def __init__(self, model_config: dict):
super().__init__()
model_config = Dict(model_config)
backbone_type = model_config.backbone.pop('type')
neck_type = model_config.neck.pop('type')
head_type = model_config.head.pop('type')
self.backbone = build_backbone(backbone_type, **model_config.backbone)
self.neck = build_neck(neck_type, in_channels=self.backbone.out_channels, **model_config.neck)
self.head = build_head(head_type, in_channels=self.neck.out_channels, **model_config.head)
self.name = f'{backbone_type}_{neck_type}_{head_type}'
def __init__(self, model_config: dict):
"""
PANnet
:param model_config: 模型配置
"""
super().__init__()
model_config = Dict(model_config)
backbone_type = model_config.backbone.pop('type')
neck_type = model_config.neck.pop('type')
head_type = model_config.head.pop('type')
self.normalize = Normalize([0.485 * 255, 0.456 * 255, 0.406 * 255],
[0.229 * 255, 0.224 * 255, 0.225 * 255])
self.backbone = build_backbone(backbone_type, **model_config.backbone)
self.neck = build_neck(neck_type,
in_channels=self.backbone.out_channels,
**model_config.neck)
self.head = build_head(head_type,
in_channels=self.neck.out_channels,
**model_config.head)
self.name = f'{backbone_type}_{neck_type}_{head_type}'
def __init__(
self,
num_classes,
backbone='resnet50',
pretrained=True,
pooling='avg_pooling',
pooling_size=1,
head='BNHead',
bn_where='after',
batch_norm_bias=True,
use_tqdm=True,
is_inference=False):
super(Baseline, self).__init__()
self.head_name = head
self.num_classes = num_classes
self.is_inference = is_inference
self.backbone, feature_dim = build_backbone(backbone, pretrained=pretrained, progress=use_tqdm)
self.global_pooling = build_pooling(pooling, pooling_size)
self.head = build_head(head, feature_dim, self.num_classes, bias_freeze=not batch_norm_bias, bn_where=bn_where, pooling_size=pooling_size)