class NormalizeParam:
# the type of normalizer used for network
# see also ModelParam.pretrain.fixed_param for the freeze of gamma/beta
normalizer = normalizer_factory(type="fixbn") # freeze bn stats
normalizer = normalizer_factory(
type="localbn") # use bn stats in one GPU
normalizer = normalizer_factory(
type="syncbn",
ndev=len(KvstoreParam.gpus)) # use bn stats across GPUs
normalizer = normalizer_factory(type="gn") # use GroupNorm
class BackboneParam:
fp16 = General.fp16
# normalizer = NormalizeParam.normalizer
normalizer = normalizer_factory(type="fixbn")
depth = 101
num_c3_block = 4
num_c4_block = 23
class NormalizeParam:
normalizer = normalizer_factory(type="syncbn",
ndev=len(KvstoreParam.gpus),
wd_mult=1.0,
lr_mult=1.0,
eps=1e-4,
mom=0.997)
class NeckParam:
fp16 = General.fp16
normalizer = normalizer_factory(type="syncbn",
ndev=len(KvstoreParam.gpus))
dim_reduced = 256
num_stage = 6
S0_kernel = 1
class RpnParam:
fp16 = General.fp16
normalizer = normalizer_factory(
type="fixbn") # old model does not use BN in RPN head
batch_image = General.batch_image
use_groupsoftmax = General.use_groupsoftmax
num_class = (1 + 2) if use_groupsoftmax else 2
class anchor_generate:
scale = (2, 4, 8, 16, 32)
ratio = (0.5, 1.0, 2.0)
stride = 16
image_anchor = 256
class head:
conv_channel = 512
mean = (0, 0, 0, 0)
std = (1, 1, 1, 1)
class proposal:
pre_nms_top_n = 12000 if is_train else 6000
post_nms_top_n = 2000 if is_train else 1000
nms_thr = 0.7
min_bbox_side = 0
class subsample_proposal:
proposal_wo_gt = True
image_roi = 256
fg_fraction = 0.25
fg_thr = 0.5
bg_thr_hi = 0.5
bg_thr_lo = 0.0
class bbox_target:
num_reg_class = 2
class_agnostic = True
weight = (1.0, 1.0, 1.0, 1.0)
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.1, 0.1, 0.2, 0.2)
class NormalizeParam:
# normalizer = normalizer_factory(type="syncbn", ndev=len(KvstoreParam.gpus))
normalizer = normalizer_factory(type="fixbn")
class NormalizeParam:
normalizer = normalizer_factory(type="fixbn")
class NeckParam:
fp16 = General.fp16
normalizer = normalizer_factory(type="localbn")
class NormalizeParam:
normalizer = normalizer_factory(type="syncbn", ndev=8, wd_mult=1.0)
class BackboneParam:
fp16 = General.fp16
# normalizer = NormalizeParam.normalizer
normalizer = normalizer_factory(type="fixbn")
class NormalizeParam:
normalizer = normalizer_factory(type="localbn",
ndev=len(KvstoreParam.gpus))
class NormalizeParam:
if is_train:
normalizer = normalizer_factory(type="syncbn",
ndev=len(KvstoreParam.gpus))
else:
normalizer = normalizer_factory(type="fixbn")
class NormalizeParam:
normalizer = normalizer_factory(type="localbn", wd_mult=0.0)
class NeckParam:
fp16 = General.fp16
normalizer = normalizer_factory(type="dummy")
