config edit, dataset create script, donut train config edit
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dataset_create.ipynb
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822
dataset_create.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from huggingface_hub import login\n",
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"from datasets import load_dataset\n",
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"import os\n",
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"import json\n",
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"import shutil"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f0476002f8d14822a24f1376cfe29a07",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"login(os.environ.get(\"HUG_TOKKEN\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_train = pd.read_csv('../fiszki-ocr/train/in.tsv', sep='\\t', header=None, index_col=False)\n",
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"files = [file[0] for file in df_train.iloc()]\n",
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"df_train_out = pd.read_csv('../fiszki-ocr/train/expected.tsv', sep='\\t', header=None, index_col=False)\n",
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"files_out = [file_out[0] for file_out in df_train_out.iloc()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"whole = []\n",
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"for file, out in zip(files, files_out):\n",
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" whole.append({\"file_name\": file, \"ground_truth\": json.dumps({\"gt_parse\": {\"text_sequance\": out}}, ensure_ascii=False)})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"train = whole[:85]\n",
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"validation = whole[85:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_files = [file.get(\"file_name\") for file in train]\n",
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"validation_files = [file.get(\"file_name\") for file in validation]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"for image in os.listdir(\"../fiszki-ocr/images\"):\n",
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" if image in train_files:\n",
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" shutil.copy(f\"/home/pc/work/fiszki-ocr/images/{image}\", f\"./images-split-fiszki/train/{image}\")\n",
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" if image in validation_files:\n",
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" shutil.copy(f\"/home/pc/work/fiszki-ocr/images/{image}\", f\"./images-split-fiszki/validation/{image}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"with open('./images-split-fiszki/train/metadata.jsonl', 'w', encoding='utf-8') as f:\n",
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" for entry in train:\n",
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" json.dump(entry, f, ensure_ascii=False)\n",
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" f.write(\"\\n\")\n",
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"with open('./images-split-fiszki/validation/metadata.jsonl', 'w', encoding='utf-8') as f:\n",
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" for entry in validation:\n",
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" json.dump(entry, f, ensure_ascii=False)\n",
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" f.write(\"\\n\")\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"model_id": "ca154573c11a44a8a1fa7dede4c54e26",
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"version_major": 2,
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"version_minor": 0
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"output_type": "display_data"
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using custom data configuration images-split-fiszki-0b6e02834f7867a1\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading and preparing dataset imagefolder/images-split-fiszki to /home/pc/.cache/huggingface/datasets/imagefolder/images-split-fiszki-0b6e02834f7867a1/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...\n",
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{
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"name": "stdout",
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"output_type": "stream",
|
||||
"text": [
|
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"Dataset imagefolder downloaded and prepared to /home/pc/.cache/huggingface/datasets/imagefolder/images-split-fiszki-0b6e02834f7867a1/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f. Subsequent calls will reuse this data.\n"
|
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]
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"data": {
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|
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}
|
||||
],
|
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"source": [
|
||||
"dataset = load_dataset('./images-split-fiszki')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
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"text": [
|
||||
"Pushing split train to the Hub.\n"
|
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]
|
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|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Pushing split validation to the Hub.\n"
|
||||
]
|
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},
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"data": {
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|
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{
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|
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|
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|
||||
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|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dataset.push_to_hub(\"Zombely/fiszki-ocr-train\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "hug_donut",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.15 (main, Nov 4 2022, 16:13:54) \n[GCC 11.2.0]"
|
||||
},
|
||||
"orig_nbformat": 4,
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "8f1c1b41577d000ca6512e75d22d324bbd1d5e060e99f4f49d98cf0adf636690"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -21,17 +21,17 @@ from pytorch_lightning.plugins import CheckpointIO
|
||||
|
||||
|
||||
|
||||
DATASET_PATH = "Zombely/pl-text-images-5000-whole"
|
||||
DATASET_PATH = "Zombely/fiszki-ocr-train"
|
||||
PRETRAINED_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
|
||||
START_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned"
|
||||
OUTPUT_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
|
||||
LOGGING_PATH = "plwiki-proto-ft-second-iter"
|
||||
START_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
|
||||
OUTPUT_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v3"
|
||||
LOGGING_PATH = "fiszki-ocr-fine-tune"
|
||||
CHECKPOINT_PATH = "./checkpoint"
|
||||
|
||||
|
||||
train_config = {
|
||||
"max_epochs":1,
|
||||
"val_check_interval":1.0, # how many times we want to validate during an epoch
|
||||
"val_check_interval":0.5, # how many times we want to validate during an epoch
|
||||
"check_val_every_n_epoch":1,
|
||||
"gradient_clip_val":1.0,
|
||||
"num_training_samples_per_epoch": 800,
|
||||
@ -362,7 +362,7 @@ custom_ckpt = CustomCheckpointIO()
|
||||
|
||||
|
||||
trainer = pl.Trainer(
|
||||
accelerator="gpu", # change to gpu
|
||||
accelerator="gpu" if torch.cuda.is_available() else 'cpu', # change to gpu
|
||||
devices=1,
|
||||
max_epochs=train_config.get("max_epochs"),
|
||||
val_check_interval=train_config.get("val_check_interval"),
|
||||
|
||||
Loading…
Reference in New Issue
Block a user