# 可取值：['densenet-s']
ENCODER_NAME = densenet_lite_136
# 可取值：['fc', 'gru', 'lstm']
DECODER_NAME = fc
MODEL_NAME = $(ENCODER_NAME)-$(DECODER_NAME)

INDEX_DIR = /data/jinlong/ocr_data
TRAIN_CONFIG_FP = docs/examples/train_config_gpu.json

train:
	CUDA_VISIBLE_DEVICES=1 cnocr train -m $(MODEL_NAME) --index-dir $(INDEX_DIR) --train-config-fp $(TRAIN_CONFIG_FP)

finetune:
	CUDA_VISIBLE_DEVICES=0 cnocr train --finetuning -m densenet_lite_136-gru --index-dir /data/jinlong/ocr_data/finetuning-general --train-config-fp docs/examples/train_config_finetune.json \
	-p runs/CnOCR-Rec/ga6ubc1s/checkpoints/cnocr-v2.2-densenet_lite_136-gru-epoch=029-val-complete_match-epoch=0.7936-model.ckpt

# 训练模型
train-number-pure:
	CUDA_VISIBLE_DEVICES=1 cnocr train -m number-$(MODEL_NAME) --index-dir data/output_number_pure --train-config-fp docs/examples/train_config_number.json

evaluate-number-pure:
	cnocr evaluate -m number-$(MODEL_NAME) -i data/output_number_pure/dev.tsv --image-folder data/output_number_pure --batch-size 128 -c cuda:0 -o eval_results/number-$(MODEL_NAME)

evaluate:
	cnocr evaluate -m $(MODEL_NAME) -i $(REC_DATA_ROOT_DIR)/test-part.txt --image-folder $(REC_DATA_ROOT_DIR) --batch-size 128 -c cuda:0 -o eval_results/$(MODEL_NAME)-$(EPOCH)

filter:
	python scripts/filter_samples.py --sample_file $(REC_DATA_ROOT_DIR)/test-part.txt --badcases_file evaluate/$(MODEL_NAME)-$(EPOCH)/badcases.txt --distance_thrsh 2 -o $(REC_DATA_ROOT_DIR)/new.txt

predict:
	cnocr predict -m $(MODEL_NAME) -f docs/examples/rand_cn1.png



.PHONY: train predict evaluate filter