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
很感谢大佬提供的代码。请问采用迁移的方式改变网络之后，训练的配置还是和量化之前的配置一样嘛，比如反向传播的时候需不要采用STE方式呢？我训练的配置还是用的量化前的配置，但是我用量化之后的网络加载我自己已经训练好的模型参数并进行训练的时候，并没有很快就收敛，这种情况正常嘛？