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# 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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