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class paddle.vision.models. ShuffleNetV2 ( scale: float = 1.0, act: _ActivationType | None = 'relu', num_classes: int = 1000, with_pool: bool = True ) [source]
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ShuffleNetV2 model from “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”.

Parameters

  • scale (float, optional) – Scale of output channels. Default: True.

  • act (str, optional) – Activation function of neural network. Default: “relu”.

  • num_classes (int, optional) – Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000.

  • with_pool (bool, optional) – Use pool before the last fc layer or not. Default: True.

Returns

Layer. An instance of ShuffleNetV2 model.

Examples

>>> import paddle
>>> from paddle.vision.models import ShuffleNetV2

>>> shufflenet_v2_swish = ShuffleNetV2(scale=1.0, act="swish")
>>> x = paddle.rand([1, 3, 224, 224])
>>> out = shufflenet_v2_swish(x)
>>> print(out.shape)
[1, 1000]
forward ( inputs: Tensor ) → Tensor

forward?

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments