0.00028, ips: 47.6082 samples/sec | ETA 01:13:56
2022-09-07 12:36:35 [INFO]	[TRAIN] epoch: 55, iter: 5160/13950, loss: 0.1246, DSC: 88.2818, lr: 0.006600, batch_cost: 0.5056, reader_cost: 0.00035, ips: 47.4721 samples/sec | ETA 01:14:03
2022-09-07 12:36:40 [INFO]	[TRAIN] epoch: 55, iter: 5170/13950, loss: 0.1364, DSC: 87.2114, lr: 0.006593, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4849 samples/sec | ETA 01:13:57
2022-09-07 12:36:45 [INFO]	[TRAIN] epoch: 55, iter: 5180/13950, loss: 0.1153, DSC: 89.3343, lr: 0.006586, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5187 samples/sec | ETA 01:13:49
2022-09-07 12:36:50 [INFO]	[TRAIN] epoch: 55, iter: 5190/13950, loss: 0.1100, DSC: 89.7588, lr: 0.006579, batch_cost: 0.5050, reader_cost: 0.00026, ips: 47.5222 samples/sec | ETA 01:13:44
2022-09-07 12:36:55 [INFO]	[TRAIN] epoch: 55, iter: 5200/13950, loss: 0.1144, DSC: 89.4644, lr: 0.006573, batch_cost: 0.5048, reader_cost: 0.00034, ips: 47.5396 samples/sec | ETA 01:13:37
2022-09-07 12:37:01 [INFO]	[TRAIN] epoch: 56, iter: 5210/13950, loss: 0.1526, DSC: 85.6447, lr: 0.006566, batch_cost: 0.6026, reader_cost: 0.11727, ips: 39.8265 samples/sec | ETA 01:27:46
2022-09-07 12:37:06 [INFO]	[TRAIN] epoch: 56, iter: 5220/13950, loss: 0.1172, DSC: 89.1067, lr: 0.006559, batch_cost: 0.5114, reader_cost: 0.00027, ips: 46.9325 samples/sec | ETA 01:14:24
2022-09-07 12:37:11 [INFO]	[TRAIN] epoch: 56, iter: 5230/13950, loss: 0.1186, DSC: 89.0139, lr: 0.006552, batch_cost: 0.5044, reader_cost: 0.00035, ips: 47.5788 samples/sec | ETA 01:13:18
2022-09-07 12:37:17 [INFO]	[TRAIN] epoch: 56, iter: 5240/13950, loss: 0.1183, DSC: 88.9207, lr: 0.006546, batch_cost: 0.5022, reader_cost: 0.00026, ips: 47.7894 samples/sec | ETA 01:12:54
2022-09-07 12:37:22 [INFO]	[TRAIN] epoch: 56, iter: 5250/13950, loss: 0.1231, DSC: 88.5389, lr: 0.006539, batch_cost: 0.5043, reader_cost: 0.00041, ips: 47.5873 samples/sec | ETA 01:13:07
2022-09-07 12:37:27 [INFO]	[TRAIN] epoch: 56, iter: 5260/13950, loss: 0.1155, DSC: 89.3077, lr: 0.006532, batch_cost: 0.5028, reader_cost: 0.00025, ips: 47.7348 samples/sec | ETA 01:12:49
2022-09-07 12:37:32 [INFO]	[TRAIN] epoch: 56, iter: 5270/13950, loss: 0.1135, DSC: 89.4213, lr: 0.006525, batch_cost: 0.5032, reader_cost: 0.00027, ips: 47.6968 samples/sec | ETA 01:12:47
2022-09-07 12:37:37 [INFO]	[TRAIN] epoch: 56, iter: 5280/13950, loss: 0.1111, DSC: 89.7731, lr: 0.006518, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6572 samples/sec | ETA 01:12:46
2022-09-07 12:37:42 [INFO]	[TRAIN] epoch: 56, iter: 5290/13950, loss: 0.1323, DSC: 87.6354, lr: 0.006512, batch_cost: 0.5050, reader_cost: 0.00033, ips: 47.5264 samples/sec | ETA 01:12:53
2022-09-07 12:37:47 [INFO]	[TRAIN] epoch: 56, iter: 5300/13950, loss: 0.1294, DSC: 87.8431, lr: 0.006505, batch_cost: 0.5032, reader_cost: 0.00022, ips: 47.6901 samples/sec | ETA 01:12:33
2022-09-07 12:37:53 [INFO]	[TRAIN] epoch: 57, iter: 5310/13950, loss: 0.1902, DSC: 81.8065, lr: 0.006498, batch_cost: 0.6062, reader_cost: 0.11881, ips: 39.5918 samples/sec | ETA 01:27:17
2022-09-07 12:37:58 [INFO]	[TRAIN] epoch: 57, iter: 5320/13950, loss: 0.1322, DSC: 87.7281, lr: 0.006491, batch_cost: 0.5064, reader_cost: 0.00027, ips: 47.3901 samples/sec | ETA 01:12:50
2022-09-07 12:38:03 [INFO]	[TRAIN] epoch: 57, iter: 5330/13950, loss: 0.1326, DSC: 87.7314, lr: 0.006485, batch_cost: 0.5059, reader_cost: 0.00033, ips: 47.4440 samples/sec | ETA 01:12:40
2022-09-07 12:38:08 [INFO]	[TRAIN] epoch: 57, iter: 5340/13950, loss: 0.1281, DSC: 88.0884, lr: 0.006478, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6378 samples/sec | ETA 01:12:17
2022-09-07 12:38:13 [INFO]	[TRAIN] epoch: 57, iter: 5350/13950, loss: 0.1234, DSC: 88.5145, lr: 0.006471, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5497 samples/sec | ETA 01:12:20
2022-09-07 12:38:18 [INFO]	[TRAIN] epoch: 57, iter: 5360/13950, loss: 0.1397, DSC: 86.9090, lr: 0.006464, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5207 samples/sec | ETA 01:12:18
2022-09-07 12:38:23 [INFO]	[TRAIN] epoch: 57, iter: 5370/13950, loss: 0.1188, DSC: 89.0113, lr: 0.006458, batch_cost: 0.5039, reader_cost: 0.00027, ips: 47.6265 samples/sec | ETA 01:12:03
2022-09-07 12:38:28 [INFO]	[TRAIN] epoch: 57, iter: 5380/13950, loss: 0.1260, DSC: 88.2354, lr: 0.006451, batch_cost: 0.5040, reader_cost: 0.00033, ips: 47.6200 samples/sec | ETA 01:11:59
2022-09-07 12:38:33 [INFO]	[TRAIN] epoch: 57, iter: 5390/13950, loss: 0.1289, DSC: 87.8548, lr: 0.006444, batch_cost: 0.5037, reader_cost: 0.00025, ips: 47.6507 samples/sec | ETA 01:11:51
2022-09-07 12:38:39 [INFO]	[TRAIN] epoch: 58, iter: 5400/13950, loss: 0.1832, DSC: 82.4263, lr: 0.006437, batch_cost: 0.5991, reader_cost: 0.11432, ips: 40.0582 samples/sec | ETA 01:25:22
2022-09-07 12:38:44 [INFO]	[TRAIN] epoch: 58, iter: 5410/13950, loss: 0.1038, DSC: 90.5556, lr: 0.006430, batch_cost: 0.5019, reader_cost: 0.00026, ips: 47.8173 samples/sec | ETA 01:11:26
2022-09-07 12:38:49 [INFO]	[TRAIN] epoch: 58, iter: 5420/13950, loss: 0.1444, DSC: 86.3499, lr: 0.006424, batch_cost: 0.5009, reader_cost: 0.00027, ips: 47.9110 samples/sec | ETA 01:11:12
2022-09-07 12:38:54 [INFO]	[TRAIN] epoch: 58, iter: 5430/13950, loss: 0.1086, DSC: 89.9062, lr: 0.006417, batch_cost: 0.5047, reader_cost: 0.00029, ips: 47.5516 samples/sec | ETA 01:11:40
2022-09-07 12:38:59 [INFO]	[TRAIN] epoch: 58, iter: 5440/13950, loss: 0.1153, DSC: 89.3315, lr: 0.006410, batch_cost: 0.5039, reader_cost: 0.00026, ips: 47.6238 samples/sec | ETA 01:11:28
2022-09-07 12:39:04 [INFO]	[TRAIN] epoch: 58, iter: 5450/13950, loss: 0.1191, DSC: 89.0811, lr: 0.006403, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5662 samples/sec | ETA 01:11:28
2022-09-07 12:39:09 [INFO]	[TRAIN] epoch: 58, iter: 5460/13950, loss: 0.1397, DSC: 86.7790, lr: 0.006397, batch_cost: 0.5044, reader_cost: 0.00032, ips: 47.5782 samples/sec | ETA 01:11:22
2022-09-07 12:39:14 [INFO]	[TRAIN] epoch: 58, iter: 5470/13950, loss: 0.1363, DSC: 87.1961, lr: 0.006390, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5771 samples/sec | ETA 01:11:17
2022-09-07 12:39:19 [INFO]	[TRAIN] epoch: 58, iter: 5480/13950, loss: 0.1097, DSC: 89.8432, lr: 0.006383, batch_cost: 0.5032, reader_cost: 0.00025, ips: 47.6980 samples/sec | ETA 01:11:01
2022-09-07 12:39:25 [INFO]	[TRAIN] epoch: 59, iter: 5490/13950, loss: 0.1800, DSC: 82.7680, lr: 0.006376, batch_cost: 0.5897, reader_cost: 0.10422, ips: 40.6980 samples/sec | ETA 01:23:08
2022-09-07 12:39:30 [INFO]	[TRAIN] epoch: 59, iter: 5500/13950, loss: 0.1162, DSC: 89.2048, lr: 0.006369, batch_cost: 0.5047, reader_cost: 0.00026, ips: 47.5527 samples/sec | ETA 01:11:04
2022-09-07 12:39:35 [INFO]	[TRAIN] epoch: 59, iter: 5510/13950, loss: 0.1153, DSC: 89.2791, lr: 0.006363, batch_cost: 0.5032, reader_cost: 0.00027, ips: 47.6924 samples/sec | ETA 01:10:47
2022-09-07 12:39:40 [INFO]	[TRAIN] epoch: 59, iter: 5520/13950, loss: 0.1131, DSC: 89.5481, lr: 0.006356, batch_cost: 0.5042, reader_cost: 0.00026, ips: 47.6007 samples/sec | ETA 01:10:50
2022-09-07 12:39:46 [INFO]	[TRAIN] epoch: 59, iter: 5530/13950, loss: 0.1345, DSC: 87.3978, lr: 0.006349, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5582 samples/sec | ETA 01:10:49
2022-09-07 12:39:51 [INFO]	[TRAIN] epoch: 59, iter: 5540/13950, loss: 0.1335, DSC: 87.3959, lr: 0.006342, batch_cost: 0.5038, reader_cost: 0.00034, ips: 47.6425 samples/sec | ETA 01:10:36
2022-09-07 12:39:56 [INFO]	[TRAIN] epoch: 59, iter: 5550/13950, loss: 0.1308, DSC: 87.8041, lr: 0.006336, batch_cost: 0.5053, reader_cost: 0.00028, ips: 47.4977 samples/sec | ETA 01:10:44
2022-09-07 12:40:01 [INFO]	[TRAIN] epoch: 59, iter: 5560/13950, loss: 0.1145, DSC: 89.3374, lr: 0.006329, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6199 samples/sec | ETA 01:10:28
2022-09-07 12:40:06 [INFO]	[TRAIN] epoch: 59, iter: 5570/13950, loss: 0.1284, DSC: 88.0313, lr: 0.006322, batch_cost: 0.5058, reader_cost: 0.00027, ips: 47.4522 samples/sec | ETA 01:10:38
2022-09-07 12:40:10 [INFO]	[TRAIN] epoch: 60, iter: 5580/13950, loss: 0.1548, DSC: 85.3165, lr: 0.006315, batch_cost: 0.4734, reader_cost: 0.00021, ips: 50.6934 samples/sec | ETA 01:06:02
2022-09-07 12:40:17 [INFO]	[TRAIN] epoch: 60, iter: 5590/13950, loss: 0.1296, DSC: 87.9370, lr: 0.006308, batch_cost: 0.6455, reader_cost: 0.12930, ips: 37.1814 samples/sec | ETA 01:29:56
2022-09-07 12:40:22 [INFO]	[TRAIN] epoch: 60, iter: 5600/13950, loss: 0.1212, DSC: 88.6232, lr: 0.006302, batch_cost: 0.5013, reader_cost: 0.00026, ips: 47.8753 samples/sec | ETA 01:09:45
2022-09-07 12:40:27 [INFO]	[TRAIN] epoch: 60, iter: 5610/13950, loss: 0.1413, DSC: 86.9201, lr: 0.006295, batch_cost: 0.5022, reader_cost: 0.00032, ips: 47.7852 samples/sec | ETA 01:09:48
2022-09-07 12:40:32 [INFO]	[TRAIN] epoch: 60, iter: 5620/13950, loss: 0.1368, DSC: 87.2366, lr: 0.006288, batch_cost: 0.5037, reader_cost: 0.00033, ips: 47.6481 samples/sec | ETA 01:09:55
2022-09-07 12:40:37 [INFO]	[TRAIN] epoch: 60, iter: 5630/13950, loss: 0.0989, DSC: 90.9064, lr: 0.006281, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6207 samples/sec | ETA 01:09:53
2022-09-07 12:40:42 [INFO]	[TRAIN] epoch: 60, iter: 5640/13950, loss: 0.1164, DSC: 89.2447, lr: 0.006274, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6220 samples/sec | ETA 01:09:47
2022-09-07 12:40:47 [INFO]	[TRAIN] epoch: 60, iter: 5650/13950, loss: 0.1367, DSC: 87.0570, lr: 0.006268, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5508 samples/sec | ETA 01:09:49
2022-09-07 12:40:52 [INFO]	[TRAIN] epoch: 60, iter: 5660/13950, loss: 0.1017, DSC: 90.6229, lr: 0.006261, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6341 samples/sec | ETA 01:09:36
2022-09-07 12:40:57 [INFO]	[TRAIN] epoch: 60, iter: 5670/13950, loss: 0.1280, DSC: 87.9166, lr: 0.006254, batch_cost: 0.5046, reader_cost: 0.00026, ips: 47.5599 samples/sec | ETA 01:09:38
2022-09-07 12:41:03 [INFO]	[TRAIN] epoch: 61, iter: 5680/13950, loss: 0.1127, DSC: 89.6883, lr: 0.006247, batch_cost: 0.6085, reader_cost: 0.12641, ips: 39.4398 samples/sec | ETA 01:23:52
2022-09-07 12:41:08 [INFO]	[TRAIN] epoch: 61, iter: 5690/13950, loss: 0.1081, DSC: 89.9437, lr: 0.006240, batch_cost: 0.5057, reader_cost: 0.00027, ips: 47.4610 samples/sec | ETA 01:09:36
2022-09-07 12:41:13 [INFO]	[TRAIN] epoch: 61, iter: 5700/13950, loss: 0.1149, DSC: 89.4849, lr: 0.006234, batch_cost: 0.5049, reader_cost: 0.00027, ips: 47.5345 samples/sec | ETA 01:09:25
2022-09-07 12:41:18 [INFO]	[TRAIN] epoch: 61, iter: 5710/13950, loss: 0.1097, DSC: 89.8453, lr: 0.006227, batch_cost: 0.5035, reader_cost: 0.00027, ips: 47.6706 samples/sec | ETA 01:09:08
2022-09-07 12:41:23 [INFO]	[TRAIN] epoch: 61, iter: 5720/13950, loss: 0.1260, DSC: 88.2496, lr: 0.006220, batch_cost: 0.5042, reader_cost: 0.00027, ips: 47.5993 samples/sec | ETA 01:09:09
2022-09-07 12:41:28 [INFO]	[TRAIN] epoch: 61, iter: 5730/13950, loss: 0.1290, DSC: 87.8707, lr: 0.006213, batch_cost: 0.5045, reader_cost: 0.00027, ips: 47.5758 samples/sec | ETA 01:09:06
2022-09-07 12:41:34 [INFO]	[TRAIN] epoch: 61, iter: 5740/13950, loss: 0.1141, DSC: 89.3188, lr: 0.006206, batch_cost: 0.5034, reader_cost: 0.00025, ips: 47.6797 samples/sec | ETA 01:08:52
2022-09-07 12:41:39 [INFO]	[TRAIN] epoch: 61, iter: 5750/13950, loss: 0.1160, DSC: 89.1875, lr: 0.006200, batch_cost: 0.5034, reader_cost: 0.00026, ips: 47.6793 samples/sec | ETA 01:08:47
2022-09-07 12:41:44 [INFO]	[TRAIN] epoch: 61, iter: 5760/13950, loss: 0.1119, DSC: 89.5966, lr: 0.006193, batch_cost: 0.5045, reader_cost: 0.00027, ips: 47.5702 samples/sec | ETA 01:08:51
2022-09-07 12:41:50 [INFO]	[TRAIN] epoch: 62, iter: 5770/13950, loss: 0.1176, DSC: 89.0844, lr: 0.006186, batch_cost: 0.6228, reader_cost: 0.12740, ips: 38.5376 samples/sec | ETA 01:24:54
2022-09-07 12:41:55 [INFO]	[TRAIN] epoch: 62, iter: 5780/13950, loss: 0.1119, DSC: 89.7961, lr: 0.006179, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5769 samples/sec | ETA 01:08:41
2022-09-07 12:42:00 [INFO]	[TRAIN] epoch: 62, iter: 5790/13950, loss: 0.1286, DSC: 87.9979, lr: 0.006172, batch_cost: 0.5039, reader_cost: 0.00030, ips: 47.6265 samples/sec | ETA 01:08:31
2022-09-07 12:42:05 [INFO]	[TRAIN] epoch: 62, iter: 5800/13950, loss: 0.1155, DSC: 89.3383, lr: 0.006166, batch_cost: 0.5034, reader_cost: 0.00027, ips: 47.6719 samples/sec | ETA 01:08:23
2022-09-07 12:42:10 [INFO]	[TRAIN] epoch: 62, iter: 5810/13950, loss: 0.1203, DSC: 88.7517, lr: 0.006159, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5407 samples/sec | ETA 01:08:29
2022-09-07 12:42:15 [INFO]	[TRAIN] epoch: 62, iter: 5820/13950, loss: 0.1289, DSC: 88.0388, lr: 0.006152, batch_cost: 0.5037, reader_cost: 0.00034, ips: 47.6431 samples/sec | ETA 01:08:15
2022-09-07 12:42:20 [INFO]	[TRAIN] epoch: 62, iter: 5830/13950, loss: 0.1319, DSC: 87.6399, lr: 0.006145, batch_cost: 0.5042, reader_cost: 0.00027, ips: 47.6039 samples/sec | ETA 01:08:13
2022-09-07 12:42:25 [INFO]	[TRAIN] epoch: 62, iter: 5840/13950, loss: 0.1077, DSC: 89.8426, lr: 0.006138, batch_cost: 0.5048, reader_cost: 0.00027, ips: 47.5472 samples/sec | ETA 01:08:13
2022-09-07 12:42:30 [INFO]	[TRAIN] epoch: 62, iter: 5850/13950, loss: 0.1198, DSC: 88.7799, lr: 0.006132, batch_cost: 0.5037, reader_cost: 0.00028, ips: 47.6480 samples/sec | ETA 01:07:59
2022-09-07 12:42:36 [INFO]	[TRAIN] epoch: 63, iter: 5860/13950, loss: 0.1241, DSC: 88.3794, lr: 0.006125, batch_cost: 0.5777, reader_cost: 0.10188, ips: 41.5415 samples/sec | ETA 01:17:53
2022-09-07 12:42:41 [INFO]	[TRAIN] epoch: 63, iter: 5870/13950, loss: 0.1070, DSC: 90.0139, lr: 0.006118, batch_cost: 0.5091, reader_cost: 0.00027, ips: 47.1377 samples/sec | ETA 01:08:33
2022-09-07 12:42:46 [INFO]	[TRAIN] epoch: 63, iter: 5880/13950, loss: 0.1100, DSC: 89.7223, lr: 0.006111, batch_cost: 0.5010, reader_cost: 0.00032, ips: 47.8996 samples/sec | ETA 01:07:23
2022-09-07 12:42:51 [INFO]	[TRAIN] epoch: 63, iter: 5890/13950, loss: 0.1169, DSC: 89.1254, lr: 0.006104, batch_cost: 0.5022, reader_cost: 0.00026, ips: 47.7859 samples/sec | ETA 01:07:28
2022-09-07 12:42:56 [INFO]	[TRAIN] epoch: 63, iter: 5900/13950, loss: 0.1191, DSC: 88.9422, lr: 0.006097, batch_cost: 0.5043, reader_cost: 0.00034, ips: 47.5871 samples/sec | ETA 01:07:39
2022-09-07 12:43:01 [INFO]	[TRAIN] epoch: 63, iter: 5910/13950, loss: 0.1087, DSC: 90.1262, lr: 0.006091, batch_cost: 0.5030, reader_cost: 0.00027, ips: 47.7166 samples/sec | ETA 01:07:23
2022-09-07 12:43:06 [INFO]	[TRAIN] epoch: 63, iter: 5920/13950, loss: 0.1059, DSC: 90.1794, lr: 0.006084, batch_cost: 0.5023, reader_cost: 0.00032, ips: 47.7807 samples/sec | ETA 01:07:13
2022-09-07 12:43:11 [INFO]	[TRAIN] epoch: 63, iter: 5930/13950, loss: 0.1289, DSC: 87.8523, lr: 0.006077, batch_cost: 0.5041, reader_cost: 0.00032, ips: 47.6138 samples/sec | ETA 01:07:22
2022-09-07 12:43:16 [INFO]	[TRAIN] epoch: 63, iter: 5940/13950, loss: 0.1257, DSC: 88.2526, lr: 0.006070, batch_cost: 0.5059, reader_cost: 0.00028, ips: 47.4362 samples/sec | ETA 01:07:32
2022-09-07 12:43:21 [INFO]	[TRAIN] epoch: 63, iter: 5950/13950, loss: 0.1313, DSC: 87.6119, lr: 0.006063, batch_cost: 0.5026, reader_cost: 0.00025, ips: 47.7478 samples/sec | ETA 01:07:01
2022-09-07 12:43:27 [INFO]	[TRAIN] epoch: 64, iter: 5960/13950, loss: 0.1779, DSC: 82.8361, lr: 0.006057, batch_cost: 0.5984, reader_cost: 0.11007, ips: 40.1041 samples/sec | ETA 01:19:41
2022-09-07 12:43:32 [INFO]	[TRAIN] epoch: 64, iter: 5970/13950, loss: 0.1059, DSC: 90.1754, lr: 0.006050, batch_cost: 0.5017, reader_cost: 0.00026, ips: 47.8342 samples/sec | ETA 01:06:43
2022-09-07 12:43:37 [INFO]	[TRAIN] epoch: 64, iter: 5980/13950, loss: 0.1130, DSC: 89.5836, lr: 0.006043, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6509 samples/sec | ETA 01:06:54
2022-09-07 12:43:42 [INFO]	[TRAIN] epoch: 64, iter: 5990/13950, loss: 0.1179, DSC: 89.1358, lr: 0.006036, batch_cost: 0.5035, reader_cost: 0.00026, ips: 47.6637 samples/sec | ETA 01:06:48
2022-09-07 12:43:47 [INFO]	[TRAIN] epoch: 64, iter: 6000/13950, loss: 0.0978, DSC: 90.9657, lr: 0.006029, batch_cost: 0.5047, reader_cost: 0.00028, ips: 47.5529 samples/sec | ETA 01:06:52
2022-09-07 12:43:47 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0215 - reader cost: 1.1749
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2786 - reader cost: 0.5903
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0539 - reader cost: 0.4015 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.2255 - reader cost: 0.4747
 5/12 [===========>..................] - ETA: 8s - batch_cost: 1.1514 - reader cost: 0.3840
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.1006 - reader cost: 0.3295
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0287 - reader cost: 0.2850
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1315 - reader cost: 0.3356
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.1035 - reader cost: 0.3006
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0764 - reader cost: 0.2737
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0247 - reader cost: 0.2503
12/12 [==============================] - 12s 987ms/step - batch_cost: 0.9868 - reader cost: 0.2312
2022-09-07 12:43:59 [INFO]	[EVAL] #Images: 12, Dice: 0.8228, Loss: 0.196450
2022-09-07 12:43:59 [INFO]	[EVAL] Class dice: 
[0.9952 0.9029 0.5266 0.888  0.8458 0.9464 0.639  0.8849 0.7765]
2022-09-07 12:44:05 [INFO]	[EVAL] The model with the best validation mDice (0.8228) was saved at iter 6000.
2022-09-07 12:44:10 [INFO]	[TRAIN] epoch: 64, iter: 6010/13950, loss: 0.1156, DSC: 89.2012, lr: 0.006022, batch_cost: 0.5057, reader_cost: 0.00042, ips: 47.4550 samples/sec | ETA 01:06:55
2022-09-07 12:44:15 [INFO]	[TRAIN] epoch: 64, iter: 6020/13950, loss: 0.1276, DSC: 88.0324, lr: 0.006016, batch_cost: 0.5063, reader_cost: 0.00030, ips: 47.4028 samples/sec | ETA 01:06:54
2022-09-07 12:44:20 [INFO]	[TRAIN] epoch: 64, iter: 6030/13950, loss: 0.1236, DSC: 88.4134, lr: 0.006009, batch_cost: 0.5046, reader_cost: 0.00030, ips: 47.5632 samples/sec | ETA 01:06:36
2022-09-07 12:44:25 [INFO]	[TRAIN] epoch: 64, iter: 6040/13950, loss: 0.1169, DSC: 89.0706, lr: 0.006002, batch_cost: 0.5052, reader_cost: 0.00036, ips: 47.5015 samples/sec | ETA 01:06:36
2022-09-07 12:44:31 [INFO]	[TRAIN] epoch: 65, iter: 6050/13950, loss: 0.1075, DSC: 90.0084, lr: 0.005995, batch_cost: 0.5781, reader_cost: 0.09324, ips: 41.5124 samples/sec | ETA 01:16:07
2022-09-07 12:44:36 [INFO]	[TRAIN] epoch: 65, iter: 6060/13950, loss: 0.1133, DSC: 89.3818, lr: 0.005988, batch_cost: 0.5023, reader_cost: 0.00029, ips: 47.7802 samples/sec | ETA 01:06:03
2022-09-07 12:44:41 [INFO]	[TRAIN] epoch: 65, iter: 6070/13950, loss: 0.1082, DSC: 89.9094, lr: 0.005981, batch_cost: 0.5057, reader_cost: 0.00035, ips: 47.4556 samples/sec | ETA 01:06:25
2022-09-07 12:44:46 [INFO]	[TRAIN] epoch: 65, iter: 6080/13950, loss: 0.1272, DSC: 88.1694, lr: 0.005975, batch_cost: 0.5032, reader_cost: 0.00026, ips: 47.6946 samples/sec | ETA 01:06:00
2022-09-07 12:44:51 [INFO]	[TRAIN] epoch: 65, iter: 6090/13950, loss: 0.1024, DSC: 90.6441, lr: 0.005968, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5775 samples/sec | ETA 01:06:04
2022-09-07 12:44:57 [INFO]	[TRAIN] epoch: 65, iter: 6100/13950, loss: 0.1177, DSC: 89.0767, lr: 0.005961, batch_cost: 0.5084, reader_cost: 0.00036, ips: 47.2115 samples/sec | ETA 01:06:30
2022-09-07 12:45:02 [INFO]	[TRAIN] epoch: 65, iter: 6110/13950, loss: 0.1131, DSC: 89.4381, lr: 0.005954, batch_cost: 0.5035, reader_cost: 0.00028, ips: 47.6702 samples/sec | ETA 01:05:47
2022-09-07 12:45:07 [INFO]	[TRAIN] epoch: 65, iter: 6120/13950, loss: 0.1182, DSC: 88.8784, lr: 0.005947, batch_cost: 0.5026, reader_cost: 0.00026, ips: 47.7495 samples/sec | ETA 01:05:35
2022-09-07 12:45:12 [INFO]	[TRAIN] epoch: 65, iter: 6130/13950, loss: 0.1114, DSC: 89.6813, lr: 0.005940, batch_cost: 0.5038, reader_cost: 0.00034, ips: 47.6420 samples/sec | ETA 01:05:39
2022-09-07 12:45:18 [INFO]	[TRAIN] epoch: 66, iter: 6140/13950, loss: 0.1526, DSC: 85.4504, lr: 0.005934, batch_cost: 0.6134, reader_cost: 0.12094, ips: 39.1267 samples/sec | ETA 01:19:50
2022-09-07 12:45:23 [INFO]	[TRAIN] epoch: 66, iter: 6150/13950, loss: 0.1140, DSC: 89.4350, lr: 0.005927, batch_cost: 0.5152, reader_cost: 0.00027, ips: 46.5874 samples/sec | ETA 01:06:58
2022-09-07 12:45:28 [INFO]	[TRAIN] epoch: 66, iter: 6160/13950, loss: 0.1108, DSC: 89.6925, lr: 0.005920, batch_cost: 0.5027, reader_cost: 0.00026, ips: 47.7450 samples/sec | ETA 01:05:15
2022-09-07 12:45:33 [INFO]	[TRAIN] epoch: 66, iter: 6170/13950, loss: 0.1138, DSC: 89.4153, lr: 0.005913, batch_cost: 0.5034, reader_cost: 0.00027, ips: 47.6747 samples/sec | ETA 01:05:16
2022-09-07 12:45:38 [INFO]	[TRAIN] epoch: 66, iter: 6180/13950, loss: 0.1068, DSC: 90.0250, lr: 0.005906, batch_cost: 0.5021, reader_cost: 0.00033, ips: 47.8037 samples/sec | ETA 01:05:00
2022-09-07 12:45:43 [INFO]	[TRAIN] epoch: 66, iter: 6190/13950, loss: 0.0965, DSC: 90.9401, lr: 0.005899, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6605 samples/sec | ETA 01:05:07
2022-09-07 12:45:48 [INFO]	[TRAIN] epoch: 66, iter: 6200/13950, loss: 0.1116, DSC: 89.5798, lr: 0.005893, batch_cost: 0.5025, reader_cost: 0.00027, ips: 47.7607 samples/sec | ETA 01:04:54
2022-09-07 12:45:53 [INFO]	[TRAIN] epoch: 66, iter: 6210/13950, loss: 0.1132, DSC: 89.5574, lr: 0.005886, batch_cost: 0.5011, reader_cost: 0.00026, ips: 47.8981 samples/sec | ETA 01:04:38
2022-09-07 12:45:58 [INFO]	[TRAIN] epoch: 66, iter: 6220/13950, loss: 0.1149, DSC: 89.4291, lr: 0.005879, batch_cost: 0.5046, reader_cost: 0.00028, ips: 47.5669 samples/sec | ETA 01:05:00
2022-09-07 12:46:03 [INFO]	[TRAIN] epoch: 66, iter: 6230/13950, loss: 0.0994, DSC: 90.8137, lr: 0.005872, batch_cost: 0.5006, reader_cost: 0.00022, ips: 47.9422 samples/sec | ETA 01:04:24
2022-09-07 12:46:09 [INFO]	[TRAIN] epoch: 67, iter: 6240/13950, loss: 0.1178, DSC: 89.1006, lr: 0.005865, batch_cost: 0.5949, reader_cost: 0.11094, ips: 40.3408 samples/sec | ETA 01:16:26
2022-09-07 12:46:14 [INFO]	[TRAIN] epoch: 67, iter: 6250/13950, loss: 0.1230, DSC: 88.4725, lr: 0.005858, batch_cost: 0.5024, reader_cost: 0.00035, ips: 47.7736 samples/sec | ETA 01:04:28
2022-09-07 12:46:19 [INFO]	[TRAIN] epoch: 67, iter: 6260/13950, loss: 0.1180, DSC: 89.0806, lr: 0.005852, batch_cost: 0.5017, reader_cost: 0.00031, ips: 47.8385 samples/sec | ETA 01:04:17
2022-09-07 12:46:24 [INFO]	[TRAIN] epoch: 67, iter: 6270/13950, loss: 0.1154, DSC: 89.2669, lr: 0.005845, batch_cost: 0.5064, reader_cost: 0.00026, ips: 47.3944 samples/sec | ETA 01:04:49
2022-09-07 12:46:29 [INFO]	[TRAIN] epoch: 67, iter: 6280/13950, loss: 0.1139, DSC: 89.3720, lr: 0.005838, batch_cost: 0.5023, reader_cost: 0.00026, ips: 47.7766 samples/sec | ETA 01:04:12
2022-09-07 12:46:34 [INFO]	[TRAIN] epoch: 67, iter: 6290/13950, loss: 0.1061, DSC: 90.1717, lr: 0.005831, batch_cost: 0.5024, reader_cost: 0.00025, ips: 47.7678 samples/sec | ETA 01:04:08
2022-09-07 12:46:39 [INFO]	[TRAIN] epoch: 67, iter: 6300/13950, loss: 0.1270, DSC: 88.0936, lr: 0.005824, batch_cost: 0.5035, reader_cost: 0.00026, ips: 47.6671 samples/sec | ETA 01:04:11
2022-09-07 12:46:44 [INFO]	[TRAIN] epoch: 67, iter: 6310/13950, loss: 0.1334, DSC: 87.4223, lr: 0.005817, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5822 samples/sec | ETA 01:04:13
2022-09-07 12:46:49 [INFO]	[TRAIN] epoch: 67, iter: 6320/13950, loss: 0.1151, DSC: 89.3535, lr: 0.005810, batch_cost: 0.5039, reader_cost: 0.00024, ips: 47.6309 samples/sec | ETA 01:04:04
2022-09-07 12:46:55 [INFO]	[TRAIN] epoch: 68, iter: 6330/13950, loss: 0.1284, DSC: 88.0112, lr: 0.005804, batch_cost: 0.6022, reader_cost: 0.11623, ips: 39.8537 samples/sec | ETA 01:16:28
2022-09-07 12:47:00 [INFO]	[TRAIN] epoch: 68, iter: 6340/13950, loss: 0.1079, DSC: 90.0302, lr: 0.005797, batch_cost: 0.5072, reader_cost: 0.00031, ips: 47.3179 samples/sec | ETA 01:04:19
2022-09-07 12:47:05 [INFO]	[TRAIN] epoch: 68, iter: 6350/13950, loss: 0.1263, DSC: 88.1297, lr: 0.005790, batch_cost: 0.5064, reader_cost: 0.00029, ips: 47.3893 samples/sec | ETA 01:04:08
2022-09-07 12:47:11 [INFO]	[TRAIN] epoch: 68, iter: 6360/13950, loss: 0.1354, DSC: 87.2801, lr: 0.005783, batch_cost: 0.5054, reader_cost: 0.00029, ips: 47.4907 samples/sec | ETA 01:03:55
2022-09-07 12:47:16 [INFO]	[TRAIN] epoch: 68, iter: 6370/13950, loss: 0.1109, DSC: 89.6948, lr: 0.005776, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5852 samples/sec | ETA 01:03:43
2022-09-07 12:47:21 [INFO]	[TRAIN] epoch: 68, iter: 6380/13950, loss: 0.1087, DSC: 89.9933, lr: 0.005769, batch_cost: 0.5034, reader_cost: 0.00027, ips: 47.6735 samples/sec | ETA 01:03:30
2022-09-07 12:47:26 [INFO]	[TRAIN] epoch: 68, iter: 6390/13950, loss: 0.1278, DSC: 87.9051, lr: 0.005762, batch_cost: 0.5059, reader_cost: 0.00028, ips: 47.4424 samples/sec | ETA 01:03:44
2022-09-07 12:47:31 [INFO]	[TRAIN] epoch: 68, iter: 6400/13950, loss: 0.1175, DSC: 88.9448, lr: 0.005756, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6539 samples/sec | ETA 01:03:22
2022-09-07 12:47:36 [INFO]	[TRAIN] epoch: 68, iter: 6410/13950, loss: 0.1319, DSC: 87.6974, lr: 0.005749, batch_cost: 0.5024, reader_cost: 0.00027, ips: 47.7689 samples/sec | ETA 01:03:08
2022-09-07 12:47:41 [INFO]	[TRAIN] epoch: 69, iter: 6420/13950, loss: 0.1297, DSC: 87.8415, lr: 0.005742, batch_cost: 0.5743, reader_cost: 0.09475, ips: 41.7918 samples/sec | ETA 01:12:04
2022-09-07 12:47:47 [INFO]	[TRAIN] epoch: 69, iter: 6430/13950, loss: 0.1120, DSC: 89.5966, lr: 0.005735, batch_cost: 0.5060, reader_cost: 0.00033, ips: 47.4352 samples/sec | ETA 01:03:24
2022-09-07 12:47:52 [INFO]	[TRAIN] epoch: 69, iter: 6440/13950, loss: 0.1260, DSC: 88.2198, lr: 0.005728, batch_cost: 0.5022, reader_cost: 0.00026, ips: 47.7920 samples/sec | ETA 01:02:51
2022-09-07 12:47:57 [INFO]	[TRAIN] epoch: 69, iter: 6450/13950, loss: 0.1125, DSC: 89.4700, lr: 0.005721, batch_cost: 0.5038, reader_cost: 0.00034, ips: 47.6368 samples/sec | ETA 01:02:58
2022-09-07 12:48:02 [INFO]	[TRAIN] epoch: 69, iter: 6460/13950, loss: 0.1195, DSC: 88.8845, lr: 0.005714, batch_cost: 0.5041, reader_cost: 0.00033, ips: 47.6068 samples/sec | ETA 01:02:55
2022-09-07 12:48:07 [INFO]	[TRAIN] epoch: 69, iter: 6470/13950, loss: 0.1043, DSC: 90.4826, lr: 0.005708, batch_cost: 0.5060, reader_cost: 0.00027, ips: 47.4305 samples/sec | ETA 01:03:04
2022-09-07 12:48:12 [INFO]	[TRAIN] epoch: 69, iter: 6480/13950, loss: 0.1141, DSC: 89.4259, lr: 0.005701, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6215 samples/sec | ETA 01:02:44
2022-09-07 12:48:17 [INFO]	[TRAIN] epoch: 69, iter: 6490/13950, loss: 0.1426, DSC: 86.6148, lr: 0.005694, batch_cost: 0.5034, reader_cost: 0.00026, ips: 47.6799 samples/sec | ETA 01:02:35
2022-09-07 12:48:22 [INFO]	[TRAIN] epoch: 69, iter: 6500/13950, loss: 0.1084, DSC: 89.7668, lr: 0.005687, batch_cost: 0.5052, reader_cost: 0.00027, ips: 47.5057 samples/sec | ETA 01:02:43
2022-09-07 12:48:27 [INFO]	[TRAIN] epoch: 70, iter: 6510/13950, loss: 0.1376, DSC: 87.0429, lr: 0.005680, batch_cost: 0.4749, reader_cost: 0.00023, ips: 50.5376 samples/sec | ETA 00:58:53
2022-09-07 12:48:33 [INFO]	[TRAIN] epoch: 70, iter: 6520/13950, loss: 0.1111, DSC: 89.6812, lr: 0.005673, batch_cost: 0.6223, reader_cost: 0.10736, ips: 38.5687 samples/sec | ETA 01:17:03
2022-09-07 12:48:38 [INFO]	[TRAIN] epoch: 70, iter: 6530/13950, loss: 0.1039, DSC: 90.2907, lr: 0.005666, batch_cost: 0.5024, reader_cost: 0.00026, ips: 47.7682 samples/sec | ETA 01:02:08
2022-09-07 12:48:43 [INFO]	[TRAIN] epoch: 70, iter: 6540/13950, loss: 0.1185, DSC: 88.8070, lr: 0.005659, batch_cost: 0.5045, reader_cost: 0.00027, ips: 47.5756 samples/sec | ETA 01:02:18
2022-09-07 12:48:48 [INFO]	[TRAIN] epoch: 70, iter: 6550/13950, loss: 0.1121, DSC: 89.5797, lr: 0.005653, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5669 samples/sec | ETA 01:02:13
2022-09-07 12:48:53 [INFO]	[TRAIN] epoch: 70, iter: 6560/13950, loss: 0.1030, DSC: 90.5999, lr: 0.005646, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6117 samples/sec | ETA 01:02:05
2022-09-07 12:48:58 [INFO]	[TRAIN] epoch: 70, iter: 6570/13950, loss: 0.1229, DSC: 88.5259, lr: 0.005639, batch_cost: 0.5052, reader_cost: 0.00035, ips: 47.5106 samples/sec | ETA 01:02:08
2022-09-07 12:49:03 [INFO]	[TRAIN] epoch: 70, iter: 6580/13950, loss: 0.1116, DSC: 89.6592, lr: 0.005632, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6347 samples/sec | ETA 01:01:53
2022-09-07 12:49:08 [INFO]	[TRAIN] epoch: 70, iter: 6590/13950, loss: 0.1022, DSC: 90.5187, lr: 0.005625, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5156 samples/sec | ETA 01:01:57
2022-09-07 12:49:13 [INFO]	[TRAIN] epoch: 70, iter: 6600/13950, loss: 0.1128, DSC: 89.4664, lr: 0.005618, batch_cost: 0.5029, reader_cost: 0.00024, ips: 47.7226 samples/sec | ETA 01:01:36
2022-09-07 12:49:19 [INFO]	[TRAIN] epoch: 71, iter: 6610/13950, loss: 0.1492, DSC: 85.7792, lr: 0.005611, batch_cost: 0.6087, reader_cost: 0.13047, ips: 39.4308 samples/sec | ETA 01:14:27
2022-09-07 12:49:24 [INFO]	[TRAIN] epoch: 71, iter: 6620/13950, loss: 0.1335, DSC: 87.4239, lr: 0.005604, batch_cost: 0.5042, reader_cost: 0.00029, ips: 47.5961 samples/sec | ETA 01:01:36
2022-09-07 12:49:29 [INFO]	[TRAIN] epoch: 71, iter: 6630/13950, loss: 0.1039, DSC: 90.3849, lr: 0.005598, batch_cost: 0.5066, reader_cost: 0.00028, ips: 47.3726 samples/sec | ETA 01:01:48
2022-09-07 12:49:34 [INFO]	[TRAIN] epoch: 71, iter: 6640/13950, loss: 0.1060, DSC: 90.2133, lr: 0.005591, batch_cost: 0.5024, reader_cost: 0.00034, ips: 47.7701 samples/sec | ETA 01:01:12
2022-09-07 12:49:39 [INFO]	[TRAIN] epoch: 71, iter: 6650/13950, loss: 0.1108, DSC: 89.6812, lr: 0.005584, batch_cost: 0.5027, reader_cost: 0.00026, ips: 47.7400 samples/sec | ETA 01:01:09
2022-09-07 12:49:44 [INFO]	[TRAIN] epoch: 71, iter: 6660/13950, loss: 0.1113, DSC: 89.6441, lr: 0.005577, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6606 samples/sec | ETA 01:01:10
2022-09-07 12:49:49 [INFO]	[TRAIN] epoch: 71, iter: 6670/13950, loss: 0.1154, DSC: 89.2908, lr: 0.005570, batch_cost: 0.5031, reader_cost: 0.00026, ips: 47.7020 samples/sec | ETA 01:01:02
2022-09-07 12:49:54 [INFO]	[TRAIN] epoch: 71, iter: 6680/13950, loss: 0.1180, DSC: 88.9316, lr: 0.005563, batch_cost: 0.5046, reader_cost: 0.00035, ips: 47.5662 samples/sec | ETA 01:01:08
2022-09-07 12:50:00 [INFO]	[TRAIN] epoch: 71, iter: 6690/13950, loss: 0.1058, DSC: 90.1429, lr: 0.005556, batch_cost: 0.5032, reader_cost: 0.00032, ips: 47.6942 samples/sec | ETA 01:00:53
2022-09-07 12:50:05 [INFO]	[TRAIN] epoch: 72, iter: 6700/13950, loss: 0.2013, DSC: 80.6741, lr: 0.005549, batch_cost: 0.5974, reader_cost: 0.11160, ips: 40.1732 samples/sec | ETA 01:12:11
2022-09-07 12:50:11 [INFO]	[TRAIN] epoch: 72, iter: 6710/13950, loss: 0.1035, DSC: 90.3424, lr: 0.005542, batch_cost: 0.5073, reader_cost: 0.00029, ips: 47.3131 samples/sec | ETA 01:01:12
2022-09-07 12:50:16 [INFO]	[TRAIN] epoch: 72, iter: 6720/13950, loss: 0.1382, DSC: 87.0531, lr: 0.005536, batch_cost: 0.5070, reader_cost: 0.00027, ips: 47.3349 samples/sec | ETA 01:01:05
2022-09-07 12:50:21 [INFO]	[TRAIN] epoch: 72, iter: 6730/13950, loss: 0.1082, DSC: 89.8710, lr: 0.005529, batch_cost: 0.5045, reader_cost: 0.00025, ips: 47.5679 samples/sec | ETA 01:00:42
2022-09-07 12:50:26 [INFO]	[TRAIN] epoch: 72, iter: 6740/13950, loss: 0.1197, DSC: 88.8375, lr: 0.005522, batch_cost: 0.5055, reader_cost: 0.00026, ips: 47.4808 samples/sec | ETA 01:00:44
2022-09-07 12:50:31 [INFO]	[TRAIN] epoch: 72, iter: 6750/13950, loss: 0.1290, DSC: 87.9836, lr: 0.005515, batch_cost: 0.5056, reader_cost: 0.00027, ips: 47.4659 samples/sec | ETA 01:00:40
2022-09-07 12:50:36 [INFO]	[TRAIN] epoch: 72, iter: 6760/13950, loss: 0.0986, DSC: 90.8812, lr: 0.005508, batch_cost: 0.5057, reader_cost: 0.00027, ips: 47.4555 samples/sec | ETA 01:00:36
2022-09-07 12:50:41 [INFO]	[TRAIN] epoch: 72, iter: 6770/13950, loss: 0.1170, DSC: 89.0493, lr: 0.005501, batch_cost: 0.5042, reader_cost: 0.00026, ips: 47.5964 samples/sec | ETA 01:00:20
2022-09-07 12:50:46 [INFO]	[TRAIN] epoch: 72, iter: 6780/13950, loss: 0.1168, DSC: 89.1228, lr: 0.005494, batch_cost: 0.5042, reader_cost: 0.00026, ips: 47.6046 samples/sec | ETA 01:00:14
2022-09-07 12:50:52 [INFO]	[TRAIN] epoch: 73, iter: 6790/13950, loss: 0.1535, DSC: 85.5291, lr: 0.005487, batch_cost: 0.6096, reader_cost: 0.13153, ips: 39.3708 samples/sec | ETA 01:12:44
2022-09-07 12:50:57 [INFO]	[TRAIN] epoch: 73, iter: 6800/13950, loss: 0.1032, DSC: 90.3975, lr: 0.005480, batch_cost: 0.5067, reader_cost: 0.00030, ips: 47.3616 samples/sec | ETA 01:00:23
2022-09-07 12:51:02 [INFO]	[TRAIN] epoch: 73, iter: 6810/13950, loss: 0.0993, DSC: 90.7641, lr: 0.005474, batch_cost: 0.5030, reader_cost: 0.00027, ips: 47.7092 samples/sec | ETA 00:59:51
2022-09-07 12:51:07 [INFO]	[TRAIN] epoch: 73, iter: 6820/13950, loss: 0.1242, DSC: 88.5089, lr: 0.005467, batch_cost: 0.5028, reader_cost: 0.00026, ips: 47.7373 samples/sec | ETA 00:59:44
2022-09-07 12:51:12 [INFO]	[TRAIN] epoch: 73, iter: 6830/13950, loss: 0.1135, DSC: 89.2463, lr: 0.005460, batch_cost: 0.5116, reader_cost: 0.00028, ips: 46.9128 samples/sec | ETA 01:00:42
2022-09-07 12:51:17 [INFO]	[TRAIN] epoch: 73, iter: 6840/13950, loss: 0.1115, DSC: 89.8784, lr: 0.005453, batch_cost: 0.5066, reader_cost: 0.00028, ips: 47.3760 samples/sec | ETA 01:00:01
2022-09-07 12:51:22 [INFO]	[TRAIN] epoch: 73, iter: 6850/13950, loss: 0.1163, DSC: 89.0861, lr: 0.005446, batch_cost: 0.5061, reader_cost: 0.00028, ips: 47.4183 samples/sec | ETA 00:59:53
2022-09-07 12:51:27 [INFO]	[TRAIN] epoch: 73, iter: 6860/13950, loss: 0.1370, DSC: 87.0207, lr: 0.005439, batch_cost: 0.5062, reader_cost: 0.00036, ips: 47.4103 samples/sec | ETA 00:59:49
2022-09-07 12:51:33 [INFO]	[TRAIN] epoch: 73, iter: 6870/13950, loss: 0.1005, DSC: 90.6869, lr: 0.005432, batch_cost: 0.5043, reader_cost: 0.00026, ips: 47.5944 samples/sec | ETA 00:59:30
2022-09-07 12:51:38 [INFO]	[TRAIN] epoch: 73, iter: 6880/13950, loss: 0.1157, DSC: 89.2178, lr: 0.005425, batch_cost: 0.5030, reader_cost: 0.00023, ips: 47.7146 samples/sec | ETA 00:59:16
2022-09-07 12:51:44 [INFO]	[TRAIN] epoch: 74, iter: 6890/13950, loss: 0.1557, DSC: 85.1345, lr: 0.005418, batch_cost: 0.6019, reader_cost: 0.10093, ips: 39.8731 samples/sec | ETA 01:10:49
2022-09-07 12:51:49 [INFO]	[TRAIN] epoch: 74, iter: 6900/13950, loss: 0.1174, DSC: 89.0241, lr: 0.005411, batch_cost: 0.5032, reader_cost: 0.00026, ips: 47.6984 samples/sec | ETA 00:59:07
2022-09-07 12:51:54 [INFO]	[TRAIN] epoch: 74, iter: 6910/13950, loss: 0.1228, DSC: 88.4674, lr: 0.005404, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5637 samples/sec | ETA 00:59:12
2022-09-07 12:51:59 [INFO]	[TRAIN] epoch: 74, iter: 6920/13950, loss: 0.1230, DSC: 88.3681, lr: 0.005398, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5653 samples/sec | ETA 00:59:07
2022-09-07 12:52:04 [INFO]	[TRAIN] epoch: 74, iter: 6930/13950, loss: 0.1240, DSC: 88.4042, lr: 0.005391, batch_cost: 0.5063, reader_cost: 0.00028, ips: 47.4068 samples/sec | ETA 00:59:13
2022-09-07 12:52:09 [INFO]	[TRAIN] epoch: 74, iter: 6940/13950, loss: 0.1593, DSC: 84.8317, lr: 0.005384, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4832 samples/sec | ETA 00:59:03
2022-09-07 12:52:14 [INFO]	[TRAIN] epoch: 74, iter: 6950/13950, loss: 0.1065, DSC: 90.1258, lr: 0.005377, batch_cost: 0.5121, reader_cost: 0.00029, ips: 46.8685 samples/sec | ETA 00:59:44
2022-09-07 12:52:19 [INFO]	[TRAIN] epoch: 74, iter: 6960/13950, loss: 0.1122, DSC: 89.5447, lr: 0.005370, batch_cost: 0.5097, reader_cost: 0.00042, ips: 47.0823 samples/sec | ETA 00:59:23
2022-09-07 12:52:24 [INFO]	[TRAIN] epoch: 74, iter: 6970/13950, loss: 0.1038, DSC: 90.5072, lr: 0.005363, batch_cost: 0.5054, reader_cost: 0.00024, ips: 47.4839 samples/sec | ETA 00:58:47
2022-09-07 12:52:30 [INFO]	[TRAIN] epoch: 75, iter: 6980/13950, loss: 0.1650, DSC: 84.2011, lr: 0.005356, batch_cost: 0.5961, reader_cost: 0.10652, ips: 40.2603 samples/sec | ETA 01:09:14
2022-09-07 12:52:35 [INFO]	[TRAIN] epoch: 75, iter: 6990/13950, loss: 0.1168, DSC: 89.1743, lr: 0.005349, batch_cost: 0.5058, reader_cost: 0.00026, ips: 47.4509 samples/sec | ETA 00:58:40
2022-09-07 12:52:40 [INFO]	[TRAIN] epoch: 75, iter: 7000/13950, loss: 0.1146, DSC: 89.2467, lr: 0.005342, batch_cost: 0.5035, reader_cost: 0.00026, ips: 47.6706 samples/sec | ETA 00:58:19
2022-09-07 12:52:40 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 21s - batch_cost: 1.9985 - reader cost: 1.1505
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2674 - reader cost: 0.5778
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0468 - reader cost: 0.3926 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.1948 - reader cost: 0.4434
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1269 - reader cost: 0.3588
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0778 - reader cost: 0.3059
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0094 - reader cost: 0.2650
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1104 - reader cost: 0.3138
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0846 - reader cost: 0.2812
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0598 - reader cost: 0.2561
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0096 - reader cost: 0.2343
12/12 [==============================] - 12s 973ms/step - batch_cost: 0.9732 - reader cost: 0.2167
2022-09-07 12:52:52 [INFO]	[EVAL] #Images: 12, Dice: 0.8217, Loss: 0.197279
2022-09-07 12:52:52 [INFO]	[EVAL] Class dice: 
[0.9956 0.9003 0.5369 0.8869 0.8455 0.9483 0.6182 0.8856 0.7777]
2022-09-07 12:52:54 [INFO]	[EVAL] The model with the best validation mDice (0.8228) was saved at iter 6000.
2022-09-07 12:52:59 [INFO]	[TRAIN] epoch: 75, iter: 7010/13950, loss: 0.1027, DSC: 90.4148, lr: 0.005335, batch_cost: 0.5049, reader_cost: 0.00044, ips: 47.5341 samples/sec | ETA 00:58:24
2022-09-07 12:53:04 [INFO]	[TRAIN] epoch: 75, iter: 7020/13950, loss: 0.1323, DSC: 87.4645, lr: 0.005328, batch_cost: 0.5045, reader_cost: 0.00029, ips: 47.5672 samples/sec | ETA 00:58:16
2022-09-07 12:53:09 [INFO]	[TRAIN] epoch: 75, iter: 7030/13950, loss: 0.1151, DSC: 89.2632, lr: 0.005322, batch_cost: 0.5039, reader_cost: 0.00035, ips: 47.6306 samples/sec | ETA 00:58:06
2022-09-07 12:53:14 [INFO]	[TRAIN] epoch: 75, iter: 7040/13950, loss: 0.1219, DSC: 88.6040, lr: 0.005315, batch_cost: 0.5079, reader_cost: 0.00030, ips: 47.2511 samples/sec | ETA 00:58:29
2022-09-07 12:53:19 [INFO]	[TRAIN] epoch: 75, iter: 7050/13950, loss: 0.1051, DSC: 90.2332, lr: 0.005308, batch_cost: 0.5187, reader_cost: 0.00030, ips: 46.2667 samples/sec | ETA 00:59:39
2022-09-07 12:53:24 [INFO]	[TRAIN] epoch: 75, iter: 7060/13950, loss: 0.1168, DSC: 89.0919, lr: 0.005301, batch_cost: 0.5088, reader_cost: 0.00046, ips: 47.1743 samples/sec | ETA 00:58:25
2022-09-07 12:53:30 [INFO]	[TRAIN] epoch: 76, iter: 7070/13950, loss: 0.1239, DSC: 88.3764, lr: 0.005294, batch_cost: 0.6001, reader_cost: 0.11956, ips: 39.9960 samples/sec | ETA 01:08:48
2022-09-07 12:53:36 [INFO]	[TRAIN] epoch: 76, iter: 7080/13950, loss: 0.1043, DSC: 90.3652, lr: 0.005287, batch_cost: 0.5095, reader_cost: 0.00028, ips: 47.1045 samples/sec | ETA 00:58:20
2022-09-07 12:53:41 [INFO]	[TRAIN] epoch: 76, iter: 7090/13950, loss: 0.1119, DSC: 89.6391, lr: 0.005280, batch_cost: 0.5062, reader_cost: 0.00027, ips: 47.4108 samples/sec | ETA 00:57:52
2022-09-07 12:53:46 [INFO]	[TRAIN] epoch: 76, iter: 7100/13950, loss: 0.1125, DSC: 89.4386, lr: 0.005273, batch_cost: 0.5043, reader_cost: 0.00026, ips: 47.5937 samples/sec | ETA 00:57:34
2022-09-07 12:53:51 [INFO]	[TRAIN] epoch: 76, iter: 7110/13950, loss: 0.1019, DSC: 90.6672, lr: 0.005266, batch_cost: 0.5089, reader_cost: 0.00029, ips: 47.1616 samples/sec | ETA 00:58:00
2022-09-07 12:53:56 [INFO]	[TRAIN] epoch: 76, iter: 7120/13950, loss: 0.1244, DSC: 88.3398, lr: 0.005259, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5698 samples/sec | ETA 00:57:25
2022-09-07 12:54:01 [INFO]	[TRAIN] epoch: 76, iter: 7130/13950, loss: 0.1153, DSC: 89.2250, lr: 0.005252, batch_cost: 0.5027, reader_cost: 0.00025, ips: 47.7466 samples/sec | ETA 00:57:08
2022-09-07 12:54:06 [INFO]	[TRAIN] epoch: 76, iter: 7140/13950, loss: 0.1163, DSC: 89.0380, lr: 0.005245, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5222 samples/sec | ETA 00:57:19
2022-09-07 12:54:11 [INFO]	[TRAIN] epoch: 76, iter: 7150/13950, loss: 0.1188, DSC: 88.8254, lr: 0.005238, batch_cost: 0.5052, reader_cost: 0.00028, ips: 47.5037 samples/sec | ETA 00:57:15
2022-09-07 12:54:16 [INFO]	[TRAIN] epoch: 76, iter: 7160/13950, loss: 0.1126, DSC: 89.4391, lr: 0.005231, batch_cost: 0.5029, reader_cost: 0.00030, ips: 47.7224 samples/sec | ETA 00:56:54
2022-09-07 12:54:22 [INFO]	[TRAIN] epoch: 77, iter: 7170/13950, loss: 0.1221, DSC: 88.6319, lr: 0.005225, batch_cost: 0.6485, reader_cost: 0.12619, ips: 37.0060 samples/sec | ETA 01:13:17
2022-09-07 12:54:28 [INFO]	[TRAIN] epoch: 77, iter: 7180/13950, loss: 0.1248, DSC: 88.3525, lr: 0.005218, batch_cost: 0.5060, reader_cost: 0.00028, ips: 47.4308 samples/sec | ETA 00:57:05
2022-09-07 12:54:33 [INFO]	[TRAIN] epoch: 77, iter: 7190/13950, loss: 0.1036, DSC: 90.4679, lr: 0.005211, batch_cost: 0.5070, reader_cost: 0.00028, ips: 47.3389 samples/sec | ETA 00:57:07
2022-09-07 12:54:38 [INFO]	[TRAIN] epoch: 77, iter: 7200/13950, loss: 0.1317, DSC: 87.6229, lr: 0.005204, batch_cost: 0.5058, reader_cost: 0.00026, ips: 47.4541 samples/sec | ETA 00:56:53
2022-09-07 12:54:43 [INFO]	[TRAIN] epoch: 77, iter: 7210/13950, loss: 0.1027, DSC: 90.3653, lr: 0.005197, batch_cost: 0.5065, reader_cost: 0.00026, ips: 47.3817 samples/sec | ETA 00:56:53
2022-09-07 12:54:48 [INFO]	[TRAIN] epoch: 77, iter: 7220/13950, loss: 0.0884, DSC: 91.7640, lr: 0.005190, batch_cost: 0.5057, reader_cost: 0.00035, ips: 47.4550 samples/sec | ETA 00:56:43
2022-09-07 12:54:53 [INFO]	[TRAIN] epoch: 77, iter: 7230/13950, loss: 0.1029, DSC: 90.4834, lr: 0.005183, batch_cost: 0.5053, reader_cost: 0.00034, ips: 47.4989 samples/sec | ETA 00:56:35
2022-09-07 12:54:58 [INFO]	[TRAIN] epoch: 77, iter: 7240/13950, loss: 0.1079, DSC: 89.9202, lr: 0.005176, batch_cost: 0.5052, reader_cost: 0.00027, ips: 47.5094 samples/sec | ETA 00:56:29
2022-09-07 12:55:03 [INFO]	[TRAIN] epoch: 77, iter: 7250/13950, loss: 0.1031, DSC: 90.4190, lr: 0.005169, batch_cost: 0.5029, reader_cost: 0.00023, ips: 47.7198 samples/sec | ETA 00:56:09
2022-09-07 12:55:09 [INFO]	[TRAIN] epoch: 78, iter: 7260/13950, loss: 0.1395, DSC: 86.8691, lr: 0.005162, batch_cost: 0.6007, reader_cost: 0.11005, ips: 39.9543 samples/sec | ETA 01:06:58
2022-09-07 12:55:14 [INFO]	[TRAIN] epoch: 78, iter: 7270/13950, loss: 0.1082, DSC: 89.9573, lr: 0.005155, batch_cost: 0.5064, reader_cost: 0.00028, ips: 47.3973 samples/sec | ETA 00:56:22
2022-09-07 12:55:19 [INFO]	[TRAIN] epoch: 78, iter: 7280/13950, loss: 0.1189, DSC: 88.8440, lr: 0.005148, batch_cost: 0.5044, reader_cost: 0.00032, ips: 47.5829 samples/sec | ETA 00:56:04
2022-09-07 12:55:24 [INFO]	[TRAIN] epoch: 78, iter: 7290/13950, loss: 0.0920, DSC: 91.5992, lr: 0.005141, batch_cost: 0.5070, reader_cost: 0.00028, ips: 47.3394 samples/sec | ETA 00:56:16
2022-09-07 12:55:29 [INFO]	[TRAIN] epoch: 78, iter: 7300/13950, loss: 0.1009, DSC: 90.5932, lr: 0.005134, batch_cost: 0.5032, reader_cost: 0.00026, ips: 47.6904 samples/sec | ETA 00:55:46
2022-09-07 12:55:34 [INFO]	[TRAIN] epoch: 78, iter: 7310/13950, loss: 0.1152, DSC: 89.1662, lr: 0.005127, batch_cost: 0.5034, reader_cost: 0.00026, ips: 47.6756 samples/sec | ETA 00:55:42
2022-09-07 12:55:39 [INFO]	[TRAIN] epoch: 78, iter: 7320/13950, loss: 0.1125, DSC: 89.4952, lr: 0.005120, batch_cost: 0.5032, reader_cost: 0.00026, ips: 47.6988 samples/sec | ETA 00:55:35
2022-09-07 12:55:44 [INFO]	[TRAIN] epoch: 78, iter: 7330/13950, loss: 0.1106, DSC: 89.7142, lr: 0.005113, batch_cost: 0.5077, reader_cost: 0.00028, ips: 47.2688 samples/sec | ETA 00:56:01
2022-09-07 12:55:49 [INFO]	[TRAIN] epoch: 78, iter: 7340/13950, loss: 0.1169, DSC: 89.0001, lr: 0.005107, batch_cost: 0.5049, reader_cost: 0.00027, ips: 47.5306 samples/sec | ETA 00:55:37
2022-09-07 12:55:55 [INFO]	[TRAIN] epoch: 79, iter: 7350/13950, loss: 0.1058, DSC: 90.3304, lr: 0.005100, batch_cost: 0.6122, reader_cost: 0.11741, ips: 39.2029 samples/sec | ETA 01:07:20
2022-09-07 12:56:01 [INFO]	[TRAIN] epoch: 79, iter: 7360/13950, loss: 0.1335, DSC: 87.3204, lr: 0.005093, batch_cost: 0.5152, reader_cost: 0.00026, ips: 46.5881 samples/sec | ETA 00:56:34
2022-09-07 12:56:06 [INFO]	[TRAIN] epoch: 79, iter: 7370/13950, loss: 0.1057, DSC: 90.1161, lr: 0.005086, batch_cost: 0.5045, reader_cost: 0.00025, ips: 47.5728 samples/sec | ETA 00:55:19
2022-09-07 12:56:11 [INFO]	[TRAIN] epoch: 79, iter: 7380/13950, loss: 0.0906, DSC: 91.5897, lr: 0.005079, batch_cost: 0.5046, reader_cost: 0.00031, ips: 47.5599 samples/sec | ETA 00:55:15
2022-09-07 12:56:16 [INFO]	[TRAIN] epoch: 79, iter: 7390/13950, loss: 0.1146, DSC: 89.2415, lr: 0.005072, batch_cost: 0.5046, reader_cost: 0.00032, ips: 47.5652 samples/sec | ETA 00:55:09
2022-09-07 12:56:21 [INFO]	[TRAIN] epoch: 79, iter: 7400/13950, loss: 0.1069, DSC: 90.2153, lr: 0.005065, batch_cost: 0.5039, reader_cost: 0.00026, ips: 47.6316 samples/sec | ETA 00:55:00
2022-09-07 12:56:26 [INFO]	[TRAIN] epoch: 79, iter: 7410/13950, loss: 0.0940, DSC: 91.3520, lr: 0.005058, batch_cost: 0.5062, reader_cost: 0.00027, ips: 47.4122 samples/sec | ETA 00:55:10
2022-09-07 12:56:31 [INFO]	[TRAIN] epoch: 79, iter: 7420/13950, loss: 0.1204, DSC: 88.6888, lr: 0.005051, batch_cost: 0.5023, reader_cost: 0.00025, ips: 47.7833 samples/sec | ETA 00:54:39
2022-09-07 12:56:36 [INFO]	[TRAIN] epoch: 79, iter: 7430/13950, loss: 0.1167, DSC: 89.0796, lr: 0.005044, batch_cost: 0.5040, reader_cost: 0.00027, ips: 47.6205 samples/sec | ETA 00:54:45
2022-09-07 12:56:41 [INFO]	[TRAIN] epoch: 80, iter: 7440/13950, loss: 0.1048, DSC: 90.2440, lr: 0.005037, batch_cost: 0.4731, reader_cost: 0.00022, ips: 50.7248 samples/sec | ETA 00:51:20
2022-09-07 12:56:47 [INFO]	[TRAIN] epoch: 80, iter: 7450/13950, loss: 0.1070, DSC: 90.0428, lr: 0.005030, batch_cost: 0.6296, reader_cost: 0.11250, ips: 38.1188 samples/sec | ETA 01:08:12
2022-09-07 12:56:52 [INFO]	[TRAIN] epoch: 80, iter: 7460/13950, loss: 0.1028, DSC: 90.4407, lr: 0.005023, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4905 samples/sec | ETA 00:54:39
2022-09-07 12:56:57 [INFO]	[TRAIN] epoch: 80, iter: 7470/13950, loss: 0.1177, DSC: 88.9626, lr: 0.005016, batch_cost: 0.5076, reader_cost: 0.00036, ips: 47.2801 samples/sec | ETA 00:54:49
2022-09-07 12:57:02 [INFO]	[TRAIN] epoch: 80, iter: 7480/13950, loss: 0.1177, DSC: 89.0419, lr: 0.005009, batch_cost: 0.5075, reader_cost: 0.00037, ips: 47.2898 samples/sec | ETA 00:54:43
2022-09-07 12:57:07 [INFO]	[TRAIN] epoch: 80, iter: 7490/13950, loss: 0.1173, DSC: 89.0439, lr: 0.005002, batch_cost: 0.5053, reader_cost: 0.00029, ips: 47.4936 samples/sec | ETA 00:54:24
2022-09-07 12:57:12 [INFO]	[TRAIN] epoch: 80, iter: 7500/13950, loss: 0.1031, DSC: 90.3767, lr: 0.004995, batch_cost: 0.5053, reader_cost: 0.00029, ips: 47.5010 samples/sec | ETA 00:54:18
2022-09-07 12:57:17 [INFO]	[TRAIN] epoch: 80, iter: 7510/13950, loss: 0.1185, DSC: 88.7945, lr: 0.004988, batch_cost: 0.5048, reader_cost: 0.00028, ips: 47.5474 samples/sec | ETA 00:54:10
2022-09-07 12:57:22 [INFO]	[TRAIN] epoch: 80, iter: 7520/13950, loss: 0.0978, DSC: 90.9899, lr: 0.004981, batch_cost: 0.5050, reader_cost: 0.00029, ips: 47.5289 samples/sec | ETA 00:54:06
2022-09-07 12:57:27 [INFO]	[TRAIN] epoch: 80, iter: 7530/13950, loss: 0.0976, DSC: 91.0078, lr: 0.004974, batch_cost: 0.5052, reader_cost: 0.00026, ips: 47.5106 samples/sec | ETA 00:54:03
2022-09-07 12:57:34 [INFO]	[TRAIN] epoch: 81, iter: 7540/13950, loss: 0.1434, DSC: 86.3962, lr: 0.004967, batch_cost: 0.6296, reader_cost: 0.13601, ips: 38.1175 samples/sec | ETA 01:07:15
2022-09-07 12:57:39 [INFO]	[TRAIN] epoch: 81, iter: 7550/13950, loss: 0.1023, DSC: 90.4992, lr: 0.004960, batch_cost: 0.5052, reader_cost: 0.00029, ips: 47.5013 samples/sec | ETA 00:53:53
2022-09-07 12:57:44 [INFO]	[TRAIN] epoch: 81, iter: 7560/13950, loss: 0.1292, DSC: 87.9070, lr: 0.004953, batch_cost: 0.5056, reader_cost: 0.00027, ips: 47.4701 samples/sec | ETA 00:53:50
2022-09-07 12:57:49 [INFO]	[TRAIN] epoch: 81, iter: 7570/13950, loss: 0.1263, DSC: 87.9539, lr: 0.004946, batch_cost: 0.5073, reader_cost: 0.00029, ips: 47.3105 samples/sec | ETA 00:53:56
2022-09-07 12:57:54 [INFO]	[TRAIN] epoch: 81, iter: 7580/13950, loss: 0.1140, DSC: 89.4758, lr: 0.004939, batch_cost: 0.5050, reader_cost: 0.00036, ips: 47.5209 samples/sec | ETA 00:53:37
2022-09-07 12:57:59 [INFO]	[TRAIN] epoch: 81, iter: 7590/13950, loss: 0.1038, DSC: 90.3420, lr: 0.004932, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5540 samples/sec | ETA 00:53:29
2022-09-07 12:58:04 [INFO]	[TRAIN] epoch: 81, iter: 7600/13950, loss: 0.1025, DSC: 90.4837, lr: 0.004925, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5856 samples/sec | ETA 00:53:22
2022-09-07 12:58:09 [INFO]	[TRAIN] epoch: 81, iter: 7610/13950, loss: 0.1100, DSC: 89.6487, lr: 0.004918, batch_cost: 0.5055, reader_cost: 0.00028, ips: 47.4785 samples/sec | ETA 00:53:24
2022-09-07 12:58:14 [INFO]	[TRAIN] epoch: 81, iter: 7620/13950, loss: 0.1028, DSC: 90.5466, lr: 0.004911, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6368 samples/sec | ETA 00:53:09
2022-09-07 12:58:20 [INFO]	[TRAIN] epoch: 82, iter: 7630/13950, loss: 0.1323, DSC: 87.5731, lr: 0.004904, batch_cost: 0.6257, reader_cost: 0.14499, ips: 38.3548 samples/sec | ETA 01:05:54
2022-09-07 12:58:25 [INFO]	[TRAIN] epoch: 82, iter: 7640/13950, loss: 0.1173, DSC: 89.0597, lr: 0.004897, batch_cost: 0.5038, reader_cost: 0.00035, ips: 47.6416 samples/sec | ETA 00:52:58
2022-09-07 12:58:30 [INFO]	[TRAIN] epoch: 82, iter: 7650/13950, loss: 0.1202, DSC: 88.8334, lr: 0.004890, batch_cost: 0.5054, reader_cost: 0.00028, ips: 47.4870 samples/sec | ETA 00:53:04
2022-09-07 12:58:35 [INFO]	[TRAIN] epoch: 82, iter: 7660/13950, loss: 0.1226, DSC: 88.5753, lr: 0.004883, batch_cost: 0.5032, reader_cost: 0.00028, ips: 47.6994 samples/sec | ETA 00:52:44
2022-09-07 12:58:40 [INFO]	[TRAIN] epoch: 82, iter: 7670/13950, loss: 0.1086, DSC: 90.0072, lr: 0.004877, batch_cost: 0.5025, reader_cost: 0.00028, ips: 47.7567 samples/sec | ETA 00:52:35
2022-09-07 12:58:46 [INFO]	[TRAIN] epoch: 82, iter: 7680/13950, loss: 0.1125, DSC: 89.5266, lr: 0.004870, batch_cost: 0.5057, reader_cost: 0.00035, ips: 47.4622 samples/sec | ETA 00:52:50
2022-09-07 12:58:51 [INFO]	[TRAIN] epoch: 82, iter: 7690/13950, loss: 0.1031, DSC: 90.3471, lr: 0.004863, batch_cost: 0.5060, reader_cost: 0.00030, ips: 47.4296 samples/sec | ETA 00:52:47
2022-09-07 12:58:56 [INFO]	[TRAIN] epoch: 82, iter: 7700/13950, loss: 0.1036, DSC: 90.2535, lr: 0.004856, batch_cost: 0.5044, reader_cost: 0.00029, ips: 47.5800 samples/sec | ETA 00:52:32
2022-09-07 12:59:01 [INFO]	[TRAIN] epoch: 82, iter: 7710/13950, loss: 0.1018, DSC: 90.5939, lr: 0.004849, batch_cost: 0.5039, reader_cost: 0.00036, ips: 47.6253 samples/sec | ETA 00:52:24
2022-09-07 12:59:07 [INFO]	[TRAIN] epoch: 83, iter: 7720/13950, loss: 0.1763, DSC: 83.0278, lr: 0.004842, batch_cost: 0.6031, reader_cost: 0.12322, ips: 39.7932 samples/sec | ETA 01:02:37
2022-09-07 12:59:12 [INFO]	[TRAIN] epoch: 83, iter: 7730/13950, loss: 0.1104, DSC: 89.6267, lr: 0.004835, batch_cost: 0.5089, reader_cost: 0.00030, ips: 47.1612 samples/sec | ETA 00:52:45
2022-09-07 12:59:17 [INFO]	[TRAIN] epoch: 83, iter: 7740/13950, loss: 0.1118, DSC: 89.5736, lr: 0.004828, batch_cost: 0.5035, reader_cost: 0.00027, ips: 47.6647 samples/sec | ETA 00:52:06
2022-09-07 12:59:22 [INFO]	[TRAIN] epoch: 83, iter: 7750/13950, loss: 0.1261, DSC: 88.1489, lr: 0.004821, batch_cost: 0.5052, reader_cost: 0.00030, ips: 47.5017 samples/sec | ETA 00:52:12
2022-09-07 12:59:27 [INFO]	[TRAIN] epoch: 83, iter: 7760/13950, loss: 0.1060, DSC: 90.1364, lr: 0.004814, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5638 samples/sec | ETA 00:52:03
2022-09-07 12:59:32 [INFO]	[TRAIN] epoch: 83, iter: 7770/13950, loss: 0.0912, DSC: 91.6382, lr: 0.004807, batch_cost: 0.5056, reader_cost: 0.00036, ips: 47.4637 samples/sec | ETA 00:52:04
2022-09-07 12:59:37 [INFO]	[TRAIN] epoch: 83, iter: 7780/13950, loss: 0.0985, DSC: 90.8266, lr: 0.004800, batch_cost: 0.5065, reader_cost: 0.00036, ips: 47.3814 samples/sec | ETA 00:52:05
2022-09-07 12:59:42 [INFO]	[TRAIN] epoch: 83, iter: 7790/13950, loss: 0.1208, DSC: 88.7745, lr: 0.004793, batch_cost: 0.5031, reader_cost: 0.00028, ips: 47.7023 samples/sec | ETA 00:51:39
2022-09-07 12:59:47 [INFO]	[TRAIN] epoch: 83, iter: 7800/13950, loss: 0.1330, DSC: 87.3595, lr: 0.004786, batch_cost: 0.5035, reader_cost: 0.00028, ips: 47.6703 samples/sec | ETA 00:51:36
2022-09-07 12:59:52 [INFO]	[TRAIN] epoch: 83, iter: 7810/13950, loss: 0.0938, DSC: 91.3663, lr: 0.004779, batch_cost: 0.5053, reader_cost: 0.00026, ips: 47.4996 samples/sec | ETA 00:51:42
2022-09-07 12:59:58 [INFO]	[TRAIN] epoch: 84, iter: 7820/13950, loss: 0.1997, DSC: 80.6765, lr: 0.004772, batch_cost: 0.6051, reader_cost: 0.12007, ips: 39.6629 samples/sec | ETA 01:01:49
2022-09-07 13:00:03 [INFO]	[TRAIN] epoch: 84, iter: 7830/13950, loss: 0.1002, DSC: 90.6924, lr: 0.004765, batch_cost: 0.5038, reader_cost: 0.00029, ips: 47.6406 samples/sec | ETA 00:51:23
2022-09-07 13:00:08 [INFO]	[TRAIN] epoch: 84, iter: 7840/13950, loss: 0.1118, DSC: 89.5898, lr: 0.004758, batch_cost: 0.5053, reader_cost: 0.00029, ips: 47.4979 samples/sec | ETA 00:51:27
2022-09-07 13:00:13 [INFO]	[TRAIN] epoch: 84, iter: 7850/13950, loss: 0.1150, DSC: 89.1495, lr: 0.004751, batch_cost: 0.5042, reader_cost: 0.00028, ips: 47.5981 samples/sec | ETA 00:51:15
2022-09-07 13:00:18 [INFO]	[TRAIN] epoch: 84, iter: 7860/13950, loss: 0.0856, DSC: 92.1819, lr: 0.004744, batch_cost: 0.5057, reader_cost: 0.00033, ips: 47.4547 samples/sec | ETA 00:51:19
2022-09-07 13:00:23 [INFO]	[TRAIN] epoch: 84, iter: 7870/13950, loss: 0.1225, DSC: 88.4728, lr: 0.004737, batch_cost: 0.5049, reader_cost: 0.00036, ips: 47.5368 samples/sec | ETA 00:51:09
2022-09-07 13:00:29 [INFO]	[TRAIN] epoch: 84, iter: 7880/13950, loss: 0.1098, DSC: 89.7778, lr: 0.004730, batch_cost: 0.5033, reader_cost: 0.00028, ips: 47.6813 samples/sec | ETA 00:50:55
2022-09-07 13:00:34 [INFO]	[TRAIN] epoch: 84, iter: 7890/13950, loss: 0.0962, DSC: 91.1339, lr: 0.004723, batch_cost: 0.5050, reader_cost: 0.00036, ips: 47.5244 samples/sec | ETA 00:51:00
2022-09-07 13:00:39 [INFO]	[TRAIN] epoch: 84, iter: 7900/13950, loss: 0.1134, DSC: 89.4012, lr: 0.004715, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6406 samples/sec | ETA 00:50:47
2022-09-07 13:00:45 [INFO]	[TRAIN] epoch: 85, iter: 7910/13950, loss: 0.1736, DSC: 83.2873, lr: 0.004708, batch_cost: 0.6104, reader_cost: 0.12823, ips: 39.3214 samples/sec | ETA 01:01:26
2022-09-07 13:00:50 [INFO]	[TRAIN] epoch: 85, iter: 7920/13950, loss: 0.1194, DSC: 88.8196, lr: 0.004701, batch_cost: 0.5032, reader_cost: 0.00030, ips: 47.6909 samples/sec | ETA 00:50:34
2022-09-07 13:00:55 [INFO]	[TRAIN] epoch: 85, iter: 7930/13950, loss: 0.1060, DSC: 90.1029, lr: 0.004694, batch_cost: 0.5050, reader_cost: 0.00030, ips: 47.5217 samples/sec | ETA 00:50:40
2022-09-07 13:01:00 [INFO]	[TRAIN] epoch: 85, iter: 7940/13950, loss: 0.1038, DSC: 90.3474, lr: 0.004687, batch_cost: 0.5029, reader_cost: 0.00029, ips: 47.7214 samples/sec | ETA 00:50:22
2022-09-07 13:01:05 [INFO]	[TRAIN] epoch: 85, iter: 7950/13950, loss: 0.1147, DSC: 89.2020, lr: 0.004680, batch_cost: 0.5035, reader_cost: 0.00035, ips: 47.6635 samples/sec | ETA 00:50:21
2022-09-07 13:01:10 [INFO]	[TRAIN] epoch: 85, iter: 7960/13950, loss: 0.0999, DSC: 90.6965, lr: 0.004673, batch_cost: 0.5066, reader_cost: 0.00028, ips: 47.3756 samples/sec | ETA 00:50:34
2022-09-07 13:01:15 [INFO]	[TRAIN] epoch: 85, iter: 7970/13950, loss: 0.1254, DSC: 88.3155, lr: 0.004666, batch_cost: 0.5060, reader_cost: 0.00028, ips: 47.4352 samples/sec | ETA 00:50:25
2022-09-07 13:01:20 [INFO]	[TRAIN] epoch: 85, iter: 7980/13950, loss: 0.1152, DSC: 89.2395, lr: 0.004659, batch_cost: 0.5057, reader_cost: 0.00033, ips: 47.4576 samples/sec | ETA 00:50:19
2022-09-07 13:01:25 [INFO]	[TRAIN] epoch: 85, iter: 7990/13950, loss: 0.0985, DSC: 90.8423, lr: 0.004652, batch_cost: 0.5049, reader_cost: 0.00027, ips: 47.5385 samples/sec | ETA 00:50:08
2022-09-07 13:01:31 [INFO]	[TRAIN] epoch: 86, iter: 8000/13950, loss: 0.1156, DSC: 89.3239, lr: 0.004645, batch_cost: 0.6042, reader_cost: 0.11864, ips: 39.7238 samples/sec | ETA 00:59:54
2022-09-07 13:01:31 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0015 - reader cost: 1.1563
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2679 - reader cost: 0.5809
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0464 - reader cost: 0.3950 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.1974 - reader cost: 0.4488
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1290 - reader cost: 0.3633
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0856 - reader cost: 0.3158
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0161 - reader cost: 0.2736
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1152 - reader cost: 0.3209
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0891 - reader cost: 0.2877
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0637 - reader cost: 0.2620
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0131 - reader cost: 0.2397
12/12 [==============================] - 12s 976ms/step - batch_cost: 0.9762 - reader cost: 0.2216
2022-09-07 13:01:43 [INFO]	[EVAL] #Images: 12, Dice: 0.8242, Loss: 0.195469
2022-09-07 13:01:43 [INFO]	[EVAL] Class dice: 
[0.9958 0.9027 0.5371 0.8913 0.8444 0.9474 0.6408 0.8825 0.7759]
2022-09-07 13:01:49 [INFO]	[EVAL] The model with the best validation mDice (0.8242) was saved at iter 8000.
2022-09-07 13:01:54 [INFO]	[TRAIN] epoch: 86, iter: 8010/13950, loss: 0.0995, DSC: 90.8515, lr: 0.004638, batch_cost: 0.5025, reader_cost: 0.00038, ips: 47.7638 samples/sec | ETA 00:49:44
2022-09-07 13:01:59 [INFO]	[TRAIN] epoch: 86, iter: 8020/13950, loss: 0.1129, DSC: 89.3898, lr: 0.004631, batch_cost: 0.5059, reader_cost: 0.00030, ips: 47.4416 samples/sec | ETA 00:49:59
2022-09-07 13:02:04 [INFO]	[TRAIN] epoch: 86, iter: 8030/13950, loss: 0.1115, DSC: 89.6473, lr: 0.004624, batch_cost: 0.5064, reader_cost: 0.00027, ips: 47.3964 samples/sec | ETA 00:49:57
2022-09-07 13:02:09 [INFO]	[TRAIN] epoch: 86, iter: 8040/13950, loss: 0.1082, DSC: 89.8812, lr: 0.004617, batch_cost: 0.5052, reader_cost: 0.00028, ips: 47.5080 samples/sec | ETA 00:49:45
2022-09-07 13:02:14 [INFO]	[TRAIN] epoch: 86, iter: 8050/13950, loss: 0.1167, DSC: 89.0581, lr: 0.004610, batch_cost: 0.5058, reader_cost: 0.00033, ips: 47.4482 samples/sec | ETA 00:49:44
2022-09-07 13:02:19 [INFO]	[TRAIN] epoch: 86, iter: 8060/13950, loss: 0.1243, DSC: 88.2949, lr: 0.004603, batch_cost: 0.5054, reader_cost: 0.00034, ips: 47.4867 samples/sec | ETA 00:49:36
2022-09-07 13:02:24 [INFO]	[TRAIN] epoch: 86, iter: 8070/13950, loss: 0.1042, DSC: 90.2703, lr: 0.004596, batch_cost: 0.5045, reader_cost: 0.00026, ips: 47.5736 samples/sec | ETA 00:49:26
2022-09-07 13:02:29 [INFO]	[TRAIN] epoch: 86, iter: 8080/13950, loss: 0.1310, DSC: 87.5044, lr: 0.004589, batch_cost: 0.5039, reader_cost: 0.00025, ips: 47.6260 samples/sec | ETA 00:49:18
2022-09-07 13:02:34 [INFO]	[TRAIN] epoch: 86, iter: 8090/13950, loss: 0.1094, DSC: 89.9093, lr: 0.004582, batch_cost: 0.5026, reader_cost: 0.00023, ips: 47.7558 samples/sec | ETA 00:49:04
2022-09-07 13:02:40 [INFO]	[TRAIN] epoch: 87, iter: 8100/13950, loss: 0.1226, DSC: 88.4750, lr: 0.004575, batch_cost: 0.6051, reader_cost: 0.12163, ips: 39.6636 samples/sec | ETA 00:58:59
2022-09-07 13:02:45 [INFO]	[TRAIN] epoch: 87, iter: 8110/13950, loss: 0.1023, DSC: 90.5147, lr: 0.004568, batch_cost: 0.5031, reader_cost: 0.00026, ips: 47.7010 samples/sec | ETA 00:48:58
2022-09-07 13:02:50 [INFO]	[TRAIN] epoch: 87, iter: 8120/13950, loss: 0.0997, DSC: 90.8197, lr: 0.004561, batch_cost: 0.5035, reader_cost: 0.00035, ips: 47.6667 samples/sec | ETA 00:48:55
2022-09-07 13:02:55 [INFO]	[TRAIN] epoch: 87, iter: 8130/13950, loss: 0.0993, DSC: 90.7836, lr: 0.004554, batch_cost: 0.5076, reader_cost: 0.00029, ips: 47.2784 samples/sec | ETA 00:49:14
2022-09-07 13:03:00 [INFO]	[TRAIN] epoch: 87, iter: 8140/13950, loss: 0.1109, DSC: 89.6445, lr: 0.004547, batch_cost: 0.5047, reader_cost: 0.00026, ips: 47.5574 samples/sec | ETA 00:48:52
2022-09-07 13:03:05 [INFO]	[TRAIN] epoch: 87, iter: 8150/13950, loss: 0.1054, DSC: 90.1057, lr: 0.004540, batch_cost: 0.5031, reader_cost: 0.00026, ips: 47.7068 samples/sec | ETA 00:48:37
2022-09-07 13:03:10 [INFO]	[TRAIN] epoch: 87, iter: 8160/13950, loss: 0.1061, DSC: 90.1260, lr: 0.004533, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6368 samples/sec | ETA 00:48:37
2022-09-07 13:03:15 [INFO]	[TRAIN] epoch: 87, iter: 8170/13950, loss: 0.1067, DSC: 90.0109, lr: 0.004526, batch_cost: 0.5038, reader_cost: 0.00029, ips: 47.6395 samples/sec | ETA 00:48:31
2022-09-07 13:03:20 [INFO]	[TRAIN] epoch: 87, iter: 8180/13950, loss: 0.1067, DSC: 90.0814, lr: 0.004519, batch_cost: 0.5018, reader_cost: 0.00024, ips: 47.8243 samples/sec | ETA 00:48:15
2022-09-07 13:03:26 [INFO]	[TRAIN] epoch: 88, iter: 8190/13950, loss: 0.1265, DSC: 88.0762, lr: 0.004512, batch_cost: 0.5976, reader_cost: 0.11618, ips: 40.1623 samples/sec | ETA 00:57:22
2022-09-07 13:03:31 [INFO]	[TRAIN] epoch: 88, iter: 8200/13950, loss: 0.1109, DSC: 89.6735, lr: 0.004505, batch_cost: 0.5011, reader_cost: 0.00028, ips: 47.8955 samples/sec | ETA 00:48:01
2022-09-07 13:03:36 [INFO]	[TRAIN] epoch: 88, iter: 8210/13950, loss: 0.1272, DSC: 88.0034, lr: 0.004498, batch_cost: 0.5025, reader_cost: 0.00027, ips: 47.7574 samples/sec | ETA 00:48:04
2022-09-07 13:03:41 [INFO]	[TRAIN] epoch: 88, iter: 8220/13950, loss: 0.1159, DSC: 89.2047, lr: 0.004490, batch_cost: 0.5056, reader_cost: 0.00030, ips: 47.4728 samples/sec | ETA 00:48:16
2022-09-07 13:03:47 [INFO]	[TRAIN] epoch: 88, iter: 8230/13950, loss: 0.1332, DSC: 87.3409, lr: 0.004483, batch_cost: 0.5053, reader_cost: 0.00028, ips: 47.4948 samples/sec | ETA 00:48:10
2022-09-07 13:03:52 [INFO]	[TRAIN] epoch: 88, iter: 8240/13950, loss: 0.1193, DSC: 88.8571, lr: 0.004476, batch_cost: 0.5100, reader_cost: 0.00031, ips: 47.0629 samples/sec | ETA 00:48:31
2022-09-07 13:03:57 [INFO]	[TRAIN] epoch: 88, iter: 8250/13950, loss: 0.1101, DSC: 89.6981, lr: 0.004469, batch_cost: 0.5036, reader_cost: 0.00029, ips: 47.6575 samples/sec | ETA 00:47:50
2022-09-07 13:04:02 [INFO]	[TRAIN] epoch: 88, iter: 8260/13950, loss: 0.1056, DSC: 90.1698, lr: 0.004462, batch_cost: 0.5043, reader_cost: 0.00026, ips: 47.5908 samples/sec | ETA 00:47:49
2022-09-07 13:04:07 [INFO]	[TRAIN] epoch: 88, iter: 8270/13950, loss: 0.1177, DSC: 88.8668, lr: 0.004455, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6488 samples/sec | ETA 00:47:40
2022-09-07 13:04:13 [INFO]	[TRAIN] epoch: 89, iter: 8280/13950, loss: 0.1574, DSC: 85.2631, lr: 0.004448, batch_cost: 0.6084, reader_cost: 0.12294, ips: 39.4496 samples/sec | ETA 00:57:29
2022-09-07 13:04:18 [INFO]	[TRAIN] epoch: 89, iter: 8290/13950, loss: 0.1305, DSC: 87.7090, lr: 0.004441, batch_cost: 0.5099, reader_cost: 0.00028, ips: 47.0688 samples/sec | ETA 00:48:05
2022-09-07 13:04:23 [INFO]	[TRAIN] epoch: 89, iter: 8300/13950, loss: 0.1179, DSC: 88.9250, lr: 0.004434, batch_cost: 0.5068, reader_cost: 0.00036, ips: 47.3583 samples/sec | ETA 00:47:43
2022-09-07 13:04:28 [INFO]	[TRAIN] epoch: 89, iter: 8310/13950, loss: 0.1130, DSC: 89.4948, lr: 0.004427, batch_cost: 0.5056, reader_cost: 0.00028, ips: 47.4705 samples/sec | ETA 00:47:31
2022-09-07 13:04:33 [INFO]	[TRAIN] epoch: 89, iter: 8320/13950, loss: 0.1033, DSC: 90.3459, lr: 0.004420, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6376 samples/sec | ETA 00:47:16
2022-09-07 13:04:38 [INFO]	[TRAIN] epoch: 89, iter: 8330/13950, loss: 0.1033, DSC: 90.4365, lr: 0.004413, batch_cost: 0.5049, reader_cost: 0.00026, ips: 47.5323 samples/sec | ETA 00:47:17
2022-09-07 13:04:43 [INFO]	[TRAIN] epoch: 89, iter: 8340/13950, loss: 0.1101, DSC: 89.6924, lr: 0.004406, batch_cost: 0.5047, reader_cost: 0.00025, ips: 47.5556 samples/sec | ETA 00:47:11
2022-09-07 13:04:48 [INFO]	[TRAIN] epoch: 89, iter: 8350/13950, loss: 0.1005, DSC: 90.6100, lr: 0.004399, batch_cost: 0.5059, reader_cost: 0.00026, ips: 47.4390 samples/sec | ETA 00:47:13
2022-09-07 13:04:53 [INFO]	[TRAIN] epoch: 89, iter: 8360/13950, loss: 0.1212, DSC: 88.6164, lr: 0.004392, batch_cost: 0.5075, reader_cost: 0.00028, ips: 47.2932 samples/sec | ETA 00:47:16
2022-09-07 13:04:58 [INFO]	[TRAIN] epoch: 90, iter: 8370/13950, loss: 0.1101, DSC: 89.6951, lr: 0.004385, batch_cost: 0.4735, reader_cost: 0.00022, ips: 50.6907 samples/sec | ETA 00:44:01
2022-09-07 13:05:04 [INFO]	[TRAIN] epoch: 90, iter: 8380/13950, loss: 0.0982, DSC: 90.9003, lr: 0.004377, batch_cost: 0.6427, reader_cost: 0.12984, ips: 37.3416 samples/sec | ETA 00:59:39
2022-09-07 13:05:09 [INFO]	[TRAIN] epoch: 90, iter: 8390/13950, loss: 0.1163, DSC: 89.1121, lr: 0.004370, batch_cost: 0.5013, reader_cost: 0.00033, ips: 47.8736 samples/sec | ETA 00:46:27
2022-09-07 13:05:15 [INFO]	[TRAIN] epoch: 90, iter: 8400/13950, loss: 0.1217, DSC: 88.5395, lr: 0.004363, batch_cost: 0.5046, reader_cost: 0.00033, ips: 47.5669 samples/sec | ETA 00:46:40
2022-09-07 13:05:20 [INFO]	[TRAIN] epoch: 90, iter: 8410/13950, loss: 0.0898, DSC: 91.7430, lr: 0.004356, batch_cost: 0.5034, reader_cost: 0.00026, ips: 47.6748 samples/sec | ETA 00:46:28
2022-09-07 13:05:25 [INFO]	[TRAIN] epoch: 90, iter: 8420/13950, loss: 0.1116, DSC: 89.5730, lr: 0.004349, batch_cost: 0.5047, reader_cost: 0.00028, ips: 47.5536 samples/sec | ETA 00:46:30
2022-09-07 13:05:30 [INFO]	[TRAIN] epoch: 90, iter: 8430/13950, loss: 0.1214, DSC: 88.5189, lr: 0.004342, batch_cost: 0.5032, reader_cost: 0.00026, ips: 47.6913 samples/sec | ETA 00:46:17
2022-09-07 13:05:35 [INFO]	[TRAIN] epoch: 90, iter: 8440/13950, loss: 0.1200, DSC: 88.8074, lr: 0.004335, batch_cost: 0.5047, reader_cost: 0.00028, ips: 47.5485 samples/sec | ETA 00:46:21
2022-09-07 13:05:40 [INFO]	[TRAIN] epoch: 90, iter: 8450/13950, loss: 0.1309, DSC: 87.6663, lr: 0.004328, batch_cost: 0.5048, reader_cost: 0.00033, ips: 47.5429 samples/sec | ETA 00:46:16
2022-09-07 13:05:45 [INFO]	[TRAIN] epoch: 90, iter: 8460/13950, loss: 0.1163, DSC: 89.0787, lr: 0.004321, batch_cost: 0.5031, reader_cost: 0.00023, ips: 47.7075 samples/sec | ETA 00:46:01
2022-09-07 13:05:51 [INFO]	[TRAIN] epoch: 91, iter: 8470/13950, loss: 0.1985, DSC: 80.9746, lr: 0.004314, batch_cost: 0.6181, reader_cost: 0.13044, ips: 38.8290 samples/sec | ETA 00:56:27
2022-09-07 13:05:56 [INFO]	[TRAIN] epoch: 91, iter: 8480/13950, loss: 0.1163, DSC: 89.3575, lr: 0.004307, batch_cost: 0.5059, reader_cost: 0.00035, ips: 47.4362 samples/sec | ETA 00:46:07
2022-09-07 13:06:01 [INFO]	[TRAIN] epoch: 91, iter: 8490/13950, loss: 0.1138, DSC: 89.4815, lr: 0.004300, batch_cost: 0.5041, reader_cost: 0.00034, ips: 47.6067 samples/sec | ETA 00:45:52
2022-09-07 13:06:06 [INFO]	[TRAIN] epoch: 91, iter: 8500/13950, loss: 0.1137, DSC: 89.4737, lr: 0.004293, batch_cost: 0.5052, reader_cost: 0.00027, ips: 47.5094 samples/sec | ETA 00:45:53
2022-09-07 13:06:11 [INFO]	[TRAIN] epoch: 91, iter: 8510/13950, loss: 0.1101, DSC: 89.7368, lr: 0.004285, batch_cost: 0.5058, reader_cost: 0.00027, ips: 47.4462 samples/sec | ETA 00:45:51
2022-09-07 13:06:16 [INFO]	[TRAIN] epoch: 91, iter: 8520/13950, loss: 0.1100, DSC: 89.6976, lr: 0.004278, batch_cost: 0.5065, reader_cost: 0.00029, ips: 47.3795 samples/sec | ETA 00:45:50
2022-09-07 13:06:21 [INFO]	[TRAIN] epoch: 91, iter: 8530/13950, loss: 0.1292, DSC: 87.9688, lr: 0.004271, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6163 samples/sec | ETA 00:45:31
2022-09-07 13:06:26 [INFO]	[TRAIN] epoch: 91, iter: 8540/13950, loss: 0.1013, DSC: 90.5701, lr: 0.004264, batch_cost: 0.5052, reader_cost: 0.00028, ips: 47.5090 samples/sec | ETA 00:45:32
2022-09-07 13:06:31 [INFO]	[TRAIN] epoch: 91, iter: 8550/13950, loss: 0.1121, DSC: 89.5509, lr: 0.004257, batch_cost: 0.5068, reader_cost: 0.00032, ips: 47.3566 samples/sec | ETA 00:45:36
2022-09-07 13:06:38 [INFO]	[TRAIN] epoch: 92, iter: 8560/13950, loss: 0.1275, DSC: 88.0904, lr: 0.004250, batch_cost: 0.6135, reader_cost: 0.12387, ips: 39.1208 samples/sec | ETA 00:55:06
2022-09-07 13:06:43 [INFO]	[TRAIN] epoch: 92, iter: 8570/13950, loss: 0.0999, DSC: 90.7629, lr: 0.004243, batch_cost: 0.5056, reader_cost: 0.00027, ips: 47.4658 samples/sec | ETA 00:45:20
2022-09-07 13:06:48 [INFO]	[TRAIN] epoch: 92, iter: 8580/13950, loss: 0.0960, DSC: 91.0563, lr: 0.004236, batch_cost: 0.5074, reader_cost: 0.00035, ips: 47.2957 samples/sec | ETA 00:45:24
2022-09-07 13:06:53 [INFO]	[TRAIN] epoch: 92, iter: 8590/13950, loss: 0.1081, DSC: 89.9561, lr: 0.004229, batch_cost: 0.5068, reader_cost: 0.00028, ips: 47.3552 samples/sec | ETA 00:45:16
2022-09-07 13:06:58 [INFO]	[TRAIN] epoch: 92, iter: 8600/13950, loss: 0.1110, DSC: 89.6804, lr: 0.004222, batch_cost: 0.5072, reader_cost: 0.00029, ips: 47.3189 samples/sec | ETA 00:45:13
2022-09-07 13:07:03 [INFO]	[TRAIN] epoch: 92, iter: 8610/13950, loss: 0.0993, DSC: 90.7877, lr: 0.004214, batch_cost: 0.5060, reader_cost: 0.00029, ips: 47.4326 samples/sec | ETA 00:45:01
2022-09-07 13:07:08 [INFO]	[TRAIN] epoch: 92, iter: 8620/13950, loss: 0.1308, DSC: 87.6279, lr: 0.004207, batch_cost: 0.5062, reader_cost: 0.00036, ips: 47.4164 samples/sec | ETA 00:44:57
2022-09-07 13:07:13 [INFO]	[TRAIN] epoch: 92, iter: 8630/13950, loss: 0.1209, DSC: 88.6601, lr: 0.004200, batch_cost: 0.5061, reader_cost: 0.00030, ips: 47.4207 samples/sec | ETA 00:44:52
2022-09-07 13:07:18 [INFO]	[TRAIN] epoch: 92, iter: 8640/13950, loss: 0.1145, DSC: 89.1662, lr: 0.004193, batch_cost: 0.5055, reader_cost: 0.00029, ips: 47.4746 samples/sec | ETA 00:44:44
2022-09-07 13:07:24 [INFO]	[TRAIN] epoch: 93, iter: 8650/13950, loss: 0.1271, DSC: 88.0173, lr: 0.004186, batch_cost: 0.5867, reader_cost: 0.10696, ips: 40.9047 samples/sec | ETA 00:51:49
2022-09-07 13:07:29 [INFO]	[TRAIN] epoch: 93, iter: 8660/13950, loss: 0.1066, DSC: 90.0772, lr: 0.004179, batch_cost: 0.5169, reader_cost: 0.00027, ips: 46.4321 samples/sec | ETA 00:45:34
2022-09-07 13:07:34 [INFO]	[TRAIN] epoch: 93, iter: 8670/13950, loss: 0.1062, DSC: 90.0354, lr: 0.004172, batch_cost: 0.5045, reader_cost: 0.00026, ips: 47.5691 samples/sec | ETA 00:44:23
2022-09-07 13:07:39 [INFO]	[TRAIN] epoch: 93, iter: 8680/13950, loss: 0.1237, DSC: 88.3449, lr: 0.004165, batch_cost: 0.5029, reader_cost: 0.00026, ips: 47.7261 samples/sec | ETA 00:44:10
2022-09-07 13:07:44 [INFO]	[TRAIN] epoch: 93, iter: 8690/13950, loss: 0.1000, DSC: 90.7847, lr: 0.004158, batch_cost: 0.5039, reader_cost: 0.00032, ips: 47.6257 samples/sec | ETA 00:44:10
2022-09-07 13:07:49 [INFO]	[TRAIN] epoch: 93, iter: 8700/13950, loss: 0.1310, DSC: 87.5868, lr: 0.004151, batch_cost: 0.5034, reader_cost: 0.00027, ips: 47.6713 samples/sec | ETA 00:44:03
2022-09-07 13:07:54 [INFO]	[TRAIN] epoch: 93, iter: 8710/13950, loss: 0.1143, DSC: 89.4399, lr: 0.004143, batch_cost: 0.5058, reader_cost: 0.00028, ips: 47.4471 samples/sec | ETA 00:44:10
2022-09-07 13:07:59 [INFO]	[TRAIN] epoch: 93, iter: 8720/13950, loss: 0.1243, DSC: 88.3776, lr: 0.004136, batch_cost: 0.5045, reader_cost: 0.00026, ips: 47.5700 samples/sec | ETA 00:43:58
2022-09-07 13:08:04 [INFO]	[TRAIN] epoch: 93, iter: 8730/13950, loss: 0.1194, DSC: 88.8606, lr: 0.004129, batch_cost: 0.5052, reader_cost: 0.00030, ips: 47.5103 samples/sec | ETA 00:43:56
2022-09-07 13:08:09 [INFO]	[TRAIN] epoch: 93, iter: 8740/13950, loss: 0.1156, DSC: 89.1627, lr: 0.004122, batch_cost: 0.5035, reader_cost: 0.00025, ips: 47.6699 samples/sec | ETA 00:43:43
2022-09-07 13:08:16 [INFO]	[TRAIN] epoch: 94, iter: 8750/13950, loss: 0.0962, DSC: 91.1913, lr: 0.004115, batch_cost: 0.6444, reader_cost: 0.15004, ips: 37.2466 samples/sec | ETA 00:55:50
2022-09-07 13:08:21 [INFO]	[TRAIN] epoch: 94, iter: 8760/13950, loss: 0.1049, DSC: 90.2445, lr: 0.004108, batch_cost: 0.5019, reader_cost: 0.00025, ips: 47.8221 samples/sec | ETA 00:43:24
2022-09-07 13:08:26 [INFO]	[TRAIN] epoch: 94, iter: 8770/13950, loss: 0.1093, DSC: 89.7973, lr: 0.004101, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6558 samples/sec | ETA 00:43:28
2022-09-07 13:08:31 [INFO]	[TRAIN] epoch: 94, iter: 8780/13950, loss: 0.1195, DSC: 88.8745, lr: 0.004094, batch_cost: 0.5030, reader_cost: 0.00032, ips: 47.7128 samples/sec | ETA 00:43:20
2022-09-07 13:08:36 [INFO]	[TRAIN] epoch: 94, iter: 8790/13950, loss: 0.0949, DSC: 91.2195, lr: 0.004086, batch_cost: 0.5052, reader_cost: 0.00028, ips: 47.5060 samples/sec | ETA 00:43:26
2022-09-07 13:08:41 [INFO]	[TRAIN] epoch: 94, iter: 8800/13950, loss: 0.0912, DSC: 91.4879, lr: 0.004079, batch_cost: 0.5026, reader_cost: 0.00025, ips: 47.7522 samples/sec | ETA 00:43:08
2022-09-07 13:08:46 [INFO]	[TRAIN] epoch: 94, iter: 8810/13950, loss: 0.0989, DSC: 90.7664, lr: 0.004072, batch_cost: 0.5034, reader_cost: 0.00026, ips: 47.6763 samples/sec | ETA 00:43:07
2022-09-07 13:08:51 [INFO]	[TRAIN] epoch: 94, iter: 8820/13950, loss: 0.1209, DSC: 88.7517, lr: 0.004065, batch_cost: 0.5052, reader_cost: 0.00027, ips: 47.5055 samples/sec | ETA 00:43:11
2022-09-07 13:08:56 [INFO]	[TRAIN] epoch: 94, iter: 8830/13950, loss: 0.1101, DSC: 89.7358, lr: 0.004058, batch_cost: 0.5065, reader_cost: 0.00027, ips: 47.3841 samples/sec | ETA 00:43:13
2022-09-07 13:09:02 [INFO]	[TRAIN] epoch: 95, iter: 8840/13950, loss: 0.1523, DSC: 85.4549, lr: 0.004051, batch_cost: 0.5992, reader_cost: 0.11034, ips: 40.0537 samples/sec | ETA 00:51:01
2022-09-07 13:09:07 [INFO]	[TRAIN] epoch: 95, iter: 8850/13950, loss: 0.1159, DSC: 89.2141, lr: 0.004044, batch_cost: 0.5057, reader_cost: 0.00028, ips: 47.4572 samples/sec | ETA 00:42:59
2022-09-07 13:09:12 [INFO]	[TRAIN] epoch: 95, iter: 8860/13950, loss: 0.1055, DSC: 90.1282, lr: 0.004037, batch_cost: 0.5060, reader_cost: 0.00039, ips: 47.4278 samples/sec | ETA 00:42:55
2022-09-07 13:09:17 [INFO]	[TRAIN] epoch: 95, iter: 8870/13950, loss: 0.1164, DSC: 89.1617, lr: 0.004029, batch_cost: 0.5066, reader_cost: 0.00026, ips: 47.3738 samples/sec | ETA 00:42:53
2022-09-07 13:09:22 [INFO]	[TRAIN] epoch: 95, iter: 8880/13950, loss: 0.1070, DSC: 89.9916, lr: 0.004022, batch_cost: 0.5051, reader_cost: 0.00026, ips: 47.5174 samples/sec | ETA 00:42:40
2022-09-07 13:09:27 [INFO]	[TRAIN] epoch: 95, iter: 8890/13950, loss: 0.1171, DSC: 89.0623, lr: 0.004015, batch_cost: 0.5048, reader_cost: 0.00027, ips: 47.5412 samples/sec | ETA 00:42:34
2022-09-07 13:09:33 [INFO]	[TRAIN] epoch: 95, iter: 8900/13950, loss: 0.1024, DSC: 90.4930, lr: 0.004008, batch_cost: 0.5060, reader_cost: 0.00028, ips: 47.4311 samples/sec | ETA 00:42:35
2022-09-07 13:09:38 [INFO]	[TRAIN] epoch: 95, iter: 8910/13950, loss: 0.1006, DSC: 90.5677, lr: 0.004001, batch_cost: 0.5055, reader_cost: 0.00036, ips: 47.4735 samples/sec | ETA 00:42:27
2022-09-07 13:09:43 [INFO]	[TRAIN] epoch: 95, iter: 8920/13950, loss: 0.1153, DSC: 89.1768, lr: 0.003994, batch_cost: 0.5032, reader_cost: 0.00027, ips: 47.6909 samples/sec | ETA 00:42:11
2022-09-07 13:09:49 [INFO]	[TRAIN] epoch: 96, iter: 8930/13950, loss: 0.1472, DSC: 86.0125, lr: 0.003987, batch_cost: 0.6013, reader_cost: 0.11774, ips: 39.9156 samples/sec | ETA 00:50:18
2022-09-07 13:09:54 [INFO]	[TRAIN] epoch: 96, iter: 8940/13950, loss: 0.1234, DSC: 88.3611, lr: 0.003979, batch_cost: 0.5063, reader_cost: 0.00028, ips: 47.4006 samples/sec | ETA 00:42:16
2022-09-07 13:09:59 [INFO]	[TRAIN] epoch: 96, iter: 8950/13950, loss: 0.1102, DSC: 89.6869, lr: 0.003972, batch_cost: 0.5054, reader_cost: 0.00028, ips: 47.4844 samples/sec | ETA 00:42:07
2022-09-07 13:10:04 [INFO]	[TRAIN] epoch: 96, iter: 8960/13950, loss: 0.1014, DSC: 90.6236, lr: 0.003965, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6059 samples/sec | ETA 00:41:55
2022-09-07 13:10:09 [INFO]	[TRAIN] epoch: 96, iter: 8970/13950, loss: 0.0965, DSC: 91.1441, lr: 0.003958, batch_cost: 0.5063, reader_cost: 0.00028, ips: 47.4055 samples/sec | ETA 00:42:01
2022-09-07 13:10:14 [INFO]	[TRAIN] epoch: 96, iter: 8980/13950, loss: 0.1107, DSC: 89.6855, lr: 0.003951, batch_cost: 0.5054, reader_cost: 0.00034, ips: 47.4862 samples/sec | ETA 00:41:51
2022-09-07 13:10:19 [INFO]	[TRAIN] epoch: 96, iter: 8990/13950, loss: 0.1101, DSC: 89.7361, lr: 0.003944, batch_cost: 0.5055, reader_cost: 0.00033, ips: 47.4813 samples/sec | ETA 00:41:47
2022-09-07 13:10:24 [INFO]	[TRAIN] epoch: 96, iter: 9000/13950, loss: 0.0891, DSC: 91.7267, lr: 0.003936, batch_cost: 0.5044, reader_cost: 0.00026, ips: 47.5778 samples/sec | ETA 00:41:36
2022-09-07 13:10:24 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0047 - reader cost: 1.1542
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2693 - reader cost: 0.5797
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0476 - reader cost: 0.3943 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.2108 - reader cost: 0.4595
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1397 - reader cost: 0.3719
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0976 - reader cost: 0.3264
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0264 - reader cost: 0.2825
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1300 - reader cost: 0.3345
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.1023 - reader cost: 0.2997
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0760 - reader cost: 0.2729
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0242 - reader cost: 0.2495
12/12 [==============================] - 12s 986ms/step - batch_cost: 0.9860 - reader cost: 0.2305
2022-09-07 13:10:36 [INFO]	[EVAL] #Images: 12, Dice: 0.8251, Loss: 0.193682
2022-09-07 13:10:36 [INFO]	[EVAL] Class dice: 
[0.9959 0.9037 0.5531 0.8896 0.8478 0.9536 0.627  0.8801 0.775 ]
2022-09-07 13:10:42 [INFO]	[EVAL] The model with the best validation mDice (0.8251) was saved at iter 9000.
2022-09-07 13:10:47 [INFO]	[TRAIN] epoch: 96, iter: 9010/13950, loss: 0.0939, DSC: 91.2522, lr: 0.003929, batch_cost: 0.5002, reader_cost: 0.00038, ips: 47.9768 samples/sec | ETA 00:41:11
2022-09-07 13:10:52 [INFO]	[TRAIN] epoch: 96, iter: 9020/13950, loss: 0.1047, DSC: 90.2409, lr: 0.003922, batch_cost: 0.5016, reader_cost: 0.00026, ips: 47.8479 samples/sec | ETA 00:41:12
2022-09-07 13:10:58 [INFO]	[TRAIN] epoch: 97, iter: 9030/13950, loss: 0.1401, DSC: 86.6343, lr: 0.003915, batch_cost: 0.6049, reader_cost: 0.11574, ips: 39.6780 samples/sec | ETA 00:49:35
2022-09-07 13:11:03 [INFO]	[TRAIN] epoch: 97, iter: 9040/13950, loss: 0.0980, DSC: 90.9018, lr: 0.003908, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5919 samples/sec | ETA 00:41:16
2022-09-07 13:11:08 [INFO]	[TRAIN] epoch: 97, iter: 9050/13950, loss: 0.1124, DSC: 89.4543, lr: 0.003901, batch_cost: 0.5041, reader_cost: 0.00026, ips: 47.6123 samples/sec | ETA 00:41:09
2022-09-07 13:11:13 [INFO]	[TRAIN] epoch: 97, iter: 9060/13950, loss: 0.0988, DSC: 90.7281, lr: 0.003894, batch_cost: 0.5054, reader_cost: 0.00028, ips: 47.4897 samples/sec | ETA 00:41:11
2022-09-07 13:11:18 [INFO]	[TRAIN] epoch: 97, iter: 9070/13950, loss: 0.0979, DSC: 90.9924, lr: 0.003886, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6584 samples/sec | ETA 00:40:57
2022-09-07 13:11:23 [INFO]	[TRAIN] epoch: 97, iter: 9080/13950, loss: 0.1004, DSC: 90.8291, lr: 0.003879, batch_cost: 0.5058, reader_cost: 0.00027, ips: 47.4454 samples/sec | ETA 00:41:03
2022-09-07 13:11:28 [INFO]	[TRAIN] epoch: 97, iter: 9090/13950, loss: 0.1022, DSC: 90.5118, lr: 0.003872, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5703 samples/sec | ETA 00:40:51
2022-09-07 13:11:33 [INFO]	[TRAIN] epoch: 97, iter: 9100/13950, loss: 0.1128, DSC: 89.4746, lr: 0.003865, batch_cost: 0.5044, reader_cost: 0.00025, ips: 47.5818 samples/sec | ETA 00:40:46
2022-09-07 13:11:38 [INFO]	[TRAIN] epoch: 97, iter: 9110/13950, loss: 0.0867, DSC: 91.9380, lr: 0.003858, batch_cost: 0.5019, reader_cost: 0.00023, ips: 47.8136 samples/sec | ETA 00:40:29
2022-09-07 13:11:45 [INFO]	[TRAIN] epoch: 98, iter: 9120/13950, loss: 0.1177, DSC: 88.9933, lr: 0.003850, batch_cost: 0.6353, reader_cost: 0.15068, ips: 37.7779 samples/sec | ETA 00:51:08
2022-09-07 13:11:50 [INFO]	[TRAIN] epoch: 98, iter: 9130/13950, loss: 0.1206, DSC: 88.5728, lr: 0.003843, batch_cost: 0.4992, reader_cost: 0.00025, ips: 48.0776 samples/sec | ETA 00:40:06
2022-09-07 13:11:55 [INFO]	[TRAIN] epoch: 98, iter: 9140/13950, loss: 0.1232, DSC: 88.4178, lr: 0.003836, batch_cost: 0.5027, reader_cost: 0.00027, ips: 47.7429 samples/sec | ETA 00:40:17
2022-09-07 13:12:00 [INFO]	[TRAIN] epoch: 98, iter: 9150/13950, loss: 0.1019, DSC: 90.5883, lr: 0.003829, batch_cost: 0.5050, reader_cost: 0.00037, ips: 47.5255 samples/sec | ETA 00:40:23
2022-09-07 13:12:05 [INFO]	[TRAIN] epoch: 98, iter: 9160/13950, loss: 0.1186, DSC: 88.8021, lr: 0.003822, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5810 samples/sec | ETA 00:40:16
2022-09-07 13:12:10 [INFO]	[TRAIN] epoch: 98, iter: 9170/13950, loss: 0.1046, DSC: 90.2999, lr: 0.003815, batch_cost: 0.5043, reader_cost: 0.00034, ips: 47.5890 samples/sec | ETA 00:40:10
2022-09-07 13:12:15 [INFO]	[TRAIN] epoch: 98, iter: 9180/13950, loss: 0.1066, DSC: 90.0031, lr: 0.003807, batch_cost: 0.5049, reader_cost: 0.00027, ips: 47.5302 samples/sec | ETA 00:40:08
2022-09-07 13:12:20 [INFO]	[TRAIN] epoch: 98, iter: 9190/13950, loss: 0.0967, DSC: 90.9977, lr: 0.003800, batch_cost: 0.5031, reader_cost: 0.00027, ips: 47.7031 samples/sec | ETA 00:39:54
2022-09-07 13:12:25 [INFO]	[TRAIN] epoch: 98, iter: 9200/13950, loss: 0.0991, DSC: 90.8900, lr: 0.003793, batch_cost: 0.5046, reader_cost: 0.00028, ips: 47.5579 samples/sec | ETA 00:39:57
2022-09-07 13:12:31 [INFO]	[TRAIN] epoch: 99, iter: 9210/13950, loss: 0.1372, DSC: 86.9340, lr: 0.003786, batch_cost: 0.6218, reader_cost: 0.13788, ips: 38.5973 samples/sec | ETA 00:49:07
2022-09-07 13:12:36 [INFO]	[TRAIN] epoch: 99, iter: 9220/13950, loss: 0.1234, DSC: 88.4612, lr: 0.003779, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6578 samples/sec | ETA 00:39:41
2022-09-07 13:12:41 [INFO]	[TRAIN] epoch: 99, iter: 9230/13950, loss: 0.1252, DSC: 88.2268, lr: 0.003771, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5567 samples/sec | ETA 00:39:42
2022-09-07 13:12:46 [INFO]	[TRAIN] epoch: 99, iter: 9240/13950, loss: 0.1272, DSC: 88.0530, lr: 0.003764, batch_cost: 0.5046, reader_cost: 0.00034, ips: 47.5611 samples/sec | ETA 00:39:36
2022-09-07 13:12:51 [INFO]	[TRAIN] epoch: 99, iter: 9250/13950, loss: 0.1259, DSC: 88.1653, lr: 0.003757, batch_cost: 0.5026, reader_cost: 0.00025, ips: 47.7490 samples/sec | ETA 00:39:22
2022-09-07 13:12:56 [INFO]	[TRAIN] epoch: 99, iter: 9260/13950, loss: 0.1080, DSC: 89.8757, lr: 0.003750, batch_cost: 0.5052, reader_cost: 0.00034, ips: 47.5045 samples/sec | ETA 00:39:29
2022-09-07 13:13:01 [INFO]	[TRAIN] epoch: 99, iter: 9270/13950, loss: 0.0997, DSC: 90.7276, lr: 0.003743, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5733 samples/sec | ETA 00:39:20
2022-09-07 13:13:06 [INFO]	[TRAIN] epoch: 99, iter: 9280/13950, loss: 0.0908, DSC: 91.6018, lr: 0.003736, batch_cost: 0.5050, reader_cost: 0.00035, ips: 47.5287 samples/sec | ETA 00:39:18
2022-09-07 13:13:11 [INFO]	[TRAIN] epoch: 99, iter: 9290/13950, loss: 0.1042, DSC: 90.3245, lr: 0.003728, batch_cost: 0.5040, reader_cost: 0.00034, ips: 47.6192 samples/sec | ETA 00:39:08
2022-09-07 13:13:16 [INFO]	[TRAIN] epoch: 100, iter: 9300/13950, loss: 0.1262, DSC: 88.0065, lr: 0.003721, batch_cost: 0.4737, reader_cost: 0.00022, ips: 50.6650 samples/sec | ETA 00:36:42
2022-09-07 13:13:23 [INFO]	[TRAIN] epoch: 100, iter: 9310/13950, loss: 0.1093, DSC: 89.8013, lr: 0.003714, batch_cost: 0.6323, reader_cost: 0.11650, ips: 37.9591 samples/sec | ETA 00:48:53
2022-09-07 13:13:28 [INFO]	[TRAIN] epoch: 100, iter: 9320/13950, loss: 0.1047, DSC: 90.3644, lr: 0.003707, batch_cost: 0.5057, reader_cost: 0.00028, ips: 47.4550 samples/sec | ETA 00:39:01
2022-09-07 13:13:33 [INFO]	[TRAIN] epoch: 100, iter: 9330/13950, loss: 0.1039, DSC: 90.2195, lr: 0.003700, batch_cost: 0.5040, reader_cost: 0.00027, ips: 47.6188 samples/sec | ETA 00:38:48
2022-09-07 13:13:38 [INFO]	[TRAIN] epoch: 100, iter: 9340/13950, loss: 0.1216, DSC: 88.5827, lr: 0.003692, batch_cost: 0.5050, reader_cost: 0.00026, ips: 47.5284 samples/sec | ETA 00:38:47
2022-09-07 13:13:43 [INFO]	[TRAIN] epoch: 100, iter: 9350/13950, loss: 0.1196, DSC: 88.8530, lr: 0.003685, batch_cost: 0.5054, reader_cost: 0.00034, ips: 47.4903 samples/sec | ETA 00:38:44
2022-09-07 13:13:48 [INFO]	[TRAIN] epoch: 100, iter: 9360/13950, loss: 0.1079, DSC: 89.9366, lr: 0.003678, batch_cost: 0.5074, reader_cost: 0.00028, ips: 47.2978 samples/sec | ETA 00:38:49
2022-09-07 13:13:53 [INFO]	[TRAIN] epoch: 100, iter: 9370/13950, loss: 0.1139, DSC: 89.2903, lr: 0.003671, batch_cost: 0.5077, reader_cost: 0.00029, ips: 47.2702 samples/sec | ETA 00:38:45
2022-09-07 13:13:58 [INFO]	[TRAIN] epoch: 100, iter: 9380/13950, loss: 0.0972, DSC: 90.9955, lr: 0.003663, batch_cost: 0.5027, reader_cost: 0.00027, ips: 47.7434 samples/sec | ETA 00:38:17
2022-09-07 13:14:03 [INFO]	[TRAIN] epoch: 100, iter: 9390/13950, loss: 0.1184, DSC: 88.9434, lr: 0.003656, batch_cost: 0.5015, reader_cost: 0.00023, ips: 47.8549 samples/sec | ETA 00:38:06
2022-09-07 13:14:09 [INFO]	[TRAIN] epoch: 101, iter: 9400/13950, loss: 0.1571, DSC: 84.9317, lr: 0.003649, batch_cost: 0.6206, reader_cost: 0.11578, ips: 38.6706 samples/sec | ETA 00:47:03
2022-09-07 13:14:14 [INFO]	[TRAIN] epoch: 101, iter: 9410/13950, loss: 0.1023, DSC: 90.3973, lr: 0.003642, batch_cost: 0.5066, reader_cost: 0.00028, ips: 47.3762 samples/sec | ETA 00:38:19
2022-09-07 13:14:19 [INFO]	[TRAIN] epoch: 101, iter: 9420/13950, loss: 0.0925, DSC: 91.4672, lr: 0.003635, batch_cost: 0.5038, reader_cost: 0.00025, ips: 47.6423 samples/sec | ETA 00:38:02
2022-09-07 13:14:24 [INFO]	[TRAIN] epoch: 101, iter: 9430/13950, loss: 0.1030, DSC: 90.4158, lr: 0.003627, batch_cost: 0.5053, reader_cost: 0.00040, ips: 47.4935 samples/sec | ETA 00:38:04
2022-09-07 13:14:29 [INFO]	[TRAIN] epoch: 101, iter: 9440/13950, loss: 0.1259, DSC: 88.1581, lr: 0.003620, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4867 samples/sec | ETA 00:37:59
2022-09-07 13:14:34 [INFO]	[TRAIN] epoch: 101, iter: 9450/13950, loss: 0.0999, DSC: 90.7184, lr: 0.003613, batch_cost: 0.5037, reader_cost: 0.00026, ips: 47.6447 samples/sec | ETA 00:37:46
2022-09-07 13:14:39 [INFO]	[TRAIN] epoch: 101, iter: 9460/13950, loss: 0.1126, DSC: 89.4636, lr: 0.003606, batch_cost: 0.5037, reader_cost: 0.00025, ips: 47.6446 samples/sec | ETA 00:37:41
2022-09-07 13:14:44 [INFO]	[TRAIN] epoch: 101, iter: 9470/13950, loss: 0.0994, DSC: 90.7797, lr: 0.003598, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6104 samples/sec | ETA 00:37:38
2022-09-07 13:14:50 [INFO]	[TRAIN] epoch: 101, iter: 9480/13950, loss: 0.0947, DSC: 91.2349, lr: 0.003591, batch_cost: 0.5051, reader_cost: 0.00025, ips: 47.5185 samples/sec | ETA 00:37:37
2022-09-07 13:14:56 [INFO]	[TRAIN] epoch: 102, iter: 9490/13950, loss: 0.1893, DSC: 81.5823, lr: 0.003584, batch_cost: 0.6154, reader_cost: 0.12713, ips: 38.9997 samples/sec | ETA 00:45:44
2022-09-07 13:15:01 [INFO]	[TRAIN] epoch: 102, iter: 9500/13950, loss: 0.1067, DSC: 90.0027, lr: 0.003577, batch_cost: 0.5034, reader_cost: 0.00027, ips: 47.6736 samples/sec | ETA 00:37:20
2022-09-07 13:15:06 [INFO]	[TRAIN] epoch: 102, iter: 9510/13950, loss: 0.1178, DSC: 88.9784, lr: 0.003570, batch_cost: 0.5041, reader_cost: 0.00028, ips: 47.6141 samples/sec | ETA 00:37:17
2022-09-07 13:15:11 [INFO]	[TRAIN] epoch: 102, iter: 9520/13950, loss: 0.1028, DSC: 90.4048, lr: 0.003562, batch_cost: 0.5045, reader_cost: 0.00027, ips: 47.5673 samples/sec | ETA 00:37:15
2022-09-07 13:15:16 [INFO]	[TRAIN] epoch: 102, iter: 9530/13950, loss: 0.0959, DSC: 91.0701, lr: 0.003555, batch_cost: 0.5034, reader_cost: 0.00026, ips: 47.6760 samples/sec | ETA 00:37:05
2022-09-07 13:15:21 [INFO]	[TRAIN] epoch: 102, iter: 9540/13950, loss: 0.0971, DSC: 91.0367, lr: 0.003548, batch_cost: 0.5025, reader_cost: 0.00027, ips: 47.7591 samples/sec | ETA 00:36:56
2022-09-07 13:15:26 [INFO]	[TRAIN] epoch: 102, iter: 9550/13950, loss: 0.1094, DSC: 89.7727, lr: 0.003541, batch_cost: 0.5040, reader_cost: 0.00028, ips: 47.6158 samples/sec | ETA 00:36:57
2022-09-07 13:15:31 [INFO]	[TRAIN] epoch: 102, iter: 9560/13950, loss: 0.1255, DSC: 88.0760, lr: 0.003533, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6506 samples/sec | ETA 00:36:51
2022-09-07 13:15:36 [INFO]	[TRAIN] epoch: 102, iter: 9570/13950, loss: 0.1231, DSC: 88.5628, lr: 0.003526, batch_cost: 0.5038, reader_cost: 0.00029, ips: 47.6424 samples/sec | ETA 00:36:46
2022-09-07 13:15:42 [INFO]	[TRAIN] epoch: 103, iter: 9580/13950, loss: 0.1830, DSC: 82.3395, lr: 0.003519, batch_cost: 0.6023, reader_cost: 0.12566, ips: 39.8443 samples/sec | ETA 00:43:52
2022-09-07 13:15:47 [INFO]	[TRAIN] epoch: 103, iter: 9590/13950, loss: 0.1066, DSC: 90.0096, lr: 0.003512, batch_cost: 0.5078, reader_cost: 0.00029, ips: 47.2637 samples/sec | ETA 00:36:53
2022-09-07 13:15:52 [INFO]	[TRAIN] epoch: 103, iter: 9600/13950, loss: 0.1218, DSC: 88.5798, lr: 0.003504, batch_cost: 0.5029, reader_cost: 0.00032, ips: 47.7261 samples/sec | ETA 00:36:27
2022-09-07 13:15:57 [INFO]	[TRAIN] epoch: 103, iter: 9610/13950, loss: 0.1120, DSC: 89.5220, lr: 0.003497, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5169 samples/sec | ETA 00:36:32
2022-09-07 13:16:02 [INFO]	[TRAIN] epoch: 103, iter: 9620/13950, loss: 0.1116, DSC: 89.6259, lr: 0.003490, batch_cost: 0.5045, reader_cost: 0.00025, ips: 47.5728 samples/sec | ETA 00:36:24
2022-09-07 13:16:07 [INFO]	[TRAIN] epoch: 103, iter: 9630/13950, loss: 0.1074, DSC: 89.8889, lr: 0.003483, batch_cost: 0.5043, reader_cost: 0.00032, ips: 47.5906 samples/sec | ETA 00:36:18
2022-09-07 13:16:12 [INFO]	[TRAIN] epoch: 103, iter: 9640/13950, loss: 0.1014, DSC: 90.6715, lr: 0.003475, batch_cost: 0.5048, reader_cost: 0.00027, ips: 47.5477 samples/sec | ETA 00:36:15
2022-09-07 13:16:17 [INFO]	[TRAIN] epoch: 103, iter: 9650/13950, loss: 0.1058, DSC: 90.0689, lr: 0.003468, batch_cost: 0.5037, reader_cost: 0.00034, ips: 47.6460 samples/sec | ETA 00:36:05
2022-09-07 13:16:22 [INFO]	[TRAIN] epoch: 103, iter: 9660/13950, loss: 0.1226, DSC: 88.5142, lr: 0.003461, batch_cost: 0.5024, reader_cost: 0.00025, ips: 47.7724 samples/sec | ETA 00:35:55
2022-09-07 13:16:27 [INFO]	[TRAIN] epoch: 103, iter: 9670/13950, loss: 0.1009, DSC: 90.5742, lr: 0.003454, batch_cost: 0.5045, reader_cost: 0.00025, ips: 47.5746 samples/sec | ETA 00:35:59
2022-09-07 13:16:33 [INFO]	[TRAIN] epoch: 104, iter: 9680/13950, loss: 0.1561, DSC: 84.9395, lr: 0.003446, batch_cost: 0.5983, reader_cost: 0.10038, ips: 40.1131 samples/sec | ETA 00:42:34
2022-09-07 13:16:38 [INFO]	[TRAIN] epoch: 104, iter: 9690/13950, loss: 0.1148, DSC: 89.2196, lr: 0.003439, batch_cost: 0.5007, reader_cost: 0.00027, ips: 47.9373 samples/sec | ETA 00:35:32
2022-09-07 13:16:43 [INFO]	[TRAIN] epoch: 104, iter: 9700/13950, loss: 0.1106, DSC: 89.7273, lr: 0.003432, batch_cost: 0.5015, reader_cost: 0.00027, ips: 47.8564 samples/sec | ETA 00:35:31
2022-09-07 13:16:48 [INFO]	[TRAIN] epoch: 104, iter: 9710/13950, loss: 0.1215, DSC: 88.6841, lr: 0.003425, batch_cost: 0.5012, reader_cost: 0.00026, ips: 47.8814 samples/sec | ETA 00:35:25
2022-09-07 13:16:53 [INFO]	[TRAIN] epoch: 104, iter: 9720/13950, loss: 0.1017, DSC: 90.5151, lr: 0.003417, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6615 samples/sec | ETA 00:35:30
2022-09-07 13:16:59 [INFO]	[TRAIN] epoch: 104, iter: 9730/13950, loss: 0.0855, DSC: 92.0753, lr: 0.003410, batch_cost: 0.5041, reader_cost: 0.00029, ips: 47.6083 samples/sec | ETA 00:35:27
2022-09-07 13:17:04 [INFO]	[TRAIN] epoch: 104, iter: 9740/13950, loss: 0.0981, DSC: 90.8360, lr: 0.003403, batch_cost: 0.5048, reader_cost: 0.00029, ips: 47.5460 samples/sec | ETA 00:35:25
2022-09-07 13:17:09 [INFO]	[TRAIN] epoch: 104, iter: 9750/13950, loss: 0.1108, DSC: 89.6505, lr: 0.003395, batch_cost: 0.5058, reader_cost: 0.00037, ips: 47.4469 samples/sec | ETA 00:35:24
2022-09-07 13:17:14 [INFO]	[TRAIN] epoch: 104, iter: 9760/13950, loss: 0.1373, DSC: 87.1192, lr: 0.003388, batch_cost: 0.5058, reader_cost: 0.00027, ips: 47.4521 samples/sec | ETA 00:35:19
2022-09-07 13:17:20 [INFO]	[TRAIN] epoch: 105, iter: 9770/13950, loss: 0.1275, DSC: 87.9014, lr: 0.003381, batch_cost: 0.6067, reader_cost: 0.11540, ips: 39.5575 samples/sec | ETA 00:42:16
2022-09-07 13:17:25 [INFO]	[TRAIN] epoch: 105, iter: 9780/13950, loss: 0.1051, DSC: 90.1893, lr: 0.003374, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6532 samples/sec | ETA 00:35:00
2022-09-07 13:17:30 [INFO]	[TRAIN] epoch: 105, iter: 9790/13950, loss: 0.0957, DSC: 91.1189, lr: 0.003366, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5669 samples/sec | ETA 00:34:58
2022-09-07 13:17:35 [INFO]	[TRAIN] epoch: 105, iter: 9800/13950, loss: 0.1005, DSC: 90.7486, lr: 0.003359, batch_cost: 0.5068, reader_cost: 0.00026, ips: 47.3591 samples/sec | ETA 00:35:03
2022-09-07 13:17:40 [INFO]	[TRAIN] epoch: 105, iter: 9810/13950, loss: 0.1207, DSC: 88.5969, lr: 0.003352, batch_cost: 0.5051, reader_cost: 0.00026, ips: 47.5169 samples/sec | ETA 00:34:51
2022-09-07 13:17:45 [INFO]	[TRAIN] epoch: 105, iter: 9820/13950, loss: 0.0961, DSC: 91.0312, lr: 0.003345, batch_cost: 0.5033, reader_cost: 0.00027, ips: 47.6811 samples/sec | ETA 00:34:38
2022-09-07 13:17:50 [INFO]	[TRAIN] epoch: 105, iter: 9830/13950, loss: 0.1009, DSC: 90.5472, lr: 0.003337, batch_cost: 0.5027, reader_cost: 0.00026, ips: 47.7398 samples/sec | ETA 00:34:31
2022-09-07 13:17:55 [INFO]	[TRAIN] epoch: 105, iter: 9840/13950, loss: 0.1026, DSC: 90.4774, lr: 0.003330, batch_cost: 0.5051, reader_cost: 0.00028, ips: 47.5116 samples/sec | ETA 00:34:36
2022-09-07 13:18:00 [INFO]	[TRAIN] epoch: 105, iter: 9850/13950, loss: 0.1026, DSC: 90.5110, lr: 0.003323, batch_cost: 0.5037, reader_cost: 0.00026, ips: 47.6427 samples/sec | ETA 00:34:25
2022-09-07 13:18:06 [INFO]	[TRAIN] epoch: 106, iter: 9860/13950, loss: 0.1690, DSC: 83.7778, lr: 0.003315, batch_cost: 0.6044, reader_cost: 0.12298, ips: 39.7101 samples/sec | ETA 00:41:11
2022-09-07 13:18:11 [INFO]	[TRAIN] epoch: 106, iter: 9870/13950, loss: 0.0897, DSC: 91.7213, lr: 0.003308, batch_cost: 0.5070, reader_cost: 0.00028, ips: 47.3383 samples/sec | ETA 00:34:28
2022-09-07 13:18:16 [INFO]	[TRAIN] epoch: 106, iter: 9880/13950, loss: 0.1201, DSC: 88.7344, lr: 0.003301, batch_cost: 0.5052, reader_cost: 0.00034, ips: 47.5039 samples/sec | ETA 00:34:16
2022-09-07 13:18:21 [INFO]	[TRAIN] epoch: 106, iter: 9890/13950, loss: 0.1069, DSC: 90.0488, lr: 0.003293, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5208 samples/sec | ETA 00:34:10
2022-09-07 13:18:26 [INFO]	[TRAIN] epoch: 106, iter: 9900/13950, loss: 0.1254, DSC: 88.1911, lr: 0.003286, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6398 samples/sec | ETA 00:34:00
2022-09-07 13:18:31 [INFO]	[TRAIN] epoch: 106, iter: 9910/13950, loss: 0.1051, DSC: 90.1681, lr: 0.003279, batch_cost: 0.5061, reader_cost: 0.00027, ips: 47.4257 samples/sec | ETA 00:34:04
2022-09-07 13:18:36 [INFO]	[TRAIN] epoch: 106, iter: 9920/13950, loss: 0.1182, DSC: 88.8880, lr: 0.003272, batch_cost: 0.5062, reader_cost: 0.00027, ips: 47.4140 samples/sec | ETA 00:33:59
2022-09-07 13:18:42 [INFO]	[TRAIN] epoch: 106, iter: 9930/13950, loss: 0.1084, DSC: 89.8265, lr: 0.003264, batch_cost: 0.5040, reader_cost: 0.00033, ips: 47.6200 samples/sec | ETA 00:33:46
2022-09-07 13:18:47 [INFO]	[TRAIN] epoch: 106, iter: 9940/13950, loss: 0.1071, DSC: 89.9553, lr: 0.003257, batch_cost: 0.5055, reader_cost: 0.00029, ips: 47.4822 samples/sec | ETA 00:33:46
2022-09-07 13:18:52 [INFO]	[TRAIN] epoch: 106, iter: 9950/13950, loss: 0.1026, DSC: 90.4672, lr: 0.003250, batch_cost: 0.5049, reader_cost: 0.00025, ips: 47.5332 samples/sec | ETA 00:33:39
2022-09-07 13:18:58 [INFO]	[TRAIN] epoch: 107, iter: 9960/13950, loss: 0.1473, DSC: 85.8557, lr: 0.003242, batch_cost: 0.6029, reader_cost: 0.11252, ips: 39.8099 samples/sec | ETA 00:40:05
2022-09-07 13:19:03 [INFO]	[TRAIN] epoch: 107, iter: 9970/13950, loss: 0.0917, DSC: 91.5378, lr: 0.003235, batch_cost: 0.5061, reader_cost: 0.00028, ips: 47.4250 samples/sec | ETA 00:33:34
2022-09-07 13:19:08 [INFO]	[TRAIN] epoch: 107, iter: 9980/13950, loss: 0.1180, DSC: 88.9848, lr: 0.003228, batch_cost: 0.5027, reader_cost: 0.00025, ips: 47.7430 samples/sec | ETA 00:33:15
2022-09-07 13:19:13 [INFO]	[TRAIN] epoch: 107, iter: 9990/13950, loss: 0.1102, DSC: 89.6959, lr: 0.003220, batch_cost: 0.5064, reader_cost: 0.00026, ips: 47.3898 samples/sec | ETA 00:33:25
2022-09-07 13:19:18 [INFO]	[TRAIN] epoch: 107, iter: 10000/13950, loss: 0.1002, DSC: 90.7229, lr: 0.003213, batch_cost: 0.5052, reader_cost: 0.00025, ips: 47.5018 samples/sec | ETA 00:33:15
2022-09-07 13:19:18 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0109 - reader cost: 1.1633
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2728 - reader cost: 0.5840
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0495 - reader cost: 0.3968 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.1973 - reader cost: 0.4467
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1293 - reader cost: 0.3616
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0813 - reader cost: 0.3095
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0124 - reader cost: 0.2681
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1126 - reader cost: 0.3164
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0870 - reader cost: 0.2837
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0614 - reader cost: 0.2582
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0109 - reader cost: 0.2362
12/12 [==============================] - 12s 974ms/step - batch_cost: 0.9741 - reader cost: 0.2182
2022-09-07 13:19:30 [INFO]	[EVAL] #Images: 12, Dice: 0.8262, Loss: 0.193562
2022-09-07 13:19:30 [INFO]	[EVAL] Class dice: 
[0.996  0.9037 0.5551 0.8896 0.8469 0.9517 0.6368 0.883  0.7731]
2022-09-07 13:19:35 [INFO]	[EVAL] The model with the best validation mDice (0.8262) was saved at iter 10000.
2022-09-07 13:19:40 [INFO]	[TRAIN] epoch: 107, iter: 10010/13950, loss: 0.1126, DSC: 89.4637, lr: 0.003206, batch_cost: 0.5039, reader_cost: 0.00043, ips: 47.6330 samples/sec | ETA 00:33:05
2022-09-07 13:19:45 [INFO]	[TRAIN] epoch: 107, iter: 10020/13950, loss: 0.1138, DSC: 89.2782, lr: 0.003198, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6386 samples/sec | ETA 00:32:59
2022-09-07 13:19:50 [INFO]	[TRAIN] epoch: 107, iter: 10030/13950, loss: 0.1009, DSC: 90.6054, lr: 0.003191, batch_cost: 0.5030, reader_cost: 0.00025, ips: 47.7145 samples/sec | ETA 00:32:51
2022-09-07 13:19:55 [INFO]	[TRAIN] epoch: 107, iter: 10040/13950, loss: 0.1133, DSC: 89.3390, lr: 0.003184, batch_cost: 0.5034, reader_cost: 0.00024, ips: 47.6735 samples/sec | ETA 00:32:48
2022-09-07 13:20:01 [INFO]	[TRAIN] epoch: 108, iter: 10050/13950, loss: 0.1041, DSC: 90.3946, lr: 0.003176, batch_cost: 0.5790, reader_cost: 0.09007, ips: 41.4506 samples/sec | ETA 00:37:38
2022-09-07 13:20:06 [INFO]	[TRAIN] epoch: 108, iter: 10060/13950, loss: 0.0929, DSC: 91.3357, lr: 0.003169, batch_cost: 0.5075, reader_cost: 0.00029, ips: 47.2867 samples/sec | ETA 00:32:54
2022-09-07 13:20:11 [INFO]	[TRAIN] epoch: 108, iter: 10070/13950, loss: 0.1230, DSC: 88.4233, lr: 0.003162, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5546 samples/sec | ETA 00:32:38
2022-09-07 13:20:16 [INFO]	[TRAIN] epoch: 108, iter: 10080/13950, loss: 0.1017, DSC: 90.5565, lr: 0.003154, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5135 samples/sec | ETA 00:32:34
2022-09-07 13:20:21 [INFO]	[TRAIN] epoch: 108, iter: 10090/13950, loss: 0.1258, DSC: 88.0882, lr: 0.003147, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6390 samples/sec | ETA 00:32:24
2022-09-07 13:20:26 [INFO]	[TRAIN] epoch: 108, iter: 10100/13950, loss: 0.1109, DSC: 89.6217, lr: 0.003140, batch_cost: 0.5053, reader_cost: 0.00027, ips: 47.4987 samples/sec | ETA 00:32:25
2022-09-07 13:20:32 [INFO]	[TRAIN] epoch: 108, iter: 10110/13950, loss: 0.1058, DSC: 90.1717, lr: 0.003132, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5538 samples/sec | ETA 00:32:18
2022-09-07 13:20:37 [INFO]	[TRAIN] epoch: 108, iter: 10120/13950, loss: 0.1231, DSC: 88.4618, lr: 0.003125, batch_cost: 0.5053, reader_cost: 0.00027, ips: 47.5012 samples/sec | ETA 00:32:15
2022-09-07 13:20:42 [INFO]	[TRAIN] epoch: 108, iter: 10130/13950, loss: 0.1025, DSC: 90.3842, lr: 0.003118, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6136 samples/sec | ETA 00:32:05
2022-09-07 13:20:48 [INFO]	[TRAIN] epoch: 109, iter: 10140/13950, loss: 0.1772, DSC: 82.9716, lr: 0.003110, batch_cost: 0.6151, reader_cost: 0.13377, ips: 39.0167 samples/sec | ETA 00:39:03
2022-09-07 13:20:53 [INFO]	[TRAIN] epoch: 109, iter: 10150/13950, loss: 0.1032, DSC: 90.3394, lr: 0.003103, batch_cost: 0.5078, reader_cost: 0.00027, ips: 47.2606 samples/sec | ETA 00:32:09
2022-09-07 13:20:58 [INFO]	[TRAIN] epoch: 109, iter: 10160/13950, loss: 0.0985, DSC: 90.9113, lr: 0.003096, batch_cost: 0.5048, reader_cost: 0.00027, ips: 47.5463 samples/sec | ETA 00:31:53
2022-09-07 13:21:03 [INFO]	[TRAIN] epoch: 109, iter: 10170/13950, loss: 0.1205, DSC: 88.7001, lr: 0.003088, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6051 samples/sec | ETA 00:31:45
2022-09-07 13:21:08 [INFO]	[TRAIN] epoch: 109, iter: 10180/13950, loss: 0.1086, DSC: 89.9057, lr: 0.003081, batch_cost: 0.5053, reader_cost: 0.00027, ips: 47.4995 samples/sec | ETA 00:31:44
2022-09-07 13:21:13 [INFO]	[TRAIN] epoch: 109, iter: 10190/13950, loss: 0.1097, DSC: 89.6538, lr: 0.003074, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6527 samples/sec | ETA 00:31:33
2022-09-07 13:21:18 [INFO]	[TRAIN] epoch: 109, iter: 10200/13950, loss: 0.0947, DSC: 91.2164, lr: 0.003066, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6134 samples/sec | ETA 00:31:30
2022-09-07 13:21:23 [INFO]	[TRAIN] epoch: 109, iter: 10210/13950, loss: 0.1099, DSC: 89.6771, lr: 0.003059, batch_cost: 0.5057, reader_cost: 0.00033, ips: 47.4560 samples/sec | ETA 00:31:31
2022-09-07 13:21:28 [INFO]	[TRAIN] epoch: 109, iter: 10220/13950, loss: 0.1017, DSC: 90.6211, lr: 0.003052, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5941 samples/sec | ETA 00:31:20
2022-09-07 13:21:33 [INFO]	[TRAIN] epoch: 110, iter: 10230/13950, loss: 0.1231, DSC: 88.3610, lr: 0.003044, batch_cost: 0.4738, reader_cost: 0.00021, ips: 50.6579 samples/sec | ETA 00:29:22
2022-09-07 13:21:39 [INFO]	[TRAIN] epoch: 110, iter: 10240/13950, loss: 0.0927, DSC: 91.4539, lr: 0.003037, batch_cost: 0.6246, reader_cost: 0.10849, ips: 38.4227 samples/sec | ETA 00:38:37
2022-09-07 13:21:44 [INFO]	[TRAIN] epoch: 110, iter: 10250/13950, loss: 0.1116, DSC: 89.5409, lr: 0.003029, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5777 samples/sec | ETA 00:31:06
2022-09-07 13:21:49 [INFO]	[TRAIN] epoch: 110, iter: 10260/13950, loss: 0.1082, DSC: 89.8004, lr: 0.003022, batch_cost: 0.5045, reader_cost: 0.00030, ips: 47.5708 samples/sec | ETA 00:31:01
2022-09-07 13:21:54 [INFO]	[TRAIN] epoch: 110, iter: 10270/13950, loss: 0.1024, DSC: 90.4193, lr: 0.003015, batch_cost: 0.5049, reader_cost: 0.00027, ips: 47.5339 samples/sec | ETA 00:30:58
2022-09-07 13:21:59 [INFO]	[TRAIN] epoch: 110, iter: 10280/13950, loss: 0.0929, DSC: 91.4219, lr: 0.003007, batch_cost: 0.5049, reader_cost: 0.00037, ips: 47.5322 samples/sec | ETA 00:30:53
2022-09-07 13:22:04 [INFO]	[TRAIN] epoch: 110, iter: 10290/13950, loss: 0.0977, DSC: 91.0180, lr: 0.003000, batch_cost: 0.5039, reader_cost: 0.00028, ips: 47.6248 samples/sec | ETA 00:30:44
2022-09-07 13:22:09 [INFO]	[TRAIN] epoch: 110, iter: 10300/13950, loss: 0.1196, DSC: 88.7371, lr: 0.002993, batch_cost: 0.5057, reader_cost: 0.00028, ips: 47.4630 samples/sec | ETA 00:30:45
2022-09-07 13:22:14 [INFO]	[TRAIN] epoch: 110, iter: 10310/13950, loss: 0.1010, DSC: 90.4902, lr: 0.002985, batch_cost: 0.5038, reader_cost: 0.00028, ips: 47.6396 samples/sec | ETA 00:30:33
2022-09-07 13:22:19 [INFO]	[TRAIN] epoch: 110, iter: 10320/13950, loss: 0.0881, DSC: 91.8262, lr: 0.002978, batch_cost: 0.5007, reader_cost: 0.00030, ips: 47.9364 samples/sec | ETA 00:30:17
2022-09-07 13:22:25 [INFO]	[TRAIN] epoch: 111, iter: 10330/13950, loss: 0.1047, DSC: 90.2315, lr: 0.002970, batch_cost: 0.6024, reader_cost: 0.11309, ips: 39.8394 samples/sec | ETA 00:36:20
2022-09-07 13:22:31 [INFO]	[TRAIN] epoch: 111, iter: 10340/13950, loss: 0.1002, DSC: 90.5791, lr: 0.002963, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6426 samples/sec | ETA 00:30:18
2022-09-07 13:22:36 [INFO]	[TRAIN] epoch: 111, iter: 10350/13950, loss: 0.0970, DSC: 90.9255, lr: 0.002956, batch_cost: 0.5028, reader_cost: 0.00034, ips: 47.7355 samples/sec | ETA 00:30:09
2022-09-07 13:22:41 [INFO]	[TRAIN] epoch: 111, iter: 10360/13950, loss: 0.0947, DSC: 91.3332, lr: 0.002948, batch_cost: 0.5031, reader_cost: 0.00034, ips: 47.7025 samples/sec | ETA 00:30:06
2022-09-07 13:22:46 [INFO]	[TRAIN] epoch: 111, iter: 10370/13950, loss: 0.1039, DSC: 90.2593, lr: 0.002941, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6211 samples/sec | ETA 00:30:04
2022-09-07 13:22:51 [INFO]	[TRAIN] epoch: 111, iter: 10380/13950, loss: 0.1086, DSC: 89.9390, lr: 0.002934, batch_cost: 0.5042, reader_cost: 0.00026, ips: 47.6031 samples/sec | ETA 00:29:59
2022-09-07 13:22:56 [INFO]	[TRAIN] epoch: 111, iter: 10390/13950, loss: 0.1132, DSC: 89.3891, lr: 0.002926, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5743 samples/sec | ETA 00:29:55
2022-09-07 13:23:01 [INFO]	[TRAIN] epoch: 111, iter: 10400/13950, loss: 0.1079, DSC: 89.8211, lr: 0.002919, batch_cost: 0.5032, reader_cost: 0.00025, ips: 47.6961 samples/sec | ETA 00:29:46
2022-09-07 13:23:06 [INFO]	[TRAIN] epoch: 111, iter: 10410/13950, loss: 0.1080, DSC: 89.9399, lr: 0.002911, batch_cost: 0.5075, reader_cost: 0.00025, ips: 47.2873 samples/sec | ETA 00:29:56
2022-09-07 13:23:12 [INFO]	[TRAIN] epoch: 112, iter: 10420/13950, loss: 0.1288, DSC: 87.9272, lr: 0.002904, batch_cost: 0.5960, reader_cost: 0.11122, ips: 40.2700 samples/sec | ETA 00:35:03
2022-09-07 13:23:17 [INFO]	[TRAIN] epoch: 112, iter: 10430/13950, loss: 0.1220, DSC: 88.5924, lr: 0.002897, batch_cost: 0.5070, reader_cost: 0.00035, ips: 47.3398 samples/sec | ETA 00:29:44
2022-09-07 13:23:22 [INFO]	[TRAIN] epoch: 112, iter: 10440/13950, loss: 0.0986, DSC: 90.9270, lr: 0.002889, batch_cost: 0.5061, reader_cost: 0.00032, ips: 47.4205 samples/sec | ETA 00:29:36
2022-09-07 13:23:27 [INFO]	[TRAIN] epoch: 112, iter: 10450/13950, loss: 0.1103, DSC: 89.6648, lr: 0.002882, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6598 samples/sec | ETA 00:29:22
2022-09-07 13:23:32 [INFO]	[TRAIN] epoch: 112, iter: 10460/13950, loss: 0.1036, DSC: 90.3184, lr: 0.002874, batch_cost: 0.5058, reader_cost: 0.00040, ips: 47.4490 samples/sec | ETA 00:29:25
2022-09-07 13:23:37 [INFO]	[TRAIN] epoch: 112, iter: 10470/13950, loss: 0.1070, DSC: 89.9261, lr: 0.002867, batch_cost: 0.5045, reader_cost: 0.00026, ips: 47.5712 samples/sec | ETA 00:29:15
2022-09-07 13:23:42 [INFO]	[TRAIN] epoch: 112, iter: 10480/13950, loss: 0.1069, DSC: 89.9365, lr: 0.002860, batch_cost: 0.5064, reader_cost: 0.00028, ips: 47.3937 samples/sec | ETA 00:29:17
2022-09-07 13:23:47 [INFO]	[TRAIN] epoch: 112, iter: 10490/13950, loss: 0.1080, DSC: 89.9070, lr: 0.002852, batch_cost: 0.5052, reader_cost: 0.00027, ips: 47.5096 samples/sec | ETA 00:29:07
2022-09-07 13:23:52 [INFO]	[TRAIN] epoch: 112, iter: 10500/13950, loss: 0.1004, DSC: 90.6549, lr: 0.002845, batch_cost: 0.5096, reader_cost: 0.00030, ips: 47.0962 samples/sec | ETA 00:29:18
2022-09-07 13:23:58 [INFO]	[TRAIN] epoch: 113, iter: 10510/13950, loss: 0.1610, DSC: 84.7031, lr: 0.002837, batch_cost: 0.5921, reader_cost: 0.11103, ips: 40.5305 samples/sec | ETA 00:33:56
2022-09-07 13:24:03 [INFO]	[TRAIN] epoch: 113, iter: 10520/13950, loss: 0.0970, DSC: 91.0812, lr: 0.002830, batch_cost: 0.5149, reader_cost: 0.00031, ips: 46.6086 samples/sec | ETA 00:29:26
2022-09-07 13:24:08 [INFO]	[TRAIN] epoch: 113, iter: 10530/13950, loss: 0.0976, DSC: 91.0519, lr: 0.002822, batch_cost: 0.5039, reader_cost: 0.00034, ips: 47.6284 samples/sec | ETA 00:28:43
2022-09-07 13:24:13 [INFO]	[TRAIN] epoch: 113, iter: 10540/13950, loss: 0.1011, DSC: 90.5259, lr: 0.002815, batch_cost: 0.5040, reader_cost: 0.00034, ips: 47.6188 samples/sec | ETA 00:28:38
2022-09-07 13:24:18 [INFO]	[TRAIN] epoch: 113, iter: 10550/13950, loss: 0.1066, DSC: 90.0618, lr: 0.002808, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6337 samples/sec | ETA 00:28:33
2022-09-07 13:24:24 [INFO]	[TRAIN] epoch: 113, iter: 10560/13950, loss: 0.1007, DSC: 90.6164, lr: 0.002800, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6535 samples/sec | ETA 00:28:27
2022-09-07 13:24:29 [INFO]	[TRAIN] epoch: 113, iter: 10570/13950, loss: 0.1125, DSC: 89.3672, lr: 0.002793, batch_cost: 0.5031, reader_cost: 0.00026, ips: 47.7033 samples/sec | ETA 00:28:20
2022-09-07 13:24:34 [INFO]	[TRAIN] epoch: 113, iter: 10580/13950, loss: 0.1133, DSC: 89.3467, lr: 0.002785, batch_cost: 0.5061, reader_cost: 0.00028, ips: 47.4206 samples/sec | ETA 00:28:25
2022-09-07 13:24:39 [INFO]	[TRAIN] epoch: 113, iter: 10590/13950, loss: 0.1060, DSC: 90.0141, lr: 0.002778, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6435 samples/sec | ETA 00:28:12
2022-09-07 13:24:44 [INFO]	[TRAIN] epoch: 113, iter: 10600/13950, loss: 0.0962, DSC: 91.0205, lr: 0.002770, batch_cost: 0.5030, reader_cost: 0.00024, ips: 47.7172 samples/sec | ETA 00:28:04
2022-09-07 13:24:50 [INFO]	[TRAIN] epoch: 114, iter: 10610/13950, loss: 0.0991, DSC: 90.8683, lr: 0.002763, batch_cost: 0.6093, reader_cost: 0.11405, ips: 39.3871 samples/sec | ETA 00:33:55
2022-09-07 13:24:55 [INFO]	[TRAIN] epoch: 114, iter: 10620/13950, loss: 0.0966, DSC: 90.9654, lr: 0.002755, batch_cost: 0.5057, reader_cost: 0.00029, ips: 47.4609 samples/sec | ETA 00:28:03
2022-09-07 13:25:00 [INFO]	[TRAIN] epoch: 114, iter: 10630/13950, loss: 0.0942, DSC: 91.2813, lr: 0.002748, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6608 samples/sec | ETA 00:27:51
2022-09-07 13:25:05 [INFO]	[TRAIN] epoch: 114, iter: 10640/13950, loss: 0.1043, DSC: 90.2846, lr: 0.002741, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5138 samples/sec | ETA 00:27:51
2022-09-07 13:25:10 [INFO]	[TRAIN] epoch: 114, iter: 10650/13950, loss: 0.1289, DSC: 87.8316, lr: 0.002733, batch_cost: 0.5031, reader_cost: 0.00026, ips: 47.7030 samples/sec | ETA 00:27:40
2022-09-07 13:25:15 [INFO]	[TRAIN] epoch: 114, iter: 10660/13950, loss: 0.0907, DSC: 91.5618, lr: 0.002726, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6348 samples/sec | ETA 00:27:37
2022-09-07 13:25:20 [INFO]	[TRAIN] epoch: 114, iter: 10670/13950, loss: 0.1111, DSC: 89.5782, lr: 0.002718, batch_cost: 0.5027, reader_cost: 0.00032, ips: 47.7426 samples/sec | ETA 00:27:28
2022-09-07 13:25:25 [INFO]	[TRAIN] epoch: 114, iter: 10680/13950, loss: 0.1073, DSC: 90.0974, lr: 0.002711, batch_cost: 0.5047, reader_cost: 0.00037, ips: 47.5575 samples/sec | ETA 00:27:30
2022-09-07 13:25:30 [INFO]	[TRAIN] epoch: 114, iter: 10690/13950, loss: 0.1223, DSC: 88.4668, lr: 0.002703, batch_cost: 0.5021, reader_cost: 0.00025, ips: 47.7969 samples/sec | ETA 00:27:16
2022-09-07 13:25:36 [INFO]	[TRAIN] epoch: 115, iter: 10700/13950, loss: 0.1193, DSC: 88.7187, lr: 0.002696, batch_cost: 0.6248, reader_cost: 0.11549, ips: 38.4122 samples/sec | ETA 00:33:50
2022-09-07 13:25:41 [INFO]	[TRAIN] epoch: 115, iter: 10710/13950, loss: 0.1030, DSC: 90.3251, lr: 0.002688, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6582 samples/sec | ETA 00:27:11
2022-09-07 13:25:46 [INFO]	[TRAIN] epoch: 115, iter: 10720/13950, loss: 0.1012, DSC: 90.5595, lr: 0.002681, batch_cost: 0.5028, reader_cost: 0.00026, ips: 47.7294 samples/sec | ETA 00:27:04
2022-09-07 13:25:51 [INFO]	[TRAIN] epoch: 115, iter: 10730/13950, loss: 0.1300, DSC: 87.5842, lr: 0.002673, batch_cost: 0.5030, reader_cost: 0.00025, ips: 47.7105 samples/sec | ETA 00:26:59
2022-09-07 13:25:56 [INFO]	[TRAIN] epoch: 115, iter: 10740/13950, loss: 0.1156, DSC: 89.1193, lr: 0.002666, batch_cost: 0.5058, reader_cost: 0.00028, ips: 47.4489 samples/sec | ETA 00:27:03
2022-09-07 13:26:02 [INFO]	[TRAIN] epoch: 115, iter: 10750/13950, loss: 0.0975, DSC: 90.8769, lr: 0.002659, batch_cost: 0.5047, reader_cost: 0.00027, ips: 47.5549 samples/sec | ETA 00:26:54
2022-09-07 13:26:07 [INFO]	[TRAIN] epoch: 115, iter: 10760/13950, loss: 0.1091, DSC: 89.8548, lr: 0.002651, batch_cost: 0.5053, reader_cost: 0.00029, ips: 47.5002 samples/sec | ETA 00:26:51
2022-09-07 13:26:12 [INFO]	[TRAIN] epoch: 115, iter: 10770/13950, loss: 0.1115, DSC: 89.6587, lr: 0.002644, batch_cost: 0.5024, reader_cost: 0.00026, ips: 47.7670 samples/sec | ETA 00:26:37
2022-09-07 13:26:17 [INFO]	[TRAIN] epoch: 115, iter: 10780/13950, loss: 0.1090, DSC: 89.8216, lr: 0.002636, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6086 samples/sec | ETA 00:26:38
2022-09-07 13:26:23 [INFO]	[TRAIN] epoch: 116, iter: 10790/13950, loss: 0.1666, DSC: 83.9733, lr: 0.002629, batch_cost: 0.6110, reader_cost: 0.13161, ips: 39.2786 samples/sec | ETA 00:32:10
2022-09-07 13:26:28 [INFO]	[TRAIN] epoch: 116, iter: 10800/13950, loss: 0.0998, DSC: 90.7489, lr: 0.002621, batch_cost: 0.5166, reader_cost: 0.00026, ips: 46.4610 samples/sec | ETA 00:27:07
2022-09-07 13:26:33 [INFO]	[TRAIN] epoch: 116, iter: 10810/13950, loss: 0.0940, DSC: 91.3090, lr: 0.002614, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5935 samples/sec | ETA 00:26:23
2022-09-07 13:26:38 [INFO]	[TRAIN] epoch: 116, iter: 10820/13950, loss: 0.0981, DSC: 90.8985, lr: 0.002606, batch_cost: 0.5052, reader_cost: 0.00026, ips: 47.5075 samples/sec | ETA 00:26:21
2022-09-07 13:26:43 [INFO]	[TRAIN] epoch: 116, iter: 10830/13950, loss: 0.1143, DSC: 89.3141, lr: 0.002599, batch_cost: 0.5035, reader_cost: 0.00032, ips: 47.6654 samples/sec | ETA 00:26:10
2022-09-07 13:26:48 [INFO]	[TRAIN] epoch: 116, iter: 10840/13950, loss: 0.1282, DSC: 87.8716, lr: 0.002591, batch_cost: 0.5035, reader_cost: 0.00027, ips: 47.6643 samples/sec | ETA 00:26:05
2022-09-07 13:26:53 [INFO]	[TRAIN] epoch: 116, iter: 10850/13950, loss: 0.1110, DSC: 89.6425, lr: 0.002584, batch_cost: 0.5056, reader_cost: 0.00028, ips: 47.4654 samples/sec | ETA 00:26:07
2022-09-07 13:26:58 [INFO]	[TRAIN] epoch: 116, iter: 10860/13950, loss: 0.1027, DSC: 90.3509, lr: 0.002576, batch_cost: 0.5086, reader_cost: 0.00029, ips: 47.1854 samples/sec | ETA 00:26:11
2022-09-07 13:27:03 [INFO]	[TRAIN] epoch: 116, iter: 10870/13950, loss: 0.1262, DSC: 88.0567, lr: 0.002569, batch_cost: 0.5037, reader_cost: 0.00028, ips: 47.6475 samples/sec | ETA 00:25:51
2022-09-07 13:27:08 [INFO]	[TRAIN] epoch: 116, iter: 10880/13950, loss: 0.1002, DSC: 90.5848, lr: 0.002561, batch_cost: 0.5043, reader_cost: 0.00025, ips: 47.5949 samples/sec | ETA 00:25:48
2022-09-07 13:27:14 [INFO]	[TRAIN] epoch: 117, iter: 10890/13950, loss: 0.1569, DSC: 84.8707, lr: 0.002554, batch_cost: 0.6130, reader_cost: 0.12556, ips: 39.1506 samples/sec | ETA 00:31:15
2022-09-07 13:27:19 [INFO]	[TRAIN] epoch: 117, iter: 10900/13950, loss: 0.1118, DSC: 89.6633, lr: 0.002546, batch_cost: 0.5016, reader_cost: 0.00029, ips: 47.8431 samples/sec | ETA 00:25:29
2022-09-07 13:27:24 [INFO]	[TRAIN] epoch: 117, iter: 10910/13950, loss: 0.0913, DSC: 91.5218, lr: 0.002539, batch_cost: 0.5036, reader_cost: 0.00034, ips: 47.6539 samples/sec | ETA 00:25:31
2022-09-07 13:27:30 [INFO]	[TRAIN] epoch: 117, iter: 10920/13950, loss: 0.1013, DSC: 90.5778, lr: 0.002531, batch_cost: 0.5042, reader_cost: 0.00030, ips: 47.5972 samples/sec | ETA 00:25:27
2022-09-07 13:27:35 [INFO]	[TRAIN] epoch: 117, iter: 10930/13950, loss: 0.0925, DSC: 91.3695, lr: 0.002524, batch_cost: 0.5034, reader_cost: 0.00030, ips: 47.6722 samples/sec | ETA 00:25:20
2022-09-07 13:27:40 [INFO]	[TRAIN] epoch: 117, iter: 10940/13950, loss: 0.1095, DSC: 89.6501, lr: 0.002516, batch_cost: 0.5064, reader_cost: 0.00028, ips: 47.3909 samples/sec | ETA 00:25:24
2022-09-07 13:27:45 [INFO]	[TRAIN] epoch: 117, iter: 10950/13950, loss: 0.0971, DSC: 91.0126, lr: 0.002509, batch_cost: 0.5051, reader_cost: 0.00029, ips: 47.5151 samples/sec | ETA 00:25:15
2022-09-07 13:27:50 [INFO]	[TRAIN] epoch: 117, iter: 10960/13950, loss: 0.0943, DSC: 91.1948, lr: 0.002501, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5127 samples/sec | ETA 00:25:10
2022-09-07 13:27:55 [INFO]	[TRAIN] epoch: 117, iter: 10970/13950, loss: 0.1126, DSC: 89.4513, lr: 0.002493, batch_cost: 0.5054, reader_cost: 0.00041, ips: 47.4890 samples/sec | ETA 00:25:06
2022-09-07 13:28:01 [INFO]	[TRAIN] epoch: 118, iter: 10980/13950, loss: 0.1898, DSC: 81.5777, lr: 0.002486, batch_cost: 0.6519, reader_cost: 0.13000, ips: 36.8157 samples/sec | ETA 00:32:16
2022-09-07 13:28:06 [INFO]	[TRAIN] epoch: 118, iter: 10990/13950, loss: 0.1083, DSC: 89.7976, lr: 0.002478, batch_cost: 0.5068, reader_cost: 0.00028, ips: 47.3553 samples/sec | ETA 00:25:00
2022-09-07 13:28:11 [INFO]	[TRAIN] epoch: 118, iter: 11000/13950, loss: 0.0899, DSC: 91.7102, lr: 0.002471, batch_cost: 0.5060, reader_cost: 0.00027, ips: 47.4319 samples/sec | ETA 00:24:52
2022-09-07 13:28:11 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0044 - reader cost: 1.1563
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2699 - reader cost: 0.5809
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0486 - reader cost: 0.3949 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.2057 - reader cost: 0.4529
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1368 - reader cost: 0.3667
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0895 - reader cost: 0.3162
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0191 - reader cost: 0.2737
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1204 - reader cost: 0.3238
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0936 - reader cost: 0.2902
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0684 - reader cost: 0.2643
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0174 - reader cost: 0.2417
12/12 [==============================] - 12s 980ms/step - batch_cost: 0.9799 - reader cost: 0.2233
2022-09-07 13:28:23 [INFO]	[EVAL] #Images: 12, Dice: 0.8252, Loss: 0.194721
2022-09-07 13:28:23 [INFO]	[EVAL] Class dice: 
[0.9961 0.904  0.5463 0.8849 0.8478 0.9489 0.632  0.8845 0.7822]
2022-09-07 13:28:25 [INFO]	[EVAL] The model with the best validation mDice (0.8262) was saved at iter 10000.
2022-09-07 13:28:30 [INFO]	[TRAIN] epoch: 118, iter: 11010/13950, loss: 0.1203, DSC: 88.8826, lr: 0.002463, batch_cost: 0.5042, reader_cost: 0.00040, ips: 47.5975 samples/sec | ETA 00:24:42
2022-09-07 13:28:35 [INFO]	[TRAIN] epoch: 118, iter: 11020/13950, loss: 0.1184, DSC: 88.8782, lr: 0.002456, batch_cost: 0.5069, reader_cost: 0.00028, ips: 47.3422 samples/sec | ETA 00:24:45
2022-09-07 13:28:41 [INFO]	[TRAIN] epoch: 118, iter: 11030/13950, loss: 0.1084, DSC: 89.7696, lr: 0.002448, batch_cost: 0.5052, reader_cost: 0.00027, ips: 47.5043 samples/sec | ETA 00:24:35
2022-09-07 13:28:46 [INFO]	[TRAIN] epoch: 118, iter: 11040/13950, loss: 0.1264, DSC: 88.0140, lr: 0.002441, batch_cost: 0.5086, reader_cost: 0.00030, ips: 47.1881 samples/sec | ETA 00:24:40
2022-09-07 13:28:51 [INFO]	[TRAIN] epoch: 118, iter: 11050/13950, loss: 0.1067, DSC: 90.0283, lr: 0.002433, batch_cost: 0.5069, reader_cost: 0.00029, ips: 47.3463 samples/sec | ETA 00:24:30
2022-09-07 13:28:56 [INFO]	[TRAIN] epoch: 118, iter: 11060/13950, loss: 0.0973, DSC: 90.9635, lr: 0.002426, batch_cost: 0.5057, reader_cost: 0.00039, ips: 47.4586 samples/sec | ETA 00:24:21
2022-09-07 13:29:02 [INFO]	[TRAIN] epoch: 119, iter: 11070/13950, loss: 0.1233, DSC: 88.2246, lr: 0.002418, batch_cost: 0.6032, reader_cost: 0.11423, ips: 39.7861 samples/sec | ETA 00:28:57
2022-09-07 13:29:07 [INFO]	[TRAIN] epoch: 119, iter: 11080/13950, loss: 0.1163, DSC: 89.1041, lr: 0.002411, batch_cost: 0.5379, reader_cost: 0.00027, ips: 44.6198 samples/sec | ETA 00:25:43
2022-09-07 13:29:12 [INFO]	[TRAIN] epoch: 119, iter: 11090/13950, loss: 0.1051, DSC: 90.2917, lr: 0.002403, batch_cost: 0.5026, reader_cost: 0.00027, ips: 47.7482 samples/sec | ETA 00:23:57
2022-09-07 13:29:17 [INFO]	[TRAIN] epoch: 119, iter: 11100/13950, loss: 0.1122, DSC: 89.5734, lr: 0.002395, batch_cost: 0.5049, reader_cost: 0.00028, ips: 47.5369 samples/sec | ETA 00:23:58
2022-09-07 13:29:22 [INFO]	[TRAIN] epoch: 119, iter: 11110/13950, loss: 0.1031, DSC: 90.3483, lr: 0.002388, batch_cost: 0.5048, reader_cost: 0.00034, ips: 47.5470 samples/sec | ETA 00:23:53
2022-09-07 13:29:27 [INFO]	[TRAIN] epoch: 119, iter: 11120/13950, loss: 0.1204, DSC: 88.5967, lr: 0.002380, batch_cost: 0.5033, reader_cost: 0.00025, ips: 47.6850 samples/sec | ETA 00:23:44
2022-09-07 13:29:32 [INFO]	[TRAIN] epoch: 119, iter: 11130/13950, loss: 0.1075, DSC: 89.8799, lr: 0.002373, batch_cost: 0.5037, reader_cost: 0.00025, ips: 47.6480 samples/sec | ETA 00:23:40
2022-09-07 13:29:37 [INFO]	[TRAIN] epoch: 119, iter: 11140/13950, loss: 0.0932, DSC: 91.3634, lr: 0.002365, batch_cost: 0.5075, reader_cost: 0.00028, ips: 47.2942 samples/sec | ETA 00:23:45
2022-09-07 13:29:42 [INFO]	[TRAIN] epoch: 119, iter: 11150/13950, loss: 0.1075, DSC: 90.0626, lr: 0.002358, batch_cost: 0.5056, reader_cost: 0.00027, ips: 47.4687 samples/sec | ETA 00:23:35
2022-09-07 13:29:47 [INFO]	[TRAIN] epoch: 120, iter: 11160/13950, loss: 0.1247, DSC: 88.2918, lr: 0.002350, batch_cost: 0.4754, reader_cost: 0.00029, ips: 50.4798 samples/sec | ETA 00:22:06
2022-09-07 13:29:54 [INFO]	[TRAIN] epoch: 120, iter: 11170/13950, loss: 0.0984, DSC: 90.7899, lr: 0.002342, batch_cost: 0.6283, reader_cost: 0.11075, ips: 38.2013 samples/sec | ETA 00:29:06
2022-09-07 13:29:59 [INFO]	[TRAIN] epoch: 120, iter: 11180/13950, loss: 0.1144, DSC: 89.2426, lr: 0.002335, batch_cost: 0.5058, reader_cost: 0.00034, ips: 47.4498 samples/sec | ETA 00:23:21
2022-09-07 13:30:04 [INFO]	[TRAIN] epoch: 120, iter: 11190/13950, loss: 0.0916, DSC: 91.5975, lr: 0.002327, batch_cost: 0.5030, reader_cost: 0.00026, ips: 47.7140 samples/sec | ETA 00:23:08
2022-09-07 13:30:09 [INFO]	[TRAIN] epoch: 120, iter: 11200/13950, loss: 0.1032, DSC: 90.3392, lr: 0.002320, batch_cost: 0.5151, reader_cost: 0.00027, ips: 46.5896 samples/sec | ETA 00:23:36
2022-09-07 13:30:14 [INFO]	[TRAIN] epoch: 120, iter: 11210/13950, loss: 0.1395, DSC: 86.7380, lr: 0.002312, batch_cost: 0.5037, reader_cost: 0.00028, ips: 47.6487 samples/sec | ETA 00:23:00
2022-09-07 13:30:19 [INFO]	[TRAIN] epoch: 120, iter: 11220/13950, loss: 0.1035, DSC: 90.4001, lr: 0.002304, batch_cost: 0.5046, reader_cost: 0.00026, ips: 47.5607 samples/sec | ETA 00:22:57
2022-09-07 13:30:24 [INFO]	[TRAIN] epoch: 120, iter: 11230/13950, loss: 0.0860, DSC: 92.0375, lr: 0.002297, batch_cost: 0.5064, reader_cost: 0.00035, ips: 47.3911 samples/sec | ETA 00:22:57
2022-09-07 13:30:29 [INFO]	[TRAIN] epoch: 120, iter: 11240/13950, loss: 0.1143, DSC: 89.2225, lr: 0.002289, batch_cost: 0.5053, reader_cost: 0.00027, ips: 47.4951 samples/sec | ETA 00:22:49
2022-09-07 13:30:34 [INFO]	[TRAIN] epoch: 120, iter: 11250/13950, loss: 0.1268, DSC: 88.0885, lr: 0.002282, batch_cost: 0.5042, reader_cost: 0.00030, ips: 47.6026 samples/sec | ETA 00:22:41
2022-09-07 13:30:40 [INFO]	[TRAIN] epoch: 121, iter: 11260/13950, loss: 0.1115, DSC: 89.6030, lr: 0.002274, batch_cost: 0.6075, reader_cost: 0.10892, ips: 39.5057 samples/sec | ETA 00:27:14
2022-09-07 13:30:45 [INFO]	[TRAIN] epoch: 121, iter: 11270/13950, loss: 0.0973, DSC: 90.9081, lr: 0.002266, batch_cost: 0.5043, reader_cost: 0.00026, ips: 47.5931 samples/sec | ETA 00:22:31
2022-09-07 13:30:50 [INFO]	[TRAIN] epoch: 121, iter: 11280/13950, loss: 0.1016, DSC: 90.4861, lr: 0.002259, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5597 samples/sec | ETA 00:22:27
2022-09-07 13:30:55 [INFO]	[TRAIN] epoch: 121, iter: 11290/13950, loss: 0.1161, DSC: 89.1928, lr: 0.002251, batch_cost: 0.5051, reader_cost: 0.00028, ips: 47.5138 samples/sec | ETA 00:22:23
2022-09-07 13:31:00 [INFO]	[TRAIN] epoch: 121, iter: 11300/13950, loss: 0.0914, DSC: 91.5646, lr: 0.002244, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6229 samples/sec | ETA 00:22:15
2022-09-07 13:31:05 [INFO]	[TRAIN] epoch: 121, iter: 11310/13950, loss: 0.1194, DSC: 88.6990, lr: 0.002236, batch_cost: 0.5039, reader_cost: 0.00026, ips: 47.6322 samples/sec | ETA 00:22:10
2022-09-07 13:31:10 [INFO]	[TRAIN] epoch: 121, iter: 11320/13950, loss: 0.1085, DSC: 89.9074, lr: 0.002228, batch_cost: 0.5080, reader_cost: 0.00031, ips: 47.2404 samples/sec | ETA 00:22:16
2022-09-07 13:31:15 [INFO]	[TRAIN] epoch: 121, iter: 11330/13950, loss: 0.1081, DSC: 89.8056, lr: 0.002221, batch_cost: 0.5039, reader_cost: 0.00026, ips: 47.6314 samples/sec | ETA 00:22:00
2022-09-07 13:31:20 [INFO]	[TRAIN] epoch: 121, iter: 11340/13950, loss: 0.1108, DSC: 89.6634, lr: 0.002213, batch_cost: 0.5027, reader_cost: 0.00032, ips: 47.7427 samples/sec | ETA 00:21:52
2022-09-07 13:31:26 [INFO]	[TRAIN] epoch: 122, iter: 11350/13950, loss: 0.1245, DSC: 88.4492, lr: 0.002206, batch_cost: 0.6009, reader_cost: 0.11044, ips: 39.9407 samples/sec | ETA 00:26:02
2022-09-07 13:31:32 [INFO]	[TRAIN] epoch: 122, iter: 11360/13950, loss: 0.1186, DSC: 88.9398, lr: 0.002198, batch_cost: 0.5086, reader_cost: 0.00031, ips: 47.1930 samples/sec | ETA 00:21:57
2022-09-07 13:31:37 [INFO]	[TRAIN] epoch: 122, iter: 11370/13950, loss: 0.1182, DSC: 88.8161, lr: 0.002190, batch_cost: 0.5088, reader_cost: 0.00030, ips: 47.1699 samples/sec | ETA 00:21:52
2022-09-07 13:31:42 [INFO]	[TRAIN] epoch: 122, iter: 11380/13950, loss: 0.1014, DSC: 90.6203, lr: 0.002183, batch_cost: 0.5053, reader_cost: 0.00028, ips: 47.4975 samples/sec | ETA 00:21:38
2022-09-07 13:31:47 [INFO]	[TRAIN] epoch: 122, iter: 11390/13950, loss: 0.1074, DSC: 90.0322, lr: 0.002175, batch_cost: 0.5057, reader_cost: 0.00028, ips: 47.4561 samples/sec | ETA 00:21:34
2022-09-07 13:31:52 [INFO]	[TRAIN] epoch: 122, iter: 11400/13950, loss: 0.1026, DSC: 90.3625, lr: 0.002167, batch_cost: 0.5063, reader_cost: 0.00034, ips: 47.4040 samples/sec | ETA 00:21:31
2022-09-07 13:31:57 [INFO]	[TRAIN] epoch: 122, iter: 11410/13950, loss: 0.1076, DSC: 89.8651, lr: 0.002160, batch_cost: 0.5065, reader_cost: 0.00029, ips: 47.3855 samples/sec | ETA 00:21:26
2022-09-07 13:32:02 [INFO]	[TRAIN] epoch: 122, iter: 11420/13950, loss: 0.1024, DSC: 90.4281, lr: 0.002152, batch_cost: 0.5049, reader_cost: 0.00033, ips: 47.5357 samples/sec | ETA 00:21:17
2022-09-07 13:32:07 [INFO]	[TRAIN] epoch: 122, iter: 11430/13950, loss: 0.1119, DSC: 89.4850, lr: 0.002144, batch_cost: 0.5039, reader_cost: 0.00027, ips: 47.6274 samples/sec | ETA 00:21:09
2022-09-07 13:32:13 [INFO]	[TRAIN] epoch: 123, iter: 11440/13950, loss: 0.1154, DSC: 89.2040, lr: 0.002137, batch_cost: 0.6377, reader_cost: 0.14942, ips: 37.6373 samples/sec | ETA 00:26:40
2022-09-07 13:32:19 [INFO]	[TRAIN] epoch: 123, iter: 11450/13950, loss: 0.1181, DSC: 88.8760, lr: 0.002129, batch_cost: 0.5201, reader_cost: 0.00026, ips: 46.1467 samples/sec | ETA 00:21:40
2022-09-07 13:32:24 [INFO]	[TRAIN] epoch: 123, iter: 11460/13950, loss: 0.1068, DSC: 89.9351, lr: 0.002121, batch_cost: 0.5011, reader_cost: 0.00025, ips: 47.8945 samples/sec | ETA 00:20:47
2022-09-07 13:32:29 [INFO]	[TRAIN] epoch: 123, iter: 11470/13950, loss: 0.1003, DSC: 90.6129, lr: 0.002114, batch_cost: 0.5027, reader_cost: 0.00025, ips: 47.7387 samples/sec | ETA 00:20:46
2022-09-07 13:32:34 [INFO]	[TRAIN] epoch: 123, iter: 11480/13950, loss: 0.1174, DSC: 88.9637, lr: 0.002106, batch_cost: 0.5027, reader_cost: 0.00024, ips: 47.7419 samples/sec | ETA 00:20:41
2022-09-07 13:32:39 [INFO]	[TRAIN] epoch: 123, iter: 11490/13950, loss: 0.1263, DSC: 88.0481, lr: 0.002098, batch_cost: 0.5025, reader_cost: 0.00024, ips: 47.7574 samples/sec | ETA 00:20:36
2022-09-07 13:32:44 [INFO]	[TRAIN] epoch: 123, iter: 11500/13950, loss: 0.1121, DSC: 89.4636, lr: 0.002091, batch_cost: 0.5027, reader_cost: 0.00025, ips: 47.7424 samples/sec | ETA 00:20:31
2022-09-07 13:32:49 [INFO]	[TRAIN] epoch: 123, iter: 11510/13950, loss: 0.1059, DSC: 90.0726, lr: 0.002083, batch_cost: 0.5035, reader_cost: 0.00026, ips: 47.6617 samples/sec | ETA 00:20:28
2022-09-07 13:32:54 [INFO]	[TRAIN] epoch: 123, iter: 11520/13950, loss: 0.1013, DSC: 90.6178, lr: 0.002075, batch_cost: 0.5034, reader_cost: 0.00035, ips: 47.6723 samples/sec | ETA 00:20:23
2022-09-07 13:32:59 [INFO]	[TRAIN] epoch: 123, iter: 11530/13950, loss: 0.1141, DSC: 89.3167, lr: 0.002068, batch_cost: 0.5003, reader_cost: 0.00023, ips: 47.9750 samples/sec | ETA 00:20:10
2022-09-07 13:33:05 [INFO]	[TRAIN] epoch: 124, iter: 11540/13950, loss: 0.1027, DSC: 90.3761, lr: 0.002060, batch_cost: 0.5918, reader_cost: 0.10666, ips: 40.5571 samples/sec | ETA 00:23:46
2022-09-07 13:33:10 [INFO]	[TRAIN] epoch: 124, iter: 11550/13950, loss: 0.0945, DSC: 91.1483, lr: 0.002052, batch_cost: 0.5024, reader_cost: 0.00026, ips: 47.7675 samples/sec | ETA 00:20:05
2022-09-07 13:33:15 [INFO]	[TRAIN] epoch: 124, iter: 11560/13950, loss: 0.1312, DSC: 87.5416, lr: 0.002045, batch_cost: 0.5053, reader_cost: 0.00029, ips: 47.4991 samples/sec | ETA 00:20:07
2022-09-07 13:33:20 [INFO]	[TRAIN] epoch: 124, iter: 11570/13950, loss: 0.1067, DSC: 90.0015, lr: 0.002037, batch_cost: 0.5072, reader_cost: 0.00027, ips: 47.3181 samples/sec | ETA 00:20:07
2022-09-07 13:33:25 [INFO]	[TRAIN] epoch: 124, iter: 11580/13950, loss: 0.1336, DSC: 87.2876, lr: 0.002029, batch_cost: 0.5065, reader_cost: 0.00028, ips: 47.3868 samples/sec | ETA 00:20:00
2022-09-07 13:33:30 [INFO]	[TRAIN] epoch: 124, iter: 11590/13950, loss: 0.0934, DSC: 91.4013, lr: 0.002021, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6384 samples/sec | ETA 00:19:48
2022-09-07 13:33:35 [INFO]	[TRAIN] epoch: 124, iter: 11600/13950, loss: 0.1055, DSC: 90.1851, lr: 0.002014, batch_cost: 0.5040, reader_cost: 0.00028, ips: 47.6145 samples/sec | ETA 00:19:44
2022-09-07 13:33:40 [INFO]	[TRAIN] epoch: 124, iter: 11610/13950, loss: 0.1059, DSC: 90.2203, lr: 0.002006, batch_cost: 0.5024, reader_cost: 0.00025, ips: 47.7699 samples/sec | ETA 00:19:35
2022-09-07 13:33:45 [INFO]	[TRAIN] epoch: 124, iter: 11620/13950, loss: 0.1033, DSC: 90.3181, lr: 0.001998, batch_cost: 0.5027, reader_cost: 0.00025, ips: 47.7381 samples/sec | ETA 00:19:31
2022-09-07 13:33:52 [INFO]	[TRAIN] epoch: 125, iter: 11630/13950, loss: 0.1093, DSC: 89.7380, lr: 0.001991, batch_cost: 0.6594, reader_cost: 0.16391, ips: 36.3942 samples/sec | ETA 00:25:29
2022-09-07 13:33:57 [INFO]	[TRAIN] epoch: 125, iter: 11640/13950, loss: 0.0811, DSC: 92.5293, lr: 0.001983, batch_cost: 0.5069, reader_cost: 0.00028, ips: 47.3453 samples/sec | ETA 00:19:30
2022-09-07 13:34:02 [INFO]	[TRAIN] epoch: 125, iter: 11650/13950, loss: 0.0976, DSC: 90.9197, lr: 0.001975, batch_cost: 0.5066, reader_cost: 0.00027, ips: 47.3715 samples/sec | ETA 00:19:25
2022-09-07 13:34:07 [INFO]	[TRAIN] epoch: 125, iter: 11660/13950, loss: 0.1099, DSC: 89.6917, lr: 0.001967, batch_cost: 0.5041, reader_cost: 0.00026, ips: 47.6099 samples/sec | ETA 00:19:14
2022-09-07 13:34:12 [INFO]	[TRAIN] epoch: 125, iter: 11670/13950, loss: 0.0917, DSC: 91.5013, lr: 0.001960, batch_cost: 0.5036, reader_cost: 0.00025, ips: 47.6583 samples/sec | ETA 00:19:08
2022-09-07 13:34:17 [INFO]	[TRAIN] epoch: 125, iter: 11680/13950, loss: 0.1059, DSC: 90.1624, lr: 0.001952, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6105 samples/sec | ETA 00:19:04
2022-09-07 13:34:22 [INFO]	[TRAIN] epoch: 125, iter: 11690/13950, loss: 0.1152, DSC: 89.0424, lr: 0.001944, batch_cost: 0.5049, reader_cost: 0.00028, ips: 47.5321 samples/sec | ETA 00:19:01
2022-09-07 13:34:27 [INFO]	[TRAIN] epoch: 125, iter: 11700/13950, loss: 0.1145, DSC: 89.3141, lr: 0.001937, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5826 samples/sec | ETA 00:18:54
2022-09-07 13:34:32 [INFO]	[TRAIN] epoch: 125, iter: 11710/13950, loss: 0.1093, DSC: 89.8016, lr: 0.001929, batch_cost: 0.5041, reader_cost: 0.00026, ips: 47.6118 samples/sec | ETA 00:18:49
2022-09-07 13:34:38 [INFO]	[TRAIN] epoch: 126, iter: 11720/13950, loss: 0.1281, DSC: 87.9994, lr: 0.001921, batch_cost: 0.5890, reader_cost: 0.10992, ips: 40.7459 samples/sec | ETA 00:21:53
2022-09-07 13:34:43 [INFO]	[TRAIN] epoch: 126, iter: 11730/13950, loss: 0.1318, DSC: 87.5218, lr: 0.001913, batch_cost: 0.5064, reader_cost: 0.00026, ips: 47.3900 samples/sec | ETA 00:18:44
2022-09-07 13:34:48 [INFO]	[TRAIN] epoch: 126, iter: 11740/13950, loss: 0.0816, DSC: 92.4856, lr: 0.001906, batch_cost: 0.5033, reader_cost: 0.00027, ips: 47.6812 samples/sec | ETA 00:18:32
2022-09-07 13:34:53 [INFO]	[TRAIN] epoch: 126, iter: 11750/13950, loss: 0.0947, DSC: 91.2998, lr: 0.001898, batch_cost: 0.5049, reader_cost: 0.00028, ips: 47.5334 samples/sec | ETA 00:18:30
2022-09-07 13:34:58 [INFO]	[TRAIN] epoch: 126, iter: 11760/13950, loss: 0.1050, DSC: 90.1343, lr: 0.001890, batch_cost: 0.5060, reader_cost: 0.00029, ips: 47.4303 samples/sec | ETA 00:18:28
2022-09-07 13:35:03 [INFO]	[TRAIN] epoch: 126, iter: 11770/13950, loss: 0.1203, DSC: 88.6452, lr: 0.001882, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5897 samples/sec | ETA 00:18:19
2022-09-07 13:35:08 [INFO]	[TRAIN] epoch: 126, iter: 11780/13950, loss: 0.1120, DSC: 89.5841, lr: 0.001874, batch_cost: 0.5027, reader_cost: 0.00025, ips: 47.7462 samples/sec | ETA 00:18:10
2022-09-07 13:35:13 [INFO]	[TRAIN] epoch: 126, iter: 11790/13950, loss: 0.1087, DSC: 89.7454, lr: 0.001867, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6610 samples/sec | ETA 00:18:07
2022-09-07 13:35:18 [INFO]	[TRAIN] epoch: 126, iter: 11800/13950, loss: 0.0927, DSC: 91.3923, lr: 0.001859, batch_cost: 0.5034, reader_cost: 0.00026, ips: 47.6787 samples/sec | ETA 00:18:02
2022-09-07 13:35:23 [INFO]	[TRAIN] epoch: 126, iter: 11810/13950, loss: 0.1155, DSC: 89.1387, lr: 0.001851, batch_cost: 0.5044, reader_cost: 0.00023, ips: 47.5777 samples/sec | ETA 00:17:59
2022-09-07 13:35:29 [INFO]	[TRAIN] epoch: 127, iter: 11820/13950, loss: 0.1166, DSC: 89.0815, lr: 0.001843, batch_cost: 0.6131, reader_cost: 0.12047, ips: 39.1425 samples/sec | ETA 00:21:45
2022-09-07 13:35:34 [INFO]	[TRAIN] epoch: 127, iter: 11830/13950, loss: 0.1240, DSC: 88.1466, lr: 0.001836, batch_cost: 0.5046, reader_cost: 0.00034, ips: 47.5598 samples/sec | ETA 00:17:49
2022-09-07 13:35:39 [INFO]	[TRAIN] epoch: 127, iter: 11840/13950, loss: 0.0954, DSC: 91.1326, lr: 0.001828, batch_cost: 0.5052, reader_cost: 0.00027, ips: 47.5081 samples/sec | ETA 00:17:45
2022-09-07 13:35:45 [INFO]	[TRAIN] epoch: 127, iter: 11850/13950, loss: 0.1145, DSC: 89.3314, lr: 0.001820, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6493 samples/sec | ETA 00:17:37
2022-09-07 13:35:50 [INFO]	[TRAIN] epoch: 127, iter: 11860/13950, loss: 0.1263, DSC: 88.1160, lr: 0.001812, batch_cost: 0.5032, reader_cost: 0.00025, ips: 47.6953 samples/sec | ETA 00:17:31
2022-09-07 13:35:55 [INFO]	[TRAIN] epoch: 127, iter: 11870/13950, loss: 0.1079, DSC: 89.8588, lr: 0.001804, batch_cost: 0.5056, reader_cost: 0.00034, ips: 47.4692 samples/sec | ETA 00:17:31
2022-09-07 13:36:00 [INFO]	[TRAIN] epoch: 127, iter: 11880/13950, loss: 0.1007, DSC: 90.5945, lr: 0.001797, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4880 samples/sec | ETA 00:17:26
2022-09-07 13:36:05 [INFO]	[TRAIN] epoch: 127, iter: 11890/13950, loss: 0.0989, DSC: 90.7980, lr: 0.001789, batch_cost: 0.5068, reader_cost: 0.00027, ips: 47.3589 samples/sec | ETA 00:17:23
2022-09-07 13:36:10 [INFO]	[TRAIN] epoch: 127, iter: 11900/13950, loss: 0.0863, DSC: 91.9917, lr: 0.001781, batch_cost: 0.5028, reader_cost: 0.00024, ips: 47.7344 samples/sec | ETA 00:17:10
2022-09-07 13:36:16 [INFO]	[TRAIN] epoch: 128, iter: 11910/13950, loss: 0.1130, DSC: 89.3882, lr: 0.001773, batch_cost: 0.6057, reader_cost: 0.11895, ips: 39.6237 samples/sec | ETA 00:20:35
2022-09-07 13:36:21 [INFO]	[TRAIN] epoch: 128, iter: 11920/13950, loss: 0.1203, DSC: 88.7462, lr: 0.001765, batch_cost: 0.5048, reader_cost: 0.00026, ips: 47.5418 samples/sec | ETA 00:17:04
2022-09-07 13:36:26 [INFO]	[TRAIN] epoch: 128, iter: 11930/13950, loss: 0.1066, DSC: 90.1072, lr: 0.001757, batch_cost: 0.5059, reader_cost: 0.00027, ips: 47.4441 samples/sec | ETA 00:17:01
2022-09-07 13:36:31 [INFO]	[TRAIN] epoch: 128, iter: 11940/13950, loss: 0.1136, DSC: 89.2658, lr: 0.001750, batch_cost: 0.5041, reader_cost: 0.00026, ips: 47.6059 samples/sec | ETA 00:16:53
2022-09-07 13:36:36 [INFO]	[TRAIN] epoch: 128, iter: 11950/13950, loss: 0.0891, DSC: 91.9144, lr: 0.001742, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6057 samples/sec | ETA 00:16:48
2022-09-07 13:36:41 [INFO]	[TRAIN] epoch: 128, iter: 11960/13950, loss: 0.1062, DSC: 90.0330, lr: 0.001734, batch_cost: 0.5035, reader_cost: 0.00026, ips: 47.6692 samples/sec | ETA 00:16:41
2022-09-07 13:36:46 [INFO]	[TRAIN] epoch: 128, iter: 11970/13950, loss: 0.1291, DSC: 87.7405, lr: 0.001726, batch_cost: 0.5037, reader_cost: 0.00025, ips: 47.6518 samples/sec | ETA 00:16:37
2022-09-07 13:36:51 [INFO]	[TRAIN] epoch: 128, iter: 11980/13950, loss: 0.1004, DSC: 90.5690, lr: 0.001718, batch_cost: 0.5044, reader_cost: 0.00027, ips: 47.5799 samples/sec | ETA 00:16:33
2022-09-07 13:36:56 [INFO]	[TRAIN] epoch: 128, iter: 11990/13950, loss: 0.0991, DSC: 90.7732, lr: 0.001710, batch_cost: 0.5060, reader_cost: 0.00035, ips: 47.4302 samples/sec | ETA 00:16:31
2022-09-07 13:37:02 [INFO]	[TRAIN] epoch: 129, iter: 12000/13950, loss: 0.1411, DSC: 86.5513, lr: 0.001703, batch_cost: 0.6136, reader_cost: 0.12025, ips: 39.1114 samples/sec | ETA 00:19:56
2022-09-07 13:37:02 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0615 - reader cost: 1.2160
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2992 - reader cost: 0.6106
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0674 - reader cost: 0.4145 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.2141 - reader cost: 0.4636
 5/12 [===========>..................] - ETA: 8s - batch_cost: 1.1428 - reader cost: 0.3752
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0922 - reader cost: 0.3203
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0215 - reader cost: 0.2773
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1215 - reader cost: 0.3258
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0947 - reader cost: 0.2919
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0690 - reader cost: 0.2658
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0182 - reader cost: 0.2431
12/12 [==============================] - 12s 981ms/step - batch_cost: 0.9809 - reader cost: 0.2246
2022-09-07 13:37:14 [INFO]	[EVAL] #Images: 12, Dice: 0.8239, Loss: 0.196395
2022-09-07 13:37:14 [INFO]	[EVAL] Class dice: 
[0.9962 0.9018 0.5463 0.8856 0.8463 0.9501 0.6291 0.8832 0.7764]
2022-09-07 13:37:16 [INFO]	[EVAL] The model with the best validation mDice (0.8262) was saved at iter 10000.
2022-09-07 13:37:21 [INFO]	[TRAIN] epoch: 129, iter: 12010/13950, loss: 0.1095, DSC: 89.7479, lr: 0.001695, batch_cost: 0.5042, reader_cost: 0.00040, ips: 47.5979 samples/sec | ETA 00:16:18
2022-09-07 13:37:26 [INFO]	[TRAIN] epoch: 129, iter: 12020/13950, loss: 0.0987, DSC: 90.7991, lr: 0.001687, batch_cost: 0.5047, reader_cost: 0.00028, ips: 47.5570 samples/sec | ETA 00:16:13
2022-09-07 13:37:31 [INFO]	[TRAIN] epoch: 129, iter: 12030/13950, loss: 0.1050, DSC: 90.1530, lr: 0.001679, batch_cost: 0.5042, reader_cost: 0.00027, ips: 47.6011 samples/sec | ETA 00:16:08
2022-09-07 13:37:36 [INFO]	[TRAIN] epoch: 129, iter: 12040/13950, loss: 0.0900, DSC: 91.7055, lr: 0.001671, batch_cost: 0.5048, reader_cost: 0.00027, ips: 47.5476 samples/sec | ETA 00:16:04
2022-09-07 13:37:41 [INFO]	[TRAIN] epoch: 129, iter: 12050/13950, loss: 0.1089, DSC: 89.7267, lr: 0.001663, batch_cost: 0.5041, reader_cost: 0.00033, ips: 47.6132 samples/sec | ETA 00:15:57
2022-09-07 13:37:47 [INFO]	[TRAIN] epoch: 129, iter: 12060/13950, loss: 0.0998, DSC: 90.6560, lr: 0.001655, batch_cost: 0.5040, reader_cost: 0.00025, ips: 47.6159 samples/sec | ETA 00:15:52
2022-09-07 13:37:52 [INFO]	[TRAIN] epoch: 129, iter: 12070/13950, loss: 0.1101, DSC: 89.7749, lr: 0.001648, batch_cost: 0.5047, reader_cost: 0.00026, ips: 47.5542 samples/sec | ETA 00:15:48
2022-09-07 13:37:57 [INFO]	[TRAIN] epoch: 129, iter: 12080/13950, loss: 0.0969, DSC: 90.9276, lr: 0.001640, batch_cost: 0.5054, reader_cost: 0.00028, ips: 47.4884 samples/sec | ETA 00:15:45
2022-09-07 13:38:01 [INFO]	[TRAIN] epoch: 130, iter: 12090/13950, loss: 0.1471, DSC: 85.9318, lr: 0.001632, batch_cost: 0.4734, reader_cost: 0.00021, ips: 50.7012 samples/sec | ETA 00:14:40
2022-09-07 13:38:08 [INFO]	[TRAIN] epoch: 130, iter: 12100/13950, loss: 0.1018, DSC: 90.6249, lr: 0.001624, batch_cost: 0.6465, reader_cost: 0.12437, ips: 37.1240 samples/sec | ETA 00:19:55
2022-09-07 13:38:13 [INFO]	[TRAIN] epoch: 130, iter: 12110/13950, loss: 0.1145, DSC: 89.2148, lr: 0.001616, batch_cost: 0.5071, reader_cost: 0.00026, ips: 47.3288 samples/sec | ETA 00:15:33
2022-09-07 13:38:18 [INFO]	[TRAIN] epoch: 130, iter: 12120/13950, loss: 0.0924, DSC: 91.4190, lr: 0.001608, batch_cost: 0.5053, reader_cost: 0.00027, ips: 47.4984 samples/sec | ETA 00:15:24
2022-09-07 13:38:23 [INFO]	[TRAIN] epoch: 130, iter: 12130/13950, loss: 0.1062, DSC: 90.0547, lr: 0.001600, batch_cost: 0.5063, reader_cost: 0.00027, ips: 47.4000 samples/sec | ETA 00:15:21
2022-09-07 13:38:28 [INFO]	[TRAIN] epoch: 130, iter: 12140/13950, loss: 0.1024, DSC: 90.3828, lr: 0.001592, batch_cost: 0.5083, reader_cost: 0.00027, ips: 47.2153 samples/sec | ETA 00:15:20
2022-09-07 13:38:33 [INFO]	[TRAIN] epoch: 130, iter: 12150/13950, loss: 0.1284, DSC: 87.8928, lr: 0.001584, batch_cost: 0.5025, reader_cost: 0.00032, ips: 47.7600 samples/sec | ETA 00:15:04
2022-09-07 13:38:38 [INFO]	[TRAIN] epoch: 130, iter: 12160/13950, loss: 0.1160, DSC: 89.0327, lr: 0.001576, batch_cost: 0.5037, reader_cost: 0.00025, ips: 47.6463 samples/sec | ETA 00:15:01
2022-09-07 13:38:43 [INFO]	[TRAIN] epoch: 130, iter: 12170/13950, loss: 0.0993, DSC: 90.7447, lr: 0.001568, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6606 samples/sec | ETA 00:14:56
2022-09-07 13:38:48 [INFO]	[TRAIN] epoch: 130, iter: 12180/13950, loss: 0.1020, DSC: 90.4448, lr: 0.001561, batch_cost: 0.5040, reader_cost: 0.00032, ips: 47.6160 samples/sec | ETA 00:14:52
2022-09-07 13:38:54 [INFO]	[TRAIN] epoch: 131, iter: 12190/13950, loss: 0.1597, DSC: 84.6800, lr: 0.001553, batch_cost: 0.6028, reader_cost: 0.11180, ips: 39.8130 samples/sec | ETA 00:17:40
2022-09-07 13:38:59 [INFO]	[TRAIN] epoch: 131, iter: 12200/13950, loss: 0.1086, DSC: 89.8490, lr: 0.001545, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5206 samples/sec | ETA 00:14:43
2022-09-07 13:39:04 [INFO]	[TRAIN] epoch: 131, iter: 12210/13950, loss: 0.1179, DSC: 88.8297, lr: 0.001537, batch_cost: 0.5061, reader_cost: 0.00028, ips: 47.4241 samples/sec | ETA 00:14:40
2022-09-07 13:39:09 [INFO]	[TRAIN] epoch: 131, iter: 12220/13950, loss: 0.0939, DSC: 91.3515, lr: 0.001529, batch_cost: 0.5073, reader_cost: 0.00044, ips: 47.3096 samples/sec | ETA 00:14:37
2022-09-07 13:39:14 [INFO]	[TRAIN] epoch: 131, iter: 12230/13950, loss: 0.1025, DSC: 90.4279, lr: 0.001521, batch_cost: 0.5043, reader_cost: 0.00033, ips: 47.5897 samples/sec | ETA 00:14:27
2022-09-07 13:39:20 [INFO]	[TRAIN] epoch: 131, iter: 12240/13950, loss: 0.0848, DSC: 92.1951, lr: 0.001513, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5168 samples/sec | ETA 00:14:23
2022-09-07 13:39:25 [INFO]	[TRAIN] epoch: 131, iter: 12250/13950, loss: 0.1149, DSC: 89.1208, lr: 0.001505, batch_cost: 0.5052, reader_cost: 0.00035, ips: 47.5079 samples/sec | ETA 00:14:18
2022-09-07 13:39:30 [INFO]	[TRAIN] epoch: 131, iter: 12260/13950, loss: 0.1043, DSC: 90.2019, lr: 0.001497, batch_cost: 0.5050, reader_cost: 0.00028, ips: 47.5294 samples/sec | ETA 00:14:13
2022-09-07 13:39:35 [INFO]	[TRAIN] epoch: 131, iter: 12270/13950, loss: 0.1072, DSC: 89.9494, lr: 0.001489, batch_cost: 0.5055, reader_cost: 0.00025, ips: 47.4794 samples/sec | ETA 00:14:09
2022-09-07 13:39:41 [INFO]	[TRAIN] epoch: 132, iter: 12280/13950, loss: 0.1200, DSC: 88.7014, lr: 0.001481, batch_cost: 0.5992, reader_cost: 0.11253, ips: 40.0539 samples/sec | ETA 00:16:40
2022-09-07 13:39:46 [INFO]	[TRAIN] epoch: 132, iter: 12290/13950, loss: 0.0892, DSC: 91.7751, lr: 0.001473, batch_cost: 0.5038, reader_cost: 0.00028, ips: 47.6368 samples/sec | ETA 00:13:56
2022-09-07 13:39:51 [INFO]	[TRAIN] epoch: 132, iter: 12300/13950, loss: 0.1020, DSC: 90.4722, lr: 0.001465, batch_cost: 0.5025, reader_cost: 0.00032, ips: 47.7630 samples/sec | ETA 00:13:49
2022-09-07 13:39:56 [INFO]	[TRAIN] epoch: 132, iter: 12310/13950, loss: 0.1170, DSC: 88.9923, lr: 0.001457, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5218 samples/sec | ETA 00:13:48
2022-09-07 13:40:01 [INFO]	[TRAIN] epoch: 132, iter: 12320/13950, loss: 0.0988, DSC: 90.7656, lr: 0.001449, batch_cost: 0.5017, reader_cost: 0.00032, ips: 47.8367 samples/sec | ETA 00:13:37
2022-09-07 13:40:06 [INFO]	[TRAIN] epoch: 132, iter: 12330/13950, loss: 0.0896, DSC: 91.6413, lr: 0.001441, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5253 samples/sec | ETA 00:13:38
2022-09-07 13:40:11 [INFO]	[TRAIN] epoch: 132, iter: 12340/13950, loss: 0.1104, DSC: 89.6137, lr: 0.001433, batch_cost: 0.5071, reader_cost: 0.00027, ips: 47.3284 samples/sec | ETA 00:13:36
2022-09-07 13:40:16 [INFO]	[TRAIN] epoch: 132, iter: 12350/13950, loss: 0.1020, DSC: 90.4505, lr: 0.001425, batch_cost: 0.5059, reader_cost: 0.00027, ips: 47.4359 samples/sec | ETA 00:13:29
2022-09-07 13:40:21 [INFO]	[TRAIN] epoch: 132, iter: 12360/13950, loss: 0.1182, DSC: 88.9135, lr: 0.001417, batch_cost: 0.5042, reader_cost: 0.00026, ips: 47.5988 samples/sec | ETA 00:13:21
2022-09-07 13:40:27 [INFO]	[TRAIN] epoch: 133, iter: 12370/13950, loss: 0.1442, DSC: 86.3536, lr: 0.001409, batch_cost: 0.5806, reader_cost: 0.10327, ips: 41.3359 samples/sec | ETA 00:15:17
2022-09-07 13:40:32 [INFO]	[TRAIN] epoch: 133, iter: 12380/13950, loss: 0.1111, DSC: 89.4621, lr: 0.001401, batch_cost: 0.5144, reader_cost: 0.00028, ips: 46.6539 samples/sec | ETA 00:13:27
2022-09-07 13:40:37 [INFO]	[TRAIN] epoch: 133, iter: 12390/13950, loss: 0.1332, DSC: 87.2575, lr: 0.001393, batch_cost: 0.5042, reader_cost: 0.00026, ips: 47.6030 samples/sec | ETA 00:13:06
2022-09-07 13:40:42 [INFO]	[TRAIN] epoch: 133, iter: 12400/13950, loss: 0.1126, DSC: 89.4197, lr: 0.001385, batch_cost: 0.5078, reader_cost: 0.00029, ips: 47.2643 samples/sec | ETA 00:13:07
2022-09-07 13:40:47 [INFO]	[TRAIN] epoch: 133, iter: 12410/13950, loss: 0.1122, DSC: 89.5835, lr: 0.001377, batch_cost: 0.5054, reader_cost: 0.00037, ips: 47.4884 samples/sec | ETA 00:12:58
2022-09-07 13:40:52 [INFO]	[TRAIN] epoch: 133, iter: 12420/13950, loss: 0.0863, DSC: 91.9906, lr: 0.001369, batch_cost: 0.5055, reader_cost: 0.00029, ips: 47.4755 samples/sec | ETA 00:12:53
2022-09-07 13:40:57 [INFO]	[TRAIN] epoch: 133, iter: 12430/13950, loss: 0.1007, DSC: 90.6645, lr: 0.001361, batch_cost: 0.5066, reader_cost: 0.00038, ips: 47.3723 samples/sec | ETA 00:12:50
2022-09-07 13:41:02 [INFO]	[TRAIN] epoch: 133, iter: 12440/13950, loss: 0.0939, DSC: 91.3423, lr: 0.001353, batch_cost: 0.5038, reader_cost: 0.00030, ips: 47.6365 samples/sec | ETA 00:12:40
2022-09-07 13:41:07 [INFO]	[TRAIN] epoch: 133, iter: 12450/13950, loss: 0.1029, DSC: 90.3898, lr: 0.001345, batch_cost: 0.5056, reader_cost: 0.00029, ips: 47.4662 samples/sec | ETA 00:12:38
2022-09-07 13:41:12 [INFO]	[TRAIN] epoch: 133, iter: 12460/13950, loss: 0.0893, DSC: 91.7524, lr: 0.001337, batch_cost: 0.5042, reader_cost: 0.00027, ips: 47.6027 samples/sec | ETA 00:12:31
2022-09-07 13:41:19 [INFO]	[TRAIN] epoch: 134, iter: 12470/13950, loss: 0.1077, DSC: 89.9058, lr: 0.001329, batch_cost: 0.6132, reader_cost: 0.12288, ips: 39.1378 samples/sec | ETA 00:15:07
2022-09-07 13:41:24 [INFO]	[TRAIN] epoch: 134, iter: 12480/13950, loss: 0.0932, DSC: 91.2499, lr: 0.001320, batch_cost: 0.5036, reader_cost: 0.00026, ips: 47.6606 samples/sec | ETA 00:12:20
2022-09-07 13:41:29 [INFO]	[TRAIN] epoch: 134, iter: 12490/13950, loss: 0.1097, DSC: 89.7128, lr: 0.001312, batch_cost: 0.5040, reader_cost: 0.00026, ips: 47.6166 samples/sec | ETA 00:12:15
2022-09-07 13:41:34 [INFO]	[TRAIN] epoch: 134, iter: 12500/13950, loss: 0.1193, DSC: 88.6490, lr: 0.001304, batch_cost: 0.5043, reader_cost: 0.00026, ips: 47.5902 samples/sec | ETA 00:12:11
2022-09-07 13:41:39 [INFO]	[TRAIN] epoch: 134, iter: 12510/13950, loss: 0.0949, DSC: 91.2188, lr: 0.001296, batch_cost: 0.5047, reader_cost: 0.00026, ips: 47.5495 samples/sec | ETA 00:12:06
2022-09-07 13:41:44 [INFO]	[TRAIN] epoch: 134, iter: 12520/13950, loss: 0.1276, DSC: 87.9646, lr: 0.001288, batch_cost: 0.5050, reader_cost: 0.00027, ips: 47.5244 samples/sec | ETA 00:12:02
2022-09-07 13:41:49 [INFO]	[TRAIN] epoch: 134, iter: 12530/13950, loss: 0.1027, DSC: 90.5163, lr: 0.001280, batch_cost: 0.5042, reader_cost: 0.00033, ips: 47.5973 samples/sec | ETA 00:11:56
2022-09-07 13:41:54 [INFO]	[TRAIN] epoch: 134, iter: 12540/13950, loss: 0.1091, DSC: 89.7771, lr: 0.001272, batch_cost: 0.5055, reader_cost: 0.00034, ips: 47.4777 samples/sec | ETA 00:11:52
2022-09-07 13:41:59 [INFO]	[TRAIN] epoch: 134, iter: 12550/13950, loss: 0.0882, DSC: 91.7319, lr: 0.001264, batch_cost: 0.5055, reader_cost: 0.00027, ips: 47.4817 samples/sec | ETA 00:11:47
2022-09-07 13:42:05 [INFO]	[TRAIN] epoch: 135, iter: 12560/13950, loss: 0.1186, DSC: 88.7873, lr: 0.001256, batch_cost: 0.5934, reader_cost: 0.10210, ips: 40.4440 samples/sec | ETA 00:13:44
2022-09-07 13:42:10 [INFO]	[TRAIN] epoch: 135, iter: 12570/13950, loss: 0.1118, DSC: 89.4049, lr: 0.001248, batch_cost: 0.5047, reader_cost: 0.00028, ips: 47.5543 samples/sec | ETA 00:11:36
2022-09-07 13:42:15 [INFO]	[TRAIN] epoch: 135, iter: 12580/13950, loss: 0.1037, DSC: 90.3659, lr: 0.001239, batch_cost: 0.5051, reader_cost: 0.00036, ips: 47.5170 samples/sec | ETA 00:11:31
2022-09-07 13:42:20 [INFO]	[TRAIN] epoch: 135, iter: 12590/13950, loss: 0.0805, DSC: 92.6246, lr: 0.001231, batch_cost: 0.5054, reader_cost: 0.00027, ips: 47.4828 samples/sec | ETA 00:11:27
2022-09-07 13:42:25 [INFO]	[TRAIN] epoch: 135, iter: 12600/13950, loss: 0.0833, DSC: 92.2893, lr: 0.001223, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6562 samples/sec | ETA 00:11:19
2022-09-07 13:42:30 [INFO]	[TRAIN] epoch: 135, iter: 12610/13950, loss: 0.1145, DSC: 89.2939, lr: 0.001215, batch_cost: 0.5044, reader_cost: 0.00030, ips: 47.5805 samples/sec | ETA 00:11:15
2022-09-07 13:42:35 [INFO]	[TRAIN] epoch: 135, iter: 12620/13950, loss: 0.1179, DSC: 88.9230, lr: 0.001207, batch_cost: 0.5038, reader_cost: 0.00028, ips: 47.6404 samples/sec | ETA 00:11:10
2022-09-07 13:42:40 [INFO]	[TRAIN] epoch: 135, iter: 12630/13950, loss: 0.0975, DSC: 90.9377, lr: 0.001199, batch_cost: 0.5045, reader_cost: 0.00028, ips: 47.5751 samples/sec | ETA 00:11:05
2022-09-07 13:42:45 [INFO]	[TRAIN] epoch: 135, iter: 12640/13950, loss: 0.1195, DSC: 88.7928, lr: 0.001190, batch_cost: 0.5049, reader_cost: 0.00029, ips: 47.5387 samples/sec | ETA 00:11:01
2022-09-07 13:42:51 [INFO]	[TRAIN] epoch: 136, iter: 12650/13950, loss: 0.1327, DSC: 87.3860, lr: 0.001182, batch_cost: 0.5863, reader_cost: 0.10394, ips: 40.9313 samples/sec | ETA 00:12:42
2022-09-07 13:42:56 [INFO]	[TRAIN] epoch: 136, iter: 12660/13950, loss: 0.1069, DSC: 90.0322, lr: 0.001174, batch_cost: 0.5094, reader_cost: 0.00027, ips: 47.1113 samples/sec | ETA 00:10:57
2022-09-07 13:43:01 [INFO]	[TRAIN] epoch: 136, iter: 12670/13950, loss: 0.1080, DSC: 89.8574, lr: 0.001166, batch_cost: 0.5037, reader_cost: 0.00027, ips: 47.6496 samples/sec | ETA 00:10:44
2022-09-07 13:43:06 [INFO]	[TRAIN] epoch: 136, iter: 12680/13950, loss: 0.0893, DSC: 91.7653, lr: 0.001158, batch_cost: 0.5055, reader_cost: 0.00028, ips: 47.4822 samples/sec | ETA 00:10:41
2022-09-07 13:43:11 [INFO]	[TRAIN] epoch: 136, iter: 12690/13950, loss: 0.1162, DSC: 88.9645, lr: 0.001150, batch_cost: 0.5050, reader_cost: 0.00028, ips: 47.5244 samples/sec | ETA 00:10:36
2022-09-07 13:43:16 [INFO]	[TRAIN] epoch: 136, iter: 12700/13950, loss: 0.1061, DSC: 90.0085, lr: 0.001141, batch_cost: 0.5071, reader_cost: 0.00032, ips: 47.3289 samples/sec | ETA 00:10:33
2022-09-07 13:43:21 [INFO]	[TRAIN] epoch: 136, iter: 12710/13950, loss: 0.1240, DSC: 88.2052, lr: 0.001133, batch_cost: 0.5068, reader_cost: 0.00038, ips: 47.3525 samples/sec | ETA 00:10:28
2022-09-07 13:43:27 [INFO]	[TRAIN] epoch: 136, iter: 12720/13950, loss: 0.1074, DSC: 90.0181, lr: 0.001125, batch_cost: 0.5031, reader_cost: 0.00027, ips: 47.7077 samples/sec | ETA 00:10:18
2022-09-07 13:43:32 [INFO]	[TRAIN] epoch: 136, iter: 12730/13950, loss: 0.1106, DSC: 89.6117, lr: 0.001117, batch_cost: 0.5038, reader_cost: 0.00030, ips: 47.6403 samples/sec | ETA 00:10:14
2022-09-07 13:43:37 [INFO]	[TRAIN] epoch: 136, iter: 12740/13950, loss: 0.1064, DSC: 90.1042, lr: 0.001108, batch_cost: 0.5008, reader_cost: 0.00022, ips: 47.9208 samples/sec | ETA 00:10:05
2022-09-07 13:43:43 [INFO]	[TRAIN] epoch: 137, iter: 12750/13950, loss: 0.1128, DSC: 89.4501, lr: 0.001100, batch_cost: 0.6191, reader_cost: 0.11470, ips: 38.7667 samples/sec | ETA 00:12:22
2022-09-07 13:43:48 [INFO]	[TRAIN] epoch: 137, iter: 12760/13950, loss: 0.1115, DSC: 89.5561, lr: 0.001092, batch_cost: 0.5064, reader_cost: 0.00030, ips: 47.3934 samples/sec | ETA 00:10:02
2022-09-07 13:43:53 [INFO]	[TRAIN] epoch: 137, iter: 12770/13950, loss: 0.1099, DSC: 89.6958, lr: 0.001084, batch_cost: 0.5068, reader_cost: 0.00029, ips: 47.3525 samples/sec | ETA 00:09:58
2022-09-07 13:43:58 [INFO]	[TRAIN] epoch: 137, iter: 12780/13950, loss: 0.0883, DSC: 91.6762, lr: 0.001075, batch_cost: 0.5043, reader_cost: 0.00029, ips: 47.5874 samples/sec | ETA 00:09:50
2022-09-07 13:44:03 [INFO]	[TRAIN] epoch: 137, iter: 12790/13950, loss: 0.1177, DSC: 88.8432, lr: 0.001067, batch_cost: 0.5036, reader_cost: 0.00027, ips: 47.6538 samples/sec | ETA 00:09:44
2022-09-07 13:44:08 [INFO]	[TRAIN] epoch: 137, iter: 12800/13950, loss: 0.1124, DSC: 89.5049, lr: 0.001059, batch_cost: 0.5044, reader_cost: 0.00026, ips: 47.5784 samples/sec | ETA 00:09:40
2022-09-07 13:44:13 [INFO]	[TRAIN] epoch: 137, iter: 12810/13950, loss: 0.1078, DSC: 89.8718, lr: 0.001051, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6107 samples/sec | ETA 00:09:34
2022-09-07 13:44:18 [INFO]	[TRAIN] epoch: 137, iter: 12820/13950, loss: 0.0818, DSC: 92.4544, lr: 0.001042, batch_cost: 0.5049, reader_cost: 0.00035, ips: 47.5319 samples/sec | ETA 00:09:30
2022-09-07 13:44:23 [INFO]	[TRAIN] epoch: 137, iter: 12830/13950, loss: 0.1012, DSC: 90.6181, lr: 0.001034, batch_cost: 0.5068, reader_cost: 0.00028, ips: 47.3521 samples/sec | ETA 00:09:27
2022-09-07 13:44:29 [INFO]	[TRAIN] epoch: 138, iter: 12840/13950, loss: 0.1112, DSC: 89.6363, lr: 0.001026, batch_cost: 0.6055, reader_cost: 0.11509, ips: 39.6362 samples/sec | ETA 00:11:12
2022-09-07 13:44:34 [INFO]	[TRAIN] epoch: 138, iter: 12850/13950, loss: 0.1340, DSC: 87.2273, lr: 0.001017, batch_cost: 0.5033, reader_cost: 0.00027, ips: 47.6832 samples/sec | ETA 00:09:13
2022-09-07 13:44:39 [INFO]	[TRAIN] epoch: 138, iter: 12860/13950, loss: 0.1046, DSC: 90.1865, lr: 0.001009, batch_cost: 0.5033, reader_cost: 0.00027, ips: 47.6898 samples/sec | ETA 00:09:08
2022-09-07 13:44:44 [INFO]	[TRAIN] epoch: 138, iter: 12870/13950, loss: 0.1005, DSC: 90.6121, lr: 0.001001, batch_cost: 0.5079, reader_cost: 0.00035, ips: 47.2516 samples/sec | ETA 00:09:08
2022-09-07 13:44:49 [INFO]	[TRAIN] epoch: 138, iter: 12880/13950, loss: 0.1081, DSC: 89.9331, lr: 0.000992, batch_cost: 0.5029, reader_cost: 0.00027, ips: 47.7248 samples/sec | ETA 00:08:58
2022-09-07 13:44:54 [INFO]	[TRAIN] epoch: 138, iter: 12890/13950, loss: 0.1146, DSC: 89.2388, lr: 0.000984, batch_cost: 0.5044, reader_cost: 0.00034, ips: 47.5844 samples/sec | ETA 00:08:54
2022-09-07 13:44:59 [INFO]	[TRAIN] epoch: 138, iter: 12900/13950, loss: 0.0967, DSC: 90.9947, lr: 0.000976, batch_cost: 0.5030, reader_cost: 0.00026, ips: 47.7168 samples/sec | ETA 00:08:48
2022-09-07 13:45:05 [INFO]	[TRAIN] epoch: 138, iter: 12910/13950, loss: 0.1000, DSC: 90.6043, lr: 0.000967, batch_cost: 0.5033, reader_cost: 0.00033, ips: 47.6823 samples/sec | ETA 00:08:43
2022-09-07 13:45:10 [INFO]	[TRAIN] epoch: 138, iter: 12920/13950, loss: 0.1027, DSC: 90.4340, lr: 0.000959, batch_cost: 0.5024, reader_cost: 0.00025, ips: 47.7665 samples/sec | ETA 00:08:37
2022-09-07 13:45:16 [INFO]	[TRAIN] epoch: 139, iter: 12930/13950, loss: 0.1409, DSC: 86.6257, lr: 0.000951, batch_cost: 0.6089, reader_cost: 0.12602, ips: 39.4169 samples/sec | ETA 00:10:21
2022-09-07 13:45:21 [INFO]	[TRAIN] epoch: 139, iter: 12940/13950, loss: 0.0920, DSC: 91.4904, lr: 0.000942, batch_cost: 0.5035, reader_cost: 0.00032, ips: 47.6657 samples/sec | ETA 00:08:28
2022-09-07 13:45:26 [INFO]	[TRAIN] epoch: 139, iter: 12950/13950, loss: 0.1032, DSC: 90.2330, lr: 0.000934, batch_cost: 0.5101, reader_cost: 0.00030, ips: 47.0500 samples/sec | ETA 00:08:30
2022-09-07 13:45:31 [INFO]	[TRAIN] epoch: 139, iter: 12960/13950, loss: 0.0973, DSC: 90.9233, lr: 0.000925, batch_cost: 0.5015, reader_cost: 0.00027, ips: 47.8569 samples/sec | ETA 00:08:16
2022-09-07 13:45:36 [INFO]	[TRAIN] epoch: 139, iter: 12970/13950, loss: 0.1237, DSC: 88.3092, lr: 0.000917, batch_cost: 0.5035, reader_cost: 0.00026, ips: 47.6657 samples/sec | ETA 00:08:13
2022-09-07 13:45:41 [INFO]	[TRAIN] epoch: 139, iter: 12980/13950, loss: 0.1116, DSC: 89.4745, lr: 0.000909, batch_cost: 0.5023, reader_cost: 0.00024, ips: 47.7817 samples/sec | ETA 00:08:07
2022-09-07 13:45:46 [INFO]	[TRAIN] epoch: 139, iter: 12990/13950, loss: 0.1041, DSC: 90.1934, lr: 0.000900, batch_cost: 0.5061, reader_cost: 0.00026, ips: 47.4243 samples/sec | ETA 00:08:05
2022-09-07 13:45:51 [INFO]	[TRAIN] epoch: 139, iter: 13000/13950, loss: 0.0911, DSC: 91.5787, lr: 0.000892, batch_cost: 0.5038, reader_cost: 0.00026, ips: 47.6344 samples/sec | ETA 00:07:58
2022-09-07 13:45:51 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0022 - reader cost: 1.1556
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2711 - reader cost: 0.5823
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0490 - reader cost: 0.3960 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.1996 - reader cost: 0.4479
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1307 - reader cost: 0.3625
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0797 - reader cost: 0.3076
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0111 - reader cost: 0.2664
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1096 - reader cost: 0.3129
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0836 - reader cost: 0.2804
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0590 - reader cost: 0.2554
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0090 - reader cost: 0.2337
12/12 [==============================] - 12s 973ms/step - batch_cost: 0.9725 - reader cost: 0.2160
2022-09-07 13:46:03 [INFO]	[EVAL] #Images: 12, Dice: 0.8245, Loss: 0.195284
2022-09-07 13:46:03 [INFO]	[EVAL] Class dice: 
[0.9963 0.9032 0.5518 0.8913 0.8462 0.9522 0.6211 0.8839 0.7742]
2022-09-07 13:46:05 [INFO]	[EVAL] The model with the best validation mDice (0.8262) was saved at iter 10000.
2022-09-07 13:46:10 [INFO]	[TRAIN] epoch: 139, iter: 13010/13950, loss: 0.0951, DSC: 91.2464, lr: 0.000883, batch_cost: 0.5039, reader_cost: 0.00041, ips: 47.6277 samples/sec | ETA 00:07:53
2022-09-07 13:46:15 [INFO]	[TRAIN] epoch: 140, iter: 13020/13950, loss: 0.1183, DSC: 88.8897, lr: 0.000875, batch_cost: 0.4746, reader_cost: 0.00022, ips: 50.5680 samples/sec | ETA 00:07:21
2022-09-07 13:46:21 [INFO]	[TRAIN] epoch: 140, iter: 13030/13950, loss: 0.1135, DSC: 89.2387, lr: 0.000866, batch_cost: 0.6266, reader_cost: 0.11000, ips: 38.2991 samples/sec | ETA 00:09:36
2022-09-07 13:46:26 [INFO]	[TRAIN] epoch: 140, iter: 13040/13950, loss: 0.1109, DSC: 89.6055, lr: 0.000858, batch_cost: 0.5101, reader_cost: 0.00027, ips: 47.0483 samples/sec | ETA 00:07:44
2022-09-07 13:46:31 [INFO]	[TRAIN] epoch: 140, iter: 13050/13950, loss: 0.0963, DSC: 91.0565, lr: 0.000849, batch_cost: 0.5020, reader_cost: 0.00027, ips: 47.8049 samples/sec | ETA 00:07:31
2022-09-07 13:46:36 [INFO]	[TRAIN] epoch: 140, iter: 13060/13950, loss: 0.0958, DSC: 91.0953, lr: 0.000841, batch_cost: 0.5042, reader_cost: 0.00029, ips: 47.5988 samples/sec | ETA 00:07:28
2022-09-07 13:46:41 [INFO]	[TRAIN] epoch: 140, iter: 13070/13950, loss: 0.1000, DSC: 90.6051, lr: 0.000832, batch_cost: 0.5043, reader_cost: 0.00028, ips: 47.5905 samples/sec | ETA 00:07:23
2022-09-07 13:46:46 [INFO]	[TRAIN] epoch: 140, iter: 13080/13950, loss: 0.0944, DSC: 91.2150, lr: 0.000824, batch_cost: 0.5038, reader_cost: 0.00027, ips: 47.6396 samples/sec | ETA 00:07:18
2022-09-07 13:46:51 [INFO]	[TRAIN] epoch: 140, iter: 13090/13950, loss: 0.1046, DSC: 90.3469, lr: 0.000815, batch_cost: 0.5044, reader_cost: 0.00028, ips: 47.5860 samples/sec | ETA 00:07:13
2022-09-07 13:46:56 [INFO]	[TRAIN] epoch: 140, iter: 13100/13950, loss: 0.1093, DSC: 89.7696, lr: 0.000807, batch_cost: 0.5045, reader_cost: 0.00038, ips: 47.5765 samples/sec | ETA 00:07:08
2022-09-07 13:47:01 [INFO]	[TRAIN] epoch: 140, iter: 13110/13950, loss: 0.0850, DSC: 92.1254, lr: 0.000798, batch_cost: 0.5031, reader_cost: 0.00024, ips: 47.6995 samples/sec | ETA 00:07:02
2022-09-07 13:47:07 [INFO]	[TRAIN] epoch: 141, iter: 13120/13950, loss: 0.1891, DSC: 81.6612, lr: 0.000790, batch_cost: 0.6028, reader_cost: 0.11646, ips: 39.8138 samples/sec | ETA 00:08:20
2022-09-07 13:47:13 [INFO]	[TRAIN] epoch: 141, iter: 13130/13950, loss: 0.0852, DSC: 92.0366, lr: 0.000781, batch_cost: 0.5063, reader_cost: 0.00028, ips: 47.4067 samples/sec | ETA 00:06:55
2022-09-07 13:47:18 [INFO]	[TRAIN] epoch: 141, iter: 13140/13950, loss: 0.0905, DSC: 91.6322, lr: 0.000773, batch_cost: 0.5040, reader_cost: 0.00033, ips: 47.6214 samples/sec | ETA 00:06:48
2022-09-07 13:47:23 [INFO]	[TRAIN] epoch: 141, iter: 13150/13950, loss: 0.0996, DSC: 90.7388, lr: 0.000764, batch_cost: 0.5042, reader_cost: 0.00033, ips: 47.6038 samples/sec | ETA 00:06:43
2022-09-07 13:47:28 [INFO]	[TRAIN] epoch: 141, iter: 13160/13950, loss: 0.0942, DSC: 91.1769, lr: 0.000756, batch_cost: 0.5052, reader_cost: 0.00026, ips: 47.5095 samples/sec | ETA 00:06:39
2022-09-07 13:47:33 [INFO]	[TRAIN] epoch: 141, iter: 13170/13950, loss: 0.0848, DSC: 92.2365, lr: 0.000747, batch_cost: 0.5100, reader_cost: 0.00026, ips: 47.0634 samples/sec | ETA 00:06:37
2022-09-07 13:47:38 [INFO]	[TRAIN] epoch: 141, iter: 13180/13950, loss: 0.1132, DSC: 89.4317, lr: 0.000738, batch_cost: 0.5057, reader_cost: 0.00027, ips: 47.4583 samples/sec | ETA 00:06:29
2022-09-07 13:47:43 [INFO]	[TRAIN] epoch: 141, iter: 13190/13950, loss: 0.1023, DSC: 90.4469, lr: 0.000730, batch_cost: 0.5053, reader_cost: 0.00026, ips: 47.4940 samples/sec | ETA 00:06:24
2022-09-07 13:47:48 [INFO]	[TRAIN] epoch: 141, iter: 13200/13950, loss: 0.1110, DSC: 89.5355, lr: 0.000721, batch_cost: 0.5031, reader_cost: 0.00025, ips: 47.7010 samples/sec | ETA 00:06:17
2022-09-07 13:47:54 [INFO]	[TRAIN] epoch: 142, iter: 13210/13950, loss: 0.1854, DSC: 82.1184, lr: 0.000712, batch_cost: 0.5958, reader_cost: 0.10859, ips: 40.2813 samples/sec | ETA 00:07:20
2022-09-07 13:47:59 [INFO]	[TRAIN] epoch: 142, iter: 13220/13950, loss: 0.0964, DSC: 91.0216, lr: 0.000704, batch_cost: 0.5055, reader_cost: 0.00027, ips: 47.4815 samples/sec | ETA 00:06:08
2022-09-07 13:48:04 [INFO]	[TRAIN] epoch: 142, iter: 13230/13950, loss: 0.0933, DSC: 91.3161, lr: 0.000695, batch_cost: 0.5068, reader_cost: 0.00029, ips: 47.3518 samples/sec | ETA 00:06:04
2022-09-07 13:48:09 [INFO]	[TRAIN] epoch: 142, iter: 13240/13950, loss: 0.0885, DSC: 91.8058, lr: 0.000686, batch_cost: 0.5058, reader_cost: 0.00028, ips: 47.4477 samples/sec | ETA 00:05:59
2022-09-07 13:48:14 [INFO]	[TRAIN] epoch: 142, iter: 13250/13950, loss: 0.1483, DSC: 85.8302, lr: 0.000678, batch_cost: 0.5057, reader_cost: 0.00029, ips: 47.4575 samples/sec | ETA 00:05:54
2022-09-07 13:48:19 [INFO]	[TRAIN] epoch: 142, iter: 13260/13950, loss: 0.0945, DSC: 91.1798, lr: 0.000669, batch_cost: 0.5058, reader_cost: 0.00027, ips: 47.4532 samples/sec | ETA 00:05:48
2022-09-07 13:48:24 [INFO]	[TRAIN] epoch: 142, iter: 13270/13950, loss: 0.1197, DSC: 88.7592, lr: 0.000660, batch_cost: 0.5063, reader_cost: 0.00027, ips: 47.4063 samples/sec | ETA 00:05:44
2022-09-07 13:48:29 [INFO]	[TRAIN] epoch: 142, iter: 13280/13950, loss: 0.0961, DSC: 91.0637, lr: 0.000652, batch_cost: 0.5057, reader_cost: 0.00033, ips: 47.4573 samples/sec | ETA 00:05:38
2022-09-07 13:48:34 [INFO]	[TRAIN] epoch: 142, iter: 13290/13950, loss: 0.1030, DSC: 90.4175, lr: 0.000643, batch_cost: 0.5180, reader_cost: 0.00027, ips: 46.3312 samples/sec | ETA 00:05:41
2022-09-07 13:48:40 [INFO]	[TRAIN] epoch: 143, iter: 13300/13950, loss: 0.1331, DSC: 87.3566, lr: 0.000634, batch_cost: 0.5925, reader_cost: 0.11420, ips: 40.5030 samples/sec | ETA 00:06:25
2022-09-07 13:48:46 [INFO]	[TRAIN] epoch: 143, iter: 13310/13950, loss: 0.0907, DSC: 91.5390, lr: 0.000625, batch_cost: 0.5136, reader_cost: 0.00033, ips: 46.7286 samples/sec | ETA 00:05:28
2022-09-07 13:48:51 [INFO]	[TRAIN] epoch: 143, iter: 13320/13950, loss: 0.1030, DSC: 90.2580, lr: 0.000616, batch_cost: 0.5083, reader_cost: 0.00031, ips: 47.2172 samples/sec | ETA 00:05:20
2022-09-07 13:48:56 [INFO]	[TRAIN] epoch: 143, iter: 13330/13950, loss: 0.1042, DSC: 90.2948, lr: 0.000608, batch_cost: 0.5071, reader_cost: 0.00030, ips: 47.3283 samples/sec | ETA 00:05:14
2022-09-07 13:49:01 [INFO]	[TRAIN] epoch: 143, iter: 13340/13950, loss: 0.1125, DSC: 89.4904, lr: 0.000599, batch_cost: 0.5034, reader_cost: 0.00034, ips: 47.6768 samples/sec | ETA 00:05:07
2022-09-07 13:49:06 [INFO]	[TRAIN] epoch: 143, iter: 13350/13950, loss: 0.0992, DSC: 90.7313, lr: 0.000590, batch_cost: 0.5053, reader_cost: 0.00027, ips: 47.4944 samples/sec | ETA 00:05:03
2022-09-07 13:49:11 [INFO]	[TRAIN] epoch: 143, iter: 13360/13950, loss: 0.0866, DSC: 91.9451, lr: 0.000581, batch_cost: 0.5040, reader_cost: 0.00027, ips: 47.6162 samples/sec | ETA 00:04:57
2022-09-07 13:49:16 [INFO]	[TRAIN] epoch: 143, iter: 13370/13950, loss: 0.1018, DSC: 90.4893, lr: 0.000572, batch_cost: 0.5055, reader_cost: 0.00028, ips: 47.4814 samples/sec | ETA 00:04:53
2022-09-07 13:49:21 [INFO]	[TRAIN] epoch: 143, iter: 13380/13950, loss: 0.1151, DSC: 89.1348, lr: 0.000563, batch_cost: 0.5057, reader_cost: 0.00028, ips: 47.4572 samples/sec | ETA 00:04:48
2022-09-07 13:49:26 [INFO]	[TRAIN] epoch: 143, iter: 13390/13950, loss: 0.0997, DSC: 90.8135, lr: 0.000555, batch_cost: 0.5038, reader_cost: 0.00024, ips: 47.6411 samples/sec | ETA 00:04:42
2022-09-07 13:49:32 [INFO]	[TRAIN] epoch: 144, iter: 13400/13950, loss: 0.1030, DSC: 90.4830, lr: 0.000546, batch_cost: 0.5967, reader_cost: 0.10790, ips: 40.2245 samples/sec | ETA 00:05:28
2022-09-07 13:49:37 [INFO]	[TRAIN] epoch: 144, iter: 13410/13950, loss: 0.0959, DSC: 91.0977, lr: 0.000537, batch_cost: 0.5123, reader_cost: 0.00028, ips: 46.8497 samples/sec | ETA 00:04:36
2022-09-07 13:49:42 [INFO]	[TRAIN] epoch: 144, iter: 13420/13950, loss: 0.0831, DSC: 92.3158, lr: 0.000528, batch_cost: 0.5062, reader_cost: 0.00027, ips: 47.4167 samples/sec | ETA 00:04:28
2022-09-07 13:49:47 [INFO]	[TRAIN] epoch: 144, iter: 13430/13950, loss: 0.1014, DSC: 90.5372, lr: 0.000519, batch_cost: 0.5075, reader_cost: 0.00028, ips: 47.2912 samples/sec | ETA 00:04:23
2022-09-07 13:49:52 [INFO]	[TRAIN] epoch: 144, iter: 13440/13950, loss: 0.1105, DSC: 89.6965, lr: 0.000510, batch_cost: 0.5056, reader_cost: 0.00028, ips: 47.4708 samples/sec | ETA 00:04:17
2022-09-07 13:49:57 [INFO]	[TRAIN] epoch: 144, iter: 13450/13950, loss: 0.0869, DSC: 91.8989, lr: 0.000501, batch_cost: 0.5063, reader_cost: 0.00028, ips: 47.3998 samples/sec | ETA 00:04:13
2022-09-07 13:50:02 [INFO]	[TRAIN] epoch: 144, iter: 13460/13950, loss: 0.0969, DSC: 90.9926, lr: 0.000492, batch_cost: 0.5041, reader_cost: 0.00033, ips: 47.6059 samples/sec | ETA 00:04:07
2022-09-07 13:50:07 [INFO]	[TRAIN] epoch: 144, iter: 13470/13950, loss: 0.1105, DSC: 89.5762, lr: 0.000483, batch_cost: 0.5055, reader_cost: 0.00027, ips: 47.4763 samples/sec | ETA 00:04:02
2022-09-07 13:50:12 [INFO]	[TRAIN] epoch: 144, iter: 13480/13950, loss: 0.0959, DSC: 91.0513, lr: 0.000474, batch_cost: 0.5054, reader_cost: 0.00026, ips: 47.4907 samples/sec | ETA 00:03:57
2022-09-07 13:50:19 [INFO]	[TRAIN] epoch: 145, iter: 13490/13950, loss: 0.1204, DSC: 88.6341, lr: 0.000465, batch_cost: 0.6071, reader_cost: 0.11838, ips: 39.5301 samples/sec | ETA 00:04:39
2022-09-07 13:50:24 [INFO]	[TRAIN] epoch: 145, iter: 13500/13950, loss: 0.1171, DSC: 88.8875, lr: 0.000456, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5927 samples/sec | ETA 00:03:46
2022-09-07 13:50:29 [INFO]	[TRAIN] epoch: 145, iter: 13510/13950, loss: 0.0896, DSC: 91.7397, lr: 0.000447, batch_cost: 0.5035, reader_cost: 0.00027, ips: 47.6627 samples/sec | ETA 00:03:41
2022-09-07 13:50:34 [INFO]	[TRAIN] epoch: 145, iter: 13520/13950, loss: 0.0921, DSC: 91.4287, lr: 0.000437, batch_cost: 0.5037, reader_cost: 0.00025, ips: 47.6477 samples/sec | ETA 00:03:36
2022-09-07 13:50:39 [INFO]	[TRAIN] epoch: 145, iter: 13530/13950, loss: 0.1042, DSC: 90.1931, lr: 0.000428, batch_cost: 0.5092, reader_cost: 0.00029, ips: 47.1363 samples/sec | ETA 00:03:33
2022-09-07 13:50:44 [INFO]	[TRAIN] epoch: 145, iter: 13540/13950, loss: 0.0906, DSC: 91.6694, lr: 0.000419, batch_cost: 0.5039, reader_cost: 0.00027, ips: 47.6304 samples/sec | ETA 00:03:26
2022-09-07 13:50:49 [INFO]	[TRAIN] epoch: 145, iter: 13550/13950, loss: 0.1150, DSC: 89.1555, lr: 0.000410, batch_cost: 0.5037, reader_cost: 0.00032, ips: 47.6478 samples/sec | ETA 00:03:21
2022-09-07 13:50:54 [INFO]	[TRAIN] epoch: 145, iter: 13560/13950, loss: 0.0858, DSC: 92.1212, lr: 0.000401, batch_cost: 0.5051, reader_cost: 0.00027, ips: 47.5192 samples/sec | ETA 00:03:16
2022-09-07 13:50:59 [INFO]	[TRAIN] epoch: 145, iter: 13570/13950, loss: 0.1227, DSC: 88.3537, lr: 0.000391, batch_cost: 0.5053, reader_cost: 0.00027, ips: 47.4998 samples/sec | ETA 00:03:12
2022-09-07 13:51:05 [INFO]	[TRAIN] epoch: 146, iter: 13580/13950, loss: 0.1071, DSC: 89.9036, lr: 0.000382, batch_cost: 0.5975, reader_cost: 0.11529, ips: 40.1680 samples/sec | ETA 00:03:41
2022-09-07 13:51:10 [INFO]	[TRAIN] epoch: 146, iter: 13590/13950, loss: 0.1158, DSC: 89.0230, lr: 0.000373, batch_cost: 0.5070, reader_cost: 0.00027, ips: 47.3330 samples/sec | ETA 00:03:02
2022-09-07 13:51:15 [INFO]	[TRAIN] epoch: 146, iter: 13600/13950, loss: 0.1046, DSC: 90.2076, lr: 0.000364, batch_cost: 0.5071, reader_cost: 0.00029, ips: 47.3273 samples/sec | ETA 00:02:57
2022-09-07 13:51:20 [INFO]	[TRAIN] epoch: 146, iter: 13610/13950, loss: 0.0953, DSC: 91.0821, lr: 0.000354, batch_cost: 0.5052, reader_cost: 0.00034, ips: 47.5070 samples/sec | ETA 00:02:51
2022-09-07 13:51:25 [INFO]	[TRAIN] epoch: 146, iter: 13620/13950, loss: 0.1043, DSC: 90.2099, lr: 0.000345, batch_cost: 0.5058, reader_cost: 0.00035, ips: 47.4489 samples/sec | ETA 00:02:46
2022-09-07 13:51:30 [INFO]	[TRAIN] epoch: 146, iter: 13630/13950, loss: 0.1069, DSC: 90.0580, lr: 0.000336, batch_cost: 0.5048, reader_cost: 0.00027, ips: 47.5457 samples/sec | ETA 00:02:41
2022-09-07 13:51:35 [INFO]	[TRAIN] epoch: 146, iter: 13640/13950, loss: 0.1118, DSC: 89.4087, lr: 0.000326, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5626 samples/sec | ETA 00:02:36
2022-09-07 13:51:40 [INFO]	[TRAIN] epoch: 146, iter: 13650/13950, loss: 0.0894, DSC: 91.7791, lr: 0.000317, batch_cost: 0.5068, reader_cost: 0.00027, ips: 47.3525 samples/sec | ETA 00:02:32
2022-09-07 13:51:45 [INFO]	[TRAIN] epoch: 146, iter: 13660/13950, loss: 0.1087, DSC: 89.8491, lr: 0.000307, batch_cost: 0.5035, reader_cost: 0.00034, ips: 47.6693 samples/sec | ETA 00:02:26
2022-09-07 13:51:50 [INFO]	[TRAIN] epoch: 146, iter: 13670/13950, loss: 0.0973, DSC: 90.9583, lr: 0.000298, batch_cost: 0.5013, reader_cost: 0.00022, ips: 47.8788 samples/sec | ETA 00:02:20
2022-09-07 13:51:56 [INFO]	[TRAIN] epoch: 147, iter: 13680/13950, loss: 0.1687, DSC: 83.7116, lr: 0.000288, batch_cost: 0.5976, reader_cost: 0.10875, ips: 40.1583 samples/sec | ETA 00:02:41
2022-09-07 13:52:01 [INFO]	[TRAIN] epoch: 147, iter: 13690/13950, loss: 0.1028, DSC: 90.3135, lr: 0.000279, batch_cost: 0.5042, reader_cost: 0.00028, ips: 47.5968 samples/sec | ETA 00:02:11
2022-09-07 13:52:06 [INFO]	[TRAIN] epoch: 147, iter: 13700/13950, loss: 0.1055, DSC: 90.0257, lr: 0.000269, batch_cost: 0.5027, reader_cost: 0.00027, ips: 47.7395 samples/sec | ETA 00:02:05
2022-09-07 13:52:11 [INFO]	[TRAIN] epoch: 147, iter: 13710/13950, loss: 0.0987, DSC: 90.7889, lr: 0.000259, batch_cost: 0.5046, reader_cost: 0.00028, ips: 47.5585 samples/sec | ETA 00:02:01
2022-09-07 13:52:17 [INFO]	[TRAIN] epoch: 147, iter: 13720/13950, loss: 0.1102, DSC: 89.7093, lr: 0.000250, batch_cost: 0.5049, reader_cost: 0.00027, ips: 47.5363 samples/sec | ETA 00:01:56
2022-09-07 13:52:22 [INFO]	[TRAIN] epoch: 147, iter: 13730/13950, loss: 0.1086, DSC: 89.8467, lr: 0.000240, batch_cost: 0.5025, reader_cost: 0.00027, ips: 47.7591 samples/sec | ETA 00:01:50
2022-09-07 13:52:27 [INFO]	[TRAIN] epoch: 147, iter: 13740/13950, loss: 0.0991, DSC: 90.7753, lr: 0.000230, batch_cost: 0.5023, reader_cost: 0.00026, ips: 47.7755 samples/sec | ETA 00:01:45
2022-09-07 13:52:32 [INFO]	[TRAIN] epoch: 147, iter: 13750/13950, loss: 0.0924, DSC: 91.4320, lr: 0.000220, batch_cost: 0.5020, reader_cost: 0.00025, ips: 47.8043 samples/sec | ETA 00:01:40
2022-09-07 13:52:37 [INFO]	[TRAIN] epoch: 147, iter: 13760/13950, loss: 0.1151, DSC: 89.1979, lr: 0.000210, batch_cost: 0.5040, reader_cost: 0.00025, ips: 47.6207 samples/sec | ETA 00:01:35
2022-09-07 13:52:43 [INFO]	[TRAIN] epoch: 148, iter: 13770/13950, loss: 0.1091, DSC: 89.8373, lr: 0.000200, batch_cost: 0.6099, reader_cost: 0.11157, ips: 39.3496 samples/sec | ETA 00:01:49
2022-09-07 13:52:48 [INFO]	[TRAIN] epoch: 148, iter: 13780/13950, loss: 0.0923, DSC: 91.4312, lr: 0.000190, batch_cost: 0.5032, reader_cost: 0.00028, ips: 47.6992 samples/sec | ETA 00:01:25
2022-09-07 13:52:53 [INFO]	[TRAIN] epoch: 148, iter: 13790/13950, loss: 0.1014, DSC: 90.5898, lr: 0.000180, batch_cost: 0.5043, reader_cost: 0.00027, ips: 47.5871 samples/sec | ETA 00:01:20
2022-09-07 13:52:58 [INFO]	[TRAIN] epoch: 148, iter: 13800/13950, loss: 0.1070, DSC: 89.8748, lr: 0.000170, batch_cost: 0.5046, reader_cost: 0.00027, ips: 47.5597 samples/sec | ETA 00:01:15
2022-09-07 13:53:03 [INFO]	[TRAIN] epoch: 148, iter: 13810/13950, loss: 0.1150, DSC: 89.1494, lr: 0.000160, batch_cost: 0.5066, reader_cost: 0.00027, ips: 47.3790 samples/sec | ETA 00:01:10
2022-09-07 13:53:08 [INFO]	[TRAIN] epoch: 148, iter: 13820/13950, loss: 0.0982, DSC: 90.8891, lr: 0.000150, batch_cost: 0.5029, reader_cost: 0.00026, ips: 47.7206 samples/sec | ETA 00:01:05
2022-09-07 13:53:13 [INFO]	[TRAIN] epoch: 148, iter: 13830/13950, loss: 0.0966, DSC: 91.0248, lr: 0.000139, batch_cost: 0.5033, reader_cost: 0.00033, ips: 47.6877 samples/sec | ETA 00:01:00
2022-09-07 13:53:18 [INFO]	[TRAIN] epoch: 148, iter: 13840/13950, loss: 0.0975, DSC: 90.9313, lr: 0.000129, batch_cost: 0.5033, reader_cost: 0.00026, ips: 47.6848 samples/sec | ETA 00:00:55
2022-09-07 13:53:23 [INFO]	[TRAIN] epoch: 148, iter: 13850/13950, loss: 0.1111, DSC: 89.5363, lr: 0.000119, batch_cost: 0.5028, reader_cost: 0.00025, ips: 47.7329 samples/sec | ETA 00:00:50
2022-09-07 13:53:29 [INFO]	[TRAIN] epoch: 149, iter: 13860/13950, loss: 0.1448, DSC: 86.0927, lr: 0.000108, batch_cost: 0.5878, reader_cost: 0.10569, ips: 40.8278 samples/sec | ETA 00:00:52
2022-09-07 13:53:34 [INFO]	[TRAIN] epoch: 149, iter: 13870/13950, loss: 0.0974, DSC: 90.9445, lr: 0.000097, batch_cost: 0.5045, reader_cost: 0.00027, ips: 47.5732 samples/sec | ETA 00:00:40
2022-09-07 13:53:39 [INFO]	[TRAIN] epoch: 149, iter: 13880/13950, loss: 0.1129, DSC: 89.3879, lr: 0.000086, batch_cost: 0.5029, reader_cost: 0.00025, ips: 47.7214 samples/sec | ETA 00:00:35
2022-09-07 13:53:44 [INFO]	[TRAIN] epoch: 149, iter: 13890/13950, loss: 0.1066, DSC: 90.0768, lr: 0.000075, batch_cost: 0.5041, reader_cost: 0.00027, ips: 47.6115 samples/sec | ETA 00:00:30
2022-09-07 13:53:49 [INFO]	[TRAIN] epoch: 149, iter: 13900/13950, loss: 0.0988, DSC: 90.7768, lr: 0.000064, batch_cost: 0.5054, reader_cost: 0.00028, ips: 47.4844 samples/sec | ETA 00:00:25
2022-09-07 13:53:54 [INFO]	[TRAIN] epoch: 149, iter: 13910/13950, loss: 0.0983, DSC: 90.7874, lr: 0.000053, batch_cost: 0.5055, reader_cost: 0.00036, ips: 47.4823 samples/sec | ETA 00:00:20
2022-09-07 13:53:59 [INFO]	[TRAIN] epoch: 149, iter: 13920/13950, loss: 0.0986, DSC: 90.8573, lr: 0.000041, batch_cost: 0.5033, reader_cost: 0.00029, ips: 47.6846 samples/sec | ETA 00:00:15
2022-09-07 13:54:04 [INFO]	[TRAIN] epoch: 149, iter: 13930/13950, loss: 0.1040, DSC: 90.2238, lr: 0.000029, batch_cost: 0.5068, reader_cost: 0.00035, ips: 47.3517 samples/sec | ETA 00:00:10
2022-09-07 13:54:09 [INFO]	[TRAIN] epoch: 149, iter: 13940/13950, loss: 0.0995, DSC: 90.7883, lr: 0.000016, batch_cost: 0.5042, reader_cost: 0.00026, ips: 47.5971 samples/sec | ETA 00:00:05
2022-09-07 13:54:14 [INFO]	[TRAIN] epoch: 150, iter: 13950/13950, loss: 0.1380, DSC: 86.7337, lr: 0.000002, batch_cost: 0.4747, reader_cost: 0.00023, ips: 50.5601 samples/sec | ETA 00:00:00
2022-09-07 13:54:14 [INFO]	Start evaluating (total_samples: 12, total_iters: 12)...

 1/12 [=>............................] - ETA: 22s - batch_cost: 2.0089 - reader cost: 1.1622
 2/12 [====>.........................] - ETA: 12s - batch_cost: 1.2731 - reader cost: 0.5838
 3/12 [======>.......................] - ETA: 9s - batch_cost: 1.0501 - reader cost: 0.3970 
 4/12 [=========>....................] - ETA: 9s - batch_cost: 1.2120 - reader cost: 0.4597
 5/12 [===========>..................] - ETA: 7s - batch_cost: 1.1411 - reader cost: 0.3720
 6/12 [==============>...............] - ETA: 6s - batch_cost: 1.0900 - reader cost: 0.3171
 7/12 [================>.............] - ETA: 5s - batch_cost: 1.0201 - reader cost: 0.2746
 8/12 [===================>..........] - ETA: 4s - batch_cost: 1.1202 - reader cost: 0.3237
 9/12 [=====================>........] - ETA: 3s - batch_cost: 1.0935 - reader cost: 0.2901
10/12 [========================>.....] - ETA: 2s - batch_cost: 1.0680 - reader cost: 0.2642
11/12 [==========================>...] - ETA: 1s - batch_cost: 1.0169 - reader cost: 0.2417
12/12 [==============================] - 12s 980ms/step - batch_cost: 0.9793 - reader cost: 0.2233
2022-09-07 13:54:26 [INFO]	[EVAL] #Images: 12, Dice: 0.8247, Loss: 0.195750
2022-09-07 13:54:26 [INFO]	[EVAL] Class dice: 
[0.9963 0.9029 0.5532 0.8883 0.8457 0.9499 0.6257 0.8837 0.777 ]
2022-09-07 13:54:28 [INFO]	[EVAL] The model with the best validation mDice (0.8262) was saved at iter 10000.
Cannot find suitable count function for <class 'medicalseg.models.backbones.resnet.StdConv2d'>. Treat it as zero FLOPs.
Cannot find suitable count function for <class 'paddle.nn.layer.norm.GroupNorm'>. Treat it as zero FLOPs.
<class 'paddle.nn.layer.activation.ReLU'>'s flops has been counted
<class 'paddle.nn.layer.conv.Conv2D'>'s flops has been counted
<class 'paddle.nn.layer.common.Dropout'>'s flops has been counted
Cannot find suitable count function for <class 'paddle.nn.layer.norm.LayerNorm'>. Treat it as zero FLOPs.
<class 'paddle.nn.layer.common.Linear'>'s flops has been counted
Cannot find suitable count function for <class 'paddle.nn.layer.activation.Softmax'>. Treat it as zero FLOPs.
Customize Function has been applied to <class 'paddle.nn.layer.norm.SyncBatchNorm'>
Cannot find suitable count function for <class 'paddle.nn.layer.common.UpsamplingBilinear2D'>. Treat it as zero FLOPs.
Cannot find suitable count function for <class 'paddle.nn.layer.common.Identity'>. Treat it as zero FLOPs.
Total Flops: 24621563904     Total Params: 105129433