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from .defaults import _C as cfg
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Executable
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from yacs.config import CfgNode as CN
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_C = CN()
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_C.MODEL = CN()
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_C.MODEL.META_ARCHITECTURE = 'SSDDetector'
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_C.MODEL.DEVICE = "cuda"
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# match default boxes to any ground truth with jaccard overlap higher than a threshold (0.5)
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_C.MODEL.THRESHOLD = 0.5
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_C.MODEL.NUM_CLASSES = 21
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# Hard negative mining
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_C.MODEL.NEG_POS_RATIO = 3
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_C.MODEL.CENTER_VARIANCE = 0.1
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_C.MODEL.SIZE_VARIANCE = 0.2
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# ---------------------------------------------------------------------------- #
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# Backbone
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# ---------------------------------------------------------------------------- #
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_C.MODEL.BACKBONE = CN()
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_C.MODEL.BACKBONE.NAME = 'vgg'
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_C.MODEL.BACKBONE.OUT_CHANNELS = (512, 1024, 512, 256, 256, 256)
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_C.MODEL.BACKBONE.PRETRAINED = True
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# -----------------------------------------------------------------------------
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# PRIORS
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# -----------------------------------------------------------------------------
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_C.MODEL.PRIORS = CN()
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_C.MODEL.PRIORS.FEATURE_MAPS = [38, 19, 10, 5, 3, 1]
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_C.MODEL.PRIORS.STRIDES = [8, 16, 32, 64, 100, 300]
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_C.MODEL.PRIORS.MIN_SIZES = [30, 60, 111, 162, 213, 264]
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_C.MODEL.PRIORS.MAX_SIZES = [60, 111, 162, 213, 264, 315]
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_C.MODEL.PRIORS.ASPECT_RATIOS = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
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# When has 1 aspect ratio, every location has 4 boxes, 2 ratio 6 boxes.
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# #boxes = 2 + #ratio * 2
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_C.MODEL.PRIORS.BOXES_PER_LOCATION = [4, 6, 6, 6, 4, 4] # number of boxes per feature map location
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_C.MODEL.PRIORS.CLIP = True
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# -----------------------------------------------------------------------------
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# Box Head
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# -----------------------------------------------------------------------------
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_C.MODEL.BOX_HEAD = CN()
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_C.MODEL.BOX_HEAD.NAME = 'SSDBoxHead'
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_C.MODEL.BOX_HEAD.PREDICTOR = 'SSDBoxPredictor'
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# -----------------------------------------------------------------------------
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# INPUT
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# -----------------------------------------------------------------------------
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_C.INPUT = CN()
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# Image size
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_C.INPUT.IMAGE_SIZE = 300
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# Values to be used for image normalization, RGB layout
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_C.INPUT.PIXEL_MEAN = [123, 117, 104]
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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.TRAIN = ()
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# List of the dataset names for testing, as present in paths_catalog.py
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_C.DATASETS.TEST = ()
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_C.DATASETS.INF = ()
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# -----------------------------------------------------------------------------
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# DataLoader
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# -----------------------------------------------------------------------------
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_C.DATA_LOADER = CN()
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# Number of data loading threads
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_C.DATA_LOADER.NUM_WORKERS = 8
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_C.DATA_LOADER.PIN_MEMORY = True
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# ---------------------------------------------------------------------------- #
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# Solver
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# ---------------------------------------------------------------------------- #
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_C.SOLVER = CN()
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# train configs
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_C.SOLVER.MAX_ITER = 120000
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_C.SOLVER.LR_STEPS = [80000, 100000]
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_C.SOLVER.GAMMA = 0.1
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_C.SOLVER.BATCH_SIZE = 4#32
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_C.SOLVER.LR = 1e-3
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_C.SOLVER.MOMENTUM = 0.9
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_C.SOLVER.WEIGHT_DECAY = 5e-4
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_C.SOLVER.WARMUP_FACTOR = 1.0 / 3
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_C.SOLVER.WARMUP_ITERS = 500
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# ---------------------------------------------------------------------------- #
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# Specific test options
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# ---------------------------------------------------------------------------- #
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_C.TEST = CN()
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_C.TEST.NMS_THRESHOLD = 0.4 #0.45
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_C.TEST.CONFIDENCE_THRESHOLD = 0.3#0.01
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_C.TEST.MAX_PER_CLASS = -1
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_C.TEST.MAX_PER_IMAGE = 100
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_C.TEST.BATCH_SIZE = 1
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_C.OUTPUT_DIR = 'outputs'
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Executable
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MODEL:
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NUM_CLASSES: 21
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BACKBONE:
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OUT_CHANNELS: (512, 1024, 512, 256, 256, 256, 256)
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PRIORS:
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FEATURE_MAPS: [64, 32, 16, 8, 4, 2, 1]
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STRIDES: [8, 16, 32, 64, 128, 256, 512]
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MIN_SIZES: [35.84, 76.8, 153.6, 230.4, 307.2, 384.0, 460.8]
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MAX_SIZES: [76.8, 153.6, 230.4, 307.2, 384.0, 460.8, 537.65]
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ASPECT_RATIOS: [[2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]]
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BOXES_PER_LOCATION: [4, 6, 6, 6, 6, 4, 4]
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INPUT:
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IMAGE_SIZE: 512
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DATASETS:
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#TRAIN: ("voc_2007_birdtrain", "voc_2007_birdval")
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#TRAIN: ("voc_2007_trainval", )
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#TEST: ("voc_2007_test", )
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#TEST: ("vocbird_2019_test", )
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TRAIN: ("voc_3_train",)
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TEST: ("voc_3_test", )
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INF: ("inference_hatcher",)
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SOLVER:
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MAX_ITER: 175000
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LR_STEPS: [80000, 100000]
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GAMMA: 0.1
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BATCH_SIZE: 16
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LR: 1e-5
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#1e-3
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OUTPUT_DIR: '/data1/Hatcher/model/data_MPKFNet/weigth/detection/vgg_ssd512_voc0712VOC3/'
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Executable
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import os
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class DatasetCatalog:
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DATA_DIR = ''
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DATASETS = {
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'voc_ICCV_train': {
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"data_dir": "VOCiccvtrain",
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"split": "trainyolo"
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},
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'voc_ICCV_test': {
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"data_dir": "VOCiccvtest",
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"split": "testyolo"
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},
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'voc_2007_train': {
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"data_dir": "VOC2007",
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"split": "train"
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},
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'voc_3_train': {
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"data_dir": "VOC3",
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"split": "train"
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},
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'voc_3_test': {
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"data_dir": "VOC3",
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"split": "test"
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},
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'voc_2007_val': {
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"data_dir": "VOC2007",
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"split": "val"
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},
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'voc_2007_trainval': {
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"data_dir": "VOC2007",
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"split": "trainval"
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},
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'voc_2007_test': {
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"data_dir": "VOC2007",
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"split": "test"
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},
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'vocbird_2019_test': {
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"data_dir": "HatcherW",
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"split": "test"
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},
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'voc_2012_train': {
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"data_dir": "VOC2012",
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"split": "train"
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},
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'voc_2012_val': {
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"data_dir": "VOC2012",
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"split": "val"
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},
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'voc_2012_trainval': {
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"data_dir": "VOC2012",
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"split": "trainval"
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},
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'voc_2012_test': {
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"data_dir": "VOC2012",
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"split": "test"
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},
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'coco_2014_valminusminival': {
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"data_dir": "val2014",
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"ann_file": "annotations/instances_valminusminival2014.json"
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},
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'coco_2014_minival': {
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"data_dir": "val2014",
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"ann_file": "annotations/instances_minival2014.json"
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},
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'coco_2014_train': {
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"data_dir": "train2014",
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"ann_file": "annotations/instances_train2014.json"
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},
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'coco_2014_val': {
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"data_dir": "val2014",
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"ann_file": "annotations/instances_val2014.json"
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},
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"voc_2007_birdtest": {
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"data_dir": "VOC2007",
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"split": "bird_test"
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},
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"voc_2007_birdval": {
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"data_dir": "VOC2007",
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"split": "bird_trainval"
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},
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"voc_2007_birdtrain": {
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"data_dir": "VOC2007",
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"split": "bird_train"
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},
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}
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@staticmethod
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def get(name):
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if "voc" in name:
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voc_root = DatasetCatalog.DATA_DIR
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if 'VOC_ROOT' in os.environ:
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voc_root = os.environ['VOC_ROOT']
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attrs = DatasetCatalog.DATASETS[name]
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args = dict(
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data_dir=os.path.join(voc_root, attrs["data_dir"]),
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split=attrs["split"],
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)
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return dict(factory="VOCDataset", args=args)
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elif "coco" in name:
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coco_root = DatasetCatalog.DATA_DIR
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if 'COCO_ROOT' in os.environ:
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coco_root = os.environ['COCO_ROOT']
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attrs = DatasetCatalog.DATASETS[name]
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args = dict(
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data_dir=os.path.join(coco_root, attrs["data_dir"]),
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ann_file=os.path.join(coco_root, attrs["ann_file"]),
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)
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return dict(factory="COCODataset", args=args)
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raise RuntimeError("Dataset not available: {}".format(name))
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