def __init__(self, cfg, scales=[0.5, 1, 1.5, 2]):
# super(MultiScaleNetV2, self).__init__()
nn.Module.__init__(self)
self.backbone = build_backbone(cfg)
self.scales = scales
self.desc_extractor = DescExtractor(cfg, scales, self.backbone)
self.desc_evaluator = MultiDescEvaluator(cfg)
def __init__(self, cfg):
super(DescExtractor, self).__init__()
self.backbone = build_backbone(cfg)
self.regress = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=1, stride=1, padding=0),
nn.ReLU(True),
nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(128, 128, kernel_size=1, stride=1, padding=0))
def __init__(self, cfg, scales):
"""
DescExtractor
:param scales: a list of scales, like [0.5, 1, 2]
"""
super(DescExtractor, self).__init__()
self.scales = scales
self.backbone = build_backbone(cfg)
self.regress = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=1, stride=1, padding=0),
nn.ReLU(True),
nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(128, 128, kernel_size=1, stride=1, padding=0)
)
def __init__(self, cfg):
super(FeatureExtractor, self).__init__()
self.backbone = build_backbone(cfg)
self.conv_out = nn.Conv2d(512, 128, 3, 1, 1)
def __init__(self, cfg):
super(DescriptorExtractor, self).__init__()
self.backbone = build_backbone(cfg)
