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- # Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
- enable_modelarts: False
- # Url for modelarts
- data_url: ""
- train_url: ""
- checkpoint_url: ""
- # Path for local
- data_path: "/cache/data"
- output_path: "/cache/train"
- load_path: "/cache/checkpoint_path"
- device_target: "Ascend"
- enable_profiling: False
-
- # ==============================================================================
- # prepare *.mindrecord* data
- coco_data_dir: "/tmp/"
- mindrecord_dir: "" # also used by train.py
- mindrecord_prefix: "coco_hp.train.mind"
- # train related
- visual_image: "false"
- save_result_dir: ""
- device_id: 0
- device_num: 1
-
- distribute: 'false'
- need_profiler: "false"
- profiler_path: "./profiler"
- epoch_size: 1
- train_steps: -1
- enable_save_ckpt: "true"
- do_shuffle: "true"
- enable_data_sink: "true"
- data_sink_steps: 1
- save_checkpoint_path: ""
- load_checkpoint_path: ""
- save_checkpoint_steps: 1000
- save_checkpoint_num: 1
- # test related
- data_dir: ""
- run_mode: "test"
- enable_eval: "true"
- # export related
- export_load_ckpt: ''
- export_format: ''
- export_name: ''
-
- dataset_config:
- num_classes: 1
- num_joints: 17
- max_objs: 32
- input_res: [512, 512]
- output_res: [128, 128]
- rand_crop: False
- shift: 0.1
- scale: 0.4
- aug_rot: 0.0
- rotate: 0
- flip_prop: 0.5
- mean: np.array([0.40789654, 0.44719302, 0.47026115], dtype=np.float32)
- std: np.array([0.28863828, 0.27408164, 0.27809835], dtype=np.float32)
- flip_idx: [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
- edges: [[0, 1], [0, 2], [1, 3], [2, 4], [4, 6], [3, 5], [5, 6],
- [5, 7], [7, 9], [6, 8], [8, 10], [6, 12], [5, 11], [11, 12],
- [12, 14], [14, 16], [11, 13], [13, 15]]
- eig_val: np.array([0.2141788, 0.01817699, 0.00341571], dtype=np.float32)
- eig_vec: np.array([[-0.58752847, -0.69563484, 0.41340352],
- [-0.5832747, 0.00994535, -0.81221408],
- [-0.56089297, 0.71832671, 0.41158938]], dtype=np.float32)
- categories: [{"supercategory": "person",
- "id": 1,
- "name": "person",
- "keypoints": ["nose", "left_eye", "right_eye", "left_ear", "right_ear",
- "left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
- "left_wrist", "right_wrist", "left_hip", "right_hip",
- "left_knee", "right_knee", "left_ankle", "right_ankle"],
- "skeleton": [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13],
- [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3], [1, 2], [1, 3],
- [2, 4], [3, 5], [4, 6], [5, 7]]}]
-
- net_config:
- down_ratio: 4
- last_level: 6
- final_kernel: 1
- stage_levels: [1, 1, 1, 2, 2, 1]
- stage_channels: [16, 32, 64, 128, 256, 512]
- head_conv: 256
- dense_hp: True
- hm_hp: True
- reg_hp_offset: True
- reg_offset: True
- hm_weight: 1
- off_weight: 1
- wh_weight: 0.1
- hp_weight: 1
- hm_hp_weight: 1
- mse_loss: False
- reg_loss: 'l1'
-
- train_config:
- batch_size: 32
- loss_scale_value: 1024
- optimizer: 'Adam'
- lr_schedule: 'MultiDecay'
- Adam:
- weight_decay: 0.0
- decay_filter: "lambda x: x.name.endswith('.bias') or x.name.endswith('.beta') or x.name.endswith('.gamma')"
- PolyDecay:
- learning_rate: 0.00012 # 1.2e-4
- end_learning_rate: 0.0000005 # 5e-7
- power: 5.0
- eps: 0.0000001 # 1e-7
- warmup_steps: 2000
- MultiDecay:
- learning_rate: 0.00012 # 1.2e-4
- eps: 0.0000001 # 1e-7
- warmup_steps: 2000
- multi_epochs: [270, 300]
- factor: 10
-
- eval_config:
- soft_nms: True
- keep_res: True
- multi_scales: [1.0]
- pad: 31
- K: 100
- score_thresh: 0.3
-
- export_config:
- input_res: dataset_config.input_res
- ckpt_file: "./ckpt_file.ckpt"
- export_format: "MINDIR"
- export_name: "CenterNet_MultiPose"
-
- ---
-
- # Help description for each configuration
- enable_modelarts: "Whether training on modelarts, default: False"
- data_url: "Url for modelarts"
- train_url: "Url for modelarts"
- data_path: "The location of the input data."
- output_path: "The location of the output file."
- device_target: "Running platform, choose from Ascend, GPU or CPU, and default is Ascend."
- enable_profiling: 'Whether enable profiling while training, default: False'
-
- distribute: "Run distribute, default is false."
- need_profiler: "Profiling to parsing runtime info, default is false."
- profiler_path: "The path to save profiling data"
- epoch_size: "Epoch size, default is 1."
- train_steps: "Training Steps, default is -1, i.e. run all steps according to epoch number."
- device_id: "Device id, default is 0."
- device_num: "Use device nums, default is 1."
- enable_save_ckpt: "Enable save checkpoint, default is true."
- do_shuffle: "Enable shuffle for dataset, default is true."
- enable_data_sink: "Enable data sink, default is true."
- data_sink_steps: "Sink steps for each epoch, default is 1."
- save_checkpoint_path: "Save checkpoint path"
- load_checkpoint_path: "Load checkpoint file path"
- save_checkpoint_steps: "Save checkpoint steps, default is 1000."
- save_checkpoint_num: "Save checkpoint numbers, default is 1."
- mindrecord_dir: "Mindrecord dataset files directory"
- mindrecord_prefix: "Prefix of MindRecord dataset filename."
- visual_image: "Visulize the ground truth and predicted image"
- save_result_dir: "The path to save the predict results"
-
- data_dir: "Dataset directory, the absolute image path is joined by the data_dir, and the relative path in anno_path"
- run_mode: "test or validation, default is test."
- enable_eval: "Whether evaluate accuracy after prediction"
- ---
-
- device_target: ['Ascend', 'CPU']
- distribute: ["true", "false"]
- need_profiler: ["true", "false"]
- enable_save_ckpt: ["true", "false"]
- do_shuffle: ["true", "false"]
- enable_data_sink: ["true", "false"]
- export_format: ["MINDIR"]
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