def ResNet152(include_top=True,
pretrained=True,
freeze_features=False,
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=(3, 224, 224)
resnet152 =ResNet(bottleneck, [3, 8, 36, 3], input_shape,num_classes=classes,include_top=include_top, model_name='resnet152')
if pretrained==True:
download_model_from_google_drive(model_urls['resnet152'],dirname,'resnet152.pth')
recovery_model=load(os.path.join(dirname,'resnet152.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(recovery_model,input_shape=input_shape, include_top=include_top, classes=classes, freeze_features=freeze_features)
resnet152.model = recovery_model
else:
resnet152.model = _make_recovery_model_include_top(resnet152.model, include_top=include_top, classes=classes, freeze_features=False)
resnet152.model.input_shape = input_shape
resnet152.model.to(_device)
return resnet152
#
#
# resnet34=ResNet(basic_block, [3, 4, 6, 3], (3, 224, 224))
# resnet50=ResNet(bottleneck, [3, 4, 6, 3], (3, 224, 224))
# resnet101=ResNet(bottleneck, [3, 4, 23, 3], (3, 224, 224))
# resnet152=ResNet(bottleneck, [3, 8, 36, 3], (3, 224, 224))
def VGG11(include_top=True,
pretrained=True,
freeze_features=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 = (3, 224, 224)
vgg11 = make_vgg_layers(cfgs['A'], classes)
vgg11.input_shape = input_shape
if pretrained:
download_model_from_google_drive('1PV9-AwgD1v-JxDRzduOjjGduIR7MDhPW',
dirname, 'vgg11.pth')
recovery_model = load(os.path.join(dirname, 'vgg11.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model.name = 'vgg11'
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
vgg11.model = recovery_model
else:
vgg11.model = _make_recovery_model_include_top(vgg11.model,
include_top=include_top,
classes=classes,
freeze_features=True)
vgg11.model.input_shape = input_shape
vgg11.model.to(_device)
return vgg11
def VGG13(include_top=True,
pretrained=True,
freeze_features=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 = (3, 224, 224)
vgg13 = make_vgg_layers(cfgs['B'], classes)
if pretrained == True:
download_model_from_google_drive('1wx67gmQ8eHWXs2mhJmNl-t-cFNw7dJ7O',
dirname, 'vgg13.pth')
recovery_model = load(os.path.join(dirname, 'vgg13.pth'))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
vgg13.model = recovery_model
else:
vgg13.model = _make_recovery_model_include_top(vgg13.model,
include_top=include_top,
classes=classes,
freeze_features=True)
vgg13.model.input_shape = input_shape
vgg13.model.to(_device)
return vgg13
def VGG16(include_top=True,
pretrained=True,
freeze_features=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 = (3, 224, 224)
vgg16 = make_vgg_layers(cfgs['D'], classes)
vgg16.input_shape = input_shape
if pretrained == True:
download_model_from_google_drive('1uXiH5MSy1rvxrHjW4uB9E2BHMM8b0Fwr',
dirname, 'vgg16.pth')
recovery_model = load(os.path.join(dirname, 'vgg16.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model.name = 'vgg16'
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
vgg16.model = recovery_model
else:
vgg16.model = _make_recovery_model_include_top(vgg16.model,
include_top=include_top,
classes=classes,
freeze_features=True)
vgg16.model.input_shape = input_shape
vgg16.model.to(_device)
return vgg16
def VGG19(include_top=True,
pretrained=True,
freeze_features=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 = (3, 224, 224)
vgg19 = make_vgg_layers(cfgs['E'], classes)
vgg19.input_shape = input_shape
if pretrained == True:
download_model_from_google_drive('1nqQJLYMzeiUX9hji39-rrBUG42YyjhYg',
dirname, 'vgg19.pth')
recovery_model = load(os.path.join(dirname, 'vgg19.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model.name = 'vgg19'
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
vgg19.model = recovery_model
else:
vgg19.model = _make_recovery_model_include_top(vgg19.model,
include_top=include_top,
classes=classes,
freeze_features=True)
vgg19.model.input_shape = input_shape
vgg19.model.to(_device)
return vgg19
def EfficientNetB5(include_top=True,
pretrained=True,
freeze_features=True,
input_shape=(456, 456, 3),
classes=1000,
**kwargs):
"""
Args:
freeze_features ():
include_top ():
pretrained ():
input_shape ():
classes ():
**kwargs ():
Returns:
"""
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (456, 456, 3)
effb5 = EfficientNet(1.6,
2.2,
default_size=input_shape,
dropout_rate=0.4,
model_name='efficientnet-b5',
include_top=include_top,
num_classes=classes)
with tf.device(get_device()):
if pretrained == True:
download_model_from_google_drive(
'1o_JQkIFUP1_-9AkiTs-x8q7gyDMyqiJz', dirname,
'efficientnet-b5_tf.pth')
recovery_model = load(
os.path.join(dirname, 'efficientnet-b5_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb5.model = recovery_model
else:
effb5.model = _make_recovery_model_include_top(
effb5.model,
include_top=include_top,
classes=classes,
freeze_features=True)
effb5.model.input_shape = input_shape
effb5.model.name = 'efficientnet-b5'
return effb5
def EfficientNetB7(include_top=True,
pretrained=True,
freeze_features=True,
input_shape=(600, 600, 3),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (600, 600, 3)
effb7 = EfficientNet(2.0,
3.1,
default_size=input_shape,
dropout_rate=0.5,
model_name='efficientnet-b7',
include_top=include_top,
num_classes=classes)
with tf.device(get_device()):
if pretrained:
download_model_from_google_drive(
'1NcsqfYpnIXme8nk8qrrvNAVQOGK-EOZz', dirname,
'efficientnet-b7_tf.pth')
recovery_model = load(
os.path.join(dirname, 'efficientnet-b7_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb7.model = recovery_model
else:
effb7.model = _make_recovery_model_include_top(
effb7.model,
include_top=include_top,
classes=classes,
freeze_features=True)
effb7.model.input_shape = input_shape
effb7.model.name = 'efficientnet-b7'
return effb7
def EfficientNetB6(include_top=True,
pretrained=True,
freeze_features=True,
input_shape=(528, 528, 3),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (528, 528, 3)
effb6 = EfficientNet(1.8,
2.6,
default_size=input_shape,
dropout_rate=0.5,
model_name='efficientnet-b6',
include_top=include_top,
num_classes=classes)
with tf.device(get_device()):
if pretrained == True:
download_model_from_google_drive(
'1-dUqwaLzv2V7m8w4jkvFBlHTCsZq54JY', dirname,
'efficientnet-b6_tf.pth')
recovery_model = load(
os.path.join(dirname, 'efficientnet-b6_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb6.model = recovery_model
else:
effb6.model = _make_recovery_model_include_top(
effb6.model,
include_top=include_top,
classes=classes,
freeze_features=True)
effb6.model.input_shape = input_shape
effb6.model.name = 'efficientnet-b6'
return effb6
def EfficientNetB4(include_top=True,
pretrained=True,
freeze_features=True,
input_shape=(380, 380, 3),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (380, 380, 3)
effb4 = EfficientNet(1.4,
1.8,
380,
0.4,
model_name='efficientnet-b4',
include_top=include_top,
num_classes=classes)
with tf.device(get_device()):
if pretrained == True:
download_model_from_google_drive(
'1eOUvMemIysmAa0ePdGKz01NO1EYWI2Dp', dirname,
'efficientnet-b4_tf.pth')
recovery_model = load(
sanitize_path(os.path.join(dirname, 'efficientnet-b4_tf.pth')))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb4.model = recovery_model
else:
effb4.model = _make_recovery_model_include_top(
effb4.model,
include_top=include_top,
classes=classes,
freeze_features=True)
effb4.model.input_shape = input_shape
effb4.model.name = 'efficientnet-b4'
return effb4
def EfficientNetB3(include_top=True,
pretrained=True,
freeze_features=True,
input_shape=(300, 300, 3),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (300, 300, 3)
effb3 = EfficientNet(1.2,
1.4,
default_size=input_shape,
dropout_rate=0.3,
model_name='efficientnet-b3',
include_top=include_top,
num_classes=classes)
with tf.device(get_device()):
if pretrained == True:
download_model_from_google_drive(
'1LgG4bsYnkY-uj6sLqbebRP0va3gDEkgS', dirname,
'efficientnet-b3_tf.pth')
recovery_model = load(
os.path.join(dirname, 'efficientnet-b3_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb3.model = recovery_model
else:
effb3.model = _make_recovery_model_include_top(
effb3.model,
include_top=include_top,
classes=classes,
freeze_features=True)
effb3.model.input_shape = input_shape
effb3.model.name = 'efficientnet-b3'
return effb3
def EfficientNetB2(include_top=True,
pretrained=True,
freeze_features=True,
input_shape=(260, 260, 3),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (260, 260, 3)
effb2 = EfficientNet(1.1,
1.2,
default_size=input_shape,
dropout_rate=0.3,
model_name='efficientnet-b2',
include_top=include_top,
num_classes=classes)
with tf.device(get_device()):
if pretrained == True:
download_model_from_google_drive(
'1YQgy7PTgj8VereQfaxKJCZshIZK_uqtI', dirname,
'efficientnet-b2_tf.pth')
recovery_model = load(
os.path.join(dirname, 'efficientnet-b2_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb2.model = recovery_model
else:
effb2.model = _make_recovery_model_include_top(
effb2.model,
include_top=include_top,
classes=classes,
freeze_features=True)
effb2.model.input_shape = input_shape
effb2.model.name = 'efficientnet-b2'
return effb2
def EfficientNetB1(include_top=True,
pretrained=True,
freeze_features=True,
input_shape=(240, 240, 3),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (240, 240, 3)
effb1 = EfficientNet(1.0,
1.1,
default_size=input_shape,
dropout_rate=0.2,
model_name='efficientnet-b1',
include_top=include_top,
num_classes=classes)
with tf.device(get_device()):
if pretrained == True:
download_model_from_google_drive(
'1zCWDn4lwHCn4exAnGfBSPh9YHYTGdIYt', dirname,
'efficientnet-b1_tf.pth')
recovery_model = load(
os.path.join(dirname, 'efficientnet-b1_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb1.model = recovery_model
else:
effb1.model = _make_recovery_model_include_top(
effb1.model,
include_top=include_top,
classes=classes,
freeze_features=True)
effb1.model.input_shape = input_shape
effb1.model.name = 'efficientnet-b1'
return effb1
def EfficientNetB0(include_top=True,
pretrained=True,
freeze_features=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)
effb0 = EfficientNet(1.0,
1.0,
default_size=input_shape,
dropout_rate=0.2,
model_name='efficientnet-b0',
include_top=include_top,
num_classes=classes)
with tf.device(get_device()):
if pretrained:
download_model_from_google_drive(
'1pO4wRWY6N4e7U_7E2H-NhBPEF4MlR4ru', dirname,
'efficientnet-b0_tf.pth')
recovery_model = load(
os.path.join(dirname, 'efficientnet-b0_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb0.model = recovery_model
else:
effb0.model = _make_recovery_model_include_top(
effb0.model,
include_top=include_top,
classes=classes,
freeze_features=True)
effb0.model.input_shape = input_shape
effb0.model.name = 'efficientnet-b0'
return effb0
def SE_ResNet50(include_top=True,
pretrained=True,
freeze_features=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 = (3, 224, 224)
resnet50 = SE_ResNet(se_bottleneck, [3, 4, 6, 3],
input_shape,
num_classes=classes,
include_top=include_top,
model_name='se_resnet50')
if pretrained == True:
download_model_from_google_drive(model_urls['se_resnet50'], dirname,
'se_resnet50.pth')
recovery_model = load(os.path.join(dirname, 'se_resnet50.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model.name = 'se_resnet50'
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
default_shape=(3, 224, 224),
input_shape=input_shape,
freeze_features=freeze_features)
resnet50.model = recovery_model
else:
resnet50.model = _make_recovery_model_include_top(
resnet50.model,
include_top=include_top,
classes=classes,
default_shape=(3, 224, 224),
input_shape=input_shape,
freeze_features=True)
resnet50.model.input_shape = input_shape
resnet50.model.to(_device)
return resnet50
def MobileNetV2(include_top=True,
pretrained=True,
freeze_features=True,
input_shape=(3, 224, 224),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 224, 224)
mob = MobileNet(input_shape=(3, 224, 224),
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('1ULenXTjOO5PdT3fHv6N8bPXEfoJAn5yL',
dirname, 'mobilenet_v2.pth')
recovery_model = load(os.path.join(dirname, 'mobilenet_v2.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
mob.model = recovery_model
else:
mob.model = _make_recovery_model_include_top(mob.model,
include_top=include_top,
classes=classes,
freeze_features=True)
mob.model.input_shape = input_shape
mob.model.to(_device)
return mob
def ResNet18(include_top=True,
pretrained=True,
freeze_features=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 = (3, 224, 224)
resnet18 = ResNet(basic_block, [2, 2, 2, 2],
input_shape,
use_bias=False,
include_top=include_top,
model_name='resnet18')
with tf.device(get_device()):
if pretrained:
download_model_from_google_drive(
'1vReSW_l8fldyYQ6ay5HCYFGoMaGbdW2T', dirname,
'resnet50_tf.pth')
recovery_model = load(os.path.join(dirname, 'resnet50_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
resnet18.model = recovery_model
else:
resnet18.model = _make_recovery_model_include_top(
resnet18.model,
include_top=include_top,
classes=classes,
freeze_features=True)
resnet18.model.input_shape = input_shape
return resnet18
def EfficientNetB6(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3, 528, 528),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 528, 528)
effb6 = EfficientNet(1.8,
2.6,
input_shape,
0.5,
model_name='efficientnet-b6',
include_top=include_top,
num_classes=classes)
if pretrained:
download_model_from_google_drive('1XJrKmcmMObN_nnjP2Z-YH_BQ3img58qF',
dirname, 'efficientnet-b6.pth')
recovery_model = fix_layer(
load(os.path.join(dirname, 'efficientnet-b6.pth')))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb6.model = recovery_model
else:
effb6.model = _make_recovery_model_include_top(effb6.model,
include_top=include_top,
classes=classes,
freeze_features=False)
effb6.model.input_shape = input_shape
effb6.model.to(get_device())
return effb6
def ResNet101(include_top=True,
pretrained=True,
freeze_features=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)
resnet101 = ResNet(bottleneck, [3, 4, 23, 3],
input_shape,
num_classes=classes,
include_top=include_top,
use_bias=True,
model_name='resnet101')
with tf.device(get_device()):
if pretrained == True:
download_model_from_google_drive(
'13QYdFX3CvsNiegi-iUX1PUC0KKKgPNwr', dirname,
'resnet101_tf.pth')
recovery_model = load(os.path.join(dirname, 'resnet101_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
resnet101.model = recovery_model
else:
resnet101.model = _make_recovery_model_include_top(
resnet101.model,
include_top=include_top,
classes=classes,
freeze_features=True)
resnet101.model.input_shape = input_shape
return resnet101
def EfficientNetB7(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3, 600, 600),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 600, 600)
effb7 = EfficientNet(2.0,
3.1,
input_shape,
0.5,
model_name='efficientnet-b7',
include_top=include_top,
num_classes=classes)
if pretrained:
download_model_from_google_drive('1M2DfvsNPRCWSo_CeXnUCQOR46rvOrhLl',
dirname, 'efficientnet-b7.pth')
recovery_model = fix_layer(
load(os.path.join(dirname, 'efficientnet-b7.pth')))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb7.model = recovery_model
else:
effb7.model = _make_recovery_model_include_top(effb7.model,
include_top=include_top,
classes=classes,
freeze_features=False)
effb7.model.input_shape = input_shape
effb7.model.to(get_device())
return effb7
def EfficientNetB4(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3, 380, 380),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 380, 380)
effb4 = EfficientNet(1.4,
1.8,
input_shape,
0.4,
model_name='efficientnet-b4',
include_top=include_top,
num_classes=classes)
if pretrained:
download_model_from_google_drive('1X4ZOBR_ETRHZJeffJHvCmWTTy9_aW8SP',
dirname, 'efficientnet-b4.pth')
recovery_model = fix_layer(
load(sanitize_path(os.path.join(dirname, 'efficientnet-b4.pth'))))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb4.model = recovery_model
else:
effb4.model = _make_recovery_model_include_top(effb4.model,
include_top=include_top,
classes=classes,
freeze_features=False)
effb4.model.input_shape = input_shape
effb4.model.to(get_device())
return effb4
def EfficientNetB5(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3, 456, 456),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 456, 456)
effb5 = EfficientNet(1.6,
2.2,
input_shape,
0.4,
model_name='efficientnet-b5',
include_top=include_top,
num_classes=classes)
if pretrained:
download_model_from_google_drive('17iTD12G9oW3jYAui84MKtdY4gjd9vpgG',
dirname, 'efficientnet-b5.pth')
recovery_model = fix_layer(
load(os.path.join(dirname, 'efficientnet-b5.pth')))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb5.model = recovery_model
else:
effb5.model = _make_recovery_model_include_top(effb5.model,
include_top=include_top,
classes=classes,
freeze_features=False)
effb5.model.input_shape = input_shape
effb5.model.to(get_device())
return effb5
def EfficientNetB2(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3, 260, 260),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 260, 260)
effb2 = EfficientNet(1.1,
1.2,
input_shape,
0.3,
model_name='efficientnet-b2',
include_top=include_top,
num_classes=classes)
if pretrained:
download_model_from_google_drive('1PjqhB7WJasF_hqOwYtSBNSXSGBY-cRLU',
dirname, 'efficientnet-b2.pth')
recovery_model = fix_layer(
load(os.path.join(dirname, 'efficientnet-b2.pth')))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb2.model = recovery_model
else:
effb2.model = _make_recovery_model_include_top(effb2.model,
include_top=include_top,
classes=classes,
freeze_features=False)
effb2.model.input_shape = input_shape
effb2.model.to(get_device())
return effb2
def EfficientNetB3(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3, 300, 300),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 300, 300)
effb3 = EfficientNet(1.2,
1.4,
input_shape,
0.3,
model_name='efficientnet-b3',
include_top=include_top,
num_classes=classes)
if pretrained:
download_model_from_google_drive('11tMxdYdFfaEREwnESO4cwjtcoEB42zB_',
dirname, 'efficientnet-b3.pth')
recovery_model = fix_layer(
load(os.path.join(dirname, 'efficientnet-b3.pth')))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb3.model = recovery_model
else:
effb3.model = _make_recovery_model_include_top(effb3.model,
include_top=include_top,
classes=classes,
freeze_features=False)
effb3.model.input_shape = input_shape
effb3.model.to(get_device())
return effb3
def EfficientNetB1(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3, 240, 240),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 240, 240)
effb1 = EfficientNet(1.0,
1.1,
input_shape,
0.2,
model_name='efficientnet-b1',
include_top=include_top,
num_classes=classes)
if pretrained:
download_model_from_google_drive('1F3BtnAjmDz4G9RS9Q0hqU_K7WWXCni1G',
dirname, 'efficientnet-b1.pth')
recovery_model = fix_layer(
load(os.path.join(dirname, 'efficientnet-b1.pth')))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb1.model = recovery_model
else:
effb1.model = _make_recovery_model_include_top(effb1.model,
include_top=include_top,
classes=classes,
freeze_features=False)
effb1.model.input_shape = input_shape
effb1.model.to(get_device())
return effb1
def EfficientNetB0(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3, 224, 224),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
else:
input_shape = (3, 224, 224)
effb0 = EfficientNet(1.0,
1.0,
input_shape,
0.2,
model_name='efficientnet-b0',
include_top=include_top,
num_classes=classes)
if pretrained:
download_model_from_google_drive('1bxnoDerzoNfiZZLft4ocD3DAgx4v6aTN',
dirname, 'efficientnet-b0.pth')
recovery_model = fix_layer(
load(os.path.join(dirname, 'efficientnet-b0.pth')))
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
effb0.model = recovery_model
else:
effb0.model = _make_recovery_model_include_top(effb0.model,
include_top=include_top,
classes=classes,
freeze_features=False)
effb0.model.input_shape = input_shape
effb0.model.to(get_device())
return effb0
def ResNet18(include_top=True,
pretrained=True,
freeze_features=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 = (3, 224, 224)
resnet18 = ResNet(basic_block, [2, 2, 2, 2],
input_shape,
use_bias=False,
include_top=include_top,
model_name='resnet18')
if pretrained == True:
download_model_from_google_drive(model_urls['resnet18'], dirname,
'resnet18.pth')
recovery_model = load(os.path.join(dirname, 'resnet18.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(
recovery_model,
input_shape=input_shape,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
resnet18.model = recovery_model
else:
resnet18.model = _make_recovery_model_include_top(
resnet18.model,
include_top=include_top,
classes=classes,
freeze_features=True)
resnet18.model.input_shape = input_shape
resnet18.model.to(_device)
return resnet18
def SEResNet_IR_50_512(include_top=True,
pretrained=True,
freeze_features=False,
input_shape=(3,112,112),
classes=1000,
**kwargs):
if input_shape is not None and len(input_shape)==3:
input_shape=tuple(input_shape)
else:
input_shape=(3, 112, 112)
seresnet = SEResNet_IR(include_top=include_top,num_layers=50,Bottleneck=BottleNeck_IR_SE,drop_ratio=0.4,feature_dim=512,input_shape=input_shape)
if pretrained == True:
download_model_from_google_drive('1aLYbFvtvsV2gQ16D_vwzrKdbCgij7IoZ', dirname, 'arcface_se_50_512.pth')
recovery_model = load(os.path.join(dirname, 'arcface_se_50_512.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model.name = 'arcface_se_50_512'
recovery_model = _make_recovery_model_include_top(recovery_model, include_top=include_top, classes=classes, freeze_features=freeze_features)
seresnet.model = recovery_model
else:
seresnet.model = _make_recovery_model_include_top(seresnet.model, include_top=include_top, classes=classes, freeze_features=False)
seresnet.model.input_shape = input_shape
seresnet.model.to(_device)
return seresnet
def ResNet152(include_top=True,
pretrained=True,
freeze_features=False,
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)
resnet152 =ResNet(bottleneck, [3, 8, 36, 3], input_shape,num_classes=classes,include_top=include_top, model_name='resnet152')
with tf.device(get_device()):
if pretrained==True:
download_model_from_google_drive('1TeVBB5ynW9E4_EgxIdjugLT8oaXnQH_c',dirname,'resnet152_tf.pth')
recovery_model=load(os.path.join(dirname,'resnet152_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model = _make_recovery_model_include_top(recovery_model, include_top=include_top, classes=classes, freeze_features=freeze_features)
resnet152.model = recovery_model
else:
resnet152.model = _make_recovery_model_include_top(resnet152.model, include_top=include_top, classes=classes, freeze_features=False)
resnet152.model.input_shape = input_shape
return resnet152
def SE_ResNet101(include_top=True,
pretrained=True,
freeze_features=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 = (3, 224, 224)
resnet101 = SE_ResNet(se_bottleneck, [3, 4, 23, 3],
input_shape,
num_classes=classes,
include_top=include_top,
model_name='resnet101')
if pretrained == True:
download_model_from_google_drive('1BO92ONsWnLQPWs9JVS-4SdpoPo5M4ABR',
dirname, 'se_resnet101.pth')
recovery_model = load(os.path.join(dirname, 'se_resnet101.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model.name = 'se_resnet101'
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
resnet101.model = recovery_model
else:
resnet101.model = _make_recovery_model_include_top(
resnet101.model,
include_top=include_top,
classes=classes,
freeze_features=True)
resnet101.model.input_shape = input_shape
resnet101.model.to(_device)
return resnet101
def DenseNet201(include_top=True,
pretrained=True,
input_shape=(224, 224, 3),
freeze_features=False,
classes=1000,
**kwargs):
"""
Constructor the image classicication model with DenseNet201 as backbond
Args:
freeze_features ():
include_top ():
pretrained (bool): If True, returns a model pre-trained on ImageNet.
input_shape (tuple or list): the default input image size in CHW order (C, H, W)
classes (int): number of classes
References
Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf
Returns:
the image classicication model with DenseNet201
Examples:
>>> dense201 = DenseNet201(include_top=True,pretrained=True,input_shape=(224,224,3),classes=1000)
>>> 'n02124075' in dense201.infer_single_image(get_image_from_google_drive('1SwablQsZO8mBuB84xnr1IoOisE3pm03l'),1).key_list[0]
True
"""
if input_shape is not None and len(input_shape) == 3:
input_shape = tuple(input_shape)
densenet201 = DenseNet([6, 12, 48, 32],
32,
64,
include_top=include_top,
pretrained=True,
input_shape=input_shape,
num_classes=classes,
name='densenet201')
with tf.device(get_device()):
if pretrained == True:
download_model_from_google_drive(
'1dJfgus11jXVoCLWfZqTgZ6jKKtrB70om', dirname,
'densenet201_tf.pth')
recovery_model = load(os.path.join(dirname, 'densenet201_tf.pth'))
recovery_model = fix_layer(recovery_model)
recovery_model._name = 'densenet201'
recovery_model = _make_recovery_model_include_top(
recovery_model,
include_top=include_top,
classes=classes,
freeze_features=freeze_features)
densenet201.model = recovery_model
else:
densenet201.model = _make_recovery_model_include_top(
densenet201.model,
include_top=include_top,
classes=classes,
freeze_features=False)
densenet201.model.input_shape = input_shape
return densenet201
