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The models subpackage contains backbones extracting features for sementic segmentation models.
ResNet_vd backbone from "Bag of Tricks for Image Classification with Convolutional Neural Networks"
class paddleseg.models.backbones.Resnet_vd(
layers = 50,
output_stride = None,
multi_grid = (1, 1, 1),
lr_mult_list = (0.1, 0.1, 0.2, 0.2),
pretrained = None
)- layers (int, optional): The layers of ResNet_vd. The supported layers are [18, 34, 50, 101, 152, 200]. Default: 50
- output_stride (int, optional): Compared with the input image, the zoom stride of the output feature, this parameter will affect the downsampling multiple.This parameter should be 8 or 16. It is 8 or 16. Default: 8
- multi_grid (tuple|list, optional): The grid of stage4. Used to expand the receptive field of convolution. Defult: (1, 1, 1)
- pretrained (str, optional): The path of pretrained model.
paddleseg.models.backbones.ResNet18_vd(**args)Return a object of ResNet_vd class which layers is 18.
paddleseg.models.backbones.ResNet34_vd(**args)Return a object of ResNet_vd class which layers is 34.
paddleseg.models.backbones.ResNet50_vd(**args)Return a object of ResNet_vd class which layers is 50.
paddleseg.models.backbones.ResNet101_vd(**args)Return a object of ResNet_vd class which layers is 101.
paddleseg.models.backbones.ResNet152_vd(**args)Return a object of ResNet_vd class which layers is 152.
padddelseg.models.backbones.ResNet200_vd(**args)Return a object of ResNet_vd class which layers is 200.
HRNet backbone from "HRNet：Deep High-Resolution Representation Learning for Visual Recognition"
class paddleseg.models.backbones.HRNet(
pretrained = None,
stage1_num_modules = 1,
stage1_num_blocks = (4,),
stage1_num_channels = (64,),
stage2_num_modules = 1,
stage2_num_blocks = (4, 4),
stage2_num_channels = (18, 36),
stage3_num_modules = 4,
stage3_num_blocks = (4, 4, 4),
stage3_num_channels = (18, 36, 72),
stage4_num_modules = 3,
stage4_num_blocks = (4, 4, 4, 4),
stage4_num_channels = (18, 36, 72, 14),
has_se = False,
align_corners = False
)- pretrained (str, optional): The path of pretrained model.
- stage1_num_modules (int, optional): Number of modules for stage1. Default: 1
- stage1_num_blocks (list, optional): Number of blocks per module for stage1.Default: (4,)
- stage1_num_channels (list, optional): Number of channels per branch for stage1. Default: (64,)
- stage2_num_modules (int, optional): Number of modules for stage2. Default 1
- stage2_num_blocks (list, optional): Number of blocks per module for stage2. Default: (4, 4)
- stage2_num_channels (list, optional): Number of channels per branch for stage2. Default: (18, 36)
- stage3_num_modules (int, optional): Number of modules for stage3. Default 4
- stage3_num_blocks (list, optional): Number of blocks per module for stage3. Default: (4, 4, 4)
- stage3_num_channels (list, optional): Number of channels per branch for stage3. Default: (18, 36, 72)
- stage4_num_modules (int, optional): Number of modules for stage4. Default 3
- stage4_num_blocks (list, optional): Number of blocks per module for stage4. Default: (4, 4, 4, 4)
- stage4_num_channels (list, optional): Number of channels per branch for stage4. Default: (18, 36, 72, 144)
- has_se (bool, optional): Whether to use Squeeze-and-Excitation module. Default False
- align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size is even, e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False
paddleseg.models.backbones.HRNet_W18_Small_V1(**kwargs)Return a object of HRNet class which width is 18 and it is smaller than HRNet_W18_Small_V2.
paddleseg.models.backbones.HRNet_W18_Small_V2(**kwargs)Return a object of HRNet class which width is 18 and it is smaller than HRNet_W18.
paddleseg.models.backbones.HRNet_W18(**kwargs)Return a object of HRNet class which width is 18.
paddleseg.models.backbones.HRNet_W30(**kwargs)Return a object of HRNet class which width is 30.
paddleseg.models.backbones.HRNet_W32(**kwargs)Return a object of HRNet class which width is 32.
paddleseg.models.backbones.HRNet_W40(**kwargs)Return a object of HRNet class which width is 40.
paddleseg.models.backbones.HRNet_W44(**kwargs)Return a object of HRNet class which width is 44.
paddleseg.models.backbones.HRNet_W48(**kwargs)Return a object of HRNet class which width is 48.
paddleseg.models.backbones.HRNet_W60(**kwargs)Return a object of HRNet class which width is 60.
paddleseg.models.backbones.HRNet_W64(**kwargs)Return a object of HRNet class which width is 64.
MobileNetV3 backbone from "Searching for MobileNetV3".
class paddleseg.models.backbones.MobileNetV3(
pretrained = None,
scale = 1.0,
model_name = "small",
output_stride = None
)- pretrained (str, optional): The path of pretrained model.
- scale (float, optional): The scale of channels. Recommendation: Compared with the small model, set a higher scale for the large model. Default: 1.0
- model_name (str, optional): Model name. It determines the type of MobileNetV3. The value is 'small' or 'large'. Defualt: 'small'
- output_stride (int, optional): The stride of output features compared to input images. The value should be one of [2, 4, 8, 16, 32]. Default: None
paddleseg.models.backbones.MobileNetV3_small_x0_35(**args)Return a object of MobileNetV3 class which scale is 0.35 and model_name is small.
paddleseg.models.backbones.MobileNetV3_small_x0_5(**args)Return a object of MobileNetV3 class which scale is 0.5 and model_name is small.
paddleseg.models.backbones.MobileNetV3_small_x0_75(**args)Return a object of MobileNetV3 class which scale is 0.75 and model_name is small.
paddleseg.models.backbones.MobileNetV3_small_x1_0(**args)Return a object of MobileNetV3 class which scale is 1.0 and model_name is small.
paddleseg.models.backbones.MobileNetV3_small_x1_25(**args)Return a object of MobileNetV3 class which scale is 1.25 and model_name is small.
paddleseg.models.backbones.MobileNetV3_large_x0_35(**args)Return a object of MobileNetV3 class which scale is 0.35 and model_name is large.
paddleseg.models.backbones.MobileNetV3_large_x0_5(**args)Return a object of MobileNetV3 class which scale is 0.5 and model_name is large.
paddleseg.models.backbones.MobileNetV3_large_x0_75(**args)Return a object of MobileNetV3 class which scale is 0.75 and model_name is large.
paddleseg.models.backbones.MobileNetV3_large_x1_0(**args)Return a object of MobileNetV3 class which scale is 1.0 and model_name is large.
paddleseg.models.backbones.MobileNetV3_large_x1_25(**args)Return a object of MobileNetV3 class which scale is 1.25 and model_name is large.
Xception backbone of DeepLabV3+ from "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation"
class paddleseg.models.backbones.XceptionDeeplab(
backbone,
pretrained = None,
output_stride = 16
)- backbone (str): Which type of Xception_DeepLab to select. It should be one of ('xception_41', 'xception_65', 'xception_71').
- pretrained (str, optional): The path of pretrained model.
- output_stride (int, optional): The stride of output features compared to input images. It is 8 or 16. Default: 16
paddleseg.models.backbones.Xception41_deeplab(**args)Return a object of XceptionDeeplab class which layers is 41.
paddleseg.models.backbones.Xception65_deeplab(**args)Return a object of XceptionDeeplab class which layers is 65.
paddleseg.models.backbones.Xception71_deeplab(**args)Return a object of XceptionDeeplab class which layers is 71.