def VGG19(include_top=True,
pretrained=True,
input_shape=None,
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (224, 224, 3)
vgg19 = make_vgg_layers(cfgs['E'], classes)
vgg19.input_shape = input_shape
if pretrained == True:
download_model_from_google_drive('1nXKMsYklBimtqs7ZRv0dQ-RIqNvgopVh',
dirname, 'vgg19_tf.pth')
recovery_model = load(os.path.join(dirname, 'vgg19_tf.pth'))
recovery_model.name = 'vgg19'
recovery_model.eval()
with tf.device(get_device()):
if include_top == False:
[recovery_model.remove_at(-1) for i in range(7)]
else:
if classes != 1000:
recovery_model.remove_at(-1)
recovery_model.add_module(
'fc3', Dense(classes, activation=None, name='fc3'))
recovery_model.add_module('softmax',
SoftMax(name='softmax'))
vgg19.model = recovery_model
return vgg19
def MobileNetV2(include_top=True,
pretrained=True,
input_shape=(224, 224, 3),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (224, 224, 3)
mob = MobileNet(input_shape=(224, 224, 3),
classes=classes,
use_bias=False,
width_mult=1.0,
round_nearest=8,
include_top=include_top,
model_name='mobilenet')
if pretrained == True:
download_model_from_google_drive('15LtLJHpvimV6cFGqAwJ4QALNEjeATrKe',
dirname, 'mobilenet_v2_tf.pth')
recovery_model = load(os.path.join(dirname, 'mobilenet_v2_tf.pth'))
recovery_model.eval()
if include_top == False:
recovery_model.remove_at(-1)
else:
if classes != 1000:
new_fc = Dense(classes, activation=None, name='fc')
new_fc.input_shape = recovery_model.fc.input_shape
recovery_model.fc = new_fc
mob.model = recovery_model
return mob
def VGG16(include_top=True,
pretrained=True,
input_shape=None,
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (224, 224, 3)
vgg16 = make_vgg_layers(cfgs['D'], classes)
vgg16.input_shape = input_shape
if pretrained == True:
download_model_from_google_drive('1fozCY4Yv_ud5UGpv7q4M9tcxZ2ryDCTb',
dirname, 'vgg16_tf.pth')
recovery_model = load(os.path.join(dirname, 'vgg16_tf.pth'))
recovery_model.name = 'vgg16'
recovery_model.eval()
with tf.device(get_device()):
if include_top == False:
[recovery_model.remove_at(-1) for i in range(7)]
else:
if classes != 1000:
recovery_model.remove_at(-1)
recovery_model.add_module(
'fc3', Dense(classes, activation=None, name='fc3'))
recovery_model.add_module('softmax',
SoftMax(name='softmax'))
vgg16.model = recovery_model
return vgg16