HardSwish. The effect of replacing ReLU with HardSwish is similar to that of BlurPool, that although the training loss is lower (not as low as BlurPool though), the validation loss is very similar. I believe the same explanation applies to swish activation. (Bells & …
25.02.2021 · It seems that the nn.Hardswish caused this problem, actually the nightly version of PyTorch has addressed this problem, so the unit-test is passed. If you are using PyTorch 1.7.x, you can replace it to an export friendly version of Hardswish as …
Hard Swish. Introduced by Howard et al. in Searching for MobileNetV3. Edit. Hard Swish is a type of activation function based on Swish, but replaces the computationally expensive sigmoid with a piecewise linear analogue: h-swish ( x) = x ReLU6 ( x + 3) 6. Source: Searching for MobileNetV3. Read Paper See Code.
Hardswish¶ class torch.nn.quantized. Hardswish (scale, zero_point) [source] ¶ This is the quantized version of Hardswish. Parameters. scale – quantization scale of the output tensor. zero_point – quantization zero point of the output tensor