mirror of https://github.com/JDAI-CV/fast-reid.git
136 lines
4.2 KiB
Python
136 lines
4.2 KiB
Python
from yacs.config import CfgNode as CN
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# -----------------------------------------------------------------------------
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# Convention about Training / Test specific parameters
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# -----------------------------------------------------------------------------
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# Whenever an argument can be either used for training or for testing, the
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# corresponding name will be post-fixed by a _TRAIN for a training parameter,
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# or _TEST for a test-specific parameter.
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# For example, the number of images during training will be
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# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
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# IMAGES_PER_BATCH_TEST
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# -----------------------------------------------------------------------------
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# Config definition
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# -----------------------------------------------------------------------------
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_C = CN()
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# -----------------------------------------------------------------------------
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# MODEL
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# -----------------------------------------------------------------------------
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_C.MODEL = CN()
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_C.MODEL.NAME = 'baseline'
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_C.MODEL.DIST_BACKEND = 'dp'
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# Model backbone
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_C.MODEL.BACKBONE = 'resnet50'
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# Last stride for backbone
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_C.MODEL.LAST_STRIDE = 1
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# If use IBN block
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_C.MODEL.WITH_IBN = False
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# Global Context Block configuration
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_C.MODEL.STAGE_WITH_GCB = (False, False, False, False)
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_C.MODEL.GCB = CN()
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_C.MODEL.GCB.ratio = 1./16.
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# If use imagenet pretrain model
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_C.MODEL.PRETRAIN = True
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# Pretrain model path
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_C.MODEL.PRETRAIN_PATH = ''
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# Checkpoint for continuing training
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_C.MODEL.CHECKPOINT = ''
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_C.MODEL.VERSION = ''
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#
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# -----------------------------------------------------------------------------
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# INPUT
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# -----------------------------------------------------------------------------
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_C.INPUT = CN()
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# Size of the image during training
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_C.INPUT.SIZE_TRAIN = [256, 128]
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# Size of the image during test
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_C.INPUT.SIZE_TEST = [256, 128]
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# Random probability for image horizontal flip
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_C.INPUT.DO_FLIP = True
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_C.INPUT.FLIP_PROB = 0.5
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# Values to be used for image normalization
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_C.INPUT.PIXEL_MEAN = [0.485, 0.456, 0.406]
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# Values to be used for image normalization
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_C.INPUT.PIXEL_STD = [0.229, 0.224, 0.225]
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# Value of padding size
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_C.INPUT.DO_PAD = True
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_C.INPUT.PADDING_MODE = 'constant'
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_C.INPUT.PADDING = 10
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# Random lightning and contrast change
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_C.INPUT.DO_LIGHTING = False
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_C.INPUT.MAX_LIGHTING = 0.2
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_C.INPUT.P_LIGHTING = 0.75
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# Random erasing
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_C.INPUT.DO_RE = True
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_C.INPUT.RE_PROB = 0.5
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# -----------------------------------------------------------------------------
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# Dataset
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# -----------------------------------------------------------------------------
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_C.DATASETS = CN()
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# List of the dataset names for training
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_C.DATASETS.NAMES = ("market1501",)
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# List of the dataset names for testing
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_C.DATASETS.TEST_NAMES = "market1501"
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# -----------------------------------------------------------------------------
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# DataLoader
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# -----------------------------------------------------------------------------
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_C.DATALOADER = CN()
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# Sampler for data loading
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_C.DATALOADER.SAMPLER = 'softmax'
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# Number of instance for each person
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_C.DATALOADER.NUM_INSTANCE = 4
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_C.DATALOADER.NUM_WORKERS = 8
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# ---------------------------------------------------------------------------- #
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# Solver
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# ---------------------------------------------------------------------------- #
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_C.SOLVER = CN()
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_C.SOLVER.DIST = False
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_C.SOLVER.OPT = "adam"
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_C.SOLVER.LOSSTYPE = ("softmax",)
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_C.SOLVER.MAX_EPOCHS = 50
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_C.SOLVER.BASE_LR = 3e-4
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_C.SOLVER.BIAS_LR_FACTOR = 1
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_C.SOLVER.MOMENTUM = 0.9
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_C.SOLVER.MARGIN = 0.3
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_C.SOLVER.WEIGHT_DECAY = 0.0005
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_C.SOLVER.WEIGHT_DECAY_BIAS = 0.
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_C.SOLVER.GAMMA = 0.1
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_C.SOLVER.STEPS = (30, 55)
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_C.SOLVER.WARMUP_FACTOR = 0.1
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_C.SOLVER.WARMUP_ITERS = 10
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_C.SOLVER.WARMUP_METHOD = "linear"
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_C.SOLVER.LOG_INTERVAL = 30
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_C.SOLVER.EVAL_PERIOD = 50
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# Number of images per batch
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# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
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# see 2 images per batch
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_C.SOLVER.IMS_PER_BATCH = 64
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# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
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# see 2 images per batch
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_C.TEST = CN()
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_C.TEST.IMS_PER_BATCH = 128
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_C.TEST.NORM = True
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_C.TEST.WEIGHT = ""
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# ---------------------------------------------------------------------------- #
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# Misc options
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# ---------------------------------------------------------------------------- #
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_C.OUTPUT_DIR = "logs/" |