def get_yolov4_config(input_size=None,
num_classes=80,
num_anchors=3,
depth_multiplier=1.,
width_multiplier=1.,
label_assignment="iou",
name="YOLOv4"):
h = Config()
if input_size is None:
input_size = default_input_sizes[name]
h.detector = name
h.dtype = "float16"
h.data_format = "channels_last"
h.num_classes = num_classes
h.depth_multiplier = depth_multiplier_dict[name]
h.width_multiplier = width_multiplier_dict[name]
if name not in model:
raise ValueError(name + " not in ", list(model.keys()))
h.model = model[name]
h.min_level = min_level_dict[name]
h.max_level = max_level_dict[name]
h.strides = strides_dict[name]
h.anchors = anchors_dict[name]
h.num_anchors = len(anchors_dict[name][0]) // 2
h.input_size = input_size if isinstance(input_size, (tuple, list)) else (input_size, input_size)
h.label_assignment = label_assignment
h.anchor_threshold = 0.2
h.gr = 1.
h.bbox_loss = dict(loss="CIoULoss", weight=1., reduction="none")
h.label_loss = dict(loss="BinaryCrossEntropy", weight=1., from_logits=True, reduction="none") # .631 if finetuning else weight = 1.0
h.conf_loss = dict(loss="BinaryCrossEntropy", weight=1., from_logits=True, reduction="none") # 0.911 if finetuning else weight = 1.
h.balance = [1., 1., 1.] # [4.0, 1.0, 0.4] # if num_level == 3 else [4.0, 1.0, 0.4, 0.1]
h.box_weight = 0.05 # 0.0296 if finetune else 0.05
h.label_weight = .5 # 0.243 if finetune else 0.5
h.conf_weight = 1.0 # 0.301 if finetune else 1.0
h.weight_decay = 0.0005
h.excluding_weight_names = ["predicted"]
h.train=dict(dataset=dict(dataset="COCODataset",
batch_size=8,
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), keep_ratio=True),
dict(augmentation="Pad", size_divisor=32)
],
# mixup=dict(alpha=8.0, prob=0.5),
mosaic=dict(size=input_size, min_image_scale=0.25, prob=1.),
num_samples=118287),
pretrained_weights_path="/data/bail/pretrained_weights/darknet53-notop/darknet53.ckpt",
optimizer=dict(optimizer="SGD", momentum=0.937),
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=480,
# learning_rate_scheduler=dict(scheduler="PiecewiseConstantDecay",
# boundaries=[24, 32],
# values=[0.012, 0.0012, 0.00012]),
learning_rate_scheduler=dict(scheduler="CosineDecay", initial_learning_rate=0.012),
warmup=dict(warmup_learning_rate=0.0012, steps=12000)),
checkpoint_dir="checkpoints/%s" % name,
summary_dir="logs/%s" % name,
log_every_n_steps=100,
save_ckpt_steps=10000)
h.val=dict(dataset=dict(dataset="COCODataset",
batch_size=8,
dataset_dir="/data/bail/COCO",
training=False,
augmentations=[
dict(Resize=dict(size=(input_size, input_size), strides=32, min_scale=1., max_scale=1.0))
# dict(ResizeV2=dict(short_side=800, long_side=1333, strides=64, min_scale=1.0, max_scale=1.0))
]),
samples=5000)
# h.test=dict(nms="NonMaxSuppressionWithQuality",
# pre_nms_size=5000,
# post_nms_size=100,
# iou_threshold=0.6,
# score_threshold=0.5,
# sigma=0.5,
# nms_type="nms")
h.test=dict(nms="CombinedNonMaxSuppression",
pre_nms_size=2000,
post_nms_size=100,
iou_threshold=0.6,
score_threshold=0.35)
return h
def default_configs(phi, batch_size=4, image_size=512, num_classes=3):
h = Config()
h.dtype = "float32"
# backbone
h.model = dict(
model="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=True),
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=[
-1,
],
frozen_stages=[
-1,
],
num_classes=num_classes)
# loss
h.use_sigmoid = False
h.loss = dict(loss="CrossEntropy",
weight=1.,
from_logits=True,
reduction="none")
h.weight_decay = 4e-5
# dataset
h.num_classes = num_classes
h.train = dict(dataset=dict(
dataset="ShuaidaoDataset",
batch_size=batch_size,
dataset_dir=
"/home/bail/Data/data1/Dataset/RongChuangActions/shuaidao/shuaidaoThree/train",
training=True,
augmentations=[
dict(Resize=dict(input_size=image_size)),
# dict(RandomDistortColor=dict()),
dict(RandAugment=dict(num_layers=2,
magnitude=10.,
cutout_const=40.,
translate_const=100.))
]))
h.val = dict(dataset=dict(
dataset="ShuaidaoDataset",
batch_size=batch_size,
dataset_dir=
"/home/bail/Data/data1/Dataset/RongChuangActions/shuaidao/shuaidaoThree/val",
training=False,
augmentations=[dict(Resize=dict(input_size=image_size))]))
# train
h.pretrained_weights_path = "/data/bail/pretrained_weights/efficientnet-b%d.h5" % phi
h.optimizer = dict(optimizer="SGD", momentum=0.9)
h.lookahead = None
h.learning_rate_scheduler = dict(scheduler="CosineDecay",
initial_learning_rate=0.01,
warmup_steps=1200,
warmup_learning_rate=0.001,
train_steps=32001)
h.checkpoint_dir = "checkpoints/efficientnet_b%d_shuaidao" % phi
h.summary_dir = "logs/efficientnet_b%d_shuaidao" % phi
h.gradient_clip_norm = 0.
h.log_every_n_steps = 100
h.save_ckpt_steps = 1000
h.val_every_n_steps = 1000
return h
def get_yolov5_config(num_classes=80, depth_multiple=.33, width_multiple=.50, name="yolov5s"):
h = Config()
h.detector = "YOLOv5"
h.dtype = "float16"
h.num_classes = num_classes
h.depth_multiple = depth_multiple # 0.33 0.67 1.0 1.33
h.width_multiple = width_multiple # 0.50 0.75 1.0 1.25
h.model = [
# [from, number, module, args]
# backbone
[-1, 1, "Focus", dict(filters=64, kernel_size=3)], # 0-P1/2
[-1, 1, "Conv", dict(filters=128, kernel_size=3, strides=2)],
[-1, 3, "BottleneckCSP", dict(filters=128)], # 2-P3/8
[-1, 1, "Conv", dict(filters=256, kernel_size=3, strides=2)],
[-1, 9, "BottleneckCSP", dict(filters=256)], # 4-P4/16
[-1, 1, "Conv", dict(filters=512, kernel_size=3, strides=2)],
[-1, 9, "BottleneckCSP", dict(filters=512)], # 6-P5/32
[-1, 1, "Conv", dict(filters=1024, kernel_size=3, strides=2)],
[-1, 1, "SpatialPyramidPooling", dict(filters=1024, pool_sizes=[5, 9, 13])],
[-1, 3, "BottleneckCSP", dict(filters=1024, shortcut=False)], # 9
# head
[-1, 1, "Conv", dict(filters=512, kernel_size=1, strides=1)],
[-1, 1, "Upsample", dict(size=2, interpolation="nearest")],
[[-1, 6], 1, "Concat", dict(axis=-1)], # concat backbone P4
[-1, 3, "BottleneckCSP", dict(filters=512, shortcut=False)], # 13
[-1, 1, "Conv", dict(filters=256, kernel_size=1, strides=1)],
[-1, 1, "Upsample", dict(size=2, interpolation="nearest")],
[[-1, 4], 1, "Concat", dict(axis=-1)], # concat backbone P3
[-1, 3, "BottleneckCSP", dict(filters=256, shortcut=False)], # 17 (P3/8-small)
[-1, 1, "Conv", dict(filters=256, kernel_size=3, strides=2)],
[[-1, 14], 1, "Concat", dict(axis=-1)], # concat head P4
[-1, 3, "BottleneckCSP", dict(filters=512, shortcut=False)], # 20 (P4/16-medium)
[-1, 1, "Conv", dict(filters=512, kernel_size=3, strides=2)],
[[-1, 10], 1, "Concat", dict(axis=-1)], # concat head P5
[-1, 3, "BottleneckCSP", dict(filters=1024, shortcut=False)], # 23 (P5-large)
[[17, 20, 23], 1, "Detect", dict(anchors="anchors", num_classes="num_classes")]
]
h.min_level = 3
h.max_level = 5
h.strides = [8, 16, 32]
h.anchors = [[10, 13, 16, 30, 33, 23],
[30, 61, 62, 45, 59, 119],
[116, 90, 156, 198, 373, 326]]
h.num_anchors = 3
h.anchor_threshold = 4. # 2.91 if finetuning else 4.0
h.gr = 1.
h.bbox_loss = dict(loss="CIoULoss", weight=1., reduction="none")
h.label_loss = dict(loss="BinaryCrossEntropy", weight=1., from_logits=True, reduction="none") # .631 if finetuning else weight = 1.0
h.conf_loss = dict(loss="BinaryCrossEntropy", weight=1., from_logits=True, reduction="none") # 0.911 if finetuning else weight = 1.
h.balance = [1., 1., 1.] # [4.0, 1.0, 0.4] # if num_level == 3 else [4.0, 1.0, 0.4, 0.1]
h.box_weight = 0.05 # 0.0296 if finetune else 0.05
h.label_weight = .5 # 0.243 if finetune else 0.5
h.conf_weight = 1.0 # 0.301 if finetune else 1.0
h.weight_decay = 0.0005
h.train=dict(dataset=dict(dataset="COCODataset",
batch_size=8,
dataset_dir="/data/bail/COCO",
training=True,
augmentations=[
dict(FlipLeftToRight=dict(probability=0.5)),
dict(RandomDistortColor=dict(probability=1.)),
# dict(Resize=dict(size=(640, 640), strides=32, min_scale=.5, max_scale=2.)),
dict(ResizeV2=dict(short_side=640, long_side=1024, strides=32, min_scale=1.0, max_scale=1.0))
],
# mixup=dict(alpha=8.0, prob=0.5),
mosaic=dict(size=640, min_image_scale=0.25, prob=1.),
num_samples=118287),
pretrained_weights_path="/data/bail/pretrained_weights/darknet53-notop/darknet53.ckpt",
optimizer=dict(optimizer="SGD", momentum=0.937),
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=480,
# learning_rate_scheduler=dict(scheduler="PiecewiseConstantDecay",
# boundaries=[24, 32],
# values=[0.012, 0.0012, 0.00012]),
learning_rate_scheduler=dict(scheduler="CosineDecay", initial_learning_rate=0.012),
warmup=dict(warmup_learning_rate=0.0012, steps=12000)),
checkpoint_dir="checkpoints/%s" % name,
summary_dir="logs/%s" % name,
log_every_n_steps=100,
save_ckpt_steps=10000)
h.val=dict(dataset=dict(dataset="COCODataset",
batch_size=8,
dataset_dir="/data/bail/COCO",
training=False,
augmentations=[
dict(Resize=dict(size=(640, 640), strides=32, min_scale=1., max_scale=1.0))
# dict(ResizeV2=dict(short_side=800, long_side=1333, strides=64, min_scale=1.0, max_scale=1.0))
]),
samples=5000)
h.test=dict(nms="NonMaxSuppressionWithQuality",
pre_nms_size=5000,
post_nms_size=100,
iou_threshold=0.6,
score_threshold=0.05,
sigma=0.5,
nms_type="nms")
# h.test=dict(nms="CombinedNonMaxSuppression",
# pre_nms_size=5000,
# post_nms_size=100,
# iou_threshold=0.6,
# score_threshold=0.05)
return h
def default_configs(name, batch_size=4, image_size=512):
h = Config()
h.dtype = "float32"
# backbone
h.model = dict(
model=name,
convolution="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=True),
activation=dict(activation="relu"),
strides=[2, 1, 2, 2, 2, 1, 2, 1],
dilation_rates=[1, 1, 1, 1, 1, 1, 1, 1],
output_indices=[
-1,
],
frozen_stages=[
-1,
],
num_classes=1)
# loss
h.use_sigmoid = True
h.loss = dict(loss="BinaryCrossEntropy",
weight=1.,
from_logits=True,
reduction="none")
h.weight_decay = 4e-5
# dataset
h.num_classes = 1
h.train = dict(dataset=dict(
dataset="SmokingDataset",
batch_size=batch_size,
dataset_dir="/data/bail/smoking/train",
training=True,
augmentations=[
dict(Resize=dict(input_size=image_size)),
# dict(RandAugment=dict(num_layers=2,
# magnitude=10.,
# cutout_const=40.,
# translate_const=100.))
]))
h.val = dict(
dataset=dict(dataset="SmokingDataset",
batch_size=batch_size,
dataset_dir="/data/bail/smoking/val",
training=False,
augmentations=[dict(Resize=dict(input_size=image_size))]))
# train
h.pretrained_weights_path = "/data/bail/pretrained_weights/efficientnet-b%d.h5" % phi
h.optimizer = dict(optimizer="SGD", momentum=0.9)
h.lookahead = None
h.learning_rate_scheduler = dict(scheduler="CosineDecay",
initial_learning_rate=0.016,
warmup_steps=800,
warmup_learning_rate=0.001,
train_steps=40001)
h.checkpoint_dir = "checkpoints/%s" % name
h.summary_dir = "logs/%s" % name
h.gradient_clip_norm = 0.
h.log_every_n_steps = 100
h.save_ckpt_steps = 2000
h.val_every_n_steps = 2000
return h
