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wider_naive_hrnet_18_256x192_coco.yml
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wider_naive_hrnet_18_256x192_coco.yml
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use_gpu: true
log_iter: 5
save_dir: output
snapshot_epoch: 10
weights: output/wider_naive_hrnet_18_256x192_coco/model_final
epoch: 210
num_joints: &num_joints 17
pixel_std: &pixel_std 200
metric: KeyPointTopDownCOCOEval
num_classes: 1
train_height: &train_height 256
train_width: &train_width 192
trainsize: &trainsize [*train_width, *train_height]
hmsize: &hmsize [48, 64]
flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
#####model
architecture: TopDownHRNet
TopDownHRNet:
backbone: LiteHRNet
post_process: HRNetPostProcess
flip_perm: *flip_perm
num_joints: *num_joints
width: &width 40
loss: KeyPointMSELoss
use_dark: false
LiteHRNet:
network_type: wider_naive
freeze_at: -1
freeze_norm: false
return_idx: [0]
KeyPointMSELoss:
use_target_weight: true
loss_scale: 1.0
#####optimizer
LearningRate:
base_lr: 0.002
schedulers:
- !PiecewiseDecay
milestones: [170, 200]
gamma: 0.1
- !LinearWarmup
start_factor: 0.001
steps: 500
OptimizerBuilder:
optimizer:
type: Adam
regularizer:
factor: 0.0
type: L2
#####data
TrainDataset:
!KeypointTopDownCocoDataset
image_dir: train2017
anno_path: annotations/person_keypoints_train2017.json
dataset_dir: dataset/coco
num_joints: *num_joints
trainsize: *trainsize
pixel_std: *pixel_std
use_gt_bbox: True
EvalDataset:
!KeypointTopDownCocoDataset
image_dir: val2017
anno_path: annotations/person_keypoints_val2017.json
dataset_dir: dataset/coco
num_joints: *num_joints
trainsize: *trainsize
pixel_std: *pixel_std
use_gt_bbox: True
image_thre: 0.0
TestDataset:
!ImageFolder
anno_path: dataset/coco/keypoint_imagelist.txt
worker_num: 2
global_mean: &global_mean [0.485, 0.456, 0.406]
global_std: &global_std [0.229, 0.224, 0.225]
TrainReader:
sample_transforms:
- RandomFlipHalfBodyTransform:
scale: 0.25
rot: 30
num_joints_half_body: 8
prob_half_body: 0.3
pixel_std: *pixel_std
trainsize: *trainsize
upper_body_ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
flip_pairs: *flip_perm
- TopDownAffine:
trainsize: *trainsize
- ToHeatmapsTopDown:
hmsize: *hmsize
sigma: 2
batch_transforms:
- NormalizeImage:
mean: *global_mean
std: *global_std
is_scale: true
- Permute: {}
batch_size: 64
shuffle: true
drop_last: false
EvalReader:
sample_transforms:
- TopDownAffine:
trainsize: *trainsize
batch_transforms:
- NormalizeImage:
mean: *global_mean
std: *global_std
is_scale: true
- Permute: {}
batch_size: 16
TestReader:
inputs_def:
image_shape: [3, *train_height, *train_width]
sample_transforms:
- Decode: {}
- TopDownEvalAffine:
trainsize: *trainsize
- NormalizeImage:
mean: *global_mean
std: *global_std
is_scale: true
- Permute: {}
batch_size: 1