def bulid_bcnn_1(self):
input_tensor = Input(shape=self.input_shape)
if self.base_model == 'inception_v3':
self.basenet1 = inception_v3.InceptionV3(input_shape=self.input_shape, classes=self.num_classes,
input_tensor=input_tensor, include_top=False, weights='imagenet')
self.basenet2 = self.basenet1
self.model_detector = self.basenet1
self.model_extractor = self.basenet2
self.output = self.build_bilinear_cnn(last_conv_layer=-1)
elif self.base_model == 'resnet_50':
self.basenet1 = resnet_50.ResNet50(include_top=False, weights='imagenet',
input_tensor=input_tensor, input_shape=self.input_shape)
self.basenet2 = self.basenet1
self.model_detector = self.basenet1
self.model_extractor = self.basenet2
self.output = self.build_bilinear_cnn(last_conv_layer=-2)
elif self.base_model == 'inception_resnet_v2':
self.basenet1 = inception_resnet_v2.InceptionResNetV2(include_top=False, weights='imagenet',
input_shape=self.input_shape,
input_tensor=input_tensor)
self.basenet2 = self.basenet1
self.model_detector = self.basenet1
self.model_extractor = self.basenet2
self.output = self.build_bilinear_cnn(last_conv_layer=-1)
self.model = Model(input_tensor, self.output)
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()
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 build_attention(self):
input_tensor = Input(shape=self.input_shape)
if self.base_model == 'inception_v3':
self.model = inception_v3.InceptionV3(input_shape=self.input_shape, classes=self.num_classes, include_top=True,
weights=None,
input_tensor=input_tensor, attention_module=self.attention_module)
self.model.load_weights('inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
by_name=True)
elif self.base_model == 'resnet_50':
self.model = resnet_50.ResNet50(input_shape=self.input_shape, classes=self.num_classes, include_top=True,
weights=None,
input_tensor=input_tensor, attention_module=self.attention_module)
self.model.load_weights('resnet_50_weights_tf_dim_ordering_tf_kernels_notop.h5',
by_name=True)
elif self.base_model == 'inception_resnet_v2':
self.model = inception_resnet_v2.InceptionResNetV2(input_shape=self.input_shape, classes=self.num_classes,
include_top=True, weights=None, input_tensor=input_tensor,
attention_module=self.attention_module)
self.model.load_weights('inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5',
by_name=True)
def bulid_bcnn_0(self):
input_tensor = Input(shape=self.input_shape)
if self.base_model == 'inception_v3':
self.basenet1 = inception_v3.InceptionV3(input_shape=self.input_shape, classes=self.num_classes,
input_tensor=input_tensor, include_top=False, weights='imagenet')
self.model_detector = self.basenet1
x = GlobalAveragePooling2D(name='avg_pool')(self.basenet1.output)
self.output = Dense(self.num_classes, activation='softmax', name='predictions')(x)
elif self.base_model == 'resnet_50':
self.basenet1 = resnet_50.ResNet50(include_top=False, input_tensor=input_tensor, weights='imagenet',
input_shape=self.input_shape)
self.model_detector = self.basenet1
x = Flatten()(self.basenet1.output)
self.output = Dense(self.num_classes, activation='softmax')(x)
elif self.base_model == 'inception_resnet_v2':
self.basenet1 = inception_resnet_v2.InceptionResNetV2(input_shape=self.input_shape, classes=self.num_classes,
include_top=False, weights='imagenet',
input_tensor=input_tensor)
self.model_detector = self.basenet1
x = GlobalAveragePooling2D(name='avg_pool')(self.basenet1.output)
self.output = Dense(self.num_classes, activation='softmax', name='predictions')(x)
self.model = Model(input_tensor, self.output)
parse = argparse.ArgumentParser(description='command for testing keras model with fp16 and fp32')
parse.add_argument('--model', type=str, default='mobilenet', help='support vgg16, resnet50, densenet121, \
inceptionv3, inception_resnet, xception, mobilenet, squeezenet')
parse.add_argument('--dtype', type=str, default='float32')
parse.add_argument('--alpha', type=float, default=0.5, help='alpha for mobilenet')
args = parse.parse_args()
K.set_floatx(args.dtype)
# create model
if args.model == 'vgg':
model = vgg16.VGG16(input_shape=(224, 224, 3))
elif args.model == 'inception':
model = inception_v3.InceptionV3(input_shape=(299, 299, 3))
elif args.model == 'inception_resnet':
model = inception_resnet_v2.InceptionResNetV2(input_shape=(299, 299, 3))
elif args.model == 'xception':
model = xception.Xception(input_shape=(299, 299, 3))
elif args.model == 'resnet':
model = resnet_50.ResNet50(input_shape=(224, 224, 3))
elif args.model == 'densenet':
model = densenet_121.DenseNet(reduction=0.5, classes=1000)
elif args.model == 'squeezenet':
model = squeezenet.SqueezeNet(input_shape=(227, 227, 3), classes=1000)
elif args.model == 'mobilenet':
model = mobilenet.MobileNet(input_shape=(224, 224, 3), alpha=args.alpha)
else:
raise ValueError("Do not support {}".format(args.model))
model.summary()
model_name = args.model if args.model != 'mobilenet' else args.model + '_' + str(args.alpha)
model.load_weights('./weights/{}'.format(weights[model_name]), by_name=True)
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.")