__C = edict()#让dict操作dict元素像操作属性一样
# Consumers can get config by:
# from fast_rcnn_config import cfg
cfg = __C
#
# Training options
#
__C.TRAIN = edict()
# Initial learning rate初始学习率
__C.TRAIN.LEARNING_RATE = 0.001
# Momentum
__C.TRAIN.MOMENTUM = 0.9
# Weight decay, for regularization
__C.TRAIN.WEIGHT_DECAY = 0.0001
# Factor for reducing the learning rate
__C.TRAIN.GAMMA = 0.1
# Step size for reducing the learning rate, currently only support one step
__C.TRAIN.STEPSIZE = [30000]
# Iteration intervals for showing the loss during training, on command line interface
__C.TRAIN.DISPLAY = 10
# Whether to double the learning rate for bias
__C.TRAIN.DOUBLE_BIAS = True
# Whether to initialize the weights with truncated normal distribution
__C.TRAIN.TRUNCATED = False
# Whether to have weight decay on bias as well
__C.TRAIN.BIAS_DECAY = False
# Whether to add ground truth boxes to the pool when sampling regions
__C.TRAIN.USE_GT = False
# Whether to use aspect-ratio grouping of training images, introduced merely for saving
# GPU memory
__C.TRAIN.ASPECT_GROUPING = False
# The number of snapshots kept, older ones are deleted to save space
__C.TRAIN.SNAPSHOT_KEPT = 3
# The time interval for saving tensorflow summaries
__C.TRAIN.SUMMARY_INTERVAL = 180
# Scale to use during training (can list multiple scales)
# The scale is the pixel size of an image's shortest side
__C.TRAIN.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TRAIN.MAX_SIZE = 1000
# Images to use per minibatch
__C.TRAIN.IMS_PER_BATCH = 1
# Minibatch size (number of regions of interest [ROIs])
__C.TRAIN.BATCH_SIZE = 128
# Fraction of minibatch that is labeled foreground (i.e. class > 0)
__C.TRAIN.FG_FRACTION = 0.25
# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
__C.TRAIN.FG_THRESH = 0.5
# Overlap threshold for a ROI to be considered background (class = 0 if
# overlap in [LO, HI))
__C.TRAIN.BG_THRESH_HI = 0.5
__C.TRAIN.BG_THRESH_LO = 0.1
# Use horizontally-flipped images during training?
__C.TRAIN.USE_FLIPPED = True
# Train bounding-box regressors
__C.TRAIN.BBOX_REG = True
# Overlap required between a ROI and ground-truth box in order for that ROI to
# be used as a bounding-box regression training example
__C.TRAIN.BBOX_THRESH = 0.5
# Iterations between snapshots
__C.TRAIN.SNAPSHOT_ITERS = 5000
# solver.prototxt specifies the snapshot path prefix, this adds an optional
# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
__C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn'
# Normalize the targets (subtract empirical mean, divide by empirical stddev)
__C.TRAIN.BBOX_NORMALIZE_TARGETS = True
# Deprecated (inside weights)
__C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Normalize the targets using "precomputed" (or made up) means and stdevs
# (BBOX_NORMALIZE_TARGETS must also be True)
__C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = True
__C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
__C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
# Train using these proposals
__C.TRAIN.PROPOSAL_METHOD = 'gt'
# Make minibatches from images that have similar aspect ratios (i.e. both
# tall and thin or both short and wide) in order to avoid wasting computation
# on zero-padding.
# Use RPN to detect objects
__C.TRAIN.HAS_RPN = True
# IOU >= thresh: positive example
__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
# IOU < thresh: negative example
__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
# If an anchor satisfied by positive and negative conditions set to negative
__C.TRAIN.RPN_CLOBBER_POSITIVES = False
# Max number of foreground examples
__C.TRAIN.RPN_FG_FRACTION = 0.5
# Total number of examples
__C.TRAIN.RPN_BATCHSIZE = 256
# NMS threshold used on RPN proposals
__C.TRAIN.RPN_NMS_THRESH = 0.7
# Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000
# Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TRAIN.RPN_POST_NMS_TOP_N = 2000
# Deprecated (outside weights)
__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Give the positive RPN examples weight of p * 1 / {num positives}
# and give negatives a weight of (1 - p)
# Set to -1.0 to use uniform example weighting
__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0
# Whether to use all ground truth bounding boxes for training,
# For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd''
__C.TRAIN.USE_ALL_GT = True
#
# Testing options
#
__C.TEST = edict()
# Scale to use during testing (can NOT list multiple scales)
# The scale is the pixel size of an image's shortest side
__C.TEST.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TEST.MAX_SIZE = 1000
# Overlap threshold used for non-maximum suppression (suppress boxes with
# IoU >= this threshold)
__C.TEST.NMS = 0.3
# Experimental: treat the (K+1) units in the cls_score layer as linear
# predictors (trained, eg, with one-vs-rest SVMs).
__C.TEST.SVM = False
# Test using bounding-box regressors
__C.TEST.BBOX_REG = True
# Propose boxes
__C.TEST.HAS_RPN = False
# Test using these proposals
__C.TEST.PROPOSAL_METHOD = 'gt'
## NMS threshold used on RPN proposals
__C.TEST.RPN_NMS_THRESH = 0.7
# Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TEST.RPN_PRE_NMS_TOP_N = 6000
# Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TEST.RPN_POST_NMS_TOP_N = 300
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
# __C.TEST.RPN_MIN_SIZE = 16
# Testing mode, default to be 'nms', 'top' is slower but better
# See report for details
__C.TEST.MODE = 'nms'
# Only useful when TEST.MODE is 'top', specifies the number of top proposals to select
__C.TEST.RPN_TOP_N = 5000
#
# ResNet options
#
__C.RESNET = edict()
# Option to set if max-pooling is appended after crop_and_resize.
# if true, the region will be resized to a square of 2xPOOLING_SIZE,
# then 2x2 max-pooling is applied; otherwise the region will be directly
# resized to a square of POOLING_SIZE
__C.RESNET.MAX_POOL = False
# Number of fixed blocks during training, by default the first of all 4 blocks is fixed
# Range: 0 (none) to 3 (all)
__C.RESNET.FIXED_BLOCKS = 1
#
# MobileNet options
#
__C.MOBILENET = edict()
# Whether to regularize the depth-wise filters during training
__C.MOBILENET.REGU_DEPTH = False
# Number of fixed layers during training, by default the bottom 5 of 14 layers is fixed
# Range: 0 (none) to 12 (all)
__C.MOBILENET.FIXED_LAYERS = 5
# Weight decay for the mobilenet weights
__C.MOBILENET.WEIGHT_DECAY = 0.00004
# Depth multiplier
__C.MOBILENET.DEPTH_MULTIPLIER = 1.
#
# MISC
#
# Pixel mean values (BGR order) as a (1, 1, 3) array
# We use the same pixel mean for all networks even though it's not exactly what
# they were trained with
__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
# For reproducibility
__C.RNG_SEED = 3
# Root directory of project
__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))
# Data directory
__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))
# Name (or path to) the matlab executable
__C.MATLAB = 'matlab'
# Place outputs under an experiments directory
__C.EXP_DIR = 'default'
# Use GPU implementation of non-maximum suppression
__C.USE_GPU_NMS = True
# Default pooling mode, only 'crop' is available
__C.POOLING_MODE = 'crop'
# Size of the pooled region after RoI pooling
__C.POOLING_SIZE = 7
# Anchor scales for RPN
__C.ANCHOR_SCALES = [8,16,32]
# Anchor ratios for RPN
__C.ANCHOR_RATIOS = [0.5,1,2]
# Number of filters for the RPN layer
__C.RPN_CHANNELS = 512
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