def get_config(is_train):
class General:
log_frequency = 10
name = __name__.rsplit("/")[-1].rsplit(".")[-1]
batch_image = 1 if is_train else 1
fp16 = False
class Trident:
num_branch = 3 if is_train else 1
train_scaleaware = False
test_scaleaware = False
branch_ids = range(num_branch) if is_train else [1]
branch_dilates = [1, 2, 3] if is_train else [2]
valid_ranges = [(0, -1), (0, -1), (0, -1)] if is_train else [(0, -1)]
valid_ranges_on_origin = True
branch_bn_shared = True
branch_conv_shared = True
branch_deform = False
assert num_branch == len(branch_ids)
assert num_branch == len(valid_ranges)
class KvstoreParam:
kvstore = "local"
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
depth = 101
num_branch = Trident.num_branch
branch_ids = Trident.branch_ids
branch_dilates = Trident.branch_dilates
branch_bn_shared = Trident.branch_bn_shared
branch_conv_shared = Trident.branch_conv_shared
branch_deform = Trident.branch_deform
class NeckParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
class RpnParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
batch_image = General.batch_image * Trident.num_branch
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 = 500 if is_train else 300
nms_thr = 0.7
min_bbox_side = 0
class subsample_proposal:
proposal_wo_gt = True
image_roi = 128
fg_fraction = 0.5
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 = 128
batch_image = General.batch_image * Trident.num_branch
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,
num_branch=Trident.num_branch,
scaleaware=Trident.train_scaleaware)
rpn_test_sym = None
test_sym = None
else:
train_sym = None
rpn_test_sym = detector.get_rpn_test_symbol(backbone, neck, rpn_head,
Trident.num_branch)
test_sym = detector.get_test_symbol(backbone,
neck,
rpn_head,
roi_extractor,
bbox_head,
num_branch=Trident.num_branch)
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%s_v1b" % BackboneParam.depth
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 = 5
class schedule:
begin_epoch = 0
end_epoch = 12
lr_iter = [
120000 * 16 //
(len(KvstoreParam.gpus) * KvstoreParam.batch_image), 160000 *
16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)
]
class warmup:
type = "gradual"
lr = 0.0
iter = 3000 * 16 // (len(KvstoreParam.gpus) *
KvstoreParam.batch_image)
class TestParam:
min_det_score = 0.001
max_det_per_image = 100
process_roidb = lambda x: x
if Trident.test_scaleaware:
process_output = lambda x, y: process_branch_outputs(
x, Trident.num_branch, Trident.valid_ranges, Trident.
valid_ranges_on_origin)
else:
process_output = lambda x, y: x
process_rpn_output = lambda x, y: process_branch_rpn_outputs(
x, Trident.num_branch)
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 = tuple(i * 255 for i in (0.485, 0.456, 0.406)) # RGB order
std = tuple(i * 255 for i in (0.229, 0.224, 0.225))
class ResizeParam:
short = 800
long = 1200 if is_train else 2000
class PadParam:
short = 800
long = 1200 if is_train else 2000
max_num_gt = 100
class ScaleRange:
valid_ranges = Trident.valid_ranges
cal_on_origin = Trident.valid_ranges_on_origin # True: valid_ranges on origin image scale / valid_ranges on resized image scale
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 trident:
invalid_anchor_threshd = 0.3
class RenameParam:
mapping = dict(image="data")
from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \
ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \
RenameRecord, Norm2DImage
from models.tridentnet.input import ScaleAwareRange, TridentAnchorTarget2D
if is_train:
transform = [
ReadRoiRecord(None),
Norm2DImage(NormParam),
Resize2DImageBbox(ResizeParam),
Flip2DImageBbox(),
Pad2DImageBbox(PadParam),
ConvertImageFromHwcToChw(),
ScaleAwareRange(ScaleRange),
TridentAnchorTarget2D(AnchorTarget2DParam),
RenameRecord(RenameParam.mapping)
]
data_name = ["data", "im_info", "gt_bbox"]
if Trident.train_scaleaware:
data_name.append("valid_ranges")
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
def get_config(is_train):
class General:
log_frequency = 20
depth = 101
name = __name__.rsplit("/")[-1].rsplit(".")[-1]
batch_image = 3 if is_train else 1
fp16 = True
class Trident:
num_branch = 3
train_scaleaware = True
test_scaleaware = True
branch_ids = range(num_branch)
branch_dilates = [1, 2, 3]
valid_ranges = [(0, 150), (50, 270), (150, -1)]
valid_ranges_on_origin = False
branch_bn_shared = False
branch_conv_shared = True
branch_deform = True
class KvstoreParam:
kvstore = "local"
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
depth = General.depth
normalizer = NormalizeParam.normalizer
num_branch = Trident.num_branch
branch_ids = Trident.branch_ids
branch_dilates = Trident.branch_dilates
branch_bn_shared = Trident.branch_bn_shared
branch_conv_shared = Trident.branch_conv_shared
branch_deform = Trident.branch_deform
class NeckParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
class RpnParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
batch_image = General.batch_image * Trident.num_branch
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 = 500 if is_train else 1000
nms_thr = 0.7
min_bbox_side = 0
class subsample_proposal:
proposal_wo_gt = True
image_roi = 128
fg_fraction = 0.5
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 = 128
batch_image = General.batch_image * Trident.num_branch
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_train2014", "coco_valminusminival2014", "coco_minival2014")
else:
image_set = ("coco_test-dev2017", )
# image_set = ("coco_minival2014", )
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,
num_branch=Trident.num_branch, scaleaware=Trident.train_scaleaware)
test_sym = None
else:
train_sym = None
test_sym = detector.get_test_symbol(
backbone, neck, rpn_head, roi_extractor, bbox_head, num_branch=Trident.num_branch)
class ModelParam:
train_symbol = train_sym
test_symbol = test_sym
from_scratch = False
random = True
memonger = True
memonger_until = "stage3_unit21_plus"
class pretrain:
prefix = "pretrain_model/resnet-%d" % General.depth
epoch = 0
fixed_param = []
def process_weight(sym, arg_params, aux_params):
import re
import logging
logger = logging.getLogger()
# for trident non-shared initialization
for k in sym.list_arguments():
branch_name = re.sub('_branch\d+', '', k)
if k != branch_name and branch_name in arg_params:
arg_params[k] = arg_params[branch_name]
logger.info('init arg {} with {}'.format(k, branch_name))
for k in sym.list_auxiliary_states():
branch_name = re.sub('_branch\d+', '', k)
if k != branch_name and branch_name in aux_params:
aux_params[k] = aux_params[branch_name]
logger.info('init aux {} with {}'.format(k, branch_name))
class OptimizeParam:
class optimizer:
type = "sgd"
lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image
momentum = 0.9
wd = 0.0001
clip_gradient = 5
class schedule:
begin_epoch = 0
end_epoch = 18
lr_iter = [180000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image),
240000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)]
class warmup:
type = "gradual"
lr = 0.0
iter = 3000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)
class TestScaleParam:
short_ranges = [600, 800, 1000, 1200]
long_ranges = [2000, 2000, 2000, 2000]
@staticmethod
def add_resize_info(roidb):
ms_roidb = []
for r_ in roidb:
for short, long in zip(TestScaleParam.short_ranges, TestScaleParam.long_ranges):
r = r_.copy()
r["resize_long"] = long
r["resize_short"] = short
ms_roidb.append(r)
return ms_roidb
class TestParam:
min_det_score = 0.001
max_det_per_image = 100
process_roidb = TestScaleParam.add_resize_info
if Trident.test_scaleaware:
process_output = lambda x, y: process_branch_outputs(
x, Trident.num_branch, Trident.valid_ranges, Trident.valid_ranges_on_origin)
else:
process_output = lambda x, y: x
class model:
prefix = "experiments/{}/checkpoint".format(General.name)
epoch = OptimizeParam.schedule.end_epoch
class nms:
from operator_py.nms import cython_soft_nms_wrapper
type = cython_soft_nms_wrapper
thr = 0.5
class coco:
annotation = "data/coco/annotations/instances_minival2014.json"
# data processing
class ResizeParam:
short = 800
long = 1200 if is_train else 2000
class RandResizeParam:
short = None # generate on the fly
long = None
short_ranges = [600, 800, 1000, 1200]
long_ranges = [2000, 2000, 2000, 2000]
class RandCropParam:
mode = "center" # random or center
short = 800
long = 1200
class PadParam:
short = 800
long = 1200 if is_train else 2000
max_num_gt = 100
class ScaleRange:
valid_ranges = Trident.valid_ranges
cal_on_origin = Trident.valid_ranges_on_origin # True: valid_ranges on origin image scale / valid_ranges on resized image scale
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 trident:
invalid_anchor_threshd = 0.3
class RenameParam:
mapping = dict(image="data")
from core.detection_input import ReadRoiRecord, RandResize2DImageBbox, RandCrop2DImageBbox, Resize2DImageBboxByRoidb, \
ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \
RenameRecord
from models.tridentnet.input import ScaleAwareRange, TridentAnchorTarget2D
if is_train:
transform = [
ReadRoiRecord(None),
RandResize2DImageBbox(RandResizeParam),
RandCrop2DImageBbox(RandCropParam),
Flip2DImageBbox(),
Pad2DImageBbox(PadParam),
ConvertImageFromHwcToChw(),
ScaleAwareRange(ScaleRange),
TridentAnchorTarget2D(AnchorTarget2DParam),
RenameRecord(RenameParam.mapping)
]
data_name = ["data", "im_info", "gt_bbox"]
if Trident.train_scaleaware:
data_name.append("valid_ranges")
label_name = ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"]
else:
transform = [
ReadRoiRecord(None),
Resize2DImageBboxByRoidb(),
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
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_train2014", "coco_valminusminival2014")
else:
image_set = ("coco_minival2014", )
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)
test_sym = None
else:
train_sym = None
test_sym = detector.get_test_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head)
class ModelParam:
train_symbol = train_sym
test_symbol = 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
def get_config(is_train):
class General:
use_groupsoftmax = True
log_frequency = 20
name = __name__.rsplit("/")[-1].rsplit(".")[-1]
batch_image = 2 if is_train else 1
fp16 = True
class KvstoreParam:
kvstore = "local"
batch_image = General.batch_image
gpus = [0, 1, 2, 3, 4, 5, 6, 7]
fp16 = General.fp16
class NormalizeParam:
if is_train:
normalizer = normalizer_factory(type="syncbn",
ndev=len(KvstoreParam.gpus))
else:
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 = 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 BboxParam:
fp16 = General.fp16
normalizer = NormalizeParam.normalizer
num_class = 1 + 83
image_roi = 256
batch_image = General.batch_image
use_groupsoftmax = General.use_groupsoftmax
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_train2014", "coco_valminusminival2014",
"cctsdb_train")
else:
image_set = ("coco_minival2014", )
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-101"
epoch = 0
fixed_param = []
class OptimizeParam:
class optimizer:
type = "sgd"
lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image
momentum = 0.9
wd = 0.0001
clip_gradient = 5
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 = 3000 * 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 = "/ws/data/opendata/coco/annotations/instances_minival2014.json"
# data processing
class GroupParam:
# box 83 classes
boxv0 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, \
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, \
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83], dtype=np.float32)
#COCO benchmark
boxv1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, \
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, \
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 0, 0, 0 ], dtype=np.float32)
#CCTSDB benchmark
boxv2 = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 82, 83], dtype=np.float32)
rpnv0 = np.array([0, 1, 2], dtype=np.float32) # rpn 3 classes
rpnv1 = np.array([0, 1, 0], dtype=np.float32) # COCO benchmark
rpnv2 = np.array([0, 0, 2], dtype=np.float32) # CCTSDB benchmark
rpn_groups = [rpnv0, rpnv1, rpnv2]
box_groups = [boxv0, boxv1, boxv2]
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)
use_groupsoftmax = General.use_groupsoftmax
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
def gtclass2rpn(gtclass):
class_gap = 80
gtclass[gtclass > class_gap] = -1
gtclass[gtclass > 0] = 1
gtclass[gtclass < 0] = 2
return gtclass
class RenameParam:
mapping = dict(image="data")
from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \
ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \
RenameRecord, AnchorTarget2D, GroupRead
if is_train:
transform = [
ReadRoiRecord(None),
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"]
if General.use_groupsoftmax:
data_name.append("rpn_group")
data_name.append("box_group")
transform.append(GroupRead(GroupParam))
else:
transform = [
ReadRoiRecord(None),
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
