From 8006c39d83daf47fa9f6ac97479fcbe6e686a302 Mon Sep 17 00:00:00 2001
From: s444415 <mickoz5@st.amu.edu.pl>
Date: Tue, 3 Jan 2023 13:15:05 +0100
Subject: [PATCH] config edit, dataset create script, donut train config edit

---
 config.yaml => config-eval.yaml |   0
 dataset_create.ipynb            | 822 ++++++++++++++++++++++++++++++++
 donut-train.py                  |  12 +-
 3 files changed, 828 insertions(+), 6 deletions(-)
 rename config.yaml => config-eval.yaml (100%)
 create mode 100644 dataset_create.ipynb

diff --git a/config.yaml b/config-eval.yaml
similarity index 100%
rename from config.yaml
rename to config-eval.yaml
diff --git a/dataset_create.ipynb b/dataset_create.ipynb
new file mode 100644
index 0000000..4fbcd73
--- /dev/null
+++ b/dataset_create.ipynb
@@ -0,0 +1,822 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from huggingface_hub import login\n",
+    "from datasets import load_dataset\n",
+    "import os\n",
+    "import json\n",
+    "import shutil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0476002f8d14822a24f1376cfe29a07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "login(os.environ.get(\"HUG_TOKKEN\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train = pd.read_csv('../fiszki-ocr/train/in.tsv', sep='\\t', header=None, index_col=False)\n",
+    "files = [file[0] for file in df_train.iloc()]\n",
+    "df_train_out = pd.read_csv('../fiszki-ocr/train/expected.tsv', sep='\\t', header=None, index_col=False)\n",
+    "files_out = [file_out[0] for file_out in df_train_out.iloc()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "whole = []\n",
+    "for file, out in zip(files, files_out):\n",
+    "        whole.append({\"file_name\": file, \"ground_truth\": json.dumps({\"gt_parse\": {\"text_sequance\": out}}, ensure_ascii=False)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = whole[:85]\n",
+    "validation = whole[85:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_files = [file.get(\"file_name\") for file in train]\n",
+    "validation_files = [file.get(\"file_name\") for file in validation]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for image in os.listdir(\"../fiszki-ocr/images\"):\n",
+    "    if image in train_files:\n",
+    "        shutil.copy(f\"/home/pc/work/fiszki-ocr/images/{image}\", f\"./images-split-fiszki/train/{image}\")\n",
+    "    if image in validation_files:\n",
+    "        shutil.copy(f\"/home/pc/work/fiszki-ocr/images/{image}\", f\"./images-split-fiszki/validation/{image}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "with open('./images-split-fiszki/train/metadata.jsonl', 'w', encoding='utf-8') as f:\n",
+    "    for entry in train:\n",
+    "        json.dump(entry, f, ensure_ascii=False)\n",
+    "        f.write(\"\\n\")\n",
+    "with open('./images-split-fiszki/validation/metadata.jsonl', 'w', encoding='utf-8') as f:\n",
+    "    for entry in validation:\n",
+    "        json.dump(entry, f, ensure_ascii=False)\n",
+    "        f.write(\"\\n\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca154573c11a44a8a1fa7dede4c54e26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Resolving data files:   0%|          | 0/86 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using custom data configuration images-split-fiszki-0b6e02834f7867a1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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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+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3db88be1336b4ea0b03638761a6d69e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96090b6f92eb46be8b44dde7d96f225a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "419401799c864422b9669c66c44159bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating validation split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "835ebdd301dc469dbb0ad6f1838403a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset = load_dataset('./images-split-fiszki')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pushing split train to the Hub.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a92c7f7732054b479a26b1f32621cf20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d934bd92d4af41e492ecac230b635903",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pushing split validation to the Hub.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd0c40c587e84870ad9c3089d401b80a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eeac023e001349a48133da21f7656378",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "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
+}
diff --git a/donut-train.py b/donut-train.py
index 7ba98f0..95dee08 100644
--- a/donut-train.py
+++ b/donut-train.py
@@ -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"),