def __init__(self, pretrained=False, freeze=False): super(CRAFT, self).__init__() #basenet self.basenet = vgg16_bn(pretrained, freeze) #U-net self.upconv1 = double_conv(1024, 512, 256) self.upconv2 = double_conv(512, 256, 128) self.upconv3 = double_conv(256, 128, 64) self.upconv4 = double_conv(128, 64, 32) num_class = 2 self.conv_cls = nn.Sequential( nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 16, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(16, 16, kernel_size=1), nn.ReLU(inplace=True), nn.Conv2d(16, num_class, kernel_size=1), ) init_weights(self.upconv1.modules()) init_weights(self.upconv2.modules()) init_weights(self.upconv3.modules()) init_weights(self.upconv4.modules()) init_weights(self.conv_cls.modules())
def __init__(self, pretrained=True, freeze=False): super(Detector, self).__init__() """ Base network """ # self.net = vgg16_bn(pretrained, freeze) # self.net.load_state_dict(copyStateDict(torch.load('vgg16_bn-6c64b313.pth'))) # self.basenet = self.net self.basenet = vgg16_bn(pretrained, freeze) """ U network """ self.upconv1 = double_conv(1024, 512, 256) self.upconv2 = double_conv(512, 256, 128) self.upconv3 = double_conv(256, 128, 64) self.upconv4 = double_conv(128, 64, 32) num_class = 2 self.conv_cls = nn.Sequential( nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 16, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(16, 16, kernel_size=1), nn.ReLU(inplace=True), nn.Conv2d(16, num_class, kernel_size=1), ) init_weights(self.upconv1.modules()) init_weights(self.upconv2.modules()) init_weights(self.upconv3.modules()) init_weights(self.upconv4.modules()) init_weights(self.conv_cls.modules())
def __init__(self, pretrained=False, freeze=False): super(CRAFT, self).__init__() """ Base network """ self.basenet = vgg16_bn(pretrained, freeze) """ U network """ self.upconv1 = double_conv(1024, 512, 256) self.upconv2 = double_conv(512, 256, 128) self.upconv3 = double_conv(256, 128, 64) self.upconv4 = double_conv(128, 64, 32) self.roialign_s16 = ROIAlign((1, 1), 1 / 16, 0) self.roialign_s8 = ROIAlign((1, 1), 1 / 8, 0) self.roialign_s4 = ROIAlign((1, 1), 1 / 4, 0) self.roialign_s2 = ROIAlign((1, 1), 1 / 2, 0) num_class = 2 self.conv_cls = nn.Sequential( nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 16, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(16, 16, kernel_size=1), nn.ReLU(inplace=True), nn.Conv2d(16, num_class, kernel_size=1), ) init_weights(self.upconv1.modules()) init_weights(self.upconv2.modules()) init_weights(self.upconv3.modules()) init_weights(self.upconv4.modules()) init_weights(self.conv_cls.modules())
def init_vgg(self, pretrained, freeze): from basenet.vgg16_bn import vgg16_bn """ Base network """ self.basenet = vgg16_bn(pretrained, freeze) """ U network """ self.upconv1 = double_conv(1024, 512, 256) self.upconv2 = double_conv(512, 256, 128) self.upconv3 = double_conv(256, 128, 64) self.upconv4 = double_conv(128, 64, 32) return 32
def __init__(self, pretrained=False, freeze=False): super(CRAFT, self).__init__() """ Base network """ self.basenet = vgg16_bn(pretrained, freeze) """ U network """ self.upconv1 = double_conv(1024, 512, 256) self.upconv2 = double_conv(512, 256, 128) self.upconv3 = double_conv(256, 128, 64) self.upconv4 = double_conv(128, 64, 32) #num_class = 2 self.conv_cls = Capsulenet.CapsNet() init_weights(self.upconv1.modules()) init_weights(self.upconv2.modules()) init_weights(self.upconv3.modules()) init_weights(self.upconv4.modules())