forked from mindspore-Ecosystem/mindspore
210 lines
5.9 KiB
YAML
210 lines
5.9 KiB
YAML
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
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enable_modelarts: False
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data_url: ""
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train_url: ""
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checkpoint_url: ""
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data_path: "/cache/data"
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output_path: "/cache/train"
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load_path: "/cache/checkpoint_path"
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checkpoint_file: './checkpoint/mask_rcnn-12_7393.ckpt'
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device_target: Ascend
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enable_profiling: False
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# ==============================================================================
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modelarts_dataset_unzip_name: 'cocodataset'
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need_modelarts_dataset_unzip: True
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# MaskRcnn training
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only_create_dataset: False
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run_distribute: False
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do_train: True
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do_eval: False
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dataset: "coco"
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pre_trained: './maskrcnn_mobilenetv1/maskrcnn_mobilenetv1_12_7393.ckpt'
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device_id: 0
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device_num: 1
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rank_id: 0
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# MaskRcnn evaluation
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ann_file: "./annotations/instances_val2017.json"
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checkpoint_path: './maskrcnn_mobilenetv1/maskrcnn_mobilenetv1_12_7393.ckpt'
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# maskrcnn mobilnetv1 export"
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batch_size_export: 1
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ckpt_file: '/cache/data/cocodataset/checkpoint/mask_rcnn-12_7393.ckpt' #############
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file_name: "maskrcnn_mobilenetv1"
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file_format: "MINDIR"
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# maskrcnn-mobilenetv1 inference
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img_path: '' # "image file path."
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result_path: '' # "result file path."
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# ==============================================================================
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# config
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img_width: 1280
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img_height: 768
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keep_ratio: True
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flip_ratio: 0.5
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expand_ratio: 1.0
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max_instance_count: 128
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mask_shape: (28, 28)
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# anchor
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feature_shapes: [(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)]
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anchor_scales: [8]
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anchor_ratios: [0.5, 1.0, 2.0]
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anchor_strides: [4, 8, 16, 32, 64]
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num_anchors: 3
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# fpn
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fpn_in_channels: [128, 256, 512, 1024]
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fpn_out_channels: 256
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fpn_num_outs: 5
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# rpn
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rpn_in_channels: 256
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rpn_feat_channels: 256
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rpn_loss_cls_weight: 1.0
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rpn_loss_reg_weight: 1.0
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rpn_cls_out_channels: 1
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rpn_target_means: [0., 0., 0., 0.]
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rpn_target_stds: [1.0, 1.0, 1.0, 1.0]
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# bbox_assign_sampler
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neg_iou_thr: 0.3
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pos_iou_thr: 0.7
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min_pos_iou: 0.3
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num_bboxes: 245520
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num_gts: 128
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num_expected_neg: 256
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num_expected_pos: 128
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# proposal
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activate_num_classes: 2
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use_sigmoid_cls: True
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# roi_align
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roi_layer: dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2)
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roi_align_out_channels: 256
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roi_align_featmap_strides: [4, 8, 16, 32]
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roi_align_finest_scale: 56
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roi_sample_num: 640
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# bbox_assign_sampler_stage2
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neg_iou_thr_stage2: 0.5
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pos_iou_thr_stage2: 0.5
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min_pos_iou_stage2: 0.5
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num_bboxes_stage2: 2000
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num_expected_pos_stage2: 128
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num_expected_neg_stage2: 512
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num_expected_total_stage2: 512
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# rcnn
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rcnn_num_layers: 2
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rcnn_in_channels: 256
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rcnn_fc_out_channels: 1024
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rcnn_mask_out_channels: 256
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rcnn_loss_cls_weight: 1
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rcnn_loss_reg_weight: 1
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rcnn_loss_mask_fb_weight: 1
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rcnn_target_means: [0., 0., 0., 0.]
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rcnn_target_stds: [0.1, 0.1, 0.2, 0.2]
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# train proposal
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rpn_proposal_nms_across_levels: False
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rpn_proposal_nms_pre: 2000
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rpn_proposal_nms_post: 2000
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rpn_proposal_max_num: 2000
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rpn_proposal_nms_thr: 0.7
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rpn_proposal_min_bbox_size: 0
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# test proposal
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rpn_nms_across_levels: False
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rpn_nms_pre: 1000
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rpn_nms_post: 1000
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rpn_max_num: 1000
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rpn_nms_thr: 0.7
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rpn_min_bbox_min_size: 0
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test_score_thr: 0.05
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test_iou_thr: 0.5
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test_max_per_img: 100
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test_batch_size: 2
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rpn_head_use_sigmoid: True
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rpn_head_weight: 1.0
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mask_thr_binary: 0.5
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# LR
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base_lr: 0.04
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base_step: 58633
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total_epoch: 13
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warmup_step: 500
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warmup_ratio: 1/3.0
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sgd_momentum: 0.9
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# train
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batch_size: 2
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loss_scale: 1
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momentum: 0.91
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weight_decay: 0.0001 # 1e-4
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pretrain_epoch_size: 0
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epoch_size: 12
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save_checkpoint: True
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save_checkpoint_epochs: 12
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keep_checkpoint_max: 12
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save_checkpoint_path: "./"
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mindrecord_dir: "./MindRecord_COCO"
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coco_root: "/cache/data"
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train_data_type: "train2017"
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val_data_type: "val2017"
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instance_set: "annotations/instances_{}.json"
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coco_classes: ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
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'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
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'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
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'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
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'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
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'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
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'kite', 'baseball bat', 'baseball glove', 'skateboard',
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'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
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'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
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'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
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'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
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'refrigerator', 'book', 'clock', 'vase', 'scissors',
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'teddy bear', 'hair drier', 'toothbrush')
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num_classes: 81
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---
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# Config description for each option
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enable_modelarts: 'Whether training on modelarts, default: False'
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data_url: 'Dataset url for obs'
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train_url: 'Training output url for obs'
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data_path: 'Dataset path for local'
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output_path: 'Training output path for local'
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ann_file: 'Ann file, default is val.json.'
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dataset: "Dataset, default is coco."
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checkpoint_path: "Checkpoint file path."
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file_name: "output file name."
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device_target: 'Target device type'
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enable_profiling: 'Whether enable profiling while training, default: False'
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only_create_dataset: 'If set it true, only create Mindrecord, default is false.'
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run_distribute: 'Run distribute, default is false.'
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do_train: 'Do train or not, default is true.'
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do_eval: 'Do eval or not, default is false.'
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pre_trained: 'Pretrain file path.'
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device_id: 'Device id, default is 0.'
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device_num: 'Use device nums, default is 1.'
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rank_id: 'Rank id, default is 0.'
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file_format: 'file format'
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img_path: "image file path."
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result_path: "result file path."
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---
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device_target: ['Ascend', 'GPU', 'CPU']
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file_format: ["AIR", "ONNX", "MINDIR"]
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