261 lines
12 KiB
YAML
261 lines
12 KiB
YAML
ENV:
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USE_PL: False
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# SET GLOBAL SYSTEM
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SOLVER:
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# NAME DESCRIPTION: TYPE: default: 'TrainValSolver'
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NAME: TrainValSolver
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# RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
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RESUME_FROM:
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# MAX_EPOCHS DESCRIPTION: Max epochs for training. TYPE: int default: 10
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MAX_EPOCHS: 200
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# NUM_FOLDS DESCRIPTION: Num folds for training. TYPE: int default: 0
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NUM_FOLDS: 1
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# WORK_DIR DESCRIPTION: Save dir of the training log or model. TYPE: str default: ''
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WORK_DIR: ./cache/save_data/example/
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LOG_FILE: std_log.txt
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# EVAL_INTERVAL DESCRIPTION: Eval the model interval. TYPE: int default: 1
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EVAL_INTERVAL: 1
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ACCU_STEP: 1
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# DO_FINAL_EVAL DESCRIPTION: If do final evaluation or not. TYPE: bool default: False
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DO_FINAL_EVAL: True
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# SAVE_EVAL_DATA DESCRIPTION: If save the evaluation data or not. TYPE: bool default: False
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SAVE_EVAL_DATA: True
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# EXTRA_KEYS DESCRIPTION: The extra keys for metric. TYPE: list default: []
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EXTRA_KEYS: []
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# TRAIN_DATA DESCRIPTION: Train data config. TYPE: default: ''
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TRAIN_DATA:
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# NAME DESCRIPTION: TYPE: default: 'ImageClassifyPublicDataset'
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NAME: ImageClassifyPublicDataset
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# DATASET DESCRIPTION: the public dataset name TYPE: str default: 'cifar10'
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DATASET: cifar10
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# DATA_ROOT DESCRIPTION: the download data save path TYPE: str default: ''
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DATA_ROOT: cifar10
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# MODE DESCRIPTION: test TYPE: str default: test
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MODE: train
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# PIN_MEMORY DESCRIPTION: pin_memory for data loader TYPE: bool default: False
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PIN_MEMORY: True
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# BATCH_SIZE DESCRIPTION: batch size for data TYPE: int default: 4
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BATCH_SIZE: 96
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# NUM_WORKERS DESCRIPTION: num workers for fetching data! TYPE: int default: 1
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NUM_WORKERS: 4
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# TRANSFORMS DESCRIPTION: TYPE: default:
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TRANSFORMS:
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# - DESCRIPTION: TYPE: default:
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- # NAME DESCRIPTION: TYPE: default: 'RandomResizedCrop'
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NAME: RandomResizedCrop
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SIZE: 32
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# RATIO DESCRIPTION: ratio TYPE: list default: [0.75, 1.3333333333333333]
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RATIO: [0.75, 1.33]
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# SCALE DESCRIPTION: scale TYPE: list default: [0.08, 1.0]
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SCALE: [0.8, 1.0]
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# INTERPOLATION DESCRIPTION: interpolation TYPE: str default: 'blilinear'
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INTERPOLATION: bilinear
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# INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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INPUT_KEY: img
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# OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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OUTPUT_KEY: img
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# BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
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BACKEND: pillow
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- # NAME DESCRIPTION: TYPE: default: 'RandomHorizontalFlip'
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NAME: RandomHorizontalFlip
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# P DESCRIPTION: P TYPE: float default: 0.5
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P: 0.5
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# INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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INPUT_KEY: img
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# OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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OUTPUT_KEY: img
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# BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
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BACKEND: pillow
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- # NAME DESCRIPTION: TYPE: default: 'ImageToTensor'
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NAME: ImageToTensor
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# INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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INPUT_KEY: img
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# OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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OUTPUT_KEY: img
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# BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
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BACKEND: pillow
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- # NAME DESCRIPTION: TYPE: default: 'Normalize'
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NAME: Normalize
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# MEAN DESCRIPTION: mean TYPE: list default: []
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MEAN: [0.4914, 0.4822, 0.4465]
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# STD DESCRIPTION: std TYPE: list default: []
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STD: [0.2023, 0.1994, 0.2010]
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# INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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INPUT_KEY: img
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# OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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OUTPUT_KEY: img
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# BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
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BACKEND: pillow
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- NAME: ToTensor
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# KEYS DESCRIPTION: keys TYPE: list default: []
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KEYS: ["img", "label"]
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- # NAME DESCRIPTION: TYPE: default: 'Select'
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NAME: Select
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# KEYS DESCRIPTION: keys TYPE: list default: []
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KEYS: ["img", "label"]
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# META_KEYS DESCRIPTION: meta keys TYPE: list default: []
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META_KEYS: []
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# EVAL_DATA DESCRIPTION: Eval data config. TYPE: default: ''
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EVAL_DATA:
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# NAME DESCRIPTION: TYPE: default: 'ImageClassifyPublicDataset'
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NAME: ImageClassifyPublicDataset
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# DATASET DESCRIPTION: the public dataset name TYPE: str default: 'cifar10'
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DATASET: cifar10
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# DATA_ROOT DESCRIPTION: the download data save path TYPE: str default: ''
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DATA_ROOT: ./cache/cache_data/cifar10
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# MODE DESCRIPTION: test TYPE: str default: test
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MODE: test
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# PIN_MEMORY DESCRIPTION: pin_memory for data loader TYPE: bool default: False
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PIN_MEMORY: True
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# BATCH_SIZE DESCRIPTION: batch size for data TYPE: int default: 4
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BATCH_SIZE: 96
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# NUM_WORKERS DESCRIPTION: num workers for fetching data! TYPE: int default: 1
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NUM_WORKERS: 4
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# TRANSFORMS DESCRIPTION: TYPE: default:
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TRANSFORMS:
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# - DESCRIPTION: TYPE: default:
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- # NAME DESCRIPTION: TYPE: default: 'Resize'
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NAME: Resize
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SIZE: 32
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# INTERPOLATION DESCRIPTION: interpolation TYPE: str default: 'blilinear'
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INTERPOLATION: bilinear
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# INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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INPUT_KEY: img
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# OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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OUTPUT_KEY: img
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# BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
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BACKEND: pillow
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- # NAME DESCRIPTION: TYPE: default: 'ImageToTensor'
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NAME: ImageToTensor
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# INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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INPUT_KEY: img
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# OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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OUTPUT_KEY: img
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# BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
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BACKEND: pillow
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- # NAME DESCRIPTION: TYPE: default: 'Normalize'
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NAME: Normalize
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# MEAN DESCRIPTION: mean TYPE: list default: []
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MEAN: [0.4914, 0.4822, 0.4465]
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# STD DESCRIPTION: std TYPE: list default: []
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STD: [0.2023, 0.1994, 0.2010]
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# INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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INPUT_KEY: img
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# OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
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OUTPUT_KEY: img
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# BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
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BACKEND: pillow
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- NAME: ToTensor
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# KEYS DESCRIPTION: keys TYPE: list default: []
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KEYS: ["img", "label"]
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- # NAME DESCRIPTION: TYPE: default: 'Select'
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NAME: Select
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# KEYS DESCRIPTION: keys TYPE: list default: []
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KEYS: ["img", "label"]
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# META_KEYS DESCRIPTION: meta keys TYPE: list default: []
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META_KEYS: []
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# TRAIN_HOOKS DESCRIPTION: TYPE: default: ''
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TRAIN_HOOKS:
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- # NAME DESCRIPTION: TYPE: default: 'LogHook'
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NAME: LogHook
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# LOG_INTERVAL DESCRIPTION: the interval for log print! TYPE: int default: 10
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LOG_INTERVAL: 10
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# EVAL_HOOKS DESCRIPTION: TYPE: default: ''
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EVAL_HOOKS:
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- # NAME DESCRIPTION: TYPE: default: 'LogHook'
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NAME: LogHook
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# LOG_INTERVAL DESCRIPTION: the interval for log print! TYPE: int default: 10
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LOG_INTERVAL: 10
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# TEST_HOOKS DESCRIPTION: TYPE: default: ''
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MODEL:
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# NAME DESCRIPTION: TYPE: default: 'Classifier'
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NAME: Classifier
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# ACT_NAME DESCRIPTION: the activation function for logits, select from [softmax, sigmoid]! TYPE: str default: 'softmax'
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ACT_NAME: softmax
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# FREEZE_BN DESCRIPTION: if freeze bn of not TYPE: bool default: False
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FREEZE_BN: False
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# BACKBONE DESCRIPTION: TYPE: default: ''
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BACKBONE:
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# NAME DESCRIPTION: TYPE: default: 'ResNet'
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NAME: ResNet
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# DEPTH DESCRIPTION: the depth of network for resnet! TYPE: int default: 18
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DEPTH: 18
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# PRETRAINED DESCRIPTION: if load the official pretrained model or not. TYPE: bool default: False
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PRETRAINED: false
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#
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KERNEL_SIZE: 3
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# USE_RELU DESCRIPTION: use relu or not! TYPE: bool default: True
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USE_RELU: True
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# USE_MAXPOOL DESCRIPTION: use maxpool or not! TYPE: bool default: True
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USE_MAXPOOL: false
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# FIRST_CONV_STRIDE DESCRIPTION: first conv stride 1 or 2! TYPE: int default: 1
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FIRST_CONV_STRIDE: 1
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# FIRST_MAX_POOL_STRIDE DESCRIPTION: first max pool stride 1 or 2! TYPE: int default: 1
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FIRST_MAX_POOL_STRIDE: 1
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# NECK DESCRIPTION: TYPE: default: ''
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NECK:
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# NAME DESCRIPTION: TYPE: default: 'GlobalAveragePooling'
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NAME: GlobalAveragePooling
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# DIM DESCRIPTION: GlobalAveragePooling dim! TYPE: int default: 2
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DIM: 2
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# HEAD DESCRIPTION: TYPE: default: ''
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HEAD:
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# NAME DESCRIPTION: TYPE: default: 'ClassifierHead'
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NAME: ClassifierHead
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# DIM DESCRIPTION: representation dim! TYPE: int default: 512
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DIM: 512
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# NUM_CLASSES DESCRIPTION: number of classes. TYPE: int default: 10
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NUM_CLASSES: 10
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# DROPOUT_RATE DESCRIPTION: dropout rate, default 0. TYPE: float default: 0.0
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DROPOUT_RATE: 0.0
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METRIC:
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# NAME DESCRIPTION: TYPE: default: 'AccuracyMetric'
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NAME: AccuracyMetric
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# TOPK DESCRIPTION: topk accuracy! TYPE: int default: 1
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TOPK: 1
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# LOSS DESCRIPTION: TYPE: default: ''
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LOSS:
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# NAME DESCRIPTION: TYPE: default: 'CrossEntropy'
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NAME: CrossEntropy
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# REDUCE DESCRIPTION: reduce is False, returns a loss per batch element instead and ignores :attr: size_average. Default: True TYPE: NoneType default: None
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# REDUCE: None
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# SIZE_AVERAGE DESCRIPTION: Deprecated (see :attr: reduction). By default,the losses are averaged over each loss element in the batch. Note that forsome losses, there are multiple elements per sample. If the field :attr: size_averageis set to False, the losses are instead summed for each minibatch. Ignoredwhen :attr: reduce is False. Default: True TYPE: NoneType default: None
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# SIZE_AVERAGE: None
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# IGNORE_INDEX DESCRIPTION: Specifies a target value that is ignoredand does not contribute to the input gradient. When :attr: size_average isTrue, the loss is averaged over non-ignored targets. Note that:attr: ignore_index is only applicable when the target contains class indices. TYPE: int default: -100
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# IGNORE_INDEX: -100
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# REDUCTION DESCRIPTION: Specifies the reduction to apply to the output:'none' | 'mean' | 'sum'. 'none': no reduction willbe applied, 'mean': the weighted mean of the output is taken,'sum': the output will be summed. Note: :attr: size_averageand :attr:`reduce` are in the process of being deprecated, and inthe meantime, specifying either of those two args will override:attr:`reduction`. Default: 'mean' TYPE: str default: 'mean'
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# REDUCTION: mean
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# LABEL_SMOOTHING DESCRIPTION: A float in [0.0, 1.0]. Specifies the amountof smoothing when computing the loss, where 0.0 means no smoothing. TYPE: float default: 0.0
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# LABEL_SMOOTHING: 0.0
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# OPTIMIZER DESCRIPTION: TYPE: default: ''
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OPTIMIZER:
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# NAME DESCRIPTION: TYPE: default: 'SGD'
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NAME: SGD
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# LEARNING_RATE DESCRIPTION: the initial learning rate! TYPE: float default: 0.1
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LEARNING_RATE: 0.01
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# MOMENTUM DESCRIPTION: the momentum! TYPE: int default: 0
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MOMENTUM: 0.9
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# DAMPENING DESCRIPTION: the dampening! TYPE: int default: 0
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DAMPENING: 0
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# WEIGHT_DECAY DESCRIPTION: the weight decay! TYPE: int default: 0
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WEIGHT_DECAY: 5e-4
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# NESTEROV DESCRIPTION: the nesterov! TYPE: bool default: False
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NESTEROV: False
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# LR_SCHEDULER DESCRIPTION: TYPE: default: ''
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LR_SCHEDULER:
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# NAME DESCRIPTION: TYPE: default: 'CosineAnnealingLR'
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NAME: CosineAnnealingLR
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# T_MAX DESCRIPTION: the T max! TYPE: float default: 1.0
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T_MAX: 200.0
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# ETA_MIN DESCRIPTION: the eta min! TYPE: int default: 0
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ETA_MIN: 0
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# LAST_EPOCH DESCRIPTION: the last epoch! TYPE: int default: -1
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LAST_EPOCH: -1
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# METRICS DESCRIPTION: TYPE: default: ''
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METRICS:
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- # NAME DESCRIPTION: TYPE: default: 'AccuracyMetric'
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NAME: AccuracyMetric
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# TOPK DESCRIPTION: topk accuracy! TYPE: int default: 1
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TOPK: 1
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KEYS: ["logits", "label"]
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