856 lines
26 KiB
Python
856 lines
26 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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dependencies = ['paddle', 'numpy']
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import paddle
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from ppcls.modeling.architectures import alexnet as _alexnet
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from ppcls.modeling.architectures import vgg as _vgg
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from ppcls.modeling.architectures import resnet as _resnet
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from ppcls.modeling.architectures import squeezenet as _squeezenet
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from ppcls.modeling.architectures import densenet as _densenet
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from ppcls.modeling.architectures import inception_v3 as _inception_v3
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from ppcls.modeling.architectures import inception_v4 as _inception_v4
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from ppcls.modeling.architectures import googlenet as _googlenet
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from ppcls.modeling.architectures import shufflenet_v2 as _shufflenet_v2
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from ppcls.modeling.architectures import mobilenet_v1 as _mobilenet_v1
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from ppcls.modeling.architectures import mobilenet_v2 as _mobilenet_v2
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from ppcls.modeling.architectures import mobilenet_v3 as _mobilenet_v3
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from ppcls.modeling.architectures import resnext as _resnext
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def _load_pretrained_parameters(model, name):
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url = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/{}_pretrained.pdparams'.format(name)
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path = paddle.utils.download.get_weights_path_from_url(url)
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model.set_state_dict(paddle.load(path))
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return model
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def AlexNet(pretrained=False, **kwargs):
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"""
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AlexNet
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `AlexNet` model depends on args.
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"""
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model = _alexnet.AlexNet(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'AlexNet')
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return model
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def VGG11(pretrained=False, **kwargs):
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"""
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VGG11
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
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Returns:
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model: nn.Layer. Specific `VGG11` model depends on args.
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"""
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model = _vgg.VGG11(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'VGG11')
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return model
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def VGG13(pretrained=False, **kwargs):
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"""
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VGG13
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
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Returns:
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model: nn.Layer. Specific `VGG13` model depends on args.
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"""
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model = _vgg.VGG13(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'VGG13')
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return model
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def VGG16(pretrained=False, **kwargs):
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"""
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VGG16
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
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Returns:
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model: nn.Layer. Specific `VGG16` model depends on args.
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"""
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model = _vgg.VGG16(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'VGG16')
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return model
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def VGG19(pretrained=False, **kwargs):
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"""
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VGG19
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
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Returns:
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model: nn.Layer. Specific `VGG19` model depends on args.
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"""
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model = _vgg.VGG19(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'VGG19')
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return model
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def ResNet18(pretrained=False, **kwargs):
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"""
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ResNet18
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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input_image_channel: int=3. The number of input image channels
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data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
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Returns:
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model: nn.Layer. Specific `ResNet18` model depends on args.
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"""
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model = _resnet.ResNet18(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'ResNet18')
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return model
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def ResNet34(pretrained=False, **kwargs):
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"""
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ResNet34
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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input_image_channel: int=3. The number of input image channels
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data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
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Returns:
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model: nn.Layer. Specific `ResNet34` model depends on args.
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"""
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model = _resnet.ResNet34(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'ResNet34')
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return model
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def ResNet50(pretrained=False, **kwargs):
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"""
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ResNet50
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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input_image_channel: int=3. The number of input image channels
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data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
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Returns:
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model: nn.Layer. Specific `ResNet50` model depends on args.
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"""
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model = _resnet.ResNet50(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'ResNet50')
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return model
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def ResNet101(pretrained=False, **kwargs):
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"""
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ResNet101
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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input_image_channel: int=3. The number of input image channels
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data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
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Returns:
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model: nn.Layer. Specific `ResNet101` model depends on args.
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"""
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model = _resnet.ResNet101(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'ResNet101')
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return model
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def ResNet152(pretrained=False, **kwargs):
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"""
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ResNet152
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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input_image_channel: int=3. The number of input image channels
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data_format: str='NCHW'. The data format of batch input images, should in ('NCHW', 'NHWC')
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Returns:
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model: nn.Layer. Specific `ResNet152` model depends on args.
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"""
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model = _resnet.ResNet152(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'ResNet152')
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return model
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def SqueezeNet1_0(pretrained=False, **kwargs):
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"""
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SqueezeNet1_0
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `SqueezeNet1_0` model depends on args.
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"""
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model = _squeezenet.SqueezeNet1_0(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'SqueezeNet1_0')
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return model
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def SqueezeNet1_1(pretrained=False, **kwargs):
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"""
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SqueezeNet1_1
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `SqueezeNet1_1` model depends on args.
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"""
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model = _squeezenet.SqueezeNet1_1(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'SqueezeNet1_1')
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return model
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def DenseNet121(pretrained=False, **kwargs):
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"""
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DenseNet121
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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dropout: float=0. Probability of setting units to zero.
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bn_size: int=4. The number of channals per group
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Returns:
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model: nn.Layer. Specific `DenseNet121` model depends on args.
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"""
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model = _densenet.DenseNet121(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'DenseNet121')
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return model
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def DenseNet161(pretrained=False, **kwargs):
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"""
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DenseNet161
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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dropout: float=0. Probability of setting units to zero.
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bn_size: int=4. The number of channals per group
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Returns:
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model: nn.Layer. Specific `DenseNet161` model depends on args.
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"""
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model = _densenet.DenseNet161(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'DenseNet161')
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return model
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def DenseNet169(pretrained=False, **kwargs):
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"""
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DenseNet169
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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dropout: float=0. Probability of setting units to zero.
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bn_size: int=4. The number of channals per group
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Returns:
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model: nn.Layer. Specific `DenseNet169` model depends on args.
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"""
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model = _densenet.DenseNet169(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'DenseNet169')
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return model
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def DenseNet201(pretrained=False, **kwargs):
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"""
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DenseNet201
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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dropout: float=0. Probability of setting units to zero.
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bn_size: int=4. The number of channals per group
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Returns:
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model: nn.Layer. Specific `DenseNet201` model depends on args.
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"""
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model = _densenet.DenseNet201(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'DenseNet201')
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return model
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def DenseNet264(pretrained=False, **kwargs):
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"""
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DenseNet264
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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dropout: float=0. Probability of setting units to zero.
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bn_size: int=4. The number of channals per group
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Returns:
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model: nn.Layer. Specific `DenseNet264` model depends on args.
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"""
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model = _densenet.DenseNet264(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'DenseNet264')
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return model
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def InceptionV3(pretrained=False, **kwargs):
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"""
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InceptionV3
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `InceptionV3` model depends on args.
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"""
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model = _inception_v3.InceptionV3(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'InceptionV3')
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return model
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def InceptionV4(pretrained=False, **kwargs):
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"""
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InceptionV4
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `InceptionV4` model depends on args.
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"""
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model = _inception_v4.InceptionV4(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'InceptionV4')
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return model
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def GoogLeNet(pretrained=False, **kwargs):
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"""
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GoogLeNet
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `GoogLeNet` model depends on args.
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"""
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model = _googlenet.GoogLeNet(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'GoogLeNet')
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return model
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def ShuffleNet(pretrained=False, **kwargs):
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"""
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ShuffleNet
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `ShuffleNet` model depends on args.
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"""
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model = _shufflenet_v2.ShuffleNet(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'ShuffleNet')
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return model
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def MobileNetV1(pretrained=False, **kwargs):
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"""
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MobileNetV1
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `MobileNetV1` model depends on args.
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"""
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model = _mobilenet_v1.MobileNetV1(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'MobileNetV1')
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return model
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def MobileNetV1_x0_25(pretrained=False, **kwargs):
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"""
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MobileNetV1_x0_25
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `MobileNetV1_x0_25` model depends on args.
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"""
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model = _mobilenet_v1.MobileNetV1_x0_25(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'MobileNetV1_x0_25')
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return model
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def MobileNetV1_x0_5(pretrained=False, **kwargs):
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"""
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MobileNetV1_x0_5
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `MobileNetV1_x0_5` model depends on args.
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"""
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model = _mobilenet_v1.MobileNetV1_x0_5(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'MobileNetV1_x0_5')
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return model
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def MobileNetV1_x0_75(pretrained=False, **kwargs):
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"""
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MobileNetV1_x0_75
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `MobileNetV1_x0_75` model depends on args.
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"""
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model = _mobilenet_v1.MobileNetV1_x0_75(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'MobileNetV1_x0_75')
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return model
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def MobileNetV2_x0_25(pretrained=False, **kwargs):
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"""
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MobileNetV2_x0_25
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Args:
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pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
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kwargs:
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class_dim: int=1000. Output dim of last fc layer.
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Returns:
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model: nn.Layer. Specific `MobileNetV2_x0_25` model depends on args.
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"""
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model = _mobilenet_v2.MobileNetV2_x0_25(**kwargs)
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if pretrained:
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model = _load_pretrained_parameters(model, 'MobileNetV2_x0_25')
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return model
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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 |