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提交 789fcb2c 编写于 作者: L lyuwenyu
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add explanation for models

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Showing with 438 addition and 93 deletion
hubconf.py
浏览文件 @ 789fcb2c
......@@ -69,8 +69,15 @@ def _load_pretrained_parameters(model, name):
def AlexNet(pretrained=False, **kwargs):
'''AlexNet
'''
"""
AlexNet
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `AlexNet` model depends on args.
"""
model = _alexnet.AlexNet(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'AlexNet')
......@@ -78,10 +85,17 @@ def AlexNet(pretrained=False, **kwargs):
return model
def VGG11(pretrained=False, **kwargs):
'''VGG11
'''
"""
VGG11
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
Returns:
model: nn.Layer. Specific `VGG11` model depends on args.
"""
model = _vgg.VGG11(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'VGG11')
......@@ -90,8 +104,16 @@ def VGG11(pretrained=False, **kwargs):
def VGG13(pretrained=False, **kwargs):
'''VGG13
'''
"""
VGG13
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
Returns:
model: nn.Layer. Specific `VGG13` model depends on args.
"""
model = _vgg.VGG13(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'VGG13')
......@@ -100,8 +122,16 @@ def VGG13(pretrained=False, **kwargs):
def VGG16(pretrained=False, **kwargs):
'''VGG16
'''
"""
VGG16
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
Returns:
model: nn.Layer. Specific `VGG16` model depends on args.
"""
model = _vgg.VGG16(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'VGG16')
......@@ -110,8 +140,16 @@ def VGG16(pretrained=False, **kwargs):
def VGG19(pretrained=False, **kwargs):
'''VGG19
'''
"""
VGG19
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
Returns:
model: nn.Layer. Specific `VGG19` model depends on args.
"""
model = _vgg.VGG19(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'VGG19')
......@@ -122,8 +160,17 @@ def VGG19(pretrained=False, **kwargs):
def ResNet18(pretrained=False, **kwargs):
'''ResNet18
'''
"""
ResNet18
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
input_image_channel: int=3. The number of input image channels
data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
Returns:
model: nn.Layer. Specific `ResNet18` model depends on args.
"""
model = _resnet.ResNet18(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNet18')
......@@ -132,8 +179,17 @@ def ResNet18(pretrained=False, **kwargs):
def ResNet34(pretrained=False, **kwargs):
'''ResNet34
'''
"""
ResNet34
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
input_image_channel: int=3. The number of input image channels
data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
Returns:
model: nn.Layer. Specific `ResNet34` model depends on args.
"""
model = _resnet.ResNet34(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNet34')
......@@ -142,8 +198,17 @@ def ResNet34(pretrained=False, **kwargs):
def ResNet50(pretrained=False, **kwargs):
'''ResNet50
'''
"""
ResNet50
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
input_image_channel: int=3. The number of input image channels
data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
Returns:
model: nn.Layer. Specific `ResNet50` model depends on args.
"""
model = _resnet.ResNet50(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNet50')
......@@ -152,8 +217,17 @@ def ResNet50(pretrained=False, **kwargs):
def ResNet101(pretrained=False, **kwargs):
'''ResNet101
'''
"""
ResNet101
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
input_image_channel: int=3. The number of input image channels
data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
Returns:
model: nn.Layer. Specific `ResNet101` model depends on args.
"""
model = _resnet.ResNet101(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNet101')
......@@ -162,8 +236,17 @@ def ResNet101(pretrained=False, **kwargs):
def ResNet152(pretrained=False, **kwargs):
'''ResNet152
'''
"""
ResNet152
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
input_image_channel: int=3. The number of input image channels
data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
Returns:
model: nn.Layer. Specific `ResNet152` model depends on args.
"""
model = _resnet.ResNet152(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNet152')
......@@ -173,8 +256,15 @@ def ResNet152(pretrained=False, **kwargs):
def SqueezeNet1_0(pretrained=False, **kwargs):
'''SqueezeNet1_0
'''
"""
SqueezeNet1_0
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `SqueezeNet1_0` model depends on args.
"""
model = _squeezenet.SqueezeNet1_0(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'SqueezeNet1_0')
......@@ -183,8 +273,15 @@ def SqueezeNet1_0(pretrained=False, **kwargs):
def SqueezeNet1_1(pretrained=False, **kwargs):
'''SqueezeNet1_1
'''
"""
SqueezeNet1_1
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `SqueezeNet1_1` model depends on args.
"""
model = _squeezenet.SqueezeNet1_1(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'SqueezeNet1_1')
......@@ -195,8 +292,17 @@ def SqueezeNet1_1(pretrained=False, **kwargs):
def DenseNet121(pretrained=False, **kwargs):
'''DenseNet121
'''
"""
DenseNet121
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
dropout: float=0. Probability of setting units to zero.
bn_size: int=4. The number of channals per group
Returns:
model: nn.Layer. Specific `DenseNet121` model depends on args.
"""
model = _densenet.DenseNet121(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'DenseNet121')
......@@ -205,8 +311,17 @@ def DenseNet121(pretrained=False, **kwargs):
def DenseNet161(pretrained=False, **kwargs):
'''DenseNet161
'''
"""
DenseNet161
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
dropout: float=0. Probability of setting units to zero.
bn_size: int=4. The number of channals per group
Returns:
model: nn.Layer. Specific `DenseNet161` model depends on args.
"""
model = _densenet.DenseNet161(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'DenseNet161')
......@@ -215,8 +330,17 @@ def DenseNet161(pretrained=False, **kwargs):
def DenseNet169(pretrained=False, **kwargs):
'''DenseNet169
'''
"""
DenseNet169
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
dropout: float=0. Probability of setting units to zero.
bn_size: int=4. The number of channals per group
Returns:
model: nn.Layer. Specific `DenseNet169` model depends on args.
"""
model = _densenet.DenseNet169(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'DenseNet169')
......@@ -225,8 +349,17 @@ def DenseNet169(pretrained=False, **kwargs):
def DenseNet201(pretrained=False, **kwargs):
'''DenseNet201
'''
"""
DenseNet201
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
dropout: float=0. Probability of setting units to zero.
bn_size: int=4. The number of channals per group
Returns:
model: nn.Layer. Specific `DenseNet201` model depends on args.
"""
model = _densenet.DenseNet201(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'DenseNet201')
......@@ -235,8 +368,17 @@ def DenseNet201(pretrained=False, **kwargs):
def DenseNet264(pretrained=False, **kwargs):
'''DenseNet264
'''
"""
DenseNet264
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
dropout: float=0. Probability of setting units to zero.
bn_size: int=4. The number of channals per group
Returns:
model: nn.Layer. Specific `DenseNet264` model depends on args.
"""
model = _densenet.DenseNet264(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'DenseNet264')
......@@ -246,8 +388,15 @@ def DenseNet264(pretrained=False, **kwargs):
def InceptionV3(pretrained=False, **kwargs):
'''InceptionV3
'''
"""
InceptionV3
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `InceptionV3` model depends on args.
"""
model = _inception_v3.InceptionV3(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'InceptionV3')
......@@ -256,8 +405,15 @@ def InceptionV3(pretrained=False, **kwargs):
def InceptionV4(pretrained=False, **kwargs):
'''InceptionV4
'''
"""
InceptionV4
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `InceptionV4` model depends on args.
"""
model = _inception_v4.InceptionV4(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'InceptionV4')
......@@ -267,8 +423,15 @@ def InceptionV4(pretrained=False, **kwargs):
def GoogLeNet(pretrained=False, **kwargs):
'''GoogLeNet
'''
"""
GoogLeNet
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `GoogLeNet` model depends on args.
"""
model = _googlenet.GoogLeNet(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'GoogLeNet')
......@@ -278,8 +441,15 @@ def GoogLeNet(pretrained=False, **kwargs):
def ShuffleNet(pretrained=False, **kwargs):
'''ShuffleNet
'''
"""
ShuffleNet
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `ShuffleNet` model depends on args.
"""
model = _shufflenet_v2.ShuffleNet(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ShuffleNet')
......@@ -289,8 +459,15 @@ def ShuffleNet(pretrained=False, **kwargs):
def MobileNetV1(pretrained=False, **kwargs):
'''MobileNetV1
'''
"""
MobileNetV1
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV1` model depends on args.
"""
model = _mobilenet_v1.MobileNetV1(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV1')
......@@ -299,8 +476,15 @@ def MobileNetV1(pretrained=False, **kwargs):
def MobileNetV1_x0_25(pretrained=False, **kwargs):
'''MobileNetV1_x0_25
'''
"""
MobileNetV1_x0_25
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_25` model depends on args.
"""
model = _mobilenet_v1.MobileNetV1_x0_25(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV1_x0_25')
......@@ -309,8 +493,15 @@ def MobileNetV1_x0_25(pretrained=False, **kwargs):
def MobileNetV1_x0_5(pretrained=False, **kwargs):
'''MobileNetV1_x0_5
'''
"""
MobileNetV1_x0_5
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_5` model depends on args.
"""
model = _mobilenet_v1.MobileNetV1_x0_5(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV1_x0_5')
......@@ -319,8 +510,15 @@ def MobileNetV1_x0_5(pretrained=False, **kwargs):
def MobileNetV1_x0_75(pretrained=False, **kwargs):
'''MobileNetV1_x0_75
'''
"""
MobileNetV1_x0_75
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_75` model depends on args.
"""
model = _mobilenet_v1.MobileNetV1_x0_75(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV1_x0_75')
......@@ -329,8 +527,15 @@ def MobileNetV1_x0_75(pretrained=False, **kwargs):
def MobileNetV2_x0_25(pretrained=False, **kwargs):
'''MobileNetV2_x0_25
'''
"""
MobileNetV2_x0_25
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV2_x0_25` model depends on args.
"""
model = _mobilenet_v2.MobileNetV2_x0_25(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV2_x0_25')
......@@ -339,8 +544,15 @@ def MobileNetV2_x0_25(pretrained=False, **kwargs):
def MobileNetV2_x0_5(pretrained=False, **kwargs):
'''MobileNetV2_x0_5
'''
"""
MobileNetV2_x0_5
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV2_x0_5` model depends on args.
"""
model = _mobilenet_v2.MobileNetV2_x0_5(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV2_x0_5')
......@@ -349,8 +561,15 @@ def MobileNetV2_x0_5(pretrained=False, **kwargs):
def MobileNetV2_x0_75(pretrained=False, **kwargs):
'''MobileNetV2_x0_75
'''
"""
MobileNetV2_x0_75
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV2_x0_75` model depends on args.
"""
model = _mobilenet_v2.MobileNetV2_x0_75(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV2_x0_75')
......@@ -359,8 +578,15 @@ def MobileNetV2_x0_75(pretrained=False, **kwargs):
def MobileNetV2_x1_5(pretrained=False, **kwargs):
'''MobileNetV2_x1_5
'''
"""
MobileNetV2_x1_5
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV2_x1_5` model depends on args.
"""
model = _mobilenet_v2.MobileNetV2_x1_5(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV2_x1_5')
......@@ -369,8 +595,15 @@ def MobileNetV2_x1_5(pretrained=False, **kwargs):
def MobileNetV2_x2_0(pretrained=False, **kwargs):
'''MobileNetV2_x2_0
'''
"""
MobileNetV2_x2_0
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV2_x2_0` model depends on args.
"""
model = _mobilenet_v2.MobileNetV2_x2_0(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV2_x2_0')
......@@ -379,8 +612,15 @@ def MobileNetV2_x2_0(pretrained=False, **kwargs):
def MobileNetV3_large_x0_35(pretrained=False, **kwargs):
'''MobileNetV3_large_x0_35
'''
"""
MobileNetV3_large_x0_35
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x0_35` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_large_x0_35(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_large_x0_35')
......@@ -389,8 +629,15 @@ def MobileNetV3_large_x0_35(pretrained=False, **kwargs):
def MobileNetV3_large_x0_5(pretrained=False, **kwargs):
'''MobileNetV3_large_x0_5
'''
"""
MobileNetV3_large_x0_5
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x0_5` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_large_x0_5(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_large_x0_5')
......@@ -399,8 +646,15 @@ def MobileNetV3_large_x0_5(pretrained=False, **kwargs):
def MobileNetV3_large_x0_75(pretrained=False, **kwargs):
'''MobileNetV3_large_x0_75
'''
"""
MobileNetV3_large_x0_75
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x0_75` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_large_x0_75(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_large_x0_75')
......@@ -409,8 +663,15 @@ def MobileNetV3_large_x0_75(pretrained=False, **kwargs):
def MobileNetV3_large_x1_0(pretrained=False, **kwargs):
'''MobileNetV3_large_x1_0
'''
"""
MobileNetV3_large_x1_0
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x1_0` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_large_x1_0(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_large_x1_0')
......@@ -419,8 +680,15 @@ def MobileNetV3_large_x1_0(pretrained=False, **kwargs):
def MobileNetV3_large_x1_25(pretrained=False, **kwargs):
'''MobileNetV3_large_x1_25
'''
"""
MobileNetV3_large_x1_25
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x1_25` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_large_x1_25(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_large_x1_25')
......@@ -429,8 +697,15 @@ def MobileNetV3_large_x1_25(pretrained=False, **kwargs):
def MobileNetV3_small_x0_35(pretrained=False, **kwargs):
'''MobileNetV3_small_x0_35
'''
"""
MobileNetV3_small_x0_35
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x0_35` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_small_x0_35(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_small_x0_35')
......@@ -439,8 +714,15 @@ def MobileNetV3_small_x0_35(pretrained=False, **kwargs):
def MobileNetV3_small_x0_5(pretrained=False, **kwargs):
'''MobileNetV3_small_x0_5
'''
"""
MobileNetV3_small_x0_5
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x0_5` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_small_x0_5(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_small_x0_5')
......@@ -449,8 +731,15 @@ def MobileNetV3_small_x0_5(pretrained=False, **kwargs):
def MobileNetV3_small_x0_75(pretrained=False, **kwargs):
'''MobileNetV3_small_x0_75
'''
"""
MobileNetV3_small_x0_75
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x0_75` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_small_x0_75(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_small_x0_75')
......@@ -459,8 +748,15 @@ def MobileNetV3_small_x0_75(pretrained=False, **kwargs):
def MobileNetV3_small_x1_0(pretrained=False, **kwargs):
'''MobileNetV3_small_x1_0
'''
"""
MobileNetV3_small_x1_0
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x1_0` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_small_x1_0(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_small_x1_0')
......@@ -469,8 +765,15 @@ def MobileNetV3_small_x1_0(pretrained=False, **kwargs):
def MobileNetV3_small_x1_25(pretrained=False, **kwargs):
'''MobileNetV3_small_x1_25
'''
"""
MobileNetV3_small_x1_25
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x1_25` model depends on args.
"""
model = _mobilenet_v3.MobileNetV3_small_x1_25(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'MobileNetV3_small_x1_25')
......@@ -480,8 +783,15 @@ def MobileNetV3_small_x1_25(pretrained=False, **kwargs):
def ResNeXt101_32x4d(pretrained=False, **kwargs):
'''ResNeXt101_32x4d
'''
"""
ResNeXt101_32x4d
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `ResNeXt101_32x4d` model depends on args.
"""
model = _resnext.ResNeXt101_32x4d(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNeXt101_32x4d')
......@@ -490,8 +800,15 @@ def ResNeXt101_32x4d(pretrained=False, **kwargs):
def ResNeXt101_64x4d(pretrained=False, **kwargs):
'''ResNeXt101_64x4d
'''
"""
ResNeXt101_64x4d
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `ResNeXt101_64x4d` model depends on args.
"""
model = _resnext.ResNeXt101_64x4d(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNeXt101_64x4d')
......@@ -500,8 +817,15 @@ def ResNeXt101_64x4d(pretrained=False, **kwargs):
def ResNeXt152_32x4d(pretrained=False, **kwargs):
'''ResNeXt152_32x4d
'''
"""
ResNeXt152_32x4d
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `ResNeXt152_32x4d` model depends on args.
"""
model = _resnext.ResNeXt152_32x4d(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNeXt152_32x4d')
......@@ -510,8 +834,15 @@ def ResNeXt152_32x4d(pretrained=False, **kwargs):
def ResNeXt152_64x4d(pretrained=False, **kwargs):
'''ResNeXt152_64x4d
'''
"""
ResNeXt152_64x4d
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `ResNeXt152_64x4d` model depends on args.
"""
model = _resnext.ResNeXt152_64x4d(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNeXt152_64x4d')
......@@ -520,8 +851,15 @@ def ResNeXt152_64x4d(pretrained=False, **kwargs):
def ResNeXt50_32x4d(pretrained=False, **kwargs):
'''ResNeXt50_32x4d
'''
"""
ResNeXt50_32x4d
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `ResNeXt50_32x4d` model depends on args.
"""
model = _resnext.ResNeXt50_32x4d(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNeXt50_32x4d')
......@@ -530,8 +868,15 @@ def ResNeXt50_32x4d(pretrained=False, **kwargs):
def ResNeXt50_64x4d(pretrained=False, **kwargs):
'''ResNeXt50_64x4d
'''
"""
ResNeXt50_64x4d
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_dim: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `ResNeXt50_64x4d` model depends on args.
"""
model = _resnext.ResNeXt50_64x4d(**kwargs)
if pretrained:
model = _load_pretrained_parameters(model, 'ResNeXt50_64x4d')
......
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