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[Feature][MMSIG] Add UniFormer Pose Estimation to Projects folder#2501

Merged
Tau-J merged 14 commits into
open-mmlab:dev-1.xfrom
xin-li-67:uniformer_projects
Jul 24, 2023
Merged

[Feature][MMSIG] Add UniFormer Pose Estimation to Projects folder#2501
Tau-J merged 14 commits into
open-mmlab:dev-1.xfrom
xin-li-67:uniformer_projects

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@xin-li-67

@xin-li-67 xin-li-67 commented Jul 1, 2023

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Motivation

MMSIG task

Modification

projects/uniformer/*

BC-breaking (Optional)

Use cases (Optional)

Checklist

Before PR:

  • I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
  • Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
  • Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
  • New functionalities are covered by complete unit tests. If not, please add more unit tests to ensure correctness.
  • The documentation has been modified accordingly, including docstring or example tutorials.

After PR:

  • CLA has been signed and all committers have signed the CLA in this PR.

@xin-li-67 xin-li-67 changed the base branch from main to dev-1.x July 1, 2023 11:33
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codecov Bot commented Jul 1, 2023 •
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Codecov Report

Patch coverage has no change and project coverage change: -0.05 ⚠️

Comparison is base (4233a61) 80.82% compared to head (f93c8ed) 80.77%.

❗ Current head f93c8ed differs from pull request most recent head 9ee9014. Consider uploading reports for the commit 9ee9014 to get more accurate results

Additional details and impacted files 
@@             Coverage Diff             @@
##           dev-1.x    #2501      +/-   ##
===========================================
- Coverage    80.82%   80.77%   -0.05%     
===========================================
  Files          230      230              
  Lines        14437    14437              
  Branches      2498     2498              
===========================================
- Hits         11668    11662       -6     
- Misses        2129     2136       +7     
+ Partials       640      639       -1
Flag Coverage Δ
unittests 80.77% <ø> (-0.05%) ⬇️

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see 2 files with indirect coverage changes

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@xin-li-67

xin-li-67 commented Jul 21, 2023

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test result sample of projects/uniformer/configs/td-hm_uniformer-s-8xb128-210e_coco-384x288.py:

Loads checkpoint by local backend from path: projects/uniformer/pose_model/top_down_384x288_global_small.pth
07/21 17:46:03 - mmengine - INFO - Load checkpoint from projects/uniformer/pose_model/top_down_384x288_global_small.pth
07/21 17:46:53 - mmengine - INFO - Epoch(test) [ 50/407]    eta: 0:05:55  time: 0.996443  data_time: 0.062145  memory: 6542  
07/21 17:47:39 - mmengine - INFO - Epoch(test) [100/407]    eta: 0:04:56  time: 0.933922  data_time: 0.034630  memory: 6542  
07/21 17:48:26 - mmengine - INFO - Epoch(test) [150/407]    eta: 0:04:05  time: 0.930108  data_time: 0.034428  memory: 6542  
07/21 17:49:13 - mmengine - INFO - Epoch(test) [200/407]    eta: 0:03:16  time: 0.937324  data_time: 0.039884  memory: 6542  
07/21 17:50:00 - mmengine - INFO - Epoch(test) [250/407]    eta: 0:02:28  time: 0.938158  data_time: 0.035234  memory: 6542  
07/21 17:50:46 - mmengine - INFO - Epoch(test) [300/407]    eta: 0:01:41  time: 0.929169  data_time: 0.036719  memory: 6542  
07/21 17:51:32 - mmengine - INFO - Epoch(test) [350/407]    eta: 0:00:53  time: 0.927817  data_time: 0.034636  memory: 6542  
07/21 17:52:19 - mmengine - INFO - Epoch(test) [400/407]    eta: 0:00:06  time: 0.929423  data_time: 0.035859  memory: 6542  
07/21 17:52:39 - mmengine - INFO - Evaluating CocoMetric...
Loading and preparing results...
DONE (t=1.54s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=3.81s).
Accumulating evaluation results...
DONE (t=0.12s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.759
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.906
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.830
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.722
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.830
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.810
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.944
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.873
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.768
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.873
07/21 17:52:44 - mmengine - INFO - Epoch(test) [407/407]    coco/AP: 0.758717  coco/AP .5: 0.906003  coco/AP .75: 0.829609  coco/AP (M): 0.721588  coco/AP (L): 0.829766  coco/AR: 0.810217  coco/AR .5: 0.943955  coco/AR .75: 0.873111  coco/AR (M): 0.767850  coco/AR (L): 0.872612  data_time: 0.039037  time: 0.939339

@xin-li-67 xin-li-67 changed the title [WIP] Add UniFormer Pose Estimation to Projects folder Add UniFormer Pose Estimation to Projects folder Jul 23, 2023
@xin-li-67

xin-li-67 commented Jul 23, 2023

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With the latest commit, I have fixed the error which blocked the training process, and now I can run training on a single GPU, and the log is quite similar to the original one. Here is a part of it:

2023/07/22 14:32:59 - 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.
2023/07/22 14:32:59 - 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                      
(NORMAL      ) SyncBuffersHook                    
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train:
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
2023/07/22 14:34:04 - mmengine - INFO - LR is set based on batch size of 1024 and the current batch size is 32. Scaling the original LR by 0.03125.
2023/07/22 14:34:10 - mmengine - INFO - load model from: /root/mmpose/projects/uniformer/pretrained/uniformer_small_in1k.pth
2023/07/22 14:34:10 - mmengine - INFO - Loads checkpoint by local backend from path: /root/mmpose/projects/uniformer/pretrained/uniformer_small_in1k.pth
2023/07/22 14:34:10 - mmengine - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: model

missing keys in source state_dict: patch_embed1.norm.weight, patch_embed1.norm.bias, patch_embed1.proj.weight, patch_embed1.proj.bias, patch_embed2.norm.weight, patch_embed2.norm.bias, patch_embed2.proj.weight, patch_embed2.proj.bias, patch_embed3.norm.weight, patch_embed3.norm.bias, patch_embed3.proj.weight, patch_embed3.proj.bias, patch_embed4.norm.weight, patch_embed4.norm.bias, patch_embed4.proj.weight, patch_embed4.proj.bias, blocks1.0.pos_embed.weight, blocks1.0.pos_embed.bias, blocks1.0.norm1.weight, blocks1.0.norm1.bias, blocks1.0.norm1.running_mean, blocks1.0.norm1.running_var, blocks1.0.conv1.weight, blocks1.0.conv1.bias, blocks1.0.conv2.weight, blocks1.0.conv2.bias, blocks1.0.attn.weight, blocks1.0.attn.bias, blocks1.0.norm2.weight, blocks1.0.norm2.bias, blocks1.0.norm2.running_mean, blocks1.0.norm2.running_var, blocks1.0.mlp.fc1.weight, blocks1.0.mlp.fc1.bias, blocks1.0.mlp.fc2.weight, blocks1.0.mlp.fc2.bias, blocks1.1.pos_embed.weight, blocks1.1.pos_embed.bias, blocks1.1.norm1.weight, blocks1.1.norm1.bias, blocks1.1.norm1.running_mean, blocks1.1.norm1.running_var, blocks1.1.conv1.weight, blocks1.1.conv1.bias, blocks1.1.conv2.weight, blocks1.1.conv2.bias, blocks1.1.attn.weight, blocks1.1.attn.bias, blocks1.1.norm2.weight, blocks1.1.norm2.bias, blocks1.1.norm2.running_mean, blocks1.1.norm2.running_var, blocks1.1.mlp.fc1.weight, blocks1.1.mlp.fc1.bias, blocks1.1.mlp.fc2.weight, blocks1.1.mlp.fc2.bias, blocks1.2.pos_embed.weight, blocks1.2.pos_embed.bias, blocks1.2.norm1.weight, blocks1.2.norm1.bias, blocks1.2.norm1.running_mean, blocks1.2.norm1.running_var, blocks1.2.conv1.weight, blocks1.2.conv1.bias, blocks1.2.conv2.weight, blocks1.2.conv2.bias, blocks1.2.attn.weight, blocks1.2.attn.bias, blocks1.2.norm2.weight, blocks1.2.norm2.bias, blocks1.2.norm2.running_mean, blocks1.2.norm2.running_var, blocks1.2.mlp.fc1.weight, blocks1.2.mlp.fc1.bias, blocks1.2.mlp.fc2.weight, blocks1.2.mlp.fc2.bias, norm1.weight, norm1.bias, blocks2.0.pos_embed.weight, blocks2.0.pos_embed.bias, blocks2.0.norm1.weight, blocks2.0.norm1.bias, blocks2.0.norm1.running_mean, blocks2.0.norm1.running_var, blocks2.0.conv1.weight, blocks2.0.conv1.bias, blocks2.0.conv2.weight, blocks2.0.conv2.bias, blocks2.0.attn.weight, blocks2.0.attn.bias, blocks2.0.norm2.weight, blocks2.0.norm2.bias, blocks2.0.norm2.running_mean, blocks2.0.norm2.running_var, blocks2.0.mlp.fc1.weight, blocks2.0.mlp.fc1.bias, blocks2.0.mlp.fc2.weight, blocks2.0.mlp.fc2.bias, blocks2.1.pos_embed.weight, blocks2.1.pos_embed.bias, blocks2.1.norm1.weight, blocks2.1.norm1.bias, blocks2.1.norm1.running_mean, blocks2.1.norm1.running_var, blocks2.1.conv1.weight, blocks2.1.conv1.bias, blocks2.1.conv2.weight, blocks2.1.conv2.bias, blocks2.1.attn.weight, blocks2.1.attn.bias, blocks2.1.norm2.weight, blocks2.1.norm2.bias, blocks2.1.norm2.running_mean, blocks2.1.norm2.running_var, blocks2.1.mlp.fc1.weight, blocks2.1.mlp.fc1.bias, blocks2.1.mlp.fc2.weight, blocks2.1.mlp.fc2.bias, blocks2.2.pos_embed.weight, blocks2.2.pos_embed.bias, blocks2.2.norm1.weight, blocks2.2.norm1.bias, blocks2.2.norm1.running_mean, blocks2.2.norm1.running_var, blocks2.2.conv1.weight, blocks2.2.conv1.bias, blocks2.2.conv2.weight, blocks2.2.conv2.bias, blocks2.2.attn.weight, blocks2.2.attn.bias, blocks2.2.norm2.weight, blocks2.2.norm2.bias, blocks2.2.norm2.running_mean, blocks2.2.norm2.running_var, blocks2.2.mlp.fc1.weight, blocks2.2.mlp.fc1.bias, blocks2.2.mlp.fc2.weight, blocks2.2.mlp.fc2.bias, blocks2.3.pos_embed.weight, blocks2.3.pos_embed.bias, blocks2.3.norm1.weight, blocks2.3.norm1.bias, blocks2.3.norm1.running_mean, blocks2.3.norm1.running_var, blocks2.3.conv1.weight, blocks2.3.conv1.bias, blocks2.3.conv2.weight, blocks2.3.conv2.bias, blocks2.3.attn.weight, blocks2.3.attn.bias, blocks2.3.norm2.weight, blocks2.3.norm2.bias, blocks2.3.norm2.running_mean, blocks2.3.norm2.running_var, blocks2.3.mlp.fc1.weight, blocks2.3.mlp.fc1.bias, blocks2.3.mlp.fc2.weight, blocks2.3.mlp.fc2.bias, norm2.weight, norm2.bias, blocks3.0.pos_embed.weight, blocks3.0.pos_embed.bias, blocks3.0.norm1.weight, blocks3.0.norm1.bias, blocks3.0.attn.qkv.weight, blocks3.0.attn.qkv.bias, blocks3.0.attn.proj.weight, blocks3.0.attn.proj.bias, blocks3.0.norm2.weight, blocks3.0.norm2.bias, blocks3.0.mlp.fc1.weight, blocks3.0.mlp.fc1.bias, blocks3.0.mlp.fc2.weight, blocks3.0.mlp.fc2.bias, blocks3.1.pos_embed.weight, blocks3.1.pos_embed.bias, blocks3.1.norm1.weight, blocks3.1.norm1.bias, blocks3.1.attn.qkv.weight, blocks3.1.attn.qkv.bias, blocks3.1.attn.proj.weight, blocks3.1.attn.proj.bias, blocks3.1.norm2.weight, blocks3.1.norm2.bias, blocks3.1.mlp.fc1.weight, blocks3.1.mlp.fc1.bias, blocks3.1.mlp.fc2.weight, blocks3.1.mlp.fc2.bias, blocks3.2.pos_embed.weight, blocks3.2.pos_embed.bias, blocks3.2.norm1.weight, blocks3.2.norm1.bias, blocks3.2.attn.qkv.weight, blocks3.2.attn.qkv.bias, blocks3.2.attn.proj.weight, blocks3.2.attn.proj.bias, blocks3.2.norm2.weight, blocks3.2.norm2.bias, blocks3.2.mlp.fc1.weight, blocks3.2.mlp.fc1.bias, blocks3.2.mlp.fc2.weight, blocks3.2.mlp.fc2.bias, blocks3.3.pos_embed.weight, blocks3.3.pos_embed.bias, blocks3.3.norm1.weight, blocks3.3.norm1.bias, blocks3.3.attn.qkv.weight, blocks3.3.attn.qkv.bias, blocks3.3.attn.proj.weight, blocks3.3.attn.proj.bias, blocks3.3.norm2.weight, blocks3.3.norm2.bias, blocks3.3.mlp.fc1.weight, blocks3.3.mlp.fc1.bias, blocks3.3.mlp.fc2.weight, blocks3.3.mlp.fc2.bias, blocks3.4.pos_embed.weight, blocks3.4.pos_embed.bias, blocks3.4.norm1.weight, blocks3.4.norm1.bias, blocks3.4.attn.qkv.weight, blocks3.4.attn.qkv.bias, blocks3.4.attn.proj.weight, blocks3.4.attn.proj.bias, blocks3.4.norm2.weight, blocks3.4.norm2.bias, blocks3.4.mlp.fc1.weight, blocks3.4.mlp.fc1.bias, blocks3.4.mlp.fc2.weight, blocks3.4.mlp.fc2.bias, blocks3.5.pos_embed.weight, blocks3.5.pos_embed.bias, blocks3.5.norm1.weight, blocks3.5.norm1.bias, blocks3.5.attn.qkv.weight, blocks3.5.attn.qkv.bias, blocks3.5.attn.proj.weight, blocks3.5.attn.proj.bias, blocks3.5.norm2.weight, blocks3.5.norm2.bias, blocks3.5.mlp.fc1.weight, blocks3.5.mlp.fc1.bias, blocks3.5.mlp.fc2.weight, blocks3.5.mlp.fc2.bias, blocks3.6.pos_embed.weight, blocks3.6.pos_embed.bias, blocks3.6.norm1.weight, blocks3.6.norm1.bias, blocks3.6.attn.qkv.weight, blocks3.6.attn.qkv.bias, blocks3.6.attn.proj.weight, blocks3.6.attn.proj.bias, blocks3.6.norm2.weight, blocks3.6.norm2.bias, blocks3.6.mlp.fc1.weight, blocks3.6.mlp.fc1.bias, blocks3.6.mlp.fc2.weight, blocks3.6.mlp.fc2.bias, blocks3.7.pos_embed.weight, blocks3.7.pos_embed.bias, blocks3.7.norm1.weight, blocks3.7.norm1.bias, blocks3.7.attn.qkv.weight, blocks3.7.attn.qkv.bias, blocks3.7.attn.proj.weight, blocks3.7.attn.proj.bias, blocks3.7.norm2.weight, blocks3.7.norm2.bias, blocks3.7.mlp.fc1.weight, blocks3.7.mlp.fc1.bias, blocks3.7.mlp.fc2.weight, blocks3.7.mlp.fc2.bias, norm3.weight, norm3.bias, blocks4.0.pos_embed.weight, blocks4.0.pos_embed.bias, blocks4.0.norm1.weight, blocks4.0.norm1.bias, blocks4.0.attn.qkv.weight, blocks4.0.attn.qkv.bias, blocks4.0.attn.proj.weight, blocks4.0.attn.proj.bias, blocks4.0.norm2.weight, blocks4.0.norm2.bias, blocks4.0.mlp.fc1.weight, blocks4.0.mlp.fc1.bias, blocks4.0.mlp.fc2.weight, blocks4.0.mlp.fc2.bias, blocks4.1.pos_embed.weight, blocks4.1.pos_embed.bias, blocks4.1.norm1.weight, blocks4.1.norm1.bias, blocks4.1.attn.qkv.weight, blocks4.1.attn.qkv.bias, blocks4.1.attn.proj.weight, blocks4.1.attn.proj.bias, blocks4.1.norm2.weight, blocks4.1.norm2.bias, blocks4.1.mlp.fc1.weight, blocks4.1.mlp.fc1.bias, blocks4.1.mlp.fc2.weight, blocks4.1.mlp.fc2.bias, blocks4.2.pos_embed.weight, blocks4.2.pos_embed.bias, blocks4.2.norm1.weight, blocks4.2.norm1.bias, blocks4.2.attn.qkv.weight, blocks4.2.attn.qkv.bias, blocks4.2.attn.proj.weight, blocks4.2.attn.proj.bias, blocks4.2.norm2.weight, blocks4.2.norm2.bias, blocks4.2.mlp.fc1.weight, blocks4.2.mlp.fc1.bias, blocks4.2.mlp.fc2.weight, blocks4.2.mlp.fc2.bias, norm4.weight, norm4.bias

Name of parameter - Initialization information

backbone.patch_embed1.norm.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed1.norm.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed1.proj.weight - torch.Size([64, 3, 4, 4]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed1.proj.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed2.norm.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed2.norm.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed2.proj.weight - torch.Size([128, 64, 2, 2]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed2.proj.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed3.norm.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed3.norm.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed3.proj.weight - torch.Size([320, 128, 2, 2]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed3.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed4.norm.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed4.norm.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed4.proj.weight - torch.Size([512, 320, 2, 2]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.patch_embed4.proj.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.pos_embed.weight - torch.Size([64, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.pos_embed.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.norm1.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.norm1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.conv1.weight - torch.Size([64, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.conv1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.conv2.weight - torch.Size([64, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.conv2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.attn.weight - torch.Size([64, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.attn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.norm2.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.norm2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.mlp.fc1.weight - torch.Size([256, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.mlp.fc1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.mlp.fc2.weight - torch.Size([64, 256, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.0.mlp.fc2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.pos_embed.weight - torch.Size([64, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.pos_embed.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.norm1.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.norm1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.conv1.weight - torch.Size([64, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.conv1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.conv2.weight - torch.Size([64, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.conv2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.attn.weight - torch.Size([64, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.attn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.norm2.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.norm2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.mlp.fc1.weight - torch.Size([256, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.mlp.fc1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.mlp.fc2.weight - torch.Size([64, 256, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.1.mlp.fc2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.pos_embed.weight - torch.Size([64, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.pos_embed.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.norm1.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.norm1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.conv1.weight - torch.Size([64, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.conv1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.conv2.weight - torch.Size([64, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.conv2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.attn.weight - torch.Size([64, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.attn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.norm2.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.norm2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.mlp.fc1.weight - torch.Size([256, 64, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.mlp.fc1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.mlp.fc2.weight - torch.Size([64, 256, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks1.2.mlp.fc2.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.norm1.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.norm1.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.pos_embed.weight - torch.Size([128, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.pos_embed.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.norm1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.norm1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.conv1.weight - torch.Size([128, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.conv1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.conv2.weight - torch.Size([128, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.conv2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.attn.weight - torch.Size([128, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.attn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.norm2.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.norm2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.mlp.fc1.weight - torch.Size([512, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.mlp.fc1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.mlp.fc2.weight - torch.Size([128, 512, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.0.mlp.fc2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.pos_embed.weight - torch.Size([128, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.pos_embed.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.norm1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.norm1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.conv1.weight - torch.Size([128, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.conv1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.conv2.weight - torch.Size([128, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.conv2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.attn.weight - torch.Size([128, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.attn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.norm2.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.norm2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.mlp.fc1.weight - torch.Size([512, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.mlp.fc1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.mlp.fc2.weight - torch.Size([128, 512, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.1.mlp.fc2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.pos_embed.weight - torch.Size([128, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.pos_embed.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.norm1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.norm1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.conv1.weight - torch.Size([128, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.conv1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.conv2.weight - torch.Size([128, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.conv2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.attn.weight - torch.Size([128, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.attn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.norm2.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.norm2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.mlp.fc1.weight - torch.Size([512, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.mlp.fc1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.mlp.fc2.weight - torch.Size([128, 512, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.2.mlp.fc2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.pos_embed.weight - torch.Size([128, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.pos_embed.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.norm1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.norm1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.conv1.weight - torch.Size([128, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.conv1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.conv2.weight - torch.Size([128, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.conv2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.attn.weight - torch.Size([128, 1, 5, 5]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.attn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.norm2.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.norm2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.mlp.fc1.weight - torch.Size([512, 128, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.mlp.fc1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.mlp.fc2.weight - torch.Size([128, 512, 1, 1]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks2.3.mlp.fc2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.norm2.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.norm2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.pos_embed.weight - torch.Size([320, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.pos_embed.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.norm1.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.norm1.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.attn.qkv.weight - torch.Size([960, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.attn.qkv.bias - torch.Size([960]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.attn.proj.weight - torch.Size([320, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.attn.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.norm2.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.norm2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.mlp.fc1.weight - torch.Size([1280, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.mlp.fc1.bias - torch.Size([1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.mlp.fc2.weight - torch.Size([320, 1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.0.mlp.fc2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.pos_embed.weight - torch.Size([320, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.pos_embed.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.norm1.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.norm1.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.attn.qkv.weight - torch.Size([960, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.attn.qkv.bias - torch.Size([960]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.attn.proj.weight - torch.Size([320, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.attn.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.norm2.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.norm2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.mlp.fc1.weight - torch.Size([1280, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.mlp.fc1.bias - torch.Size([1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.mlp.fc2.weight - torch.Size([320, 1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.1.mlp.fc2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.pos_embed.weight - torch.Size([320, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.pos_embed.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.norm1.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.norm1.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.attn.qkv.weight - torch.Size([960, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.attn.qkv.bias - torch.Size([960]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.attn.proj.weight - torch.Size([320, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.attn.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.norm2.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.norm2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.mlp.fc1.weight - torch.Size([1280, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.mlp.fc1.bias - torch.Size([1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.mlp.fc2.weight - torch.Size([320, 1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.2.mlp.fc2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.pos_embed.weight - torch.Size([320, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.pos_embed.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.norm1.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.norm1.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.attn.qkv.weight - torch.Size([960, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.attn.qkv.bias - torch.Size([960]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.attn.proj.weight - torch.Size([320, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.attn.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.norm2.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.norm2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.mlp.fc1.weight - torch.Size([1280, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.mlp.fc1.bias - torch.Size([1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.mlp.fc2.weight - torch.Size([320, 1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.3.mlp.fc2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.pos_embed.weight - torch.Size([320, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.pos_embed.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.norm1.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.norm1.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.attn.qkv.weight - torch.Size([960, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.attn.qkv.bias - torch.Size([960]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.attn.proj.weight - torch.Size([320, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.attn.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.norm2.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.norm2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.mlp.fc1.weight - torch.Size([1280, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.mlp.fc1.bias - torch.Size([1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.mlp.fc2.weight - torch.Size([320, 1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.4.mlp.fc2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.pos_embed.weight - torch.Size([320, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.pos_embed.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.norm1.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.norm1.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.attn.qkv.weight - torch.Size([960, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.attn.qkv.bias - torch.Size([960]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.attn.proj.weight - torch.Size([320, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.attn.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.norm2.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.norm2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.mlp.fc1.weight - torch.Size([1280, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.mlp.fc1.bias - torch.Size([1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.mlp.fc2.weight - torch.Size([320, 1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.5.mlp.fc2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.pos_embed.weight - torch.Size([320, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.pos_embed.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.norm1.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.norm1.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.attn.qkv.weight - torch.Size([960, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.attn.qkv.bias - torch.Size([960]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.attn.proj.weight - torch.Size([320, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.attn.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.norm2.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.norm2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.mlp.fc1.weight - torch.Size([1280, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.mlp.fc1.bias - torch.Size([1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.mlp.fc2.weight - torch.Size([320, 1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.6.mlp.fc2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.pos_embed.weight - torch.Size([320, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.pos_embed.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.norm1.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.norm1.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.attn.qkv.weight - torch.Size([960, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.attn.qkv.bias - torch.Size([960]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.attn.proj.weight - torch.Size([320, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.attn.proj.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.norm2.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.norm2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.mlp.fc1.weight - torch.Size([1280, 320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.mlp.fc1.bias - torch.Size([1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.mlp.fc2.weight - torch.Size([320, 1280]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks3.7.mlp.fc2.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.norm3.weight - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.norm3.bias - torch.Size([320]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.pos_embed.weight - torch.Size([512, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.pos_embed.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.attn.qkv.weight - torch.Size([1536, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.attn.qkv.bias - torch.Size([1536]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.attn.proj.weight - torch.Size([512, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.attn.proj.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.mlp.fc1.weight - torch.Size([2048, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.mlp.fc1.bias - torch.Size([2048]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.mlp.fc2.weight - torch.Size([512, 2048]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.0.mlp.fc2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.pos_embed.weight - torch.Size([512, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.pos_embed.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.attn.qkv.weight - torch.Size([1536, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.attn.qkv.bias - torch.Size([1536]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.attn.proj.weight - torch.Size([512, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.attn.proj.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.mlp.fc1.weight - torch.Size([2048, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.mlp.fc1.bias - torch.Size([2048]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.mlp.fc2.weight - torch.Size([512, 2048]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.1.mlp.fc2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.pos_embed.weight - torch.Size([512, 1, 3, 3]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.pos_embed.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.attn.qkv.weight - torch.Size([1536, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.attn.qkv.bias - torch.Size([1536]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.attn.proj.weight - torch.Size([512, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.attn.proj.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.mlp.fc1.weight - torch.Size([2048, 512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.mlp.fc1.bias - torch.Size([2048]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.mlp.fc2.weight - torch.Size([512, 2048]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.blocks4.2.mlp.fc2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.norm4.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

backbone.norm4.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

head.deconv_layers.0.weight - torch.Size([512, 256, 4, 4]): 
NormalInit: mean=0, std=0.001, bias=0 

head.deconv_layers.1.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

head.deconv_layers.1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

head.deconv_layers.3.weight - torch.Size([256, 256, 4, 4]): 
NormalInit: mean=0, std=0.001, bias=0 

head.deconv_layers.4.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

head.deconv_layers.4.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

head.deconv_layers.6.weight - torch.Size([256, 256, 4, 4]): 
NormalInit: mean=0, std=0.001, bias=0 

head.deconv_layers.7.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

head.deconv_layers.7.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of TopdownPoseEstimator  

head.final_layer.weight - torch.Size([17, 256, 1, 1]): 
NormalInit: mean=0, std=0.001, bias=0 

head.final_layer.bias - torch.Size([17]): 
NormalInit: mean=0, std=0.001, bias=0 
2023/07/22 14:34:10 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
2023/07/22 14:34:10 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
2023/07/22 14:34:10 - mmengine - INFO - Checkpoints will be saved to /root/mmpose/work_dirs/td-hm_uniformer-s-8xb128-210e_coco-256x192.
2023/07/22 14:34:22 - mmengine - INFO - Epoch(train)   [1][  50/4682]  lr: 6.193637e-06  eta: 2 days, 17:10:57  time: 0.238675  data_time: 0.088113  memory: 2769  loss: 0.002342  loss_kpt: 0.002342  acc_pose: 0.025147
2023/07/22 14:34:33 - mmengine - INFO - Epoch(train)   [1][ 100/4682]  lr: 1.244990e-05  eta: 2 days, 13:50:20  time: 0.214210  data_time: 0.070430  memory: 2769  loss: 0.002225  loss_kpt: 0.002225  acc_pose: 0.063447
2023/07/22 14:34:44 - mmengine - INFO - Epoch(train)   [1][ 150/4682]  lr: 1.870616e-05  eta: 2 days, 13:04:57  time: 0.218167  data_time: 0.071140  memory: 2769  loss: 0.002192  loss_kpt: 0.002192  acc_pose: 0.088471
2023/07/22 14:34:56 - mmengine - INFO - Epoch(train)   [1][ 200/4682]  lr: 2.496242e-05  eta: 2 days, 13:38:20  time: 0.231884  data_time: 0.085791  memory: 2769  loss: 0.002208  loss_kpt: 0.002208  acc_pose: 0.068286
2023/07/22 14:35:06 - mmengine - INFO - Epoch(train)   [1][ 250/4682]  lr: 3.121869e-05  eta: 2 days, 13:07:07  time: 0.216263  data_time: 0.070398  memory: 2769  loss: 0.002174  loss_kpt: 0.002174  acc_pose: 0.134264
2023/07/22 14:35:17 - mmengine - INFO - Epoch(train)   [1][ 300/4682]  lr: 3.747495e-05  eta: 2 days, 12:45:42  time: 0.216062  data_time: 0.071069  memory: 2769  loss: 0.002154  loss_kpt: 0.002154  acc_pose: 0.088193
2023/07/22 14:35:28 - mmengine - INFO - Epoch(train)   [1][ 350/4682]  lr: 4.373121e-05  eta: 2 days, 12:29:12  time: 0.215576  data_time: 0.070412  memory: 2769  loss: 0.002122  loss_kpt: 0.002122  acc_pose: 0.120076
2023/07/22 14:35:39 - mmengine - INFO - Epoch(train)   [1][ 400/4682]  lr: 4.998747e-05  eta: 2 days, 12:23:18  time: 0.218757  data_time: 0.069984  memory: 2769  loss: 0.002127  loss_kpt: 0.002127  acc_pose: 0.137982
2023/07/22 14:35:50 - mmengine - INFO - Epoch(train)   [1][ 450/4682]  lr: 5.624374e-05  eta: 2 days, 12:10:00  time: 0.213989  data_time: 0.068708  memory: 2769  loss: 0.002121  loss_kpt: 0.002121  acc_pose: 0.125615
2023/07/22 14:36:01 - mmengine - INFO - Epoch(train)   [1][ 500/4682]  lr: 6.250000e-05  eta: 2 days, 12:24:21  time: 0.229271  data_time: 0.084079  memory: 2769  loss: 0.002046  loss_kpt: 0.002046  acc_pose: 0.100560
2023/07/22 14:36:12 - mmengine - INFO - Epoch(train)   [1][ 550/4682]  lr: 6.250000e-05  eta: 2 days, 12:11:56  time: 0.213064  data_time: 0.067762  memory: 2769  loss: 0.002069  loss_kpt: 0.002069  acc_pose: 0.101174
2023/07/22 14:36:23 - mmengine - INFO - Epoch(train)   [1][ 600/4682]  lr: 6.250000e-05  eta: 2 days, 12:05:39  time: 0.216078  data_time: 0.068032  memory: 2769  loss: 0.002138  loss_kpt: 0.002138  acc_pose: 0.123952
2023/07/22 14:36:34 - mmengine - INFO - Epoch(train)   [1][ 650/4682]  lr: 6.250000e-05  eta: 2 days, 12:11:38  time: 0.225055  data_time: 0.075938  memory: 2769  loss: 0.002037  loss_kpt: 0.002037  acc_pose: 0.181745
2023/07/22 14:36:45 - mmengine - INFO - Epoch(train)   [1][ 700/4682]  lr: 6.250000e-05  eta: 2 days, 12:07:41  time: 0.217328  data_time: 0.070013  memory: 2769  loss: 0.002077  loss_kpt: 0.002077  acc_pose: 0.156886
2023/07/22 14:36:55 - mmengine - INFO - Epoch(train)   [1][ 750/4682]  lr: 6.250000e-05  eta: 2 days, 12:02:47  time: 0.215984  data_time: 0.070167  memory: 2769  loss: 0.002072  loss_kpt: 0.002072  acc_pose: 0.183034
2023/07/22 14:37:06 - mmengine - INFO - Epoch(train)   [1][ 800/4682]  lr: 6.250000e-05  eta: 2 days, 12:01:03  time: 0.218508  data_time: 0.070894  memory: 2769  loss: 0.002069  loss_kpt: 0.002069  acc_pose: 0.116141
2023/07/22 14:37:19 - mmengine - INFO - Epoch(train)   [1][ 850/4682]  lr: 6.250000e-05  eta: 2 days, 12:28:41  time: 0.248821  data_time: 0.101981  memory: 2769  loss: 0.002058  loss_kpt: 0.002058  acc_pose: 0.152378
2023/07/22 14:37:30 - mmengine - INFO - Epoch(train)   [1][ 900/4682]  lr: 6.250000e-05  eta: 2 days, 12:24:00  time: 0.216682  data_time: 0.069223  memory: 2769  loss: 0.002018  loss_kpt: 0.002018  acc_pose: 0.162742
2023/07/22 14:37:41 - mmengine - INFO - Epoch(train)   [1][ 950/4682]  lr: 6.250000e-05  eta: 2 days, 12:24:08  time: 0.221729  data_time: 0.070929  memory: 2769  loss: 0.002030  loss_kpt: 0.002030  acc_pose: 0.157651
2023/07/22 14:37:52 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 14:37:52 - mmengine - INFO - Epoch(train)   [1][1000/4682]  lr: 6.250000e-05  eta: 2 days, 12:22:31  time: 0.219609  data_time: 0.071100  memory: 2769  loss: 0.002040  loss_kpt: 0.002040  acc_pose: 0.212475
2023/07/22 14:38:03 - mmengine - INFO - Epoch(train)   [1][1050/4682]  lr: 6.250000e-05  eta: 2 days, 12:19:44  time: 0.217952  data_time: 0.070976  memory: 2769  loss: 0.002039  loss_kpt: 0.002039  acc_pose: 0.177589
2023/07/22 14:38:14 - mmengine - INFO - Epoch(train)   [1][1100/4682]  lr: 6.250000e-05  eta: 2 days, 12:17:50  time: 0.218819  data_time: 0.071152  memory: 2769  loss: 0.001997  loss_kpt: 0.001997  acc_pose: 0.181745
2023/07/22 14:38:24 - mmengine - INFO - Epoch(train)   [1][1150/4682]  lr: 6.250000e-05  eta: 2 days, 12:12:54  time: 0.214366  data_time: 0.069868  memory: 2769  loss: 0.002044  loss_kpt: 0.002044  acc_pose: 0.247371
2023/07/22 14:38:35 - mmengine - INFO - Epoch(train)   [1][1200/4682]  lr: 6.250000e-05  eta: 2 days, 12:09:43  time: 0.216320  data_time: 0.070588  memory: 2769  loss: 0.001998  loss_kpt: 0.001998  acc_pose: 0.182767
2023/07/22 14:38:46 - mmengine - INFO - Epoch(train)   [1][1250/4682]  lr: 6.250000e-05  eta: 2 days, 12:10:15  time: 0.221654  data_time: 0.072237  memory: 2769  loss: 0.002001  loss_kpt: 0.002001  acc_pose: 0.192956
2023/07/22 14:38:57 - mmengine - INFO - Epoch(train)   [1][1300/4682]  lr: 6.250000e-05  eta: 2 days, 12:07:12  time: 0.216048  data_time: 0.067855  memory: 2769  loss: 0.001997  loss_kpt: 0.001997  acc_pose: 0.205860
2023/07/22 14:39:08 - mmengine - INFO - Epoch(train)   [1][1350/4682]  lr: 6.250000e-05  eta: 2 days, 12:05:19  time: 0.217611  data_time: 0.069244  memory: 2769  loss: 0.002010  loss_kpt: 0.002010  acc_pose: 0.177655
2023/07/22 14:39:19 - mmengine - INFO - Epoch(train)   [1][1400/4682]  lr: 6.250000e-05  eta: 2 days, 12:03:52  time: 0.218147  data_time: 0.071651  memory: 2769  loss: 0.002012  loss_kpt: 0.002012  acc_pose: 0.169542
2023/07/22 14:39:30 - mmengine - INFO - Epoch(train)   [1][1450/4682]  lr: 6.250000e-05  eta: 2 days, 12:03:20  time: 0.219611  data_time: 0.072449  memory: 2769  loss: 0.001992  loss_kpt: 0.001992  acc_pose: 0.252034
2023/07/22 14:39:41 - mmengine - INFO - Epoch(train)   [1][1500/4682]  lr: 6.250000e-05  eta: 2 days, 12:09:23  time: 0.231631  data_time: 0.084115  memory: 2769  loss: 0.002023  loss_kpt: 0.002023  acc_pose: 0.185021
2023/07/22 14:39:52 - mmengine - INFO - Epoch(train)   [1][1550/4682]  lr: 6.250000e-05  eta: 2 days, 12:07:28  time: 0.217318  data_time: 0.070157  memory: 2769  loss: 0.001949  loss_kpt: 0.001949  acc_pose: 0.195677
2023/07/22 14:40:03 - mmengine - INFO - Epoch(train)   [1][1600/4682]  lr: 6.250000e-05  eta: 2 days, 12:05:48  time: 0.217586  data_time: 0.070765  memory: 2769  loss: 0.002003  loss_kpt: 0.002003  acc_pose: 0.202623
2023/07/22 14:40:14 - mmengine - INFO - Epoch(train)   [1][1650/4682]  lr: 6.250000e-05  eta: 2 days, 12:04:59  time: 0.219127  data_time: 0.071461  memory: 2769  loss: 0.001976  loss_kpt: 0.001976  acc_pose: 0.170747
2023/07/22 14:40:25 - mmengine - INFO - Epoch(train)   [1][1700/4682]  lr: 6.250000e-05  eta: 2 days, 12:05:16  time: 0.221335  data_time: 0.070725  memory: 2769  loss: 0.001958  loss_kpt: 0.001958  acc_pose: 0.270792
2023/07/22 14:40:36 - mmengine - INFO - Epoch(train)   [1][1750/4682]  lr: 6.250000e-05  eta: 2 days, 12:02:18  time: 0.214426  data_time: 0.068187  memory: 2769  loss: 0.001904  loss_kpt: 0.001904  acc_pose: 0.204290
2023/07/22 14:40:47 - mmengine - INFO - Epoch(train)   [1][1800/4682]  lr: 6.250000e-05  eta: 2 days, 12:01:06  time: 0.217989  data_time: 0.070411  memory: 2769  loss: 0.001948  loss_kpt: 0.001948  acc_pose: 0.202963
2023/07/22 14:40:57 - mmengine - INFO - Epoch(train)   [1][1850/4682]  lr: 6.250000e-05  eta: 2 days, 11:58:54  time: 0.215582  data_time: 0.068443  memory: 2769  loss: 0.001918  loss_kpt: 0.001918  acc_pose: 0.250597
2023/07/22 14:41:08 - mmengine - INFO - Epoch(train)   [1][1900/4682]  lr: 6.250000e-05  eta: 2 days, 11:57:27  time: 0.217119  data_time: 0.070256  memory: 2769  loss: 0.001875  loss_kpt: 0.001875  acc_pose: 0.208480
2023/07/22 14:41:19 - mmengine - INFO - Epoch(train)   [1][1950/4682]  lr: 6.250000e-05  eta: 2 days, 11:57:06  time: 0.219533  data_time: 0.069367  memory: 2769  loss: 0.001934  loss_kpt: 0.001934  acc_pose: 0.274872
2023/07/22 14:41:30 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 14:41:30 - mmengine - INFO - Epoch(train)   [1][2000/4682]  lr: 6.250000e-05  eta: 2 days, 11:55:49  time: 0.217253  data_time: 0.068804  memory: 2769  loss: 0.001888  loss_kpt: 0.001888  acc_pose: 0.344551
2023/07/22 14:41:41 - mmengine - INFO - Epoch(train)   [1][2050/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:12  time: 0.213803  data_time: 0.066914  memory: 2769  loss: 0.001902  loss_kpt: 0.001902  acc_pose: 0.159386
2023/07/22 14:41:52 - mmengine - INFO - Epoch(train)   [1][2100/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:51  time: 0.216723  data_time: 0.069914  memory: 2769  loss: 0.001916  loss_kpt: 0.001916  acc_pose: 0.237694
2023/07/22 14:42:03 - mmengine - INFO - Epoch(train)   [1][2150/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:26  time: 0.216423  data_time: 0.069900  memory: 2769  loss: 0.001918  loss_kpt: 0.001918  acc_pose: 0.260449
2023/07/22 14:42:13 - mmengine - INFO - Epoch(train)   [1][2200/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:06  time: 0.216480  data_time: 0.069685  memory: 2769  loss: 0.001904  loss_kpt: 0.001904  acc_pose: 0.303737
2023/07/22 14:42:24 - mmengine - INFO - Epoch(train)   [1][2250/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:44  time: 0.219002  data_time: 0.070634  memory: 2769  loss: 0.001892  loss_kpt: 0.001892  acc_pose: 0.248940
2023/07/22 14:42:36 - mmengine - INFO - Epoch(train)   [1][2300/4682]  lr: 6.250000e-05  eta: 2 days, 11:54:11  time: 0.235337  data_time: 0.086533  memory: 2769  loss: 0.001909  loss_kpt: 0.001909  acc_pose: 0.245801
2023/07/22 14:42:47 - mmengine - INFO - Epoch(train)   [1][2350/4682]  lr: 6.250000e-05  eta: 2 days, 11:54:04  time: 0.220070  data_time: 0.072084  memory: 2769  loss: 0.001897  loss_kpt: 0.001897  acc_pose: 0.228306
2023/07/22 14:42:58 - mmengine - INFO - Epoch(train)   [1][2400/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:57  time: 0.217078  data_time: 0.068393  memory: 2769  loss: 0.001846  loss_kpt: 0.001846  acc_pose: 0.324069
2023/07/22 14:43:09 - mmengine - INFO - Epoch(train)   [1][2450/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:16  time: 0.218289  data_time: 0.070318  memory: 2769  loss: 0.001890  loss_kpt: 0.001890  acc_pose: 0.288004
2023/07/22 14:43:20 - mmengine - INFO - Epoch(train)   [1][2500/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:27  time: 0.217856  data_time: 0.070853  memory: 2769  loss: 0.001838  loss_kpt: 0.001838  acc_pose: 0.192583
2023/07/22 14:43:31 - mmengine - INFO - Epoch(train)   [1][2550/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:23  time: 0.216951  data_time: 0.069948  memory: 2769  loss: 0.001860  loss_kpt: 0.001860  acc_pose: 0.141178
2023/07/22 14:43:42 - mmengine - INFO - Epoch(train)   [1][2600/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:43  time: 0.230845  data_time: 0.082083  memory: 2769  loss: 0.001872  loss_kpt: 0.001872  acc_pose: 0.358413
2023/07/22 14:43:53 - mmengine - INFO - Epoch(train)   [1][2650/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:38  time: 0.216960  data_time: 0.067662  memory: 2769  loss: 0.001869  loss_kpt: 0.001869  acc_pose: 0.262805
2023/07/22 14:44:04 - mmengine - INFO - Epoch(train)   [1][2700/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:27  time: 0.219842  data_time: 0.070447  memory: 2769  loss: 0.001856  loss_kpt: 0.001856  acc_pose: 0.302262
2023/07/22 14:44:15 - mmengine - INFO - Epoch(train)   [1][2750/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:47  time: 0.214871  data_time: 0.066616  memory: 2769  loss: 0.001869  loss_kpt: 0.001869  acc_pose: 0.253689
2023/07/22 14:44:26 - mmengine - INFO - Epoch(train)   [1][2800/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:37  time: 0.216359  data_time: 0.068392  memory: 2769  loss: 0.001831  loss_kpt: 0.001831  acc_pose: 0.297514
2023/07/22 14:44:36 - mmengine - INFO - Epoch(train)   [1][2850/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:25  time: 0.216140  data_time: 0.067781  memory: 2769  loss: 0.001805  loss_kpt: 0.001805  acc_pose: 0.306196
2023/07/22 14:44:48 - mmengine - INFO - Epoch(train)   [1][2900/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:33  time: 0.238467  data_time: 0.088258  memory: 2769  loss: 0.001801  loss_kpt: 0.001801  acc_pose: 0.312398
2023/07/22 14:44:59 - mmengine - INFO - Epoch(train)   [1][2950/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:38  time: 0.220876  data_time: 0.071713  memory: 2769  loss: 0.001849  loss_kpt: 0.001849  acc_pose: 0.312699
2023/07/22 14:45:10 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 14:45:10 - mmengine - INFO - Epoch(train)   [1][3000/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:36  time: 0.220552  data_time: 0.070842  memory: 2769  loss: 0.001837  loss_kpt: 0.001837  acc_pose: 0.216881
2023/07/22 14:45:21 - mmengine - INFO - Epoch(train)   [1][3050/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:20  time: 0.219633  data_time: 0.069806  memory: 2769  loss: 0.001814  loss_kpt: 0.001814  acc_pose: 0.309035
2023/07/22 14:45:32 - mmengine - INFO - Epoch(train)   [1][3100/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:54  time: 0.219044  data_time: 0.071651  memory: 2769  loss: 0.001802  loss_kpt: 0.001802  acc_pose: 0.220713
2023/07/22 14:45:43 - mmengine - INFO - Epoch(train)   [1][3150/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:56  time: 0.216899  data_time: 0.068939  memory: 2769  loss: 0.001801  loss_kpt: 0.001801  acc_pose: 0.334904
2023/07/22 14:45:54 - mmengine - INFO - Epoch(train)   [1][3200/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:20  time: 0.218274  data_time: 0.071068  memory: 2769  loss: 0.001809  loss_kpt: 0.001809  acc_pose: 0.260054
2023/07/22 14:46:05 - mmengine - INFO - Epoch(train)   [1][3250/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:28  time: 0.217118  data_time: 0.068969  memory: 2769  loss: 0.001812  loss_kpt: 0.001812  acc_pose: 0.250341
2023/07/22 14:46:16 - mmengine - INFO - Epoch(train)   [1][3300/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:52  time: 0.218194  data_time: 0.070332  memory: 2769  loss: 0.001810  loss_kpt: 0.001810  acc_pose: 0.296835
2023/07/22 14:46:26 - mmengine - INFO - Epoch(train)   [1][3350/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:04  time: 0.213188  data_time: 0.064518  memory: 2769  loss: 0.001807  loss_kpt: 0.001807  acc_pose: 0.303540
2023/07/22 14:46:38 - mmengine - INFO - Epoch(train)   [1][3400/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:11  time: 0.233414  data_time: 0.085296  memory: 2769  loss: 0.001820  loss_kpt: 0.001820  acc_pose: 0.321955
2023/07/22 14:46:49 - mmengine - INFO - Epoch(train)   [1][3450/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:17  time: 0.216865  data_time: 0.067705  memory: 2769  loss: 0.001838  loss_kpt: 0.001838  acc_pose: 0.429913
2023/07/22 14:47:00 - mmengine - INFO - Epoch(train)   [1][3500/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:18  time: 0.220740  data_time: 0.072034  memory: 2769  loss: 0.001760  loss_kpt: 0.001760  acc_pose: 0.327256
2023/07/22 14:47:11 - mmengine - INFO - Epoch(train)   [1][3550/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:22  time: 0.225282  data_time: 0.073994  memory: 2769  loss: 0.001783  loss_kpt: 0.001783  acc_pose: 0.312063
2023/07/22 14:47:22 - mmengine - INFO - Epoch(train)   [1][3600/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:50  time: 0.218429  data_time: 0.070870  memory: 2769  loss: 0.001785  loss_kpt: 0.001785  acc_pose: 0.276079
2023/07/22 14:47:33 - mmengine - INFO - Epoch(train)   [1][3650/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:37  time: 0.219818  data_time: 0.070142  memory: 2769  loss: 0.001771  loss_kpt: 0.001771  acc_pose: 0.268007
2023/07/22 14:47:45 - mmengine - INFO - Epoch(train)   [1][3700/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:25  time: 0.233418  data_time: 0.084324  memory: 2769  loss: 0.001789  loss_kpt: 0.001789  acc_pose: 0.282685
2023/07/22 14:47:56 - mmengine - INFO - Epoch(train)   [1][3750/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:16  time: 0.215661  data_time: 0.068282  memory: 2769  loss: 0.001733  loss_kpt: 0.001733  acc_pose: 0.289396
2023/07/22 14:48:07 - mmengine - INFO - Epoch(train)   [1][3800/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:05  time: 0.220118  data_time: 0.071339  memory: 2769  loss: 0.001777  loss_kpt: 0.001777  acc_pose: 0.330327
2023/07/22 14:48:18 - mmengine - INFO - Epoch(train)   [1][3850/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:53  time: 0.219957  data_time: 0.071816  memory: 2769  loss: 0.001771  loss_kpt: 0.001771  acc_pose: 0.306766
2023/07/22 14:48:29 - mmengine - INFO - Epoch(train)   [1][3900/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:44  time: 0.220199  data_time: 0.070299  memory: 2769  loss: 0.001763  loss_kpt: 0.001763  acc_pose: 0.408379
2023/07/22 14:48:40 - mmengine - INFO - Epoch(train)   [1][3950/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:41  time: 0.220680  data_time: 0.071206  memory: 2769  loss: 0.001723  loss_kpt: 0.001723  acc_pose: 0.331735
2023/07/22 14:48:51 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 14:48:51 - mmengine - INFO - Epoch(train)   [1][4000/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:02  time: 0.217765  data_time: 0.067463  memory: 2769  loss: 0.001758  loss_kpt: 0.001758  acc_pose: 0.276604
2023/07/22 14:49:01 - mmengine - INFO - Epoch(train)   [1][4050/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:21  time: 0.217575  data_time: 0.069876  memory: 2769  loss: 0.001735  loss_kpt: 0.001735  acc_pose: 0.264040
2023/07/22 14:49:12 - mmengine - INFO - Epoch(train)   [1][4100/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:06  time: 0.219668  data_time: 0.071190  memory: 2769  loss: 0.001745  loss_kpt: 0.001745  acc_pose: 0.303010
2023/07/22 14:49:23 - mmengine - INFO - Epoch(train)   [1][4150/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:07  time: 0.215914  data_time: 0.067799  memory: 2769  loss: 0.001749  loss_kpt: 0.001749  acc_pose: 0.337708
2023/07/22 14:49:34 - mmengine - INFO - Epoch(train)   [1][4200/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:51  time: 0.219527  data_time: 0.072417  memory: 2769  loss: 0.001757  loss_kpt: 0.001757  acc_pose: 0.374240
2023/07/22 14:49:46 - mmengine - INFO - Epoch(train)   [1][4250/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:34  time: 0.229830  data_time: 0.082045  memory: 2769  loss: 0.001735  loss_kpt: 0.001735  acc_pose: 0.337551
2023/07/22 14:49:57 - mmengine - INFO - Epoch(train)   [1][4300/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:57  time: 0.217770  data_time: 0.069308  memory: 2769  loss: 0.001732  loss_kpt: 0.001732  acc_pose: 0.346927
2023/07/22 14:50:07 - mmengine - INFO - Epoch(train)   [1][4350/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:09  time: 0.216777  data_time: 0.069380  memory: 2769  loss: 0.001723  loss_kpt: 0.001723  acc_pose: 0.381805
2023/07/22 14:50:18 - mmengine - INFO - Epoch(train)   [1][4400/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:13  time: 0.215922  data_time: 0.067738  memory: 2769  loss: 0.001733  loss_kpt: 0.001733  acc_pose: 0.369610
2023/07/22 14:50:29 - mmengine - INFO - Epoch(train)   [1][4450/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:20  time: 0.221661  data_time: 0.070542  memory: 2769  loss: 0.001743  loss_kpt: 0.001743  acc_pose: 0.432129
2023/07/22 14:50:40 - mmengine - INFO - Epoch(train)   [1][4500/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:48  time: 0.218011  data_time: 0.069297  memory: 2769  loss: 0.001714  loss_kpt: 0.001714  acc_pose: 0.343639
2023/07/22 14:50:51 - mmengine - INFO - Epoch(train)   [1][4550/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:11  time: 0.217507  data_time: 0.069888  memory: 2769  loss: 0.001740  loss_kpt: 0.001740  acc_pose: 0.400089
2023/07/22 14:51:02 - mmengine - INFO - Epoch(train)   [1][4600/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:46  time: 0.218590  data_time: 0.070766  memory: 2769  loss: 0.001733  loss_kpt: 0.001733  acc_pose: 0.294756
2023/07/22 14:51:13 - mmengine - INFO - Epoch(train)   [1][4650/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:15  time: 0.218027  data_time: 0.069461  memory: 2769  loss: 0.001701  loss_kpt: 0.001701  acc_pose: 0.332855
2023/07/22 14:51:20 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 14:51:31 - mmengine - INFO - Epoch(train)   [2][  50/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:26  time: 0.225645  data_time: 0.075325  memory: 2769  loss: 0.001687  loss_kpt: 0.001687  acc_pose: 0.337012
2023/07/22 14:51:42 - mmengine - INFO - Epoch(train)   [2][ 100/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:55  time: 0.223896  data_time: 0.072022  memory: 2769  loss: 0.001708  loss_kpt: 0.001708  acc_pose: 0.409553
2023/07/22 14:51:54 - mmengine - INFO - Epoch(train)   [2][ 150/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:22  time: 0.223711  data_time: 0.073127  memory: 2769  loss: 0.001702  loss_kpt: 0.001702  acc_pose: 0.340780
2023/07/22 14:52:05 - mmengine - INFO - Epoch(train)   [2][ 200/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:35  time: 0.234287  data_time: 0.085480  memory: 2769  loss: 0.001711  loss_kpt: 0.001711  acc_pose: 0.273863
2023/07/22 14:52:16 - mmengine - INFO - Epoch(train)   [2][ 250/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:30  time: 0.220832  data_time: 0.072730  memory: 2769  loss: 0.001690  loss_kpt: 0.001690  acc_pose: 0.433614
2023/07/22 14:52:27 - mmengine - INFO - Epoch(train)   [2][ 300/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:15  time: 0.219682  data_time: 0.070356  memory: 2769  loss: 0.001731  loss_kpt: 0.001731  acc_pose: 0.315223
2023/07/22 14:52:31 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 14:52:38 - mmengine - INFO - Epoch(train)   [2][ 350/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:05  time: 0.220255  data_time: 0.069510  memory: 2769  loss: 0.001698  loss_kpt: 0.001698  acc_pose: 0.330739
2023/07/22 14:52:49 - mmengine - INFO - Epoch(train)   [2][ 400/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:45  time: 0.219247  data_time: 0.069325  memory: 2769  loss: 0.001724  loss_kpt: 0.001724  acc_pose: 0.337666
2023/07/22 14:53:01 - mmengine - INFO - Epoch(train)   [2][ 450/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:30  time: 0.232288  data_time: 0.083366  memory: 2769  loss: 0.001677  loss_kpt: 0.001677  acc_pose: 0.298622
2023/07/22 14:53:12 - mmengine - INFO - Epoch(train)   [2][ 500/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:27  time: 0.227438  data_time: 0.078858  memory: 2769  loss: 0.001654  loss_kpt: 0.001654  acc_pose: 0.362969
2023/07/22 14:53:23 - mmengine - INFO - Epoch(train)   [2][ 550/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:50  time: 0.217559  data_time: 0.068187  memory: 2769  loss: 0.001678  loss_kpt: 0.001678  acc_pose: 0.286935
2023/07/22 14:53:35 - mmengine - INFO - Epoch(train)   [2][ 600/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:01  time: 0.235668  data_time: 0.070694  memory: 2769  loss: 0.001674  loss_kpt: 0.001674  acc_pose: 0.399984
2023/07/22 14:53:46 - mmengine - INFO - Epoch(train)   [2][ 650/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:46  time: 0.220053  data_time: 0.071188  memory: 2769  loss: 0.001672  loss_kpt: 0.001672  acc_pose: 0.365492
2023/07/22 14:53:57 - mmengine - INFO - Epoch(train)   [2][ 700/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:57  time: 0.216242  data_time: 0.066703  memory: 2769  loss: 0.001661  loss_kpt: 0.001661  acc_pose: 0.302438
2023/07/22 14:54:08 - mmengine - INFO - Epoch(train)   [2][ 750/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:47  time: 0.220451  data_time: 0.071079  memory: 2769  loss: 0.001660  loss_kpt: 0.001660  acc_pose: 0.423986
2023/07/22 14:54:19 - mmengine - INFO - Epoch(train)   [2][ 800/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:47  time: 0.221668  data_time: 0.072982  memory: 2769  loss: 0.001686  loss_kpt: 0.001686  acc_pose: 0.256086
2023/07/22 14:54:30 - mmengine - INFO - Epoch(train)   [2][ 850/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:04  time: 0.216771  data_time: 0.067996  memory: 2769  loss: 0.001644  loss_kpt: 0.001644  acc_pose: 0.331493
2023/07/22 14:54:41 - mmengine - INFO - Epoch(train)   [2][ 900/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:53  time: 0.220389  data_time: 0.070694  memory: 2769  loss: 0.001686  loss_kpt: 0.001686  acc_pose: 0.356108
2023/07/22 14:54:52 - mmengine - INFO - Epoch(train)   [2][ 950/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:16  time: 0.217441  data_time: 0.068453  memory: 2769  loss: 0.001645  loss_kpt: 0.001645  acc_pose: 0.416431
2023/07/22 14:55:02 - mmengine - INFO - Epoch(train)   [2][1000/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:32  time: 0.216543  data_time: 0.068501  memory: 2769  loss: 0.001684  loss_kpt: 0.001684  acc_pose: 0.459218
2023/07/22 14:55:13 - mmengine - INFO - Epoch(train)   [2][1050/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:11  time: 0.219055  data_time: 0.070455  memory: 2769  loss: 0.001674  loss_kpt: 0.001674  acc_pose: 0.400068
2023/07/22 14:55:24 - mmengine - INFO - Epoch(train)   [2][1100/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:26  time: 0.223386  data_time: 0.070582  memory: 2769  loss: 0.001666  loss_kpt: 0.001666  acc_pose: 0.369216
2023/07/22 14:55:36 - mmengine - INFO - Epoch(train)   [2][1150/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:20  time: 0.220941  data_time: 0.069895  memory: 2769  loss: 0.001662  loss_kpt: 0.001662  acc_pose: 0.378371
2023/07/22 14:55:47 - mmengine - INFO - Epoch(train)   [2][1200/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:56  time: 0.233237  data_time: 0.083369  memory: 2769  loss: 0.001662  loss_kpt: 0.001662  acc_pose: 0.401072
2023/07/22 14:55:58 - mmengine - INFO - Epoch(train)   [2][1250/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:19  time: 0.217277  data_time: 0.068816  memory: 2769  loss: 0.001647  loss_kpt: 0.001647  acc_pose: 0.406680
2023/07/22 14:56:09 - mmengine - INFO - Epoch(train)   [2][1300/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:59  time: 0.219428  data_time: 0.068816  memory: 2769  loss: 0.001606  loss_kpt: 0.001606  acc_pose: 0.403298
2023/07/22 14:56:13 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 14:56:20 - mmengine - INFO - Epoch(train)   [2][1350/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:27  time: 0.217827  data_time: 0.068824  memory: 2769  loss: 0.001631  loss_kpt: 0.001631  acc_pose: 0.400999
2023/07/22 14:56:31 - mmengine - INFO - Epoch(train)   [2][1400/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:07  time: 0.219201  data_time: 0.067744  memory: 2769  loss: 0.001673  loss_kpt: 0.001673  acc_pose: 0.393899
2023/07/22 14:56:42 - mmengine - INFO - Epoch(train)   [2][1450/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:01  time: 0.221023  data_time: 0.070715  memory: 2769  loss: 0.001647  loss_kpt: 0.001647  acc_pose: 0.310082
2023/07/22 14:56:53 - mmengine - INFO - Epoch(train)   [2][1500/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:49  time: 0.220228  data_time: 0.070339  memory: 2769  loss: 0.001657  loss_kpt: 0.001657  acc_pose: 0.389150
2023/07/22 14:57:04 - mmengine - INFO - Epoch(train)   [2][1550/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:03  time: 0.215965  data_time: 0.066791  memory: 2769  loss: 0.001663  loss_kpt: 0.001663  acc_pose: 0.416658
2023/07/22 14:57:15 - mmengine - INFO - Epoch(train)   [2][1600/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:41  time: 0.234430  data_time: 0.068986  memory: 2769  loss: 0.001657  loss_kpt: 0.001657  acc_pose: 0.388935
2023/07/22 14:57:26 - mmengine - INFO - Epoch(train)   [2][1650/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:02  time: 0.216811  data_time: 0.067282  memory: 2769  loss: 0.001636  loss_kpt: 0.001636  acc_pose: 0.377669
2023/07/22 14:57:37 - mmengine - INFO - Epoch(train)   [2][1700/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:46  time: 0.219776  data_time: 0.068558  memory: 2769  loss: 0.001636  loss_kpt: 0.001636  acc_pose: 0.409638
2023/07/22 14:57:48 - mmengine - INFO - Epoch(train)   [2][1750/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:21  time: 0.218620  data_time: 0.070469  memory: 2769  loss: 0.001649  loss_kpt: 0.001649  acc_pose: 0.349039
2023/07/22 14:57:59 - mmengine - INFO - Epoch(train)   [2][1800/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:04  time: 0.219574  data_time: 0.070088  memory: 2769  loss: 0.001590  loss_kpt: 0.001590  acc_pose: 0.519241
2023/07/22 14:58:10 - mmengine - INFO - Epoch(train)   [2][1850/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:28  time: 0.217086  data_time: 0.067470  memory: 2769  loss: 0.001633  loss_kpt: 0.001633  acc_pose: 0.438281
2023/07/22 14:58:21 - mmengine - INFO - Epoch(train)   [2][1900/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:04  time: 0.218644  data_time: 0.070237  memory: 2769  loss: 0.001624  loss_kpt: 0.001624  acc_pose: 0.376196
2023/07/22 14:58:32 - mmengine - INFO - Epoch(train)   [2][1950/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:56  time: 0.220802  data_time: 0.069396  memory: 2769  loss: 0.001622  loss_kpt: 0.001622  acc_pose: 0.458305
2023/07/22 14:58:43 - mmengine - INFO - Epoch(train)   [2][2000/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:42  time: 0.220006  data_time: 0.069541  memory: 2769  loss: 0.001628  loss_kpt: 0.001628  acc_pose: 0.244268
2023/07/22 14:58:54 - mmengine - INFO - Epoch(train)   [2][2050/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:12  time: 0.217762  data_time: 0.068479  memory: 2769  loss: 0.001629  loss_kpt: 0.001629  acc_pose: 0.360035
2023/07/22 14:59:05 - mmengine - INFO - Epoch(train)   [2][2100/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:33  time: 0.216387  data_time: 0.067631  memory: 2769  loss: 0.001620  loss_kpt: 0.001620  acc_pose: 0.432375
2023/07/22 14:59:16 - mmengine - INFO - Epoch(train)   [2][2150/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:02  time: 0.217608  data_time: 0.067910  memory: 2769  loss: 0.001639  loss_kpt: 0.001639  acc_pose: 0.461053
2023/07/22 14:59:27 - mmengine - INFO - Epoch(train)   [2][2200/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:20  time: 0.232736  data_time: 0.083698  memory: 2769  loss: 0.001621  loss_kpt: 0.001621  acc_pose: 0.363318
2023/07/22 14:59:38 - mmengine - INFO - Epoch(train)   [2][2250/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:09  time: 0.220476  data_time: 0.069601  memory: 2769  loss: 0.001612  loss_kpt: 0.001612  acc_pose: 0.423775
2023/07/22 14:59:49 - mmengine - INFO - Epoch(train)   [2][2300/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:40  time: 0.217828  data_time: 0.067601  memory: 2769  loss: 0.001628  loss_kpt: 0.001628  acc_pose: 0.288921
2023/07/22 14:59:53 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:00:00 - mmengine - INFO - Epoch(train)   [2][2350/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:54  time: 0.215318  data_time: 0.066771  memory: 2769  loss: 0.001574  loss_kpt: 0.001574  acc_pose: 0.390623
2023/07/22 15:00:11 - mmengine - INFO - Epoch(train)   [2][2400/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:45  time: 0.220696  data_time: 0.070551  memory: 2769  loss: 0.001605  loss_kpt: 0.001605  acc_pose: 0.384320
2023/07/22 15:00:22 - mmengine - INFO - Epoch(train)   [2][2450/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:45  time: 0.221823  data_time: 0.069686  memory: 2769  loss: 0.001636  loss_kpt: 0.001636  acc_pose: 0.353418
2023/07/22 15:00:33 - mmengine - INFO - Epoch(train)   [2][2500/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:15  time: 0.217565  data_time: 0.069915  memory: 2769  loss: 0.001615  loss_kpt: 0.001615  acc_pose: 0.316838
2023/07/22 15:00:44 - mmengine - INFO - Epoch(train)   [2][2550/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:58  time: 0.219580  data_time: 0.069860  memory: 2769  loss: 0.001571  loss_kpt: 0.001571  acc_pose: 0.376523
2023/07/22 15:00:56 - mmengine - INFO - Epoch(train)   [2][2600/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:16  time: 0.233512  data_time: 0.069192  memory: 2769  loss: 0.001588  loss_kpt: 0.001588  acc_pose: 0.480474
2023/07/22 15:01:06 - mmengine - INFO - Epoch(train)   [2][2650/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:29  time: 0.215090  data_time: 0.066871  memory: 2769  loss: 0.001616  loss_kpt: 0.001616  acc_pose: 0.384666
2023/07/22 15:01:17 - mmengine - INFO - Epoch(train)   [2][2700/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:47  time: 0.215675  data_time: 0.067341  memory: 2769  loss: 0.001601  loss_kpt: 0.001601  acc_pose: 0.381176
2023/07/22 15:01:28 - mmengine - INFO - Epoch(train)   [2][2750/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:21  time: 0.217987  data_time: 0.068197  memory: 2769  loss: 0.001593  loss_kpt: 0.001593  acc_pose: 0.439409
2023/07/22 15:01:39 - mmengine - INFO - Epoch(train)   [2][2800/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:34  time: 0.224119  data_time: 0.071853  memory: 2769  loss: 0.001582  loss_kpt: 0.001582  acc_pose: 0.358909
2023/07/22 15:01:50 - mmengine - INFO - Epoch(train)   [2][2850/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:36  time: 0.222361  data_time: 0.071014  memory: 2769  loss: 0.001562  loss_kpt: 0.001562  acc_pose: 0.423431
2023/07/22 15:02:01 - mmengine - INFO - Epoch(train)   [2][2900/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:09  time: 0.217877  data_time: 0.067547  memory: 2769  loss: 0.001635  loss_kpt: 0.001635  acc_pose: 0.434224
2023/07/22 15:02:12 - mmengine - INFO - Epoch(train)   [2][2950/4682]  lr: 6.250000e-05  eta: 2 days, 11:42:45  time: 0.218263  data_time: 0.069244  memory: 2769  loss: 0.001584  loss_kpt: 0.001584  acc_pose: 0.392643
2023/07/22 15:02:23 - mmengine - INFO - Epoch(train)   [2][3000/4682]  lr: 6.250000e-05  eta: 2 days, 11:42:50  time: 0.222961  data_time: 0.072229  memory: 2769  loss: 0.001605  loss_kpt: 0.001605  acc_pose: 0.488786
2023/07/22 15:02:34 - mmengine - INFO - Epoch(train)   [2][3050/4682]  lr: 6.250000e-05  eta: 2 days, 11:42:46  time: 0.221490  data_time: 0.070006  memory: 2769  loss: 0.001579  loss_kpt: 0.001579  acc_pose: 0.462665
2023/07/22 15:02:46 - mmengine - INFO - Epoch(train)   [2][3100/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:03  time: 0.224848  data_time: 0.072710  memory: 2769  loss: 0.001588  loss_kpt: 0.001588  acc_pose: 0.490568
2023/07/22 15:02:57 - mmengine - INFO - Epoch(train)   [2][3150/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:02  time: 0.221932  data_time: 0.069415  memory: 2769  loss: 0.001578  loss_kpt: 0.001578  acc_pose: 0.445818
2023/07/22 15:03:09 - mmengine - INFO - Epoch(train)   [2][3200/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:42  time: 0.238345  data_time: 0.085177  memory: 2769  loss: 0.001583  loss_kpt: 0.001583  acc_pose: 0.465223
2023/07/22 15:03:20 - mmengine - INFO - Epoch(train)   [2][3250/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:50  time: 0.223617  data_time: 0.072932  memory: 2769  loss: 0.001555  loss_kpt: 0.001555  acc_pose: 0.393350
2023/07/22 15:03:31 - mmengine - INFO - Epoch(train)   [2][3300/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:39  time: 0.220582  data_time: 0.071421  memory: 2769  loss: 0.001553  loss_kpt: 0.001553  acc_pose: 0.497468
2023/07/22 15:03:35 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:03:44 - mmengine - INFO - Epoch(train)   [2][3350/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:42  time: 0.252405  data_time: 0.102398  memory: 2769  loss: 0.001547  loss_kpt: 0.001547  acc_pose: 0.425660
2023/07/22 15:03:54 - mmengine - INFO - Epoch(train)   [2][3400/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:07  time: 0.216904  data_time: 0.069343  memory: 2769  loss: 0.001575  loss_kpt: 0.001575  acc_pose: 0.432068
2023/07/22 15:04:05 - mmengine - INFO - Epoch(train)   [2][3450/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:22  time: 0.214931  data_time: 0.066422  memory: 2769  loss: 0.001567  loss_kpt: 0.001567  acc_pose: 0.369169
2023/07/22 15:04:16 - mmengine - INFO - Epoch(train)   [2][3500/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:24  time: 0.222887  data_time: 0.071557  memory: 2769  loss: 0.001578  loss_kpt: 0.001578  acc_pose: 0.364742
2023/07/22 15:04:27 - mmengine - INFO - Epoch(train)   [2][3550/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:43  time: 0.215626  data_time: 0.068208  memory: 2769  loss: 0.001581  loss_kpt: 0.001581  acc_pose: 0.356414
2023/07/22 15:04:39 - mmengine - INFO - Epoch(train)   [2][3600/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:53  time: 0.234540  data_time: 0.071323  memory: 2769  loss: 0.001563  loss_kpt: 0.001563  acc_pose: 0.463508
2023/07/22 15:04:50 - mmengine - INFO - Epoch(train)   [2][3650/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:51  time: 0.222149  data_time: 0.072262  memory: 2769  loss: 0.001523  loss_kpt: 0.001523  acc_pose: 0.475855
2023/07/22 15:05:01 - mmengine - INFO - Epoch(train)   [2][3700/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:11  time: 0.215929  data_time: 0.068524  memory: 2769  loss: 0.001556  loss_kpt: 0.001556  acc_pose: 0.395543
2023/07/22 15:05:12 - mmengine - INFO - Epoch(train)   [2][3750/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:03  time: 0.221173  data_time: 0.070995  memory: 2769  loss: 0.001581  loss_kpt: 0.001581  acc_pose: 0.471426
2023/07/22 15:05:23 - mmengine - INFO - Epoch(train)   [2][3800/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:49  time: 0.220183  data_time: 0.069493  memory: 2769  loss: 0.001572  loss_kpt: 0.001572  acc_pose: 0.493317
2023/07/22 15:05:34 - mmengine - INFO - Epoch(train)   [2][3850/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:14  time: 0.216588  data_time: 0.068845  memory: 2769  loss: 0.001575  loss_kpt: 0.001575  acc_pose: 0.418905
2023/07/22 15:05:45 - mmengine - INFO - Epoch(train)   [2][3900/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:57  time: 0.219552  data_time: 0.070914  memory: 2769  loss: 0.001512  loss_kpt: 0.001512  acc_pose: 0.500859
2023/07/22 15:05:56 - mmengine - INFO - Epoch(train)   [2][3950/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:37  time: 0.219205  data_time: 0.069279  memory: 2769  loss: 0.001523  loss_kpt: 0.001523  acc_pose: 0.393162
2023/07/22 15:06:06 - mmengine - INFO - Epoch(train)   [2][4000/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:16  time: 0.218842  data_time: 0.068649  memory: 2769  loss: 0.001549  loss_kpt: 0.001549  acc_pose: 0.447751
2023/07/22 15:06:18 - mmengine - INFO - Epoch(train)   [2][4050/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:05  time: 0.220770  data_time: 0.069756  memory: 2769  loss: 0.001534  loss_kpt: 0.001534  acc_pose: 0.459695
2023/07/22 15:06:29 - mmengine - INFO - Epoch(train)   [2][4100/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:01  time: 0.221805  data_time: 0.072117  memory: 2769  loss: 0.001576  loss_kpt: 0.001576  acc_pose: 0.466968
2023/07/22 15:06:40 - mmengine - INFO - Epoch(train)   [2][4150/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:46  time: 0.219979  data_time: 0.070291  memory: 2769  loss: 0.001563  loss_kpt: 0.001563  acc_pose: 0.515747
2023/07/22 15:06:51 - mmengine - INFO - Epoch(train)   [2][4200/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:50  time: 0.234497  data_time: 0.085877  memory: 2769  loss: 0.001527  loss_kpt: 0.001527  acc_pose: 0.451229
2023/07/22 15:07:03 - mmengine - INFO - Epoch(train)   [2][4250/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:02  time: 0.224861  data_time: 0.072720  memory: 2769  loss: 0.001534  loss_kpt: 0.001534  acc_pose: 0.413065
2023/07/22 15:07:14 - mmengine - INFO - Epoch(train)   [2][4300/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:42  time: 0.219114  data_time: 0.069655  memory: 2769  loss: 0.001539  loss_kpt: 0.001539  acc_pose: 0.386309
2023/07/22 15:07:18 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:07:25 - mmengine - INFO - Epoch(train)   [2][4350/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:05  time: 0.227090  data_time: 0.073618  memory: 2769  loss: 0.001558  loss_kpt: 0.001558  acc_pose: 0.374963
2023/07/22 15:07:36 - mmengine - INFO - Epoch(train)   [2][4400/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:54  time: 0.220883  data_time: 0.069260  memory: 2769  loss: 0.001547  loss_kpt: 0.001547  acc_pose: 0.421333
2023/07/22 15:07:47 - mmengine - INFO - Epoch(train)   [2][4450/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:07  time: 0.225217  data_time: 0.071588  memory: 2769  loss: 0.001521  loss_kpt: 0.001521  acc_pose: 0.419866
2023/07/22 15:07:59 - mmengine - INFO - Epoch(train)   [2][4500/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:38  time: 0.228904  data_time: 0.075243  memory: 2769  loss: 0.001526  loss_kpt: 0.001526  acc_pose: 0.472045
2023/07/22 15:08:10 - mmengine - INFO - Epoch(train)   [2][4550/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:40  time: 0.223317  data_time: 0.070567  memory: 2769  loss: 0.001536  loss_kpt: 0.001536  acc_pose: 0.442823
2023/07/22 15:08:22 - mmengine - INFO - Epoch(train)   [2][4600/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:18  time: 0.241568  data_time: 0.072527  memory: 2769  loss: 0.001535  loss_kpt: 0.001535  acc_pose: 0.523795
2023/07/22 15:08:33 - mmengine - INFO - Epoch(train)   [2][4650/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:35  time: 0.226505  data_time: 0.074145  memory: 2769  loss: 0.001525  loss_kpt: 0.001525  acc_pose: 0.375999
2023/07/22 15:08:40 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:08:52 - mmengine - INFO - Epoch(train)   [3][  50/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:02  time: 0.228728  data_time: 0.077746  memory: 2769  loss: 0.001539  loss_kpt: 0.001539  acc_pose: 0.389852
2023/07/22 15:09:03 - mmengine - INFO - Epoch(train)   [3][ 100/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:33  time: 0.229128  data_time: 0.076196  memory: 2769  loss: 0.001536  loss_kpt: 0.001536  acc_pose: 0.374548
2023/07/22 15:09:14 - mmengine - INFO - Epoch(train)   [3][ 150/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:35  time: 0.223625  data_time: 0.070833  memory: 2769  loss: 0.001517  loss_kpt: 0.001517  acc_pose: 0.437881
2023/07/22 15:09:26 - mmengine - INFO - Epoch(train)   [3][ 200/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:53  time: 0.226991  data_time: 0.073356  memory: 2769  loss: 0.001556  loss_kpt: 0.001556  acc_pose: 0.403673
2023/07/22 15:09:37 - mmengine - INFO - Epoch(train)   [3][ 250/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:04  time: 0.225397  data_time: 0.071258  memory: 2769  loss: 0.001558  loss_kpt: 0.001558  acc_pose: 0.453507
2023/07/22 15:09:48 - mmengine - INFO - Epoch(train)   [3][ 300/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:10  time: 0.224612  data_time: 0.070947  memory: 2769  loss: 0.001525  loss_kpt: 0.001525  acc_pose: 0.435017
2023/07/22 15:10:00 - mmengine - INFO - Epoch(train)   [3][ 350/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:20  time: 0.225398  data_time: 0.070415  memory: 2769  loss: 0.001494  loss_kpt: 0.001494  acc_pose: 0.512108
2023/07/22 15:10:11 - mmengine - INFO - Epoch(train)   [3][ 400/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:30  time: 0.237399  data_time: 0.085648  memory: 2769  loss: 0.001521  loss_kpt: 0.001521  acc_pose: 0.386923
2023/07/22 15:10:23 - mmengine - INFO - Epoch(train)   [3][ 450/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:43  time: 0.238253  data_time: 0.086934  memory: 2769  loss: 0.001520  loss_kpt: 0.001520  acc_pose: 0.508170
2023/07/22 15:10:35 - mmengine - INFO - Epoch(train)   [3][ 500/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:08  time: 0.228809  data_time: 0.075898  memory: 2769  loss: 0.001501  loss_kpt: 0.001501  acc_pose: 0.333293
2023/07/22 15:10:46 - mmengine - INFO - Epoch(train)   [3][ 550/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:16  time: 0.225379  data_time: 0.072405  memory: 2769  loss: 0.001476  loss_kpt: 0.001476  acc_pose: 0.325959
2023/07/22 15:10:57 - mmengine - INFO - Epoch(train)   [3][ 600/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:02  time: 0.220697  data_time: 0.069189  memory: 2769  loss: 0.001509  loss_kpt: 0.001509  acc_pose: 0.460931
2023/07/22 15:11:05 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:11:08 - mmengine - INFO - Epoch(train)   [3][ 650/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:12  time: 0.225951  data_time: 0.073455  memory: 2769  loss: 0.001527  loss_kpt: 0.001527  acc_pose: 0.443181
2023/07/22 15:11:21 - mmengine - INFO - Epoch(train)   [3][ 700/4682]  lr: 6.250000e-05  eta: 2 days, 11:54:06  time: 0.247277  data_time: 0.093363  memory: 2769  loss: 0.001508  loss_kpt: 0.001508  acc_pose: 0.397818
2023/07/22 15:11:32 - mmengine - INFO - Epoch(train)   [3][ 750/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:54  time: 0.221287  data_time: 0.070861  memory: 2769  loss: 0.001506  loss_kpt: 0.001506  acc_pose: 0.444112
2023/07/22 15:11:43 - mmengine - INFO - Epoch(train)   [3][ 800/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:50  time: 0.223241  data_time: 0.071001  memory: 2769  loss: 0.001499  loss_kpt: 0.001499  acc_pose: 0.474696
2023/07/22 15:11:54 - mmengine - INFO - Epoch(train)   [3][ 850/4682]  lr: 6.250000e-05  eta: 2 days, 11:54:01  time: 0.226083  data_time: 0.073321  memory: 2769  loss: 0.001539  loss_kpt: 0.001539  acc_pose: 0.352767
2023/07/22 15:12:06 - mmengine - INFO - Epoch(train)   [3][ 900/4682]  lr: 6.250000e-05  eta: 2 days, 11:55:02  time: 0.236974  data_time: 0.069962  memory: 2769  loss: 0.001515  loss_kpt: 0.001515  acc_pose: 0.391092
2023/07/22 15:12:17 - mmengine - INFO - Epoch(train)   [3][ 950/4682]  lr: 6.250000e-05  eta: 2 days, 11:55:10  time: 0.225629  data_time: 0.072428  memory: 2769  loss: 0.001516  loss_kpt: 0.001516  acc_pose: 0.413933
2023/07/22 15:12:29 - mmengine - INFO - Epoch(train)   [3][1000/4682]  lr: 6.250000e-05  eta: 2 days, 11:55:17  time: 0.225717  data_time: 0.072638  memory: 2769  loss: 0.001517  loss_kpt: 0.001517  acc_pose: 0.568354
2023/07/22 15:12:40 - mmengine - INFO - Epoch(train)   [3][1050/4682]  lr: 6.250000e-05  eta: 2 days, 11:55:07  time: 0.221957  data_time: 0.072911  memory: 2769  loss: 0.001509  loss_kpt: 0.001509  acc_pose: 0.412208
2023/07/22 15:12:51 - mmengine - INFO - Epoch(train)   [3][1100/4682]  lr: 6.250000e-05  eta: 2 days, 11:55:01  time: 0.222699  data_time: 0.073540  memory: 2769  loss: 0.001503  loss_kpt: 0.001503  acc_pose: 0.438893
2023/07/22 15:13:02 - mmengine - INFO - Epoch(train)   [3][1150/4682]  lr: 6.250000e-05  eta: 2 days, 11:54:52  time: 0.222204  data_time: 0.071423  memory: 2769  loss: 0.001505  loss_kpt: 0.001505  acc_pose: 0.458421
2023/07/22 15:13:13 - mmengine - INFO - Epoch(train)   [3][1200/4682]  lr: 6.250000e-05  eta: 2 days, 11:54:34  time: 0.220323  data_time: 0.070108  memory: 2769  loss: 0.001493  loss_kpt: 0.001493  acc_pose: 0.550778
2023/07/22 15:13:24 - mmengine - INFO - Epoch(train)   [3][1250/4682]  lr: 6.250000e-05  eta: 2 days, 11:54:08  time: 0.218356  data_time: 0.069542  memory: 2769  loss: 0.001499  loss_kpt: 0.001499  acc_pose: 0.388994
2023/07/22 15:13:35 - mmengine - INFO - Epoch(train)   [3][1300/4682]  lr: 6.250000e-05  eta: 2 days, 11:54:00  time: 0.222448  data_time: 0.072866  memory: 2769  loss: 0.001525  loss_kpt: 0.001525  acc_pose: 0.383418
2023/07/22 15:13:46 - mmengine - INFO - Epoch(train)   [3][1350/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:24  time: 0.216230  data_time: 0.067260  memory: 2769  loss: 0.001527  loss_kpt: 0.001527  acc_pose: 0.434656
2023/07/22 15:13:57 - mmengine - INFO - Epoch(train)   [3][1400/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:50  time: 0.216686  data_time: 0.067725  memory: 2769  loss: 0.001486  loss_kpt: 0.001486  acc_pose: 0.432514
2023/07/22 15:14:09 - mmengine - INFO - Epoch(train)   [3][1450/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:46  time: 0.236521  data_time: 0.086240  memory: 2769  loss: 0.001474  loss_kpt: 0.001474  acc_pose: 0.453256
2023/07/22 15:14:20 - mmengine - INFO - Epoch(train)   [3][1500/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:22  time: 0.219015  data_time: 0.069292  memory: 2769  loss: 0.001516  loss_kpt: 0.001516  acc_pose: 0.444045
2023/07/22 15:14:30 - mmengine - INFO - Epoch(train)   [3][1550/4682]  lr: 6.250000e-05  eta: 2 days, 11:53:00  time: 0.219084  data_time: 0.069413  memory: 2769  loss: 0.001479  loss_kpt: 0.001479  acc_pose: 0.489273
2023/07/22 15:14:41 - mmengine - INFO - Epoch(train)   [3][1600/4682]  lr: 6.250000e-05  eta: 2 days, 11:52:32  time: 0.218035  data_time: 0.069572  memory: 2769  loss: 0.001488  loss_kpt: 0.001488  acc_pose: 0.459996
2023/07/22 15:14:49 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:14:52 - mmengine - INFO - Epoch(train)   [3][1650/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:59  time: 0.216720  data_time: 0.068382  memory: 2769  loss: 0.001481  loss_kpt: 0.001481  acc_pose: 0.421594
2023/07/22 15:15:03 - mmengine - INFO - Epoch(train)   [3][1700/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:33  time: 0.218246  data_time: 0.069050  memory: 2769  loss: 0.001527  loss_kpt: 0.001527  acc_pose: 0.491073
2023/07/22 15:15:14 - mmengine - INFO - Epoch(train)   [3][1750/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:08  time: 0.218465  data_time: 0.068737  memory: 2769  loss: 0.001490  loss_kpt: 0.001490  acc_pose: 0.452824
2023/07/22 15:15:25 - mmengine - INFO - Epoch(train)   [3][1800/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:40  time: 0.217915  data_time: 0.069640  memory: 2769  loss: 0.001457  loss_kpt: 0.001457  acc_pose: 0.477751
2023/07/22 15:15:37 - mmengine - INFO - Epoch(train)   [3][1850/4682]  lr: 6.250000e-05  eta: 2 days, 11:51:27  time: 0.234829  data_time: 0.069162  memory: 2769  loss: 0.001489  loss_kpt: 0.001489  acc_pose: 0.458187
2023/07/22 15:15:48 - mmengine - INFO - Epoch(train)   [3][1900/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:58  time: 0.217580  data_time: 0.068042  memory: 2769  loss: 0.001459  loss_kpt: 0.001459  acc_pose: 0.535764
2023/07/22 15:15:58 - mmengine - INFO - Epoch(train)   [3][1950/4682]  lr: 6.250000e-05  eta: 2 days, 11:50:22  time: 0.215908  data_time: 0.066592  memory: 2769  loss: 0.001455  loss_kpt: 0.001455  acc_pose: 0.499629
2023/07/22 15:16:09 - mmengine - INFO - Epoch(train)   [3][2000/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:39  time: 0.214302  data_time: 0.066167  memory: 2769  loss: 0.001461  loss_kpt: 0.001461  acc_pose: 0.375722
2023/07/22 15:16:20 - mmengine - INFO - Epoch(train)   [3][2050/4682]  lr: 6.250000e-05  eta: 2 days, 11:49:10  time: 0.217361  data_time: 0.068411  memory: 2769  loss: 0.001514  loss_kpt: 0.001514  acc_pose: 0.493885
2023/07/22 15:16:31 - mmengine - INFO - Epoch(train)   [3][2100/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:29  time: 0.214414  data_time: 0.066097  memory: 2769  loss: 0.001464  loss_kpt: 0.001464  acc_pose: 0.489009
2023/07/22 15:16:41 - mmengine - INFO - Epoch(train)   [3][2150/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:51  time: 0.215387  data_time: 0.067409  memory: 2769  loss: 0.001457  loss_kpt: 0.001457  acc_pose: 0.456484
2023/07/22 15:16:52 - mmengine - INFO - Epoch(train)   [3][2200/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:32  time: 0.219605  data_time: 0.069114  memory: 2769  loss: 0.001480  loss_kpt: 0.001480  acc_pose: 0.388902
2023/07/22 15:17:03 - mmengine - INFO - Epoch(train)   [3][2250/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:17  time: 0.220411  data_time: 0.067490  memory: 2769  loss: 0.001506  loss_kpt: 0.001506  acc_pose: 0.420182
2023/07/22 15:17:14 - mmengine - INFO - Epoch(train)   [3][2300/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:59  time: 0.219991  data_time: 0.070105  memory: 2769  loss: 0.001506  loss_kpt: 0.001506  acc_pose: 0.477168
2023/07/22 15:17:26 - mmengine - INFO - Epoch(train)   [3][2350/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:47  time: 0.221399  data_time: 0.070510  memory: 2769  loss: 0.001508  loss_kpt: 0.001508  acc_pose: 0.445495
2023/07/22 15:17:37 - mmengine - INFO - Epoch(train)   [3][2400/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:27  time: 0.219253  data_time: 0.070244  memory: 2769  loss: 0.001470  loss_kpt: 0.001470  acc_pose: 0.510175
2023/07/22 15:17:49 - mmengine - INFO - Epoch(train)   [3][2450/4682]  lr: 6.250000e-05  eta: 2 days, 11:48:01  time: 0.247133  data_time: 0.096268  memory: 2769  loss: 0.001488  loss_kpt: 0.001488  acc_pose: 0.451647
2023/07/22 15:18:00 - mmengine - INFO - Epoch(train)   [3][2500/4682]  lr: 6.250000e-05  eta: 2 days, 11:47:25  time: 0.215521  data_time: 0.066550  memory: 2769  loss: 0.001459  loss_kpt: 0.001459  acc_pose: 0.455389
2023/07/22 15:18:10 - mmengine - INFO - Epoch(train)   [3][2550/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:53  time: 0.216507  data_time: 0.068897  memory: 2769  loss: 0.001446  loss_kpt: 0.001446  acc_pose: 0.416623
2023/07/22 15:18:21 - mmengine - INFO - Epoch(train)   [3][2600/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:33  time: 0.219372  data_time: 0.070798  memory: 2769  loss: 0.001473  loss_kpt: 0.001473  acc_pose: 0.480012
2023/07/22 15:18:29 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:18:32 - mmengine - INFO - Epoch(train)   [3][2650/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:19  time: 0.220712  data_time: 0.072121  memory: 2769  loss: 0.001465  loss_kpt: 0.001465  acc_pose: 0.578642
2023/07/22 15:18:44 - mmengine - INFO - Epoch(train)   [3][2700/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:15  time: 0.223269  data_time: 0.070139  memory: 2769  loss: 0.001458  loss_kpt: 0.001458  acc_pose: 0.408636
2023/07/22 15:18:55 - mmengine - INFO - Epoch(train)   [3][2750/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:56  time: 0.219595  data_time: 0.069752  memory: 2769  loss: 0.001433  loss_kpt: 0.001433  acc_pose: 0.406640
2023/07/22 15:19:05 - mmengine - INFO - Epoch(train)   [3][2800/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:20  time: 0.215343  data_time: 0.066182  memory: 2769  loss: 0.001445  loss_kpt: 0.001445  acc_pose: 0.447996
2023/07/22 15:19:17 - mmengine - INFO - Epoch(train)   [3][2850/4682]  lr: 6.250000e-05  eta: 2 days, 11:46:09  time: 0.236678  data_time: 0.070048  memory: 2769  loss: 0.001494  loss_kpt: 0.001494  acc_pose: 0.404458
2023/07/22 15:19:28 - mmengine - INFO - Epoch(train)   [3][2900/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:53  time: 0.220321  data_time: 0.070539  memory: 2769  loss: 0.001453  loss_kpt: 0.001453  acc_pose: 0.424017
2023/07/22 15:19:39 - mmengine - INFO - Epoch(train)   [3][2950/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:29  time: 0.218431  data_time: 0.067726  memory: 2769  loss: 0.001520  loss_kpt: 0.001520  acc_pose: 0.483733
2023/07/22 15:19:50 - mmengine - INFO - Epoch(train)   [3][3000/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:06  time: 0.218577  data_time: 0.068489  memory: 2769  loss: 0.001482  loss_kpt: 0.001482  acc_pose: 0.390663
2023/07/22 15:20:01 - mmengine - INFO - Epoch(train)   [3][3050/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:43  time: 0.218434  data_time: 0.070400  memory: 2769  loss: 0.001468  loss_kpt: 0.001468  acc_pose: 0.427277
2023/07/22 15:20:12 - mmengine - INFO - Epoch(train)   [3][3100/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:29  time: 0.220808  data_time: 0.071014  memory: 2769  loss: 0.001442  loss_kpt: 0.001442  acc_pose: 0.528775
2023/07/22 15:20:23 - mmengine - INFO - Epoch(train)   [3][3150/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:18  time: 0.221541  data_time: 0.070337  memory: 2769  loss: 0.001464  loss_kpt: 0.001464  acc_pose: 0.530323
2023/07/22 15:20:34 - mmengine - INFO - Epoch(train)   [3][3200/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:05  time: 0.220914  data_time: 0.069655  memory: 2769  loss: 0.001450  loss_kpt: 0.001450  acc_pose: 0.396172
2023/07/22 15:20:45 - mmengine - INFO - Epoch(train)   [3][3250/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:57  time: 0.222383  data_time: 0.071079  memory: 2769  loss: 0.001455  loss_kpt: 0.001455  acc_pose: 0.564231
2023/07/22 15:20:57 - mmengine - INFO - Epoch(train)   [3][3300/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:01  time: 0.241309  data_time: 0.086130  memory: 2769  loss: 0.001463  loss_kpt: 0.001463  acc_pose: 0.499546
2023/07/22 15:21:08 - mmengine - INFO - Epoch(train)   [3][3350/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:34  time: 0.217348  data_time: 0.068391  memory: 2769  loss: 0.001433  loss_kpt: 0.001433  acc_pose: 0.486005
2023/07/22 15:21:19 - mmengine - INFO - Epoch(train)   [3][3400/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:26  time: 0.222563  data_time: 0.072298  memory: 2769  loss: 0.001441  loss_kpt: 0.001441  acc_pose: 0.555127
2023/07/22 15:21:31 - mmengine - INFO - Epoch(train)   [3][3450/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:20  time: 0.238730  data_time: 0.087200  memory: 2769  loss: 0.001437  loss_kpt: 0.001437  acc_pose: 0.536771
2023/07/22 15:21:43 - mmengine - INFO - Epoch(train)   [3][3500/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:18  time: 0.224136  data_time: 0.072326  memory: 2769  loss: 0.001454  loss_kpt: 0.001454  acc_pose: 0.605151
2023/07/22 15:21:54 - mmengine - INFO - Epoch(train)   [3][3550/4682]  lr: 6.250000e-05  eta: 2 days, 11:45:03  time: 0.220573  data_time: 0.069013  memory: 2769  loss: 0.001445  loss_kpt: 0.001445  acc_pose: 0.476203
2023/07/22 15:22:05 - mmengine - INFO - Epoch(train)   [3][3600/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:49  time: 0.220986  data_time: 0.070026  memory: 2769  loss: 0.001406  loss_kpt: 0.001406  acc_pose: 0.499655
2023/07/22 15:22:13 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:22:16 - mmengine - INFO - Epoch(train)   [3][3650/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:31  time: 0.219798  data_time: 0.071113  memory: 2769  loss: 0.001459  loss_kpt: 0.001459  acc_pose: 0.420060
2023/07/22 15:22:27 - mmengine - INFO - Epoch(train)   [3][3700/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:22  time: 0.222087  data_time: 0.070992  memory: 2769  loss: 0.001469  loss_kpt: 0.001469  acc_pose: 0.466726
2023/07/22 15:22:38 - mmengine - INFO - Epoch(train)   [3][3750/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:03  time: 0.219632  data_time: 0.069718  memory: 2769  loss: 0.001445  loss_kpt: 0.001445  acc_pose: 0.371630
2023/07/22 15:22:49 - mmengine - INFO - Epoch(train)   [3][3800/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:59  time: 0.223605  data_time: 0.071977  memory: 2769  loss: 0.001457  loss_kpt: 0.001457  acc_pose: 0.482615
2023/07/22 15:23:01 - mmengine - INFO - Epoch(train)   [3][3850/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:33  time: 0.233919  data_time: 0.068537  memory: 2769  loss: 0.001433  loss_kpt: 0.001433  acc_pose: 0.563241
2023/07/22 15:23:11 - mmengine - INFO - Epoch(train)   [3][3900/4682]  lr: 6.250000e-05  eta: 2 days, 11:44:08  time: 0.217995  data_time: 0.067523  memory: 2769  loss: 0.001434  loss_kpt: 0.001434  acc_pose: 0.480374
2023/07/22 15:23:23 - mmengine - INFO - Epoch(train)   [3][3950/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:54  time: 0.220818  data_time: 0.069681  memory: 2769  loss: 0.001454  loss_kpt: 0.001454  acc_pose: 0.499782
2023/07/22 15:23:33 - mmengine - INFO - Epoch(train)   [3][4000/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:32  time: 0.218618  data_time: 0.069236  memory: 2769  loss: 0.001457  loss_kpt: 0.001457  acc_pose: 0.466018
2023/07/22 15:23:44 - mmengine - INFO - Epoch(train)   [3][4050/4682]  lr: 6.250000e-05  eta: 2 days, 11:43:12  time: 0.219392  data_time: 0.069481  memory: 2769  loss: 0.001442  loss_kpt: 0.001442  acc_pose: 0.521244
2023/07/22 15:23:55 - mmengine - INFO - Epoch(train)   [3][4100/4682]  lr: 6.250000e-05  eta: 2 days, 11:42:52  time: 0.219258  data_time: 0.069111  memory: 2769  loss: 0.001449  loss_kpt: 0.001449  acc_pose: 0.488747
2023/07/22 15:24:06 - mmengine - INFO - Epoch(train)   [3][4150/4682]  lr: 6.250000e-05  eta: 2 days, 11:42:30  time: 0.218605  data_time: 0.068648  memory: 2769  loss: 0.001440  loss_kpt: 0.001440  acc_pose: 0.496123
2023/07/22 15:24:17 - mmengine - INFO - Epoch(train)   [3][4200/4682]  lr: 6.250000e-05  eta: 2 days, 11:42:16  time: 0.220647  data_time: 0.069940  memory: 2769  loss: 0.001444  loss_kpt: 0.001444  acc_pose: 0.554808
2023/07/22 15:24:28 - mmengine - INFO - Epoch(train)   [3][4250/4682]  lr: 6.250000e-05  eta: 2 days, 11:41:46  time: 0.216402  data_time: 0.067053  memory: 2769  loss: 0.001433  loss_kpt: 0.001433  acc_pose: 0.380657
2023/07/22 15:24:39 - mmengine - INFO - Epoch(train)   [3][4300/4682]  lr: 6.250000e-05  eta: 2 days, 11:41:26  time: 0.219231  data_time: 0.068180  memory: 2769  loss: 0.001423  loss_kpt: 0.001423  acc_pose: 0.489721
2023/07/22 15:24:50 - mmengine - INFO - Epoch(train)   [3][4350/4682]  lr: 6.250000e-05  eta: 2 days, 11:41:02  time: 0.218099  data_time: 0.067306  memory: 2769  loss: 0.001446  loss_kpt: 0.001446  acc_pose: 0.480642
2023/07/22 15:25:01 - mmengine - INFO - Epoch(train)   [3][4400/4682]  lr: 6.250000e-05  eta: 2 days, 11:40:46  time: 0.220216  data_time: 0.070069  memory: 2769  loss: 0.001446  loss_kpt: 0.001446  acc_pose: 0.563972
2023/07/22 15:25:13 - mmengine - INFO - Epoch(train)   [3][4450/4682]  lr: 6.250000e-05  eta: 2 days, 11:41:12  time: 0.232098  data_time: 0.083050  memory: 2769  loss: 0.001438  loss_kpt: 0.001438  acc_pose: 0.490288
2023/07/22 15:25:24 - mmengine - INFO - Epoch(train)   [3][4500/4682]  lr: 6.250000e-05  eta: 2 days, 11:40:49  time: 0.218239  data_time: 0.070104  memory: 2769  loss: 0.001445  loss_kpt: 0.001445  acc_pose: 0.442514
2023/07/22 15:25:35 - mmengine - INFO - Epoch(train)   [3][4550/4682]  lr: 6.250000e-05  eta: 2 days, 11:40:37  time: 0.221468  data_time: 0.072447  memory: 2769  loss: 0.001436  loss_kpt: 0.001436  acc_pose: 0.394671
2023/07/22 15:25:46 - mmengine - INFO - Epoch(train)   [3][4600/4682]  lr: 6.250000e-05  eta: 2 days, 11:40:19  time: 0.219582  data_time: 0.069744  memory: 2769  loss: 0.001428  loss_kpt: 0.001428  acc_pose: 0.441705
2023/07/22 15:25:54 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251
2023/07/22 15:25:57 - mmengine - INFO - Epoch(train)   [3][4650/4682]  lr: 6.250000e-05  eta: 2 days, 11:40:07  time: 0.221277  data_time: 0.069339  memory: 2769  loss: 0.001419  loss_kpt: 0.001419  acc_pose: 0.550174
2023/07/22 15:26:04 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230722_143251

@xin-li-67

xin-li-67 commented Jul 24, 2023

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Here is a part of the dist_train log running on three 3090 GPUs:

07/24 10:33:33 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230724_092951
07/24 10:33:45 - mmengine - INFO - Epoch(train)  [10][1000/1561]  lr: 2.000000e-03  eta: 21:34:51  time: 0.258567  data_time: 0.088065  memory: 2863  loss: 0.001054  loss_kpt: 0.001054  acc_pose: 0.667949
07/24 10:33:58 - mmengine - INFO - Epoch(train)  [10][1050/1561]  lr: 2.000000e-03  eta: 21:34:36  time: 0.245400  data_time: 0.073074  memory: 2863  loss: 0.001058  loss_kpt: 0.001058  acc_pose: 0.643093
07/24 10:34:10 - mmengine - INFO - Epoch(train)  [10][1100/1561]  lr: 2.000000e-03  eta: 21:34:21  time: 0.246482  data_time: 0.070497  memory: 2863  loss: 0.001037  loss_kpt: 0.001037  acc_pose: 0.620019
07/24 10:34:22 - mmengine - INFO - Epoch(train)  [10][1150/1561]  lr: 2.000000e-03  eta: 21:34:02  time: 0.241395  data_time: 0.068484  memory: 2863  loss: 0.001044  loss_kpt: 0.001044  acc_pose: 0.742400
07/24 10:34:35 - mmengine - INFO - Epoch(train)  [10][1200/1561]  lr: 2.000000e-03  eta: 21:34:05  time: 0.263412  data_time: 0.069424  memory: 2863  loss: 0.001048  loss_kpt: 0.001048  acc_pose: 0.709707
07/24 10:34:47 - mmengine - INFO - Epoch(train)  [10][1250/1561]  lr: 2.000000e-03  eta: 21:33:45  time: 0.241096  data_time: 0.069936  memory: 2863  loss: 0.001023  loss_kpt: 0.001023  acc_pose: 0.731810
07/24 10:34:59 - mmengine - INFO - Epoch(train)  [10][1300/1561]  lr: 2.000000e-03  eta: 21:33:27  time: 0.243296  data_time: 0.071560  memory: 2863  loss: 0.001052  loss_kpt: 0.001052  acc_pose: 0.672277
07/24 10:35:13 - mmengine - INFO - Epoch(train)  [10][1350/1561]  lr: 2.000000e-03  eta: 21:33:35  time: 0.268337  data_time: 0.083195  memory: 2863  loss: 0.001055  loss_kpt: 0.001055  acc_pose: 0.703284
07/24 10:35:25 - mmengine - INFO - Epoch(train)  [10][1400/1561]  lr: 2.000000e-03  eta: 21:33:18  time: 0.243694  data_time: 0.072882  memory: 2863  loss: 0.001041  loss_kpt: 0.001041  acc_pose: 0.621418
07/24 10:35:37 - mmengine - INFO - Epoch(train)  [10][1450/1561]  lr: 2.000000e-03  eta: 21:33:04  time: 0.246809  data_time: 0.071920  memory: 2863  loss: 0.001051  loss_kpt: 0.001051  acc_pose: 0.673734
07/24 10:35:49 - mmengine - INFO - Epoch(train)  [10][1500/1561]  lr: 2.000000e-03  eta: 21:32:45  time: 0.242615  data_time: 0.069541  memory: 2863  loss: 0.001041  loss_kpt: 0.001041  acc_pose: 0.693784
07/24 10:36:02 - mmengine - INFO - Epoch(train)  [10][1550/1561]  lr: 2.000000e-03  eta: 21:32:26  time: 0.241543  data_time: 0.070940  memory: 2863  loss: 0.001035  loss_kpt: 0.001035  acc_pose: 0.661962
07/24 10:36:04 - mmengine - INFO - Exp name: td-hm_uniformer-s-8xb128-210e_coco-256x192_20230724_092951
07/24 10:36:04 - mmengine - INFO - Saving checkpoint at 10 epochs
07/24 10:36:49 - mmengine - INFO - Epoch(val)  [10][ 50/136]    eta: 0:01:10  time: 0.817474  data_time: 0.176064  memory: 3365  
07/24 10:37:29 - mmengine - INFO - Epoch(val)  [10][100/136]    eta: 0:00:29  time: 0.800923  data_time: 0.157949  memory: 3365  
07/24 10:38:38 - mmengine - INFO - Evaluating CocoMetric...
Loading and preparing results...
DONE (t=4.02s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=9.73s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.538
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.815
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.588
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.508
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.598
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.608
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.866
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.662
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.567
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.666
07/24 10:38:52 - mmengine - INFO - Epoch(val) [10][136/136]    coco/AP: 0.538381  coco/AP .5: 0.814657  coco/AP .75: 0.588496  coco/AP (M): 0.508142  coco/AP (L): 0.598121  coco/AR: 0.607746  coco/AR .5: 0.866184  coco/AR .75: 0.662469  coco/AR (M): 0.566976  coco/AR (L): 0.665515  data_time: 0.175768  time: 0.816618
07/24 10:38:55 - mmengine - INFO - The best checkpoint with 0.5384 coco/AP at 10 epoch is saved to best_coco_AP_epoch_10.pth.

Seems like there is some ACC drop...

@xin-li-67

xin-li-67 commented Jul 24, 2023 •
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Testing result on projects/uniformer/configs/td-hm_uniformer-s-8xb128-210e_coco-256x192.py:

07/24 14:04:55 - mmpose - INFO - Use torch.utils.checkpoint: False
07/24 14:04:55 - mmpose - INFO - torch.utils.checkpoint number: (0, 0, 0, 0)
07/24 14:04:55 - mmpose - INFO - Use global window for all blocks in stage3
07/24 14:04:56 - mmpose - INFO - Loads checkpoint by local backend from path: /root/mmpose/projects/uniformer/pretrained/uniformer_small_in1k.pth
07/24 14:04:56 - mmpose - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: model

missing keys in source state_dict: patch_embed1.norm.weight, patch_embed1.norm.bias, patch_embed1.proj.weight, patch_embed1.proj.bias, patch_embed2.norm.weight, patch_embed2.norm.bias, patch_embed2.proj.weight, patch_embed2.proj.bias, patch_embed3.norm.weight, patch_embed3.norm.bias, patch_embed3.proj.weight, patch_embed3.proj.bias, patch_embed4.norm.weight, patch_embed4.norm.bias, patch_embed4.proj.weight, patch_embed4.proj.bias, blocks1.0.pos_embed.weight, blocks1.0.pos_embed.bias, blocks1.0.norm1.weight, blocks1.0.norm1.bias, blocks1.0.norm1.running_mean, blocks1.0.norm1.running_var, blocks1.0.conv1.weight, blocks1.0.conv1.bias, blocks1.0.conv2.weight, blocks1.0.conv2.bias, blocks1.0.attn.weight, blocks1.0.attn.bias, blocks1.0.norm2.weight, blocks1.0.norm2.bias, blocks1.0.norm2.running_mean, blocks1.0.norm2.running_var, blocks1.0.mlp.fc1.weight, blocks1.0.mlp.fc1.bias, blocks1.0.mlp.fc2.weight, blocks1.0.mlp.fc2.bias, blocks1.1.pos_embed.weight, blocks1.1.pos_embed.bias, blocks1.1.norm1.weight, blocks1.1.norm1.bias, blocks1.1.norm1.running_mean, blocks1.1.norm1.running_var, blocks1.1.conv1.weight, blocks1.1.conv1.bias, blocks1.1.conv2.weight, blocks1.1.conv2.bias, blocks1.1.attn.weight, blocks1.1.attn.bias, blocks1.1.norm2.weight, blocks1.1.norm2.bias, blocks1.1.norm2.running_mean, blocks1.1.norm2.running_var, blocks1.1.mlp.fc1.weight, blocks1.1.mlp.fc1.bias, blocks1.1.mlp.fc2.weight, blocks1.1.mlp.fc2.bias, blocks1.2.pos_embed.weight, blocks1.2.pos_embed.bias, blocks1.2.norm1.weight, blocks1.2.norm1.bias, blocks1.2.norm1.running_mean, blocks1.2.norm1.running_var, blocks1.2.conv1.weight, blocks1.2.conv1.bias, blocks1.2.conv2.weight, blocks1.2.conv2.bias, blocks1.2.attn.weight, blocks1.2.attn.bias, blocks1.2.norm2.weight, blocks1.2.norm2.bias, blocks1.2.norm2.running_mean, blocks1.2.norm2.running_var, blocks1.2.mlp.fc1.weight, blocks1.2.mlp.fc1.bias, blocks1.2.mlp.fc2.weight, blocks1.2.mlp.fc2.bias, norm1.weight, norm1.bias, blocks2.0.pos_embed.weight, blocks2.0.pos_embed.bias, blocks2.0.norm1.weight, blocks2.0.norm1.bias, blocks2.0.norm1.running_mean, blocks2.0.norm1.running_var, blocks2.0.conv1.weight, blocks2.0.conv1.bias, blocks2.0.conv2.weight, blocks2.0.conv2.bias, blocks2.0.attn.weight, blocks2.0.attn.bias, blocks2.0.norm2.weight, blocks2.0.norm2.bias, blocks2.0.norm2.running_mean, blocks2.0.norm2.running_var, blocks2.0.mlp.fc1.weight, blocks2.0.mlp.fc1.bias, blocks2.0.mlp.fc2.weight, blocks2.0.mlp.fc2.bias, blocks2.1.pos_embed.weight, blocks2.1.pos_embed.bias, blocks2.1.norm1.weight, blocks2.1.norm1.bias, blocks2.1.norm1.running_mean, blocks2.1.norm1.running_var, blocks2.1.conv1.weight, blocks2.1.conv1.bias, blocks2.1.conv2.weight, blocks2.1.conv2.bias, blocks2.1.attn.weight, blocks2.1.attn.bias, blocks2.1.norm2.weight, blocks2.1.norm2.bias, blocks2.1.norm2.running_mean, blocks2.1.norm2.running_var, blocks2.1.mlp.fc1.weight, blocks2.1.mlp.fc1.bias, blocks2.1.mlp.fc2.weight, blocks2.1.mlp.fc2.bias, blocks2.2.pos_embed.weight, blocks2.2.pos_embed.bias, blocks2.2.norm1.weight, blocks2.2.norm1.bias, blocks2.2.norm1.running_mean, blocks2.2.norm1.running_var, blocks2.2.conv1.weight, blocks2.2.conv1.bias, blocks2.2.conv2.weight, blocks2.2.conv2.bias, blocks2.2.attn.weight, blocks2.2.attn.bias, blocks2.2.norm2.weight, blocks2.2.norm2.bias, blocks2.2.norm2.running_mean, blocks2.2.norm2.running_var, blocks2.2.mlp.fc1.weight, blocks2.2.mlp.fc1.bias, blocks2.2.mlp.fc2.weight, blocks2.2.mlp.fc2.bias, blocks2.3.pos_embed.weight, blocks2.3.pos_embed.bias, blocks2.3.norm1.weight, blocks2.3.norm1.bias, blocks2.3.norm1.running_mean, blocks2.3.norm1.running_var, blocks2.3.conv1.weight, blocks2.3.conv1.bias, blocks2.3.conv2.weight, blocks2.3.conv2.bias, blocks2.3.attn.weight, blocks2.3.attn.bias, blocks2.3.norm2.weight, blocks2.3.norm2.bias, blocks2.3.norm2.running_mean, blocks2.3.norm2.running_var, blocks2.3.mlp.fc1.weight, blocks2.3.mlp.fc1.bias, blocks2.3.mlp.fc2.weight, blocks2.3.mlp.fc2.bias, norm2.weight, norm2.bias, blocks3.0.pos_embed.weight, blocks3.0.pos_embed.bias, blocks3.0.norm1.weight, blocks3.0.norm1.bias, blocks3.0.attn.qkv.weight, blocks3.0.attn.qkv.bias, blocks3.0.attn.proj.weight, blocks3.0.attn.proj.bias, blocks3.0.norm2.weight, blocks3.0.norm2.bias, blocks3.0.mlp.fc1.weight, blocks3.0.mlp.fc1.bias, blocks3.0.mlp.fc2.weight, blocks3.0.mlp.fc2.bias, blocks3.1.pos_embed.weight, blocks3.1.pos_embed.bias, blocks3.1.norm1.weight, blocks3.1.norm1.bias, blocks3.1.attn.qkv.weight, blocks3.1.attn.qkv.bias, blocks3.1.attn.proj.weight, blocks3.1.attn.proj.bias, blocks3.1.norm2.weight, blocks3.1.norm2.bias, blocks3.1.mlp.fc1.weight, blocks3.1.mlp.fc1.bias, blocks3.1.mlp.fc2.weight, blocks3.1.mlp.fc2.bias, blocks3.2.pos_embed.weight, blocks3.2.pos_embed.bias, blocks3.2.norm1.weight, blocks3.2.norm1.bias, blocks3.2.attn.qkv.weight, blocks3.2.attn.qkv.bias, blocks3.2.attn.proj.weight, blocks3.2.attn.proj.bias, blocks3.2.norm2.weight, blocks3.2.norm2.bias, blocks3.2.mlp.fc1.weight, 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07/24 14:04:56 - mmpose - INFO - Load pretrained model from /root/mmpose/projects/uniformer/pretrained/uniformer_small_in1k.pth
07/24 14:05:00 - 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.
07/24 14:05:00 - 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                      
(NORMAL      ) SyncBuffersHook                    
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train:
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
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after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
loading annotations into memory...
Done (t=0.30s)
creating index...
index created!
loading annotations into memory...
Done (t=0.18s)
creating index...
index created!
Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_256x192_global_small-d4a7fdac_20230724.pth
Downloading: "https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_256x192_global_small-d4a7fdac_20230724.pth" to /root/.cache/torch/hub/checkpoints/top_down_256x192_global_small-d4a7fdac_20230724.pth
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96.3M/96.3M [00:05<00:00, 19.8MB/s]
07/24 14:05:14 - mmengine - INFO - Load checkpoint from https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_256x192_global_small-d4a7fdac_20230724.pth
07/24 14:05:55 - mmengine - INFO - Epoch(test) [ 50/407]    eta: 0:04:51  time: 0.815450  data_time: 0.149263  memory: 2966  
07/24 14:06:37 - mmengine - INFO - Epoch(test) [100/407]    eta: 0:04:12  time: 0.831289  data_time: 0.162858  memory: 2966  
07/24 14:07:18 - mmengine - INFO - Epoch(test) [150/407]    eta: 0:03:31  time: 0.826862  data_time: 0.153038  memory: 2966  
07/24 14:08:00 - mmengine - INFO - Epoch(test) [200/407]    eta: 0:02:51  time: 0.837929  data_time: 0.170698  memory: 2966  
07/24 14:08:42 - mmengine - INFO - Epoch(test) [250/407]    eta: 0:02:10  time: 0.841122  data_time: 0.165579  memory: 2966  
07/24 14:09:23 - mmengine - INFO - Epoch(test) [300/407]    eta: 0:01:28  time: 0.828984  data_time: 0.156390  memory: 2966  
07/24 14:10:05 - mmengine - INFO - Epoch(test) [350/407]    eta: 0:00:47  time: 0.838562  data_time: 0.169481  memory: 2966  
07/24 14:10:47 - mmengine - INFO - Epoch(test) [400/407]    eta: 0:00:05  time: 0.836921  data_time: 0.168182  memory: 2966  
07/24 14:11:26 - mmengine - INFO - Evaluating CocoMetric...
Loading and preparing results...
DONE (t=3.31s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=9.63s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.740
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.903
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.821
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.705
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.809
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.795
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.941
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.866
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.754
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.855
07/24 14:11:40 - mmengine - INFO - Epoch(test) [407/407]    coco/AP: 0.740478  coco/AP .5: 0.902957  coco/AP .75: 0.821051  coco/AP (M): 0.704838  coco/AP (L): 0.808673  coco/AR: 0.794773  coco/AR .5: 0.941436  coco/AR .75: 0.866026  coco/AR (M): 0.753510  coco/AR (L): 0.855035  data_time: 0.161404  time: 0.831106

whereas the accuracy listed in the official UniFormer repo is:

Backbone Input Size AP AP50 AP75 ARM ARL AR FLOPs
UniFormer-S 256x192 74.0 90.3 82.2 66.8 76.7 79.5 4.7G

@xin-li-67

xin-li-67 commented Jul 24, 2023

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Testing result on projects/uniformer/configs/_base_/td-hm_uniformer-b-8xb128-210e_coco-256x192.py:

07/24 14:29:21 - mmpose - INFO - Use torch.utils.checkpoint: False
07/24 14:29:21 - mmpose - INFO - torch.utils.checkpoint number: (0, 0, 0, 0)
07/24 14:29:21 - mmpose - INFO - Use global window for all blocks in stage3
07/24 14:29:22 - mmpose - INFO - Loads checkpoint by local backend from path: /root/mmpose/projects/uniformer/pretrained/uniformer_base_in1k.pth
07/24 14:29:22 - mmpose - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: model

missing keys in source state_dict: patch_embed1.norm.weight, patch_embed1.norm.bias, patch_embed1.proj.weight, patch_embed1.proj.bias, patch_embed2.norm.weight, patch_embed2.norm.bias, patch_embed2.proj.weight, patch_embed2.proj.bias, patch_embed3.norm.weight, patch_embed3.norm.bias, patch_embed3.proj.weight, patch_embed3.proj.bias, patch_embed4.norm.weight, patch_embed4.norm.bias, patch_embed4.proj.weight, patch_embed4.proj.bias, blocks1.0.pos_embed.weight, blocks1.0.pos_embed.bias, blocks1.0.norm1.weight, blocks1.0.norm1.bias, blocks1.0.norm1.running_mean, blocks1.0.norm1.running_var, blocks1.0.conv1.weight, blocks1.0.conv1.bias, blocks1.0.conv2.weight, blocks1.0.conv2.bias, blocks1.0.attn.weight, blocks1.0.attn.bias, blocks1.0.norm2.weight, blocks1.0.norm2.bias, blocks1.0.norm2.running_mean, blocks1.0.norm2.running_var, blocks1.0.mlp.fc1.weight, blocks1.0.mlp.fc1.bias, blocks1.0.mlp.fc2.weight, blocks1.0.mlp.fc2.bias, blocks1.1.pos_embed.weight, blocks1.1.pos_embed.bias, 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07/24 14:29:22 - mmpose - INFO - Load pretrained model from /root/mmpose/projects/uniformer/pretrained/uniformer_base_in1k.pth
07/24 14:29:26 - 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.
07/24 14:29:26 - 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                      
(NORMAL      ) SyncBuffersHook                    
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train:
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
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before_test_iter:
(NORMAL      ) IterTimerHook                      
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after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
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after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
loading annotations into memory...
Done (t=0.30s)
creating index...
index created!
loading annotations into memory...
Done (t=0.19s)
creating index...
index created!
Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_256x192_global_base-1713bcd4_20230724.pth
Downloading: "https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_256x192_global_base-1713bcd4_20230724.pth" to /root/.cache/torch/hub/checkpoints/top_down_256x192_global_base-1713bcd4_20230724.pth
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 204M/204M [00:10<00:00, 20.9MB/s]
07/24 14:29:45 - mmengine - INFO - Load checkpoint from https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_256x192_global_base-1713bcd4_20230724.pth
07/24 14:30:50 - mmengine - INFO - Epoch(test) [ 50/407]    eta: 0:07:43  time: 1.299552  data_time: 0.199893  memory: 3075  
07/24 14:31:54 - mmengine - INFO - Epoch(test) [100/407]    eta: 0:06:36  time: 1.280605  data_time: 0.170661  memory: 3075  
07/24 14:32:58 - mmengine - INFO - Epoch(test) [150/407]    eta: 0:05:29  time: 1.268949  data_time: 0.157379  memory: 3075  
07/24 14:34:02 - mmengine - INFO - Epoch(test) [200/407]    eta: 0:04:25  time: 1.280099  data_time: 0.168987  memory: 3075  
07/24 14:35:06 - mmengine - INFO - Epoch(test) [250/407]    eta: 0:03:21  time: 1.290992  data_time: 0.181665  memory: 3075  
07/24 14:36:10 - mmengine - INFO - Epoch(test) [300/407]    eta: 0:02:17  time: 1.279991  data_time: 0.171667  memory: 3075  
07/24 14:37:14 - mmengine - INFO - Epoch(test) [350/407]    eta: 0:01:13  time: 1.278610  data_time: 0.166956  memory: 3075  
07/24 14:38:18 - mmengine - INFO - Epoch(test) [400/407]    eta: 0:00:08  time: 1.275209  data_time: 0.164871  memory: 3075  
07/24 14:38:59 - mmengine - INFO - Evaluating CocoMetric...
Loading and preparing results...
DONE (t=3.18s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=9.17s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.750
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.905
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.829
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.715
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.818
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.804
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.943
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.872
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.762
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.864
07/24 14:39:12 - mmengine - INFO - Epoch(test) [407/407]    coco/AP: 0.749641  coco/AP .5: 0.905371  coco/AP .75: 0.828859  coco/AP (M): 0.714766  coco/AP (L): 0.817848  coco/AR: 0.803526  coco/AR .5: 0.942538  coco/AR .75: 0.871851  coco/AR (M): 0.761950  coco/AR (L): 0.863768  data_time: 0.172043  time: 1.280725

whereas the accuracy listed in the official UniFormer repo is:

Backbone Input Size AP AP50 AP75 ARM ARL AR FLOPs
UniFormer-B 256x192 75.0 90.6 83.0 67.8 77.7 80.4 9.2G

@xin-li-67

xin-li-67 commented Jul 24, 2023

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Testing result on projects/uniformer/configs/_base_/td-hm_uniformer-b-8xb32-210e_coco-384x288.py:

07/24 14:42:05 - mmpose - INFO - Use torch.utils.checkpoint: False
07/24 14:42:05 - mmpose - INFO - torch.utils.checkpoint number: (0, 0, 0, 0)
07/24 14:42:05 - mmpose - INFO - Use global window for all blocks in stage3
07/24 14:42:06 - mmpose - INFO - Loads checkpoint by local backend from path: /root/mmpose/projects/uniformer/pretrained/uniformer_base_in1k.pth
07/24 14:42:06 - mmpose - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: model

missing keys in source state_dict: patch_embed1.norm.weight, patch_embed1.norm.bias, patch_embed1.proj.weight, patch_embed1.proj.bias, patch_embed2.norm.weight, patch_embed2.norm.bias, patch_embed2.proj.weight, patch_embed2.proj.bias, patch_embed3.norm.weight, patch_embed3.norm.bias, patch_embed3.proj.weight, patch_embed3.proj.bias, patch_embed4.norm.weight, patch_embed4.norm.bias, patch_embed4.proj.weight, patch_embed4.proj.bias, blocks1.0.pos_embed.weight, blocks1.0.pos_embed.bias, blocks1.0.norm1.weight, blocks1.0.norm1.bias, blocks1.0.norm1.running_mean, blocks1.0.norm1.running_var, blocks1.0.conv1.weight, blocks1.0.conv1.bias, blocks1.0.conv2.weight, blocks1.0.conv2.bias, blocks1.0.attn.weight, blocks1.0.attn.bias, blocks1.0.norm2.weight, blocks1.0.norm2.bias, blocks1.0.norm2.running_mean, blocks1.0.norm2.running_var, blocks1.0.mlp.fc1.weight, blocks1.0.mlp.fc1.bias, blocks1.0.mlp.fc2.weight, blocks1.0.mlp.fc2.bias, blocks1.1.pos_embed.weight, blocks1.1.pos_embed.bias, 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07/24 14:42:06 - mmpose - INFO - Load pretrained model from /root/mmpose/projects/uniformer/pretrained/uniformer_base_in1k.pth
07/24 14:42:10 - 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.
07/24 14:42:10 - 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                      
(NORMAL      ) SyncBuffersHook                    
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train:
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
loading annotations into memory...
Done (t=0.29s)
creating index...
index created!
loading annotations into memory...
Done (t=0.19s)
creating index...
index created!
Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_384x288_global_base-c650da38_20230724.pth
Downloading: "https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_384x288_global_base-c650da38_20230724.pth" to /root/.cache/torch/hub/checkpoints/top_down_384x288_global_base-c650da38_20230724.pth
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 204M/204M [00:09<00:00, 21.6MB/s]
07/24 14:42:29 - mmengine - INFO - Load checkpoint from https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_384x288_global_base-c650da38_20230724.pth
07/24 14:44:48 - mmengine - INFO - Epoch(test) [ 50/407]    eta: 0:16:35  time: 2.789852  data_time: 0.260804  memory: 6649  
07/24 14:47:06 - mmengine - INFO - Epoch(test) [100/407]    eta: 0:14:11  time: 2.755543  data_time: 0.209653  memory: 6649  
07/24 14:49:24 - mmengine - INFO - Epoch(test) [150/407]    eta: 0:11:51  time: 2.760203  data_time: 0.212239  memory: 6649  
07/24 14:51:42 - mmengine - INFO - Epoch(test) [200/407]    eta: 0:09:32  time: 2.752695  data_time: 0.202930  memory: 6649  
07/24 14:53:59 - mmengine - INFO - Epoch(test) [250/407]    eta: 0:07:13  time: 2.757030  data_time: 0.213792  memory: 6649  
07/24 14:56:18 - mmengine - INFO - Epoch(test) [300/407]    eta: 0:04:55  time: 2.763714  data_time: 0.216035  memory: 6649  
07/24 14:58:35 - mmengine - INFO - Epoch(test) [350/407]    eta: 0:02:37  time: 2.754733  data_time: 0.205812  memory: 6649  
07/24 15:00:53 - mmengine - INFO - Epoch(test) [400/407]    eta: 0:00:19  time: 2.756398  data_time: 0.217246  memory: 6649  
07/24 15:01:45 - mmengine - INFO - Evaluating CocoMetric...
Loading and preparing results...
DONE (t=3.20s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=9.61s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.767
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.908
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.841
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.729
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.837
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.819
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.946
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.883
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.777
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.880
07/24 15:01:58 - mmengine - INFO - Epoch(test) [407/407]    coco/AP: 0.767028  coco/AP .5: 0.907571  coco/AP .75: 0.840608  coco/AP (M): 0.729437  coco/AP (L): 0.836965  coco/AR: 0.818640  coco/AR .5: 0.946316  coco/AR .75: 0.882714  coco/AR (M): 0.776618  coco/AR (L): 0.880119  data_time: 0.216960  time: 2.759213

whereas the accuracy listed in the official UniFormer repo is:

Backbone Input Size AP AP50 AP75 ARM ARL AR FLOPs
UniFormer-B 384x288 76.7 90.8 84.0 69.3 79.7 81.4 14.8G

@xin-li-67

xin-li-67 commented Jul 24, 2023

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Testing result on rojects/uniformer/configs/td-hm_uniformer-s-8xb128-210e_coco-384x288.py:

07/24 15:12:03 - mmpose - INFO - Use torch.utils.checkpoint: False
07/24 15:12:03 - mmpose - INFO - torch.utils.checkpoint number: (0, 0, 0, 0)
07/24 15:12:03 - mmpose - INFO - Use global window for all blocks in stage3
07/24 15:12:04 - mmpose - INFO - Loads checkpoint by local backend from path: /root/mmpose/projects/uniformer/pretrained/uniformer_small_in1k.pth
07/24 15:12:04 - mmpose - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: model

missing keys in source state_dict: patch_embed1.norm.weight, patch_embed1.norm.bias, patch_embed1.proj.weight, patch_embed1.proj.bias, patch_embed2.norm.weight, patch_embed2.norm.bias, patch_embed2.proj.weight, patch_embed2.proj.bias, patch_embed3.norm.weight, patch_embed3.norm.bias, patch_embed3.proj.weight, patch_embed3.proj.bias, patch_embed4.norm.weight, patch_embed4.norm.bias, patch_embed4.proj.weight, patch_embed4.proj.bias, blocks1.0.pos_embed.weight, blocks1.0.pos_embed.bias, blocks1.0.norm1.weight, blocks1.0.norm1.bias, blocks1.0.norm1.running_mean, blocks1.0.norm1.running_var, blocks1.0.conv1.weight, blocks1.0.conv1.bias, blocks1.0.conv2.weight, blocks1.0.conv2.bias, blocks1.0.attn.weight, blocks1.0.attn.bias, blocks1.0.norm2.weight, blocks1.0.norm2.bias, blocks1.0.norm2.running_mean, blocks1.0.norm2.running_var, blocks1.0.mlp.fc1.weight, blocks1.0.mlp.fc1.bias, blocks1.0.mlp.fc2.weight, blocks1.0.mlp.fc2.bias, blocks1.1.pos_embed.weight, blocks1.1.pos_embed.bias, blocks1.1.norm1.weight, blocks1.1.norm1.bias, blocks1.1.norm1.running_mean, blocks1.1.norm1.running_var, blocks1.1.conv1.weight, blocks1.1.conv1.bias, blocks1.1.conv2.weight, blocks1.1.conv2.bias, blocks1.1.attn.weight, blocks1.1.attn.bias, blocks1.1.norm2.weight, blocks1.1.norm2.bias, blocks1.1.norm2.running_mean, blocks1.1.norm2.running_var, blocks1.1.mlp.fc1.weight, blocks1.1.mlp.fc1.bias, blocks1.1.mlp.fc2.weight, blocks1.1.mlp.fc2.bias, blocks1.2.pos_embed.weight, blocks1.2.pos_embed.bias, blocks1.2.norm1.weight, blocks1.2.norm1.bias, blocks1.2.norm1.running_mean, blocks1.2.norm1.running_var, blocks1.2.conv1.weight, blocks1.2.conv1.bias, blocks1.2.conv2.weight, blocks1.2.conv2.bias, blocks1.2.attn.weight, blocks1.2.attn.bias, blocks1.2.norm2.weight, blocks1.2.norm2.bias, blocks1.2.norm2.running_mean, blocks1.2.norm2.running_var, blocks1.2.mlp.fc1.weight, blocks1.2.mlp.fc1.bias, blocks1.2.mlp.fc2.weight, blocks1.2.mlp.fc2.bias, norm1.weight, norm1.bias, blocks2.0.pos_embed.weight, blocks2.0.pos_embed.bias, blocks2.0.norm1.weight, blocks2.0.norm1.bias, blocks2.0.norm1.running_mean, blocks2.0.norm1.running_var, blocks2.0.conv1.weight, blocks2.0.conv1.bias, blocks2.0.conv2.weight, blocks2.0.conv2.bias, blocks2.0.attn.weight, blocks2.0.attn.bias, blocks2.0.norm2.weight, blocks2.0.norm2.bias, blocks2.0.norm2.running_mean, blocks2.0.norm2.running_var, blocks2.0.mlp.fc1.weight, blocks2.0.mlp.fc1.bias, blocks2.0.mlp.fc2.weight, blocks2.0.mlp.fc2.bias, blocks2.1.pos_embed.weight, blocks2.1.pos_embed.bias, blocks2.1.norm1.weight, blocks2.1.norm1.bias, blocks2.1.norm1.running_mean, blocks2.1.norm1.running_var, blocks2.1.conv1.weight, blocks2.1.conv1.bias, blocks2.1.conv2.weight, blocks2.1.conv2.bias, blocks2.1.attn.weight, blocks2.1.attn.bias, blocks2.1.norm2.weight, blocks2.1.norm2.bias, blocks2.1.norm2.running_mean, blocks2.1.norm2.running_var, blocks2.1.mlp.fc1.weight, blocks2.1.mlp.fc1.bias, blocks2.1.mlp.fc2.weight, blocks2.1.mlp.fc2.bias, blocks2.2.pos_embed.weight, blocks2.2.pos_embed.bias, blocks2.2.norm1.weight, blocks2.2.norm1.bias, blocks2.2.norm1.running_mean, blocks2.2.norm1.running_var, blocks2.2.conv1.weight, blocks2.2.conv1.bias, blocks2.2.conv2.weight, blocks2.2.conv2.bias, blocks2.2.attn.weight, blocks2.2.attn.bias, blocks2.2.norm2.weight, blocks2.2.norm2.bias, blocks2.2.norm2.running_mean, blocks2.2.norm2.running_var, blocks2.2.mlp.fc1.weight, blocks2.2.mlp.fc1.bias, blocks2.2.mlp.fc2.weight, blocks2.2.mlp.fc2.bias, blocks2.3.pos_embed.weight, blocks2.3.pos_embed.bias, blocks2.3.norm1.weight, blocks2.3.norm1.bias, blocks2.3.norm1.running_mean, blocks2.3.norm1.running_var, blocks2.3.conv1.weight, blocks2.3.conv1.bias, blocks2.3.conv2.weight, blocks2.3.conv2.bias, blocks2.3.attn.weight, blocks2.3.attn.bias, blocks2.3.norm2.weight, blocks2.3.norm2.bias, blocks2.3.norm2.running_mean, blocks2.3.norm2.running_var, blocks2.3.mlp.fc1.weight, blocks2.3.mlp.fc1.bias, blocks2.3.mlp.fc2.weight, blocks2.3.mlp.fc2.bias, norm2.weight, norm2.bias, blocks3.0.pos_embed.weight, blocks3.0.pos_embed.bias, blocks3.0.norm1.weight, blocks3.0.norm1.bias, blocks3.0.attn.qkv.weight, blocks3.0.attn.qkv.bias, blocks3.0.attn.proj.weight, blocks3.0.attn.proj.bias, blocks3.0.norm2.weight, blocks3.0.norm2.bias, blocks3.0.mlp.fc1.weight, blocks3.0.mlp.fc1.bias, blocks3.0.mlp.fc2.weight, blocks3.0.mlp.fc2.bias, blocks3.1.pos_embed.weight, blocks3.1.pos_embed.bias, blocks3.1.norm1.weight, blocks3.1.norm1.bias, blocks3.1.attn.qkv.weight, blocks3.1.attn.qkv.bias, blocks3.1.attn.proj.weight, blocks3.1.attn.proj.bias, blocks3.1.norm2.weight, blocks3.1.norm2.bias, blocks3.1.mlp.fc1.weight, blocks3.1.mlp.fc1.bias, blocks3.1.mlp.fc2.weight, blocks3.1.mlp.fc2.bias, blocks3.2.pos_embed.weight, blocks3.2.pos_embed.bias, blocks3.2.norm1.weight, blocks3.2.norm1.bias, blocks3.2.attn.qkv.weight, blocks3.2.attn.qkv.bias, blocks3.2.attn.proj.weight, blocks3.2.attn.proj.bias, blocks3.2.norm2.weight, blocks3.2.norm2.bias, blocks3.2.mlp.fc1.weight, blocks3.2.mlp.fc1.bias, blocks3.2.mlp.fc2.weight, blocks3.2.mlp.fc2.bias, blocks3.3.pos_embed.weight, blocks3.3.pos_embed.bias, blocks3.3.norm1.weight, blocks3.3.norm1.bias, blocks3.3.attn.qkv.weight, blocks3.3.attn.qkv.bias, blocks3.3.attn.proj.weight, blocks3.3.attn.proj.bias, blocks3.3.norm2.weight, blocks3.3.norm2.bias, blocks3.3.mlp.fc1.weight, blocks3.3.mlp.fc1.bias, blocks3.3.mlp.fc2.weight, blocks3.3.mlp.fc2.bias, blocks3.4.pos_embed.weight, blocks3.4.pos_embed.bias, blocks3.4.norm1.weight, blocks3.4.norm1.bias, blocks3.4.attn.qkv.weight, blocks3.4.attn.qkv.bias, blocks3.4.attn.proj.weight, blocks3.4.attn.proj.bias, blocks3.4.norm2.weight, blocks3.4.norm2.bias, blocks3.4.mlp.fc1.weight, blocks3.4.mlp.fc1.bias, blocks3.4.mlp.fc2.weight, blocks3.4.mlp.fc2.bias, blocks3.5.pos_embed.weight, blocks3.5.pos_embed.bias, blocks3.5.norm1.weight, blocks3.5.norm1.bias, blocks3.5.attn.qkv.weight, blocks3.5.attn.qkv.bias, blocks3.5.attn.proj.weight, blocks3.5.attn.proj.bias, blocks3.5.norm2.weight, blocks3.5.norm2.bias, blocks3.5.mlp.fc1.weight, blocks3.5.mlp.fc1.bias, blocks3.5.mlp.fc2.weight, blocks3.5.mlp.fc2.bias, blocks3.6.pos_embed.weight, blocks3.6.pos_embed.bias, blocks3.6.norm1.weight, blocks3.6.norm1.bias, blocks3.6.attn.qkv.weight, blocks3.6.attn.qkv.bias, blocks3.6.attn.proj.weight, blocks3.6.attn.proj.bias, blocks3.6.norm2.weight, blocks3.6.norm2.bias, blocks3.6.mlp.fc1.weight, blocks3.6.mlp.fc1.bias, blocks3.6.mlp.fc2.weight, blocks3.6.mlp.fc2.bias, blocks3.7.pos_embed.weight, blocks3.7.pos_embed.bias, blocks3.7.norm1.weight, blocks3.7.norm1.bias, blocks3.7.attn.qkv.weight, blocks3.7.attn.qkv.bias, blocks3.7.attn.proj.weight, blocks3.7.attn.proj.bias, blocks3.7.norm2.weight, blocks3.7.norm2.bias, blocks3.7.mlp.fc1.weight, blocks3.7.mlp.fc1.bias, blocks3.7.mlp.fc2.weight, blocks3.7.mlp.fc2.bias, norm3.weight, norm3.bias, blocks4.0.pos_embed.weight, blocks4.0.pos_embed.bias, blocks4.0.norm1.weight, blocks4.0.norm1.bias, blocks4.0.attn.qkv.weight, blocks4.0.attn.qkv.bias, blocks4.0.attn.proj.weight, blocks4.0.attn.proj.bias, blocks4.0.norm2.weight, blocks4.0.norm2.bias, blocks4.0.mlp.fc1.weight, blocks4.0.mlp.fc1.bias, blocks4.0.mlp.fc2.weight, blocks4.0.mlp.fc2.bias, blocks4.1.pos_embed.weight, blocks4.1.pos_embed.bias, blocks4.1.norm1.weight, blocks4.1.norm1.bias, blocks4.1.attn.qkv.weight, blocks4.1.attn.qkv.bias, blocks4.1.attn.proj.weight, blocks4.1.attn.proj.bias, blocks4.1.norm2.weight, blocks4.1.norm2.bias, blocks4.1.mlp.fc1.weight, blocks4.1.mlp.fc1.bias, blocks4.1.mlp.fc2.weight, blocks4.1.mlp.fc2.bias, blocks4.2.pos_embed.weight, blocks4.2.pos_embed.bias, blocks4.2.norm1.weight, blocks4.2.norm1.bias, blocks4.2.attn.qkv.weight, blocks4.2.attn.qkv.bias, blocks4.2.attn.proj.weight, blocks4.2.attn.proj.bias, blocks4.2.norm2.weight, blocks4.2.norm2.bias, blocks4.2.mlp.fc1.weight, blocks4.2.mlp.fc1.bias, blocks4.2.mlp.fc2.weight, blocks4.2.mlp.fc2.bias, norm4.weight, norm4.bias

07/24 15:12:04 - mmpose - INFO - Load pretrained model from /root/mmpose/projects/uniformer/pretrained/uniformer_small_in1k.pth
07/24 15:12:08 - 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.
07/24 15:12:08 - 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                      
(NORMAL      ) SyncBuffersHook                    
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train:
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
loading annotations into memory...
Done (t=0.29s)
creating index...
index created!
loading annotations into memory...
Done (t=0.19s)
creating index...
index created!
Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_384x288_global_small-7a613f78_20230724.pth
Downloading: "https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_384x288_global_small-7a613f78_20230724.pth" to /root/.cache/torch/hub/checkpoints/top_down_384x288_global_small-7a613f78_20230724.pth
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96.3M/96.3M [00:04<00:00, 21.2MB/s]
07/24 15:12:21 - mmengine - INFO - Load checkpoint from https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_384x288_global_small-7a613f78_20230724.pth
07/24 15:13:45 - mmengine - INFO - Epoch(test) [ 50/407]    eta: 0:09:58  time: 1.675995  data_time: 0.252927  memory: 6540  
07/24 15:15:07 - mmengine - INFO - Epoch(test) [100/407]    eta: 0:08:28  time: 1.635582  data_time: 0.206682  memory: 6540  
07/24 15:16:29 - mmengine - INFO - Epoch(test) [150/407]    eta: 0:07:04  time: 1.646374  data_time: 0.215712  memory: 6540  
07/24 15:17:51 - mmengine - INFO - Epoch(test) [200/407]    eta: 0:05:41  time: 1.637084  data_time: 0.210125  memory: 6540  
07/24 15:19:13 - mmengine - INFO - Epoch(test) [250/407]    eta: 0:04:18  time: 1.642803  data_time: 0.216182  memory: 6540  
07/24 15:20:35 - mmengine - INFO - Epoch(test) [300/407]    eta: 0:02:56  time: 1.637716  data_time: 0.213193  memory: 6540  
07/24 15:21:57 - mmengine - INFO - Epoch(test) [350/407]    eta: 0:01:33  time: 1.640897  data_time: 0.214671  memory: 6540  
07/24 15:23:19 - mmengine - INFO - Epoch(test) [400/407]    eta: 0:00:11  time: 1.639230  data_time: 0.210880  memory: 6540  
07/24 15:24:03 - mmengine - INFO - Evaluating CocoMetric...
Loading and preparing results...
DONE (t=3.46s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=9.41s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.759
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.906
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.830
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.722
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.830
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.810
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.944
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.873
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.768
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.873
07/24 15:24:16 - mmengine - INFO - Epoch(test) [407/407]    coco/AP: 0.758805  coco/AP .5: 0.906079  coco/AP .75: 0.829732  coco/AP (M): 0.721798  coco/AP (L): 0.829743  coco/AR: 0.810327  coco/AR .5: 0.944112  coco/AR .75: 0.873268  coco/AR (M): 0.768069  coco/AR (L): 0.872575  data_time: 0.217041  time: 1.642961

whereas the accuracy listed in the official UniFormer repo is:

Backbone Input Size AP AP50 AP75 ARM ARL AR FLOPs
UniFormer-S 384x288 75.9 90.6 83.4 68.6 79.0 81.4 11.1G

@Ben-Louis

Ben-Louis commented Jul 24, 2023 •
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Hi, @xin-li-67, thank you for your effort and contribution. Could you please relocate the configs under configs/_base_/ to configs/? It is worth noting that the files in configs/_base/ are typically utilized by all model configs, while it is preferable to store model configs in configs/.

@xin-li-67

xin-li-67 commented Jul 24, 2023

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Hi, @xin-li-67, thank you for your effort and contribution. Could you please relocate the configs under configs/_base_/ to configs/? It is worth noting that the files in configs/_base/ are typically utilized by all model configs, while it is preferable to store model configs in configs/.

Got it! I have moved all the config files under the configs folder and updated the corresponding files. It will come up with README modifications later together.

@xin-li-67

xin-li-67 commented Jul 24, 2023

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Testing result on projects/uniformer/configs/_base_/td-hm_uniformer-b-8xb32-210e_coco-448x320.py:

07/24 15:27:05 - mmpose - INFO - Use torch.utils.checkpoint: False
07/24 15:27:05 - mmpose - INFO - torch.utils.checkpoint number: (0, 0, 0, 0)
07/24 15:27:05 - mmpose - INFO - Use global window for all blocks in stage3
07/24 15:27:06 - mmpose - INFO - Loads checkpoint by local backend from path: /root/mmpose/projects/uniformer/pretrained/uniformer_base_in1k.pth
07/24 15:27:06 - mmpose - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: model

missing keys in source state_dict: patch_embed1.norm.weight, patch_embed1.norm.bias, patch_embed1.proj.weight, patch_embed1.proj.bias, patch_embed2.norm.weight, patch_embed2.norm.bias, patch_embed2.proj.weight, patch_embed2.proj.bias, patch_embed3.norm.weight, patch_embed3.norm.bias, patch_embed3.proj.weight, patch_embed3.proj.bias, patch_embed4.norm.weight, patch_embed4.norm.bias, patch_embed4.proj.weight, patch_embed4.proj.bias, blocks1.0.pos_embed.weight, blocks1.0.pos_embed.bias, blocks1.0.norm1.weight, blocks1.0.norm1.bias, blocks1.0.norm1.running_mean, blocks1.0.norm1.running_var, blocks1.0.conv1.weight, blocks1.0.conv1.bias, blocks1.0.conv2.weight, blocks1.0.conv2.bias, blocks1.0.attn.weight, blocks1.0.attn.bias, blocks1.0.norm2.weight, blocks1.0.norm2.bias, blocks1.0.norm2.running_mean, blocks1.0.norm2.running_var, blocks1.0.mlp.fc1.weight, blocks1.0.mlp.fc1.bias, blocks1.0.mlp.fc2.weight, blocks1.0.mlp.fc2.bias, blocks1.1.pos_embed.weight, blocks1.1.pos_embed.bias, blocks1.1.norm1.weight, blocks1.1.norm1.bias, blocks1.1.norm1.running_mean, blocks1.1.norm1.running_var, blocks1.1.conv1.weight, blocks1.1.conv1.bias, blocks1.1.conv2.weight, blocks1.1.conv2.bias, blocks1.1.attn.weight, blocks1.1.attn.bias, blocks1.1.norm2.weight, blocks1.1.norm2.bias, blocks1.1.norm2.running_mean, blocks1.1.norm2.running_var, blocks1.1.mlp.fc1.weight, blocks1.1.mlp.fc1.bias, blocks1.1.mlp.fc2.weight, blocks1.1.mlp.fc2.bias, blocks1.2.pos_embed.weight, blocks1.2.pos_embed.bias, blocks1.2.norm1.weight, blocks1.2.norm1.bias, blocks1.2.norm1.running_mean, blocks1.2.norm1.running_var, blocks1.2.conv1.weight, blocks1.2.conv1.bias, blocks1.2.conv2.weight, blocks1.2.conv2.bias, blocks1.2.attn.weight, blocks1.2.attn.bias, blocks1.2.norm2.weight, blocks1.2.norm2.bias, blocks1.2.norm2.running_mean, blocks1.2.norm2.running_var, blocks1.2.mlp.fc1.weight, blocks1.2.mlp.fc1.bias, blocks1.2.mlp.fc2.weight, blocks1.2.mlp.fc2.bias, blocks1.3.pos_embed.weight, blocks1.3.pos_embed.bias, blocks1.3.norm1.weight, blocks1.3.norm1.bias, blocks1.3.norm1.running_mean, blocks1.3.norm1.running_var, blocks1.3.conv1.weight, blocks1.3.conv1.bias, blocks1.3.conv2.weight, blocks1.3.conv2.bias, blocks1.3.attn.weight, blocks1.3.attn.bias, blocks1.3.norm2.weight, blocks1.3.norm2.bias, blocks1.3.norm2.running_mean, blocks1.3.norm2.running_var, blocks1.3.mlp.fc1.weight, blocks1.3.mlp.fc1.bias, blocks1.3.mlp.fc2.weight, blocks1.3.mlp.fc2.bias, blocks1.4.pos_embed.weight, blocks1.4.pos_embed.bias, blocks1.4.norm1.weight, blocks1.4.norm1.bias, blocks1.4.norm1.running_mean, blocks1.4.norm1.running_var, blocks1.4.conv1.weight, blocks1.4.conv1.bias, blocks1.4.conv2.weight, blocks1.4.conv2.bias, blocks1.4.attn.weight, blocks1.4.attn.bias, blocks1.4.norm2.weight, blocks1.4.norm2.bias, blocks1.4.norm2.running_mean, blocks1.4.norm2.running_var, blocks1.4.mlp.fc1.weight, blocks1.4.mlp.fc1.bias, blocks1.4.mlp.fc2.weight, blocks1.4.mlp.fc2.bias, norm1.weight, norm1.bias, blocks2.0.pos_embed.weight, blocks2.0.pos_embed.bias, blocks2.0.norm1.weight, blocks2.0.norm1.bias, blocks2.0.norm1.running_mean, blocks2.0.norm1.running_var, blocks2.0.conv1.weight, blocks2.0.conv1.bias, blocks2.0.conv2.weight, blocks2.0.conv2.bias, blocks2.0.attn.weight, blocks2.0.attn.bias, blocks2.0.norm2.weight, blocks2.0.norm2.bias, blocks2.0.norm2.running_mean, blocks2.0.norm2.running_var, blocks2.0.mlp.fc1.weight, blocks2.0.mlp.fc1.bias, blocks2.0.mlp.fc2.weight, blocks2.0.mlp.fc2.bias, blocks2.1.pos_embed.weight, blocks2.1.pos_embed.bias, blocks2.1.norm1.weight, blocks2.1.norm1.bias, blocks2.1.norm1.running_mean, blocks2.1.norm1.running_var, blocks2.1.conv1.weight, blocks2.1.conv1.bias, blocks2.1.conv2.weight, blocks2.1.conv2.bias, blocks2.1.attn.weight, blocks2.1.attn.bias, blocks2.1.norm2.weight, blocks2.1.norm2.bias, blocks2.1.norm2.running_mean, blocks2.1.norm2.running_var, blocks2.1.mlp.fc1.weight, blocks2.1.mlp.fc1.bias, blocks2.1.mlp.fc2.weight, blocks2.1.mlp.fc2.bias, blocks2.2.pos_embed.weight, blocks2.2.pos_embed.bias, blocks2.2.norm1.weight, blocks2.2.norm1.bias, blocks2.2.norm1.running_mean, blocks2.2.norm1.running_var, blocks2.2.conv1.weight, blocks2.2.conv1.bias, blocks2.2.conv2.weight, blocks2.2.conv2.bias, blocks2.2.attn.weight, blocks2.2.attn.bias, blocks2.2.norm2.weight, blocks2.2.norm2.bias, blocks2.2.norm2.running_mean, blocks2.2.norm2.running_var, blocks2.2.mlp.fc1.weight, blocks2.2.mlp.fc1.bias, blocks2.2.mlp.fc2.weight, blocks2.2.mlp.fc2.bias, blocks2.3.pos_embed.weight, blocks2.3.pos_embed.bias, blocks2.3.norm1.weight, blocks2.3.norm1.bias, blocks2.3.norm1.running_mean, blocks2.3.norm1.running_var, blocks2.3.conv1.weight, blocks2.3.conv1.bias, blocks2.3.conv2.weight, blocks2.3.conv2.bias, blocks2.3.attn.weight, blocks2.3.attn.bias, blocks2.3.norm2.weight, blocks2.3.norm2.bias, blocks2.3.norm2.running_mean, blocks2.3.norm2.running_var, blocks2.3.mlp.fc1.weight, blocks2.3.mlp.fc1.bias, blocks2.3.mlp.fc2.weight, blocks2.3.mlp.fc2.bias, blocks2.4.pos_embed.weight, blocks2.4.pos_embed.bias, blocks2.4.norm1.weight, blocks2.4.norm1.bias, blocks2.4.norm1.running_mean, blocks2.4.norm1.running_var, blocks2.4.conv1.weight, blocks2.4.conv1.bias, blocks2.4.conv2.weight, blocks2.4.conv2.bias, blocks2.4.attn.weight, blocks2.4.attn.bias, blocks2.4.norm2.weight, blocks2.4.norm2.bias, blocks2.4.norm2.running_mean, blocks2.4.norm2.running_var, blocks2.4.mlp.fc1.weight, blocks2.4.mlp.fc1.bias, blocks2.4.mlp.fc2.weight, blocks2.4.mlp.fc2.bias, blocks2.5.pos_embed.weight, blocks2.5.pos_embed.bias, blocks2.5.norm1.weight, blocks2.5.norm1.bias, blocks2.5.norm1.running_mean, blocks2.5.norm1.running_var, blocks2.5.conv1.weight, blocks2.5.conv1.bias, blocks2.5.conv2.weight, blocks2.5.conv2.bias, blocks2.5.attn.weight, blocks2.5.attn.bias, blocks2.5.norm2.weight, blocks2.5.norm2.bias, blocks2.5.norm2.running_mean, blocks2.5.norm2.running_var, blocks2.5.mlp.fc1.weight, blocks2.5.mlp.fc1.bias, blocks2.5.mlp.fc2.weight, blocks2.5.mlp.fc2.bias, blocks2.6.pos_embed.weight, blocks2.6.pos_embed.bias, blocks2.6.norm1.weight, blocks2.6.norm1.bias, blocks2.6.norm1.running_mean, blocks2.6.norm1.running_var, blocks2.6.conv1.weight, blocks2.6.conv1.bias, blocks2.6.conv2.weight, blocks2.6.conv2.bias, blocks2.6.attn.weight, blocks2.6.attn.bias, blocks2.6.norm2.weight, blocks2.6.norm2.bias, blocks2.6.norm2.running_mean, blocks2.6.norm2.running_var, blocks2.6.mlp.fc1.weight, blocks2.6.mlp.fc1.bias, blocks2.6.mlp.fc2.weight, blocks2.6.mlp.fc2.bias, blocks2.7.pos_embed.weight, blocks2.7.pos_embed.bias, blocks2.7.norm1.weight, blocks2.7.norm1.bias, blocks2.7.norm1.running_mean, blocks2.7.norm1.running_var, blocks2.7.conv1.weight, blocks2.7.conv1.bias, blocks2.7.conv2.weight, blocks2.7.conv2.bias, blocks2.7.attn.weight, blocks2.7.attn.bias, blocks2.7.norm2.weight, blocks2.7.norm2.bias, blocks2.7.norm2.running_mean, blocks2.7.norm2.running_var, blocks2.7.mlp.fc1.weight, blocks2.7.mlp.fc1.bias, blocks2.7.mlp.fc2.weight, blocks2.7.mlp.fc2.bias, norm2.weight, norm2.bias, blocks3.0.pos_embed.weight, blocks3.0.pos_embed.bias, blocks3.0.norm1.weight, blocks3.0.norm1.bias, blocks3.0.attn.qkv.weight, blocks3.0.attn.qkv.bias, blocks3.0.attn.proj.weight, blocks3.0.attn.proj.bias, blocks3.0.norm2.weight, blocks3.0.norm2.bias, blocks3.0.mlp.fc1.weight, blocks3.0.mlp.fc1.bias, blocks3.0.mlp.fc2.weight, blocks3.0.mlp.fc2.bias, blocks3.1.pos_embed.weight, blocks3.1.pos_embed.bias, blocks3.1.norm1.weight, blocks3.1.norm1.bias, blocks3.1.attn.qkv.weight, blocks3.1.attn.qkv.bias, blocks3.1.attn.proj.weight, blocks3.1.attn.proj.bias, blocks3.1.norm2.weight, blocks3.1.norm2.bias, blocks3.1.mlp.fc1.weight, blocks3.1.mlp.fc1.bias, blocks3.1.mlp.fc2.weight, blocks3.1.mlp.fc2.bias, blocks3.2.pos_embed.weight, blocks3.2.pos_embed.bias, blocks3.2.norm1.weight, blocks3.2.norm1.bias, blocks3.2.attn.qkv.weight, blocks3.2.attn.qkv.bias, blocks3.2.attn.proj.weight, blocks3.2.attn.proj.bias, blocks3.2.norm2.weight, blocks3.2.norm2.bias, blocks3.2.mlp.fc1.weight, blocks3.2.mlp.fc1.bias, blocks3.2.mlp.fc2.weight, blocks3.2.mlp.fc2.bias, blocks3.3.pos_embed.weight, blocks3.3.pos_embed.bias, blocks3.3.norm1.weight, blocks3.3.norm1.bias, blocks3.3.attn.qkv.weight, blocks3.3.attn.qkv.bias, blocks3.3.attn.proj.weight, blocks3.3.attn.proj.bias, blocks3.3.norm2.weight, blocks3.3.norm2.bias, blocks3.3.mlp.fc1.weight, blocks3.3.mlp.fc1.bias, blocks3.3.mlp.fc2.weight, blocks3.3.mlp.fc2.bias, blocks3.4.pos_embed.weight, blocks3.4.pos_embed.bias, blocks3.4.norm1.weight, blocks3.4.norm1.bias, blocks3.4.attn.qkv.weight, blocks3.4.attn.qkv.bias, blocks3.4.attn.proj.weight, blocks3.4.attn.proj.bias, blocks3.4.norm2.weight, blocks3.4.norm2.bias, blocks3.4.mlp.fc1.weight, blocks3.4.mlp.fc1.bias, blocks3.4.mlp.fc2.weight, blocks3.4.mlp.fc2.bias, blocks3.5.pos_embed.weight, blocks3.5.pos_embed.bias, blocks3.5.norm1.weight, blocks3.5.norm1.bias, blocks3.5.attn.qkv.weight, blocks3.5.attn.qkv.bias, blocks3.5.attn.proj.weight, blocks3.5.attn.proj.bias, 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blocks3.13.mlp.fc2.weight, blocks3.13.mlp.fc2.bias, blocks3.14.pos_embed.weight, blocks3.14.pos_embed.bias, blocks3.14.norm1.weight, blocks3.14.norm1.bias, blocks3.14.attn.qkv.weight, blocks3.14.attn.qkv.bias, blocks3.14.attn.proj.weight, blocks3.14.attn.proj.bias, blocks3.14.norm2.weight, blocks3.14.norm2.bias, blocks3.14.mlp.fc1.weight, blocks3.14.mlp.fc1.bias, blocks3.14.mlp.fc2.weight, blocks3.14.mlp.fc2.bias, blocks3.15.pos_embed.weight, blocks3.15.pos_embed.bias, blocks3.15.norm1.weight, blocks3.15.norm1.bias, blocks3.15.attn.qkv.weight, blocks3.15.attn.qkv.bias, blocks3.15.attn.proj.weight, blocks3.15.attn.proj.bias, blocks3.15.norm2.weight, blocks3.15.norm2.bias, blocks3.15.mlp.fc1.weight, blocks3.15.mlp.fc1.bias, blocks3.15.mlp.fc2.weight, blocks3.15.mlp.fc2.bias, blocks3.16.pos_embed.weight, blocks3.16.pos_embed.bias, blocks3.16.norm1.weight, blocks3.16.norm1.bias, blocks3.16.attn.qkv.weight, blocks3.16.attn.qkv.bias, blocks3.16.attn.proj.weight, blocks3.16.attn.proj.bias, 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blocks4.4.mlp.fc1.bias, blocks4.4.mlp.fc2.weight, blocks4.4.mlp.fc2.bias, blocks4.5.pos_embed.weight, blocks4.5.pos_embed.bias, blocks4.5.norm1.weight, blocks4.5.norm1.bias, blocks4.5.attn.qkv.weight, blocks4.5.attn.qkv.bias, blocks4.5.attn.proj.weight, blocks4.5.attn.proj.bias, blocks4.5.norm2.weight, blocks4.5.norm2.bias, blocks4.5.mlp.fc1.weight, blocks4.5.mlp.fc1.bias, blocks4.5.mlp.fc2.weight, blocks4.5.mlp.fc2.bias, blocks4.6.pos_embed.weight, blocks4.6.pos_embed.bias, blocks4.6.norm1.weight, blocks4.6.norm1.bias, blocks4.6.attn.qkv.weight, blocks4.6.attn.qkv.bias, blocks4.6.attn.proj.weight, blocks4.6.attn.proj.bias, blocks4.6.norm2.weight, blocks4.6.norm2.bias, blocks4.6.mlp.fc1.weight, blocks4.6.mlp.fc1.bias, blocks4.6.mlp.fc2.weight, blocks4.6.mlp.fc2.bias, norm4.weight, norm4.bias

07/24 15:27:06 - mmpose - INFO - Load pretrained model from /root/mmpose/projects/uniformer/pretrained/uniformer_base_in1k.pth
07/24 15:27:10 - 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.
07/24 15:27:10 - 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                      
(NORMAL      ) SyncBuffersHook                    
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train:
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
loading annotations into memory...
Done (t=0.30s)
creating index...
index created!
loading annotations into memory...
Done (t=0.18s)
creating index...
index created!
Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_448x320_global_base-a05c185f_20230724.pth
Downloading: "https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_448x320_global_base-a05c185f_20230724.pth" to /root/.cache/torch/hub/checkpoints/top_down_448x320_global_base-a05c185f_20230724.pth
100%|██████████████████████████████████████████████████████████████████████████████| 204M/204M [00:11<00:00, 19.1MB/s]
07/24 15:27:30 - mmengine - INFO - Load checkpoint from https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_448x320_global_base-a05c185f_20230724.pth
07/24 15:30:33 - mmengine - INFO - Epoch(test) [ 50/407]    eta: 0:21:45  time: 3.655887  data_time: 0.280876  memory: 8555  
07/24 15:33:34 - mmengine - INFO - Epoch(test) [100/407]    eta: 0:18:37  time: 3.622624  data_time: 0.227081  memory: 8555  
07/24 15:36:35 - mmengine - INFO - Epoch(test) [150/407]    eta: 0:15:33  time: 3.623958  data_time: 0.230486  memory: 8555  
07/24 15:39:36 - mmengine - INFO - Epoch(test) [200/407]    eta: 0:12:31  time: 3.621793  data_time: 0.223173  memory: 8555  
07/24 15:42:37 - mmengine - INFO - Epoch(test) [250/407]    eta: 0:09:29  time: 3.621299  data_time: 0.229660  memory: 8555  
07/24 15:45:39 - mmengine - INFO - Epoch(test) [300/407]    eta: 0:06:28  time: 3.631738  data_time: 0.235520  memory: 8555  
07/24 15:48:40 - mmengine - INFO - Epoch(test) [350/407]    eta: 0:03:26  time: 3.627835  data_time: 0.227529  memory: 8555  
07/24 15:51:42 - mmengine - INFO - Epoch(test) [400/407]    eta: 0:00:25  time: 3.627315  data_time: 0.237593  memory: 8555  
07/24 15:52:39 - mmengine - INFO - Evaluating CocoMetric...
Loading and preparing results...
DONE (t=3.18s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=9.66s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.774
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.910
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.844
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.739
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.843
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.825
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.949
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.885
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.784
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.884
07/24 15:52:53 - mmengine - INFO - Epoch(test) [407/407]    coco/AP: 0.774310  coco/AP .5: 0.910345  coco/AP .75: 0.844331  coco/AP (M): 0.738556  coco/AP (L): 0.842938  coco/AR: 0.824748  coco/AR .5: 0.948992  coco/AR .75: 0.885076  coco/AR (M): 0.784239  coco/AR (L): 0.884244  data_time: 0.235981  time: 3.626385

whereas the accuracy listed in the official UniFormer repo is:

Backbone Input Size AP AP50 AP75 ARM ARL AR FLOPs
UniFormer-B 448x320 77.4 91.1 84.4 70.2 80.6 82.5 29.6G

@xin-li-67

xin-li-67 commented Jul 24, 2023

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Testing result on projects/uniformer/configs/td-hm_uniformer-s-8xb64-210e_coco-448x320.py:

07/24 16:02:55 - mmpose - INFO - Use torch.utils.checkpoint: False
07/24 16:02:55 - mmpose - INFO - torch.utils.checkpoint number: (0, 0, 0, 0)
07/24 16:02:55 - mmpose - INFO - Use global window for all blocks in stage3
07/24 16:02:55 - mmpose - INFO - Loads checkpoint by local backend from path: /root/mmpose/projects/uniformer/pretrained/uniformer_small_in1k.pth
07/24 16:02:55 - mmpose - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: model

missing keys in source state_dict: patch_embed1.norm.weight, patch_embed1.norm.bias, patch_embed1.proj.weight, patch_embed1.proj.bias, patch_embed2.norm.weight, patch_embed2.norm.bias, patch_embed2.proj.weight, patch_embed2.proj.bias, patch_embed3.norm.weight, patch_embed3.norm.bias, patch_embed3.proj.weight, patch_embed3.proj.bias, patch_embed4.norm.weight, patch_embed4.norm.bias, patch_embed4.proj.weight, patch_embed4.proj.bias, blocks1.0.pos_embed.weight, blocks1.0.pos_embed.bias, blocks1.0.norm1.weight, blocks1.0.norm1.bias, blocks1.0.norm1.running_mean, blocks1.0.norm1.running_var, blocks1.0.conv1.weight, blocks1.0.conv1.bias, blocks1.0.conv2.weight, blocks1.0.conv2.bias, blocks1.0.attn.weight, blocks1.0.attn.bias, blocks1.0.norm2.weight, blocks1.0.norm2.bias, blocks1.0.norm2.running_mean, blocks1.0.norm2.running_var, blocks1.0.mlp.fc1.weight, blocks1.0.mlp.fc1.bias, blocks1.0.mlp.fc2.weight, blocks1.0.mlp.fc2.bias, blocks1.1.pos_embed.weight, blocks1.1.pos_embed.bias, blocks1.1.norm1.weight, blocks1.1.norm1.bias, blocks1.1.norm1.running_mean, blocks1.1.norm1.running_var, blocks1.1.conv1.weight, blocks1.1.conv1.bias, blocks1.1.conv2.weight, blocks1.1.conv2.bias, blocks1.1.attn.weight, blocks1.1.attn.bias, blocks1.1.norm2.weight, blocks1.1.norm2.bias, blocks1.1.norm2.running_mean, blocks1.1.norm2.running_var, blocks1.1.mlp.fc1.weight, blocks1.1.mlp.fc1.bias, blocks1.1.mlp.fc2.weight, blocks1.1.mlp.fc2.bias, blocks1.2.pos_embed.weight, blocks1.2.pos_embed.bias, blocks1.2.norm1.weight, blocks1.2.norm1.bias, blocks1.2.norm1.running_mean, blocks1.2.norm1.running_var, blocks1.2.conv1.weight, blocks1.2.conv1.bias, blocks1.2.conv2.weight, blocks1.2.conv2.bias, blocks1.2.attn.weight, blocks1.2.attn.bias, blocks1.2.norm2.weight, blocks1.2.norm2.bias, blocks1.2.norm2.running_mean, blocks1.2.norm2.running_var, blocks1.2.mlp.fc1.weight, blocks1.2.mlp.fc1.bias, blocks1.2.mlp.fc2.weight, blocks1.2.mlp.fc2.bias, norm1.weight, norm1.bias, blocks2.0.pos_embed.weight, blocks2.0.pos_embed.bias, blocks2.0.norm1.weight, blocks2.0.norm1.bias, blocks2.0.norm1.running_mean, blocks2.0.norm1.running_var, blocks2.0.conv1.weight, blocks2.0.conv1.bias, blocks2.0.conv2.weight, blocks2.0.conv2.bias, blocks2.0.attn.weight, blocks2.0.attn.bias, blocks2.0.norm2.weight, blocks2.0.norm2.bias, blocks2.0.norm2.running_mean, blocks2.0.norm2.running_var, blocks2.0.mlp.fc1.weight, blocks2.0.mlp.fc1.bias, blocks2.0.mlp.fc2.weight, blocks2.0.mlp.fc2.bias, blocks2.1.pos_embed.weight, blocks2.1.pos_embed.bias, blocks2.1.norm1.weight, blocks2.1.norm1.bias, blocks2.1.norm1.running_mean, blocks2.1.norm1.running_var, blocks2.1.conv1.weight, blocks2.1.conv1.bias, blocks2.1.conv2.weight, blocks2.1.conv2.bias, blocks2.1.attn.weight, blocks2.1.attn.bias, blocks2.1.norm2.weight, blocks2.1.norm2.bias, blocks2.1.norm2.running_mean, blocks2.1.norm2.running_var, blocks2.1.mlp.fc1.weight, blocks2.1.mlp.fc1.bias, blocks2.1.mlp.fc2.weight, blocks2.1.mlp.fc2.bias, blocks2.2.pos_embed.weight, blocks2.2.pos_embed.bias, blocks2.2.norm1.weight, blocks2.2.norm1.bias, blocks2.2.norm1.running_mean, blocks2.2.norm1.running_var, blocks2.2.conv1.weight, blocks2.2.conv1.bias, blocks2.2.conv2.weight, blocks2.2.conv2.bias, blocks2.2.attn.weight, blocks2.2.attn.bias, blocks2.2.norm2.weight, blocks2.2.norm2.bias, blocks2.2.norm2.running_mean, blocks2.2.norm2.running_var, blocks2.2.mlp.fc1.weight, blocks2.2.mlp.fc1.bias, blocks2.2.mlp.fc2.weight, blocks2.2.mlp.fc2.bias, blocks2.3.pos_embed.weight, blocks2.3.pos_embed.bias, blocks2.3.norm1.weight, blocks2.3.norm1.bias, blocks2.3.norm1.running_mean, blocks2.3.norm1.running_var, blocks2.3.conv1.weight, blocks2.3.conv1.bias, blocks2.3.conv2.weight, blocks2.3.conv2.bias, blocks2.3.attn.weight, blocks2.3.attn.bias, blocks2.3.norm2.weight, blocks2.3.norm2.bias, blocks2.3.norm2.running_mean, blocks2.3.norm2.running_var, blocks2.3.mlp.fc1.weight, blocks2.3.mlp.fc1.bias, blocks2.3.mlp.fc2.weight, blocks2.3.mlp.fc2.bias, norm2.weight, norm2.bias, blocks3.0.pos_embed.weight, blocks3.0.pos_embed.bias, blocks3.0.norm1.weight, blocks3.0.norm1.bias, blocks3.0.attn.qkv.weight, blocks3.0.attn.qkv.bias, blocks3.0.attn.proj.weight, blocks3.0.attn.proj.bias, blocks3.0.norm2.weight, blocks3.0.norm2.bias, blocks3.0.mlp.fc1.weight, blocks3.0.mlp.fc1.bias, blocks3.0.mlp.fc2.weight, blocks3.0.mlp.fc2.bias, blocks3.1.pos_embed.weight, blocks3.1.pos_embed.bias, blocks3.1.norm1.weight, blocks3.1.norm1.bias, blocks3.1.attn.qkv.weight, blocks3.1.attn.qkv.bias, blocks3.1.attn.proj.weight, blocks3.1.attn.proj.bias, blocks3.1.norm2.weight, blocks3.1.norm2.bias, blocks3.1.mlp.fc1.weight, blocks3.1.mlp.fc1.bias, blocks3.1.mlp.fc2.weight, blocks3.1.mlp.fc2.bias, blocks3.2.pos_embed.weight, blocks3.2.pos_embed.bias, blocks3.2.norm1.weight, blocks3.2.norm1.bias, blocks3.2.attn.qkv.weight, blocks3.2.attn.qkv.bias, blocks3.2.attn.proj.weight, blocks3.2.attn.proj.bias, blocks3.2.norm2.weight, blocks3.2.norm2.bias, blocks3.2.mlp.fc1.weight, blocks3.2.mlp.fc1.bias, blocks3.2.mlp.fc2.weight, blocks3.2.mlp.fc2.bias, blocks3.3.pos_embed.weight, blocks3.3.pos_embed.bias, blocks3.3.norm1.weight, blocks3.3.norm1.bias, blocks3.3.attn.qkv.weight, blocks3.3.attn.qkv.bias, blocks3.3.attn.proj.weight, blocks3.3.attn.proj.bias, blocks3.3.norm2.weight, blocks3.3.norm2.bias, blocks3.3.mlp.fc1.weight, blocks3.3.mlp.fc1.bias, blocks3.3.mlp.fc2.weight, blocks3.3.mlp.fc2.bias, blocks3.4.pos_embed.weight, blocks3.4.pos_embed.bias, blocks3.4.norm1.weight, blocks3.4.norm1.bias, blocks3.4.attn.qkv.weight, blocks3.4.attn.qkv.bias, blocks3.4.attn.proj.weight, blocks3.4.attn.proj.bias, blocks3.4.norm2.weight, blocks3.4.norm2.bias, blocks3.4.mlp.fc1.weight, blocks3.4.mlp.fc1.bias, blocks3.4.mlp.fc2.weight, blocks3.4.mlp.fc2.bias, blocks3.5.pos_embed.weight, blocks3.5.pos_embed.bias, blocks3.5.norm1.weight, blocks3.5.norm1.bias, blocks3.5.attn.qkv.weight, blocks3.5.attn.qkv.bias, blocks3.5.attn.proj.weight, blocks3.5.attn.proj.bias, blocks3.5.norm2.weight, blocks3.5.norm2.bias, blocks3.5.mlp.fc1.weight, blocks3.5.mlp.fc1.bias, blocks3.5.mlp.fc2.weight, blocks3.5.mlp.fc2.bias, blocks3.6.pos_embed.weight, blocks3.6.pos_embed.bias, blocks3.6.norm1.weight, blocks3.6.norm1.bias, blocks3.6.attn.qkv.weight, blocks3.6.attn.qkv.bias, blocks3.6.attn.proj.weight, blocks3.6.attn.proj.bias, blocks3.6.norm2.weight, blocks3.6.norm2.bias, blocks3.6.mlp.fc1.weight, blocks3.6.mlp.fc1.bias, blocks3.6.mlp.fc2.weight, blocks3.6.mlp.fc2.bias, blocks3.7.pos_embed.weight, blocks3.7.pos_embed.bias, blocks3.7.norm1.weight, blocks3.7.norm1.bias, blocks3.7.attn.qkv.weight, blocks3.7.attn.qkv.bias, blocks3.7.attn.proj.weight, blocks3.7.attn.proj.bias, blocks3.7.norm2.weight, blocks3.7.norm2.bias, blocks3.7.mlp.fc1.weight, blocks3.7.mlp.fc1.bias, blocks3.7.mlp.fc2.weight, blocks3.7.mlp.fc2.bias, norm3.weight, norm3.bias, blocks4.0.pos_embed.weight, blocks4.0.pos_embed.bias, blocks4.0.norm1.weight, blocks4.0.norm1.bias, blocks4.0.attn.qkv.weight, blocks4.0.attn.qkv.bias, blocks4.0.attn.proj.weight, blocks4.0.attn.proj.bias, blocks4.0.norm2.weight, blocks4.0.norm2.bias, blocks4.0.mlp.fc1.weight, blocks4.0.mlp.fc1.bias, blocks4.0.mlp.fc2.weight, blocks4.0.mlp.fc2.bias, blocks4.1.pos_embed.weight, blocks4.1.pos_embed.bias, blocks4.1.norm1.weight, blocks4.1.norm1.bias, blocks4.1.attn.qkv.weight, blocks4.1.attn.qkv.bias, blocks4.1.attn.proj.weight, blocks4.1.attn.proj.bias, blocks4.1.norm2.weight, blocks4.1.norm2.bias, blocks4.1.mlp.fc1.weight, blocks4.1.mlp.fc1.bias, blocks4.1.mlp.fc2.weight, blocks4.1.mlp.fc2.bias, blocks4.2.pos_embed.weight, blocks4.2.pos_embed.bias, blocks4.2.norm1.weight, blocks4.2.norm1.bias, blocks4.2.attn.qkv.weight, blocks4.2.attn.qkv.bias, blocks4.2.attn.proj.weight, blocks4.2.attn.proj.bias, blocks4.2.norm2.weight, blocks4.2.norm2.bias, blocks4.2.mlp.fc1.weight, blocks4.2.mlp.fc1.bias, blocks4.2.mlp.fc2.weight, blocks4.2.mlp.fc2.bias, norm4.weight, norm4.bias

07/24 16:02:55 - mmpose - INFO - Load pretrained model from /root/mmpose/projects/uniformer/pretrained/uniformer_small_in1k.pth
07/24 16:02:59 - 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.
07/24 16:02:59 - 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                      
(NORMAL      ) SyncBuffersHook                    
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) SyncBuffersHook                    
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train:
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) PoseVisualizationHook              
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
loading annotations into memory...
Done (t=0.29s)
creating index...
index created!
loading annotations into memory...
Done (t=0.18s)
creating index...
index created!
Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_448x320_global_small-18b760de_20230724.pth
Downloading: "https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_448x320_global_small-18b760de_20230724.pth" to /root/.cache/torch/hub/checkpoints/top_down_448x320_global_small-18b760de_20230724.pth
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96.3M/96.3M [00:05<00:00, 19.8MB/s]
07/24 16:03:13 - mmengine - INFO - Load checkpoint from https://download.openmmlab.com/mmpose/v1/projects/uniformer/top_down_448x320_global_small-18b760de_20230724.pth
07/24 16:05:00 - mmengine - INFO - Epoch(test) [ 50/407]    eta: 0:12:44  time: 2.140249  data_time: 0.281717  memory: 8446  
07/24 16:06:45 - mmengine - INFO - Epoch(test) [100/407]    eta: 0:10:51  time: 2.104473  data_time: 0.235087  memory: 8446  
07/24 16:08:30 - mmengine - INFO - Epoch(test) [150/407]    eta: 0:09:03  time: 2.101846  data_time: 0.232801  memory: 8446  
07/24 16:10:16 - mmengine - INFO - Epoch(test) [200/407]    eta: 0:07:17  time: 2.103954  data_time: 0.233861  memory: 8446  
07/24 16:12:01 - mmengine - INFO - Epoch(test) [250/407]    eta: 0:05:31  time: 2.114850  data_time: 0.245181  memory: 8446  
07/24 16:13:47 - mmengine - INFO - Epoch(test) [300/407]    eta: 0:03:45  time: 2.105382  data_time: 0.237319  memory: 8446  
07/24 16:15:32 - mmengine - INFO - Epoch(test) [350/407]    eta: 0:02:00  time: 2.099765  data_time: 0.229156  memory: 8446  
07/24 16:17:17 - mmengine - INFO - Epoch(test) [400/407]    eta: 0:00:14  time: 2.104526  data_time: 0.233125  memory: 8446  
07/24 16:18:04 - mmengine - INFO - Evaluating CocoMetric...
Loading and preparing results...
DONE (t=3.50s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=9.13s).
Accumulating evaluation results...
DONE (t=0.32s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.762
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.906
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.832
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.725
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.834
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.814
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.944
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.876
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.772
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.877
07/24 16:18:17 - mmengine - INFO - Epoch(test) [407/407]    coco/AP: 0.762134  coco/AP .5: 0.906027  coco/AP .75: 0.832140  coco/AP (M): 0.725317  coco/AP (L): 0.833510  coco/AR: 0.814421  coco/AR .5: 0.944112  coco/AR .75: 0.876417  coco/AR (M): 0.772111  coco/AR (L): 0.876886  data_time: 0.240286  time: 2.107453

whereas the accuracy listed in the official UniFormer repo is:

Backbone Input Size AP AP50 AP75 ARM ARL AR FLOPs
UniFormer-S 448x320 76.2 90.6 83.2 68.6 79.4 81.4 14.8G

Tau-J
Tau-J approved these changes Jul 24, 2023
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@Tau-J Tau-J left a comment

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LGTM

@Tau-J Tau-J changed the title Add UniFormer Pose Estimation to Projects folder [Feature][MMSIG] Add UniFormer Pose Estimation to Projects folder Jul 24, 2023
@Tau-J Tau-J merged commit 85831b8 into open-mmlab:dev-1.x Jul 24, 2023
@xin-li-67 xin-li-67 deleted the uniformer_projects branch July 24, 2023 12:08
@Tau-J Tau-J mentioned this pull request Jul 14, 2023
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Tau-J added a commit that referenced this pull request Oct 12, 2023
@Tau-J @Ben-Louis
@LareinaM @xin-li-67 @Indigo6 @xiexinch @tpoisonooo @crazysteeaam @yzd-v @lawenlic @lwttttt @k-yomo @LiuYi-Up @ZhaoQiiii @Yang-Changhui @juxuan27
Dev 1.x (#2752)
6d10b2e 
* update

* [Fix] Fix HRFormer log link

* [Feature] Add Application 'Just dance' (#2528)

* [Docs] Add advanced tutorial of implement new model. (#2539)

* [Doc] Update img (#2541)

* [Feature] Support MotionBERT (#2482)

* [Fix] Fix demo scripts (#2542)

* [Fix] Fix Pose3dInferencer keypoint shape bug (#2543)

* [Enhance] Add notifications when saving visualization results (#2545)

* [Fix] MotionBERT training and flip-test (#2548)

* [Docs] Enhance docs (#2555)

* [Docs] Fix links in doc (#2557)

* [Docs] add details (#2558)

* [Refactor] 3d human pose demo (#2554)

* [Docs] Update MotionBERT docs (#2559)

* [Refactor] Update the arguments of 3d inferencer to align with the demo script (#2561)

* [Enhance] Combined dataset supports custom sampling ratio (#2562)

* [Docs] Add MultiSourceSampler docs (#2563)

* [Doc] Refine docs (#2564)

* [Feature][MMSIG] Add UniFormer Pose Estimation to Projects folder (#2501)

* [Fix] Check the compatibility of inferencer's input/output  (#2567)

* [Fix]Fix 3d visualization (#2565)

* [Feature] Add bear example in just dance (#2568)

* [Doc] Add example and openxlab link for just dance (#2571)

* [Fix] Configs' paths of VideoPose3d (#2572)

* [Docs] update docs (#2573)

* [Fix] Fix new config bug in train.py (#2575)

* [Fix] Configs' of MotionBERT (#2574)

* [Enhance] Normalization option in 3d human pose demo and inferencer (#2576)

* [Fix] Fix the incorrect labels for training vis_head with combined datasets (#2550)

* [Enhance] Enhance 3dpose demo and docs (#2578)

* [Docs] Enhance Codecs documents (#2580)

* [Feature] Add DWPose distilled WholeBody RTMPose models (#2581)

* [Docs] Add deployment docs (#2582)

* [Fix] Refine 3dpose (#2583)

* [Fix] Fix config typo in rtmpose-x (#2585)

* [Fix] Fix 3d inferencer (#2593)

* [Feature] Add a simple visualize api (#2596)

* [Feature][MMSIG] Support badcase analyze in test (#2584)

* [Fix] fix bug in flip_bbox with xyxy format (#2598)

* [Feature] Support ubody dataset (2d keypoints) (#2588)

* [Fix] Fix visualization bug in 3d pose (#2594)

* [Fix] Remove use-multi-frames option (#2601)

* [Enhance] Update demos (#2602)

* [Enhance] wholebody support  openpose style visualization (#2609)

* [Docs] Documentation regarding 3d pose (#2599)

* [CodeCamp2023-533] Migration Deepfashion topdown heatmap algorithms to 1.x (#2597)

* [Fix] fix badcase hook (#2616)

* [Fix] Update dataset mim downloading source to OpenXLab (#2614)

* [Docs] Update docs structure (#2617)

* [Docs] Refine Docs (#2619)

* [Fix] Fix numpy error (#2626)

* [Docs] Update error info and docs (#2624)

* [Fix] Fix inferencer argument name (#2627)

* [Fix] fix links for coco+aic hrnet (#2630)

* [Fix] fix a bug when visualize keypoint indices (#2631)

* [Docs] Update rtmpose docs (#2642)

* [Docs] update README.md (#2647)

* [Docs] Add onnx of RTMPose models (#2656)

* [Docs] Fix mmengine link (#2655)

* [Docs] Update QR code (#2653)

* [Feature] Add DWPose (#2643)

* [Refactor] Reorganize distillers (#2658)

* [CodeCamp2023-259]Document Writing: Advanced Tutorial - Custom Data Augmentation (#2605)

* [Docs] Fix installation docs(#2668)

* [Fix] Fix expired links in README (#2673)

* [Feature] Support multi-dataset evaluation (#2674)

* [Refactor] Specify labels to pack in codecs (#2659)

* [Refactor] update mapping tables (#2676)

* [Fix] fix link (#2677)

* [Enhance] Enable CocoMetric to get ann_file from MessageHub (#2678)

* [Fix] fix vitpose pretrained ckpts (#2687)

* [Refactor] Refactor YOLOX-Pose into mmpose core package (#2620)

* [Fix] Fix typo in COCOMetric(#2691)

* [Fix] Fix bug raised by changing bbox_center to input_center (#2693)

* [Feature] Surpport EDPose for inference(#2688)

* [Refactor] Internet for 3d hand pose estimation (#2632)

* [Fix] Change test batch_size of edpose to 1 (#2701)

* [Docs] Add OpenXLab Badge (#2698)

* [Doc] fix inferencer doc (#2702)

* [Docs] Refine dataset config tutorial (#2707)

* [Fix] modify yoloxpose test settings (#2706)

* [Fix] add compatibility for argument `return_datasample` (#2708)

* [Feature] Support ubody3d dataset (#2699)

* [Fix] Fix 3d inferencer (#2709)

* [Fix] Move ubody3d dataset to wholebody3d (#2712)

* [Refactor] Refactor config and dataset file structures (#2711)

* [Fix] give more clues when loading img failed (#2714)

* [Feature] Add demo script for 3d hand pose  (#2710)

* [Fix] Fix Internet demo (#2717)

* [codecamp: mmpose-315] 300W-LP data set support (#2716)

* [Fix] Fix the typo in YOLOX-Pose (#2719)

* [Feature] Add detectors trained on humanart (#2724)

* [Feature] Add RTMPose-Wholebody (#2721)

* [Doc] Fix github action badge in README (#2727)

* [Fix] Fix bug of dwpose (#2728)

* [Feature] Support hand3d inferencer (#2729)

* [Fix] Fix new config of RTMW (#2731)

* [Fix] Align visualization color of 3d demo (#2734)

* [Fix] Refine h36m data loading and add head_size to PackPoseInputs (#2735)

* [Refactor] Align test accuracy for AE (#2737)

* [Refactor] Separate evaluation mappings from KeypointConverter (#2738)

* [Fix] MotionbertLabel codec (#2739)

* [Fix] Fix mask shape (#2740)

* [Feature] Add training datasets of RTMW (#2743)

* [Doc] update RTMPose README (#2744)

* [Fix] skip warnings in demo (#2746)

* Bump 1.2 (#2748)

* add comments in dekr configs (#2751)

---------

Co-authored-by: Peng Lu <penglu2097@gmail.com>
Co-authored-by: Yifan Lareina WU <mhsj16lareina@gmail.com>
Co-authored-by: Xin Li <7219519+xin-li-67@users.noreply.github.com>
Co-authored-by: Indigo6 <40358785+Indigo6@users.noreply.github.com>
Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
Co-authored-by: tpoisonooo <khj.application@aliyun.com>
Co-authored-by: zhengjie.xu <jerryxuzhengjie@gmail.com>
Co-authored-by: Mesopotamia <54797851+yzd-v@users.noreply.github.com>
Co-authored-by: chaodyna <li0331_1@163.com>
Co-authored-by: lwttttt <85999869+lwttttt@users.noreply.github.com>
Co-authored-by: Kanji Yomoda <Kanji.yy@gmail.com>
Co-authored-by: LiuYi-Up <73060646+LiuYi-Up@users.noreply.github.com>
Co-authored-by: ZhaoQiiii <102809799+ZhaoQiiii@users.noreply.github.com>
Co-authored-by: Yang-ChangHui <71805205+Yang-Changhui@users.noreply.github.com>
Co-authored-by: Xuan Ju <89566272+juxuan27@users.noreply.github.com>
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@xin-li-67 @Ben-Louis @Tau-J