|
- # 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
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