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PaddleClas
未验证 提交 1303affa 编写于 作者: W Wei Shengyu 提交者： GitHub
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Merge pull request #720 from lyuwenyu/hub_L_b 

Release unnecessary dependent pkgs
上级 e8752538 72ab665b
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hubconf.py
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......@@ -12,821 +12,813 @@
# See the License for the specific language governing permissions and
# limitations under the License.
dependencies = ['paddle', 'numpy']
dependencies = ['paddle']
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
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.join(
os.path.dirname(os.path.abspath(__file__)), '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
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