micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape
def updateBN():
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
if hasattr(m.weight, 'data'):
m.weight.grad.data.add_(args.s*torch.sign(m.weight.data))
ly0303521
commented
3 years ago
想問為什麼 這段是出自那兩篇paper哪一段 目前看L1正則都是取絕對值 而不是直接加梯度