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 = False
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
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 = 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 + 80
image_roi = 256
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-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 = 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 = "data/coco/annotations/instances_minival2014.json"
# data processing
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 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
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"]
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
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
min_pos_thr = 0.0
class sample:
image_anchor = 256
pos_fraction = 0.5
class RenameParam:
mapping = dict(image="data")
transform = [
ReadRoiRecord(None),
RandResizeCrop2DImageBbox(ResizeParam),
Flip2DImageBbox(),
Pad2DImageBbox(PadParam),
ConvertImageFromHwcToChw(),
AnchorTarget2D(AnchorTarget2DParam),
RenameRecord(RenameParam.mapping)
]
DEBUG = True
with open("data/cache/coco_val2017.roidb", "rb") as fin:
roidb = pkl.load(fin)
roidb = [rec for rec in roidb if rec["gt_bbox"].shape[0] > 0]
roidb = [roidb[i] for i in np.random.choice(len(roidb), 20, replace=False)]
print(roidb[0])
flipped_roidb = []
for rec in roidb:
new_rec = rec.copy()
new_rec["flipped"] = True
def test_rpn_target():
import numpy as np
# anchor generation
stride = 16
aspects = (0.5, 1.0, 2.0)
scales = (2, 4, 8, 16, 32)
max_side = 1200
feat_h = 75
feat_w = 50
base_anchor = np.array([0, 0, stride - 1, stride - 1])
w = base_anchor[2] - base_anchor[0] + 1
h = base_anchor[3] - base_anchor[1] + 1
x_ctr = base_anchor[0] + 0.5 * (w - 1)
y_ctr = base_anchor[1] + 0.5 * (h - 1)
w_ratios = np.round(np.sqrt(w * h / aspects))
h_ratios = np.round(w_ratios * aspects)
ws = (np.outer(w_ratios, scales)).reshape(-1)
hs = (np.outer(h_ratios, scales)).reshape(-1)
base_anchor = np.stack([
x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1), x_ctr + 0.5 *
(ws - 1), y_ctr + 0.5 * (hs - 1)
],
axis=1)
shift_x = np.arange(0, max_side // stride, dtype=np.float32) * stride
shift_y = np.arange(0, max_side // stride, dtype=np.float32) * stride
grid_x, grid_y = np.meshgrid(shift_x, shift_y)
grid_x, grid_y = grid_x.reshape(-1), grid_y.reshape(-1)
grid = np.stack([grid_x, grid_y, grid_x, grid_y], axis=1)
all_anchor = grid[:, None, :] + base_anchor[None, :, :]
all_anchor = all_anchor.reshape(1, 1, max_side // stride,
max_side // stride, -1)
anchors = mx.nd.array(all_anchor, dtype="float32")
cls_prob = mx.nd.random_normal(
0, 1, shape=[1, len(aspects) * len(scales), feat_h, feat_w])
gt_bboxes = mx.nd.array([
[200, 200, 300, 300],
[300, 300, 500, 500],
[-1, -1, -1, -1],
[200, 200, 300, 300],
[400, 300, 500, 500],
[-1, -1, -1, -1],
]).reshape(2, 3, 4)
im_infos = mx.nd.array([[1200, 800, 2], [1200, 800, 2]]).reshape(2, 3)
rpn_cls_label, rpn_reg_target, rpn_reg_weight = _rpn_target_batch(
mx.ndarray, cls_prob, anchors, gt_bboxes, im_infos, 2, 15, max_side,
stride, 0, 256, 0.5, 0.7, 0.3)
print(len(np.where(rpn_cls_label[1].asnumpy() == 0)[0]))
print(np.where(rpn_cls_label[1].asnumpy() > 0))
print(np.where(rpn_reg_weight[1].asnumpy() > 0))
print(rpn_reg_target[1][np.where(rpn_reg_weight[1].asnumpy() > 0)])
rpn_cls_label, rpn_reg_target, rpn_reg_weight = _fpn_rpn_target_batch(
mx.ndarray, [cls_prob], [anchors], gt_bboxes, im_infos, 2, 15,
max_side, [stride], 0, 256, 0.5, 0.7, 0.3)
print(len(np.where(rpn_cls_label[1].asnumpy() == 0)[0]))
print(np.where(rpn_cls_label[1].asnumpy() > 0))
print(np.where(rpn_reg_weight[1].asnumpy() > 0))
print(rpn_reg_target[1][np.where(rpn_reg_weight[1].asnumpy() > 0)])
from core.detection_input import AnchorTarget2D
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
anchor_target = AnchorTarget2D(AnchorTarget2DParam)
record = {
"im_info": im_infos.asnumpy()[1],
"gt_bbox": gt_bboxes.asnumpy()[1]
}
anchor_target.apply(record)
print(len(np.where(record["rpn_cls_label"] == 0)[0]))
print(np.where(record["rpn_cls_label"] > 0))
print(np.where(record["rpn_reg_weight"] > 0))
print(record["rpn_reg_target"][np.where(record["rpn_reg_weight"] > 0)])
class AnchorTarget2DParam:
def __init__(self):
self.generate = self._generate()
class _generate:
def __init__(self):
self.stride = (4, 8, 16, 32, 64)
self.short = (200, 100, 50, 25, 13)
self.long = (334, 167, 84, 42, 21)
scales = (8)
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
