| **# config yaml guide** | |
| KeyPoint config guide，Take an example of [tinypose_256x192.yml](../../configs/keypoint/tiny_pose/tinypose_256x192.yml) | |
| ```yaml | |
| use_gpu: true #train with gpu or not | |
| log_iter: 5 #print log every 5 iter | |
| save_dir: output #the directory to save model | |
| snapshot_epoch: 10 #save model every 10 epochs | |
| weights: output/tinypose_256x192/model_final #the weight to load(without postfix “.pdparams”） | |
| epoch: 420 #the total epoch number to train | |
| num_joints: &num_joints 17 #number of joints | |
| pixel_std: &pixel_std 200 #the standard pixel length（don't care） | |
| metric: KeyPointTopDownCOCOEval #metric function | |
| num_classes: 1 #number of classes（just for object detection, don't care） | |
| train_height: &train_height 256 #the height of model input | |
| train_width: &train_width 192 #the width of model input | |
| trainsize: &trainsize [*train_width, *train_height] #the shape of model input | |
| hmsize: &hmsize [48, 64] #the shape of model output | |
| flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]] #the correspondence between left and right keypoint id, for example: left wrist become right wrist after image flip, and also the right wrist becomes left wrist | |
| \#####model | |
| architecture: TopDownHRNet #the model architecture | |
| TopDownHRNet: #TopDownHRNet configs | |
| backbone: LiteHRNet #which backbone to use | |
| post_process: HRNetPostProcess #the post_process to use | |
| flip_perm: *flip_perm #same to the upper "flip_perm" | |
| num_joints: *num_joints #the joint number（the number of output channels） | |
| width: &width 40 #backbone output channels | |
| loss: KeyPointMSELoss #loss funciton | |
| use_dark: true #whther to use DarkPose in postprocess | |
| LiteHRNet: #LiteHRNet configs | |
| network_type: wider_naive #the network type of backbone | |
| freeze_at: -1 #the branch match this id doesn't backward，-1 means all branch backward | |
| freeze_norm: false #whether to freeze normalize weights | |
| return_idx: [0] #the branch id to fetch features | |
| KeyPointMSELoss: #Loss configs | |
| use_target_weight: true #whether to use target weights | |
| loss_scale: 1.0 #loss weights，finalloss = loss*loss_scale | |
| \#####optimizer | |
| LearningRate: #LearningRate configs | |
| base_lr: 0.002 #the original base learning rate | |
| schedulers: | |
| \- !PiecewiseDecay #the scheduler to adjust learning rate | |
| milestones: [380, 410] #the milestones(epochs) to adjust learning rate | |
| gamma: 0.1 #the ratio to adjust learning rate, new_lr = lr*gamma | |
| \- !LinearWarmup #Warmup configs | |
| start_factor: 0.001 #the original ratio with respect to base_lr | |
| steps: 500 #iters used to warmup | |
| OptimizerBuilder: #Optimizer type configs | |
| optimizer: | |
| type: Adam #optimizer type: Adam | |
| regularizer: | |
| factor: 0.0 #the regularizer weight | |
| type: L2 #regularizer type: L2/L1 | |
| \#####data | |
| TrainDataset: #Train Dataset configs | |
| !KeypointTopDownCocoDataset #the dataset class to load data | |
| image_dir: "" #the image directory, relative to dataset_dir | |
| anno_path: aic_coco_train_cocoformat.json #the train datalist，coco format, relative to dataset_dir | |
| dataset_dir: dataset #the dataset directory, the image_dir and anno_path based on this directory | |
| num_joints: *num_joints #joint numbers | |
| trainsize: *trainsize #the input size of model | |
| pixel_std: *pixel_std #same to the upper "pixel_std" | |
| use_gt_bbox: True #whether to use gt bbox, commonly used in eval | |
| EvalDataset: #Eval Dataset configs | |
| !KeypointTopDownCocoDataset #the dataset class to load data | |
| image_dir: val2017 #the image directory, relative to dataset_dir | |
| anno_path: annotations/person_keypoints_val2017.json #the eval datalist，coco format, relative to dataset_dir | |
| dataset_dir: dataset/coco #the dataset directory, the image_dir and anno_path based on this directory | |
| num_joints: *num_joints #joint numbers | |
| trainsize: *trainsize #the input size of model | |
| pixel_std: *pixel_std #same to the upper "pixel_std" | |
| use_gt_bbox: True #whether to use gt bbox, commonly used in eval | |
| image_thre: 0.5 #the threshold of detected rect, used while use_gt_bbox is False | |
| TestDataset: #the test dataset without label | |
| !ImageFolder #the class to load data, find images by folder | |
| anno_path: dataset/coco/keypoint_imagelist.txt #the image list file | |
| worker_num: 2 #the workers to load Dataset | |
| global_mean: &global_mean [0.485, 0.456, 0.406] #means used to normalize image | |
| global_std: &global_std [0.229, 0.224, 0.225] #stds used to normalize image | |
| TrainReader: #TrainReader configs | |
| sample_transforms: #transform configs | |
| \- RandomFlipHalfBodyTransform: #random flip & random HalfBodyTransform | |
| scale: 0.25 #the maximum scale for size transform | |
| rot: 30 #the maximum rotation to transoform | |
| num_joints_half_body: 8 #the HalfBodyTransform is skiped while joints found is less than this number | |
| prob_half_body: 0.3 #the ratio of halfbody transform | |
| pixel_std: *pixel_std #same to upper "pixel_std" | |
| trainsize: *trainsize #the input size of model | |
| upper_body_ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] #the joint id which is belong to upper body | |
| flip_pairs: *flip_perm #same to the upper "flip_perm" | |
| \- AugmentationbyInformantionDropping: | |
| prob_cutout: 0.5 #the probability to cutout keypoint | |
| offset_factor: 0.05 #the jitter offset of cutout position, expressed as a percentage of trainwidth | |
| num_patch: 1 #the numbers of area to cutout | |
| trainsize: *trainsize #same to upper "trainsize" | |
| \- TopDownAffine: | |
| trainsize: *trainsize #same to upper "trainsize" | |
| use_udp: true #whether to use udp_unbias（just for flip eval） | |
| \- ToHeatmapsTopDown_DARK: #generate gt heatmaps | |
| hmsize: *hmsize #the size of output heatmaps | |
| sigma: 2 #the sigma of gaussin kernel which used to generate gt heatmaps | |
| batch_transforms: | |
| \- NormalizeImage: #image normalize class | |
| mean: *global_mean #mean of normalize | |
| std: *global_std #std of normalize | |
| is_scale: true #whether scale by 1/255 to every image pixels，transform pixel from [0,255] to [0,1] | |
| \- Permute: {} #channel transform from HWC to CHW | |
| batch_size: 128 #batchsize used for train | |
| shuffle: true #whether to shuffle the images before train | |
| drop_last: false #whether drop the last images which is not enogh for batchsize | |
| EvalReader: | |
| sample_transforms: #transform configs | |
| \- TopDownAffine: #Affine configs | |
| trainsize: *trainsize #same to upper "trainsize" | |
| use_udp: true #whether to use udp_unbias（just for flip eval） | |
| batch_transforms: | |
| \- NormalizeImage: #image normalize, the values should be same to values in TrainReader | |
| mean: *global_mean | |
| std: *global_std | |
| is_scale: true | |
| \- Permute: {} #channel transform from HWC to CHW | |
| batch_size: 16 #batchsize used for test | |
| TestReader: | |
| inputs_def: | |
| image_shape: [3, *train_height, *train_width] #the input dimensions used in model，CHW | |
| sample_transforms: | |
| \- Decode: {} #load image | |
| \- TopDownEvalAffine: #Affine class used in Eval | |
| trainsize: *trainsize #the input size of model | |
| \- NormalizeImage: #image normalize, the values should be same to values in TrainReader | |
| mean: *global_mean #mean of normalize | |
| std: *global_std #std of normalize | |
| is_scale: true #whether scale by 1/255 to every image pixels，transform pixel from [0,255] to [0,1] | |
| \- Permute: {} #channel transform from HWC to CHW | |
| batch_size: 1 #Test batchsize | |
| fuse_normalize: false #whether fuse the normalize into model while export model, this speedup the model infer | |
| ``` | |