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https://github.com/JDAI-CV/fast-reid.git
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71950d2c09
2. Finish multi-dataset evaluation 3. Decouple image preprocess and output postprocess with model forward for DataParallel training 4. Finish build backbone registry 5. Fix dataset sampler
168 lines
5.4 KiB
Python
168 lines
5.4 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.META_ARCHITECTURE = 'Baseline'
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# ---------------------------------------------------------------------------- #
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# Backbone options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.BACKBONE = CN()
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_C.MODEL.BACKBONE.NAME = "build_resnet_backbone"
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_C.MODEL.BACKBONE.DEPTH = 50
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_C.MODEL.BACKBONE.LAST_STRIDE = 1
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# If use IBN block in backbone
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_C.MODEL.BACKBONE.WITH_IBN = False
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# If use SE block in backbone
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_C.MODEL.BACKBONE.WITH_SE = False
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# If use ImageNet pretrain model
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_C.MODEL.BACKBONE.PRETRAIN = True
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# Pretrain model path
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_C.MODEL.BACKBONE.PRETRAIN_PATH = ''
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# ---------------------------------------------------------------------------- #
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# REID HEADS options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.REID_HEADS = CN()
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_C.MODEL.REID_HEADS.NAME = "BaselineHeads"
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# Number of identity classes
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_C.MODEL.REID_HEADS.NUM_CLASSES = 751
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_C.MODEL.REID_HEADS.MARGIN = 0.3
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_C.MODEL.REID_HEADS.SMOOTH_ON = False
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# Path (possibly with schema like catalog:// or detectron2://) to a checkpoint file
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# to be loaded to the model. You can find available models in the model zoo.
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_C.MODEL.WEIGHTS = ""
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# Values to be used for image normalization
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_C.MODEL.PIXEL_MEAN = [0.485*255, 0.456*255, 0.406*255]
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# Values to be used for image normalization
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_C.MODEL.PIXEL_STD = [0.229*255, 0.224*255, 0.225*255]
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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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# 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.BRIGHTNESS = 0.4
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_C.INPUT.CONTRAST = 0.4
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# Random erasing
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_C.INPUT.RE = CN()
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_C.INPUT.RE.DO = True
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_C.INPUT.RE.PROB = 0.5
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_C.INPUT.RE.MEAN = [0.340*255, 0.326*255, 0.316*255]
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# Cutout
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_C.INPUT.CUTOUT = CN()
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_C.INPUT.CUTOUT.DO = False
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_C.INPUT.CUTOUT.PROB = 0.5
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_C.INPUT.CUTOUT.SIZE = 64
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_C.INPUT.CUTOUT.MEAN = [0, 0, 0]
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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 = ("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.MAX_ITER = 40000
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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.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.CHECKPOINT_PERIOD = 5000
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_C.SOLVER.LOG_PERIOD = 30
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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.EVAL_PERIOD = 50
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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/"
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# Benchmark different cudnn algorithms.
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# If input images have very different sizes, this option will have large overhead
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# for about 10k iterations. It usually hurts total time, but can benefit for certain models.
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# If input images have the same or similar sizes, benchmark is often helpful.
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_C.CUDNN_BENCHMARK = False
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