From 789fcb2c1249a24fbad63668ae12b6fa0e2e1eb9 Mon Sep 17 00:00:00 2001 From: lyuwenyu Date: Sun, 25 Apr 2021 19:34:14 +0800 Subject: [PATCH] add explanation for models --- hubconf.py | 531 +++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 438 insertions(+), 93 deletions(-) diff --git a/hubconf.py b/hubconf.py index 0a72726f..39fe1105 100644 --- a/hubconf.py +++ b/hubconf.py @@ -69,8 +69,15 @@ def _load_pretrained_parameters(model, name): def AlexNet(pretrained=False, **kwargs): - '''AlexNet - ''' + """ + 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 = _alexnet.AlexNet(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'AlexNet') @@ -78,10 +85,17 @@ def AlexNet(pretrained=False, **kwargs): return model - def VGG11(pretrained=False, **kwargs): - '''VGG11 - ''' + """ + 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 = _vgg.VGG11(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'VGG11') @@ -90,8 +104,16 @@ def VGG11(pretrained=False, **kwargs): def VGG13(pretrained=False, **kwargs): - '''VGG13 - ''' + """ + 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 = _vgg.VGG13(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'VGG13') @@ -100,8 +122,16 @@ def VGG13(pretrained=False, **kwargs): def VGG16(pretrained=False, **kwargs): - '''VGG16 - ''' + """ + 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 = _vgg.VGG16(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'VGG16') @@ -110,8 +140,16 @@ def VGG16(pretrained=False, **kwargs): def VGG19(pretrained=False, **kwargs): - '''VGG19 - ''' + """ + 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 = _vgg.VGG19(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'VGG19') @@ -122,8 +160,17 @@ def VGG19(pretrained=False, **kwargs): def ResNet18(pretrained=False, **kwargs): - '''ResNet18 - ''' + """ + 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 = _resnet.ResNet18(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet18') @@ -132,8 +179,17 @@ def ResNet18(pretrained=False, **kwargs): def ResNet34(pretrained=False, **kwargs): - '''ResNet34 - ''' + """ + 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 = _resnet.ResNet34(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet34') @@ -142,8 +198,17 @@ def ResNet34(pretrained=False, **kwargs): def ResNet50(pretrained=False, **kwargs): - '''ResNet50 - ''' + """ + 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 = _resnet.ResNet50(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet50') @@ -152,8 +217,17 @@ def ResNet50(pretrained=False, **kwargs): def ResNet101(pretrained=False, **kwargs): - '''ResNet101 - ''' + """ + 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 = _resnet.ResNet101(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet101') @@ -162,8 +236,17 @@ def ResNet101(pretrained=False, **kwargs): def ResNet152(pretrained=False, **kwargs): - '''ResNet152 - ''' + """ + 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 = _resnet.ResNet152(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ResNet152') @@ -173,8 +256,15 @@ def ResNet152(pretrained=False, **kwargs): def SqueezeNet1_0(pretrained=False, **kwargs): - '''SqueezeNet1_0 - ''' + """ + 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 = _squeezenet.SqueezeNet1_0(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'SqueezeNet1_0') @@ -183,8 +273,15 @@ def SqueezeNet1_0(pretrained=False, **kwargs): def SqueezeNet1_1(pretrained=False, **kwargs): - '''SqueezeNet1_1 - ''' + """ + 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 = _squeezenet.SqueezeNet1_1(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'SqueezeNet1_1') @@ -195,8 +292,17 @@ def SqueezeNet1_1(pretrained=False, **kwargs): def DenseNet121(pretrained=False, **kwargs): - '''DenseNet121 - ''' + """ + 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 = _densenet.DenseNet121(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet121') @@ -205,8 +311,17 @@ def DenseNet121(pretrained=False, **kwargs): def DenseNet161(pretrained=False, **kwargs): - '''DenseNet161 - ''' + """ + 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 = _densenet.DenseNet161(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet161') @@ -215,8 +330,17 @@ def DenseNet161(pretrained=False, **kwargs): def DenseNet169(pretrained=False, **kwargs): - '''DenseNet169 - ''' + """ + 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 = _densenet.DenseNet169(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet169') @@ -225,8 +349,17 @@ def DenseNet169(pretrained=False, **kwargs): def DenseNet201(pretrained=False, **kwargs): - '''DenseNet201 - ''' + """ + 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 = _densenet.DenseNet201(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet201') @@ -235,8 +368,17 @@ def DenseNet201(pretrained=False, **kwargs): def DenseNet264(pretrained=False, **kwargs): - '''DenseNet264 - ''' + """ + 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 = _densenet.DenseNet264(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'DenseNet264') @@ -246,8 +388,15 @@ def DenseNet264(pretrained=False, **kwargs): def InceptionV3(pretrained=False, **kwargs): - '''InceptionV3 - ''' + """ + 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 = _inception_v3.InceptionV3(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'InceptionV3') @@ -256,8 +405,15 @@ def InceptionV3(pretrained=False, **kwargs): def InceptionV4(pretrained=False, **kwargs): - '''InceptionV4 - ''' + """ + 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 = _inception_v4.InceptionV4(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'InceptionV4') @@ -267,8 +423,15 @@ def InceptionV4(pretrained=False, **kwargs): def GoogLeNet(pretrained=False, **kwargs): - '''GoogLeNet - ''' + """ + 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 = _googlenet.GoogLeNet(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'GoogLeNet') @@ -278,8 +441,15 @@ def GoogLeNet(pretrained=False, **kwargs): def ShuffleNet(pretrained=False, **kwargs): - '''ShuffleNet - ''' + """ + ShuffleNet + 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 `ShuffleNet` model depends on args. + """ model = _shufflenet_v2.ShuffleNet(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'ShuffleNet') @@ -289,8 +459,15 @@ def ShuffleNet(pretrained=False, **kwargs): def MobileNetV1(pretrained=False, **kwargs): - '''MobileNetV1 - ''' + """ + 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 = _mobilenet_v1.MobileNetV1(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV1') @@ -299,8 +476,15 @@ def MobileNetV1(pretrained=False, **kwargs): def MobileNetV1_x0_25(pretrained=False, **kwargs): - '''MobileNetV1_x0_25 - ''' + """ + 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 = _mobilenet_v1.MobileNetV1_x0_25(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV1_x0_25') @@ -309,8 +493,15 @@ def MobileNetV1_x0_25(pretrained=False, **kwargs): def MobileNetV1_x0_5(pretrained=False, **kwargs): - '''MobileNetV1_x0_5 - ''' + """ + 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 = _mobilenet_v1.MobileNetV1_x0_5(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV1_x0_5') @@ -319,8 +510,15 @@ def MobileNetV1_x0_5(pretrained=False, **kwargs): def MobileNetV1_x0_75(pretrained=False, **kwargs): - '''MobileNetV1_x0_75 - ''' + """ + 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 = _mobilenet_v1.MobileNetV1_x0_75(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV1_x0_75') @@ -329,8 +527,15 @@ def MobileNetV1_x0_75(pretrained=False, **kwargs): def MobileNetV2_x0_25(pretrained=False, **kwargs): - '''MobileNetV2_x0_25 - ''' + """ + 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 = _mobilenet_v2.MobileNetV2_x0_25(**kwargs) if pretrained: model = _load_pretrained_parameters(model, 'MobileNetV2_x0_25') @@ -339,8 +544,15 @@ def MobileNetV2_x0_25(pretrained=False, **kwargs): def MobileNetV2_x0_5(pretrained=False, **kwargs): - '''MobileNetV2_x0_5 - ''' + """ + 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') @@ -349,8 +561,15 @@ def MobileNetV2_x0_5(pretrained=False, **kwargs): def MobileNetV2_x0_75(pretrained=False, **kwargs): - '''MobileNetV2_x0_75 - ''' + """ + 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') @@ -359,8 +578,15 @@ def MobileNetV2_x0_75(pretrained=False, **kwargs): def MobileNetV2_x1_5(pretrained=False, **kwargs): - '''MobileNetV2_x1_5 - ''' + """ + 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') @@ -369,8 +595,15 @@ def MobileNetV2_x1_5(pretrained=False, **kwargs): def MobileNetV2_x2_0(pretrained=False, **kwargs): - '''MobileNetV2_x2_0 - ''' + """ + 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') @@ -379,8 +612,15 @@ def MobileNetV2_x2_0(pretrained=False, **kwargs): def MobileNetV3_large_x0_35(pretrained=False, **kwargs): - '''MobileNetV3_large_x0_35 - ''' + """ + 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') @@ -389,8 +629,15 @@ def MobileNetV3_large_x0_35(pretrained=False, **kwargs): def MobileNetV3_large_x0_5(pretrained=False, **kwargs): - '''MobileNetV3_large_x0_5 - ''' + """ + 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') @@ -399,8 +646,15 @@ def MobileNetV3_large_x0_5(pretrained=False, **kwargs): def MobileNetV3_large_x0_75(pretrained=False, **kwargs): - '''MobileNetV3_large_x0_75 - ''' + """ + 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') @@ -409,8 +663,15 @@ def MobileNetV3_large_x0_75(pretrained=False, **kwargs): def MobileNetV3_large_x1_0(pretrained=False, **kwargs): - '''MobileNetV3_large_x1_0 - ''' + """ + 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') @@ -419,8 +680,15 @@ def MobileNetV3_large_x1_0(pretrained=False, **kwargs): def MobileNetV3_large_x1_25(pretrained=False, **kwargs): - '''MobileNetV3_large_x1_25 - ''' + """ + 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') @@ -429,8 +697,15 @@ def MobileNetV3_large_x1_25(pretrained=False, **kwargs): def MobileNetV3_small_x0_35(pretrained=False, **kwargs): - '''MobileNetV3_small_x0_35 - ''' + """ + 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') @@ -439,8 +714,15 @@ def MobileNetV3_small_x0_35(pretrained=False, **kwargs): def MobileNetV3_small_x0_5(pretrained=False, **kwargs): - '''MobileNetV3_small_x0_5 - ''' + """ + 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') @@ -449,8 +731,15 @@ def MobileNetV3_small_x0_5(pretrained=False, **kwargs): def MobileNetV3_small_x0_75(pretrained=False, **kwargs): - '''MobileNetV3_small_x0_75 - ''' + """ + 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') @@ -459,8 +748,15 @@ def MobileNetV3_small_x0_75(pretrained=False, **kwargs): def MobileNetV3_small_x1_0(pretrained=False, **kwargs): - '''MobileNetV3_small_x1_0 - ''' + """ + 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') @@ -469,8 +765,15 @@ def MobileNetV3_small_x1_0(pretrained=False, **kwargs): def MobileNetV3_small_x1_25(pretrained=False, **kwargs): - '''MobileNetV3_small_x1_25 - ''' + """ + 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') @@ -480,8 +783,15 @@ def MobileNetV3_small_x1_25(pretrained=False, **kwargs): def ResNeXt101_32x4d(pretrained=False, **kwargs): - '''ResNeXt101_32x4d - ''' + """ + 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') @@ -490,8 +800,15 @@ def ResNeXt101_32x4d(pretrained=False, **kwargs): def ResNeXt101_64x4d(pretrained=False, **kwargs): - '''ResNeXt101_64x4d - ''' + """ + 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') @@ -500,8 +817,15 @@ def ResNeXt101_64x4d(pretrained=False, **kwargs): def ResNeXt152_32x4d(pretrained=False, **kwargs): - '''ResNeXt152_32x4d - ''' + """ + 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') @@ -510,8 +834,15 @@ def ResNeXt152_32x4d(pretrained=False, **kwargs): def ResNeXt152_64x4d(pretrained=False, **kwargs): - '''ResNeXt152_64x4d - ''' + """ + 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') @@ -520,8 +851,15 @@ def ResNeXt152_64x4d(pretrained=False, **kwargs): def ResNeXt50_32x4d(pretrained=False, **kwargs): - '''ResNeXt50_32x4d - ''' + """ + 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') @@ -530,8 +868,15 @@ def ResNeXt50_32x4d(pretrained=False, **kwargs): def ResNeXt50_64x4d(pretrained=False, **kwargs): - '''ResNeXt50_64x4d - ''' + """ + 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') -- GitLab