Merge pull request #701 from lyuwenyu/hub_L_a

Support paddle hub 

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', 'numpy']
+
+import paddle
+from ppcls.modeling import architectures
+
+
+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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.GoogLeNet(**kwargs)
+    if pretrained:
+        model = _load_pretrained_parameters(model, 'GoogLeNet')
+
+    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.
+    """
+    model = architectures.ShuffleNetV2_x0_25(**kwargs)
+    if pretrained:
+        model = _load_pretrained_parameters(model, 'ShuffleNetV2_x0_25')
+
+    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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.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 = architectures.ResNeXt50_64x4d(**kwargs)
+    if pretrained:
+        model = _load_pretrained_parameters(model, 'ResNeXt50_64x4d')
+
+    return model