def __init__(self):
super(Idnet, self).__init__()
self.resnet = resnet50_features(pretrained=True)
self.conv_layer1 = nn.Conv2d(2048,
1024,
kernel_size=3,
stride=2,
padding=1)
self.conv_layer2 = nn.Conv2d(1024,
1024,
kernel_size=3,
stride=2,
padding=1)
self.conv_layer3 = nn.Conv2d(1024,
512,
kernel_size=3,
stride=2,
padding=1)
self.conv_layer4 = nn.Conv2d(512,
512,
kernel_size=3,
stride=1,
padding=1)
self.fc = nn.Linear(512, 3000)
def __init__(self, num_classes=1):
super(RetinaNet, self).__init__()
_resnet = resnet50_features(pretrained=True)
self.fpn = FeaturePyramid(_resnet)#FPN50()
self.num_classes = num_classes
self.loc_head = self._make_head(self.num_anchors*4)
self.cls_head = self._make_head(self.num_anchors*self.num_classes)
def __init__(self, classes):
super(RetinaNet, self).__init__()
self.classes = classes
_resnet = resnet50_features(pretrained=True)
self.feature_pyramid = FeaturePyramid(_resnet)
self.subnet_boxes = SubNet(mode='boxes')
self.subnet_classes = SubNet(mode='classes')
def __init__(self):
super(Idnet, self).__init__()
self.resnet = resnet50_features(pretrained=True)
self.conv_layer1 = conv3x3(2048, 1024, stride=2, padding=1)
self.conv_layer2 = conv3x3(1024, 1024, stride=2, padding=1)
self.conv_layer3 = conv3x3(1024, 512, stride=2, padding=1)
self.conv_layer4 = conv3x3(512, 512, stride=1, padding=1)
self.fc = nn.Linear(512, 3000)