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- # Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
- enable_modelarts: False
- data_url: ""
- train_url: ""
- checkpoint_url: ""
- data_path: "/cache/data"
- output_path: "/cache/train"
- load_path: "/cache/checkpoint_path"
- checkpoint_path: './checkpoint/'
- checkpoint_file: './checkpoint/'
- device_target: Asend
- enable_profiling: False
-
- pre_trained: "/cache/data"
- coco_root: "./COCO"
- ckpt_path: './'
- ann_file: "./COCO/annotations/instances_train2017.json"
- # ==============================================================================
- modelarts_dataset_unzip_name: 'cocodataset'
- need_modelarts_dataset_unzip: True
-
- img_path: ''
- result_path: ''
-
- # Training options
- num_parallel_workers: 1
- python_multiprocessing:
- img_width: 1280
- img_height: 768
- keep_ratio: True
- flip_ratio: 0.5
- expand_ratio: 1.0
-
- max_instance_count: 128
- mask_shape: [28, 28]
-
- # LR
- base_lr: 0.02
- base_step: 58633
- total_epoch: 13
- warmup_step: 500
- warmup_ratio: 0.333333
- sgd_momentum: 0.9
-
- # train
- batch_size: 1
- loss_scale: 1
- momentum: 0.91
- weight_decay: 0.0001 # 1e-4
- pretrain_epoch_size: 0
- epoch_size: 12
- save_checkpoint: True
- save_checkpoint_epochs: 1
- keep_checkpoint_max: 12
- save_checkpoint_path: "./"
-
- mindrecord_dir: "./MindRecord_COCO"
- train_data_type: "train2017"
- val_data_type: "val2017"
- instance_set: "annotations/instances_{}.json"
- coco_classes: ['background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
- 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
- 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
- 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
- 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
- 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
- 'kite', 'baseball bat', 'baseball glove', 'skateboard',
- 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
- 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
- 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
- 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
- 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
- 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
- 'refrigerator', 'book', 'clock', 'vase', 'scissors',
- 'teddy bear', 'hair drier', 'toothbrush']
- num_classes: 81
-
- only_create_dataset: False
- run_distribute: False
- do_train: True
- do_eval: False
- dataset: "coco"
- device_id: 0
- device_num: 1
- rank_id: 0
-
- # queryinst export
- file_name: "queryinst"
- file_format: "MINDIR"
- ckpt_file: '/'
- ckpt_file_local: './'
- export_input_type: float16
-
- # other
- learning_rate: 0.002
- buffer_size: 1000
- save_checkpoint_steps: 1000
- sink_size: -1
- dataset_sink_mode: True
- lr: 0.01
-
- # Model Description
- model_name: queryinst
-
- ---
- # Config description for each option
- enable_modelarts: 'Whether training on modelarts, default: False'
- data_url: 'Dataset url for obs'
- train_url: 'Training output url for obs'
- data_path: 'Dataset path for local'
- output_path: 'Training output path for local'
- ann_file: 'Ann file, default is val.json.'
-
- device_target: 'Target device type'
- enable_profiling: 'Whether enable profiling while training, default: False'
- only_create_dataset: 'If set it true, only create Mindrecord, default is false.'
- run_distribute: 'Run distribute, default is false.'
- do_train: 'Do train or not, default is true.'
- do_eval: 'Do eval or not, default is false.'
- dataset: 'Dataset, default is coco.'
- pre_trained: 'Pretrain file path.'
- device_id: 'Device id, default is 0.'
- device_num: 'Use device nums, default is 1.'
- rank_id: 'Rank id, default is 0.'
- file_format: 'file format'
- img_path: "image file path."
- result_path: "result file path."
-
- ---
- device_target: ['Ascend', 'GPU', 'CPU']
- file_format: ["AIR", "ONNX", "MINDIR"]
- export_input_type: ["float16", "float32"]
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