PaddleClas/hubconf.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


dependencies = ['paddle', 'numpy']

import paddle

from ppcls.modeling.architectures import alexnet as _alexnet
from ppcls.modeling.architectures import vgg as _vgg 
from ppcls.modeling.architectures import resnet as _resnet 
from ppcls.modeling.architectures import squeezenet as _squeezenet
from ppcls.modeling.architectures import densenet as _densenet
from ppcls.modeling.architectures import inception_v3 as _inception_v3
from ppcls.modeling.architectures import inception_v4 as _inception_v4
from ppcls.modeling.architectures import googlenet as _googlenet
from ppcls.modeling.architectures import shufflenet_v2 as _shufflenet_v2
from ppcls.modeling.architectures import mobilenet_v1 as _mobilenet_v1
from ppcls.modeling.architectures import mobilenet_v2 as _mobilenet_v2
from ppcls.modeling.architectures import mobilenet_v3 as _mobilenet_v3
from ppcls.modeling.architectures import resnext as _resnext



def _load_pretrained_parameters(model, name):
    url = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/{}_pretrained.pdparams'.format(name)
    path = paddle.utils.download.get_weights_path_from_url(url)
    model.set_state_dict(paddle.load(path))
    return model
    

def AlexNet(pretrained=False, **kwargs):
    """
    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')

    return model


def VGG11(pretrained=False, **kwargs):
    """
    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')

    return model


def VGG13(pretrained=False, **kwargs):
    """
    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')

    return model


def VGG16(pretrained=False, **kwargs):
    """
    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')

    return model


def VGG19(pretrained=False, **kwargs):
    """
    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')

    return model




def ResNet18(pretrained=False, **kwargs):
    """
    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')

    return model


def ResNet34(pretrained=False, **kwargs):
    """
    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')

    return model


def ResNet50(pretrained=False, **kwargs):
    """
    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')
        
    return model


def ResNet101(pretrained=False, **kwargs):
    """
    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')

    return model


def ResNet152(pretrained=False, **kwargs):
    """
    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')

    return model



def SqueezeNet1_0(pretrained=False, **kwargs):
    """
    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')

    return model


def SqueezeNet1_1(pretrained=False, **kwargs):
    """
    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')

    return model




def DenseNet121(pretrained=False, **kwargs):
    """
    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')

    return model


def DenseNet161(pretrained=False, **kwargs):
    """
    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')

    return model


def DenseNet169(pretrained=False, **kwargs):
    """
    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')

    return model


def DenseNet201(pretrained=False, **kwargs):
    """
    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')

    return model


def DenseNet264(pretrained=False, **kwargs):
    """
    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')

    return model



def InceptionV3(pretrained=False, **kwargs):
    """
    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')

    return model


def InceptionV4(pretrained=False, **kwargs):
    """
    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')

    return model



def GoogLeNet(pretrained=False, **kwargs):
    """
    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')

    return model



def ShuffleNet(pretrained=False, **kwargs):
    """
    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')

    return model



def MobileNetV1(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV1_x0_25(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV1_x0_5(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV1_x0_75(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV2_x0_25(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV2_x0_5(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV2_x0_75(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV2_x1_5(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV2_x2_0(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_large_x0_35(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_large_x0_5(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_large_x0_75(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_large_x1_0(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_large_x1_25(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_small_x0_35(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_small_x0_5(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_small_x0_75(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_small_x1_0(pretrained=False, **kwargs):
    """
    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')

    return model


def MobileNetV3_small_x1_25(pretrained=False, **kwargs):
    """
    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')

    return model



def ResNeXt101_32x4d(pretrained=False, **kwargs):
    """
    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')

    return model


def ResNeXt101_64x4d(pretrained=False, **kwargs):
    """
    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')

    return model


def ResNeXt152_32x4d(pretrained=False, **kwargs):
    """
    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')

    return model


def ResNeXt152_64x4d(pretrained=False, **kwargs):
    """
    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')

    return model


def ResNeXt50_32x4d(pretrained=False, **kwargs):
    """
    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')

    return model


def ResNeXt50_64x4d(pretrained=False, **kwargs):
    """
    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')

    return model