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hrnet_w32_256x256_mpii.yml
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hrnet_w32_256x256_mpii.yml
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use_gpu: true
log_iter: 5
save_dir: output
snapshot_epoch: 10
weights: output/hrnet_w32_256x256_mpii/model_final
epoch: 210
num_joints: &num_joints 16
pixel_std: &pixel_std 200
metric: KeyPointTopDownMPIIEval
num_classes: 1
train_height: &train_height 256
train_width: &train_width 256
trainsize: &trainsize [*train_width, *train_height]
hmsize: &hmsize [64, 64]
flip_perm: &flip_perm [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]]
#####model
architecture: TopDownHRNet
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/Trunc_HRNet_W32_C_pretrained.pdparams
TopDownHRNet:
backbone: HRNet
post_process: HRNetPostProcess
flip_perm: *flip_perm
num_joints: *num_joints
width: &width 32
loss: KeyPointMSELoss
HRNet:
width: *width
freeze_at: -1
freeze_norm: false
return_idx: [0]
KeyPointMSELoss:
use_target_weight: true
#####optimizer
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
milestones: [170, 200]
gamma: 0.1
- !LinearWarmup
start_factor: 0.001
steps: 1000
OptimizerBuilder:
optimizer:
type: Adam
regularizer:
factor: 0.0
type: L2
#####data
TrainDataset:
!KeypointTopDownMPIIDataset
image_dir: images
anno_path: annotations/mpii_train.json
dataset_dir: dataset/mpii
num_joints: *num_joints
EvalDataset:
!KeypointTopDownMPIIDataset
image_dir: images
anno_path: annotations/mpii_val.json
dataset_dir: dataset/mpii
num_joints: *num_joints
TestDataset:
!ImageFolder
anno_path: dataset/coco/keypoint_imagelist.txt
worker_num: 4
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: 0.5
rot: 40
num_joints_half_body: 8
prob_half_body: 0.3
pixel_std: *pixel_std
trainsize: *trainsize
upper_body_ids: [7, 8, 9, 10, 11, 12, 13, 14, 15]
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