MMEngine 快速上手#
构建模型#
首先，需要构建 MMEngine 模型，约定这个模型应当继承 BaseModel，并且其 forward 方法除了接受来自数据集的若干参数外，还需要接受额外的参数 mode：对于训练，需要 mode 接受字符串 "loss"，并返回包含 "loss" 字段的字典；对于验证，需要 mode 接受字符串 "predict"，并返回同时包含预测信息和真实信息的结果。
import set_env
import torch.nn.functional as F
import torch
from torch import nn
import torchvision
from mmengine.model import BaseModel
class MMResNet50(BaseModel):
def __init__(self, data_preprocessor: dict|nn.Module|None = None,
init_cfg: dict|None = None):
super().__init__(data_preprocessor=data_preprocessor, init_cfg=init_cfg)
self.resnet = torchvision.models.resnet50()
def forward(self, inputs: torch.Tensor,
data_samples: list|list = None,
mode: str = 'tensor') -> dict[str, torch.Tensor] | list:
x = self.resnet(inputs)
if mode == 'loss':
return {'loss': F.cross_entropy(x, data_samples)}
elif mode == 'predict':
return x, data_samples
else:
return x
/media/pc/data/lxw/envs/anaconda3x/envs/xxx/lib/python3.12/site-packages/mmengine/optim/optimizer/zero_optimizer.py:11: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.
from torch.distributed.optim import \
构建数据集和数据加载器#
其次，需要构建训练和验证所需要的数据集 (Dataset)和数据加载器 (DataLoader)。 对于基础的训练和验证功能，可以直接使用符合 PyTorch 标准的数据加载器和数据集。
from pathlib import Path
temp_dir = Path(".temp")
temp_dir.mkdir(exist_ok=True) # 创建缓存目录
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
train_dataloader = DataLoader(
batch_size=32,
shuffle=True,
dataset=torchvision.datasets.CIFAR10(
temp_dir/'data/cifar10',
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**norm_cfg)
]))
)
val_dataloader = DataLoader(
batch_size=32,
shuffle=False,
dataset=torchvision.datasets.CIFAR10(
temp_dir/'data/cifar10',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(**norm_cfg)
]))
)
Files already downloaded and verified
Files already downloaded and verified
构建评测指标#
为了进行验证和测试，需要定义模型推理结果的评测指标。
约定评测指标需要继承 BaseMetric，并实现 process 和 compute_metrics 方法。其中 process 方法接受数据集的输出和模型 mode="predict" 时的输出，此时的数据为单个批次的数据，对这一批次的数据进行处理后，保存信息至 self.results 属性。而 compute_metrics 接受 results 参数，这一参数的输入为 process 中保存的所有信息（如果是分布式环境，results 中为已收集的，包括各个进程 process 保存信息的结果），利用这些信息计算并返回保存有评测指标结果的字典
from typing import Any, Sequence
from mmengine.evaluator import BaseMetric
class Accuracy(BaseMetric):
def process(self, data_batch: Any, data_samples: Sequence[dict])->None:
"""
处理一批数据样本及其预测结果。处理后的结果应存储在`self.results`中，这将在所有批次处理完毕后用于计算指标。
Args:
data_batch: 从数据加载器获取的一批数据。
data_samples: 模型输出的一批结果。
"""
score, gt = data_samples
# 将一个批次的中间结果保存至 `self.results`
self.results.append({
'batch_size': len(gt),
'correct': (score.argmax(dim=1) == gt).sum().cpu(),
})
def compute_metrics(self, results):
total_correct = sum(item['correct'] for item in results)
total_size = sum(item['batch_size'] for item in results)
# 返回保存有评测指标结果的字典，其中键为指标名称
return dict(accuracy=100 * total_correct / total_size)
构建执行器并执行任务#
最后，利用构建好的模型，数据加载器，评测指标构建执行器 (Runner)，同时在其中配置 优化器、工作路径、训练与验证配置等选项，即可通过调用执行器的 train() 方法启动训练：
from torch.optim import SGD
from mmengine.runner import Runner
runner = Runner(
# 用以训练和验证的模型，需要满足特定的接口需求
model=MMResNet50(),
# 工作路径，用以保存训练日志、权重文件信息
work_dir=temp_dir/'./work_dir',
# 训练数据加载器，需要满足 PyTorch 数据加载器协议
train_dataloader=train_dataloader,
# 优化器包装，用于模型优化，并提供 AMP、梯度累积等附加功能
optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
# 训练配置，用于指定训练周期、验证间隔等信息
train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
# 验证数据加载器，需要满足 PyTorch 数据加载器协议
val_dataloader=val_dataloader,
# 验证配置，用于指定验证所需要的额外参数
val_cfg=dict(),
# 用于验证的评测器，这里使用默认评测器，并评测指标
val_evaluator=dict(type=Accuracy),
)
runner.train()
Show code cell output
11/22 16:43:51 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.12.2 | packaged by conda-forge | (main, Feb 16 2024, 20:50:58) [GCC 12.3.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 1560666916
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 2080 Ti
CUDA_HOME: /media/pc/data/lxw/envs/anaconda3x/envs/xxx
NVCC: Cuda compilation tools, release 12.6, V12.6.20
GCC: gcc (conda-forge gcc 12.4.0-0) 12.4.0
PyTorch: 2.5.0
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 12.3
- Built with CUDA Runtime 12.4
- NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
- CuDNN 90.1
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.4, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.5.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,
TorchVision: 0.20.0
OpenCV: 4.10.0
MMEngine: 0.10.5
Runtime environment:
dist_cfg: {'backend': 'nccl'}
seed: 1560666916
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
11/22 16:43:51 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
11/22 16:43:51 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook
--------------------
before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
--------------------
before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train_epoch:
(NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
before_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_val_epoch:
(NORMAL ) IterTimerHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_train:
(VERY_HIGH ) RuntimeInfoHook
(VERY_LOW ) CheckpointHook
--------------------
before_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_test_epoch:
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
11/22 16:43:51 - mmengine - WARNING - Dataset CIFAR10 has no metainfo. ``dataset_meta`` in visualizer will be None.
11/22 16:43:51 - mmengine - WARNING - The prefix is not set in metric class Accuracy.
11/22 16:43:51 - mmengine - WARNING - Dataset CIFAR10 has no metainfo. ``dataset_meta`` in evaluator, metric and visualizer will be None.
11/22 16:43:52 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
11/22 16:43:52 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
11/22 16:43:52 - mmengine - INFO - Checkpoints will be saved to /media/pc/data/lxw/ai/torch-book/doc/tools/mmengine/.temp/work_dir.
11/22 16:43:59 - mmengine - INFO - Epoch(train) [1][ 10/1563] lr: 1.0000e-03 eta: 1:39:16 time: 0.7632 data_time: 0.0715 memory: 382 loss: 5.2001
11/22 16:44:00 - mmengine - INFO - Epoch(train) [1][ 20/1563] lr: 1.0000e-03 eta: 0:53:29 time: 0.0603 data_time: 0.0164 memory: 382 loss: 2.7405
11/22 16:44:01 - mmengine - INFO - Epoch(train) [1][ 30/1563] lr: 1.0000e-03 eta: 0:38:11 time: 0.0597 data_time: 0.0165 memory: 382 loss: 2.6204
11/22 16:44:01 - mmengine - INFO - Epoch(train) [1][ 40/1563] lr: 1.0000e-03 eta: 0:30:28 time: 0.0576 data_time: 0.0159 memory: 382 loss: 2.6943
11/22 16:44:02 - mmengine - INFO - Epoch(train) [1][ 50/1563] lr: 1.0000e-03 eta: 0:25:56 time: 0.0614 data_time: 0.0164 memory: 382 loss: 2.5952
11/22 16:44:02 - mmengine - INFO - Epoch(train) [1][ 60/1563] lr: 1.0000e-03 eta: 0:22:53 time: 0.0606 data_time: 0.0162 memory: 382 loss: 2.6457
11/22 16:44:03 - mmengine - INFO - Epoch(train) [1][ 70/1563] lr: 1.0000e-03 eta: 0:20:43 time: 0.0613 data_time: 0.0164 memory: 382 loss: 2.5014
11/22 16:44:04 - mmengine - INFO - Epoch(train) [1][ 80/1563] lr: 1.0000e-03 eta: 0:19:05 time: 0.0602 data_time: 0.0161 memory: 382 loss: 2.6964
11/22 16:44:04 - mmengine - INFO - Epoch(train) [1][ 90/1563] lr: 1.0000e-03 eta: 0:17:48 time: 0.0609 data_time: 0.0161 memory: 382 loss: 2.5502
11/22 16:44:05 - mmengine - INFO - Epoch(train) [1][ 100/1563] lr: 1.0000e-03 eta: 0:16:47 time: 0.0601 data_time: 0.0161 memory: 382 loss: 2.7025
11/22 16:44:05 - mmengine - INFO - Epoch(train) [1][ 110/1563] lr: 1.0000e-03 eta: 0:15:56 time: 0.0596 data_time: 0.0159 memory: 382 loss: 2.6884
11/22 16:44:06 - mmengine - INFO - Epoch(train) [1][ 120/1563] lr: 1.0000e-03 eta: 0:15:13 time: 0.0598 data_time: 0.0162 memory: 382 loss: 2.4517
11/22 16:44:07 - mmengine - INFO - Epoch(train) [1][ 130/1563] lr: 1.0000e-03 eta: 0:14:37 time: 0.0594 data_time: 0.0159 memory: 382 loss: 2.4550
11/22 16:44:07 - mmengine - INFO - Epoch(train) [1][ 140/1563] lr: 1.0000e-03 eta: 0:14:07 time: 0.0614 data_time: 0.0162 memory: 382 loss: 2.6048
11/22 16:44:08 - mmengine - INFO - Epoch(train) [1][ 150/1563] lr: 1.0000e-03 eta: 0:13:39 time: 0.0584 data_time: 0.0159 memory: 382 loss: 2.5119
11/22 16:44:08 - mmengine - INFO - Epoch(train) [1][ 160/1563] lr: 1.0000e-03 eta: 0:13:15 time: 0.0591 data_time: 0.0158 memory: 382 loss: 2.6287
11/22 16:44:09 - mmengine - INFO - Epoch(train) [1][ 170/1563] lr: 1.0000e-03 eta: 0:12:54 time: 0.0591 data_time: 0.0159 memory: 382 loss: 2.5352
11/22 16:44:09 - mmengine - INFO - Epoch(train) [1][ 180/1563] lr: 1.0000e-03 eta: 0:12:35 time: 0.0595 data_time: 0.0158 memory: 382 loss: 2.3148
11/22 16:44:10 - mmengine - INFO - Epoch(train) [1][ 190/1563] lr: 1.0000e-03 eta: 0:12:19 time: 0.0610 data_time: 0.0161 memory: 382 loss: 2.4886
11/22 16:44:11 - mmengine - INFO - Epoch(train) [1][ 200/1563] lr: 1.0000e-03 eta: 0:12:04 time: 0.0604 data_time: 0.0161 memory: 382 loss: 2.6004
11/22 16:44:11 - mmengine - INFO - Epoch(train) [1][ 210/1563] lr: 1.0000e-03 eta: 0:11:51 time: 0.0608 data_time: 0.0163 memory: 382 loss: 2.4654
11/22 16:44:12 - mmengine - INFO - Epoch(train) [1][ 220/1563] lr: 1.0000e-03 eta: 0:11:38 time: 0.0609 data_time: 0.0161 memory: 382 loss: 2.4486
11/22 16:44:13 - mmengine - INFO - Epoch(train) [1][ 230/1563] lr: 1.0000e-03 eta: 0:11:29 time: 0.0652 data_time: 0.0185 memory: 382 loss: 2.4717
11/22 16:44:13 - mmengine - INFO - Epoch(train) [1][ 240/1563] lr: 1.0000e-03 eta: 0:11:16 time: 0.0543 data_time: 0.0162 memory: 382 loss: 2.8184
11/22 16:44:14 - mmengine - INFO - Epoch(train) [1][ 250/1563] lr: 1.0000e-03 eta: 0:11:05 time: 0.0545 data_time: 0.0147 memory: 382 loss: 2.5532
11/22 16:44:14 - mmengine - INFO - Epoch(train) [1][ 260/1563] lr: 1.0000e-03 eta: 0:10:55 time: 0.0567 data_time: 0.0151 memory: 382 loss: 2.5035
11/22 16:44:15 - mmengine - INFO - Epoch(train) [1][ 270/1563] lr: 1.0000e-03 eta: 0:10:46 time: 0.0573 data_time: 0.0150 memory: 382 loss: 2.4546
11/22 16:44:15 - mmengine - INFO - Epoch(train) [1][ 280/1563] lr: 1.0000e-03 eta: 0:10:35 time: 0.0502 data_time: 0.0148 memory: 382 loss: 2.4746
11/22 16:44:16 - mmengine - INFO - Epoch(train) [1][ 290/1563] lr: 1.0000e-03 eta: 0:10:27 time: 0.0538 data_time: 0.0146 memory: 382 loss: 2.4412
11/22 16:44:16 - mmengine - INFO - Epoch(train) [1][ 300/1563] lr: 1.0000e-03 eta: 0:10:19 time: 0.0551 data_time: 0.0147 memory: 382 loss: 2.8462
11/22 16:44:17 - mmengine - INFO - Epoch(train) [1][ 310/1563] lr: 1.0000e-03 eta: 0:10:13 time: 0.0617 data_time: 0.0172 memory: 382 loss: 2.6183
11/22 16:44:18 - mmengine - INFO - Epoch(train) [1][ 320/1563] lr: 1.0000e-03 eta: 0:10:11 time: 0.0776 data_time: 0.0266 memory: 382 loss: 2.5153
11/22 16:44:18 - mmengine - INFO - Epoch(train) [1][ 330/1563] lr: 1.0000e-03 eta: 0:10:07 time: 0.0658 data_time: 0.0222 memory: 382 loss: 2.6309
11/22 16:44:19 - mmengine - INFO - Epoch(train) [1][ 340/1563] lr: 1.0000e-03 eta: 0:09:59 time: 0.0520 data_time: 0.0147 memory: 382 loss: 2.4788
11/22 16:44:20 - mmengine - INFO - Epoch(train) [1][ 350/1563] lr: 1.0000e-03 eta: 0:09:53 time: 0.0540 data_time: 0.0143 memory: 382 loss: 2.3778
11/22 16:44:20 - mmengine - INFO - Epoch(train) [1][ 360/1563] lr: 1.0000e-03 eta: 0:09:47 time: 0.0544 data_time: 0.0143 memory: 382 loss: 2.3699
11/22 16:44:21 - mmengine - INFO - Epoch(train) [1][ 370/1563] lr: 1.0000e-03 eta: 0:09:41 time: 0.0534 data_time: 0.0142 memory: 382 loss: 2.3043
11/22 16:44:21 - mmengine - INFO - Epoch(train) [1][ 380/1563] lr: 1.0000e-03 eta: 0:09:35 time: 0.0530 data_time: 0.0141 memory: 382 loss: 2.2693
11/22 16:44:22 - mmengine - INFO - Epoch(train) [1][ 390/1563] lr: 1.0000e-03 eta: 0:09:30 time: 0.0537 data_time: 0.0140 memory: 382 loss: 2.2301
11/22 16:44:22 - mmengine - INFO - Epoch(train) [1][ 400/1563] lr: 1.0000e-03 eta: 0:09:25 time: 0.0529 data_time: 0.0139 memory: 382 loss: 2.2824
11/22 16:44:23 - mmengine - INFO - Epoch(train) [1][ 410/1563] lr: 1.0000e-03 eta: 0:09:20 time: 0.0529 data_time: 0.0140 memory: 382 loss: 2.2469
11/22 16:44:23 - mmengine - INFO - Epoch(train) [1][ 420/1563] lr: 1.0000e-03 eta: 0:09:15 time: 0.0525 data_time: 0.0140 memory: 382 loss: 2.2944
11/22 16:44:24 - mmengine - INFO - Epoch(train) [1][ 430/1563] lr: 1.0000e-03 eta: 0:09:11 time: 0.0527 data_time: 0.0139 memory: 382 loss: 2.2259
11/22 16:44:24 - mmengine - INFO - Epoch(train) [1][ 440/1563] lr: 1.0000e-03 eta: 0:09:06 time: 0.0526 data_time: 0.0140 memory: 382 loss: 2.3950
11/22 16:44:25 - mmengine - INFO - Epoch(train) [1][ 450/1563] lr: 1.0000e-03 eta: 0:09:02 time: 0.0526 data_time: 0.0138 memory: 382 loss: 2.2780
11/22 16:44:25 - mmengine - INFO - Epoch(train) [1][ 460/1563] lr: 1.0000e-03 eta: 0:08:58 time: 0.0522 data_time: 0.0141 memory: 382 loss: 2.2708
11/22 16:44:26 - mmengine - INFO - Epoch(train) [1][ 470/1563] lr: 1.0000e-03 eta: 0:08:54 time: 0.0523 data_time: 0.0136 memory: 382 loss: 2.2801
11/22 16:44:26 - mmengine - INFO - Epoch(train) [1][ 480/1563] lr: 1.0000e-03 eta: 0:08:50 time: 0.0528 data_time: 0.0140 memory: 382 loss: 2.2182
11/22 16:44:27 - mmengine - INFO - Epoch(train) [1][ 490/1563] lr: 1.0000e-03 eta: 0:08:46 time: 0.0523 data_time: 0.0137 memory: 382 loss: 2.3097
11/22 16:44:27 - mmengine - INFO - Epoch(train) [1][ 500/1563] lr: 1.0000e-03 eta: 0:08:43 time: 0.0525 data_time: 0.0138 memory: 382 loss: 2.2631
11/22 16:44:28 - mmengine - INFO - Epoch(train) [1][ 510/1563] lr: 1.0000e-03 eta: 0:08:40 time: 0.0550 data_time: 0.0144 memory: 382 loss: 2.4475
11/22 16:44:29 - mmengine - INFO - Epoch(train) [1][ 520/1563] lr: 1.0000e-03 eta: 0:08:37 time: 0.0557 data_time: 0.0144 memory: 382 loss: 2.2028
11/22 16:44:29 - mmengine - INFO - Epoch(train) [1][ 530/1563] lr: 1.0000e-03 eta: 0:08:34 time: 0.0569 data_time: 0.0148 memory: 382 loss: 2.2588
11/22 16:44:30 - mmengine - INFO - Epoch(train) [1][ 540/1563] lr: 1.0000e-03 eta: 0:08:31 time: 0.0563 data_time: 0.0147 memory: 382 loss: 2.2757
11/22 16:44:30 - mmengine - INFO - Epoch(train) [1][ 550/1563] lr: 1.0000e-03 eta: 0:08:29 time: 0.0594 data_time: 0.0191 memory: 382 loss: 2.2082
11/22 16:44:31 - mmengine - INFO - Epoch(train) [1][ 560/1563] lr: 1.0000e-03 eta: 0:08:26 time: 0.0524 data_time: 0.0153 memory: 382 loss: 2.3530
11/22 16:44:31 - mmengine - INFO - Epoch(train) [1][ 570/1563] lr: 1.0000e-03 eta: 0:08:24 time: 0.0554 data_time: 0.0147 memory: 382 loss: 2.2255
11/22 16:44:32 - mmengine - INFO - Epoch(train) [1][ 580/1563] lr: 1.0000e-03 eta: 0:08:21 time: 0.0570 data_time: 0.0150 memory: 382 loss: 2.3278
11/22 16:44:32 - mmengine - INFO - Epoch(train) [1][ 590/1563] lr: 1.0000e-03 eta: 0:08:19 time: 0.0574 data_time: 0.0149 memory: 382 loss: 2.3414
11/22 16:44:33 - mmengine - INFO - Epoch(train) [1][ 600/1563] lr: 1.0000e-03 eta: 0:08:17 time: 0.0572 data_time: 0.0148 memory: 382 loss: 2.3389
11/22 16:44:34 - mmengine - INFO - Epoch(train) [1][ 610/1563] lr: 1.0000e-03 eta: 0:08:15 time: 0.0572 data_time: 0.0149 memory: 382 loss: 2.1401
11/22 16:44:34 - mmengine - INFO - Epoch(train) [1][ 620/1563] lr: 1.0000e-03 eta: 0:08:13 time: 0.0572 data_time: 0.0149 memory: 382 loss: 2.2828
11/22 16:44:35 - mmengine - INFO - Epoch(train) [1][ 630/1563] lr: 1.0000e-03 eta: 0:08:13 time: 0.0776 data_time: 0.0256 memory: 382 loss: 2.1000
11/22 16:44:36 - mmengine - INFO - Epoch(train) [1][ 640/1563] lr: 1.0000e-03 eta: 0:08:14 time: 0.0787 data_time: 0.0267 memory: 382 loss: 2.1133
11/22 16:44:37 - mmengine - INFO - Epoch(train) [1][ 650/1563] lr: 1.0000e-03 eta: 0:08:14 time: 0.0742 data_time: 0.0253 memory: 382 loss: 2.1463
11/22 16:44:37 - mmengine - INFO - Epoch(train) [1][ 660/1563] lr: 1.0000e-03 eta: 0:08:13 time: 0.0722 data_time: 0.0253 memory: 382 loss: 2.0891
11/22 16:44:38 - mmengine - INFO - Epoch(train) [1][ 670/1563] lr: 1.0000e-03 eta: 0:08:11 time: 0.0494 data_time: 0.0142 memory: 382 loss: 2.1502
11/22 16:44:38 - mmengine - INFO - Epoch(train) [1][ 680/1563] lr: 1.0000e-03 eta: 0:08:08 time: 0.0542 data_time: 0.0142 memory: 382 loss: 2.1161
11/22 16:44:39 - mmengine - INFO - Epoch(train) [1][ 690/1563] lr: 1.0000e-03 eta: 0:08:06 time: 0.0554 data_time: 0.0144 memory: 382 loss: 2.0932
11/22 16:44:40 - mmengine - INFO - Epoch(train) [1][ 700/1563] lr: 1.0000e-03 eta: 0:08:06 time: 0.0682 data_time: 0.0216 memory: 382 loss: 2.1566
11/22 16:44:40 - mmengine - INFO - Epoch(train) [1][ 710/1563] lr: 1.0000e-03 eta: 0:08:06 time: 0.0751 data_time: 0.0259 memory: 382 loss: 2.2086
11/22 16:44:41 - mmengine - INFO - Epoch(train) [1][ 720/1563] lr: 1.0000e-03 eta: 0:08:06 time: 0.0754 data_time: 0.0260 memory: 382 loss: 2.0933
11/22 16:44:42 - mmengine - INFO - Epoch(train) [1][ 730/1563] lr: 1.0000e-03 eta: 0:08:06 time: 0.0747 data_time: 0.0259 memory: 382 loss: 2.2761
11/22 16:44:43 - mmengine - INFO - Epoch(train) [1][ 740/1563] lr: 1.0000e-03 eta: 0:08:05 time: 0.0754 data_time: 0.0260 memory: 382 loss: 2.0696
11/22 16:44:43 - mmengine - INFO - Epoch(train) [1][ 750/1563] lr: 1.0000e-03 eta: 0:08:05 time: 0.0755 data_time: 0.0261 memory: 382 loss: 2.1613
11/22 16:44:44 - mmengine - INFO - Epoch(train) [1][ 760/1563] lr: 1.0000e-03 eta: 0:08:04 time: 0.0561 data_time: 0.0185 memory: 382 loss: 2.1743
11/22 16:44:44 - mmengine - INFO - Epoch(train) [1][ 770/1563] lr: 1.0000e-03 eta: 0:08:01 time: 0.0478 data_time: 0.0137 memory: 382 loss: 2.1002
11/22 16:44:45 - mmengine - INFO - Epoch(train) [1][ 780/1563] lr: 1.0000e-03 eta: 0:07:59 time: 0.0522 data_time: 0.0139 memory: 382 loss: 2.1998
11/22 16:44:45 - mmengine - INFO - Epoch(train) [1][ 790/1563] lr: 1.0000e-03 eta: 0:07:57 time: 0.0538 data_time: 0.0143 memory: 382 loss: 2.1168
11/22 16:44:46 - mmengine - INFO - Epoch(train) [1][ 800/1563] lr: 1.0000e-03 eta: 0:07:55 time: 0.0539 data_time: 0.0144 memory: 382 loss: 2.2058
11/22 16:44:46 - mmengine - INFO - Epoch(train) [1][ 810/1563] lr: 1.0000e-03 eta: 0:07:53 time: 0.0536 data_time: 0.0142 memory: 382 loss: 2.2244
11/22 16:44:47 - mmengine - INFO - Epoch(train) [1][ 820/1563] lr: 1.0000e-03 eta: 0:07:51 time: 0.0521 data_time: 0.0137 memory: 382 loss: 2.2053
11/22 16:44:47 - mmengine - INFO - Epoch(train) [1][ 830/1563] lr: 1.0000e-03 eta: 0:07:49 time: 0.0527 data_time: 0.0140 memory: 382 loss: 2.1958
11/22 16:44:48 - mmengine - INFO - Epoch(train) [1][ 840/1563] lr: 1.0000e-03 eta: 0:07:47 time: 0.0526 data_time: 0.0140 memory: 382 loss: 2.2440
11/22 16:44:49 - mmengine - INFO - Epoch(train) [1][ 850/1563] lr: 1.0000e-03 eta: 0:07:46 time: 0.0545 data_time: 0.0141 memory: 382 loss: 2.2917
11/22 16:44:49 - mmengine - INFO - Epoch(train) [1][ 860/1563] lr: 1.0000e-03 eta: 0:07:44 time: 0.0526 data_time: 0.0142 memory: 382 loss: 2.1799
11/22 16:44:50 - mmengine - INFO - Epoch(train) [1][ 870/1563] lr: 1.0000e-03 eta: 0:07:42 time: 0.0520 data_time: 0.0137 memory: 382 loss: 2.1603
11/22 16:44:50 - mmengine - INFO - Epoch(train) [1][ 880/1563] lr: 1.0000e-03 eta: 0:07:40 time: 0.0525 data_time: 0.0138 memory: 382 loss: 2.1370
11/22 16:44:51 - mmengine - INFO - Epoch(train) [1][ 890/1563] lr: 1.0000e-03 eta: 0:07:38 time: 0.0522 data_time: 0.0136 memory: 382 loss: 2.0750
11/22 16:44:51 - mmengine - INFO - Epoch(train) [1][ 900/1563] lr: 1.0000e-03 eta: 0:07:37 time: 0.0528 data_time: 0.0138 memory: 382 loss: 2.1426
11/22 16:44:52 - mmengine - INFO - Epoch(train) [1][ 910/1563] lr: 1.0000e-03 eta: 0:07:35 time: 0.0538 data_time: 0.0145 memory: 382 loss: 2.0531
11/22 16:44:52 - mmengine - INFO - Epoch(train) [1][ 920/1563] lr: 1.0000e-03 eta: 0:07:34 time: 0.0653 data_time: 0.0208 memory: 382 loss: 2.1486
11/22 16:44:53 - mmengine - INFO - Epoch(train) [1][ 930/1563] lr: 1.0000e-03 eta: 0:07:34 time: 0.0758 data_time: 0.0259 memory: 382 loss: 2.0900
11/22 16:44:54 - mmengine - INFO - Epoch(train) [1][ 940/1563] lr: 1.0000e-03 eta: 0:07:34 time: 0.0740 data_time: 0.0257 memory: 382 loss: 2.1378
11/22 16:44:54 - mmengine - INFO - Epoch(train) [1][ 950/1563] lr: 1.0000e-03 eta: 0:07:32 time: 0.0476 data_time: 0.0135 memory: 382 loss: 2.0934
11/22 16:44:55 - mmengine - INFO - Epoch(train) [1][ 960/1563] lr: 1.0000e-03 eta: 0:07:31 time: 0.0512 data_time: 0.0134 memory: 382 loss: 2.1247
11/22 16:44:55 - mmengine - INFO - Epoch(train) [1][ 970/1563] lr: 1.0000e-03 eta: 0:07:29 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.9091
11/22 16:44:56 - mmengine - INFO - Epoch(train) [1][ 980/1563] lr: 1.0000e-03 eta: 0:07:27 time: 0.0523 data_time: 0.0138 memory: 382 loss: 2.0327
11/22 16:44:56 - mmengine - INFO - Epoch(train) [1][ 990/1563] lr: 1.0000e-03 eta: 0:07:26 time: 0.0516 data_time: 0.0135 memory: 382 loss: 2.0589
11/22 16:44:57 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:44:57 - mmengine - INFO - Epoch(train) [1][1000/1563] lr: 1.0000e-03 eta: 0:07:24 time: 0.0517 data_time: 0.0134 memory: 382 loss: 2.0987
11/22 16:44:57 - mmengine - INFO - Epoch(train) [1][1010/1563] lr: 1.0000e-03 eta: 0:07:23 time: 0.0518 data_time: 0.0136 memory: 382 loss: 2.1372
11/22 16:44:58 - mmengine - INFO - Epoch(train) [1][1020/1563] lr: 1.0000e-03 eta: 0:07:21 time: 0.0542 data_time: 0.0143 memory: 382 loss: 2.0680
11/22 16:44:59 - mmengine - INFO - Epoch(train) [1][1030/1563] lr: 1.0000e-03 eta: 0:07:20 time: 0.0537 data_time: 0.0142 memory: 382 loss: 2.0451
11/22 16:44:59 - mmengine - INFO - Epoch(train) [1][1040/1563] lr: 1.0000e-03 eta: 0:07:18 time: 0.0526 data_time: 0.0140 memory: 382 loss: 1.9602
11/22 16:45:00 - mmengine - INFO - Epoch(train) [1][1050/1563] lr: 1.0000e-03 eta: 0:07:17 time: 0.0518 data_time: 0.0134 memory: 382 loss: 2.1273
11/22 16:45:00 - mmengine - INFO - Epoch(train) [1][1060/1563] lr: 1.0000e-03 eta: 0:07:15 time: 0.0517 data_time: 0.0136 memory: 382 loss: 2.0634
11/22 16:45:01 - mmengine - INFO - Epoch(train) [1][1070/1563] lr: 1.0000e-03 eta: 0:07:14 time: 0.0522 data_time: 0.0137 memory: 382 loss: 2.0051
11/22 16:45:01 - mmengine - INFO - Epoch(train) [1][1080/1563] lr: 1.0000e-03 eta: 0:07:13 time: 0.0519 data_time: 0.0137 memory: 382 loss: 2.0453
11/22 16:45:02 - mmengine - INFO - Epoch(train) [1][1090/1563] lr: 1.0000e-03 eta: 0:07:11 time: 0.0517 data_time: 0.0136 memory: 382 loss: 2.0371
11/22 16:45:02 - mmengine - INFO - Epoch(train) [1][1100/1563] lr: 1.0000e-03 eta: 0:07:10 time: 0.0518 data_time: 0.0135 memory: 382 loss: 2.0868
11/22 16:45:03 - mmengine - INFO - Epoch(train) [1][1110/1563] lr: 1.0000e-03 eta: 0:07:08 time: 0.0517 data_time: 0.0136 memory: 382 loss: 2.0355
11/22 16:45:03 - mmengine - INFO - Epoch(train) [1][1120/1563] lr: 1.0000e-03 eta: 0:07:07 time: 0.0520 data_time: 0.0136 memory: 382 loss: 2.0876
11/22 16:45:04 - mmengine - INFO - Epoch(train) [1][1130/1563] lr: 1.0000e-03 eta: 0:07:06 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.9616
11/22 16:45:04 - mmengine - INFO - Epoch(train) [1][1140/1563] lr: 1.0000e-03 eta: 0:07:04 time: 0.0518 data_time: 0.0135 memory: 382 loss: 2.1710
11/22 16:45:05 - mmengine - INFO - Epoch(train) [1][1150/1563] lr: 1.0000e-03 eta: 0:07:03 time: 0.0516 data_time: 0.0134 memory: 382 loss: 2.0644
11/22 16:45:05 - mmengine - INFO - Epoch(train) [1][1160/1563] lr: 1.0000e-03 eta: 0:07:02 time: 0.0520 data_time: 0.0136 memory: 382 loss: 2.0819
11/22 16:45:06 - mmengine - INFO - Epoch(train) [1][1170/1563] lr: 1.0000e-03 eta: 0:07:00 time: 0.0518 data_time: 0.0136 memory: 382 loss: 2.1671
11/22 16:45:06 - mmengine - INFO - Epoch(train) [1][1180/1563] lr: 1.0000e-03 eta: 0:06:59 time: 0.0524 data_time: 0.0139 memory: 382 loss: 2.1896
11/22 16:45:07 - mmengine - INFO - Epoch(train) [1][1190/1563] lr: 1.0000e-03 eta: 0:06:58 time: 0.0517 data_time: 0.0134 memory: 382 loss: 2.0388
11/22 16:45:07 - mmengine - INFO - Epoch(train) [1][1200/1563] lr: 1.0000e-03 eta: 0:06:57 time: 0.0522 data_time: 0.0137 memory: 382 loss: 2.0957
11/22 16:45:08 - mmengine - INFO - Epoch(train) [1][1210/1563] lr: 1.0000e-03 eta: 0:06:55 time: 0.0519 data_time: 0.0135 memory: 382 loss: 2.0627
11/22 16:45:08 - mmengine - INFO - Epoch(train) [1][1220/1563] lr: 1.0000e-03 eta: 0:06:54 time: 0.0519 data_time: 0.0134 memory: 382 loss: 2.0116
11/22 16:45:09 - mmengine - INFO - Epoch(train) [1][1230/1563] lr: 1.0000e-03 eta: 0:06:53 time: 0.0520 data_time: 0.0136 memory: 382 loss: 2.0208
11/22 16:45:09 - mmengine - INFO - Epoch(train) [1][1240/1563] lr: 1.0000e-03 eta: 0:06:52 time: 0.0534 data_time: 0.0140 memory: 382 loss: 1.9053
11/22 16:45:10 - mmengine - INFO - Epoch(train) [1][1250/1563] lr: 1.0000e-03 eta: 0:06:51 time: 0.0547 data_time: 0.0144 memory: 382 loss: 2.0169
11/22 16:45:11 - mmengine - INFO - Epoch(train) [1][1260/1563] lr: 1.0000e-03 eta: 0:06:50 time: 0.0545 data_time: 0.0145 memory: 382 loss: 1.9237
11/22 16:45:11 - mmengine - INFO - Epoch(train) [1][1270/1563] lr: 1.0000e-03 eta: 0:06:49 time: 0.0546 data_time: 0.0144 memory: 382 loss: 2.1535
11/22 16:45:12 - mmengine - INFO - Epoch(train) [1][1280/1563] lr: 1.0000e-03 eta: 0:06:48 time: 0.0557 data_time: 0.0143 memory: 382 loss: 1.9716
11/22 16:45:12 - mmengine - INFO - Epoch(train) [1][1290/1563] lr: 1.0000e-03 eta: 0:06:47 time: 0.0562 data_time: 0.0145 memory: 382 loss: 2.0045
11/22 16:45:13 - mmengine - INFO - Epoch(train) [1][1300/1563] lr: 1.0000e-03 eta: 0:06:46 time: 0.0558 data_time: 0.0144 memory: 382 loss: 2.0869
11/22 16:45:13 - mmengine - INFO - Epoch(train) [1][1310/1563] lr: 1.0000e-03 eta: 0:06:45 time: 0.0557 data_time: 0.0143 memory: 382 loss: 1.9966
11/22 16:45:14 - mmengine - INFO - Epoch(train) [1][1320/1563] lr: 1.0000e-03 eta: 0:06:44 time: 0.0550 data_time: 0.0140 memory: 382 loss: 1.9463
11/22 16:45:14 - mmengine - INFO - Epoch(train) [1][1330/1563] lr: 1.0000e-03 eta: 0:06:43 time: 0.0530 data_time: 0.0137 memory: 382 loss: 1.8883
11/22 16:45:15 - mmengine - INFO - Epoch(train) [1][1340/1563] lr: 1.0000e-03 eta: 0:06:42 time: 0.0515 data_time: 0.0134 memory: 382 loss: 2.0122
11/22 16:45:15 - mmengine - INFO - Epoch(train) [1][1350/1563] lr: 1.0000e-03 eta: 0:06:41 time: 0.0517 data_time: 0.0137 memory: 382 loss: 1.9305
11/22 16:45:16 - mmengine - INFO - Epoch(train) [1][1360/1563] lr: 1.0000e-03 eta: 0:06:39 time: 0.0520 data_time: 0.0137 memory: 382 loss: 1.9142
11/22 16:45:16 - mmengine - INFO - Epoch(train) [1][1370/1563] lr: 1.0000e-03 eta: 0:06:38 time: 0.0537 data_time: 0.0138 memory: 382 loss: 1.9884
11/22 16:45:17 - mmengine - INFO - Epoch(train) [1][1380/1563] lr: 1.0000e-03 eta: 0:06:37 time: 0.0536 data_time: 0.0137 memory: 382 loss: 1.8882
11/22 16:45:18 - mmengine - INFO - Epoch(train) [1][1390/1563] lr: 1.0000e-03 eta: 0:06:36 time: 0.0528 data_time: 0.0135 memory: 382 loss: 2.0320
11/22 16:45:18 - mmengine - INFO - Epoch(train) [1][1400/1563] lr: 1.0000e-03 eta: 0:06:35 time: 0.0513 data_time: 0.0134 memory: 382 loss: 2.0217
11/22 16:45:19 - mmengine - INFO - Epoch(train) [1][1410/1563] lr: 1.0000e-03 eta: 0:06:34 time: 0.0519 data_time: 0.0136 memory: 382 loss: 1.9956
11/22 16:45:19 - mmengine - INFO - Epoch(train) [1][1420/1563] lr: 1.0000e-03 eta: 0:06:33 time: 0.0518 data_time: 0.0134 memory: 382 loss: 2.0142
11/22 16:45:20 - mmengine - INFO - Epoch(train) [1][1430/1563] lr: 1.0000e-03 eta: 0:06:32 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.9738
11/22 16:45:20 - mmengine - INFO - Epoch(train) [1][1440/1563] lr: 1.0000e-03 eta: 0:06:31 time: 0.0521 data_time: 0.0136 memory: 382 loss: 2.0285
11/22 16:45:21 - mmengine - INFO - Epoch(train) [1][1450/1563] lr: 1.0000e-03 eta: 0:06:30 time: 0.0514 data_time: 0.0134 memory: 382 loss: 2.0299
11/22 16:45:21 - mmengine - INFO - Epoch(train) [1][1460/1563] lr: 1.0000e-03 eta: 0:06:29 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.8035
11/22 16:45:22 - mmengine - INFO - Epoch(train) [1][1470/1563] lr: 1.0000e-03 eta: 0:06:28 time: 0.0516 data_time: 0.0134 memory: 382 loss: 2.1066
11/22 16:45:22 - mmengine - INFO - Epoch(train) [1][1480/1563] lr: 1.0000e-03 eta: 0:06:27 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.8985
11/22 16:45:23 - mmengine - INFO - Epoch(train) [1][1490/1563] lr: 1.0000e-03 eta: 0:06:26 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.8805
11/22 16:45:23 - mmengine - INFO - Epoch(train) [1][1500/1563] lr: 1.0000e-03 eta: 0:06:25 time: 0.0519 data_time: 0.0135 memory: 382 loss: 2.0064
11/22 16:45:24 - mmengine - INFO - Epoch(train) [1][1510/1563] lr: 1.0000e-03 eta: 0:06:24 time: 0.0517 data_time: 0.0136 memory: 382 loss: 1.9251
11/22 16:45:24 - mmengine - INFO - Epoch(train) [1][1520/1563] lr: 1.0000e-03 eta: 0:06:23 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.9094
11/22 16:45:25 - mmengine - INFO - Epoch(train) [1][1530/1563] lr: 1.0000e-03 eta: 0:06:22 time: 0.0517 data_time: 0.0135 memory: 382 loss: 1.9858
11/22 16:45:25 - mmengine - INFO - Epoch(train) [1][1540/1563] lr: 1.0000e-03 eta: 0:06:21 time: 0.0519 data_time: 0.0135 memory: 382 loss: 1.9386
11/22 16:45:26 - mmengine - INFO - Epoch(train) [1][1550/1563] lr: 1.0000e-03 eta: 0:06:20 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.8874
11/22 16:45:26 - mmengine - INFO - Epoch(train) [1][1560/1563] lr: 1.0000e-03 eta: 0:06:19 time: 0.0518 data_time: 0.0137 memory: 382 loss: 1.9668
11/22 16:45:27 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:45:27 - mmengine - INFO - Saving checkpoint at 1 epochs
11/22 16:45:27 - mmengine - WARNING - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers
11/22 16:45:28 - mmengine - INFO - Epoch(val) [1][ 10/313] eta: 0:00:22 time: 0.0747 data_time: 0.0111 memory: 382
11/22 16:45:29 - mmengine - INFO - Epoch(val) [1][ 20/313] eta: 0:00:13 time: 0.0198 data_time: 0.0097 memory: 225
11/22 16:45:29 - mmengine - INFO - Epoch(val) [1][ 30/313] eta: 0:00:10 time: 0.0199 data_time: 0.0096 memory: 225
11/22 16:45:29 - mmengine - INFO - Epoch(val) [1][ 40/313] eta: 0:00:09 time: 0.0181 data_time: 0.0089 memory: 225
11/22 16:45:29 - mmengine - INFO - Epoch(val) [1][ 50/313] eta: 0:00:07 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:45:29 - mmengine - INFO - Epoch(val) [1][ 60/313] eta: 0:00:07 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:45:29 - mmengine - INFO - Epoch(val) [1][ 70/313] eta: 0:00:06 time: 0.0176 data_time: 0.0086 memory: 225
11/22 16:45:30 - mmengine - INFO - Epoch(val) [1][ 80/313] eta: 0:00:05 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:45:30 - mmengine - INFO - Epoch(val) [1][ 90/313] eta: 0:00:05 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:45:30 - mmengine - INFO - Epoch(val) [1][100/313] eta: 0:00:05 time: 0.0170 data_time: 0.0083 memory: 225
11/22 16:45:30 - mmengine - INFO - Epoch(val) [1][110/313] eta: 0:00:04 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:45:30 - mmengine - INFO - Epoch(val) [1][120/313] eta: 0:00:04 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:45:30 - mmengine - INFO - Epoch(val) [1][130/313] eta: 0:00:04 time: 0.0167 data_time: 0.0082 memory: 225
11/22 16:45:31 - mmengine - INFO - Epoch(val) [1][140/313] eta: 0:00:03 time: 0.0167 data_time: 0.0082 memory: 225
11/22 16:45:31 - mmengine - INFO - Epoch(val) [1][150/313] eta: 0:00:03 time: 0.0168 data_time: 0.0082 memory: 225
11/22 16:45:31 - mmengine - INFO - Epoch(val) [1][160/313] eta: 0:00:03 time: 0.0171 data_time: 0.0084 memory: 225
11/22 16:45:31 - mmengine - INFO - Epoch(val) [1][170/313] eta: 0:00:02 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:45:31 - mmengine - INFO - Epoch(val) [1][180/313] eta: 0:00:02 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:45:32 - mmengine - INFO - Epoch(val) [1][190/313] eta: 0:00:02 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:45:32 - mmengine - INFO - Epoch(val) [1][200/313] eta: 0:00:02 time: 0.0168 data_time: 0.0082 memory: 225
11/22 16:45:32 - mmengine - INFO - Epoch(val) [1][210/313] eta: 0:00:02 time: 0.0168 data_time: 0.0082 memory: 225
11/22 16:45:32 - mmengine - INFO - Epoch(val) [1][220/313] eta: 0:00:01 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:45:32 - mmengine - INFO - Epoch(val) [1][230/313] eta: 0:00:01 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:45:32 - mmengine - INFO - Epoch(val) [1][240/313] eta: 0:00:01 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:45:33 - mmengine - INFO - Epoch(val) [1][250/313] eta: 0:00:01 time: 0.0167 data_time: 0.0082 memory: 225
11/22 16:45:33 - mmengine - INFO - Epoch(val) [1][260/313] eta: 0:00:01 time: 0.0170 data_time: 0.0083 memory: 225
11/22 16:45:33 - mmengine - INFO - Epoch(val) [1][270/313] eta: 0:00:00 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:45:33 - mmengine - INFO - Epoch(val) [1][280/313] eta: 0:00:00 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:45:33 - mmengine - INFO - Epoch(val) [1][290/313] eta: 0:00:00 time: 0.0172 data_time: 0.0084 memory: 225
11/22 16:45:33 - mmengine - INFO - Epoch(val) [1][300/313] eta: 0:00:00 time: 0.0173 data_time: 0.0086 memory: 225
11/22 16:45:34 - mmengine - INFO - Epoch(val) [1][310/313] eta: 0:00:00 time: 0.0171 data_time: 0.0085 memory: 225
11/22 16:45:34 - mmengine - INFO - Epoch(val) [1][313/313] accuracy: 35.3700 data_time: 0.0085 time: 0.0190
11/22 16:45:34 - mmengine - INFO - Epoch(train) [2][ 10/1563] lr: 1.0000e-03 eta: 0:06:19 time: 0.0469 data_time: 0.0135 memory: 382 loss: 1.9365
11/22 16:45:35 - mmengine - INFO - Epoch(train) [2][ 20/1563] lr: 1.0000e-03 eta: 0:06:18 time: 0.0506 data_time: 0.0132 memory: 382 loss: 1.8599
11/22 16:45:35 - mmengine - INFO - Epoch(train) [2][ 30/1563] lr: 1.0000e-03 eta: 0:06:17 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.8692
11/22 16:45:36 - mmengine - INFO - Epoch(train) [2][ 40/1563] lr: 1.0000e-03 eta: 0:06:16 time: 0.0520 data_time: 0.0137 memory: 382 loss: 1.8781
11/22 16:45:36 - mmengine - INFO - Epoch(train) [2][ 50/1563] lr: 1.0000e-03 eta: 0:06:15 time: 0.0522 data_time: 0.0138 memory: 382 loss: 1.9849
11/22 16:45:37 - mmengine - INFO - Epoch(train) [2][ 60/1563] lr: 1.0000e-03 eta: 0:06:14 time: 0.0520 data_time: 0.0138 memory: 382 loss: 2.0209
11/22 16:45:37 - mmengine - INFO - Epoch(train) [2][ 70/1563] lr: 1.0000e-03 eta: 0:06:13 time: 0.0534 data_time: 0.0143 memory: 382 loss: 2.0264
11/22 16:45:38 - mmengine - INFO - Epoch(train) [2][ 80/1563] lr: 1.0000e-03 eta: 0:06:13 time: 0.0516 data_time: 0.0136 memory: 382 loss: 1.7989
11/22 16:45:38 - mmengine - INFO - Epoch(train) [2][ 90/1563] lr: 1.0000e-03 eta: 0:06:12 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.8950
11/22 16:45:39 - mmengine - INFO - Epoch(train) [2][ 100/1563] lr: 1.0000e-03 eta: 0:06:11 time: 0.0513 data_time: 0.0135 memory: 382 loss: 2.0333
11/22 16:45:39 - mmengine - INFO - Epoch(train) [2][ 110/1563] lr: 1.0000e-03 eta: 0:06:10 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.8740
11/22 16:45:40 - mmengine - INFO - Epoch(train) [2][ 120/1563] lr: 1.0000e-03 eta: 0:06:09 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.8757
11/22 16:45:40 - mmengine - INFO - Epoch(train) [2][ 130/1563] lr: 1.0000e-03 eta: 0:06:08 time: 0.0516 data_time: 0.0136 memory: 382 loss: 1.7964
11/22 16:45:41 - mmengine - INFO - Epoch(train) [2][ 140/1563] lr: 1.0000e-03 eta: 0:06:07 time: 0.0517 data_time: 0.0136 memory: 382 loss: 1.9340
11/22 16:45:41 - mmengine - INFO - Epoch(train) [2][ 150/1563] lr: 1.0000e-03 eta: 0:06:06 time: 0.0516 data_time: 0.0136 memory: 382 loss: 1.8404
11/22 16:45:42 - mmengine - INFO - Epoch(train) [2][ 160/1563] lr: 1.0000e-03 eta: 0:06:05 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.8404
11/22 16:45:42 - mmengine - INFO - Epoch(train) [2][ 170/1563] lr: 1.0000e-03 eta: 0:06:04 time: 0.0515 data_time: 0.0136 memory: 382 loss: 1.8419
11/22 16:45:43 - mmengine - INFO - Epoch(train) [2][ 180/1563] lr: 1.0000e-03 eta: 0:06:03 time: 0.0512 data_time: 0.0133 memory: 382 loss: 1.8487
11/22 16:45:43 - mmengine - INFO - Epoch(train) [2][ 190/1563] lr: 1.0000e-03 eta: 0:06:03 time: 0.0517 data_time: 0.0137 memory: 382 loss: 1.8069
11/22 16:45:44 - mmengine - INFO - Epoch(train) [2][ 200/1563] lr: 1.0000e-03 eta: 0:06:02 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.9490
11/22 16:45:44 - mmengine - INFO - Epoch(train) [2][ 210/1563] lr: 1.0000e-03 eta: 0:06:01 time: 0.0518 data_time: 0.0135 memory: 382 loss: 2.0255
11/22 16:45:45 - mmengine - INFO - Epoch(train) [2][ 220/1563] lr: 1.0000e-03 eta: 0:06:00 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.8988
11/22 16:45:45 - mmengine - INFO - Epoch(train) [2][ 230/1563] lr: 1.0000e-03 eta: 0:05:59 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.9589
11/22 16:45:46 - mmengine - INFO - Epoch(train) [2][ 240/1563] lr: 1.0000e-03 eta: 0:05:58 time: 0.0513 data_time: 0.0135 memory: 382 loss: 1.9171
11/22 16:45:46 - mmengine - INFO - Epoch(train) [2][ 250/1563] lr: 1.0000e-03 eta: 0:05:57 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.9336
11/22 16:45:47 - mmengine - INFO - Epoch(train) [2][ 260/1563] lr: 1.0000e-03 eta: 0:05:56 time: 0.0515 data_time: 0.0136 memory: 382 loss: 1.9064
11/22 16:45:47 - mmengine - INFO - Epoch(train) [2][ 270/1563] lr: 1.0000e-03 eta: 0:05:56 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.8528
11/22 16:45:48 - mmengine - INFO - Epoch(train) [2][ 280/1563] lr: 1.0000e-03 eta: 0:05:55 time: 0.0519 data_time: 0.0138 memory: 382 loss: 1.9756
11/22 16:45:49 - mmengine - INFO - Epoch(train) [2][ 290/1563] lr: 1.0000e-03 eta: 0:05:54 time: 0.0517 data_time: 0.0136 memory: 382 loss: 1.8577
11/22 16:45:49 - mmengine - INFO - Epoch(train) [2][ 300/1563] lr: 1.0000e-03 eta: 0:05:53 time: 0.0509 data_time: 0.0133 memory: 382 loss: 1.7939
11/22 16:45:50 - mmengine - INFO - Epoch(train) [2][ 310/1563] lr: 1.0000e-03 eta: 0:05:52 time: 0.0517 data_time: 0.0136 memory: 382 loss: 1.8862
11/22 16:45:50 - mmengine - INFO - Epoch(train) [2][ 320/1563] lr: 1.0000e-03 eta: 0:05:51 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.8484
11/22 16:45:51 - mmengine - INFO - Epoch(train) [2][ 330/1563] lr: 1.0000e-03 eta: 0:05:50 time: 0.0519 data_time: 0.0137 memory: 382 loss: 1.8681
11/22 16:45:51 - mmengine - INFO - Epoch(train) [2][ 340/1563] lr: 1.0000e-03 eta: 0:05:50 time: 0.0526 data_time: 0.0140 memory: 382 loss: 1.9015
11/22 16:45:52 - mmengine - INFO - Epoch(train) [2][ 350/1563] lr: 1.0000e-03 eta: 0:05:49 time: 0.0515 data_time: 0.0136 memory: 382 loss: 1.8918
11/22 16:45:52 - mmengine - INFO - Epoch(train) [2][ 360/1563] lr: 1.0000e-03 eta: 0:05:48 time: 0.0516 data_time: 0.0135 memory: 382 loss: 2.0049
11/22 16:45:53 - mmengine - INFO - Epoch(train) [2][ 370/1563] lr: 1.0000e-03 eta: 0:05:47 time: 0.0512 data_time: 0.0134 memory: 382 loss: 1.8901
11/22 16:45:53 - mmengine - INFO - Epoch(train) [2][ 380/1563] lr: 1.0000e-03 eta: 0:05:46 time: 0.0515 data_time: 0.0137 memory: 382 loss: 1.9105
11/22 16:45:54 - mmengine - INFO - Epoch(train) [2][ 390/1563] lr: 1.0000e-03 eta: 0:05:46 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.8478
11/22 16:45:54 - mmengine - INFO - Epoch(train) [2][ 400/1563] lr: 1.0000e-03 eta: 0:05:45 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.8279
11/22 16:45:55 - mmengine - INFO - Epoch(train) [2][ 410/1563] lr: 1.0000e-03 eta: 0:05:44 time: 0.0512 data_time: 0.0135 memory: 382 loss: 1.8644
11/22 16:45:55 - mmengine - INFO - Epoch(train) [2][ 420/1563] lr: 1.0000e-03 eta: 0:05:43 time: 0.0511 data_time: 0.0137 memory: 382 loss: 1.9658
11/22 16:45:56 - mmengine - INFO - Epoch(train) [2][ 430/1563] lr: 1.0000e-03 eta: 0:05:42 time: 0.0532 data_time: 0.0141 memory: 382 loss: 1.7715
11/22 16:45:56 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:45:56 - mmengine - INFO - Epoch(train) [2][ 440/1563] lr: 1.0000e-03 eta: 0:05:42 time: 0.0521 data_time: 0.0140 memory: 382 loss: 2.0089
11/22 16:45:57 - mmengine - INFO - Epoch(train) [2][ 450/1563] lr: 1.0000e-03 eta: 0:05:41 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.7780
11/22 16:45:57 - mmengine - INFO - Epoch(train) [2][ 460/1563] lr: 1.0000e-03 eta: 0:05:40 time: 0.0516 data_time: 0.0136 memory: 382 loss: 1.7803
11/22 16:45:58 - mmengine - INFO - Epoch(train) [2][ 470/1563] lr: 1.0000e-03 eta: 0:05:39 time: 0.0519 data_time: 0.0137 memory: 382 loss: 1.8877
11/22 16:45:58 - mmengine - INFO - Epoch(train) [2][ 480/1563] lr: 1.0000e-03 eta: 0:05:38 time: 0.0520 data_time: 0.0137 memory: 382 loss: 1.9322
11/22 16:45:59 - mmengine - INFO - Epoch(train) [2][ 490/1563] lr: 1.0000e-03 eta: 0:05:38 time: 0.0518 data_time: 0.0137 memory: 382 loss: 1.8556
11/22 16:45:59 - mmengine - INFO - Epoch(train) [2][ 500/1563] lr: 1.0000e-03 eta: 0:05:37 time: 0.0523 data_time: 0.0140 memory: 382 loss: 1.7696
11/22 16:46:00 - mmengine - INFO - Epoch(train) [2][ 510/1563] lr: 1.0000e-03 eta: 0:05:36 time: 0.0543 data_time: 0.0145 memory: 382 loss: 1.7981
11/22 16:46:00 - mmengine - INFO - Epoch(train) [2][ 520/1563] lr: 1.0000e-03 eta: 0:05:35 time: 0.0560 data_time: 0.0145 memory: 382 loss: 1.7619
11/22 16:46:01 - mmengine - INFO - Epoch(train) [2][ 530/1563] lr: 1.0000e-03 eta: 0:05:35 time: 0.0561 data_time: 0.0145 memory: 382 loss: 1.9445
11/22 16:46:02 - mmengine - INFO - Epoch(train) [2][ 540/1563] lr: 1.0000e-03 eta: 0:05:34 time: 0.0560 data_time: 0.0144 memory: 382 loss: 1.9609
11/22 16:46:02 - mmengine - INFO - Epoch(train) [2][ 550/1563] lr: 1.0000e-03 eta: 0:05:34 time: 0.0556 data_time: 0.0145 memory: 382 loss: 1.8829
11/22 16:46:03 - mmengine - INFO - Epoch(train) [2][ 560/1563] lr: 1.0000e-03 eta: 0:05:33 time: 0.0556 data_time: 0.0144 memory: 382 loss: 1.7553
11/22 16:46:03 - mmengine - INFO - Epoch(train) [2][ 570/1563] lr: 1.0000e-03 eta: 0:05:32 time: 0.0546 data_time: 0.0140 memory: 382 loss: 1.8695
11/22 16:46:04 - mmengine - INFO - Epoch(train) [2][ 580/1563] lr: 1.0000e-03 eta: 0:05:31 time: 0.0535 data_time: 0.0136 memory: 382 loss: 1.9100
11/22 16:46:04 - mmengine - INFO - Epoch(train) [2][ 590/1563] lr: 1.0000e-03 eta: 0:05:31 time: 0.0526 data_time: 0.0136 memory: 382 loss: 1.8179
11/22 16:46:05 - mmengine - INFO - Epoch(train) [2][ 600/1563] lr: 1.0000e-03 eta: 0:05:30 time: 0.0518 data_time: 0.0137 memory: 382 loss: 1.7467
11/22 16:46:05 - mmengine - INFO - Epoch(train) [2][ 610/1563] lr: 1.0000e-03 eta: 0:05:29 time: 0.0517 data_time: 0.0137 memory: 382 loss: 1.9133
11/22 16:46:06 - mmengine - INFO - Epoch(train) [2][ 620/1563] lr: 1.0000e-03 eta: 0:05:28 time: 0.0524 data_time: 0.0140 memory: 382 loss: 1.8832
11/22 16:46:06 - mmengine - INFO - Epoch(train) [2][ 630/1563] lr: 1.0000e-03 eta: 0:05:28 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.8295
11/22 16:46:07 - mmengine - INFO - Epoch(train) [2][ 640/1563] lr: 1.0000e-03 eta: 0:05:27 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.7728
11/22 16:46:07 - mmengine - INFO - Epoch(train) [2][ 650/1563] lr: 1.0000e-03 eta: 0:05:26 time: 0.0516 data_time: 0.0136 memory: 382 loss: 1.8678
11/22 16:46:08 - mmengine - INFO - Epoch(train) [2][ 660/1563] lr: 1.0000e-03 eta: 0:05:25 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.7402
11/22 16:46:08 - mmengine - INFO - Epoch(train) [2][ 670/1563] lr: 1.0000e-03 eta: 0:05:25 time: 0.0513 data_time: 0.0134 memory: 382 loss: 1.8371
11/22 16:46:09 - mmengine - INFO - Epoch(train) [2][ 680/1563] lr: 1.0000e-03 eta: 0:05:24 time: 0.0529 data_time: 0.0141 memory: 382 loss: 1.8069
11/22 16:46:10 - mmengine - INFO - Epoch(train) [2][ 690/1563] lr: 1.0000e-03 eta: 0:05:23 time: 0.0518 data_time: 0.0133 memory: 382 loss: 1.8423
11/22 16:46:10 - mmengine - INFO - Epoch(train) [2][ 700/1563] lr: 1.0000e-03 eta: 0:05:22 time: 0.0518 data_time: 0.0137 memory: 382 loss: 1.7003
11/22 16:46:11 - mmengine - INFO - Epoch(train) [2][ 710/1563] lr: 1.0000e-03 eta: 0:05:22 time: 0.0513 data_time: 0.0138 memory: 382 loss: 1.7592
11/22 16:46:11 - mmengine - INFO - Epoch(train) [2][ 720/1563] lr: 1.0000e-03 eta: 0:05:21 time: 0.0513 data_time: 0.0136 memory: 382 loss: 1.7464
11/22 16:46:12 - mmengine - INFO - Epoch(train) [2][ 730/1563] lr: 1.0000e-03 eta: 0:05:20 time: 0.0511 data_time: 0.0134 memory: 382 loss: 1.7989
11/22 16:46:12 - mmengine - INFO - Epoch(train) [2][ 740/1563] lr: 1.0000e-03 eta: 0:05:19 time: 0.0509 data_time: 0.0135 memory: 382 loss: 1.8442
11/22 16:46:13 - mmengine - INFO - Epoch(train) [2][ 750/1563] lr: 1.0000e-03 eta: 0:05:19 time: 0.0508 data_time: 0.0135 memory: 382 loss: 1.7362
11/22 16:46:13 - mmengine - INFO - Epoch(train) [2][ 760/1563] lr: 1.0000e-03 eta: 0:05:18 time: 0.0522 data_time: 0.0135 memory: 382 loss: 1.8073
11/22 16:46:14 - mmengine - INFO - Epoch(train) [2][ 770/1563] lr: 1.0000e-03 eta: 0:05:17 time: 0.0522 data_time: 0.0135 memory: 382 loss: 1.7809
11/22 16:46:14 - mmengine - INFO - Epoch(train) [2][ 780/1563] lr: 1.0000e-03 eta: 0:05:17 time: 0.0523 data_time: 0.0136 memory: 382 loss: 1.7341
11/22 16:46:15 - mmengine - INFO - Epoch(train) [2][ 790/1563] lr: 1.0000e-03 eta: 0:05:16 time: 0.0533 data_time: 0.0138 memory: 382 loss: 1.7854
11/22 16:46:15 - mmengine - INFO - Epoch(train) [2][ 800/1563] lr: 1.0000e-03 eta: 0:05:15 time: 0.0528 data_time: 0.0137 memory: 382 loss: 1.8100
11/22 16:46:16 - mmengine - INFO - Epoch(train) [2][ 810/1563] lr: 1.0000e-03 eta: 0:05:14 time: 0.0531 data_time: 0.0137 memory: 382 loss: 1.9313
11/22 16:46:16 - mmengine - INFO - Epoch(train) [2][ 820/1563] lr: 1.0000e-03 eta: 0:05:14 time: 0.0531 data_time: 0.0138 memory: 382 loss: 1.8791
11/22 16:46:17 - mmengine - INFO - Epoch(train) [2][ 830/1563] lr: 1.0000e-03 eta: 0:05:13 time: 0.0528 data_time: 0.0138 memory: 382 loss: 1.7162
11/22 16:46:17 - mmengine - INFO - Epoch(train) [2][ 840/1563] lr: 1.0000e-03 eta: 0:05:12 time: 0.0526 data_time: 0.0135 memory: 382 loss: 1.9569
11/22 16:46:18 - mmengine - INFO - Epoch(train) [2][ 850/1563] lr: 1.0000e-03 eta: 0:05:12 time: 0.0526 data_time: 0.0135 memory: 382 loss: 1.8267
11/22 16:46:18 - mmengine - INFO - Epoch(train) [2][ 860/1563] lr: 1.0000e-03 eta: 0:05:11 time: 0.0527 data_time: 0.0135 memory: 382 loss: 1.7703
11/22 16:46:19 - mmengine - INFO - Epoch(train) [2][ 870/1563] lr: 1.0000e-03 eta: 0:05:10 time: 0.0525 data_time: 0.0135 memory: 382 loss: 1.8052
11/22 16:46:19 - mmengine - INFO - Epoch(train) [2][ 880/1563] lr: 1.0000e-03 eta: 0:05:10 time: 0.0490 data_time: 0.0138 memory: 382 loss: 1.8631
11/22 16:46:20 - mmengine - INFO - Epoch(train) [2][ 890/1563] lr: 1.0000e-03 eta: 0:05:09 time: 0.0516 data_time: 0.0138 memory: 382 loss: 1.8815
11/22 16:46:20 - mmengine - INFO - Epoch(train) [2][ 900/1563] lr: 1.0000e-03 eta: 0:05:08 time: 0.0465 data_time: 0.0137 memory: 382 loss: 1.8250
11/22 16:46:21 - mmengine - INFO - Epoch(train) [2][ 910/1563] lr: 1.0000e-03 eta: 0:05:07 time: 0.0518 data_time: 0.0138 memory: 382 loss: 1.7385
11/22 16:46:21 - mmengine - INFO - Epoch(train) [2][ 920/1563] lr: 1.0000e-03 eta: 0:05:07 time: 0.0515 data_time: 0.0137 memory: 382 loss: 1.8195
11/22 16:46:22 - mmengine - INFO - Epoch(train) [2][ 930/1563] lr: 1.0000e-03 eta: 0:05:06 time: 0.0518 data_time: 0.0137 memory: 382 loss: 1.7355
11/22 16:46:22 - mmengine - INFO - Epoch(train) [2][ 940/1563] lr: 1.0000e-03 eta: 0:05:05 time: 0.0512 data_time: 0.0135 memory: 382 loss: 1.8352
11/22 16:46:23 - mmengine - INFO - Epoch(train) [2][ 950/1563] lr: 1.0000e-03 eta: 0:05:04 time: 0.0513 data_time: 0.0136 memory: 382 loss: 1.9294
11/22 16:46:23 - mmengine - INFO - Epoch(train) [2][ 960/1563] lr: 1.0000e-03 eta: 0:05:04 time: 0.0523 data_time: 0.0138 memory: 382 loss: 1.6733
11/22 16:46:24 - mmengine - INFO - Epoch(train) [2][ 970/1563] lr: 1.0000e-03 eta: 0:05:03 time: 0.0518 data_time: 0.0138 memory: 382 loss: 1.7458
11/22 16:46:25 - mmengine - INFO - Epoch(train) [2][ 980/1563] lr: 1.0000e-03 eta: 0:05:02 time: 0.0512 data_time: 0.0135 memory: 382 loss: 1.7601
11/22 16:46:25 - mmengine - INFO - Epoch(train) [2][ 990/1563] lr: 1.0000e-03 eta: 0:05:02 time: 0.0514 data_time: 0.0136 memory: 382 loss: 1.8750
11/22 16:46:26 - mmengine - INFO - Epoch(train) [2][1000/1563] lr: 1.0000e-03 eta: 0:05:01 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.7647
11/22 16:46:26 - mmengine - INFO - Epoch(train) [2][1010/1563] lr: 1.0000e-03 eta: 0:05:00 time: 0.0531 data_time: 0.0145 memory: 382 loss: 1.7641
11/22 16:46:27 - mmengine - INFO - Epoch(train) [2][1020/1563] lr: 1.0000e-03 eta: 0:05:00 time: 0.0512 data_time: 0.0135 memory: 382 loss: 1.6066
11/22 16:46:27 - mmengine - INFO - Epoch(train) [2][1030/1563] lr: 1.0000e-03 eta: 0:04:59 time: 0.0514 data_time: 0.0136 memory: 382 loss: 1.8534
11/22 16:46:28 - mmengine - INFO - Epoch(train) [2][1040/1563] lr: 1.0000e-03 eta: 0:04:58 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.7043
11/22 16:46:28 - mmengine - INFO - Epoch(train) [2][1050/1563] lr: 1.0000e-03 eta: 0:04:58 time: 0.0514 data_time: 0.0136 memory: 382 loss: 1.6900
11/22 16:46:29 - mmengine - INFO - Epoch(train) [2][1060/1563] lr: 1.0000e-03 eta: 0:04:57 time: 0.0512 data_time: 0.0134 memory: 382 loss: 1.7221
11/22 16:46:29 - mmengine - INFO - Epoch(train) [2][1070/1563] lr: 1.0000e-03 eta: 0:04:56 time: 0.0516 data_time: 0.0137 memory: 382 loss: 1.6914
11/22 16:46:30 - mmengine - INFO - Epoch(train) [2][1080/1563] lr: 1.0000e-03 eta: 0:04:56 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.7516
11/22 16:46:30 - mmengine - INFO - Epoch(train) [2][1090/1563] lr: 1.0000e-03 eta: 0:04:55 time: 0.0518 data_time: 0.0137 memory: 382 loss: 1.7626
11/22 16:46:31 - mmengine - INFO - Epoch(train) [2][1100/1563] lr: 1.0000e-03 eta: 0:04:54 time: 0.0520 data_time: 0.0137 memory: 382 loss: 1.6875
11/22 16:46:31 - mmengine - INFO - Epoch(train) [2][1110/1563] lr: 1.0000e-03 eta: 0:04:54 time: 0.0556 data_time: 0.0146 memory: 382 loss: 1.8314
11/22 16:46:32 - mmengine - INFO - Epoch(train) [2][1120/1563] lr: 1.0000e-03 eta: 0:04:53 time: 0.0554 data_time: 0.0143 memory: 382 loss: 1.7458
11/22 16:46:32 - mmengine - INFO - Epoch(train) [2][1130/1563] lr: 1.0000e-03 eta: 0:04:52 time: 0.0555 data_time: 0.0144 memory: 382 loss: 1.7342
11/22 16:46:33 - mmengine - INFO - Epoch(train) [2][1140/1563] lr: 1.0000e-03 eta: 0:04:52 time: 0.0558 data_time: 0.0145 memory: 382 loss: 1.8697
11/22 16:46:34 - mmengine - INFO - Epoch(train) [2][1150/1563] lr: 1.0000e-03 eta: 0:04:51 time: 0.0594 data_time: 0.0162 memory: 382 loss: 1.8513
11/22 16:46:34 - mmengine - INFO - Epoch(train) [2][1160/1563] lr: 1.0000e-03 eta: 0:04:51 time: 0.0596 data_time: 0.0161 memory: 382 loss: 1.7320
11/22 16:46:35 - mmengine - INFO - Epoch(train) [2][1170/1563] lr: 1.0000e-03 eta: 0:04:50 time: 0.0583 data_time: 0.0160 memory: 382 loss: 1.7382
11/22 16:46:35 - mmengine - INFO - Epoch(train) [2][1180/1563] lr: 1.0000e-03 eta: 0:04:50 time: 0.0582 data_time: 0.0160 memory: 382 loss: 1.6267
11/22 16:46:36 - mmengine - INFO - Epoch(train) [2][1190/1563] lr: 1.0000e-03 eta: 0:04:49 time: 0.0580 data_time: 0.0158 memory: 382 loss: 1.8052
11/22 16:46:36 - mmengine - INFO - Epoch(train) [2][1200/1563] lr: 1.0000e-03 eta: 0:04:48 time: 0.0580 data_time: 0.0158 memory: 382 loss: 1.8120
11/22 16:46:37 - mmengine - INFO - Epoch(train) [2][1210/1563] lr: 1.0000e-03 eta: 0:04:48 time: 0.0561 data_time: 0.0159 memory: 382 loss: 1.7501
11/22 16:46:38 - mmengine - INFO - Epoch(train) [2][1220/1563] lr: 1.0000e-03 eta: 0:04:47 time: 0.0559 data_time: 0.0158 memory: 382 loss: 1.8753
11/22 16:46:38 - mmengine - INFO - Epoch(train) [2][1230/1563] lr: 1.0000e-03 eta: 0:04:47 time: 0.0580 data_time: 0.0159 memory: 382 loss: 1.8064
11/22 16:46:39 - mmengine - INFO - Epoch(train) [2][1240/1563] lr: 1.0000e-03 eta: 0:04:46 time: 0.0582 data_time: 0.0159 memory: 382 loss: 1.7679
11/22 16:46:39 - mmengine - INFO - Epoch(train) [2][1250/1563] lr: 1.0000e-03 eta: 0:04:46 time: 0.0569 data_time: 0.0159 memory: 382 loss: 1.7150
11/22 16:46:40 - mmengine - INFO - Epoch(train) [2][1260/1563] lr: 1.0000e-03 eta: 0:04:45 time: 0.0585 data_time: 0.0160 memory: 382 loss: 1.7895
11/22 16:46:40 - mmengine - INFO - Epoch(train) [2][1270/1563] lr: 1.0000e-03 eta: 0:04:44 time: 0.0580 data_time: 0.0158 memory: 382 loss: 1.7520
11/22 16:46:41 - mmengine - INFO - Epoch(train) [2][1280/1563] lr: 1.0000e-03 eta: 0:04:44 time: 0.0582 data_time: 0.0160 memory: 382 loss: 1.6606
11/22 16:46:42 - mmengine - INFO - Epoch(train) [2][1290/1563] lr: 1.0000e-03 eta: 0:04:43 time: 0.0580 data_time: 0.0158 memory: 382 loss: 1.6303
11/22 16:46:42 - mmengine - INFO - Epoch(train) [2][1300/1563] lr: 1.0000e-03 eta: 0:04:43 time: 0.0580 data_time: 0.0158 memory: 382 loss: 1.8094
11/22 16:46:43 - mmengine - INFO - Epoch(train) [2][1310/1563] lr: 1.0000e-03 eta: 0:04:42 time: 0.0582 data_time: 0.0160 memory: 382 loss: 1.7696
11/22 16:46:43 - mmengine - INFO - Epoch(train) [2][1320/1563] lr: 1.0000e-03 eta: 0:04:42 time: 0.0581 data_time: 0.0159 memory: 382 loss: 1.8476
11/22 16:46:44 - mmengine - INFO - Epoch(train) [2][1330/1563] lr: 1.0000e-03 eta: 0:04:41 time: 0.0581 data_time: 0.0159 memory: 382 loss: 1.7991
11/22 16:46:45 - mmengine - INFO - Epoch(train) [2][1340/1563] lr: 1.0000e-03 eta: 0:04:41 time: 0.0581 data_time: 0.0159 memory: 382 loss: 1.7261
11/22 16:46:45 - mmengine - INFO - Epoch(train) [2][1350/1563] lr: 1.0000e-03 eta: 0:04:40 time: 0.0580 data_time: 0.0158 memory: 382 loss: 1.7103
11/22 16:46:46 - mmengine - INFO - Epoch(train) [2][1360/1563] lr: 1.0000e-03 eta: 0:04:39 time: 0.0580 data_time: 0.0159 memory: 382 loss: 1.7356
11/22 16:46:46 - mmengine - INFO - Epoch(train) [2][1370/1563] lr: 1.0000e-03 eta: 0:04:39 time: 0.0581 data_time: 0.0158 memory: 382 loss: 1.8153
11/22 16:46:47 - mmengine - INFO - Epoch(train) [2][1380/1563] lr: 1.0000e-03 eta: 0:04:38 time: 0.0580 data_time: 0.0158 memory: 382 loss: 1.7560
11/22 16:46:47 - mmengine - INFO - Epoch(train) [2][1390/1563] lr: 1.0000e-03 eta: 0:04:38 time: 0.0580 data_time: 0.0158 memory: 382 loss: 1.6897
11/22 16:46:48 - mmengine - INFO - Epoch(train) [2][1400/1563] lr: 1.0000e-03 eta: 0:04:37 time: 0.0570 data_time: 0.0158 memory: 382 loss: 1.7431
11/22 16:46:49 - mmengine - INFO - Epoch(train) [2][1410/1563] lr: 1.0000e-03 eta: 0:04:37 time: 0.0551 data_time: 0.0160 memory: 382 loss: 1.7052
11/22 16:46:49 - mmengine - INFO - Epoch(train) [2][1420/1563] lr: 1.0000e-03 eta: 0:04:36 time: 0.0578 data_time: 0.0159 memory: 382 loss: 1.7328
11/22 16:46:50 - mmengine - INFO - Epoch(train) [2][1430/1563] lr: 1.0000e-03 eta: 0:04:35 time: 0.0580 data_time: 0.0159 memory: 382 loss: 1.7698
11/22 16:46:50 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:46:50 - mmengine - INFO - Epoch(train) [2][1440/1563] lr: 1.0000e-03 eta: 0:04:35 time: 0.0542 data_time: 0.0161 memory: 382 loss: 1.7988
11/22 16:46:51 - mmengine - INFO - Epoch(train) [2][1450/1563] lr: 1.0000e-03 eta: 0:04:34 time: 0.0565 data_time: 0.0157 memory: 382 loss: 1.8104
11/22 16:46:51 - mmengine - INFO - Epoch(train) [2][1460/1563] lr: 1.0000e-03 eta: 0:04:34 time: 0.0577 data_time: 0.0158 memory: 382 loss: 1.8345
11/22 16:46:52 - mmengine - INFO - Epoch(train) [2][1470/1563] lr: 1.0000e-03 eta: 0:04:33 time: 0.0581 data_time: 0.0160 memory: 382 loss: 1.8332
11/22 16:46:53 - mmengine - INFO - Epoch(train) [2][1480/1563] lr: 1.0000e-03 eta: 0:04:33 time: 0.0578 data_time: 0.0158 memory: 382 loss: 1.5920
11/22 16:46:53 - mmengine - INFO - Epoch(train) [2][1490/1563] lr: 1.0000e-03 eta: 0:04:32 time: 0.0579 data_time: 0.0159 memory: 382 loss: 1.8411
11/22 16:46:54 - mmengine - INFO - Epoch(train) [2][1500/1563] lr: 1.0000e-03 eta: 0:04:31 time: 0.0579 data_time: 0.0158 memory: 382 loss: 1.6966
11/22 16:46:54 - mmengine - INFO - Epoch(train) [2][1510/1563] lr: 1.0000e-03 eta: 0:04:31 time: 0.0580 data_time: 0.0159 memory: 382 loss: 1.7776
11/22 16:46:55 - mmengine - INFO - Epoch(train) [2][1520/1563] lr: 1.0000e-03 eta: 0:04:30 time: 0.0566 data_time: 0.0159 memory: 382 loss: 1.7656
11/22 16:46:55 - mmengine - INFO - Epoch(train) [2][1530/1563] lr: 1.0000e-03 eta: 0:04:30 time: 0.0554 data_time: 0.0158 memory: 382 loss: 1.7661
11/22 16:46:56 - mmengine - INFO - Epoch(train) [2][1540/1563] lr: 1.0000e-03 eta: 0:04:29 time: 0.0571 data_time: 0.0159 memory: 382 loss: 1.7557
11/22 16:46:57 - mmengine - INFO - Epoch(train) [2][1550/1563] lr: 1.0000e-03 eta: 0:04:29 time: 0.0578 data_time: 0.0159 memory: 382 loss: 1.7437
11/22 16:46:57 - mmengine - INFO - Epoch(train) [2][1560/1563] lr: 1.0000e-03 eta: 0:04:28 time: 0.0576 data_time: 0.0158 memory: 382 loss: 1.7960
11/22 16:46:57 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:46:57 - mmengine - INFO - Saving checkpoint at 2 epochs
11/22 16:46:58 - mmengine - INFO - Epoch(val) [2][ 10/313] eta: 0:00:06 time: 0.0222 data_time: 0.0117 memory: 382
11/22 16:46:58 - mmengine - INFO - Epoch(val) [2][ 20/313] eta: 0:00:06 time: 0.0193 data_time: 0.0095 memory: 225
11/22 16:46:59 - mmengine - INFO - Epoch(val) [2][ 30/313] eta: 0:00:05 time: 0.0174 data_time: 0.0086 memory: 225
11/22 16:46:59 - mmengine - INFO - Epoch(val) [2][ 40/313] eta: 0:00:05 time: 0.0175 data_time: 0.0087 memory: 225
11/22 16:46:59 - mmengine - INFO - Epoch(val) [2][ 50/313] eta: 0:00:04 time: 0.0174 data_time: 0.0086 memory: 225
11/22 16:46:59 - mmengine - INFO - Epoch(val) [2][ 60/313] eta: 0:00:04 time: 0.0176 data_time: 0.0087 memory: 225
11/22 16:46:59 - mmengine - INFO - Epoch(val) [2][ 70/313] eta: 0:00:04 time: 0.0176 data_time: 0.0086 memory: 225
11/22 16:46:59 - mmengine - INFO - Epoch(val) [2][ 80/313] eta: 0:00:04 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:00 - mmengine - INFO - Epoch(val) [2][ 90/313] eta: 0:00:04 time: 0.0175 data_time: 0.0087 memory: 225
11/22 16:47:00 - mmengine - INFO - Epoch(val) [2][100/313] eta: 0:00:03 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:00 - mmengine - INFO - Epoch(val) [2][110/313] eta: 0:00:03 time: 0.0176 data_time: 0.0087 memory: 225
11/22 16:47:00 - mmengine - INFO - Epoch(val) [2][120/313] eta: 0:00:03 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:00 - mmengine - INFO - Epoch(val) [2][130/313] eta: 0:00:03 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:01 - mmengine - INFO - Epoch(val) [2][140/313] eta: 0:00:03 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:01 - mmengine - INFO - Epoch(val) [2][150/313] eta: 0:00:02 time: 0.0176 data_time: 0.0087 memory: 225
11/22 16:47:01 - mmengine - INFO - Epoch(val) [2][160/313] eta: 0:00:02 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:01 - mmengine - INFO - Epoch(val) [2][170/313] eta: 0:00:02 time: 0.0176 data_time: 0.0087 memory: 225
11/22 16:47:01 - mmengine - INFO - Epoch(val) [2][180/313] eta: 0:00:02 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:01 - mmengine - INFO - Epoch(val) [2][190/313] eta: 0:00:02 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:02 - mmengine - INFO - Epoch(val) [2][200/313] eta: 0:00:02 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:02 - mmengine - INFO - Epoch(val) [2][210/313] eta: 0:00:01 time: 0.0179 data_time: 0.0088 memory: 225
11/22 16:47:02 - mmengine - INFO - Epoch(val) [2][220/313] eta: 0:00:01 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:02 - mmengine - INFO - Epoch(val) [2][230/313] eta: 0:00:01 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:02 - mmengine - INFO - Epoch(val) [2][240/313] eta: 0:00:01 time: 0.0175 data_time: 0.0086 memory: 225
11/22 16:47:02 - mmengine - INFO - Epoch(val) [2][250/313] eta: 0:00:01 time: 0.0174 data_time: 0.0086 memory: 225
11/22 16:47:03 - mmengine - INFO - Epoch(val) [2][260/313] eta: 0:00:00 time: 0.0174 data_time: 0.0086 memory: 225
11/22 16:47:03 - mmengine - INFO - Epoch(val) [2][270/313] eta: 0:00:00 time: 0.0177 data_time: 0.0087 memory: 225
11/22 16:47:03 - mmengine - INFO - Epoch(val) [2][280/313] eta: 0:00:00 time: 0.0175 data_time: 0.0087 memory: 225
11/22 16:47:03 - mmengine - INFO - Epoch(val) [2][290/313] eta: 0:00:00 time: 0.0177 data_time: 0.0087 memory: 225
11/22 16:47:03 - mmengine - INFO - Epoch(val) [2][300/313] eta: 0:00:00 time: 0.0176 data_time: 0.0087 memory: 225
11/22 16:47:04 - mmengine - INFO - Epoch(val) [2][310/313] eta: 0:00:00 time: 0.0176 data_time: 0.0087 memory: 225
11/22 16:47:04 - mmengine - INFO - Epoch(val) [2][313/313] accuracy: 43.0900 data_time: 0.0087 time: 0.0177
11/22 16:47:04 - mmengine - INFO - Epoch(train) [3][ 10/1563] lr: 1.0000e-03 eta: 0:04:27 time: 0.0536 data_time: 0.0138 memory: 382 loss: 1.7544
11/22 16:47:05 - mmengine - INFO - Epoch(train) [3][ 20/1563] lr: 1.0000e-03 eta: 0:04:27 time: 0.0531 data_time: 0.0136 memory: 382 loss: 1.8709
11/22 16:47:05 - mmengine - INFO - Epoch(train) [3][ 30/1563] lr: 1.0000e-03 eta: 0:04:26 time: 0.0539 data_time: 0.0140 memory: 382 loss: 1.7736
11/22 16:47:06 - mmengine - INFO - Epoch(train) [3][ 40/1563] lr: 1.0000e-03 eta: 0:04:25 time: 0.0540 data_time: 0.0140 memory: 382 loss: 1.7101
11/22 16:47:06 - mmengine - INFO - Epoch(train) [3][ 50/1563] lr: 1.0000e-03 eta: 0:04:25 time: 0.0541 data_time: 0.0141 memory: 382 loss: 1.6691
11/22 16:47:07 - mmengine - INFO - Epoch(train) [3][ 60/1563] lr: 1.0000e-03 eta: 0:04:24 time: 0.0539 data_time: 0.0138 memory: 382 loss: 1.7272
11/22 16:47:07 - mmengine - INFO - Epoch(train) [3][ 70/1563] lr: 1.0000e-03 eta: 0:04:24 time: 0.0542 data_time: 0.0140 memory: 382 loss: 1.6619
11/22 16:47:08 - mmengine - INFO - Epoch(train) [3][ 80/1563] lr: 1.0000e-03 eta: 0:04:23 time: 0.0538 data_time: 0.0139 memory: 382 loss: 1.6607
11/22 16:47:08 - mmengine - INFO - Epoch(train) [3][ 90/1563] lr: 1.0000e-03 eta: 0:04:22 time: 0.0541 data_time: 0.0141 memory: 382 loss: 1.6077
11/22 16:47:09 - mmengine - INFO - Epoch(train) [3][ 100/1563] lr: 1.0000e-03 eta: 0:04:22 time: 0.0541 data_time: 0.0140 memory: 382 loss: 1.6063
11/22 16:47:10 - mmengine - INFO - Epoch(train) [3][ 110/1563] lr: 1.0000e-03 eta: 0:04:21 time: 0.0544 data_time: 0.0141 memory: 382 loss: 1.6782
11/22 16:47:10 - mmengine - INFO - Epoch(train) [3][ 120/1563] lr: 1.0000e-03 eta: 0:04:20 time: 0.0549 data_time: 0.0143 memory: 382 loss: 1.8019
11/22 16:47:11 - mmengine - INFO - Epoch(train) [3][ 130/1563] lr: 1.0000e-03 eta: 0:04:20 time: 0.0542 data_time: 0.0141 memory: 382 loss: 1.6718
11/22 16:47:11 - mmengine - INFO - Epoch(train) [3][ 140/1563] lr: 1.0000e-03 eta: 0:04:19 time: 0.0520 data_time: 0.0144 memory: 382 loss: 1.6988
11/22 16:47:12 - mmengine - INFO - Epoch(train) [3][ 150/1563] lr: 1.0000e-03 eta: 0:04:19 time: 0.0540 data_time: 0.0145 memory: 382 loss: 1.7076
11/22 16:47:12 - mmengine - INFO - Epoch(train) [3][ 160/1563] lr: 1.0000e-03 eta: 0:04:18 time: 0.0562 data_time: 0.0147 memory: 382 loss: 1.6644
11/22 16:47:13 - mmengine - INFO - Epoch(train) [3][ 170/1563] lr: 1.0000e-03 eta: 0:04:17 time: 0.0559 data_time: 0.0146 memory: 382 loss: 1.7423
11/22 16:47:13 - mmengine - INFO - Epoch(train) [3][ 180/1563] lr: 1.0000e-03 eta: 0:04:17 time: 0.0535 data_time: 0.0147 memory: 382 loss: 1.6710
11/22 16:47:14 - mmengine - INFO - Epoch(train) [3][ 190/1563] lr: 1.0000e-03 eta: 0:04:16 time: 0.0535 data_time: 0.0146 memory: 382 loss: 1.6126
11/22 16:47:14 - mmengine - INFO - Epoch(train) [3][ 200/1563] lr: 1.0000e-03 eta: 0:04:16 time: 0.0534 data_time: 0.0143 memory: 382 loss: 1.7686
11/22 16:47:15 - mmengine - INFO - Epoch(train) [3][ 210/1563] lr: 1.0000e-03 eta: 0:04:15 time: 0.0521 data_time: 0.0137 memory: 382 loss: 1.6462
11/22 16:47:15 - mmengine - INFO - Epoch(train) [3][ 220/1563] lr: 1.0000e-03 eta: 0:04:14 time: 0.0525 data_time: 0.0138 memory: 382 loss: 1.6834
11/22 16:47:16 - mmengine - INFO - Epoch(train) [3][ 230/1563] lr: 1.0000e-03 eta: 0:04:14 time: 0.0526 data_time: 0.0139 memory: 382 loss: 1.8183
11/22 16:47:16 - mmengine - INFO - Epoch(train) [3][ 240/1563] lr: 1.0000e-03 eta: 0:04:13 time: 0.0533 data_time: 0.0143 memory: 382 loss: 1.5796
11/22 16:47:17 - mmengine - INFO - Epoch(train) [3][ 250/1563] lr: 1.0000e-03 eta: 0:04:12 time: 0.0507 data_time: 0.0134 memory: 382 loss: 1.6061
11/22 16:47:18 - mmengine - INFO - Epoch(train) [3][ 260/1563] lr: 1.0000e-03 eta: 0:04:12 time: 0.0520 data_time: 0.0137 memory: 382 loss: 1.7223
11/22 16:47:18 - mmengine - INFO - Epoch(train) [3][ 270/1563] lr: 1.0000e-03 eta: 0:04:11 time: 0.0514 data_time: 0.0136 memory: 382 loss: 1.6142
11/22 16:47:19 - mmengine - INFO - Epoch(train) [3][ 280/1563] lr: 1.0000e-03 eta: 0:04:10 time: 0.0514 data_time: 0.0136 memory: 382 loss: 1.6779
11/22 16:47:19 - mmengine - INFO - Epoch(train) [3][ 290/1563] lr: 1.0000e-03 eta: 0:04:10 time: 0.0514 data_time: 0.0136 memory: 382 loss: 1.8518
11/22 16:47:20 - mmengine - INFO - Epoch(train) [3][ 300/1563] lr: 1.0000e-03 eta: 0:04:09 time: 0.0530 data_time: 0.0138 memory: 382 loss: 1.6435
11/22 16:47:20 - mmengine - INFO - Epoch(train) [3][ 310/1563] lr: 1.0000e-03 eta: 0:04:09 time: 0.0539 data_time: 0.0138 memory: 382 loss: 1.6019
11/22 16:47:21 - mmengine - INFO - Epoch(train) [3][ 320/1563] lr: 1.0000e-03 eta: 0:04:08 time: 0.0532 data_time: 0.0137 memory: 382 loss: 1.6349
11/22 16:47:21 - mmengine - INFO - Epoch(train) [3][ 330/1563] lr: 1.0000e-03 eta: 0:04:07 time: 0.0529 data_time: 0.0139 memory: 382 loss: 1.7829
11/22 16:47:22 - mmengine - INFO - Epoch(train) [3][ 340/1563] lr: 1.0000e-03 eta: 0:04:07 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.7509
11/22 16:47:22 - mmengine - INFO - Epoch(train) [3][ 350/1563] lr: 1.0000e-03 eta: 0:04:06 time: 0.0510 data_time: 0.0135 memory: 382 loss: 1.7433
11/22 16:47:23 - mmengine - INFO - Epoch(train) [3][ 360/1563] lr: 1.0000e-03 eta: 0:04:05 time: 0.0516 data_time: 0.0137 memory: 382 loss: 1.6134
11/22 16:47:23 - mmengine - INFO - Epoch(train) [3][ 370/1563] lr: 1.0000e-03 eta: 0:04:05 time: 0.0511 data_time: 0.0134 memory: 382 loss: 1.6078
11/22 16:47:24 - mmengine - INFO - Epoch(train) [3][ 380/1563] lr: 1.0000e-03 eta: 0:04:04 time: 0.0510 data_time: 0.0135 memory: 382 loss: 1.6183
11/22 16:47:24 - mmengine - INFO - Epoch(train) [3][ 390/1563] lr: 1.0000e-03 eta: 0:04:04 time: 0.0531 data_time: 0.0137 memory: 382 loss: 1.7506
11/22 16:47:25 - mmengine - INFO - Epoch(train) [3][ 400/1563] lr: 1.0000e-03 eta: 0:04:03 time: 0.0531 data_time: 0.0136 memory: 382 loss: 1.7401
11/22 16:47:25 - mmengine - INFO - Epoch(train) [3][ 410/1563] lr: 1.0000e-03 eta: 0:04:02 time: 0.0537 data_time: 0.0138 memory: 382 loss: 1.6120
11/22 16:47:26 - mmengine - INFO - Epoch(train) [3][ 420/1563] lr: 1.0000e-03 eta: 0:04:02 time: 0.0532 data_time: 0.0136 memory: 382 loss: 1.7460
11/22 16:47:26 - mmengine - INFO - Epoch(train) [3][ 430/1563] lr: 1.0000e-03 eta: 0:04:01 time: 0.0521 data_time: 0.0137 memory: 382 loss: 1.7638
11/22 16:47:27 - mmengine - INFO - Epoch(train) [3][ 440/1563] lr: 1.0000e-03 eta: 0:04:00 time: 0.0511 data_time: 0.0138 memory: 382 loss: 1.6975
11/22 16:47:27 - mmengine - INFO - Epoch(train) [3][ 450/1563] lr: 1.0000e-03 eta: 0:04:00 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.6568
11/22 16:47:28 - mmengine - INFO - Epoch(train) [3][ 460/1563] lr: 1.0000e-03 eta: 0:03:59 time: 0.0531 data_time: 0.0137 memory: 382 loss: 1.7361
11/22 16:47:29 - mmengine - INFO - Epoch(train) [3][ 470/1563] lr: 1.0000e-03 eta: 0:03:59 time: 0.0541 data_time: 0.0140 memory: 382 loss: 1.6191
11/22 16:47:29 - mmengine - INFO - Epoch(train) [3][ 480/1563] lr: 1.0000e-03 eta: 0:03:58 time: 0.0539 data_time: 0.0140 memory: 382 loss: 1.6996
11/22 16:47:30 - mmengine - INFO - Epoch(train) [3][ 490/1563] lr: 1.0000e-03 eta: 0:03:57 time: 0.0538 data_time: 0.0139 memory: 382 loss: 1.6323
11/22 16:47:30 - mmengine - INFO - Epoch(train) [3][ 500/1563] lr: 1.0000e-03 eta: 0:03:57 time: 0.0539 data_time: 0.0139 memory: 382 loss: 1.6949
11/22 16:47:31 - mmengine - INFO - Epoch(train) [3][ 510/1563] lr: 1.0000e-03 eta: 0:03:56 time: 0.0541 data_time: 0.0139 memory: 382 loss: 1.7518
11/22 16:47:31 - mmengine - INFO - Epoch(train) [3][ 520/1563] lr: 1.0000e-03 eta: 0:03:56 time: 0.0538 data_time: 0.0138 memory: 382 loss: 1.7295
11/22 16:47:32 - mmengine - INFO - Epoch(train) [3][ 530/1563] lr: 1.0000e-03 eta: 0:03:55 time: 0.0541 data_time: 0.0140 memory: 382 loss: 1.7763
11/22 16:47:32 - mmengine - INFO - Epoch(train) [3][ 540/1563] lr: 1.0000e-03 eta: 0:03:54 time: 0.0539 data_time: 0.0139 memory: 382 loss: 1.6836
11/22 16:47:33 - mmengine - INFO - Epoch(train) [3][ 550/1563] lr: 1.0000e-03 eta: 0:03:54 time: 0.0539 data_time: 0.0140 memory: 382 loss: 1.7488
11/22 16:47:33 - mmengine - INFO - Epoch(train) [3][ 560/1563] lr: 1.0000e-03 eta: 0:03:53 time: 0.0538 data_time: 0.0139 memory: 382 loss: 1.6240
11/22 16:47:34 - mmengine - INFO - Epoch(train) [3][ 570/1563] lr: 1.0000e-03 eta: 0:03:53 time: 0.0537 data_time: 0.0138 memory: 382 loss: 1.6455
11/22 16:47:34 - mmengine - INFO - Epoch(train) [3][ 580/1563] lr: 1.0000e-03 eta: 0:03:52 time: 0.0538 data_time: 0.0139 memory: 382 loss: 1.8316
11/22 16:47:35 - mmengine - INFO - Epoch(train) [3][ 590/1563] lr: 1.0000e-03 eta: 0:03:51 time: 0.0540 data_time: 0.0139 memory: 382 loss: 1.5671
11/22 16:47:36 - mmengine - INFO - Epoch(train) [3][ 600/1563] lr: 1.0000e-03 eta: 0:03:51 time: 0.0538 data_time: 0.0138 memory: 382 loss: 1.7360
11/22 16:47:36 - mmengine - INFO - Epoch(train) [3][ 610/1563] lr: 1.0000e-03 eta: 0:03:50 time: 0.0539 data_time: 0.0139 memory: 382 loss: 1.7328
11/22 16:47:37 - mmengine - INFO - Epoch(train) [3][ 620/1563] lr: 1.0000e-03 eta: 0:03:50 time: 0.0539 data_time: 0.0140 memory: 382 loss: 1.6618
11/22 16:47:37 - mmengine - INFO - Epoch(train) [3][ 630/1563] lr: 1.0000e-03 eta: 0:03:49 time: 0.0537 data_time: 0.0137 memory: 382 loss: 1.6549
11/22 16:47:38 - mmengine - INFO - Epoch(train) [3][ 640/1563] lr: 1.0000e-03 eta: 0:03:48 time: 0.0536 data_time: 0.0138 memory: 382 loss: 1.7133
11/22 16:47:38 - mmengine - INFO - Epoch(train) [3][ 650/1563] lr: 1.0000e-03 eta: 0:03:48 time: 0.0540 data_time: 0.0140 memory: 382 loss: 1.7738
11/22 16:47:39 - mmengine - INFO - Epoch(train) [3][ 660/1563] lr: 1.0000e-03 eta: 0:03:47 time: 0.0546 data_time: 0.0141 memory: 382 loss: 1.7275
11/22 16:47:39 - mmengine - INFO - Epoch(train) [3][ 670/1563] lr: 1.0000e-03 eta: 0:03:47 time: 0.0539 data_time: 0.0139 memory: 382 loss: 1.7047
11/22 16:47:40 - mmengine - INFO - Epoch(train) [3][ 680/1563] lr: 1.0000e-03 eta: 0:03:46 time: 0.0532 data_time: 0.0140 memory: 382 loss: 1.6989
11/22 16:47:40 - mmengine - INFO - Epoch(train) [3][ 690/1563] lr: 1.0000e-03 eta: 0:03:46 time: 0.0537 data_time: 0.0140 memory: 382 loss: 1.7081
11/22 16:47:41 - mmengine - INFO - Epoch(train) [3][ 700/1563] lr: 1.0000e-03 eta: 0:03:45 time: 0.0541 data_time: 0.0139 memory: 382 loss: 1.5826
11/22 16:47:41 - mmengine - INFO - Epoch(train) [3][ 710/1563] lr: 1.0000e-03 eta: 0:03:44 time: 0.0541 data_time: 0.0141 memory: 382 loss: 1.6385
11/22 16:47:42 - mmengine - INFO - Epoch(train) [3][ 720/1563] lr: 1.0000e-03 eta: 0:03:44 time: 0.0538 data_time: 0.0139 memory: 382 loss: 1.6755
11/22 16:47:43 - mmengine - INFO - Epoch(train) [3][ 730/1563] lr: 1.0000e-03 eta: 0:03:43 time: 0.0541 data_time: 0.0141 memory: 382 loss: 1.6512
11/22 16:47:43 - mmengine - INFO - Epoch(train) [3][ 740/1563] lr: 1.0000e-03 eta: 0:03:43 time: 0.0540 data_time: 0.0140 memory: 382 loss: 1.7752
11/22 16:47:44 - mmengine - INFO - Epoch(train) [3][ 750/1563] lr: 1.0000e-03 eta: 0:03:42 time: 0.0538 data_time: 0.0138 memory: 382 loss: 1.6108
11/22 16:47:44 - mmengine - INFO - Epoch(train) [3][ 760/1563] lr: 1.0000e-03 eta: 0:03:41 time: 0.0538 data_time: 0.0139 memory: 382 loss: 1.7641
11/22 16:47:45 - mmengine - INFO - Epoch(train) [3][ 770/1563] lr: 1.0000e-03 eta: 0:03:41 time: 0.0539 data_time: 0.0139 memory: 382 loss: 1.7119
11/22 16:47:45 - mmengine - INFO - Epoch(train) [3][ 780/1563] lr: 1.0000e-03 eta: 0:03:40 time: 0.0538 data_time: 0.0139 memory: 382 loss: 1.6977
11/22 16:47:46 - mmengine - INFO - Epoch(train) [3][ 790/1563] lr: 1.0000e-03 eta: 0:03:40 time: 0.0540 data_time: 0.0139 memory: 382 loss: 1.7686
11/22 16:47:46 - mmengine - INFO - Epoch(train) [3][ 800/1563] lr: 1.0000e-03 eta: 0:03:39 time: 0.0542 data_time: 0.0142 memory: 382 loss: 1.6454
11/22 16:47:47 - mmengine - INFO - Epoch(train) [3][ 810/1563] lr: 1.0000e-03 eta: 0:03:38 time: 0.0512 data_time: 0.0137 memory: 382 loss: 1.5920
11/22 16:47:47 - mmengine - INFO - Epoch(train) [3][ 820/1563] lr: 1.0000e-03 eta: 0:03:38 time: 0.0519 data_time: 0.0137 memory: 382 loss: 1.7565
11/22 16:47:48 - mmengine - INFO - Epoch(train) [3][ 830/1563] lr: 1.0000e-03 eta: 0:03:37 time: 0.0521 data_time: 0.0140 memory: 382 loss: 1.6128
11/22 16:47:48 - mmengine - INFO - Epoch(train) [3][ 840/1563] lr: 1.0000e-03 eta: 0:03:37 time: 0.0538 data_time: 0.0140 memory: 382 loss: 1.6129
11/22 16:47:49 - mmengine - INFO - Epoch(train) [3][ 850/1563] lr: 1.0000e-03 eta: 0:03:36 time: 0.0539 data_time: 0.0141 memory: 382 loss: 1.6814
11/22 16:47:49 - mmengine - INFO - Epoch(train) [3][ 860/1563] lr: 1.0000e-03 eta: 0:03:35 time: 0.0543 data_time: 0.0141 memory: 382 loss: 1.6300
11/22 16:47:50 - mmengine - INFO - Epoch(train) [3][ 870/1563] lr: 1.0000e-03 eta: 0:03:35 time: 0.0531 data_time: 0.0139 memory: 382 loss: 1.8181
11/22 16:47:50 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:47:51 - mmengine - INFO - Epoch(train) [3][ 880/1563] lr: 1.0000e-03 eta: 0:03:34 time: 0.0543 data_time: 0.0141 memory: 382 loss: 1.7343
11/22 16:47:51 - mmengine - INFO - Epoch(train) [3][ 890/1563] lr: 1.0000e-03 eta: 0:03:34 time: 0.0538 data_time: 0.0139 memory: 382 loss: 1.5847
11/22 16:47:52 - mmengine - INFO - Epoch(train) [3][ 900/1563] lr: 1.0000e-03 eta: 0:03:33 time: 0.0522 data_time: 0.0139 memory: 382 loss: 1.6588
11/22 16:47:52 - mmengine - INFO - Epoch(train) [3][ 910/1563] lr: 1.0000e-03 eta: 0:03:32 time: 0.0536 data_time: 0.0139 memory: 382 loss: 1.5980
11/22 16:47:53 - mmengine - INFO - Epoch(train) [3][ 920/1563] lr: 1.0000e-03 eta: 0:03:32 time: 0.0548 data_time: 0.0143 memory: 382 loss: 1.6948
11/22 16:47:53 - mmengine - INFO - Epoch(train) [3][ 930/1563] lr: 1.0000e-03 eta: 0:03:31 time: 0.0539 data_time: 0.0139 memory: 382 loss: 1.7071
11/22 16:47:54 - mmengine - INFO - Epoch(train) [3][ 940/1563] lr: 1.0000e-03 eta: 0:03:31 time: 0.0541 data_time: 0.0139 memory: 382 loss: 1.7188
11/22 16:47:54 - mmengine - INFO - Epoch(train) [3][ 950/1563] lr: 1.0000e-03 eta: 0:03:30 time: 0.0540 data_time: 0.0139 memory: 382 loss: 1.6856
11/22 16:47:55 - mmengine - INFO - Epoch(train) [3][ 960/1563] lr: 1.0000e-03 eta: 0:03:30 time: 0.0541 data_time: 0.0140 memory: 382 loss: 1.5540
11/22 16:47:55 - mmengine - INFO - Epoch(train) [3][ 970/1563] lr: 1.0000e-03 eta: 0:03:29 time: 0.0544 data_time: 0.0140 memory: 382 loss: 1.6686
11/22 16:47:56 - mmengine - INFO - Epoch(train) [3][ 980/1563] lr: 1.0000e-03 eta: 0:03:28 time: 0.0546 data_time: 0.0144 memory: 382 loss: 1.6318
11/22 16:47:56 - mmengine - INFO - Epoch(train) [3][ 990/1563] lr: 1.0000e-03 eta: 0:03:28 time: 0.0522 data_time: 0.0140 memory: 382 loss: 1.6669
11/22 16:47:57 - mmengine - INFO - Epoch(train) [3][1000/1563] lr: 1.0000e-03 eta: 0:03:27 time: 0.0516 data_time: 0.0139 memory: 382 loss: 1.7148
11/22 16:47:57 - mmengine - INFO - Epoch(train) [3][1010/1563] lr: 1.0000e-03 eta: 0:03:27 time: 0.0517 data_time: 0.0137 memory: 382 loss: 1.6002
11/22 16:47:58 - mmengine - INFO - Epoch(train) [3][1020/1563] lr: 1.0000e-03 eta: 0:03:26 time: 0.0538 data_time: 0.0140 memory: 382 loss: 1.6180
11/22 16:47:59 - mmengine - INFO - Epoch(train) [3][1030/1563] lr: 1.0000e-03 eta: 0:03:25 time: 0.0537 data_time: 0.0139 memory: 382 loss: 1.6952
11/22 16:47:59 - mmengine - INFO - Epoch(train) [3][1040/1563] lr: 1.0000e-03 eta: 0:03:25 time: 0.0540 data_time: 0.0140 memory: 382 loss: 1.5350
11/22 16:48:00 - mmengine - INFO - Epoch(train) [3][1050/1563] lr: 1.0000e-03 eta: 0:03:24 time: 0.0539 data_time: 0.0140 memory: 382 loss: 1.6214
11/22 16:48:00 - mmengine - INFO - Epoch(train) [3][1060/1563] lr: 1.0000e-03 eta: 0:03:24 time: 0.0584 data_time: 0.0166 memory: 382 loss: 1.6768
11/22 16:48:01 - mmengine - INFO - Epoch(train) [3][1070/1563] lr: 1.0000e-03 eta: 0:03:23 time: 0.0563 data_time: 0.0163 memory: 382 loss: 1.6510
11/22 16:48:01 - mmengine - INFO - Epoch(train) [3][1080/1563] lr: 1.0000e-03 eta: 0:03:23 time: 0.0530 data_time: 0.0142 memory: 382 loss: 1.6150
11/22 16:48:02 - mmengine - INFO - Epoch(train) [3][1090/1563] lr: 1.0000e-03 eta: 0:03:22 time: 0.0580 data_time: 0.0170 memory: 382 loss: 1.5842
11/22 16:48:02 - mmengine - INFO - Epoch(train) [3][1100/1563] lr: 1.0000e-03 eta: 0:03:21 time: 0.0567 data_time: 0.0164 memory: 382 loss: 1.8733
11/22 16:48:03 - mmengine - INFO - Epoch(train) [3][1110/1563] lr: 1.0000e-03 eta: 0:03:21 time: 0.0530 data_time: 0.0143 memory: 382 loss: 1.6146
11/22 16:48:04 - mmengine - INFO - Epoch(train) [3][1120/1563] lr: 1.0000e-03 eta: 0:03:20 time: 0.0529 data_time: 0.0141 memory: 382 loss: 1.5651
11/22 16:48:04 - mmengine - INFO - Epoch(train) [3][1130/1563] lr: 1.0000e-03 eta: 0:03:20 time: 0.0705 data_time: 0.0228 memory: 382 loss: 1.6882
11/22 16:48:05 - mmengine - INFO - Epoch(train) [3][1140/1563] lr: 1.0000e-03 eta: 0:03:19 time: 0.0755 data_time: 0.0258 memory: 382 loss: 1.5309
11/22 16:48:06 - mmengine - INFO - Epoch(train) [3][1150/1563] lr: 1.0000e-03 eta: 0:03:19 time: 0.0540 data_time: 0.0171 memory: 382 loss: 1.7008
11/22 16:48:06 - mmengine - INFO - Epoch(train) [3][1160/1563] lr: 1.0000e-03 eta: 0:03:18 time: 0.0561 data_time: 0.0183 memory: 382 loss: 1.6935
11/22 16:48:07 - mmengine - INFO - Epoch(train) [3][1170/1563] lr: 1.0000e-03 eta: 0:03:18 time: 0.0485 data_time: 0.0138 memory: 382 loss: 1.5953
11/22 16:48:07 - mmengine - INFO - Epoch(train) [3][1180/1563] lr: 1.0000e-03 eta: 0:03:17 time: 0.0553 data_time: 0.0184 memory: 382 loss: 1.7106
11/22 16:48:08 - mmengine - INFO - Epoch(train) [3][1190/1563] lr: 1.0000e-03 eta: 0:03:16 time: 0.0507 data_time: 0.0132 memory: 382 loss: 1.6371
11/22 16:48:08 - mmengine - INFO - Epoch(train) [3][1200/1563] lr: 1.0000e-03 eta: 0:03:16 time: 0.0531 data_time: 0.0167 memory: 382 loss: 1.6515
11/22 16:48:09 - mmengine - INFO - Epoch(train) [3][1210/1563] lr: 1.0000e-03 eta: 0:03:15 time: 0.0476 data_time: 0.0132 memory: 382 loss: 1.5957
11/22 16:48:09 - mmengine - INFO - Epoch(train) [3][1220/1563] lr: 1.0000e-03 eta: 0:03:15 time: 0.0533 data_time: 0.0165 memory: 382 loss: 1.7322
11/22 16:48:10 - mmengine - INFO - Epoch(train) [3][1230/1563] lr: 1.0000e-03 eta: 0:03:14 time: 0.0526 data_time: 0.0139 memory: 382 loss: 1.7070
11/22 16:48:10 - mmengine - INFO - Epoch(train) [3][1240/1563] lr: 1.0000e-03 eta: 0:03:14 time: 0.0570 data_time: 0.0153 memory: 382 loss: 1.6752
11/22 16:48:11 - mmengine - INFO - Epoch(train) [3][1250/1563] lr: 1.0000e-03 eta: 0:03:13 time: 0.0903 data_time: 0.0324 memory: 382 loss: 1.5140
11/22 16:48:12 - mmengine - INFO - Epoch(train) [3][1260/1563] lr: 1.0000e-03 eta: 0:03:13 time: 0.0774 data_time: 0.0272 memory: 382 loss: 1.6021
11/22 16:48:13 - mmengine - INFO - Epoch(train) [3][1270/1563] lr: 1.0000e-03 eta: 0:03:12 time: 0.0670 data_time: 0.0222 memory: 382 loss: 1.7170
11/22 16:48:13 - mmengine - INFO - Epoch(train) [3][1280/1563] lr: 1.0000e-03 eta: 0:03:12 time: 0.0597 data_time: 0.0172 memory: 382 loss: 1.6965
11/22 16:48:14 - mmengine - INFO - Epoch(train) [3][1290/1563] lr: 1.0000e-03 eta: 0:03:11 time: 0.0571 data_time: 0.0169 memory: 382 loss: 1.5570
11/22 16:48:14 - mmengine - INFO - Epoch(train) [3][1300/1563] lr: 1.0000e-03 eta: 0:03:11 time: 0.0573 data_time: 0.0152 memory: 382 loss: 1.7423
11/22 16:48:15 - mmengine - INFO - Epoch(train) [3][1310/1563] lr: 1.0000e-03 eta: 0:03:10 time: 0.0559 data_time: 0.0145 memory: 382 loss: 1.6773
11/22 16:48:15 - mmengine - INFO - Epoch(train) [3][1320/1563] lr: 1.0000e-03 eta: 0:03:10 time: 0.0553 data_time: 0.0144 memory: 382 loss: 1.6053
11/22 16:48:16 - mmengine - INFO - Epoch(train) [3][1330/1563] lr: 1.0000e-03 eta: 0:03:09 time: 0.0540 data_time: 0.0138 memory: 382 loss: 1.7449
11/22 16:48:17 - mmengine - INFO - Epoch(train) [3][1340/1563] lr: 1.0000e-03 eta: 0:03:08 time: 0.0538 data_time: 0.0140 memory: 382 loss: 1.7399
11/22 16:48:17 - mmengine - INFO - Epoch(train) [3][1350/1563] lr: 1.0000e-03 eta: 0:03:08 time: 0.0524 data_time: 0.0139 memory: 382 loss: 1.5348
11/22 16:48:18 - mmengine - INFO - Epoch(train) [3][1360/1563] lr: 1.0000e-03 eta: 0:03:07 time: 0.0526 data_time: 0.0140 memory: 382 loss: 1.5117
11/22 16:48:18 - mmengine - INFO - Epoch(train) [3][1370/1563] lr: 1.0000e-03 eta: 0:03:07 time: 0.0524 data_time: 0.0138 memory: 382 loss: 1.6947
11/22 16:48:19 - mmengine - INFO - Epoch(train) [3][1380/1563] lr: 1.0000e-03 eta: 0:03:06 time: 0.0522 data_time: 0.0139 memory: 382 loss: 1.6303
11/22 16:48:19 - mmengine - INFO - Epoch(train) [3][1390/1563] lr: 1.0000e-03 eta: 0:03:05 time: 0.0522 data_time: 0.0139 memory: 382 loss: 1.6375
11/22 16:48:20 - mmengine - INFO - Epoch(train) [3][1400/1563] lr: 1.0000e-03 eta: 0:03:05 time: 0.0523 data_time: 0.0137 memory: 382 loss: 1.6429
11/22 16:48:20 - mmengine - INFO - Epoch(train) [3][1410/1563] lr: 1.0000e-03 eta: 0:03:04 time: 0.0525 data_time: 0.0139 memory: 382 loss: 1.6803
11/22 16:48:21 - mmengine - INFO - Epoch(train) [3][1420/1563] lr: 1.0000e-03 eta: 0:03:04 time: 0.0517 data_time: 0.0136 memory: 382 loss: 1.5705
11/22 16:48:21 - mmengine - INFO - Epoch(train) [3][1430/1563] lr: 1.0000e-03 eta: 0:03:03 time: 0.0539 data_time: 0.0139 memory: 382 loss: 1.6145
11/22 16:48:22 - mmengine - INFO - Epoch(train) [3][1440/1563] lr: 1.0000e-03 eta: 0:03:02 time: 0.0527 data_time: 0.0137 memory: 382 loss: 1.6834
11/22 16:48:22 - mmengine - INFO - Epoch(train) [3][1450/1563] lr: 1.0000e-03 eta: 0:03:02 time: 0.0523 data_time: 0.0139 memory: 382 loss: 1.6103
11/22 16:48:23 - mmengine - INFO - Epoch(train) [3][1460/1563] lr: 1.0000e-03 eta: 0:03:01 time: 0.0528 data_time: 0.0139 memory: 382 loss: 1.6688
11/22 16:48:23 - mmengine - INFO - Epoch(train) [3][1470/1563] lr: 1.0000e-03 eta: 0:03:01 time: 0.0523 data_time: 0.0138 memory: 382 loss: 1.6066
11/22 16:48:24 - mmengine - INFO - Epoch(train) [3][1480/1563] lr: 1.0000e-03 eta: 0:03:00 time: 0.0523 data_time: 0.0138 memory: 382 loss: 1.4817
11/22 16:48:24 - mmengine - INFO - Epoch(train) [3][1490/1563] lr: 1.0000e-03 eta: 0:03:00 time: 0.0525 data_time: 0.0138 memory: 382 loss: 1.5851
11/22 16:48:25 - mmengine - INFO - Epoch(train) [3][1500/1563] lr: 1.0000e-03 eta: 0:02:59 time: 0.0518 data_time: 0.0137 memory: 382 loss: 1.6215
11/22 16:48:25 - mmengine - INFO - Epoch(train) [3][1510/1563] lr: 1.0000e-03 eta: 0:02:58 time: 0.0522 data_time: 0.0138 memory: 382 loss: 1.7130
11/22 16:48:26 - mmengine - INFO - Epoch(train) [3][1520/1563] lr: 1.0000e-03 eta: 0:02:58 time: 0.0520 data_time: 0.0137 memory: 382 loss: 1.6338
11/22 16:48:27 - mmengine - INFO - Epoch(train) [3][1530/1563] lr: 1.0000e-03 eta: 0:02:57 time: 0.0528 data_time: 0.0141 memory: 382 loss: 1.6627
11/22 16:48:27 - mmengine - INFO - Epoch(train) [3][1540/1563] lr: 1.0000e-03 eta: 0:02:57 time: 0.0520 data_time: 0.0137 memory: 382 loss: 1.6814
11/22 16:48:28 - mmengine - INFO - Epoch(train) [3][1550/1563] lr: 1.0000e-03 eta: 0:02:56 time: 0.0524 data_time: 0.0139 memory: 382 loss: 1.7130
11/22 16:48:28 - mmengine - INFO - Epoch(train) [3][1560/1563] lr: 1.0000e-03 eta: 0:02:55 time: 0.0519 data_time: 0.0137 memory: 382 loss: 1.5962
11/22 16:48:28 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:48:28 - mmengine - INFO - Saving checkpoint at 3 epochs
11/22 16:48:29 - mmengine - INFO - Epoch(val) [3][ 10/313] eta: 0:00:06 time: 0.0219 data_time: 0.0116 memory: 382
11/22 16:48:29 - mmengine - INFO - Epoch(val) [3][ 20/313] eta: 0:00:05 time: 0.0181 data_time: 0.0089 memory: 225
11/22 16:48:29 - mmengine - INFO - Epoch(val) [3][ 30/313] eta: 0:00:05 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:30 - mmengine - INFO - Epoch(val) [3][ 40/313] eta: 0:00:05 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:30 - mmengine - INFO - Epoch(val) [3][ 50/313] eta: 0:00:04 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:48:30 - mmengine - INFO - Epoch(val) [3][ 60/313] eta: 0:00:04 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:48:30 - mmengine - INFO - Epoch(val) [3][ 70/313] eta: 0:00:04 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:48:30 - mmengine - INFO - Epoch(val) [3][ 80/313] eta: 0:00:04 time: 0.0171 data_time: 0.0084 memory: 225
11/22 16:48:30 - mmengine - INFO - Epoch(val) [3][ 90/313] eta: 0:00:03 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:48:31 - mmengine - INFO - Epoch(val) [3][100/313] eta: 0:00:03 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:31 - mmengine - INFO - Epoch(val) [3][110/313] eta: 0:00:03 time: 0.0170 data_time: 0.0083 memory: 225
11/22 16:48:31 - mmengine - INFO - Epoch(val) [3][120/313] eta: 0:00:03 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:31 - mmengine - INFO - Epoch(val) [3][130/313] eta: 0:00:03 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:31 - mmengine - INFO - Epoch(val) [3][140/313] eta: 0:00:03 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:31 - mmengine - INFO - Epoch(val) [3][150/313] eta: 0:00:02 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:48:32 - mmengine - INFO - Epoch(val) [3][160/313] eta: 0:00:02 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:32 - mmengine - INFO - Epoch(val) [3][170/313] eta: 0:00:02 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:32 - mmengine - INFO - Epoch(val) [3][180/313] eta: 0:00:02 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:48:32 - mmengine - INFO - Epoch(val) [3][190/313] eta: 0:00:02 time: 0.0170 data_time: 0.0083 memory: 225
11/22 16:48:32 - mmengine - INFO - Epoch(val) [3][200/313] eta: 0:00:01 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:32 - mmengine - INFO - Epoch(val) [3][210/313] eta: 0:00:01 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:48:33 - mmengine - INFO - Epoch(val) [3][220/313] eta: 0:00:01 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:33 - mmengine - INFO - Epoch(val) [3][230/313] eta: 0:00:01 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:48:33 - mmengine - INFO - Epoch(val) [3][240/313] eta: 0:00:01 time: 0.0168 data_time: 0.0082 memory: 225
11/22 16:48:33 - mmengine - INFO - Epoch(val) [3][250/313] eta: 0:00:01 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:33 - mmengine - INFO - Epoch(val) [3][260/313] eta: 0:00:00 time: 0.0168 data_time: 0.0082 memory: 225
11/22 16:48:33 - mmengine - INFO - Epoch(val) [3][270/313] eta: 0:00:00 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:48:34 - mmengine - INFO - Epoch(val) [3][280/313] eta: 0:00:00 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:48:34 - mmengine - INFO - Epoch(val) [3][290/313] eta: 0:00:00 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:48:34 - mmengine - INFO - Epoch(val) [3][300/313] eta: 0:00:00 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:48:34 - mmengine - INFO - Epoch(val) [3][310/313] eta: 0:00:00 time: 0.0171 data_time: 0.0085 memory: 225
11/22 16:48:34 - mmengine - INFO - Epoch(val) [3][313/313] accuracy: 46.4500 data_time: 0.0084 time: 0.0171
11/22 16:48:35 - mmengine - INFO - Epoch(train) [4][ 10/1563] lr: 1.0000e-03 eta: 0:02:55 time: 0.0518 data_time: 0.0133 memory: 382 loss: 1.4827
11/22 16:48:35 - mmengine - INFO - Epoch(train) [4][ 20/1563] lr: 1.0000e-03 eta: 0:02:54 time: 0.0510 data_time: 0.0132 memory: 382 loss: 1.6613
11/22 16:48:36 - mmengine - INFO - Epoch(train) [4][ 30/1563] lr: 1.0000e-03 eta: 0:02:53 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.5464
11/22 16:48:36 - mmengine - INFO - Epoch(train) [4][ 40/1563] lr: 1.0000e-03 eta: 0:02:53 time: 0.0510 data_time: 0.0132 memory: 382 loss: 1.5757
11/22 16:48:37 - mmengine - INFO - Epoch(train) [4][ 50/1563] lr: 1.0000e-03 eta: 0:02:52 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.6691
11/22 16:48:37 - mmengine - INFO - Epoch(train) [4][ 60/1563] lr: 1.0000e-03 eta: 0:02:52 time: 0.0513 data_time: 0.0133 memory: 382 loss: 1.6151
11/22 16:48:38 - mmengine - INFO - Epoch(train) [4][ 70/1563] lr: 1.0000e-03 eta: 0:02:51 time: 0.0513 data_time: 0.0133 memory: 382 loss: 1.6309
11/22 16:48:38 - mmengine - INFO - Epoch(train) [4][ 80/1563] lr: 1.0000e-03 eta: 0:02:50 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.5857
11/22 16:48:39 - mmengine - INFO - Epoch(train) [4][ 90/1563] lr: 1.0000e-03 eta: 0:02:50 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.7167
11/22 16:48:39 - mmengine - INFO - Epoch(train) [4][ 100/1563] lr: 1.0000e-03 eta: 0:02:49 time: 0.0520 data_time: 0.0136 memory: 382 loss: 1.6490
11/22 16:48:40 - mmengine - INFO - Epoch(train) [4][ 110/1563] lr: 1.0000e-03 eta: 0:02:49 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.4848
11/22 16:48:40 - mmengine - INFO - Epoch(train) [4][ 120/1563] lr: 1.0000e-03 eta: 0:02:48 time: 0.0516 data_time: 0.0133 memory: 382 loss: 1.6159
11/22 16:48:41 - mmengine - INFO - Epoch(train) [4][ 130/1563] lr: 1.0000e-03 eta: 0:02:48 time: 0.0513 data_time: 0.0133 memory: 382 loss: 1.7069
11/22 16:48:41 - mmengine - INFO - Epoch(train) [4][ 140/1563] lr: 1.0000e-03 eta: 0:02:47 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.6253
11/22 16:48:42 - mmengine - INFO - Epoch(train) [4][ 150/1563] lr: 1.0000e-03 eta: 0:02:46 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.6000
11/22 16:48:42 - mmengine - INFO - Epoch(train) [4][ 160/1563] lr: 1.0000e-03 eta: 0:02:46 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.5325
11/22 16:48:43 - mmengine - INFO - Epoch(train) [4][ 170/1563] lr: 1.0000e-03 eta: 0:02:45 time: 0.0512 data_time: 0.0133 memory: 382 loss: 1.5598
11/22 16:48:43 - mmengine - INFO - Epoch(train) [4][ 180/1563] lr: 1.0000e-03 eta: 0:02:45 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.5560
11/22 16:48:44 - mmengine - INFO - Epoch(train) [4][ 190/1563] lr: 1.0000e-03 eta: 0:02:44 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.4787
11/22 16:48:44 - mmengine - INFO - Epoch(train) [4][ 200/1563] lr: 1.0000e-03 eta: 0:02:43 time: 0.0513 data_time: 0.0134 memory: 382 loss: 1.6953
11/22 16:48:45 - mmengine - INFO - Epoch(train) [4][ 210/1563] lr: 1.0000e-03 eta: 0:02:43 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.5108
11/22 16:48:46 - mmengine - INFO - Epoch(train) [4][ 220/1563] lr: 1.0000e-03 eta: 0:02:42 time: 0.0513 data_time: 0.0134 memory: 382 loss: 1.5402
11/22 16:48:46 - mmengine - INFO - Epoch(train) [4][ 230/1563] lr: 1.0000e-03 eta: 0:02:42 time: 0.0517 data_time: 0.0135 memory: 382 loss: 1.7756
11/22 16:48:47 - mmengine - INFO - Epoch(train) [4][ 240/1563] lr: 1.0000e-03 eta: 0:02:41 time: 0.0531 data_time: 0.0144 memory: 382 loss: 1.6098
11/22 16:48:47 - mmengine - INFO - Epoch(train) [4][ 250/1563] lr: 1.0000e-03 eta: 0:02:41 time: 0.0585 data_time: 0.0159 memory: 382 loss: 1.7508
11/22 16:48:48 - mmengine - INFO - Epoch(train) [4][ 260/1563] lr: 1.0000e-03 eta: 0:02:40 time: 0.0577 data_time: 0.0156 memory: 382 loss: 1.5077
11/22 16:48:48 - mmengine - INFO - Epoch(train) [4][ 270/1563] lr: 1.0000e-03 eta: 0:02:39 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.7236
11/22 16:48:49 - mmengine - INFO - Epoch(train) [4][ 280/1563] lr: 1.0000e-03 eta: 0:02:39 time: 0.0582 data_time: 0.0156 memory: 382 loss: 1.5900
11/22 16:48:49 - mmengine - INFO - Epoch(train) [4][ 290/1563] lr: 1.0000e-03 eta: 0:02:38 time: 0.0583 data_time: 0.0159 memory: 382 loss: 1.6442
11/22 16:48:50 - mmengine - INFO - Epoch(train) [4][ 300/1563] lr: 1.0000e-03 eta: 0:02:38 time: 0.0582 data_time: 0.0157 memory: 382 loss: 1.4944
11/22 16:48:51 - mmengine - INFO - Epoch(train) [4][ 310/1563] lr: 1.0000e-03 eta: 0:02:37 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.6391
11/22 16:48:51 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:48:51 - mmengine - INFO - Epoch(train) [4][ 320/1563] lr: 1.0000e-03 eta: 0:02:37 time: 0.0585 data_time: 0.0161 memory: 382 loss: 1.6686
11/22 16:48:52 - mmengine - INFO - Epoch(train) [4][ 330/1563] lr: 1.0000e-03 eta: 0:02:36 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.6061
11/22 16:48:52 - mmengine - INFO - Epoch(train) [4][ 340/1563] lr: 1.0000e-03 eta: 0:02:36 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5049
11/22 16:48:53 - mmengine - INFO - Epoch(train) [4][ 350/1563] lr: 1.0000e-03 eta: 0:02:35 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.7170
11/22 16:48:54 - mmengine - INFO - Epoch(train) [4][ 360/1563] lr: 1.0000e-03 eta: 0:02:34 time: 0.0584 data_time: 0.0160 memory: 382 loss: 1.5866
11/22 16:48:54 - mmengine - INFO - Epoch(train) [4][ 370/1563] lr: 1.0000e-03 eta: 0:02:34 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5168
11/22 16:48:55 - mmengine - INFO - Epoch(train) [4][ 380/1563] lr: 1.0000e-03 eta: 0:02:33 time: 0.0583 data_time: 0.0159 memory: 382 loss: 1.5840
11/22 16:48:55 - mmengine - INFO - Epoch(train) [4][ 390/1563] lr: 1.0000e-03 eta: 0:02:33 time: 0.0548 data_time: 0.0157 memory: 382 loss: 1.6690
11/22 16:48:56 - mmengine - INFO - Epoch(train) [4][ 400/1563] lr: 1.0000e-03 eta: 0:02:32 time: 0.0570 data_time: 0.0160 memory: 382 loss: 1.5442
11/22 16:48:56 - mmengine - INFO - Epoch(train) [4][ 410/1563] lr: 1.0000e-03 eta: 0:02:32 time: 0.0585 data_time: 0.0159 memory: 382 loss: 1.6225
11/22 16:48:57 - mmengine - INFO - Epoch(train) [4][ 420/1563] lr: 1.0000e-03 eta: 0:02:31 time: 0.0587 data_time: 0.0161 memory: 382 loss: 1.6509
11/22 16:48:58 - mmengine - INFO - Epoch(train) [4][ 430/1563] lr: 1.0000e-03 eta: 0:02:31 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.5399
11/22 16:48:58 - mmengine - INFO - Epoch(train) [4][ 440/1563] lr: 1.0000e-03 eta: 0:02:30 time: 0.0584 data_time: 0.0160 memory: 382 loss: 1.7360
11/22 16:48:59 - mmengine - INFO - Epoch(train) [4][ 450/1563] lr: 1.0000e-03 eta: 0:02:30 time: 0.0562 data_time: 0.0156 memory: 382 loss: 1.6366
11/22 16:48:59 - mmengine - INFO - Epoch(train) [4][ 460/1563] lr: 1.0000e-03 eta: 0:02:29 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5633
11/22 16:49:00 - mmengine - INFO - Epoch(train) [4][ 470/1563] lr: 1.0000e-03 eta: 0:02:28 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.7030
11/22 16:49:00 - mmengine - INFO - Epoch(train) [4][ 480/1563] lr: 1.0000e-03 eta: 0:02:28 time: 0.0586 data_time: 0.0161 memory: 382 loss: 1.6098
11/22 16:49:01 - mmengine - INFO - Epoch(train) [4][ 490/1563] lr: 1.0000e-03 eta: 0:02:27 time: 0.0583 data_time: 0.0157 memory: 382 loss: 1.5978
11/22 16:49:02 - mmengine - INFO - Epoch(train) [4][ 500/1563] lr: 1.0000e-03 eta: 0:02:27 time: 0.0584 data_time: 0.0159 memory: 382 loss: 1.5659
11/22 16:49:02 - mmengine - INFO - Epoch(train) [4][ 510/1563] lr: 1.0000e-03 eta: 0:02:26 time: 0.0581 data_time: 0.0158 memory: 382 loss: 1.6597
11/22 16:49:03 - mmengine - INFO - Epoch(train) [4][ 520/1563] lr: 1.0000e-03 eta: 0:02:26 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.4810
11/22 16:49:03 - mmengine - INFO - Epoch(train) [4][ 530/1563] lr: 1.0000e-03 eta: 0:02:25 time: 0.0584 data_time: 0.0159 memory: 382 loss: 1.5026
11/22 16:49:04 - mmengine - INFO - Epoch(train) [4][ 540/1563] lr: 1.0000e-03 eta: 0:02:25 time: 0.0584 data_time: 0.0160 memory: 382 loss: 1.5160
11/22 16:49:05 - mmengine - INFO - Epoch(train) [4][ 550/1563] lr: 1.0000e-03 eta: 0:02:24 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.7005
11/22 16:49:05 - mmengine - INFO - Epoch(train) [4][ 560/1563] lr: 1.0000e-03 eta: 0:02:23 time: 0.0579 data_time: 0.0157 memory: 382 loss: 1.5502
11/22 16:49:06 - mmengine - INFO - Epoch(train) [4][ 570/1563] lr: 1.0000e-03 eta: 0:02:23 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.5649
11/22 16:49:06 - mmengine - INFO - Epoch(train) [4][ 580/1563] lr: 1.0000e-03 eta: 0:02:22 time: 0.0584 data_time: 0.0159 memory: 382 loss: 1.5219
11/22 16:49:07 - mmengine - INFO - Epoch(train) [4][ 590/1563] lr: 1.0000e-03 eta: 0:02:22 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.6810
11/22 16:49:07 - mmengine - INFO - Epoch(train) [4][ 600/1563] lr: 1.0000e-03 eta: 0:02:21 time: 0.0580 data_time: 0.0155 memory: 382 loss: 1.4635
11/22 16:49:08 - mmengine - INFO - Epoch(train) [4][ 610/1563] lr: 1.0000e-03 eta: 0:02:21 time: 0.0584 data_time: 0.0160 memory: 382 loss: 1.6882
11/22 16:49:09 - mmengine - INFO - Epoch(train) [4][ 620/1563] lr: 1.0000e-03 eta: 0:02:20 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.4945
11/22 16:49:09 - mmengine - INFO - Epoch(train) [4][ 630/1563] lr: 1.0000e-03 eta: 0:02:20 time: 0.0582 data_time: 0.0156 memory: 382 loss: 1.6907
11/22 16:49:10 - mmengine - INFO - Epoch(train) [4][ 640/1563] lr: 1.0000e-03 eta: 0:02:19 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.6535
11/22 16:49:10 - mmengine - INFO - Epoch(train) [4][ 650/1563] lr: 1.0000e-03 eta: 0:02:18 time: 0.0578 data_time: 0.0155 memory: 382 loss: 1.6673
11/22 16:49:11 - mmengine - INFO - Epoch(train) [4][ 660/1563] lr: 1.0000e-03 eta: 0:02:18 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5819
11/22 16:49:12 - mmengine - INFO - Epoch(train) [4][ 670/1563] lr: 1.0000e-03 eta: 0:02:17 time: 0.0590 data_time: 0.0165 memory: 382 loss: 1.5481
11/22 16:49:12 - mmengine - INFO - Epoch(train) [4][ 680/1563] lr: 1.0000e-03 eta: 0:02:17 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5307
11/22 16:49:13 - mmengine - INFO - Epoch(train) [4][ 690/1563] lr: 1.0000e-03 eta: 0:02:16 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.5848
11/22 16:49:13 - mmengine - INFO - Epoch(train) [4][ 700/1563] lr: 1.0000e-03 eta: 0:02:16 time: 0.0584 data_time: 0.0159 memory: 382 loss: 1.6722
11/22 16:49:14 - mmengine - INFO - Epoch(train) [4][ 710/1563] lr: 1.0000e-03 eta: 0:02:15 time: 0.0582 data_time: 0.0159 memory: 382 loss: 1.6036
11/22 16:49:14 - mmengine - INFO - Epoch(train) [4][ 720/1563] lr: 1.0000e-03 eta: 0:02:15 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.6059
11/22 16:49:15 - mmengine - INFO - Epoch(train) [4][ 730/1563] lr: 1.0000e-03 eta: 0:02:14 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.6429
11/22 16:49:16 - mmengine - INFO - Epoch(train) [4][ 740/1563] lr: 1.0000e-03 eta: 0:02:14 time: 0.0584 data_time: 0.0159 memory: 382 loss: 1.6281
11/22 16:49:16 - mmengine - INFO - Epoch(train) [4][ 750/1563] lr: 1.0000e-03 eta: 0:02:13 time: 0.0584 data_time: 0.0156 memory: 382 loss: 1.5211
11/22 16:49:17 - mmengine - INFO - Epoch(train) [4][ 760/1563] lr: 1.0000e-03 eta: 0:02:12 time: 0.0582 data_time: 0.0158 memory: 382 loss: 1.5727
11/22 16:49:17 - mmengine - INFO - Epoch(train) [4][ 770/1563] lr: 1.0000e-03 eta: 0:02:12 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.6995
11/22 16:49:18 - mmengine - INFO - Epoch(train) [4][ 780/1563] lr: 1.0000e-03 eta: 0:02:11 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.5949
11/22 16:49:19 - mmengine - INFO - Epoch(train) [4][ 790/1563] lr: 1.0000e-03 eta: 0:02:11 time: 0.0581 data_time: 0.0155 memory: 382 loss: 1.5910
11/22 16:49:19 - mmengine - INFO - Epoch(train) [4][ 800/1563] lr: 1.0000e-03 eta: 0:02:10 time: 0.0582 data_time: 0.0156 memory: 382 loss: 1.6577
11/22 16:49:20 - mmengine - INFO - Epoch(train) [4][ 810/1563] lr: 1.0000e-03 eta: 0:02:10 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.5431
11/22 16:49:20 - mmengine - INFO - Epoch(train) [4][ 820/1563] lr: 1.0000e-03 eta: 0:02:09 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.6076
11/22 16:49:21 - mmengine - INFO - Epoch(train) [4][ 830/1563] lr: 1.0000e-03 eta: 0:02:09 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.6482
11/22 16:49:21 - mmengine - INFO - Epoch(train) [4][ 840/1563] lr: 1.0000e-03 eta: 0:02:08 time: 0.0584 data_time: 0.0160 memory: 382 loss: 1.6101
11/22 16:49:22 - mmengine - INFO - Epoch(train) [4][ 850/1563] lr: 1.0000e-03 eta: 0:02:07 time: 0.0583 data_time: 0.0160 memory: 382 loss: 1.6150
11/22 16:49:23 - mmengine - INFO - Epoch(train) [4][ 860/1563] lr: 1.0000e-03 eta: 0:02:07 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.5988
11/22 16:49:23 - mmengine - INFO - Epoch(train) [4][ 870/1563] lr: 1.0000e-03 eta: 0:02:06 time: 0.0579 data_time: 0.0155 memory: 382 loss: 1.5681
11/22 16:49:24 - mmengine - INFO - Epoch(train) [4][ 880/1563] lr: 1.0000e-03 eta: 0:02:06 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.5544
11/22 16:49:24 - mmengine - INFO - Epoch(train) [4][ 890/1563] lr: 1.0000e-03 eta: 0:02:05 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.5334
11/22 16:49:25 - mmengine - INFO - Epoch(train) [4][ 900/1563] lr: 1.0000e-03 eta: 0:02:05 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.5827
11/22 16:49:25 - mmengine - INFO - Epoch(train) [4][ 910/1563] lr: 1.0000e-03 eta: 0:02:04 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.6359
11/22 16:49:26 - mmengine - INFO - Epoch(train) [4][ 920/1563] lr: 1.0000e-03 eta: 0:02:04 time: 0.0588 data_time: 0.0159 memory: 382 loss: 1.6748
11/22 16:49:27 - mmengine - INFO - Epoch(train) [4][ 930/1563] lr: 1.0000e-03 eta: 0:02:03 time: 0.0582 data_time: 0.0157 memory: 382 loss: 1.5071
11/22 16:49:27 - mmengine - INFO - Epoch(train) [4][ 940/1563] lr: 1.0000e-03 eta: 0:02:02 time: 0.0582 data_time: 0.0157 memory: 382 loss: 1.5332
11/22 16:49:28 - mmengine - INFO - Epoch(train) [4][ 950/1563] lr: 1.0000e-03 eta: 0:02:02 time: 0.0584 data_time: 0.0160 memory: 382 loss: 1.6278
11/22 16:49:28 - mmengine - INFO - Epoch(train) [4][ 960/1563] lr: 1.0000e-03 eta: 0:02:01 time: 0.0581 data_time: 0.0157 memory: 382 loss: 1.4036
11/22 16:49:29 - mmengine - INFO - Epoch(train) [4][ 970/1563] lr: 1.0000e-03 eta: 0:02:01 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.4834
11/22 16:49:30 - mmengine - INFO - Epoch(train) [4][ 980/1563] lr: 1.0000e-03 eta: 0:02:00 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.5523
11/22 16:49:30 - mmengine - INFO - Epoch(train) [4][ 990/1563] lr: 1.0000e-03 eta: 0:02:00 time: 0.0584 data_time: 0.0160 memory: 382 loss: 1.5490
11/22 16:49:31 - mmengine - INFO - Epoch(train) [4][1000/1563] lr: 1.0000e-03 eta: 0:01:59 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.4718
11/22 16:49:31 - mmengine - INFO - Epoch(train) [4][1010/1563] lr: 1.0000e-03 eta: 0:01:59 time: 0.0585 data_time: 0.0160 memory: 382 loss: 1.7312
11/22 16:49:32 - mmengine - INFO - Epoch(train) [4][1020/1563] lr: 1.0000e-03 eta: 0:01:58 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5452
11/22 16:49:32 - mmengine - INFO - Epoch(train) [4][1030/1563] lr: 1.0000e-03 eta: 0:01:57 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.6344
11/22 16:49:33 - mmengine - INFO - Epoch(train) [4][1040/1563] lr: 1.0000e-03 eta: 0:01:57 time: 0.0585 data_time: 0.0159 memory: 382 loss: 1.4563
11/22 16:49:34 - mmengine - INFO - Epoch(train) [4][1050/1563] lr: 1.0000e-03 eta: 0:01:56 time: 0.0587 data_time: 0.0160 memory: 382 loss: 1.6368
11/22 16:49:34 - mmengine - INFO - Epoch(train) [4][1060/1563] lr: 1.0000e-03 eta: 0:01:56 time: 0.0584 data_time: 0.0159 memory: 382 loss: 1.5848
11/22 16:49:35 - mmengine - INFO - Epoch(train) [4][1070/1563] lr: 1.0000e-03 eta: 0:01:55 time: 0.0585 data_time: 0.0158 memory: 382 loss: 1.7266
11/22 16:49:35 - mmengine - INFO - Epoch(train) [4][1080/1563] lr: 1.0000e-03 eta: 0:01:55 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.5943
11/22 16:49:36 - mmengine - INFO - Epoch(train) [4][1090/1563] lr: 1.0000e-03 eta: 0:01:54 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.5624
11/22 16:49:37 - mmengine - INFO - Epoch(train) [4][1100/1563] lr: 1.0000e-03 eta: 0:01:54 time: 0.0582 data_time: 0.0156 memory: 382 loss: 1.7034
11/22 16:49:37 - mmengine - INFO - Epoch(train) [4][1110/1563] lr: 1.0000e-03 eta: 0:01:53 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.6762
11/22 16:49:38 - mmengine - INFO - Epoch(train) [4][1120/1563] lr: 1.0000e-03 eta: 0:01:52 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.6385
11/22 16:49:38 - mmengine - INFO - Epoch(train) [4][1130/1563] lr: 1.0000e-03 eta: 0:01:52 time: 0.0584 data_time: 0.0159 memory: 382 loss: 1.6235
11/22 16:49:39 - mmengine - INFO - Epoch(train) [4][1140/1563] lr: 1.0000e-03 eta: 0:01:51 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5350
11/22 16:49:39 - mmengine - INFO - Epoch(train) [4][1150/1563] lr: 1.0000e-03 eta: 0:01:51 time: 0.0582 data_time: 0.0156 memory: 382 loss: 1.7931
11/22 16:49:40 - mmengine - INFO - Epoch(train) [4][1160/1563] lr: 1.0000e-03 eta: 0:01:50 time: 0.0579 data_time: 0.0155 memory: 382 loss: 1.6556
11/22 16:49:41 - mmengine - INFO - Epoch(train) [4][1170/1563] lr: 1.0000e-03 eta: 0:01:50 time: 0.0580 data_time: 0.0157 memory: 382 loss: 1.5397
11/22 16:49:41 - mmengine - INFO - Epoch(train) [4][1180/1563] lr: 1.0000e-03 eta: 0:01:49 time: 0.0579 data_time: 0.0155 memory: 382 loss: 1.6626
11/22 16:49:42 - mmengine - INFO - Epoch(train) [4][1190/1563] lr: 1.0000e-03 eta: 0:01:49 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.4917
11/22 16:49:42 - mmengine - INFO - Epoch(train) [4][1200/1563] lr: 1.0000e-03 eta: 0:01:48 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.4882
11/22 16:49:43 - mmengine - INFO - Epoch(train) [4][1210/1563] lr: 1.0000e-03 eta: 0:01:47 time: 0.0579 data_time: 0.0155 memory: 382 loss: 1.6437
11/22 16:49:44 - mmengine - INFO - Epoch(train) [4][1220/1563] lr: 1.0000e-03 eta: 0:01:47 time: 0.0586 data_time: 0.0160 memory: 382 loss: 1.5235
11/22 16:49:44 - mmengine - INFO - Epoch(train) [4][1230/1563] lr: 1.0000e-03 eta: 0:01:46 time: 0.0582 data_time: 0.0156 memory: 382 loss: 1.5863
11/22 16:49:45 - mmengine - INFO - Epoch(train) [4][1240/1563] lr: 1.0000e-03 eta: 0:01:46 time: 0.0579 data_time: 0.0157 memory: 382 loss: 1.5557
11/22 16:49:45 - mmengine - INFO - Epoch(train) [4][1250/1563] lr: 1.0000e-03 eta: 0:01:45 time: 0.0554 data_time: 0.0157 memory: 382 loss: 1.5434
11/22 16:49:46 - mmengine - INFO - Epoch(train) [4][1260/1563] lr: 1.0000e-03 eta: 0:01:45 time: 0.0578 data_time: 0.0154 memory: 382 loss: 1.5289
11/22 16:49:46 - mmengine - INFO - Epoch(train) [4][1270/1563] lr: 1.0000e-03 eta: 0:01:44 time: 0.0577 data_time: 0.0155 memory: 382 loss: 1.5152
11/22 16:49:47 - mmengine - INFO - Epoch(train) [4][1280/1563] lr: 1.0000e-03 eta: 0:01:44 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.5708
11/22 16:49:48 - mmengine - INFO - Epoch(train) [4][1290/1563] lr: 1.0000e-03 eta: 0:01:43 time: 0.0576 data_time: 0.0155 memory: 382 loss: 1.5456
11/22 16:49:48 - mmengine - INFO - Epoch(train) [4][1300/1563] lr: 1.0000e-03 eta: 0:01:42 time: 0.0577 data_time: 0.0155 memory: 382 loss: 1.5222
11/22 16:49:49 - mmengine - INFO - Epoch(train) [4][1310/1563] lr: 1.0000e-03 eta: 0:01:42 time: 0.0581 data_time: 0.0157 memory: 382 loss: 1.5920
11/22 16:49:49 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:49:49 - mmengine - INFO - Epoch(train) [4][1320/1563] lr: 1.0000e-03 eta: 0:01:41 time: 0.0586 data_time: 0.0160 memory: 382 loss: 1.6577
11/22 16:49:50 - mmengine - INFO - Epoch(train) [4][1330/1563] lr: 1.0000e-03 eta: 0:01:41 time: 0.0578 data_time: 0.0155 memory: 382 loss: 1.5433
11/22 16:49:50 - mmengine - INFO - Epoch(train) [4][1340/1563] lr: 1.0000e-03 eta: 0:01:40 time: 0.0576 data_time: 0.0155 memory: 382 loss: 1.6406
11/22 16:49:51 - mmengine - INFO - Epoch(train) [4][1350/1563] lr: 1.0000e-03 eta: 0:01:40 time: 0.0581 data_time: 0.0155 memory: 382 loss: 1.4883
11/22 16:49:52 - mmengine - INFO - Epoch(train) [4][1360/1563] lr: 1.0000e-03 eta: 0:01:39 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.5228
11/22 16:49:52 - mmengine - INFO - Epoch(train) [4][1370/1563] lr: 1.0000e-03 eta: 0:01:38 time: 0.0577 data_time: 0.0155 memory: 382 loss: 1.5995
11/22 16:49:53 - mmengine - INFO - Epoch(train) [4][1380/1563] lr: 1.0000e-03 eta: 0:01:38 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.5131
11/22 16:49:53 - mmengine - INFO - Epoch(train) [4][1390/1563] lr: 1.0000e-03 eta: 0:01:37 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5755
11/22 16:49:54 - mmengine - INFO - Epoch(train) [4][1400/1563] lr: 1.0000e-03 eta: 0:01:37 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5492
11/22 16:49:54 - mmengine - INFO - Epoch(train) [4][1410/1563] lr: 1.0000e-03 eta: 0:01:36 time: 0.0578 data_time: 0.0155 memory: 382 loss: 1.5689
11/22 16:49:55 - mmengine - INFO - Epoch(train) [4][1420/1563] lr: 1.0000e-03 eta: 0:01:36 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.5505
11/22 16:49:56 - mmengine - INFO - Epoch(train) [4][1430/1563] lr: 1.0000e-03 eta: 0:01:35 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.6146
11/22 16:49:56 - mmengine - INFO - Epoch(train) [4][1440/1563] lr: 1.0000e-03 eta: 0:01:35 time: 0.0582 data_time: 0.0158 memory: 382 loss: 1.5625
11/22 16:49:57 - mmengine - INFO - Epoch(train) [4][1450/1563] lr: 1.0000e-03 eta: 0:01:34 time: 0.0527 data_time: 0.0155 memory: 382 loss: 1.6290
11/22 16:49:57 - mmengine - INFO - Epoch(train) [4][1460/1563] lr: 1.0000e-03 eta: 0:01:33 time: 0.0570 data_time: 0.0155 memory: 382 loss: 1.6265
11/22 16:49:58 - mmengine - INFO - Epoch(train) [4][1470/1563] lr: 1.0000e-03 eta: 0:01:33 time: 0.0578 data_time: 0.0155 memory: 382 loss: 1.5794
11/22 16:49:58 - mmengine - INFO - Epoch(train) [4][1480/1563] lr: 1.0000e-03 eta: 0:01:32 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.4858
11/22 16:49:59 - mmengine - INFO - Epoch(train) [4][1490/1563] lr: 1.0000e-03 eta: 0:01:32 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.5724
11/22 16:50:00 - mmengine - INFO - Epoch(train) [4][1500/1563] lr: 1.0000e-03 eta: 0:01:31 time: 0.0581 data_time: 0.0158 memory: 382 loss: 1.5253
11/22 16:50:00 - mmengine - INFO - Epoch(train) [4][1510/1563] lr: 1.0000e-03 eta: 0:01:31 time: 0.0575 data_time: 0.0154 memory: 382 loss: 1.5984
11/22 16:50:01 - mmengine - INFO - Epoch(train) [4][1520/1563] lr: 1.0000e-03 eta: 0:01:30 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.4768
11/22 16:50:01 - mmengine - INFO - Epoch(train) [4][1530/1563] lr: 1.0000e-03 eta: 0:01:30 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.6302
11/22 16:50:02 - mmengine - INFO - Epoch(train) [4][1540/1563] lr: 1.0000e-03 eta: 0:01:29 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.4214
11/22 16:50:03 - mmengine - INFO - Epoch(train) [4][1550/1563] lr: 1.0000e-03 eta: 0:01:28 time: 0.0578 data_time: 0.0155 memory: 382 loss: 1.5100
11/22 16:50:03 - mmengine - INFO - Epoch(train) [4][1560/1563] lr: 1.0000e-03 eta: 0:01:28 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.5353
11/22 16:50:03 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:50:03 - mmengine - INFO - Saving checkpoint at 4 epochs
11/22 16:50:04 - mmengine - INFO - Epoch(val) [4][ 10/313] eta: 0:00:06 time: 0.0201 data_time: 0.0098 memory: 382
11/22 16:50:04 - mmengine - INFO - Epoch(val) [4][ 20/313] eta: 0:00:05 time: 0.0190 data_time: 0.0093 memory: 225
11/22 16:50:05 - mmengine - INFO - Epoch(val) [4][ 30/313] eta: 0:00:05 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:05 - mmengine - INFO - Epoch(val) [4][ 40/313] eta: 0:00:04 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:50:05 - mmengine - INFO - Epoch(val) [4][ 50/313] eta: 0:00:04 time: 0.0171 data_time: 0.0085 memory: 225
11/22 16:50:05 - mmengine - INFO - Epoch(val) [4][ 60/313] eta: 0:00:04 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:50:05 - mmengine - INFO - Epoch(val) [4][ 70/313] eta: 0:00:04 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:50:05 - mmengine - INFO - Epoch(val) [4][ 80/313] eta: 0:00:04 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:06 - mmengine - INFO - Epoch(val) [4][ 90/313] eta: 0:00:03 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:06 - mmengine - INFO - Epoch(val) [4][100/313] eta: 0:00:03 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:50:06 - mmengine - INFO - Epoch(val) [4][110/313] eta: 0:00:03 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:50:06 - mmengine - INFO - Epoch(val) [4][120/313] eta: 0:00:03 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:50:06 - mmengine - INFO - Epoch(val) [4][130/313] eta: 0:00:03 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:06 - mmengine - INFO - Epoch(val) [4][140/313] eta: 0:00:02 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:50:07 - mmengine - INFO - Epoch(val) [4][150/313] eta: 0:00:02 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:50:07 - mmengine - INFO - Epoch(val) [4][160/313] eta: 0:00:02 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:50:07 - mmengine - INFO - Epoch(val) [4][170/313] eta: 0:00:02 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:50:07 - mmengine - INFO - Epoch(val) [4][180/313] eta: 0:00:02 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:50:07 - mmengine - INFO - Epoch(val) [4][190/313] eta: 0:00:02 time: 0.0171 data_time: 0.0085 memory: 225
11/22 16:50:07 - mmengine - INFO - Epoch(val) [4][200/313] eta: 0:00:01 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:08 - mmengine - INFO - Epoch(val) [4][210/313] eta: 0:00:01 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:08 - mmengine - INFO - Epoch(val) [4][220/313] eta: 0:00:01 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:08 - mmengine - INFO - Epoch(val) [4][230/313] eta: 0:00:01 time: 0.0168 data_time: 0.0083 memory: 225
11/22 16:50:08 - mmengine - INFO - Epoch(val) [4][240/313] eta: 0:00:01 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:50:08 - mmengine - INFO - Epoch(val) [4][250/313] eta: 0:00:01 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:08 - mmengine - INFO - Epoch(val) [4][260/313] eta: 0:00:00 time: 0.0169 data_time: 0.0084 memory: 225
11/22 16:50:09 - mmengine - INFO - Epoch(val) [4][270/313] eta: 0:00:00 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:09 - mmengine - INFO - Epoch(val) [4][280/313] eta: 0:00:00 time: 0.0169 data_time: 0.0083 memory: 225
11/22 16:50:09 - mmengine - INFO - Epoch(val) [4][290/313] eta: 0:00:00 time: 0.0170 data_time: 0.0084 memory: 225
11/22 16:50:09 - mmengine - INFO - Epoch(val) [4][300/313] eta: 0:00:00 time: 0.0171 data_time: 0.0085 memory: 225
11/22 16:50:09 - mmengine - INFO - Epoch(val) [4][310/313] eta: 0:00:00 time: 0.0171 data_time: 0.0084 memory: 225
11/22 16:50:09 - mmengine - INFO - Epoch(val) [4][313/313] accuracy: 48.9200 data_time: 0.0084 time: 0.0171
11/22 16:50:10 - mmengine - INFO - Epoch(train) [5][ 10/1563] lr: 1.0000e-03 eta: 0:01:27 time: 0.0519 data_time: 0.0133 memory: 382 loss: 1.5881
11/22 16:50:10 - mmengine - INFO - Epoch(train) [5][ 20/1563] lr: 1.0000e-03 eta: 0:01:26 time: 0.0510 data_time: 0.0132 memory: 382 loss: 1.5953
11/22 16:50:11 - mmengine - INFO - Epoch(train) [5][ 30/1563] lr: 1.0000e-03 eta: 0:01:26 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.6398
11/22 16:50:11 - mmengine - INFO - Epoch(train) [5][ 40/1563] lr: 1.0000e-03 eta: 0:01:25 time: 0.0520 data_time: 0.0136 memory: 382 loss: 1.6299
11/22 16:50:12 - mmengine - INFO - Epoch(train) [5][ 50/1563] lr: 1.0000e-03 eta: 0:01:25 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.4690
11/22 16:50:12 - mmengine - INFO - Epoch(train) [5][ 60/1563] lr: 1.0000e-03 eta: 0:01:24 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.5009
11/22 16:50:13 - mmengine - INFO - Epoch(train) [5][ 70/1563] lr: 1.0000e-03 eta: 0:01:24 time: 0.0512 data_time: 0.0133 memory: 382 loss: 1.6991
11/22 16:50:13 - mmengine - INFO - Epoch(train) [5][ 80/1563] lr: 1.0000e-03 eta: 0:01:23 time: 0.0511 data_time: 0.0132 memory: 382 loss: 1.5897
11/22 16:50:14 - mmengine - INFO - Epoch(train) [5][ 90/1563] lr: 1.0000e-03 eta: 0:01:22 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.4838
11/22 16:50:15 - mmengine - INFO - Epoch(train) [5][ 100/1563] lr: 1.0000e-03 eta: 0:01:22 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.5367
11/22 16:50:15 - mmengine - INFO - Epoch(train) [5][ 110/1563] lr: 1.0000e-03 eta: 0:01:21 time: 0.0512 data_time: 0.0134 memory: 382 loss: 1.5360
11/22 16:50:16 - mmengine - INFO - Epoch(train) [5][ 120/1563] lr: 1.0000e-03 eta: 0:01:21 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.4626
11/22 16:50:16 - mmengine - INFO - Epoch(train) [5][ 130/1563] lr: 1.0000e-03 eta: 0:01:20 time: 0.0512 data_time: 0.0134 memory: 382 loss: 1.6532
11/22 16:50:17 - mmengine - INFO - Epoch(train) [5][ 140/1563] lr: 1.0000e-03 eta: 0:01:20 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.5336
11/22 16:50:17 - mmengine - INFO - Epoch(train) [5][ 150/1563] lr: 1.0000e-03 eta: 0:01:19 time: 0.0515 data_time: 0.0136 memory: 382 loss: 1.5675
11/22 16:50:18 - mmengine - INFO - Epoch(train) [5][ 160/1563] lr: 1.0000e-03 eta: 0:01:18 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.5454
11/22 16:50:18 - mmengine - INFO - Epoch(train) [5][ 170/1563] lr: 1.0000e-03 eta: 0:01:18 time: 0.0522 data_time: 0.0136 memory: 382 loss: 1.6156
11/22 16:50:19 - mmengine - INFO - Epoch(train) [5][ 180/1563] lr: 1.0000e-03 eta: 0:01:17 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.5434
11/22 16:50:19 - mmengine - INFO - Epoch(train) [5][ 190/1563] lr: 1.0000e-03 eta: 0:01:17 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.5017
11/22 16:50:20 - mmengine - INFO - Epoch(train) [5][ 200/1563] lr: 1.0000e-03 eta: 0:01:16 time: 0.0518 data_time: 0.0134 memory: 382 loss: 1.5192
11/22 16:50:20 - mmengine - INFO - Epoch(train) [5][ 210/1563] lr: 1.0000e-03 eta: 0:01:16 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.6853
11/22 16:50:21 - mmengine - INFO - Epoch(train) [5][ 220/1563] lr: 1.0000e-03 eta: 0:01:15 time: 0.0518 data_time: 0.0135 memory: 382 loss: 1.5970
11/22 16:50:21 - mmengine - INFO - Epoch(train) [5][ 230/1563] lr: 1.0000e-03 eta: 0:01:14 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.4325
11/22 16:50:22 - mmengine - INFO - Epoch(train) [5][ 240/1563] lr: 1.0000e-03 eta: 0:01:14 time: 0.0517 data_time: 0.0136 memory: 382 loss: 1.5267
11/22 16:50:22 - mmengine - INFO - Epoch(train) [5][ 250/1563] lr: 1.0000e-03 eta: 0:01:13 time: 0.0516 data_time: 0.0136 memory: 382 loss: 1.6820
11/22 16:50:23 - mmengine - INFO - Epoch(train) [5][ 260/1563] lr: 1.0000e-03 eta: 0:01:13 time: 0.0513 data_time: 0.0133 memory: 382 loss: 1.6840
11/22 16:50:23 - mmengine - INFO - Epoch(train) [5][ 270/1563] lr: 1.0000e-03 eta: 0:01:12 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.5562
11/22 16:50:24 - mmengine - INFO - Epoch(train) [5][ 280/1563] lr: 1.0000e-03 eta: 0:01:12 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.4979
11/22 16:50:24 - mmengine - INFO - Epoch(train) [5][ 290/1563] lr: 1.0000e-03 eta: 0:01:11 time: 0.0513 data_time: 0.0134 memory: 382 loss: 1.7433
11/22 16:50:25 - mmengine - INFO - Epoch(train) [5][ 300/1563] lr: 1.0000e-03 eta: 0:01:10 time: 0.0513 data_time: 0.0134 memory: 382 loss: 1.4537
11/22 16:50:25 - mmengine - INFO - Epoch(train) [5][ 310/1563] lr: 1.0000e-03 eta: 0:01:10 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.5039
11/22 16:50:26 - mmengine - INFO - Epoch(train) [5][ 320/1563] lr: 1.0000e-03 eta: 0:01:09 time: 0.0515 data_time: 0.0136 memory: 382 loss: 1.6451
11/22 16:50:26 - mmengine - INFO - Epoch(train) [5][ 330/1563] lr: 1.0000e-03 eta: 0:01:09 time: 0.0517 data_time: 0.0134 memory: 382 loss: 1.5821
11/22 16:50:27 - mmengine - INFO - Epoch(train) [5][ 340/1563] lr: 1.0000e-03 eta: 0:01:08 time: 0.0509 data_time: 0.0137 memory: 382 loss: 1.6438
11/22 16:50:27 - mmengine - INFO - Epoch(train) [5][ 350/1563] lr: 1.0000e-03 eta: 0:01:08 time: 0.0509 data_time: 0.0134 memory: 382 loss: 1.5743
11/22 16:50:28 - mmengine - INFO - Epoch(train) [5][ 360/1563] lr: 1.0000e-03 eta: 0:01:07 time: 0.0512 data_time: 0.0134 memory: 382 loss: 1.5997
11/22 16:50:28 - mmengine - INFO - Epoch(train) [5][ 370/1563] lr: 1.0000e-03 eta: 0:01:06 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.5345
11/22 16:50:29 - mmengine - INFO - Epoch(train) [5][ 380/1563] lr: 1.0000e-03 eta: 0:01:06 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.3340
11/22 16:50:29 - mmengine - INFO - Epoch(train) [5][ 390/1563] lr: 1.0000e-03 eta: 0:01:05 time: 0.0518 data_time: 0.0135 memory: 382 loss: 1.5397
11/22 16:50:30 - mmengine - INFO - Epoch(train) [5][ 400/1563] lr: 1.0000e-03 eta: 0:01:05 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.5086
11/22 16:50:30 - mmengine - INFO - Epoch(train) [5][ 410/1563] lr: 1.0000e-03 eta: 0:01:04 time: 0.0518 data_time: 0.0135 memory: 382 loss: 1.5779
11/22 16:50:31 - mmengine - INFO - Epoch(train) [5][ 420/1563] lr: 1.0000e-03 eta: 0:01:04 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.5474
11/22 16:50:32 - mmengine - INFO - Epoch(train) [5][ 430/1563] lr: 1.0000e-03 eta: 0:01:03 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.3913
11/22 16:50:32 - mmengine - INFO - Epoch(train) [5][ 440/1563] lr: 1.0000e-03 eta: 0:01:02 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.5450
11/22 16:50:33 - mmengine - INFO - Epoch(train) [5][ 450/1563] lr: 1.0000e-03 eta: 0:01:02 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.6198
11/22 16:50:33 - mmengine - INFO - Epoch(train) [5][ 460/1563] lr: 1.0000e-03 eta: 0:01:01 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.5160
11/22 16:50:34 - mmengine - INFO - Epoch(train) [5][ 470/1563] lr: 1.0000e-03 eta: 0:01:01 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.5863
11/22 16:50:34 - mmengine - INFO - Epoch(train) [5][ 480/1563] lr: 1.0000e-03 eta: 0:01:00 time: 0.0517 data_time: 0.0134 memory: 382 loss: 1.4299
11/22 16:50:35 - mmengine - INFO - Epoch(train) [5][ 490/1563] lr: 1.0000e-03 eta: 0:01:00 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.5563
11/22 16:50:35 - mmengine - INFO - Epoch(train) [5][ 500/1563] lr: 1.0000e-03 eta: 0:00:59 time: 0.0520 data_time: 0.0136 memory: 382 loss: 1.6120
11/22 16:50:36 - mmengine - INFO - Epoch(train) [5][ 510/1563] lr: 1.0000e-03 eta: 0:00:58 time: 0.0517 data_time: 0.0134 memory: 382 loss: 1.4608
11/22 16:50:36 - mmengine - INFO - Epoch(train) [5][ 520/1563] lr: 1.0000e-03 eta: 0:00:58 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.4410
11/22 16:50:37 - mmengine - INFO - Epoch(train) [5][ 530/1563] lr: 1.0000e-03 eta: 0:00:57 time: 0.0515 data_time: 0.0133 memory: 382 loss: 1.5759
11/22 16:50:37 - mmengine - INFO - Epoch(train) [5][ 540/1563] lr: 1.0000e-03 eta: 0:00:57 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.5766
11/22 16:50:38 - mmengine - INFO - Epoch(train) [5][ 550/1563] lr: 1.0000e-03 eta: 0:00:56 time: 0.0512 data_time: 0.0134 memory: 382 loss: 1.4889
11/22 16:50:38 - mmengine - INFO - Epoch(train) [5][ 560/1563] lr: 1.0000e-03 eta: 0:00:56 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.4940
11/22 16:50:39 - mmengine - INFO - Epoch(train) [5][ 570/1563] lr: 1.0000e-03 eta: 0:00:55 time: 0.0521 data_time: 0.0136 memory: 382 loss: 1.4498
11/22 16:50:39 - mmengine - INFO - Epoch(train) [5][ 580/1563] lr: 1.0000e-03 eta: 0:00:55 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.5106
11/22 16:50:40 - mmengine - INFO - Epoch(train) [5][ 590/1563] lr: 1.0000e-03 eta: 0:00:54 time: 0.0517 data_time: 0.0136 memory: 382 loss: 1.5412
11/22 16:50:40 - mmengine - INFO - Epoch(train) [5][ 600/1563] lr: 1.0000e-03 eta: 0:00:53 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.4269
11/22 16:50:41 - mmengine - INFO - Epoch(train) [5][ 610/1563] lr: 1.0000e-03 eta: 0:00:53 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.4413
11/22 16:50:41 - mmengine - INFO - Epoch(train) [5][ 620/1563] lr: 1.0000e-03 eta: 0:00:52 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.5007
11/22 16:50:42 - mmengine - INFO - Epoch(train) [5][ 630/1563] lr: 1.0000e-03 eta: 0:00:52 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.6053
11/22 16:50:42 - mmengine - INFO - Epoch(train) [5][ 640/1563] lr: 1.0000e-03 eta: 0:00:51 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.4454
11/22 16:50:43 - mmengine - INFO - Epoch(train) [5][ 650/1563] lr: 1.0000e-03 eta: 0:00:51 time: 0.0513 data_time: 0.0133 memory: 382 loss: 1.4828
11/22 16:50:43 - mmengine - INFO - Epoch(train) [5][ 660/1563] lr: 1.0000e-03 eta: 0:00:50 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.6222
11/22 16:50:44 - mmengine - INFO - Epoch(train) [5][ 670/1563] lr: 1.0000e-03 eta: 0:00:49 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.5357
11/22 16:50:44 - mmengine - INFO - Epoch(train) [5][ 680/1563] lr: 1.0000e-03 eta: 0:00:49 time: 0.0515 data_time: 0.0133 memory: 382 loss: 1.6462
11/22 16:50:45 - mmengine - INFO - Epoch(train) [5][ 690/1563] lr: 1.0000e-03 eta: 0:00:48 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.4879
11/22 16:50:45 - mmengine - INFO - Epoch(train) [5][ 700/1563] lr: 1.0000e-03 eta: 0:00:48 time: 0.0497 data_time: 0.0138 memory: 382 loss: 1.5146
11/22 16:50:46 - mmengine - INFO - Epoch(train) [5][ 710/1563] lr: 1.0000e-03 eta: 0:00:47 time: 0.0501 data_time: 0.0134 memory: 382 loss: 1.5513
11/22 16:50:46 - mmengine - INFO - Epoch(train) [5][ 720/1563] lr: 1.0000e-03 eta: 0:00:47 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.5761
11/22 16:50:47 - mmengine - INFO - Epoch(train) [5][ 730/1563] lr: 1.0000e-03 eta: 0:00:46 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.5548
11/22 16:50:47 - mmengine - INFO - Epoch(train) [5][ 740/1563] lr: 1.0000e-03 eta: 0:00:45 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.5272
11/22 16:50:48 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:50:48 - mmengine - INFO - Epoch(train) [5][ 750/1563] lr: 1.0000e-03 eta: 0:00:45 time: 0.0520 data_time: 0.0137 memory: 382 loss: 1.5802
11/22 16:50:48 - mmengine - INFO - Epoch(train) [5][ 760/1563] lr: 1.0000e-03 eta: 0:00:44 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.5568
11/22 16:50:49 - mmengine - INFO - Epoch(train) [5][ 770/1563] lr: 1.0000e-03 eta: 0:00:44 time: 0.0516 data_time: 0.0135 memory: 382 loss: 1.4462
11/22 16:50:50 - mmengine - INFO - Epoch(train) [5][ 780/1563] lr: 1.0000e-03 eta: 0:00:43 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.5174
11/22 16:50:50 - mmengine - INFO - Epoch(train) [5][ 790/1563] lr: 1.0000e-03 eta: 0:00:43 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.5304
11/22 16:50:51 - mmengine - INFO - Epoch(train) [5][ 800/1563] lr: 1.0000e-03 eta: 0:00:42 time: 0.0513 data_time: 0.0134 memory: 382 loss: 1.3804
11/22 16:50:51 - mmengine - INFO - Epoch(train) [5][ 810/1563] lr: 1.0000e-03 eta: 0:00:42 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.5559
11/22 16:50:52 - mmengine - INFO - Epoch(train) [5][ 820/1563] lr: 1.0000e-03 eta: 0:00:41 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.5324
11/22 16:50:52 - mmengine - INFO - Epoch(train) [5][ 830/1563] lr: 1.0000e-03 eta: 0:00:40 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.6753
11/22 16:50:53 - mmengine - INFO - Epoch(train) [5][ 840/1563] lr: 1.0000e-03 eta: 0:00:40 time: 0.0523 data_time: 0.0139 memory: 382 loss: 1.4945
11/22 16:50:53 - mmengine - INFO - Epoch(train) [5][ 850/1563] lr: 1.0000e-03 eta: 0:00:39 time: 0.0512 data_time: 0.0132 memory: 382 loss: 1.5660
11/22 16:50:54 - mmengine - INFO - Epoch(train) [5][ 860/1563] lr: 1.0000e-03 eta: 0:00:39 time: 0.0519 data_time: 0.0136 memory: 382 loss: 1.5264
11/22 16:50:54 - mmengine - INFO - Epoch(train) [5][ 870/1563] lr: 1.0000e-03 eta: 0:00:38 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.3853
11/22 16:50:55 - mmengine - INFO - Epoch(train) [5][ 880/1563] lr: 1.0000e-03 eta: 0:00:38 time: 0.0514 data_time: 0.0133 memory: 382 loss: 1.5576
11/22 16:50:55 - mmengine - INFO - Epoch(train) [5][ 890/1563] lr: 1.0000e-03 eta: 0:00:37 time: 0.0517 data_time: 0.0135 memory: 382 loss: 1.5043
11/22 16:50:56 - mmengine - INFO - Epoch(train) [5][ 900/1563] lr: 1.0000e-03 eta: 0:00:36 time: 0.0527 data_time: 0.0141 memory: 382 loss: 1.5948
11/22 16:50:56 - mmengine - INFO - Epoch(train) [5][ 910/1563] lr: 1.0000e-03 eta: 0:00:36 time: 0.0523 data_time: 0.0138 memory: 382 loss: 1.5114
11/22 16:50:57 - mmengine - INFO - Epoch(train) [5][ 920/1563] lr: 1.0000e-03 eta: 0:00:35 time: 0.0522 data_time: 0.0136 memory: 382 loss: 1.5999
11/22 16:50:57 - mmengine - INFO - Epoch(train) [5][ 930/1563] lr: 1.0000e-03 eta: 0:00:35 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.4599
11/22 16:50:58 - mmengine - INFO - Epoch(train) [5][ 940/1563] lr: 1.0000e-03 eta: 0:00:34 time: 0.0514 data_time: 0.0134 memory: 382 loss: 1.4754
11/22 16:50:58 - mmengine - INFO - Epoch(train) [5][ 950/1563] lr: 1.0000e-03 eta: 0:00:34 time: 0.0514 data_time: 0.0135 memory: 382 loss: 1.5212
11/22 16:50:59 - mmengine - INFO - Epoch(train) [5][ 960/1563] lr: 1.0000e-03 eta: 0:00:33 time: 0.0515 data_time: 0.0134 memory: 382 loss: 1.4693
11/22 16:50:59 - mmengine - INFO - Epoch(train) [5][ 970/1563] lr: 1.0000e-03 eta: 0:00:33 time: 0.0516 data_time: 0.0134 memory: 382 loss: 1.4739
11/22 16:51:00 - mmengine - INFO - Epoch(train) [5][ 980/1563] lr: 1.0000e-03 eta: 0:00:32 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.5810
11/22 16:51:00 - mmengine - INFO - Epoch(train) [5][ 990/1563] lr: 1.0000e-03 eta: 0:00:31 time: 0.0518 data_time: 0.0136 memory: 382 loss: 1.4858
11/22 16:51:01 - mmengine - INFO - Epoch(train) [5][1000/1563] lr: 1.0000e-03 eta: 0:00:31 time: 0.0545 data_time: 0.0174 memory: 382 loss: 1.5966
11/22 16:51:01 - mmengine - INFO - Epoch(train) [5][1010/1563] lr: 1.0000e-03 eta: 0:00:30 time: 0.0508 data_time: 0.0148 memory: 382 loss: 1.4623
11/22 16:51:02 - mmengine - INFO - Epoch(train) [5][1020/1563] lr: 1.0000e-03 eta: 0:00:30 time: 0.0517 data_time: 0.0136 memory: 382 loss: 1.6233
11/22 16:51:03 - mmengine - INFO - Epoch(train) [5][1030/1563] lr: 1.0000e-03 eta: 0:00:29 time: 0.0599 data_time: 0.0184 memory: 382 loss: 1.5946
11/22 16:51:03 - mmengine - INFO - Epoch(train) [5][1040/1563] lr: 1.0000e-03 eta: 0:00:29 time: 0.0505 data_time: 0.0137 memory: 382 loss: 1.4944
11/22 16:51:04 - mmengine - INFO - Epoch(train) [5][1050/1563] lr: 1.0000e-03 eta: 0:00:28 time: 0.0520 data_time: 0.0138 memory: 382 loss: 1.4998
11/22 16:51:04 - mmengine - INFO - Epoch(train) [5][1060/1563] lr: 1.0000e-03 eta: 0:00:28 time: 0.0516 data_time: 0.0137 memory: 382 loss: 1.5143
11/22 16:51:05 - mmengine - INFO - Epoch(train) [5][1070/1563] lr: 1.0000e-03 eta: 0:00:27 time: 0.0759 data_time: 0.0253 memory: 382 loss: 1.4463
11/22 16:51:06 - mmengine - INFO - Epoch(train) [5][1080/1563] lr: 1.0000e-03 eta: 0:00:26 time: 0.0761 data_time: 0.0264 memory: 382 loss: 1.4758
11/22 16:51:06 - mmengine - INFO - Epoch(train) [5][1090/1563] lr: 1.0000e-03 eta: 0:00:26 time: 0.0593 data_time: 0.0199 memory: 382 loss: 1.3308
11/22 16:51:07 - mmengine - INFO - Epoch(train) [5][1100/1563] lr: 1.0000e-03 eta: 0:00:25 time: 0.0473 data_time: 0.0135 memory: 382 loss: 1.6007
11/22 16:51:07 - mmengine - INFO - Epoch(train) [5][1110/1563] lr: 1.0000e-03 eta: 0:00:25 time: 0.0497 data_time: 0.0154 memory: 382 loss: 1.4717
11/22 16:51:08 - mmengine - INFO - Epoch(train) [5][1120/1563] lr: 1.0000e-03 eta: 0:00:24 time: 0.0545 data_time: 0.0158 memory: 382 loss: 1.4900
11/22 16:51:08 - mmengine - INFO - Epoch(train) [5][1130/1563] lr: 1.0000e-03 eta: 0:00:24 time: 0.0515 data_time: 0.0135 memory: 382 loss: 1.5029
11/22 16:51:09 - mmengine - INFO - Epoch(train) [5][1140/1563] lr: 1.0000e-03 eta: 0:00:23 time: 0.0518 data_time: 0.0153 memory: 382 loss: 1.4895
11/22 16:51:09 - mmengine - INFO - Epoch(train) [5][1150/1563] lr: 1.0000e-03 eta: 0:00:23 time: 0.0547 data_time: 0.0166 memory: 382 loss: 1.5550
11/22 16:51:10 - mmengine - INFO - Epoch(train) [5][1160/1563] lr: 1.0000e-03 eta: 0:00:22 time: 0.0597 data_time: 0.0169 memory: 382 loss: 1.4044
11/22 16:51:10 - mmengine - INFO - Epoch(train) [5][1170/1563] lr: 1.0000e-03 eta: 0:00:21 time: 0.0580 data_time: 0.0159 memory: 382 loss: 1.4789
11/22 16:51:11 - mmengine - INFO - Epoch(train) [5][1180/1563] lr: 1.0000e-03 eta: 0:00:21 time: 0.0858 data_time: 0.0300 memory: 382 loss: 1.4462
11/22 16:51:12 - mmengine - INFO - Epoch(train) [5][1190/1563] lr: 1.0000e-03 eta: 0:00:20 time: 0.0787 data_time: 0.0282 memory: 382 loss: 1.5601
11/22 16:51:13 - mmengine - INFO - Epoch(train) [5][1200/1563] lr: 1.0000e-03 eta: 0:00:20 time: 0.0636 data_time: 0.0207 memory: 382 loss: 1.5417
11/22 16:51:13 - mmengine - INFO - Epoch(train) [5][1210/1563] lr: 1.0000e-03 eta: 0:00:19 time: 0.0525 data_time: 0.0158 memory: 382 loss: 1.5232
11/22 16:51:14 - mmengine - INFO - Epoch(train) [5][1220/1563] lr: 1.0000e-03 eta: 0:00:19 time: 0.0571 data_time: 0.0158 memory: 382 loss: 1.4043
11/22 16:51:14 - mmengine - INFO - Epoch(train) [5][1230/1563] lr: 1.0000e-03 eta: 0:00:18 time: 0.0576 data_time: 0.0157 memory: 382 loss: 1.5698
11/22 16:51:15 - mmengine - INFO - Epoch(train) [5][1240/1563] lr: 1.0000e-03 eta: 0:00:18 time: 0.0575 data_time: 0.0156 memory: 382 loss: 1.4904
11/22 16:51:16 - mmengine - INFO - Epoch(train) [5][1250/1563] lr: 1.0000e-03 eta: 0:00:17 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.5992
11/22 16:51:16 - mmengine - INFO - Epoch(train) [5][1260/1563] lr: 1.0000e-03 eta: 0:00:16 time: 0.0581 data_time: 0.0157 memory: 382 loss: 1.4858
11/22 16:51:17 - mmengine - INFO - Epoch(train) [5][1270/1563] lr: 1.0000e-03 eta: 0:00:16 time: 0.0580 data_time: 0.0157 memory: 382 loss: 1.3606
11/22 16:51:17 - mmengine - INFO - Epoch(train) [5][1280/1563] lr: 1.0000e-03 eta: 0:00:15 time: 0.0582 data_time: 0.0157 memory: 382 loss: 1.6510
11/22 16:51:18 - mmengine - INFO - Epoch(train) [5][1290/1563] lr: 1.0000e-03 eta: 0:00:15 time: 0.0582 data_time: 0.0157 memory: 382 loss: 1.5346
11/22 16:51:19 - mmengine - INFO - Epoch(train) [5][1300/1563] lr: 1.0000e-03 eta: 0:00:14 time: 0.0582 data_time: 0.0158 memory: 382 loss: 1.3520
11/22 16:51:19 - mmengine - INFO - Epoch(train) [5][1310/1563] lr: 1.0000e-03 eta: 0:00:14 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.5014
11/22 16:51:20 - mmengine - INFO - Epoch(train) [5][1320/1563] lr: 1.0000e-03 eta: 0:00:13 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.4836
11/22 16:51:20 - mmengine - INFO - Epoch(train) [5][1330/1563] lr: 1.0000e-03 eta: 0:00:13 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.3875
11/22 16:51:21 - mmengine - INFO - Epoch(train) [5][1340/1563] lr: 1.0000e-03 eta: 0:00:12 time: 0.0582 data_time: 0.0156 memory: 382 loss: 1.4849
11/22 16:51:21 - mmengine - INFO - Epoch(train) [5][1350/1563] lr: 1.0000e-03 eta: 0:00:11 time: 0.0582 data_time: 0.0158 memory: 382 loss: 1.5298
11/22 16:51:22 - mmengine - INFO - Epoch(train) [5][1360/1563] lr: 1.0000e-03 eta: 0:00:11 time: 0.0583 data_time: 0.0159 memory: 382 loss: 1.4389
11/22 16:51:23 - mmengine - INFO - Epoch(train) [5][1370/1563] lr: 1.0000e-03 eta: 0:00:10 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.4141
11/22 16:51:23 - mmengine - INFO - Epoch(train) [5][1380/1563] lr: 1.0000e-03 eta: 0:00:10 time: 0.0581 data_time: 0.0157 memory: 382 loss: 1.4422
11/22 16:51:24 - mmengine - INFO - Epoch(train) [5][1390/1563] lr: 1.0000e-03 eta: 0:00:09 time: 0.0582 data_time: 0.0157 memory: 382 loss: 1.4260
11/22 16:51:24 - mmengine - INFO - Epoch(train) [5][1400/1563] lr: 1.0000e-03 eta: 0:00:09 time: 0.0585 data_time: 0.0161 memory: 382 loss: 1.3820
11/22 16:51:25 - mmengine - INFO - Epoch(train) [5][1410/1563] lr: 1.0000e-03 eta: 0:00:08 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.4658
11/22 16:51:25 - mmengine - INFO - Epoch(train) [5][1420/1563] lr: 1.0000e-03 eta: 0:00:07 time: 0.0583 data_time: 0.0159 memory: 382 loss: 1.4120
11/22 16:51:26 - mmengine - INFO - Epoch(train) [5][1430/1563] lr: 1.0000e-03 eta: 0:00:07 time: 0.0576 data_time: 0.0157 memory: 382 loss: 1.5193
11/22 16:51:27 - mmengine - INFO - Epoch(train) [5][1440/1563] lr: 1.0000e-03 eta: 0:00:06 time: 0.0585 data_time: 0.0159 memory: 382 loss: 1.5316
11/22 16:51:27 - mmengine - INFO - Epoch(train) [5][1450/1563] lr: 1.0000e-03 eta: 0:00:06 time: 0.0582 data_time: 0.0158 memory: 382 loss: 1.4528
11/22 16:51:28 - mmengine - INFO - Epoch(train) [5][1460/1563] lr: 1.0000e-03 eta: 0:00:05 time: 0.0584 data_time: 0.0160 memory: 382 loss: 1.5557
11/22 16:51:28 - mmengine - INFO - Epoch(train) [5][1470/1563] lr: 1.0000e-03 eta: 0:00:05 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.4487
11/22 16:51:29 - mmengine - INFO - Epoch(train) [5][1480/1563] lr: 1.0000e-03 eta: 0:00:04 time: 0.0581 data_time: 0.0156 memory: 382 loss: 1.4470
11/22 16:51:30 - mmengine - INFO - Epoch(train) [5][1490/1563] lr: 1.0000e-03 eta: 0:00:04 time: 0.0581 data_time: 0.0157 memory: 382 loss: 1.4924
11/22 16:51:30 - mmengine - INFO - Epoch(train) [5][1500/1563] lr: 1.0000e-03 eta: 0:00:03 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.5511
11/22 16:51:31 - mmengine - INFO - Epoch(train) [5][1510/1563] lr: 1.0000e-03 eta: 0:00:02 time: 0.0580 data_time: 0.0156 memory: 382 loss: 1.4933
11/22 16:51:31 - mmengine - INFO - Epoch(train) [5][1520/1563] lr: 1.0000e-03 eta: 0:00:02 time: 0.0578 data_time: 0.0156 memory: 382 loss: 1.6460
11/22 16:51:32 - mmengine - INFO - Epoch(train) [5][1530/1563] lr: 1.0000e-03 eta: 0:00:01 time: 0.0579 data_time: 0.0157 memory: 382 loss: 1.4602
11/22 16:51:32 - mmengine - INFO - Epoch(train) [5][1540/1563] lr: 1.0000e-03 eta: 0:00:01 time: 0.0584 data_time: 0.0159 memory: 382 loss: 1.4839
11/22 16:51:33 - mmengine - INFO - Epoch(train) [5][1550/1563] lr: 1.0000e-03 eta: 0:00:00 time: 0.0579 data_time: 0.0156 memory: 382 loss: 1.4155
11/22 16:51:34 - mmengine - INFO - Epoch(train) [5][1560/1563] lr: 1.0000e-03 eta: 0:00:00 time: 0.0581 data_time: 0.0157 memory: 382 loss: 1.4509
11/22 16:51:34 - mmengine - INFO - Exp name: 20241122_164350
11/22 16:51:34 - mmengine - INFO - Saving checkpoint at 5 epochs
11/22 16:51:35 - mmengine - INFO - Epoch(val) [5][ 10/313] eta: 0:00:06 time: 0.0206 data_time: 0.0100 memory: 382
11/22 16:51:35 - mmengine - INFO - Epoch(val) [5][ 20/313] eta: 0:00:06 time: 0.0204 data_time: 0.0100 memory: 225
11/22 16:51:35 - mmengine - INFO - Epoch(val) [5][ 30/313] eta: 0:00:05 time: 0.0208 data_time: 0.0102 memory: 225
11/22 16:51:35 - mmengine - INFO - Epoch(val) [5][ 40/313] eta: 0:00:05 time: 0.0205 data_time: 0.0100 memory: 225
11/22 16:51:35 - mmengine - INFO - Epoch(val) [5][ 50/313] eta: 0:00:05 time: 0.0206 data_time: 0.0100 memory: 225
11/22 16:51:36 - mmengine - INFO - Epoch(val) [5][ 60/313] eta: 0:00:05 time: 0.0203 data_time: 0.0099 memory: 225
11/22 16:51:36 - mmengine - INFO - Epoch(val) [5][ 70/313] eta: 0:00:04 time: 0.0205 data_time: 0.0100 memory: 225
11/22 16:51:36 - mmengine - INFO - Epoch(val) [5][ 80/313] eta: 0:00:04 time: 0.0204 data_time: 0.0099 memory: 225
11/22 16:51:36 - mmengine - INFO - Epoch(val) [5][ 90/313] eta: 0:00:04 time: 0.0203 data_time: 0.0099 memory: 225
11/22 16:51:36 - mmengine - INFO - Epoch(val) [5][100/313] eta: 0:00:04 time: 0.0205 data_time: 0.0100 memory: 225
11/22 16:51:37 - mmengine - INFO - Epoch(val) [5][110/313] eta: 0:00:04 time: 0.0204 data_time: 0.0099 memory: 225
11/22 16:51:37 - mmengine - INFO - Epoch(val) [5][120/313] eta: 0:00:03 time: 0.0204 data_time: 0.0099 memory: 225
11/22 16:51:37 - mmengine - INFO - Epoch(val) [5][130/313] eta: 0:00:03 time: 0.0265 data_time: 0.0132 memory: 225
11/22 16:51:37 - mmengine - INFO - Epoch(val) [5][140/313] eta: 0:00:03 time: 0.0226 data_time: 0.0110 memory: 225
11/22 16:51:38 - mmengine - INFO - Epoch(val) [5][150/313] eta: 0:00:03 time: 0.0210 data_time: 0.0102 memory: 225
11/22 16:51:38 - mmengine - INFO - Epoch(val) [5][160/313] eta: 0:00:03 time: 0.0204 data_time: 0.0099 memory: 225
11/22 16:51:38 - mmengine - INFO - Epoch(val) [5][170/313] eta: 0:00:03 time: 0.0205 data_time: 0.0099 memory: 225
11/22 16:51:38 - mmengine - INFO - Epoch(val) [5][180/313] eta: 0:00:02 time: 0.0206 data_time: 0.0100 memory: 225
11/22 16:51:38 - mmengine - INFO - Epoch(val) [5][190/313] eta: 0:00:02 time: 0.0205 data_time: 0.0100 memory: 225
11/22 16:51:39 - mmengine - INFO - Epoch(val) [5][200/313] eta: 0:00:02 time: 0.0260 data_time: 0.0125 memory: 225
11/22 16:51:39 - mmengine - INFO - Epoch(val) [5][210/313] eta: 0:00:02 time: 0.0223 data_time: 0.0108 memory: 225
11/22 16:51:39 - mmengine - INFO - Epoch(val) [5][220/313] eta: 0:00:01 time: 0.0217 data_time: 0.0105 memory: 225
11/22 16:51:39 - mmengine - INFO - Epoch(val) [5][230/313] eta: 0:00:01 time: 0.0186 data_time: 0.0091 memory: 225
11/22 16:51:39 - mmengine - INFO - Epoch(val) [5][240/313] eta: 0:00:01 time: 0.0180 data_time: 0.0089 memory: 225
11/22 16:51:40 - mmengine - INFO - Epoch(val) [5][250/313] eta: 0:00:01 time: 0.0180 data_time: 0.0089 memory: 225
11/22 16:51:40 - mmengine - INFO - Epoch(val) [5][260/313] eta: 0:00:01 time: 0.0179 data_time: 0.0088 memory: 225
11/22 16:51:40 - mmengine - INFO - Epoch(val) [5][270/313] eta: 0:00:00 time: 0.0180 data_time: 0.0089 memory: 225
11/22 16:51:40 - mmengine - INFO - Epoch(val) [5][280/313] eta: 0:00:00 time: 0.0181 data_time: 0.0089 memory: 225
11/22 16:51:40 - mmengine - INFO - Epoch(val) [5][290/313] eta: 0:00:00 time: 0.0179 data_time: 0.0088 memory: 225
11/22 16:51:41 - mmengine - INFO - Epoch(val) [5][300/313] eta: 0:00:00 time: 0.0180 data_time: 0.0089 memory: 225
11/22 16:51:41 - mmengine - INFO - Epoch(val) [5][310/313] eta: 0:00:00 time: 0.0312 data_time: 0.0154 memory: 225
11/22 16:51:41 - mmengine - INFO - Epoch(val) [5][313/313] accuracy: 49.1500 data_time: 0.0102 time: 0.0208
MMResNet50(
(data_preprocessor): BaseDataPreprocessor()
(resnet): ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=2048, out_features=1000, bias=True)
)
)