def get_model():
if model_index == 0:
return mobilenet_v1.MobileNetV1()
elif model_index == 1:
return mobilenet_v2.MobileNetV2()
elif model_index == 2:
return mobilenet_v3_large.MobileNetV3Large()
elif model_index == 3:
return mobilenet_v3_small.MobileNetV3Small()
elif model_index == 4:
return efficientnet.efficient_net_b0()
elif model_index == 5:
return efficientnet.efficient_net_b1()
elif model_index == 6:
return efficientnet.efficient_net_b2()
elif model_index == 7:
return efficientnet.efficient_net_b3()
elif model_index == 8:
return efficientnet.efficient_net_b4()
elif model_index == 9:
return efficientnet.efficient_net_b5()
elif model_index == 10:
return efficientnet.efficient_net_b6()
elif model_index == 11:
return efficientnet.efficient_net_b7()
elif model_index == 12:
return resnext.ResNeXt50()
elif model_index == 13:
return resnext.ResNeXt101()
elif model_index == 14:
return inception_v4.InceptionV4()
elif model_index == 15:
return inception_resnet_v1.InceptionResNetV1()
elif model_index == 16:
return inception_resnet_v2.InceptionResNetV2()
elif model_index == 17:
return se_resnet.se_resnet_50()
elif model_index == 18:
return se_resnet.se_resnet_101()
elif model_index == 19:
return se_resnet.se_resnet_152()
elif model_index == 20:
return squeezenet.SqueezeNet()
elif model_index == 21:
return densenet.densenet_121()
elif model_index == 22:
return densenet.densenet_169()
elif model_index == 23:
return densenet.densenet_201()
elif model_index == 24:
return densenet.densenet_264()
elif model_index == 25:
return shufflenet_v2.shufflenet_0_5x()
elif model_index == 26:
return shufflenet_v2.shufflenet_1_0x()
elif model_index == 27:
return shufflenet_v2.shufflenet_1_5x()
elif model_index == 28:
return shufflenet_v2.shufflenet_2_0x()
def get_model():
if model_index == 0:
return mobilenet_v1.MobileNetV1()
elif model_index == 1:
return mobilenet_v2.MobileNetV2()
elif model_index == 2:
return mobilenet_v3_large.MobileNetV3Large()
elif model_index == 3:
return mobilenet_v3_small.MobileNetV3Small()
elif model_index == 4:
return efficientnet.efficient_net_b0()
elif model_index == 5:
return efficientnet.efficient_net_b1()
elif model_index == 6:
return efficientnet.efficient_net_b2()
elif model_index == 7:
return efficientnet.efficient_net_b3()
elif model_index == 8:
return efficientnet.efficient_net_b4()
elif model_index == 9:
return efficientnet.efficient_net_b5()
elif model_index == 10:
return efficientnet.efficient_net_b6()
elif model_index == 11:
return efficientnet.efficient_net_b7()
elif model_index == 12:
return resnext.ResNeXt50()
elif model_index == 13:
return resnext.ResNeXt101()
elif model_index == 14:
return inception_v4.InceptionV4()
elif model_index == 15:
return inception_resnet_v1.InceptionResNetV1()
elif model_index == 16:
return inception_resnet_v2.InceptionResNetV2()
elif model_index == 17:
return se_resnet.se_resnet_50()
elif model_index == 18:
return se_resnet.se_resnet_101()
elif model_index == 19:
return se_resnet.se_resnet_152()
elif model_index == 20:
return squeezenet.SqueezeNet()
elif model_index == 21:
return densenet.densenet_121()
elif model_index == 22:
return densenet.densenet_169()
elif model_index == 23:
return densenet.densenet_201()
elif model_index == 24:
return densenet.densenet_264()
elif model_index == 25:
return shufflenet_v2.shufflenet_0_5x()
elif model_index == 26:
return shufflenet_v2.shufflenet_1_0x()
elif model_index == 27:
return shufflenet_v2.shufflenet_1_5x()
elif model_index == 28:
return shufflenet_v2.shufflenet_2_0x()
elif model_index == 29:
return resnet.resnet_18()
elif model_index == 30:
return resnet.resnet_34()
elif model_index == 31:
return resnet.resnet_50()
elif model_index == 32:
return resnet.resnet_101()
elif model_index == 33:
return resnet.resnet_152()
elif model_index == 34:
return vgg16.VGG16()
elif model_index == 35:
return vgg16_mini.VGG16()
elif model_index == 36:
return VGG16_self.VGG16()
elif model_index == 10086:
return diy_resnet.resnet_50()
else:
raise ValueError("The model_index does not exist.")