UserWarning about mmpose1.0

[ChenZhenGui]

When I run runner.val , it show :"UserWarning: Since the spatial dimensions of overlaid_image: (488, 640) and featmap: torch.Size([64, 48]) are not same, the feature map will be interpolated. This may cause mismatch problems ！" waht's my problem

[Ben-Louis]

Hi, thanks for using MMPose. Do you activate the PoseVisualizationHook during validation? And could you please provide the information about the config you use? It will help us check this problem

[ChenZhenGui]

已收到，谢谢

[ChenZhenGui]

Hi, thanks for using MMPose. Do you activate the PoseVisualizationHook during validation? And could you please provide the information about the config you use? It will help us check this problem

this is my config: base = ['../../../base/default_runtime.py',] channel_cfg = dict( num_output_channels=17, dataset_joints=17, dataset_channel=[ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], ], inference_channel=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ])

runtime

train_cfg = dict(max_epochs=300, val_interval=50)

optimizer

optim_wrapper = dict(optimizer=dict( type='AdamW', lr=5e-4, ))

resume = True

load_from = None

log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), dict(type='TensorboardLoggerHook') ])

learning policy

param_scheduler = [ dict( type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), # warm-up dict( type='MultiStepLR', begin=0, end=210, milestones=[170, 240], gamma=0.1, by_epoch=True) ]

automatically scaling LR based on the actual training batch size

auto_scale_lr = dict(base_batch_size=512)

hooks

default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater'))

codec settings

codec = dict( type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)

model settings

model = dict( type='TopdownPoseEstimator', data_preprocessor=dict( type='PoseDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True), backbone=dict( type='MyMobileViT', model_cfg={'layer1': {'out_channels': 32, 'expand_ratio': 4, 'num_blocks': 1, 'stride': 1, 'block_type': 'mv2', 'dropout': 0.1, 'ffn_dropout': 0.0, 'attn_dropout': 0.0}, 'layer2': {'out_channels': 64, 'expand_ratio': 4, 'num_blocks': 3, 'stride': 2, 'block_type': 'mv2', 'dropout': 0.1, 'ffn_dropout': 0.0, 'attn_dropout': 0.0}, 'layer3': {'out_channels': 96, 'transformer_channels': 144, 'ffn_dim': 288, 'transformer_blocks': 2, 'patch_h': 2, 'patch_w': 2, 'stride': 2, 'mv_expand_ratio': 4, 'num_heads': 4, 'block_type': 'mobilevit', 'dropout': 0.1, 'ffn_dropout': 0.0, 'attn_dropout': 0.0}, 'layer4': {'out_channels': 128, 'transformer_channels': 192, 'ffn_dim': 384, 'transformer_blocks': 4, 'patch_h': 2, 'patch_w': 2, 'stride': 2, 'mv_expand_ratio': 4, 'num_heads': 4, 'block_type': 'mobilevit', 'dropout': 0.1, 'ffn_dropout': 0.0, 'attn_dropout': 0.0}, 'layer5': {'out_channels': 160, 'transformer_channels': 240, 'ffn_dim': 480, 'transformer_blocks': 3, 'patch_h': 2, 'patch_w': 2, 'stride': 2, 'mv_expand_ratio': 4, 'num_heads': 4, 'block_type': 'mobilevit', 'dropout': 0.1, 'ffn_dropout': 0.0, 'attn_dropout': 0.0}, 'last_layer_exp_factor': 4, 'cls_dropout': 0.1}, ), head=dict( type='TopdownHeatmapSimpleHead', in_channels=640, out_channels=channel_cfg['num_output_channels'], loss_keypoint=dict(type='KeypointMSELoss', use_target_weight=True), decoder=codec), test_cfg=dict( flip_test=True, flip_mode='heatmap', shift_heatmap=True, output_heatmaps=True ))

base dataset settings

dataset_type = 'CocoDataset' data_mode = 'topdown' data_root = '/data/zgchen/ViTPose/tools/data/coco/'

pipelines

train_pipeline = [ dict(type='LoadImage', file_client_args={{base.file_client_args}}), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), dict(type='RandomHalfBody'), dict(type='RandomBBoxTransform'), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='GenerateTarget', target_type='heatmap', encoder=codec), dict(type='PackPoseInputs') ] val_pipeline = [ dict(type='LoadImage', file_client_args={{base.file_client_args}}), dict(type='GetBBoxCenterScale'), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='PackPoseInputs'), ]

data loaders

train_dataloader = dict( batch_size=48, num_workers=2, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type=dataset_type, data_root=data_root, data_mode=data_mode, ann_file='annotations/person_keypoints_train2017.json', data_prefix=dict(img='train2017/'), pipeline=train_pipeline, )) val_dataloader = dict( batch_size=48, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), dataset=dict( type=dataset_type, data_root=data_root, data_mode=data_mode, ann_file='annotations/person_keypoints_val2017.json', bbox_file='/data/zgchen/ViTPose/tools/data/coco/person_detection_results/' 'COCO_val2017_detections_AP_H_56_person.json', data_prefix=dict(img='val2017/'), test_mode=True, pipeline=val_pipeline, )) test_dataloader = val_dataloader

evaluators

val_evaluator = dict( type='CocoMetric', ann_file=data_root + 'annotations/person_keypoints_val2017.json') test_evaluator = val_evaluator

when I try to test the visualization,program error：img_path = data_batch[0]['data_samples'].get('img_path') KeyError: 0 then I try to debug and change data_batch[0]['data_samples'].get('img_path') to data_batch['data_samples'][0].get('img_path'),it works,but have new problem like my question above

[ChenZhenGui]

After changing，my verification image becomes like this：

[Ben-Louis]

The reason for this problem is that the output heatmap is not aligned with the bounding box on the original image. Thank you for pointing it out.

Could you please add

data_sample = merge_data_samples([data_sample])

after this line and check the result?

[ChenZhenGui]

It worked! thank you very much for your help！

[Ben-Louis]

Nice. Would you like to raise a PR to fix this problem?

[ChenZhenGui]

well,I don't know much about that function, let me try it now

[Ben-Louis]

Thanks for your contribution! Here is a step-by-step guideline which might be helpful

[ChenZhenGui]

thank you，and It would be better if there is a Chinese version

[ChenZhenGui]

By the way, does this modified place need to be commit together?

[Ben-Louis]

By the way, does this modified place need to be commit together?

Yes, please change it together. The Chinese version of guideline is added in the above page

[ChenZhenGui]

hello ，I'm trying to push my code, and I'm getting this error halfway through, how can I fix it?

[Ben-Louis]

You could try the following steps:

1. Fork the MMPose
2. In the terminal, execute git remote add origin git@github.com:ChenZhenGui/mmpose.git if fail, execute git remote set-url origin git@github.com:ChenZhenGui/mmpose.git
3. Then you can push your commit

[ChenZhenGui]

Thanks a lot, but it doesn't seem to pick up dev-1.x branch

[Ben-Louis]

Hi @ChenZhenGui, could you please check if you successfully commit the changes and push the commit?

• Check if the changes are committed execute git status
  • if mmpose/engine/hooks/visualization_hook.py does not appear, then execute git log and check if you are the author of the latest (top) commit
  • if mmpose/engine/hooks/visualization_hook.py appears, then
  • execute

    git add mmpose/engine/hooks/visualization_hook.py
    git commit -m 'fix visualization hook' --no-verify

    • execute git log and check if you are the author of the latest (top) commit

If you find you are the author of the latest commit, then you could push the commit with git push origin fix-visualization-hook

[ChenZhenGui]

I re-created a new branch and successfully submitted it to the dev-1.x branch, please check if my steps are correct

[ChenZhenGui]

Is it done?

[ly015]

Yes, PR #1800 has been merged into MMPose dev-1.x branch, and will be part of the next release. Thanks again for your help! We will close this issue now and feel free to re-open it or submit a new one if there are further questions.