SuperPowerful CRNN and SuperLight CRNN_Tiny

1. Refactoring the CRNN architecture based on the reference paper, improving ChineseLicensePlate testset by nearly 7 points compared to v0.3.0;
2. We have achieved a super lightweight CRNN_Tiny, which reduces the model size by at least 14 times and increases inference speed by about 3 times compared to the CRNN model;
3. Fixed the mixed-precision-training bug in train_plate.py.

Model	ARCH	Model Size (MB)	EMNIST Accuracy (%)	Training Data	Testing Data
CRNN	CONV+GRU	31	98.546	100,000	5,000
CRNN_Tiny	CONV+GRU	1.7	98.396	100,000	5,000

Model	ARCH	Model Size (MB)	ChineseLicensePlate Accuracy (%)	Training Data	Testing Data
CRNN	CONV+GRU	58	82.379	269,621	149,002
CRNN_Tiny	CONV+GRU	4	76.222	269,621	149,002