def get_onenet_config(num_classes=80):
h = Config()
input_size = (512, 512)
h.detector = "OneNet"
h.dtype = "float16"
h.data_format = "channels_last"
h.input_shape = (input_size[0], input_size[1], 3)
h.num_classes = num_classes
h.backbone = dict(backbone="ResNet18",
dropblock=None,
normalization=dict(normalization="batch_norm",
momentum=0.997,
epsilon=1e-4,
trainable=False),
activation=dict(activation="relu"),
strides=[2, 2, 2, 2, 2],
dilation_rates=[1, 1, 1, 1, 1],
output_indices=[2, 3, 4, 5],
frozen_stages=[
1,
])
h.neck = dict(neck="CenterNetDeconv",
normalization=dict(normalization="batch_norm",
momentum=0.997,
epsilon=1e-4,
trainable=False),
activation=dict(activation="relu"))
h.head = dict(head="OneNetHead",
activation=dict(activation="relu"),
feat_dims=64,
dropblock=None,
num_classes=num_classes,
strides=4,
prior=0.01,
use_sigmoid=True,
assigner=dict(assigner="MinCostAssigner",
class_weight=2.,
l1_weight=2.,
iou_weight=5.,
iou_type="giou",
alpha=0.25,
gamma=2.),
label_loss=dict(loss="FocalLoss",
alpha=0.25,
gamma=2.,
reduction="sum"),
bbox_loss=dict(loss="RegL1Loss", weight=1., reduction="sum"))
h.weight_decay = 1e-4
h.excluding_weight_names = ["predicted_box", "predicted_class"]
h.train = dict(
dataset=dict(dataset="COCODataset",
batch_size=4,
dataset_dir="/data/bail/COCO",
training=True,
augmentations=[
dict(augmentation="FlipLeftToRight", probability=0.5),
dict(augmentation="RandomDistortColor"),
dict(augmentation="Resize",
img_scale=(0.2, 2),
multiscale_mode="range",
keep_ratio=True),
dict(augmentation="RandCropOrPad",
size=(input_size, input_size),
clip_box_base_center=False),
],
num_samples=118287),
pretrained_weights_path=
"/data/bail/pretrained_weights/resnet50/resnet50.ckpt",
optimizer=dict(optimizer="SGD", momentum=0.9),
mixed_precision=dict(
loss_scale=None
), # The loss scale in mixed precision training. If None, use dynamic.
gradient_clip_norm=10.0,
scheduler=dict(train_epochs=24,
learning_rate_scheduler=dict(
scheduler="PiecewiseConstantDecay",
boundaries=[16, 22],
values=[0.02, 0.002, 0.0002]),
warmup=dict(warmup_learning_rate=0.001, steps=800)),
checkpoint_dir="checkpoints/onenet",
summary_dir="logs/onenet",
log_every_n_steps=100,
save_ckpt_steps=5000)
h.val = dict(dataset=dict(dataset="COCODataset",
batch_size=4,
dataset_dir="/data/bail/COCO",
training=False,
augmentations=[
dict(augmentation="Resize",
img_scale=[(1333, input_size)],
keep_ratio=True),
dict(augmentation="Pad", size_divisor=32)
]),
samples=5000)
h.test = dict(topk=100, score_threshold=0.3)
return h
def default_detection_configs(phi,
num_classes=90,
min_level=3,
max_level=7,
fpn_filters=64,
neck_repeats=3,
head_repeats=3,
anchor_scale=4,
num_scales=3,
batch_size=4,
image_size=512,
fpn_name="BiFPN",
fpn_input_dims=[80, 192, 320],
fusion_type="weighted_sum"):
h = Config()
h.detector = "EfficientDetD%d" % phi
h.dtype = "float32"
h.num_classes = num_classes
h.backbone = dict(
backbone="EfficientNetB%d" % phi,
convolution="depthwise_conv2d",
dropblock=None,
# dropblock=dict(keep_prob=None,
# block_size=None)
normalization=dict(normalization="batch_norm",
momentum=0.99,
epsilon=1e-3,
axis=-1,
trainable=False),
activation=dict(activation="swish"),
strides=[2, 1, 2, 2, 2, 1, 2, 1],
dilation_rates=[1, 1, 1, 1, 1, 1, 1, 1],
output_indices=[3, 4, 5],
frozen_stages=[
-1,
])
h.neck = dict(neck=fpn_name,
input_size=image_size if isinstance(
image_size, (list, tuple)) else [image_size, image_size],
num_backbone_levels=3,
feat_dims=fpn_filters,
repeats=neck_repeats,
convolution="separable_conv2d",
normalization=dict(normalization="batch_norm",
momentum=0.99,
epsilon=1e-3,
axis=-1,
trainable=False),
activation=dict(activation="swish"),
min_level=3,
max_level=7,
input_dims=fpn_input_dims,
pool_type=None,
apply_bn=True,
fusion_type=fusion_type)
# anchors parameters
strides = [2**l for l in range(min_level, max_level + 1)]
scales = [[
2**(i / num_scales) * s * anchor_scale for i in range(num_scales)
] for s in strides]
aspect_ratios = [1., 0.5, 2.]
num_scales = len(scales[0]) * len(aspect_ratios)
h.anchors = dict(aspect_ratios=aspect_ratios,
strides=strides,
scales=scales,
num_anchors=num_scales)
h.head = dict(head="RetinaNetHead",
convolution="separable_conv2d",
normalization=dict(normalization="batch_norm",
momentum=0.99,
epsilon=1e-3,
axis=-1,
trainable=False),
activation=dict(activation="swish"),
feat_dims=fpn_filters,
dropblock=None,
repeats=head_repeats,
min_level=min_level,
max_level=max_level,
use_sigmoid=True,
prior=0.01,
survival_prob=None,
data_format="channels_last",
bbox_encoder=dict(encoder="Box2Delta", weights=None),
bbox_decoder=dict(decoder="Delta2Box", weights=None),
assigner=dict(assigner="ATSSAssigner", topk=9),
sampler=dict(sampler="PseudoSampler"),
label_loss=dict(loss="FocalLoss",
gamma=2.0,
alpha=0.25,
label_smoothing=0.01,
weight=1.,
from_logits=True,
reduction="sum"),
bbox_loss=dict(loss="CIoULoss", weight=1., reduction="sum"))
h.weight_decay = 4e-5
h.train = dict(
input_size=(image_size, image_size),
dataset=dict(
dataset="COCODataset",
batch_size=batch_size,
dataset_dir="/data/bail/COCO",
training=True,
augmentations=[
dict(FlipLeftToRight=dict(probability=0.5)),
dict(RandomDistortColor=dict(probability=1.)),
dict(Resize=dict(size=(image_size, image_size),
strides=32,
min_scale=0.5,
max_scale=2.0)),
# dict(ResizeV2=dict(short_side=800, long_side=1333, strides=64, min_scale=1.0, max_scale=1.0))
],
num_samples=118287,
num_classes=num_classes),
pretrained_weights_path=
"/data/bail/pretrained_weights/resnet50/resnet50.ckpt",
optimizer=dict(optimizer="SGD", momentum=0.9),
mixed_precision=dict(
loss_scale=None
), # The loss scale in mixed precision training. If None, use dynamic.
gradient_clip_norm=.0,
scheduler=dict(
train_epochs=18,
# learning_rate_scheduler=dict(scheduler="PiecewiseConstantDecay",
# boundaries=[8, 11],
# values=[0.02, 0.002, 0.0002]),
learning_rate_scheduler=dict(scheduler="CosineDecay",
initial_learning_rate=0.02),
warmup=dict(warmup_learning_rate=0.0001, steps=800)),
checkpoint_dir="checkpoints/efficientdet-d%d" % phi,
summary_dir="logs/efficientdet-d%d" % phi,
log_every_n_steps=100,
save_ckpt_steps=5000)
h.val = dict(
dataset=dict(
dataset="COCODataset",
batch_size=batch_size,
dataset_dir="/data/bail/COCO",
training=False,
augmentations=[
dict(Resize=dict(size=(image_size, image_size),
strides=32,
min_scale=1.0,
max_scale=1.0))
# dict(ResizeV2=dict(short_side=800, long_side=1333, strides=64, min_scale=1.0, max_scale=1.0))
]),
input_size=(image_size, image_size),
samples=5000)
h.test = dict(
nms="CombinedNonMaxSuppression",
pre_nms_size=5000, # select top_k high confident detections for nms
post_nms_size=100,
iou_threshold=0.6,
score_threshold=0.1,
)
return h
def get_yolof_config(num_classes=80):
h = Config()
data_format = "channels_last"
input_size = (1024, 1024)
h.detector = "YOLOF"
h.dtype = "float16"
h.data_format = data_format
h.input_shape = (input_size[0], input_size[1], 3)
h.num_classes = num_classes
h.backbone = dict(backbone="ResNeXt101_64X4D",
dropblock=None,
normalization=dict(normalization="batch_norm", momentum=0.997, epsilon=1e-4, trainable=False),
activation=dict(activation="relu"),
strides=[2, 2, 2, 2, 2],
dilation_rates=[1, 1, 1, 1, 1],
output_indices=[5],
frozen_stages=[1, ])
h.neck=dict(neck="DilatedEncoder",
filters=512,
midfilters=128,
dilation_rates=[2, 4, 6, 8], # dilation not in stage5
# dilation_rates=[4, 8, 12, 16],
normalization=dict(normalization="batch_norm", momentum=0.9, epsilon=1e-3, axis=-1, trainable=True),
activation=dict(activation="relu"),
kernel_initializer="he_normal",
data_format=data_format)
h.anchors=dict(generator="AnchorGenerator",
aspect_ratios=[1.],
scales=[32, 64, 128, 256, 512],
strides=32,
# scales=[16, 32, 64, 128, 256, 512],
# strides=16,
num_anchors=5)
h.head=dict(head="YOLOFHead",
normalization=dict(normalization="batch_norm", momentum=0.9, epsilon=1e-3, axis=-1, trainable=True),
activation=dict(activation="relu"),
feat_dims=512,
cls_num_convs=2,
reg_num_convs=4,
kernel_initializer="he_normal",
use_sigmoid=True,
prior=0.01,
bbox_decoder=dict(decoder="Delta2Box", weights=[1., 1., 1., 1.]),
bbox_encoder=dict(encoder="Box2Delta", weights=[1., 1., 1., 1.]),
assigner=dict(assigner="UniformAssigner", match_times=8, pos_ignore_thresh=0.7, neg_ignore_thresh=0.15),
sampler=dict(sampler="PseudoSampler"),
label_loss=dict(loss="FocalLoss", alpha=0.25, gamma=2.0, weight=1., from_logits=True, reduction="sum"),
bbox_loss=dict(loss="GIoULoss", weight=2., reduction="sum"))
h.weight_decay = 1e-4
h.excluding_weight_names = ["predicted_box", "predicted_class"]
h.train=dict(dataset=dict(dataset="COCODataset",
batch_size=4,
dataset_dir="/data/bail/COCO",
training=True,
augmentations=[
dict(augmentation="FlipLeftToRight", probability=0.5),
dict(augmentation="RandomDistortColor"),
dict(augmentation="Resize", img_scale=[(1333, 800)], keep_ratio=True),
dict(augmentation="Pad", size_divisor=32)
],
num_samples=118287),
pretrained_weights_path="/data/bail/pretrained_weights/resnet50/resnet50.ckpt",
optimizer=dict(optimizer="SGD", momentum=0.9),
mixed_precision=dict(loss_scale=None), # The loss scale in mixed precision training. If None, use dynamic.
gradient_clip_norm=10.0,
scheduler=dict(train_epochs=24,
learning_rate_scheduler=dict(scheduler="PiecewiseConstantDecay",
boundaries=[16, 22],
values=[0.02, 0.002, 0.0002]),
warmup=dict(warmup_learning_rate=0.001, steps=800)),
checkpoint_dir="checkpoints/yolof",
summary_dir="logs/yolof",
log_every_n_steps=100,
save_ckpt_steps=5000)
h.val=dict(dataset=dict(dataset="COCODataset",
batch_size=4,
dataset_dir="/data/bail/COCO",
training=False,
augmentations=[
dict(augmentation="Resize", img_scale=[(1333, 800)], keep_ratio=True),
dict(augmentation="Pad", size_divisor=32),
]),
samples=5000)
h.test=dict(nms="CombinedNonMaxSuppression",
pre_nms_size=2000,
post_nms_size=100,
iou_threshold=0.5,
score_threshold=0.35)
return h
def get_gfl_config(num_classes=80):
h = Config()
input_size = (1024, 1024)
h.detector = "GFL"
h.dtype = "float16"
h.data_format = "channels_last"
h.input_shape = (input_size[0], input_size[1], 3)
h.num_classes = num_classes
h.backbone = dict(backbone="ResNet101",
dropblock=None,
normalization=dict(normalization="batch_norm", momentum=0.997, epsilon=1e-4, trainable=False),
activation=dict(activation="relu"),
strides=[2, 2, 2, 2, 2],
dilation_rates=[1, 1, 1, 1, 1],
output_indices=[3, 4, 5],
frozen_stages=[1, ])
h.neck=dict(neck="FPN",
feat_dims=256,
min_level=3,
max_level=5,
num_output_levels=5,
add_extra_convs=True,
relu_before_extra_convs=True)
h.anchors=dict(generator="AnchorGeneratorV2",
aspect_ratios=[1.],
octave_base_scale=8,
scales_per_octave=1,
strides=[8, 16, 32, 64, 128],
num_anchors=1)
h.head=dict(head="GFLHead",
normalization=dict(normalization="group_norm", groups=32),
activation=dict(activation="relu"),
feat_dims=256,
dropblock=None,
num_classes=num_classes,
repeats=4,
min_level=3,
max_level=7,
use_sigmoid=True,
prior=0.01,
reg_max=16,
bbox_decoder = dict(decoder="Distance2Box", weights=None),
bbox_encoder = dict(encoder="Box2Distance", weights=None),
assigner = dict(assigner="ATSSAssigner", topk=9),
sampler = dict(sampler="PseudoSampler"),
label_loss = dict(loss="QualityFocalLoss", beta=2.0, weight=1., from_logits=True, reduction="sum"),
bbox_loss = dict(loss="GIoULoss", weight=2., reduction="sum"),
dfl_loss = dict(loss="DistributionFocalLoss", weight=.25, reduction="sum"))
h.weight_decay = 1e-4
h.excluding_weight_names = ["predicted_box", "predicted_class"]
h.train=dict(dataset=dict(dataset="COCODataset",
batch_size=4,
dataset_dir="/data/bail/COCO",
training=True,
augmentations=[
dict(augmentation="FlipLeftToRight", probability=0.5),
dict(augmentation="RandomDistortColor"),
dict(augmentation="Resize", img_scale=[(1333, 800)], keep_ratio=True),
dict(augmentation="Pad", size_divisor=32),
],
num_samples=118287),
pretrained_weights_path="/data/bail/pretrained_weights/resnet50/resnet50.ckpt",
optimizer=dict(optimizer="SGD", momentum=0.9),
mixed_precision=dict(loss_scale=None), # The loss scale in mixed precision training. If None, use dynamic.
gradient_clip_norm=10.0,
scheduler=dict(train_epochs=24,
learning_rate_scheduler=dict(scheduler="PiecewiseConstantDecay",
boundaries=[16, 22],
values=[0.02, 0.002, 0.0002]),
warmup=dict(warmup_learning_rate=0.001, steps=800)),
checkpoint_dir="checkpoints/gfl",
summary_dir="logs/gfl",
log_every_n_steps=100,
save_ckpt_steps=5000)
h.val=dict(dataset=dict(dataset="COCODataset",
batch_size=4,
dataset_dir="/data/bail/COCO",
training=False,
augmentations=[
dict(augmentation="Resize", img_scale=[(1333, 800)], keep_ratio=True),
dict(augmentation="Pad", size_divisor=32),
]),
samples=5000)
h.test=dict(nms="CombinedNonMaxSuppression",
pre_nms_size=5000,
post_nms_size=100,
iou_threshold=0.6,
score_threshold=0.3)
return h
def default_detection_configs(phi,
min_level=3,
max_level=7,
fpn_filters=64,
neck_repeats=3,
head_repeats=3,
anchor_scale=4,
num_scales=3,
batch_size=4,
image_size=512,
fusion_type="weighted_sum"):
h = Config()
# model name
h.detector = "efficientdet-d%d" % phi
h.min_level = min_level
h.max_level = max_level
h.dtype = "float16"
# backbone
h.backbone = dict(backbone="efficientnet-b%d" % phi,
convolution="depthwise_conv2d",
dropblock=None,
# dropblock=dict(keep_prob=None,
# block_size=None)
normalization=dict(normalization="batch_norm",
momentum=0.99,
epsilon=1e-3,
axis=-1,
trainable=False),
activation=dict(activation="swish"),
strides=[2, 1, 2, 2, 2, 1, 2, 1],
dilation_rates=[1, 1, 1, 1, 1, 1, 1, 1],
output_indices=[3, 4, 5],
frozen_stages=[-1])
# neck
h.neck = dict(neck="bifpn",
repeats=neck_repeats,
convolution="separable_conv2d",
dropblock=None,
# dropblock=dict(keep_prob=None,
# block_size=None)
feat_dims=fpn_filters,
normalization=dict(normalization="batch_norm",
momentum=0.99,
epsilon=1e-3,
axis=-1,
trainable=False),
activation=dict(activation="swish"),
add_extra_conv=False, # Add extra convolution for neck
fusion_type=fusion_type,
use_multiplication=False)
# head
h.head = dict(head="RetinaNetHead",
repeats=head_repeats,
convolution="separable_conv2d",
dropblock=None,
# dropblock=dict(keep_prob=None,
# block_size=None)
feat_dims=fpn_filters,
normalization=dict(normalization="batch_norm",
momentum=0.99,
epsilon=1e-3,
axis=-1,
trainable=False),
activation=dict(activation="swish"),
prior=0.01)
# anchors parameters
strides = [2 ** l for l in range(min_level, max_level + 1)]
h.anchor = dict(aspect_ratios=[[1., 0.5, 2.]] * (max_level - min_level + 1),
scales=[
[2 ** (i / num_scales) * s * anchor_scale
for i in range(num_scales)] for s in strides
],
num_anchors=9)
# assigner
h.assigner = dict(assigner="max_iou_assigner",
pos_iou_thresh=0.5,
neg_iou_thresh=0.5)
# sampler
h.sampler = dict(sampler="pseudo_sampler")
# loss
h.use_sigmoid = True
h.label_loss=dict(loss="focal_loss",
alpha=0.25,
gamma=1.5,
label_smoothing=0.,
weight=1.,
from_logits=True,
reduction="none")
h.bbox_loss=dict(loss="smooth_l1_loss",
weight=50., # 50.
delta=.1, # .1
reduction="none")
# h.box_loss=dict(loss="giou_loss",
# weight=10.,
# reduction="none")
h.weight_decay = 4e-5
h.bbox_mean = None # [0., 0., 0., 0.]
h.bbox_std = None # [0.1, 0.1, 0.2, 0.2]
# dataset
h.num_classes = 90
h.skip_crowd_during_training = True
h.dataset = "objects365"
h.batch_size = batch_size
h.input_size = [image_size, image_size]
h.train_dataset_dir = "/home/bail/Data/data1/Dataset/Objects365/train"
h.val_dataset_dir = "/home/bail/Data/data1/Dataset/Objects365/train"
h.augmentation = [
dict(ssd_crop=dict(patch_area_range=(0.3, 1.),
aspect_ratio_range=(0.5, 2.0),
min_overlaps=(0.1, 0.3, 0.5, 0.7, 0.9),
max_attempts=100,
probability=.5)),
# dict(data_anchor_sampling=dict(anchor_scales=(16, 32, 64, 128, 256, 512),
# overlap_threshold=0.7,
# max_attempts=50,
# probability=.5)),
dict(flip_left_to_right=dict(probability=0.5)),
dict(random_distort_color=dict(probability=1.))
]
# train
h.pretrained_weights_path = "/home/bail/Workspace/pretrained_weights/efficientdet-d%d" % phi
h.optimizer = dict(optimizer="sgd", momentum=0.9)
h.lookahead = None
h.train_steps = 240000
h.learning_rate_scheduler = dict(scheduler="cosine", initial_learning_rate=0.002)
h.warmup = dict(warmup_learning_rate = 0.00001, steps = 24000)
h.checkpoint_dir = "checkpoints/efficientdet_d%d" % phi
h.summary_dir = "logs/efficientdet_d%d" % phi
h.gradient_clip_norm = .0
h.log_every_n_steps = 500
h.save_ckpt_steps = 10000
h.val_every_n_steps = 4000
h.postprocess = dict(pre_nms_size=5000, # select top_k high confident detections for nms
post_nms_size=100,
iou_threshold=0.5,
score_threshold=0.2)
return h
