更换文档检测模型
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use_gpu: true
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log_iter: 10
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save_dir: output
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snapshot_epoch: 10
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weights: output/higherhrnet_hrnet_w32_512/model_final
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epoch: 300
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num_joints: &num_joints 17
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flip_perm: &flip_perm [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
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input_size: &input_size 512
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hm_size: &hm_size 128
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hm_size_2x: &hm_size_2x 256
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max_people: &max_people 30
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metric: COCO
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IouType: keypoints
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num_classes: 1
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#####model
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architecture: HigherHRNet
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/Trunc_HRNet_W32_C_pretrained.pdparams
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HigherHRNet:
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backbone: HRNet
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hrhrnet_head: HrHRNetHead
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post_process: HrHRNetPostProcess
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flip_perm: *flip_perm
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eval_flip: true
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HRNet:
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width: &width 32
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freeze_at: -1
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freeze_norm: false
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return_idx: [0]
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HrHRNetHead:
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num_joints: *num_joints
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width: *width
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loss: HrHRNetLoss
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swahr: false
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HrHRNetLoss:
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num_joints: *num_joints
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swahr: false
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#####optimizer
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LearningRate:
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base_lr: 0.001
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schedulers:
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- !PiecewiseDecay
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milestones: [200, 260]
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gamma: 0.1
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- !LinearWarmup
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start_factor: 0.001
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steps: 1000
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OptimizerBuilder:
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optimizer:
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type: Adam
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regularizer: None
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#####data
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TrainDataset:
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!KeypointBottomUpCocoDataset
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image_dir: train2017
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anno_path: annotations/person_keypoints_train2017.json
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dataset_dir: dataset/coco
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num_joints: *num_joints
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return_bbox: False
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return_area: False
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return_class: False
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EvalDataset:
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!KeypointBottomUpCocoDataset
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image_dir: val2017
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anno_path: annotations/person_keypoints_val2017.json
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dataset_dir: dataset/coco
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num_joints: *num_joints
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test_mode: true
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return_bbox: False
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return_area: False
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return_class: False
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TestDataset:
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!ImageFolder
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anno_path: dataset/coco/keypoint_imagelist.txt
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worker_num: 8
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global_mean: &global_mean [0.485, 0.456, 0.406]
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global_std: &global_std [0.229, 0.224, 0.225]
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TrainReader:
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sample_transforms:
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- RandomAffine:
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max_degree: 30
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scale: [0.75, 1.5]
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max_shift: 0.2
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trainsize: [*input_size, *input_size]
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hmsize: [*hm_size, *hm_size_2x]
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- KeyPointFlip:
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flip_prob: 0.5
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flip_permutation: *flip_perm
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hmsize: [*hm_size, *hm_size_2x]
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- ToHeatmaps:
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num_joints: *num_joints
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hmsize: [*hm_size, *hm_size_2x]
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sigma: 2
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- TagGenerate:
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num_joints: *num_joints
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max_people: *max_people
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- NormalizePermute:
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mean: *global_mean
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std: *global_std
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batch_size: 20
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shuffle: true
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drop_last: true
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use_shared_memory: true
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EvalReader:
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sample_transforms:
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- EvalAffine:
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size: *input_size
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- NormalizeImage:
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mean: *global_mean
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std: *global_std
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is_scale: true
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- Permute: {}
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batch_size: 1
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TestReader:
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sample_transforms:
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- Decode: {}
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- EvalAffine:
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size: *input_size
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- NormalizeImage:
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mean: *global_mean
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std: *global_std
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is_scale: true
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- Permute: {}
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batch_size: 1
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@@ -0,0 +1,140 @@
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use_gpu: true
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log_iter: 10
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save_dir: output
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snapshot_epoch: 10
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weights: output/higherhrnet_hrnet_w32_512_swahr/model_final
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epoch: 300
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num_joints: &num_joints 17
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flip_perm: &flip_perm [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
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input_size: &input_size 512
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hm_size: &hm_size 128
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hm_size_2x: &hm_size_2x 256
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max_people: &max_people 30
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metric: COCO
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IouType: keypoints
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num_classes: 1
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#####model
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architecture: HigherHRNet
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/Trunc_HRNet_W32_C_pretrained.pdparams
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HigherHRNet:
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backbone: HRNet
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hrhrnet_head: HrHRNetHead
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post_process: HrHRNetPostProcess
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flip_perm: *flip_perm
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eval_flip: true
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HRNet:
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width: &width 32
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freeze_at: -1
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freeze_norm: false
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return_idx: [0]
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HrHRNetHead:
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num_joints: *num_joints
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width: *width
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loss: HrHRNetLoss
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swahr: true
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HrHRNetLoss:
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num_joints: *num_joints
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swahr: true
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#####optimizer
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LearningRate:
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base_lr: 0.001
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schedulers:
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- !PiecewiseDecay
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milestones: [200, 260]
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gamma: 0.1
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- !LinearWarmup
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start_factor: 0.001
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steps: 1000
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OptimizerBuilder:
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optimizer:
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type: Adam
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regularizer: None
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#####data
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TrainDataset:
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!KeypointBottomUpCocoDataset
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image_dir: train2017
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anno_path: annotations/person_keypoints_train2017.json
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dataset_dir: dataset/coco
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num_joints: *num_joints
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return_bbox: False
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return_area: False
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return_class: False
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EvalDataset:
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!KeypointBottomUpCocoDataset
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image_dir: val2017
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anno_path: annotations/person_keypoints_val2017.json
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dataset_dir: dataset/coco
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num_joints: *num_joints
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test_mode: true
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return_bbox: False
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return_area: False
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return_class: False
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TestDataset:
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!ImageFolder
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anno_path: dataset/coco/keypoint_imagelist.txt
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worker_num: 8
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global_mean: &global_mean [0.485, 0.456, 0.406]
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global_std: &global_std [0.229, 0.224, 0.225]
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TrainReader:
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sample_transforms:
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- RandomAffine:
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max_degree: 30
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scale: [0.75, 1.5]
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max_shift: 0.2
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trainsize: [*input_size, *input_size]
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hmsize: [*hm_size, *hm_size_2x]
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- KeyPointFlip:
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flip_prob: 0.5
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flip_permutation: *flip_perm
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hmsize: [*hm_size, *hm_size_2x]
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- ToHeatmaps:
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num_joints: *num_joints
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hmsize: [*hm_size, *hm_size_2x]
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sigma: 2
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- TagGenerate:
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num_joints: *num_joints
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max_people: *max_people
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- NormalizePermute:
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mean: *global_mean
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std: *global_std
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batch_size: 16
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shuffle: true
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drop_last: true
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use_shared_memory: true
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EvalReader:
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sample_transforms:
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- EvalAffine:
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size: *input_size
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- NormalizeImage:
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mean: *global_mean
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std: *global_std
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is_scale: true
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- Permute: {}
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batch_size: 1
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TestReader:
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sample_transforms:
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- Decode: {}
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- EvalAffine:
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size: *input_size
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- NormalizeImage:
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mean: *global_mean
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std: *global_std
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is_scale: true
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- Permute: {}
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batch_size: 1
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@@ -0,0 +1,139 @@
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use_gpu: true
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log_iter: 10
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save_dir: output
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snapshot_epoch: 10
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weights: output/higherhrnet_hrnet_w32_640/model_final
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epoch: 300
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num_joints: &num_joints 17
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flip_perm: &flip_perm [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
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input_size: &input_size 640
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hm_size: &hm_size 160
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hm_size_2x: &hm_size_2x 320
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max_people: &max_people 30
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metric: COCO
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IouType: keypoints
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num_classes: 1
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#####model
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architecture: HigherHRNet
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/Trunc_HRNet_W32_C_pretrained.pdparams
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HigherHRNet:
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backbone: HRNet
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hrhrnet_head: HrHRNetHead
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post_process: HrHRNetPostProcess
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flip_perm: *flip_perm
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eval_flip: true
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HRNet:
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width: &width 32
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freeze_at: -1
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freeze_norm: false
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return_idx: [0]
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HrHRNetHead:
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num_joints: *num_joints
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width: *width
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loss: HrHRNetLoss
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swahr: false
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HrHRNetLoss:
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num_joints: *num_joints
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swahr: false
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#####optimizer
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LearningRate:
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base_lr: 0.001
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schedulers:
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- !PiecewiseDecay
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milestones: [200, 260]
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gamma: 0.1
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- !LinearWarmup
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start_factor: 0.001
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steps: 1000
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OptimizerBuilder:
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optimizer:
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type: Adam
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regularizer: None
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#####data
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TrainDataset:
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!KeypointBottomUpCocoDataset
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image_dir: train2017
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anno_path: annotations/person_keypoints_train2017.json
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dataset_dir: dataset/coco
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num_joints: *num_joints
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return_bbox: False
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return_area: False
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return_class: False
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EvalDataset:
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!KeypointBottomUpCocoDataset
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image_dir: val2017
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anno_path: annotations/person_keypoints_val2017.json
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dataset_dir: dataset/coco
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num_joints: *num_joints
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test_mode: true
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return_bbox: False
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return_area: False
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return_class: False
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TestDataset:
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!ImageFolder
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anno_path: dataset/coco/keypoint_imagelist.txt
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worker_num: 8
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global_mean: &global_mean [0.485, 0.456, 0.406]
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global_std: &global_std [0.229, 0.224, 0.225]
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TrainReader:
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sample_transforms:
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- RandomAffine:
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max_degree: 30
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scale: [0.75, 1.5]
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max_shift: 0.2
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trainsize: [*input_size, *input_size]
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hmsize: [*hm_size, *hm_size_2x]
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- KeyPointFlip:
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flip_prob: 0.5
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flip_permutation: *flip_perm
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hmsize: [*hm_size, *hm_size_2x]
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- ToHeatmaps:
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num_joints: *num_joints
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hmsize: [*hm_size, *hm_size_2x]
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sigma: 2
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- TagGenerate:
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num_joints: *num_joints
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max_people: *max_people
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- NormalizePermute:
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mean: *global_mean
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std: *global_std
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batch_size: 20
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shuffle: true
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drop_last: true
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use_shared_memory: true
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EvalReader:
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sample_transforms:
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- EvalAffine:
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size: *input_size
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- NormalizeImage:
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mean: *global_mean
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std: *global_std
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is_scale: true
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- Permute: {}
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batch_size: 1
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TestReader:
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sample_transforms:
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- Decode: {}
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- EvalAffine:
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size: *input_size
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- NormalizeImage:
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mean: *global_mean
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std: *global_std
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is_scale: true
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- Permute: {}
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batch_size: 1
|
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