def get_config(is_train):
class General:
log_frequency = 10
name = __name__.rsplit("/")[-1].rsplit(".")[-1]
batch_image = 2 if is_train else 1
fp16 = True
class KvstoreParam:
kvstore = "nccl"
batch_image = General.batch_image
gpus = [0, 1, 2, 3, 4, 5, 6, 7]
fp16 = General.fp16
class NormalizeParam:
# normalizer = normalizer_factory(type="syncbn", ndev=len(KvstoreParam.gpus))
normalizer = normalizer_factory(type="fixbn")
class BackboneParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
class NeckParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
class RpnParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
batch_image = General.batch_image
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 = False
image_roi = 512
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 BboxParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
num_class = 1 + 80
image_roi = 512
batch_image = General.batch_image
class regress_target:
class_agnostic = True
mean = (0.0, 0.0, 0.0, 0.0)
std = (0.1, 0.1, 0.2, 0.2)
class RoiParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
out_size = 7
stride = 16
class DatasetParam:
if is_train:
image_set = ("coco_train2017", )
else:
image_set = ("coco_val2017", )
backbone = Backbone(BackboneParam)
neck = Neck(NeckParam)
rpn_head = RpnHead(RpnParam)
roi_extractor = RoiExtractor(RoiParam)
bbox_head = BboxHead(BboxParam)
detector = Detector()
if is_train:
train_sym = detector.get_train_symbol(backbone, neck, rpn_head,
roi_extractor, bbox_head)
rpn_test_sym = None
test_sym = None
else:
train_sym = None
rpn_test_sym = detector.get_rpn_test_symbol(backbone, neck, rpn_head)
test_sym = detector.get_test_symbol(backbone, neck, rpn_head,
roi_extractor, bbox_head)
class ModelParam:
train_symbol = train_sym
test_symbol = test_sym
rpn_test_symbol = rpn_test_sym
from_scratch = False
random = True
memonger = False
memonger_until = "stage3_unit21_plus"
class pretrain:
prefix = "pretrain_model/resnet-v1-50"
epoch = 0
fixed_param = ["conv0", "stage1", "gamma", "beta"]
class OptimizeParam:
class optimizer:
type = "sgd"
lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image
momentum = 0.9
wd = 0.0001
clip_gradient = 35
class schedule:
begin_epoch = 0
end_epoch = 6
lr_iter = [
60000 * 16 //
(len(KvstoreParam.gpus) * KvstoreParam.batch_image), 80000 *
16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)
]
class warmup:
type = "gradual"
lr = 0.0
iter = 750 * 16 // (len(KvstoreParam.gpus) *
KvstoreParam.batch_image)
class TestParam:
min_det_score = 0.05
max_det_per_image = 100
process_roidb = lambda x: x
process_output = lambda x, y: x
class model:
prefix = "experiments/{}/checkpoint".format(General.name)
epoch = OptimizeParam.schedule.end_epoch
class nms:
type = "nms"
thr = 0.5
class coco:
annotation = "data/coco/annotations/instances_minival2014.json"
# data processing
class NormParam:
mean = (122.7717, 115.9465, 102.9801) # RGB order
std = (1.0, 1.0, 1.0)
class ResizeParam:
short = 800
long = 1200 if is_train else 2000
class PadParam:
short = 800
long = 1200
max_num_gt = 100
class AnchorTarget2DParam:
class generate:
short = 800 // 16
long = 1200 // 16
stride = 16
scales = (2, 4, 8, 16, 32)
aspects = (0.5, 1.0, 2.0)
class assign:
allowed_border = 0
pos_thr = 0.7
neg_thr = 0.3
min_pos_thr = 0.0
class sample:
image_anchor = 256
pos_fraction = 0.5
class RenameParam:
mapping = dict(image="data")
from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \
ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \
RenameRecord, AnchorTarget2D, Norm2DImage
if is_train:
transform = [
ReadRoiRecord(None),
Norm2DImage(NormParam),
Resize2DImageBbox(ResizeParam),
Flip2DImageBbox(),
Pad2DImageBbox(PadParam),
ConvertImageFromHwcToChw(),
AnchorTarget2D(AnchorTarget2DParam),
RenameRecord(RenameParam.mapping)
]
data_name = ["data", "im_info", "gt_bbox"]
label_name = ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"]
else:
transform = [
ReadRoiRecord(None),
Norm2DImage(NormParam),
Resize2DImageBbox(ResizeParam),
ConvertImageFromHwcToChw(),
RenameRecord(RenameParam.mapping)
]
data_name = ["data", "im_info", "im_id", "rec_id"]
label_name = []
import core.detection_metric as metric
rpn_acc_metric = metric.AccWithIgnore("RpnAcc", ["rpn_cls_loss_output"],
["rpn_cls_label"])
rpn_l1_metric = metric.L1("RpnL1", ["rpn_reg_loss_output"],
["rpn_cls_label"])
# for bbox, the label is generated in network so it is an output
box_acc_metric = metric.AccWithIgnore(
"RcnnAcc", ["bbox_cls_loss_output", "bbox_label_blockgrad_output"], [])
box_l1_metric = metric.L1(
"RcnnL1", ["bbox_reg_loss_output", "bbox_label_blockgrad_output"], [])
metric_list = [
rpn_acc_metric, rpn_l1_metric, box_acc_metric, box_l1_metric
]
return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \
ModelParam, OptimizeParam, TestParam, \
transform, data_name, label_name, metric_list
