class Config(object):
log = './log_ctn_lanmu' # Path to save log
checkpoint_path = './checkpoints_lanmu' # Path to store checkpoint model
resume = './checkpoints_lanmu/latest.pth' # load checkpoint model
evaluate = None # evaluate model path
# train_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/taibiao', 'images/train2017')
# val_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/taibiao', 'images/val2017')
# dataset_annotations_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/taibiao', 'annotations')
train_dataset_path = os.path.join(
'/home/jovyan/data-vol-polefs-1/dataset/lanmu', 'images/train2017')
val_dataset_path = os.path.join(
'/home/jovyan/data-vol-polefs-1/dataset/lanmu', 'images/val2017')
dataset_annotations_path = os.path.join(
'/home/jovyan/data-vol-polefs-1/dataset/lanmu', 'annotations')
network = "resnet50_centernet"
pretrained = False
#*********************************************************************************
multi_head = False
#the pretrained centernet model to load
pre_model_dir = '/home/jovyan/data-vol-1/zhangze/code/pytorch-ImageNet-CIFAR-COCO-VOC-training/detection_experiments/resnet18_centernet_coco_distributed_apex_resize512/checkpoints/best.pth'
num_classes = [41]
#*********************************************************************************
seed = 0
input_image_size = 512
use_multi_scale = False
multi_scale_range = [0.6, 1.4]
stride = 4
train_dataset = CocoDetection(image_root_dir=train_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="train2017",
transform=transforms.Compose([
RandomFlip(flip_prob=0.5),
RandomCrop(crop_prob=0.5),
RandomTranslate(translate_prob=0.5),
Normalize(),
]))
val_dataset = CocoDetection(image_root_dir=val_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="val2017",
transform=transforms.Compose([
Normalize(),
Resize(resize=input_image_size),
]))
epochs = 140
milestones = [90, 120]
per_node_batch_size = 16
lr = 5e-4
num_workers = 4
print_interval = 100
apex = True
sync_bn = False
class Config(object):
log = './log' # Path to save log
checkpoint_path = './checkpoints' # Path to store checkpoint model
resume = './checkpoints/latest.pth' # load checkpoint model
evaluate = None # evaluate model path
train_dataset_path = os.path.join(COCO2017_path, 'images/train2017')
val_dataset_path = os.path.join(COCO2017_path, 'images/val2017')
dataset_annotations_path = os.path.join(COCO2017_path, 'annotations')
network = "resnet18_centernet"
pretrained = False
num_classes = 80
seed = 0
input_image_size = 512
use_multi_scale = True
multi_scale_range = [0.6, 1.4]
stride = 4
train_dataset = CocoDetection(image_root_dir=train_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="train2017",
transform=transforms.Compose([
RandomFlip(flip_prob=0.5),
RandomCrop(crop_prob=0.5),
RandomTranslate(translate_prob=0.5),
Normalize(),
]))
val_dataset = CocoDetection(image_root_dir=val_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="val2017",
transform=transforms.Compose([
Normalize(),
Resize(resize=input_image_size),
]))
epochs = 140
milestones = [90, 120]
per_node_batch_size = 24
lr = 5e-4
num_workers = 4
print_interval = 100
apex = True
sync_bn = False
class Config(object):
log = './log' # Path to save log
checkpoint_path = './checkpoints' # Path to store checkpoint model
resume = './checkpoints/latest.pth' # load checkpoint model
evaluate = None # evaluate model path
train_dataset_path = os.path.join(COCO2017_path, 'images/train2017')
val_dataset_path = os.path.join(COCO2017_path, 'images/val2017')
dataset_annotations_path = os.path.join(COCO2017_path, 'annotations')
network = "resnet50_retinanet"
pretrained = False
num_classes = 80
seed = 0
input_image_size = 667
train_dataset = CocoDetection(image_root_dir=train_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="train2017",
transform=transforms.Compose([
RandomFlip(flip_prob=0.5),
RandomCrop(crop_prob=0.5),
RandomTranslate(translate_prob=0.5),
Normalize(),
Resize(resize=input_image_size),
]))
val_dataset = CocoDetection(image_root_dir=val_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="val2017",
transform=transforms.Compose([
Normalize(),
Resize(resize=input_image_size),
]))
epochs = 12
batch_size = 16
lr = 1e-4
num_workers = 4
print_interval = 1
apex = True
class Config(object):
log = './log_obj365' # Path to save log
checkpoint_path = './checkpoints_obj365' # Path to store checkpoint model
resume = './checkpoints_obj365/latest.pth' # load checkpoint model
evaluate = None # evaluate model path
train_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-1/small_sample/obj365', 'images/train2017')
val_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-1/small_sample/obj365', 'images/val2017')
dataset_annotations_path = os.path.join('/home/jovyan/data-vol-polefs-1/small_sample/obj365', 'annotations')
network = "resnet50_centernet"
pretrained = False
num_classes = [365]
seed = 0
input_image_size = 512
multi_head = False
#must use at the same time
#use mlp layer after head
cls_mlp = False
#load the params to head2
load_head = False
#use selayer before head
selayer = False
#load the params to head2
load_head = False
#use ttf head in centernet head
use_ttf = False
pre_model_dir = None
use_multi_scale = False
multi_scale_range = [0.6, 1.4]
stride = 4
train_dataset = CocoDetection(image_root_dir=train_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="train2017",
transform=transforms.Compose([
RandomFlip(flip_prob=0.5),
RandomCrop(crop_prob=0.5),
RandomTranslate(translate_prob=0.5),
Normalize(),
]))
val_dataset = CocoDetection(image_root_dir=val_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="val2017",
transform=transforms.Compose([
Normalize(),
Resize(resize=input_image_size),
]))
epochs = 140
milestones = [90, 120]
per_node_batch_size = 8
lr = 5e-4
num_workers = 16
print_interval = 100
apex = True
sync_bn = False
class Config(object):
version = 10
log = './log_ctn_multi_v' + str(version) # Path to save log
checkpoint_path = './checkpoints_multi_v' + str(version) # Path to store checkpoint model
resume = checkpoint_path + '/latest.pth' # load checkpoint model
evaluate = None # evaluate model path
train_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/taibiao', 'images/train2017')
val_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/taibiao', 'images/val2017')
dataset_annotations_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/taibiao', 'annotations')
# train_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/lanmu', 'images/train2017')
# val_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/lanmu', 'images/val2017')
# dataset_annotations_path = os.path.join('/home/jovyan/data-vol-polefs-1/dataset/lanmu', 'annotations')
# train_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-2/datasets_shenhe/scripts/03-08', 'crop_images')
# val_dataset_path = os.path.join('/home/jovyan/data-vol-polefs-2/datasets_shenhe/scripts/03-08', 'train2017')
# dataset_annotations_path = os.path.join('/home/jovyan/data-vol-polefs-2/datasets_shenhe/scripts/03-08', 'annotations')
network = "resnet50_centernet"
pretrained = False
#*********************************************************************************
multi_head = True
#must use at the same time
#use mlp layer after head
cls_mlp = False
#load the params to head2
load_head = False
#use selayer before head
selayer = True
#use ttf head in centernet head
use_ttf = True
# pre_model_dir = '/home/jovyan/data-vol-1/zhangze/code/pytorch-ImageNet-CIFAR-COCO-VOC-training/detection_experiments/resnet18_centernet_coco_distributed_apex_resize512/checkpoints/best.pth'
pre_model_dir = "/home/jovyan/data-vol-1/zhangze/code/multi_task/train/checkpoints_multi_v2/best.pth"
#head1 head2 classes
num_classes = [41, 1]
#*********************************************************************************
seed = 0
input_image_size = 512
use_multi_scale = False
multi_scale_range = [0.6, 1.4]
stride = 4
train_dataset = CocoDetection(image_root_dir=train_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="train2017",
transform=transforms.Compose([
RandomFlip(flip_prob=0.5),
RandomCrop(crop_prob=0.5),
RandomTranslate(translate_prob=0.5),
Normalize(),
]))
val_dataset = CocoDetection(image_root_dir=val_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="val2017",
transform=transforms.Compose([
Normalize(),
Resize(resize=input_image_size),
]))
epochs = 140
milestones = [90, 120]
per_node_batch_size = 16
lr = 5e-4
# lr = 1e-5
num_workers = 4
print_interval = 100
apex = True
sync_bn = False
class Config(object):
version = 1
log = './multi_task/log_' + str(version) # Path to save log
checkpoint_path = '/home/jovyan/data-vol-polefs-1/small_sample/multi_task/checkpoints/v{}'.format(version) # Path to store checkpoint model
resume = '/home/jovyan/data-vol-polefs-1/small_sample/multi_task/checkpoints/v{}/latest.pth'.format(version) # load checkpoint model
pre_model_dir = '/home/jovyan/data-vol-polefs-1/small_sample/multi_task/checkpoints/v1/best.pth'.format(version-1)
evaluate = None # evaluate model path
base_path = '/home/jovyan/data-vol-polefs-1/small_sample/dataset'
train_dataset_path = os.path.join(base_path, 'images/images')
val_dataset_path = os.path.join(base_path, 'images/images')
dataset_annotations_path = os.path.join("/home/jovyan/data-vol-polefs-1/small_sample/multi_task", 'annotations/v{}'.format(version))
network = "resnet50_yolof"
seed = 0
#resize
input_image_size = 667
num_classes = 1
train_dataset = CocoDetection(image_root_dir=train_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="train",
transform=transforms.Compose([
RandomFlip(flip_prob=0.5),
RandomCrop(crop_prob=0.5),
RandomTranslate(translate_prob=0.5),
Normalize(),
Resize(resize=input_image_size)
]))
val_dataset = CocoDetection(image_root_dir=val_dataset_path,
annotation_root_dir=dataset_annotations_path,
set="val",
transform=transforms.Compose([
Normalize(),
Resize(resize=input_image_size),
]))
#***********************************************************#
#use the pretrained backbone
pretrained=True
#freeze the backbone and neck
# freeze = False
#load the previous model to train,if v1:use the coco_pretrained,else use the latest version model
# previous = False
#train multi task head
multi_task = False
#***********************************************************#
#fpn
#use yolof neck
use_yolof = True
#fpn encode channels
fpn_out_channels=512
use_p5=True
#***********************************************************#
#head
class_num=1
#use gn in head
use_GN_head=False
prior=0.01
cnt_on_reg=True
#yolof
yolof_encoder_channels = 512
#ttf up
use_ttf = True
ttf_out_channels = [256, 128]
#
dubble_run = True
#***********************************************************#
#training
epochs = 24
per_node_batch_size = 4
lr = 1e-4
num_workers = 4
print_interval = 100
eval_interval = 4
apex = True
sync_bn = False
#down sample strides
strides=[16]
#limit in decoder
limit_range=[[-1,99999]]
# limit_range=[[-1,667]]
#scales parameter in head
scales = [1.0]
#***********************************************************#
#inference
score_threshold=0.05
nms_iou_threshold=0.6
max_detection_num=1000