Open ghost opened 6 years ago
L58 in the config.
Yeah, i changed it, but besides that no more changes needed?
As far as I know, you just need to modify this place. However you also need to implement your own dataloader.
i tried it, but it still not working well (high loss, really poor detection results)
i tried to use the inspect_humanpose.ipynb for debugging my code, but when i try to Inspect the DetectionKeypointTargetLayer if i use test mode of 'training' the rois, target_bbox, target_bbox, target_keypoint_lables, target_keypoint_weight are all zero, otherwise it throws me the following error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'input_gt_keypoints' with dtype float and shape [?,?,1,3] [[Node: input_gt_keypoints = Placeholder[dtype=DT_FLOAT, shape=[?,?,1,3], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]] [[Node: proposal_targets/rois/_2851 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:
Hi @Superlee506 ,
By writing a new dataloader do you mean I have to rewrite the load mask function?
Hey, thank you for your fast reply. I tried to run the model for training data with 2 keypoints detection (not human pose estimation), but the loss of the keypoint mask branch is high, and the model does not detect the correct bboxes (regular mask r cnn does and detects very well) do you have any suggestion how transform the current implementation for 2 keypoints detection? (besides changing the number of keypoints of course)
thank you very much