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")