forked from huawei/mindspore2022
147 lines
4.7 KiB
YAML
147 lines
4.7 KiB
YAML
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unlesee you know exactly what you are doing)
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enable_modelarts: False
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# url for modelarts
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data_url: ""
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train_url: ""
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checkpoint_url: ""
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# path for local
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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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device_target: "Ascend"
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enable_profiling: False
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need_modelarts_dataset_unzip: True
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modelarts_dataset_unzip_name: "MindRecord_COCO"
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# ======================================================================================
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# common options
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distribute: False
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# ======================================================================================
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# Training options
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img_shape: [600, 600]
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num_retinanet_boxes: 67995
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match_thershold: 0.5
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nms_thershold: 0.6
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min_score: 0.1
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max_boxes: 100
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# learning rate settings
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lr: 0.1
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global_step: 0
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lr_init: 1e-6
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lr_end_rate: 5e-3
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warmup_epochs1: 2
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warmup_epochs2: 5
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warmup_epochs3: 23
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warmup_epochs4: 60
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warmup_epochs5: 160
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momentum: 0.9
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weight_decay: 1.5e-4
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# network
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num_default: [9, 9, 9, 9, 9]
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extras_out_channels: [256, 256, 256, 256, 256]
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feature_size: [75, 38, 19, 10, 5]
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aspect_ratios: [[0.5, 1.0, 2.0], [0.5, 1.0, 2.0], [0.5, 1.0, 2.0], [0.5, 1.0, 2.0], [0.5, 1.0, 2.0]]
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steps: [8, 16, 32, 64, 128]
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anchor_size: [32, 64, 128, 256, 512]
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prior_scaling: [0.1, 0.2]
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gamma: 2.0
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alpha: 0.75
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num_classes: 81
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# `mindrecord_dir` and `coco_root` are better to use absolute path.
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mindrecord_dir: "./"
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coco_root: "./"
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train_data_type: "train2017"
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val_data_type: "val2017"
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instances_set: "./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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# The annotation.json position of voc validation dataset
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voc_root: ""
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# voc original dataset
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voc_dir: ""
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# if coco or voc used, `image_dir` and `anno_path` are useless
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image_dir: ""
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anno_path: ""
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save_checkpoint: True
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save_checkpoint_epochs: 1
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keep_checkpoint_max: 10
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save_checkpoint_path: "./ckpt"
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finish_epoch: 0
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# optimiter options
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workers: 24
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mode: "sink"
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epoch_size: 550
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batch_size: 32
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pre_trained: ""
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pre_trained_epoch_size: 0
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loss_scale: 1024
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filter_weight: False
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# ======================================================================================
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# Eval options
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dataset: "coco"
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checkpoint_path: ""
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# ======================================================================================
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# export options
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device_id: 0
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file_format: "MINDIR"
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export_batch_size: 1
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file_name: "retinanet"
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# ======================================================================================
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# postprocess options
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result_path: ""
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img_path: ""
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img_id_file: ""
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---
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# Help description for each configuration
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enable_modelarts: "Whether training on modelarts default: False"
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data_url: "Url for modelarts"
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train_url: "Url for modelarts"
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data_path: "The location of input data"
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output_pah: "The location of the output file"
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device_target: "device id of GPU or Ascend. (Default: None)"
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enable_profiling: "Whether enable profiling while training default: False"
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workers: "Num parallel workers."
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lr: "Learning rate, default is 0.1."
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mode: "Run sink mode or not, default is sink."
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epoch_size: "Epoch size, default is 500."
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batch_size: "Batch size, default is 32."
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pre_trained: "Pretrained Checkpoint file path."
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pre_trained_epoch_size: "Pretrained epoch size."
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save_checkpoint_epochs: "Save checkpoint epochs, default is 1."
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loss_scale: "Loss scale, default is 1024."
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filter_weight: "Filter weight parameters, default is False."
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dataset: "Dataset, default is coco."
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device_id: "Device id, default is 0."
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file_format: "file format choices [AIR, MINDIR]"
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file_name: "output file name."
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export_batch_size: "batch size"
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result_path: "result file path."
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img_path: "image file path."
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img_id_file: "image id file."
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