ShuffleNetV2¶
- class paddle.vision.models. ShuffleNetV2 ( scale=1.0, act='relu', num_classes=1000, with_pool=True ) [source]
-
ShuffleNetV2 model from “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”
- Parameters
-
scale (float, optional) – True.
act (str, optional) – “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.
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)
-
forward
(
inputs
)
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