Python MobileNetV2 Examples

Example #1

File: train.py Project: 2605759123/Basic_CNNs_TensorFlow2

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()

Example #2

File: train.py Project: liangheng/Basic_CNNs_TensorFlow2

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()

Example #3

File: eval_mobilenet_torch.py Project: zhang405744522/amc-compressed-models

def get_model(n_class):
    print('=> Building model {}...'.format(args.model))
    if args.model == 'mobilenet_0.5flops':
        net = mobilenet_v1.MobileNet(n_class, profile='0.5flops')
        checkpoint_path = './checkpoints/torch/mobilenet_imagenet_0.5flops_70.5.pth.tar'
    elif args.model == 'mobilenet_0.5time':
        net = mobilenet_v1.MobileNet(n_class, profile='0.5time')
        checkpoint_path = './checkpoints/torch/mobilenet_imagenet_0.5time_70.2.pth.tar'
    elif args.model == 'mobilenetv2':
        net = mobilenet_v2.MobileNetV2(n_class, profile='normal')
        checkpoint_path = './checkpoints/torch/mobilenetv2_imagenet_71.814.pth.tar'
    elif args.model == 'mobilenetv2_0.7flops':
        net = mobilenet_v2.MobileNetV2(n_class, profile='0.7flops')
        checkpoint_path = './checkpoints/torch/mobilenetv2_imagenet_0.7amc_70.854.pth.tar'
    else:
        raise NotImplementedError

    print('=> Loading checkpoints..')
    checkpoint = torch.load(checkpoint_path)
    if 'state_dict' in checkpoint:
        checkpoint = checkpoint['state_dict']  # get state_dict
    net.load_state_dict(process_state_dict(checkpoint))  # remove .module

    return net

Example #4

File: test_mobilenet_v2.py Project: tamnguyenvan/ssd-tf2

import context

import tensorflow as tf
from models import mobilenet_v2

physical_devices = tf.config.experimental.list_physical_devices('GPU')
for physical_device in physical_devices:
    tf.config.experimental.set_memory_growth(physical_device, True)

model = mobilenet_v2.MobileNetV2([320, 320, 3], 81, 'ssd_mobilenet_v2')
model.summary()

x = tf.random.normal((1, 320, 320, 3))
outs = model(x)
for o in outs:
    print(o.shape)

Example #5

File: train_self_3.py Project: YUHANYU/Dongpu_Expriment

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.")