Evolving Normalization-Activation Layers

Google AI and DeepMind

• Implement EvoNorm S0 and B0 with Training Mode support
• Solve Shape Error with group_std and instance_std functions
• Solve NaN Error Issue with S0
• Fix Error with shape in running variance calculation in EvoNorm B0
• Solve NaN Error Issue with B0

Figure 1. Left: Computation graph of a searched normalization activation layer that is batch-independent, named EvoNorm-S0. Right: ResNet-50 results with EvoNorm-S0 as the batch size over 8 workers varies from 1024 to 32 on ImageNet. EvoNorm-S0 also outperforms both BN and GN-based layers on MobileNetV2 and Mask R-CNN.

Usage:

from evonorm2d import EvoNorm2D
# For B0 version
evoB0 = EvoNorm2D(input, affine = True, version = 'B0', training = True)

# For S0 version 
evoS0 = EvoNorm2D(input)