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 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
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
