Open alaa-shubbak opened 1 year ago
ly015
commented
1 year ago The error information indicates that the cat here is a string, which should be a dict. Could you please verify that your annotation files follow the standard COCO format? Or would you like to share the annotation file so we can reproduce and fix this problem?
alaa-shubbak
commented
1 year ago sure i can share the annotation file of my train dataset as bellow : annotation_train.txt the test dataset has the same format.
alaa-shubbak
commented
1 year ago here also the images and the test annotations. annotation_test.txt images.zip
please help.
ly015
commented
1 year ago Hi, the reason seems to be that in your annotation file the 'categories' field is a dict, which should be a list of dict in COCO format. You can refer to the COCO offical site for details.
alaa-shubbak
commented
1 year ago Thanks . Now my model is being able to train.
and i got a results as .pth for different epochs.
I would like to ask about testing the result and show the keypoint and skeleton over the images.
I used those two commands:
first : python tools/test.py configs xx.pth --eval mAP --work-dir folder .
from this i got a file of results_keypoints.json
which i use it for the second command
second:python demo/top_down_img_demo.py configs xxx.pth --img-root --json-file result_keypoints.json --out-img-root vis_results_exc
I am not sure if i am doing the right in extract test images with skeleton.
when running the second command , i got this error :
AssertionError: annotation file format <class 'list'> not supported
can you please help
ly015
commented
1 year ago The script tools/test.py is for quantitatively evaluating the trained model in metrics like 'AP'. It's output result_keypoints.json are not for visualization.
demo/top_down_image_demo.py can be used to visualize the inference results of a trained model. And the argument --json-file should be the annotation file, like the annotation_train.txt and annotation_test.txt attached above (whose content is actually in json format).
IssaIssa1
commented
1 year ago @ly015 the test.py file saves the prediction into json file. is there a way to call evaluation in this file directly without rerunning and writing the file again?
alaa-shubbak
commented
1 year ago @ly015 I try to test my trained model on some images and want to visualize the results as skeleton.
I use the same command:
demo/top_down_image_demo.py
with my config, .pth file ,and direction of the images. I did not use any json file
i got this error message:
loading annotations into memory... Traceback (most recent call last): File "/home/shubbak/mmpose/demo/top_down_img_demo.py", line 130, in <module> main() File "/home/shubbak/mmpose/demo/top_down_img_demo.py", line 58, in main coco = COCO(args.json_file) File "/home/shubbak/env_pose/lib/python3.9/site-packages/xtcocotools/coco.py", line 93, in __init__ with open(annotation_file, 'r') as f: FileNotFoundError: [Errno 2] No such file or directory: ''
is there any method that i can test my trained model on a group of image without having annotation json file, and at same time visualize the results?
Checklist
I try to train my model on my custom dataset , and i got this error message :
TypeError: Excavator2Dataset: string indices must be integersA clear and concise description of what the bug is.
Reproduction
base = [ '../base/default_runtime.py', '../base/datasets/coco_ex.py' ] evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict( type='Adam', lr=5e-4, ) optimizer_config = dict(grad_clip=None)
learning policy
lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[170, 200]) total_epochs = 210 log_config = dict( interval=1, hooks=[ dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
channel_cfg = dict( num_output_channels=4, dataset_joints=4, dataset_channel=[ [0, 1, 2, 3], ], inference_channel=[ 0, 1, 2, 3 ])
model settings
model = dict( type='TopDown', pretrained='https://download.openmmlab.com/mmpose/' 'pretrain_models/hrnet_w32-36af842e.pth', backbone=dict( type='HRNet', in_channels=3, extra=dict( stage1=dict( num_modules=1, num_branches=1, block='BOTTLENECK', num_blocks=(4, ), num_channels=(64, )), stage2=dict( num_modules=1, num_branches=2, block='BASIC', num_blocks=(4, 4), num_channels=(32, 64)), stage3=dict( num_modules=4, num_branches=3, block='BASIC', num_blocks=(4, 4, 4), num_channels=(32, 64, 128)), stage4=dict( num_modules=3, num_branches=4, block='BASIC', num_blocks=(4, 4, 4, 4), num_channels=(32, 64, 128, 256))), ), keypoint_head=dict( type='TopdownHeatmapSimpleHead', in_channels=32, out_channels=channel_cfg['num_output_channels'], num_deconv_layers=0, extra=dict(final_conv_kernel=1, ), loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), train_cfg=dict(), test_cfg=dict( flip_test=True, post_process='default', shift_heatmap=True, modulate_kernel=11))
data_cfg = dict( image_size=[256, 256], heatmap_size=[64, 64], num_output_channels=channel_cfg['num_output_channels'], num_joints=channel_cfg['dataset_joints'], dataset_channel=channel_cfg['dataset_channel'], inference_channel=channel_cfg['inference_channel'], soft_nms=False, nms_thr=1.0, oks_thr=0.9, vis_thr=0.2, use_gt_bbox=True, det_bbox_thr=0.0, bbox_file='', )
train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='TopDownGetBboxCenterScale', padding=1.25), dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3), dict(type='TopDownRandomFlip', flip_prob=0.5), dict( type='TopDownHalfBodyTransform', num_joints_half_body=8, prob_half_body=0.3), dict( type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), dict(type='TopDownAffine'), dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), dict(type='TopDownGenerateTarget', sigma=2), dict( type='Collect', keys=['img', 'target', 'target_weight'], meta_keys=[ 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', 'rotation', 'bbox_score', 'flip_pairs' ]), ]
val_pipeline = [ dict(type='LoadImageFromFile'), dict(type='TopDownGetBboxCenterScale', padding=1.25), dict(type='TopDownAffine'), dict(type='ToTensor'), dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), dict( type='Collect', keys=['img'], meta_keys=[ 'image_file', 'center', 'scale', 'rotation', 'bbox_score', 'flip_pairs' ]), ]
test_pipeline = val_pipeline
data_root = 'dataset/exc' data = dict( samples_per_gpu=64, workers_per_gpu=2, val_dataloader=dict(samples_per_gpu=32), test_dataloader=dict(samples_per_gpu=32), train=dict( type='Excavator2Dataset', ann_file=f'{data_root}/annotations/annotation_train.json', img_prefix=f'{data_root}/images/', data_cfg=data_cfg, pipeline=train_pipeline, dataset_info={{base.dataset_info}}), val=dict( type='Excavator2Dataset', ann_file=f'{data_root}/annotations/annotation_test.json', img_prefix=f'{data_root}/images/', data_cfg=data_cfg, pipeline=val_pipeline, dataset_info={{base.dataset_info}}), test=dict( type='Excavator2Dataset', ann_file=f'{data_root}/annotations/annotation_test.json', img_prefix=f'{data_root}/images/', data_cfg=data_cfg, pipeline=test_pipeline, dataset_info={{base.dataset_info}}), )