162 lines
5.6 KiB
Python
162 lines
5.6 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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_C.MODEL = CN()
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# Using cuda or cpu for training
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_C.MODEL.DEVICE = "cuda"
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# ID number of GPU
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_C.MODEL.DEVICE_ID = '0'
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# Name of backbone
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_C.MODEL.NAME = 'resnet50'
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# Last stride of backbone
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_C.MODEL.LAST_STRIDE = 1
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# Path to pretrained model of backbone
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_C.MODEL.PRETRAIN_PATH = ''
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# Use ImageNet pretrained model to initialize backbone or use self trained model to initialize the whole model
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# Options: 'imagenet' or 'self'
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_C.MODEL.PRETRAIN_CHOICE = 'imagenet'
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# If train with BNNeck, options: 'bnneck' or 'no'
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_C.MODEL.NECK = 'bnneck'
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# If train loss include center loss, options: 'yes' or 'no'. Loss with center loss has different optimizer configuration
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_C.MODEL.IF_WITH_CENTER = 'no'
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# The loss type of metric loss
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# options:['triplet'](without center loss) or ['center','triplet_center'](with center loss)
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_C.MODEL.METRIC_LOSS_TYPE = 'triplet'
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# For example, if loss type is cross entropy loss + triplet loss + center loss
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# the setting should be: _C.MODEL.METRIC_LOSS_TYPE = 'triplet_center' and _C.MODEL.IF_WITH_CENTER = 'yes'
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# If train with label smooth, options: 'on', 'off'
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_C.MODEL.IF_LABELSMOOTH = 'on'
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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 = [384, 128]
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# Size of the image during test
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_C.INPUT.SIZE_TEST = [384, 128]
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# Random probability for image horizontal flip
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_C.INPUT.PROB = 0.5
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# Random probability for random erasing
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_C.INPUT.RE_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.PADDING = 10
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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, as present in paths_catalog.py
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_C.DATASETS.NAMES = ('market1501')
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# Root directory where datasets should be used (and downloaded if not found)
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_C.DATASETS.ROOT_DIR = ('./data')
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# -----------------------------------------------------------------------------
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# DataLoader
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# -----------------------------------------------------------------------------
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_C.DATALOADER = CN()
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# Number of data loading threads
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_C.DATALOADER.NUM_WORKERS = 8
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# Sampler for data loading
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_C.DATALOADER.SAMPLER = 'softmax'
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# Number of instance for one batch
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_C.DATALOADER.NUM_INSTANCE = 16
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# ---------------------------------------------------------------------------- #
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# Solver
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# ---------------------------------------------------------------------------- #
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_C.SOLVER = CN()
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# Name of optimizer
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_C.SOLVER.OPTIMIZER_NAME = "Adam"
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# Number of max epoches
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_C.SOLVER.MAX_EPOCHS = 50
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# Base learning rate
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_C.SOLVER.BASE_LR = 3e-4
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# Factor of learning bias
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_C.SOLVER.BIAS_LR_FACTOR = 2
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# Momentum
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_C.SOLVER.MOMENTUM = 0.9
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# Margin of triplet loss
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_C.SOLVER.MARGIN = 0.3
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# Margin of cluster ;pss
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_C.SOLVER.CLUSTER_MARGIN = 0.3
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# Learning rate of SGD to learn the centers of center loss
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_C.SOLVER.CENTER_LR = 0.5
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# Balanced weight of center loss
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_C.SOLVER.CENTER_LOSS_WEIGHT = 0.0005
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# Settings of range loss
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_C.SOLVER.RANGE_K = 2
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_C.SOLVER.RANGE_MARGIN = 0.3
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_C.SOLVER.RANGE_ALPHA = 0
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_C.SOLVER.RANGE_BETA = 1
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_C.SOLVER.RANGE_LOSS_WEIGHT = 1
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# Settings of weight decay
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_C.SOLVER.WEIGHT_DECAY = 0.0005
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_C.SOLVER.WEIGHT_DECAY_BIAS = 0.
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# decay rate of learning rate
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_C.SOLVER.GAMMA = 0.1
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# decay step of learning rate
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_C.SOLVER.STEPS = (30, 55)
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# warm up factor
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_C.SOLVER.WARMUP_FACTOR = 1.0 / 3
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# iterations of warm up
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_C.SOLVER.WARMUP_ITERS = 500
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# method of warm up, option: 'constant','linear'
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_C.SOLVER.WARMUP_METHOD = "linear"
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# epoch number of saving checkpoints
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_C.SOLVER.CHECKPOINT_PERIOD = 50
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# iteration of display training log
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_C.SOLVER.LOG_PERIOD = 100
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# epoch number of validation
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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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# Number of images per batch during test
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_C.TEST.IMS_PER_BATCH = 128
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# If test with re-ranking, options: 'yes','no'
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_C.TEST.RE_RANKING = 'no'
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# Path to trained model
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_C.TEST.WEIGHT = ""
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# Which feature of BNNeck to be used for test, before or after BNNneck, options: 'before' or 'after'
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_C.TEST.NECK_FEAT = 'after'
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# Whether feature is nomalized before test, if yes, it is equivalent to cosine distance
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_C.TEST.FEAT_NORM = 'yes'
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# ---------------------------------------------------------------------------- #
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# Misc options
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# ---------------------------------------------------------------------------- #
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# Path to checkpoint and saved log of trained model
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_C.OUTPUT_DIR = ""
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