Hi Shree, thanks for your answer.
I tried the script setting:
TESSDATA=extracted # here I have the eng.lstm and
eng.trainedata
LANGDATA=langdata-master # all langdata downladed by OCR-D
MODEL_NAME = eng
CONTINUE_FROM = eng
First I run the old Makefile to create the boxes.
$ make training MODEL_NAME=eng
I stop it as soon as the training starts:
At iteration 400/400/400, Mean rms=6.657%, delta=40.765%, char
train=100.827%, word train=100%, skip ratio=0%, New worst char error =
100.827 wrote checkpoint.
At iteration 500/500/500, Mean rms=6.644%, delta=40.423%, char
train=100.662%, word train=100%, skip ratio=0%, New worst char error =
100.662 wrote checkpoint.
^Cmake: *** Deleting file 'data/checkpoints/eng_checkpoint'
Makefile:110: recipe for target 'data/checkpoints/eng_checkpoint' failed
make: *** [data/checkpoints/eng_checkpoint] Interrupt
Notice that the data/checkpoints/eng_checkpoint file is deleted, I do not
know if it is relevant or not.
then I switch to the new one and I get this:
$ make training
mkdir -p data/checkpoints
lstmtraining \
--continue_from extracted/eng.lstm \
--old_traineddata extracted/eng.traineddata \
--traineddata data/eng/eng.traineddata \
--model_output data/checkpoints/eng \
--debug_interval -1 \
--train_listfile data/list.train \
--eval_listfile data/list.eval \
--sequential_training \
--max_iterations 3000
Loaded file extracted/eng.lstm, unpacking...
Warning: LSTMTrainer deserialized an LSTMRecognizer!
Code range changed from 111 to 76!
Num (Extended) outputs,weights in Series:
1,36,0,1:1, 0
Num (Extended) outputs,weights in Series:
C3,3:9, 0
Ft16:16, 160
Total weights = 160
[C3,3Ft16]:16, 160
Mp3,3:16, 0
Lfys64:64, 20736
Lfx96:96, 61824
Lrx96:96, 74112
Lfx512:512, 1247232
Fc76:76, 0
Total weights = 1404064
Previous null char=110 mapped to 75
Continuing from extracted/eng.lstm
Loaded 1/1 pages (1-1) of document
data/train/mueller_waldhornist_1821_0130_010.lstmf
Loaded 1/1 pages (1-1) of document
data/train/bismarck_erinnerungen02_1898_0274_002.lstmf
Loaded 1/1 pages (1-1) of document
data/train/spyri_heidi_1880_0062_005.lstmf
Loaded 1/1 pages (1-1) of document
data/train/novalis_ofterdingen_1802_0210_001.lstmf
Iteration 0: ALIGNED TRUTH : Sparoͤfen kauft' ich auch und Sorgenstuͤhle,
Iteration 0: BEST OCR TEXT : l bd o D V fc ds ft hs D t' dsu PM )k ,„cGs D
t' D„Gs 'A AKG„9„t d tft ü!Vt Eb ht Ac )k uF ' K,cGPFVts
File data/train/mueller_waldhornist_1821_0130_010.lstmf page 0 :
!int_mode_:Error:Assert failed:in file weightmatrix.cpp, line 244
!int_mode_:Error:Assert failed:in file weightmatrix.cpp, line 244
Makefile:113: recipe for target 'data/checkpoints/eng_checkpoint' failed
make: *** [data/checkpoints/eng_checkpoint] Segmentation fault
What am I doing wrong?
Lorenzo
2018-06-29 14:08 GMT+02:00 Shree Devi Kumar <shreesh...@gmail.com>:
> I modified the makefile for ocrd-train to do fine-tuning. It is pasted
> below:
>
> export
>
> SHELL := /bin/bash
> LOCAL := $(PWD)/usr
> PATH := $(LOCAL)/bin:$(PATH)
> HOME := /home/ubuntu
> TESSDATA = $(HOME)/tessdata_best
> LANGDATA = $(HOME)/langdata
>
> # Name of the model to be built
> MODEL_NAME = frk
>
> # Name of the model to continue from
> CONTINUE_FROM = frk
>
> # Normalization Mode - see src/training/language_specific.sh for details
> NORM_MODE = 2
>
> # Tesseract model repo to use. Default: $(TESSDATA_REPO)
> TESSDATA_REPO = _best
>
> # Train directory
> TRAIN := data/train
>
> # BEGIN-EVAL makefile-parser --make-help Makefile
>
> help:
> @echo ""
> @echo " Targets"
> @echo ""
> @echo " unicharset Create unicharset"
> @echo " lists Create lists of lstmf filenames for training
> and eval"
> @echo " training Start training"
> @echo " proto-model Build the proto model"
> @echo " leptonica Build leptonica"
> @echo " tesseract Build tesseract"
> @echo " tesseract-langs Download tesseract-langs"
> @echo " langdata Download langdata"
> @echo " clean Clean all generated files"
> @echo ""
> @echo " Variables"
> @echo ""
> @echo " MODEL_NAME Name of the model to be built"
> @echo " CORES No of cores to use for compiling
> leptonica/tesseract"
> @echo " LEPTONICA_VERSION Leptonica version. Default:
> $(LEPTONICA_VERSION)"
> @echo " TESSERACT_VERSION Tesseract commit. Default:
> $(TESSERACT_VERSION)"
> @echo " LANGDATA_VERSION Tesseract langdata version. Default:
> $(LANGDATA_VERSION)"
> @echo " TESSDATA_REPO Tesseract model repo to use. Default:
> $(TESSDATA_REPO)"
> @echo " TRAIN Train directory"
> @echo " RATIO_TRAIN Ratio of train / eval training data"
>
> # END-EVAL
>
> # Ratio of train / eval training data
> RATIO_TRAIN := 0.90
>
> ALL_BOXES = data/all-boxes
> ALL_LSTMF = data/all-lstmf
>
> # Create unicharset
> unicharset: data/unicharset
>
> # Create lists of lstmf filenames for training and eval
> lists: $(ALL_LSTMF) data/list.train data/list.eval
>
> data/list.train: $(ALL_LSTMF)
> total=`cat $(ALL_LSTMF) | wc -l` \
> no=`echo "$$total * $(RATIO_TRAIN) / 1" | bc`; \
> head -n "$$no" $(ALL_LSTMF) > "$@"
>
> data/list.eval: $(ALL_LSTMF)
> total=`cat $(ALL_LSTMF) | wc -l` \
> no=`echo "($$total - $$total * $(RATIO_TRAIN)) / 1" | bc`; \
> tail -n "+$$no" $(ALL_LSTMF) > "$@"
>
> # Start training
> training: data/$(MODEL_NAME).traineddata
>
> data/unicharset: $(ALL_BOXES)
> combine_tessdata -u $(TESSDATA)/$(CONTINUE_FROM).traineddata
> $(TESSDATA)/$(CONTINUE_FROM).
> unicharset_extractor --output_unicharset "$(TRAIN)/my.unicharset"
> --norm_mode $(NORM_MODE) "$(ALL_BOXES)"
> merge_unicharsets $(TESSDATA)/$(CONTINUE_FROM).lstm-unicharset
> $(TRAIN)/my.unicharset "$@"
> $(ALL_BOXES): $(sort $(patsubst %.tif,%.box,$(wildcard $(TRAIN)/*.tif)))
> find $(TRAIN) -name '*.box' -exec cat {} \; > "$@"
> $(TRAIN)/%.box: $(TRAIN)/%.tif $(TRAIN)/%-gt.txt
> python generate_line_box.py -i "$(TRAIN)/$*.tif" -t "$(TRAIN)/$*-gt.txt" >
> "$@"
>
> $(ALL_LSTMF): $(sort $(patsubst %.tif,%.lstmf,$(wildcard $(TRAIN)/*.tif)))
> find $(TRAIN) -name '*.lstmf' -exec echo {} \; | sort -R -o "$@"
>
> $(TRAIN)/%.lstmf: $(TRAIN)/%.box
> tesseract $(TRAIN)/$*.tif $(TRAIN)/$* --psm 6 lstm.train
>
> # Build the proto model
> proto-model: data/$(MODEL_NAME)/$(MODEL_NAME).traineddata
>
> data/$(MODEL_NAME)/$(MODEL_NAME).traineddata: $(LANGDATA) data/unicharset
> combine_lang_model \
> --input_unicharset data/unicharset \
> --script_dir $(LANGDATA) \
> --words $(LANGDATA)/$(MODEL_NAME)/$(MODEL_NAME).wordlist \
> --numbers $(LANGDATA)/$(MODEL_NAME)/$(MODEL_NAME).numbers \
> --puncs $(LANGDATA)/$(MODEL_NAME)/$(MODEL_NAME).punc \
> --output_dir data/ \
> --lang $(MODEL_NAME)
>
> data/checkpoints/$(MODEL_NAME)_checkpoint: unicharset lists proto-model
> mkdir -p data/checkpoints
> lstmtraining \
> --continue_from $(TESSDATA)/$(CONTINUE_FROM).lstm \
> --old_traineddata $(TESSDATA)/$(CONTINUE_FROM).traineddata \
> --traineddata data/$(MODEL_NAME)/$(MODEL_NAME).traineddata \
> --model_output data/checkpoints/$(MODEL_NAME) \
> --debug_interval -1 \
> --train_listfile data/list.train \
> --eval_listfile data/list.eval \
> --sequential_training \
> --max_iterations 3000
>
> data/$(MODEL_NAME).traineddata: data/checkpoints/$(MODEL_NAME)_checkpoint
> lstmtraining \
> --stop_training \
> --continue_from $^ \
> --old_traineddata $(TESSDATA)/$(CONTINUE_FROM).traineddata \
> --traineddata data/$(MODEL_NAME)/$(MODEL_NAME).traineddata \
> --model_output $@
>
> # Clean all generated files
> clean:
> find data/train -name '*.box' -delete
> find data/train -name '*.lstmf' -delete
> rm -rf data/all-*
> rm -rf data/list.*
> rm -rf data/$(MODEL_NAME)
> rm -rf data/unicharset
> rm -rf data/checkpoints
>
> On Fri, Jun 29, 2018 at 5:31 PM Lorenzo Bolzani <l.bolz...@gmail.com>
> wrote:
>
>>
>>
>> Hi,
>> I'm trying to do fine tuning of an existing model using line images and
>> text labels. I'm running this version:
>>
>> tesseract 4.0.0-beta.3-56-g5fda
>> leptonica-1.76.0
>> libgif 5.1.4 : libjpeg 8d (libjpeg-turbo 1.4.2) : libpng 1.2.54 :
>> libtiff 4.0.6 : zlib 1.2.8 : libwebp 0.4.4 : libopenjp2 2.3.0
>> Found AVX2
>> Found AVX
>> Found SSE
>>
>>
>>
>> I used OCR-D to generate lstmf files for the demo data.
>>
>> If I run the make command it works fine.
>>
>> make training MODEL_NAME=prova
>>
>> Now I isolated this command from the build:
>>
>> lstmtraining \
>> --traineddata data/prova/prova.traineddata \
>> --net_spec "[1,36,0,1 Ct3,3,16 Mp3,3 Lfys48 Lfx96 Lrx96 Lfx256 O1c`head
>> -n1 data/unicharset`]" \
>> --model_output data/checkpoints/prova \
>> --learning_rate 20e-4 \
>> --train_listfile data/list.train \
>> --eval_listfile data/list.eval \
>> --max_iterations 10000
>>
>> and it works fine.
>>
>> Now I'm trying to modify it to fine tune the existing eng model. I made a
>> few attempts, all ending into different errors (see the attached file for
>> full output).
>>
>> I used:
>>
>> combine_tessdata -e /usr/local/share/tessdata/eng.traineddata
>> extracted/eng.lstm
>>
>> to extract the eng.lstm model.
>>
>> This seems to works but I'm not sure it is the correct.
>>
>> lstmtraining \
>> --continue_from extracted/eng.lstm \
>> --traineddata data/prova/prova.traineddata \
>> --old_traineddata extracted/eng.traineddata \
>> --model_output data/checkpoints/prova \
>> --learning_rate 20e-4 \
>> --train_listfile data/list.train \
>> --eval_listfile data/list.eval \
>> --max_iterations 10000
>>
>> (extracted/eng.traineddata is just a copy of eng.traineddata)
>>
>>
>> The training resume exactly with the RMS of prova_checkpoint (6%) so it
>> looks like it is training from that checkpoint, not the eng.lstm.
>>
>> Is this correct? What should I change?
>>
>> I'm following this guide:
>>
>> https://github.com/tesseract-ocr/tesseract/wiki/
>> TrainingTesseract-4.00#fine-tuning-for--a-few-characters
>>
>>
>> I think continue_from and traineddata should refer to the eng model and
>> old_traineddata should point to prova.traineddata, but if I do that I get a
>> segmentation fault:
>>
>> [...]
>> !int_mode_:Error:Assert failed:in file weightmatrix.cpp, line 244
>> !int_mode_:Error:Assert failed:in file weightmatrix.cpp, line 244
>> Segmentation fault
>>
>> What am I missing?
>>
>>
>> Thanks, bye
>>
>> Lorenzo
>>
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>
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