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- # Runtime
- use_gpu: true
- use_xpu: false
- use_mlu: false
- use_npu: false
- log_iter: 20
- save_dir: output
- snapshot_epoch: 1
- print_flops: false
- print_params: false
- use_ema: true
- # Dataset
- metric: COCO
- num_classes: 80
- TrainDataset:
- name: COCODataSet
- image_dir: train2017
- anno_path: annotations/instances_train2017.json
- dataset_dir: dataset/coco
- data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
- EvalDataset:
- name: COCODataSet
- image_dir: val2017
- anno_path: annotations/instances_val2017.json
- dataset_dir: dataset/coco
- allow_empty: true
- TestDataset:
- name: ImageFolder
- anno_path: annotations/instances_val2017.json # also support txt (like VOC's label_list.txt)
- dataset_dir: dataset/coco # if set, anno_path will be 'dataset_dir/anno_path'
- # Reader
- worker_num: 4
- TrainReader:
- inputs_def:
- image_shape: [3, 512, 512]
- sample_transforms:
- - Decode: {}
- - FlipWarpAffine: {keep_res: False, input_h: 512, input_w: 512, use_random: True}
- - CenterRandColor: {}
- - Lighting: {eigval: [0.2141788, 0.01817699, 0.00341571], eigvec: [[-0.58752847, -0.69563484, 0.41340352], [-0.5832747, 0.00994535, -0.81221408], [-0.56089297, 0.71832671, 0.41158938]]}
- - NormalizeImage: {mean: [0.40789655, 0.44719303, 0.47026116], std: [0.2886383 , 0.27408165, 0.27809834], is_scale: False}
- - Permute: {}
- - Gt2CenterNetTarget: {down_ratio: 4, max_objs: 128}
- batch_size: 16
- shuffle: True
- drop_last: True
- use_shared_memory: True
- EvalReader:
- sample_transforms:
- - Decode: {}
- - WarpAffine: {keep_res: True, input_h: 512, input_w: 512}
- - NormalizeImage: {mean: [0.40789655, 0.44719303, 0.47026116], std: [0.2886383 , 0.27408165, 0.27809834]}
- - Permute: {}
- batch_size: 1
- TestReader:
- inputs_def:
- image_shape: [3, 512, 512]
- sample_transforms:
- - Decode: {}
- - WarpAffine: {keep_res: True, input_h: 512, input_w: 512}
- - NormalizeImage: {mean: [0.40789655, 0.44719303, 0.47026116], std: [0.2886383 , 0.27408165, 0.27809834], is_scale: True}
- - Permute: {}
- batch_size: 1
- # Model
- architecture: CenterNet
- pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_pretrained.pdparams
- norm_type: sync_bn
- use_ema: true
- ema_decay: 0.9998
- CenterNet:
- backbone: ResNet
- neck: CenterNetDLAFPN
- head: CenterNetHead
- post_process: CenterNetPostProcess
- ResNet:
- depth: 50
- variant: d
- return_idx: [0, 1, 2, 3]
- freeze_at: -1
- norm_decay: 0.
- dcn_v2_stages: [3]
- CenterNetDLAFPN:
- first_level: 0
- last_level: 4
- down_ratio: 4
- dcn_v2: False
- CenterNetHead:
- head_planes: 256
- regress_ltrb: False
- CenterNetPostProcess:
- max_per_img: 100
- regress_ltrb: False
- # Optimizer
- epoch: 140
- LearningRate:
- base_lr: 0.0005
- schedulers:
- - !PiecewiseDecay
- gamma: 0.1
- milestones: [90, 120]
- use_warmup: False
- OptimizerBuilder:
- optimizer:
- type: Adam
- regularizer: NULL
- # Exporting the model
- export:
- post_process: True # Whether post-processing is included in the network when export model.
- nms: True # Whether NMS is included in the network when export model.
- benchmark: False # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
- fuse_conv_bn: False
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