def __init__(self, opt, logger=None):
super(Model, self).__init__(config, kwargs)
self.opt = opt
# cfgfile = 'yolo-voc.cfg'
# self.detector = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
# in_features = self.detector.roi_heads.box_predictor.cls_score.in_features
#
# # replace the pre-trained head with a new one
# self.detector.roi_heads.box_predictor = FastRCNNPredictor(in_features, config.DATA.NUM_CLASSESS + 1)
self.detector = yolov4(inference=True,
n_classes=config.DATA.NUM_CLASSESS)
# """
# 预训练模型
# """
# pretrained_dict = torch.load('pretrained/yolov4.pth')
# self.detector.load_state_dict(pretrained_dict)
self.yolov4loss = Yolo_loss(device=opt.device, batch=opt.batch_size)
#####################
# Init weights
#####################
# normal_init(self.detector)
if opt.debug:
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join('checkpoints', opt.tag)
def __init__(self, opt, logger=None):
super(Model, self).__init__()
self.opt = opt
self.logger = logger
# 根据YoloV2和YoloV3使用不同的配置文件
if opt.model == 'Yolo2':
cfgfile = 'configs/yolo2-voc.cfg'
elif opt.model == 'Yolo3':
cfgfile = 'configs/yolo3-coco.cfg'
# 初始化detector
self.detector = Darknet(cfgfile, device=opt.device).to(opt.device)
print_network(self.detector, logger=logger)
# 在--load之前加载weights文件(可选)
if opt.weights:
utils.color_print('Load Yolo weights from %s.' % opt.weights, 3)
self.detector.load_weights(opt.weights)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.classifier = Classifier() #.cuda(device=opt.device)
#####################
# Init weights
#####################
# self.classifier.apply(weights_init)
print_network(self.classifier)
self.optimizer = optim.Adam(self.classifier.parameters(),
lr=opt.lr,
betas=(0.95, 0.999))
# load networks
if opt.load:
pretrained_path = opt.load
self.load_network(self.classifier, 'G', opt.which_epoch,
pretrained_path)
# if self.training:
# self.load_network(self.discriminitor, 'D', opt.which_epoch, pretrained_path)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, opt, logger=None):
super(Model, self).__init__()
self.opt = opt
self.logger = logger
kargs = {}
if opt.scale:
min_size = opt.scale
max_size = int(min_size / 3 * 4)
kargs = {
'min_size': min_size,
'max_size': max_size,
}
kargs.update({'box_nms_thresh': nms_thresh})
# 定义backbone和Faster RCNN模型
if opt.backbone is None or opt.backbone.lower() in [
'res50', 'resnet50'
]:
# 默认是带fpn的resnet50
self.detector = fasterrcnn_resnet50_fpn(pretrained=False, **kargs)
in_features = self.detector.roi_heads.box_predictor.cls_score.in_features
# replace the pre-trained head with a new one
self.detector.roi_heads.box_predictor = FastRCNNPredictor(
in_features, opt.num_classes + 1)
elif opt.backbone.lower() in ['vgg16', 'vgg']:
backbone = vgg16_backbone()
self.detector = FasterRCNN(backbone,
num_classes=opt.num_classes + 1,
**kargs)
elif opt.backbone.lower() in ['res101', 'resnet101']:
# 不带FPN的resnet101
backbone = res101_backbone()
self.detector = FasterRCNN(backbone,
num_classes=opt.num_classes + 1,
**kargs)
elif opt.backbone.lower() in ['res', 'resnet']:
raise RuntimeError(
f'backbone "{opt.backbone}" is ambiguous, please specify layers.'
)
else:
raise NotImplementedError(f'no such backbone: {opt.backbone}')
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.classifier = Classifier(opt.model)
# self.classifier.apply(weights_init) # 初始化权重
print_network(self.classifier)
self.optimizer = get_optimizer(opt, self.classifier)
self.scheduler = get_scheduler(opt, self.optimizer)
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.cleaner = FFA().to(device=opt.device)
#####################
# Init weights
#####################
# normal_init(self.cleaner)
print_network(self.cleaner)
self.g_optimizer = get_optimizer(opt, self.cleaner)
self.scheduler = get_scheduler(opt, self.g_optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, opt, logger=None):
super(Model, self).__init__(config, kwargs)
self.opt = opt
self.detector = get_net().to(device=opt.device)
#####################
# Init weights
#####################
# normal_init(self.detector)
if opt.debug:
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join('checkpoints', opt.tag)
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.detector = torchvision.models.detection.fasterrcnn_resnet50_fpn(
pretrained=False)
# self.detector = FasterRCNN_VGG()
in_features = self.detector.roi_heads.box_predictor.cls_score.in_features
# replace the pre-trained head with a new one
self.detector.roi_heads.box_predictor = FastRCNNPredictor(
in_features, opt.num_classes + 1)
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, config, **kwargs):
super(Model, self).__init__(config, kwargs)
self.config = config
# 根据YoloV2和YoloV3使用不同的配置文件
if config.MODEL.NAME == 'Yolo2':
cfgfile = 'configs/networks/yolo2-voc.cfg'
elif config.MODEL.NAME == 'Yolo3':
cfgfile = 'configs/networks/yolo3-coco.cfg'
# 初始化detector
self.detector = Darknet(cfgfile, device=opt.device).to(opt.device)
if opt.debug:
print_network(self.detector)
# 在--load之前加载weights文件(可选)
if opt.load and opt.load[-2:] != 'pt':
if is_first_gpu():
utils.color_print('Load Yolo weights from %s.' % opt.load, 3)
self.detector.load_weights(opt.load)
elif 'LOAD' in config.MODEL and config.MODEL.LOAD[-2:] != 'pt':
if is_first_gpu():
utils.color_print(
'Load Yolo weights from %s.' % config.MODEL.LOAD, 3)
self.detector.load_weights(config.MODEL.LOAD)
self.to(opt.device)
# 多GPU支持
if is_distributed():
self.detector = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
self.detector)
self.detector = torch.nn.parallel.DistributedDataParallel(
self.detector,
find_unused_parameters=False,
device_ids=[opt.local_rank],
output_device=opt.local_rank)
# self.detector = torch.nn.parallel.DistributedDataParallel(self.detector, device_ids=[opt.local_rank], output_device=opt.local_rank)
self.optimizer = get_optimizer(config, self.detector)
self.scheduler = get_scheduler(config, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join('checkpoints', opt.tag)
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
cfgfile = 'configs/yolov5x.yaml'
self.detector = Yolo5(cfgfile)
self.detector.hyp = hyp
self.detector.gr = 1.0
self.detector.nc = opt.num_classes
#####################
# Init weights
#####################
# normal_init(self.detector)
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
self.it = 0
def __init__(self, opt, logger=None):
super(Model, self).__init__(config, kwargs)
self.opt = opt
cfgfile = 'configs/yolov5x.yaml'
self.detector = Yolo5(cfgfile)
self.detector.hyp = hyp
self.detector.gr = 1.0
self.detector.nc = config.DATA.NUM_CLASSESS
#####################
# Init weights
#####################
# normal_init(self.detector)
if opt.debug:
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join('checkpoints', opt.tag)
self.it = 0
def __init__(self, opt, logger=None):
super(Model, self).__init__()
self.opt = opt
# cfgfile = 'yolo-voc.cfg'
# self.detector = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
# in_features = self.detector.roi_heads.box_predictor.cls_score.in_features
#
# # replace the pre-trained head with a new one
# self.detector.roi_heads.box_predictor = FastRCNNPredictor(in_features, opt.num_classes + 1)
self.detector = Retina_50(opt.num_classes,pretrained=True)
#####################
# Init weights
#####################
# normal_init(self.detector)
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.classifier = Classifier()
#####################
# Init weights
#####################
# self.classifier.apply(weights_init)
print_network(self.classifier)
self.optimizer = optim.Adam(self.classifier.parameters(),
lr=opt.lr,
betas=(0.95, 0.999))
self.scheduler = get_scheduler(opt, self.optimizer)
# load networks
# if opt.load:
# pretrained_path = opt.load
# self.load_network(self.classifier, 'G', opt.which_epoch, pretrained_path)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
def __init__(self, opt, logger=None):
super(Model, self).__init__()
self.opt = opt
self.detector = SSDDetector(opt).to(device=opt.device)
#####################
# Init weights
#####################
# normal_init(self.detector)
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
CENTER_VARIANCE = 0.1
SIZE_VARIANCE = 0.2
THRESHOLD = 0.5
self.target_transform = SSDTargetTransform(
PriorBox(opt)(), CENTER_VARIANCE, SIZE_VARIANCE, THRESHOLD)
def __init__(self, config, **kwargs):
super(Model, self).__init__(config, kwargs)
self.config = config
self.detector = SSDDetector(config).to(device=opt.device)
#####################
# Init weights
#####################
# normal_init(self.detector)
if opt.debug:
print_network(self.detector)
self.optimizer = get_optimizer(config, self.detector)
self.scheduler = get_scheduler(config, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join('checkpoints', opt.tag)
CENTER_VARIANCE = 0.1
SIZE_VARIANCE = 0.2
THRESHOLD = 0.5
self.target_transform = SSDTargetTransform(
PriorBox(config)(), CENTER_VARIANCE, SIZE_VARIANCE, THRESHOLD)
def __init__(self, config, **kwargs):
super(Model, self).__init__(config, kwargs)
self.config = config
kargs = {}
if 'SCALE' in config.DATA:
scale = config.DATA.SCALE
if isinstance(scale, int):
min_size = scale
max_size = int(min_size / 3 * 5)
else:
min_size, max_size = config.DATA.SCALE
kargs = {
'min_size': min_size,
'max_size': max_size,
}
kargs.update({'box_nms_thresh': config.TEST.NMS_THRESH})
# 多卡使用 SyncBN
if is_distributed():
kargs.update({'norm_layer': torch.nn.SyncBatchNorm})
# 定义backbone和Faster RCNN模型
if config.MODEL.BACKBONE is None or config.MODEL.BACKBONE.lower() in [
'res50', 'resnet50'
]:
# 默认是带fpn的resnet50
self.detector = fasterrcnn_resnet50_fpn(pretrained=False, **kargs)
in_features = self.detector.roi_heads.box_predictor.cls_score.in_features
# replace the pre-trained head with a new one
self.detector.roi_heads.box_predictor = FastRCNNPredictor(
in_features, config.DATA.NUM_CLASSESS + 1)
elif config.MODEL.BACKBONE.lower() in ['vgg16', 'vgg']:
backbone = vgg16_backbone()
self.detector = FasterRCNN(backbone,
num_classes=config.DATA.NUM_CLASSESS +
1,
**kargs)
elif config.MODEL.BACKBONE.lower() in ['res101', 'resnet101']:
# 不带FPN的resnet101
backbone = res101_backbone()
self.detector = FasterRCNN(backbone,
num_classes=config.DATA.NUM_CLASSESS +
1,
**kargs)
elif config.MODEL.BACKBONE.lower() in ['res', 'resnet']:
raise RuntimeError(
f'backbone "{config.MODEL.BACKBONE}" is ambiguous, please specify layers.'
)
else:
raise NotImplementedError(
f'no such backbone: {config.MODEL.BACKBONE}')
if opt.debug and is_first_gpu():
print_network(self.detector)
self.to(opt.device)
# 多GPU支持
if is_distributed():
self.detector = torch.nn.parallel.DistributedDataParallel(
self.detector,
find_unused_parameters=False,
device_ids=[opt.local_rank],
output_device=opt.local_rank)
# self.detector = torch.nn.parallel.DistributedDataParallel(self.detector, device_ids=[opt.local_rank], output_device=opt.local_rank)
self.optimizer = get_optimizer(config, self.detector)
self.scheduler = get_scheduler(config, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join('checkpoints', opt.tag)
def __init__(self, opt, logger=None):
super(Model, self).__init__()
self.opt = opt
if opt.scale:
min_size = opt.scale
max_size = int(min_size / 3 * 5)
else:
min_size = 800
max_size = 1333
# anchor_sizes = ((16,), (32,), (64,), (128,), (512,)) # ,( 4,), (256,), (512,))
# aspect_ratios = ((0.2, 0.5, 1.0, 2.0, 5.0),) * len(anchor_sizes)
# rpn_anchor_generator = AnchorGenerator(
# anchor_sizes, aspect_ratios
# )
kargs = {
'min_size': min_size,
'max_size': max_size,
'cascade_iou_thr': [0.5, 0.6, 0.7],
}
# 定义backbone和Faster RCNN模型
if opt.backbone is None or opt.backbone.lower() in [
'res50', 'resnet50'
]:
# 默认是带fpn的resnet50
self.detector = cascadercnn_resnet50_fpn(pretrained=False, **kargs)
in_features = self.detector.roi_heads[
0].box_predictor.cls_score.in_features
# replace the pre-trained head with a new one
self.detector.roi_heads[0].box_predictor = FastRCNNPredictor(
in_features, opt.num_classes + 1)
self.detector.roi_heads[1].box_predictor = FastRCNNPredictor(
in_features, opt.num_classes + 1)
self.detector.roi_heads[2].box_predictor = FastRCNNPredictor(
in_features, opt.num_classes + 1)
elif opt.backbone.lower() in ['vgg16', 'vgg']:
backbone = vgg16_backbone()
self.detector = CascadeRCNN(backbone,
num_classes=opt.num_classes + 1,
**kargs)
elif opt.backbone.lower() in ['res101', 'resnet101']:
# 不带FPN的resnet101
backbone = res101_backbone()
self.detector = CascadeRCNN(backbone,
num_classes=opt.num_classes + 1,
**kargs)
elif opt.backbone.lower() in ['res', 'resnet']:
raise RuntimeError(
f'backbone "{opt.backbone}" is ambiguous, please specify layers.'
)
else:
raise NotImplementedError(f'no such backbone: {opt.backbone}')
print_network(self.detector)
self.optimizer = get_optimizer(opt, self.detector)
self.scheduler = get_scheduler(opt, self.optimizer)
self.avg_meters = ExponentialMovingAverage(0.95)
self.save_dir = os.path.join(opt.checkpoint_dir, opt.tag)
