hubconf.py

# Copyright (c) 2021 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']

import paddle
import os
import sys


class _SysPathG(object):
    """
    _SysPathG used to add/clean path for sys.path. Making sure minimal pkgs dependents by skiping parent dirs.

    __enter__
        add path into sys.path
    __exit__
        clean user's sys.path to avoid unexpect behaviors
    """

    def __init__(self, path):
        self.path = path

    def __enter__(self, ):
        sys.path.insert(0, self.path)

    def __exit__(self, type, value, traceback):
        _p = sys.path.pop(0)
        assert _p == self.path, 'Make sure sys.path cleaning {} correctly.'.format(
            self.path)


with _SysPathG(os.path.dirname(os.path.abspath(__file__)), ):
    import ppcls
    import ppcls.arch.backbone as backbone

    def ppclas_init():
        if ppcls.utils.logger._logger is None:
            ppcls.utils.logger.init_logger()

    ppclas_init()

    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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.AlexNet(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.VGG11(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.VGG13(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.VGG16(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.VGG19(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNet18(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNet34(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNet50(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNet101(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNet152(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.SqueezeNet1_0(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.SqueezeNet1_1(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.DenseNet121(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.DenseNet161(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.DenseNet169(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.DenseNet201(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.DenseNet264(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.InceptionV3(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.InceptionV4(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.GoogLeNet(**kwargs)

        return model

    def shufflenetv2_x0_25(pretrained=False, **kwargs):
        """
        ShuffleNetV2_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 `ShuffleNetV2_x0_25` model depends on args.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ShuffleNetV2_x0_25(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV1(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV1_x0_25(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV1_x0_5(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV1_x0_75(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV2_x0_25(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV2_x0_5(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV2_x0_75(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV2_x1_5(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV2_x2_0(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_large_x0_35(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_large_x0_5(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_large_x0_75(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_large_x1_0(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_large_x1_25(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_small_x0_35(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_small_x0_5(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_small_x0_75(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_small_x1_0(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.MobileNetV3_small_x1_25(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNeXt101_32x4d(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNeXt101_64x4d(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNeXt152_32x4d(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNeXt152_64x4d(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNeXt50_32x4d(**kwargs)

        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.ResNeXt50_64x4d(**kwargs)

        return model

    def darknet53(pretrained=False, **kwargs):
        """
        DarkNet53
        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.
        """
        kwargs.update({'pretrained': pretrained})
        model = backbone.DarkNet53(**kwargs)

        return model