调整训练的轮次和数据集位置

修改该文件可调整训练中涉及的一些参数，default_config.yaml文件在提供的数据包中的根路径下，涉及修改的部分已经加了中文注释
# 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
output_dir: "/cache"
data_path: "/cache/data"
output_path: "/cache/train"
load_path: "/cache/checkpoint_path"
device_target: "Ascend"
need_modelarts_dataset_unzip: True
modelarts_dataset_unzip_name: "coco"

# ==============================================================================
# Train options
data_dir: "motorcycle-coco/"
per_batch_size: 32
yolov5_version: "yolov5s"
pretrained_backbone: ""
resume_yolov5: ""
pretrained_checkpoint: ""

lr_scheduler: "cosine_annealing"
lr: 0.013
lr_epochs: "220,250"
lr_gamma: 0.1
eta_min: 0.0
T_max: 300
#训练轮次
max_epoch: 2
warmup_epochs: 20
weight_decay: 0.0005
momentum: 0.9
loss_scale: 1024
label_smooth: 0
label_smooth_factor: 0.1
log_interval: 100
#ckpt输出位置
ckpt_path: "outputs/"
ckpt_interval: 1
is_save_on_master: 1
is_distributed: 0
rank: 0
group_size: 1
need_profiler: 0
training_shape: ""
resize_rate: 10
is_modelArts: 0

# Eval options
pretrained: ""
#log输出位置
log_path: "outputs/"
ann_val_file: "annotations/val.json"
eval_nms_thresh: 0.6
eval_shape: ""
ignore_threshold: 0.7
test_ignore_threshold: 0.001
multi_label: True
multi_label_thresh: 0.1

# Export options
device_id: 0
batch_size: 1
testing_shape: 640
ckpt_file: ""
file_name: "yolov5"
file_format: "MINDIR"
dataset_path: ""
ann_file: ""


# Other default config
hue: 0.015
saturation: 1.5
value: 0.4
jitter: 0.3

multi_scale: [[320, 320],
              [352, 352],
              [384, 384],
              [416, 416],
              [448, 448],
              [480, 480],
              [512, 512],
              [544, 544],
              [576, 576],
              [608, 608],
              [640, 640],
              [672, 672],
              [704, 704],
              [736, 736],
              [768, 768]]
num_classes: 80
max_box: 150

# h->w
anchor_scales: [[12, 16],
                [19, 36],
                [40, 28],
                [36, 75],
                [76, 55],
                [72, 146],
                [142, 110],
                [192, 243],
                [459, 401]]

out_channel: 255  # 3 * (num_classes + 5)

input_shape: [[3, 32, 64, 128, 256, 512, 1],
              [3, 48, 96, 192, 384, 768, 2],
              [3, 64, 128, 256, 512, 1024, 3],
              [3, 80, 160, 320, 640, 1280, 4]]

# test_param
test_img_shape: [640, 640]

labels: [ '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' ]

coco_ids: [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27,
            28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53,
            54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80,
            81, 82, 84, 85, 86, 87, 88, 89, 90 ]

result_files: './result_Files'

---

# Help description for each configuration
# Train options
data_dir: "Train dataset directory."
per_batch_size: "Batch size for Training."
pretrained_backbone: "The ckpt file of CspDarkNet53."
resume_yolov5: "The ckpt file of YOLOv5, which used to fine tune."
pretrained_checkpoint: "The ckpt file of YOLOv5CspDarkNet53."
lr_scheduler: "Learning rate scheduler, options: exponential, cosine_annealing."
lr: "Learning rate."
lr_epochs: "Epoch of changing of lr changing, split with ','."
lr_gamma: "Decrease lr by a factor of exponential lr_scheduler."
eta_min: "Eta_min in cosine_annealing scheduler."
T_max: "T-max in cosine_annealing scheduler."
max_epoch: "Max epoch num to train the model."
warmup_epochs: "Warmup epochs."
weight_decay: "Weight decay factor."
momentum: "Momentum."
loss_scale: "Static loss scale."
label_smooth: "Whether to use label smooth in CE."
label_smooth_factor: "Smooth strength of original one-hot."
log_interval: "Logging interval steps."
ckpt_path: "Checkpoint save location."
ckpt_interval: "Save checkpoint interval."
is_save_on_master: "Save ckpt on master or all rank, 1 for master, 0 for all ranks."
is_distributed: "Distribute train or not, 1 for yes, 0 for no."
rank: "Local rank of distributed."
group_size: "World size of device."
need_profiler: "Whether use profiler. 0 for no, 1 for yes."
training_shape: "Fix training shape."
resize_rate: "Resize rate for multi-scale training."
ann_file: "path to annotation"
each_multiscale: "Apply multi-scale for each scale"
labels: "the label of train data"
multi_label: "use multi label to nms"
multi_label_thresh: "multi label thresh"

# Eval options
pretrained: "model_path, local pretrained model to load"
log_path: "checkpoint save location"
ann_val_file: "path to annotation"

# Export options
device_id: "Device id for export"
batch_size: "batch size for export"
testing_shape: "shape for test"
ckpt_file: "Checkpoint file path for export"
file_name: "output file name for export"
file_format: "file format for export"
result_files: 'path to 310 infer result floder'