Commit 1e85e7d9 authored by Ross Girshick's avatar Ross Girshick

pylint cleanups

parent c69e33db
......@@ -31,72 +31,72 @@ cfg = __C
# Training options
#
__C.TRAIN = edict()
__C.TRAIN = edict()
# Scales to use during training (can list multiple scales)
# Each scale is the pixel size of an image's shortest side
__C.TRAIN.SCALES = (600,)
__C.TRAIN.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TRAIN.MAX_SIZE = 1000
__C.TRAIN.MAX_SIZE = 1000
# Images to use per minibatch
__C.TRAIN.IMS_PER_BATCH = 2
__C.TRAIN.IMS_PER_BATCH = 2
# Minibatch size (number of regions of interest [ROIs])
__C.TRAIN.BATCH_SIZE = 128
__C.TRAIN.BATCH_SIZE = 128
# Fraction of minibatch that is labeled foreground (i.e. class > 0)
__C.TRAIN.FG_FRACTION = 0.25
__C.TRAIN.FG_FRACTION = 0.25
# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
__C.TRAIN.FG_THRESH = 0.5
__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
__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
__C.TRAIN.USE_FLIPPED = True
# Train bounding-box regressors
__C.TRAIN.BBOX_REG = True
__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
__C.TRAIN.BBOX_THRESH = 0.5
# Iterations between snapshots
__C.TRAIN.SNAPSHOT_ITERS = 10000
__C.TRAIN.SNAPSHOT_ITERS = 10000
# solver.prototxt specifies the snapshot path prefix, this adds an optional
# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
__C.TRAIN.SNAPSHOT_INFIX = ''
__C.TRAIN.SNAPSHOT_INFIX = ''
#
# Testing options
#
__C.TEST = edict()
__C.TEST = edict()
# Scales to use during testing (can list multiple scales)
# Each scale is the pixel size of an image's shortest side
__C.TEST.SCALES = (600,)
__C.TEST.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TEST.MAX_SIZE = 1000
__C.TEST.MAX_SIZE = 1000
# Overlap threshold used for non-maximum suppression (suppress boxes with
# IoU >= this threshold)
__C.TEST.NMS = 0.3
__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
__C.TEST.SVM = False
# Test using bounding-box regressors
__C.TEST.BBOX_REG = True
__C.TEST.BBOX_REG = True
#
# MISC
......@@ -107,30 +107,30 @@ __C.TEST.BBOX_REG = True
# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor
# for identifying duplicate boxes.
# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG_16
__C.DEDUP_BOXES = 1./16.
__C.DEDUP_BOXES = 1./16.
# Pixel mean values (BGR order) as a (1, 1, 3) array
# These are the values originally used for training VGG_16
__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
# For reproducibility
__C.RNG_SEED = 3
__C.RNG_SEED = 3
# A small number that's used many times
__C.EPS = 1e-14
__C.EPS = 1e-14
# Root directory of project
__C.ROOT_DIR = osp.join(osp.dirname(__file__), '..', '..')
__C.ROOT_DIR = ops.abspath(osp.join(osp.dirname(__file__), '..', '..'))
# Place outputs under an experiments directory
__C.EXP_DIR = 'default'
__C.EXP_DIR = 'default'
def get_output_path(imdb, net):
path = os.path.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name)
path = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))
if net is None:
return path
else:
return os.path.join(path, net.name)
return osp.join(path, net.name)
def _merge_a_into_b(a, b):
"""
......@@ -146,8 +146,9 @@ def _merge_a_into_b(a, b):
# the types must match, too
if type(b[k]) is not type(v):
raise ValueError('Type mismatch ({} vs. {}) for config key: {}'.
format(type(b[k]), type(v), k))
raise ValueError(('Type mismatch ({} vs. {}) '
'for config key: {}').format(type(b[k]),
type(v), k))
# recursively merge dicts
if type(v) is edict:
......@@ -165,7 +166,6 @@ def cfg_from_file(filename):
file.
"""
import yaml
global __C
with open(filename, 'r') as f:
yaml_cfg = edict(yaml.load(f))
......
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