From 04a4bddd9757182dbd126f592933e642e3c08f6f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=89=E6=B4=8B=E4=B8=89=E6=B4=8B?= <1258009915@qq.com>
Date: Mon, 4 Mar 2024 05:38:30 +0000
Subject: [PATCH 1/5] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E4=BA=86functional.py?=
 =?UTF-8?q?=E4=B8=AD=E7=9A=84tokenize=5Ffn(=E5=9B=BE=E7=89=87=E4=BB=A5png?=
 =?UTF-8?q?=E5=BD=A2=E5=BC=8F=E8=80=8C=E9=9D=9E=E6=95=B0=E7=BB=84=E7=9A=84?=
 =?UTF-8?q?=E5=BD=A2=E5=BC=8F=E4=BF=9D=E5=AD=98=EF=BC=8C=E9=98=B2=E6=AD=A2?=
 =?UTF-8?q?=E5=8D=A0=E7=94=A8=E8=BF=87=E5=A4=9A=E7=A1=AC=E7=9B=98=E7=A9=BA?=
 =?UTF-8?q?=E9=97=B4)?=
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---
 src/models/ocr_model/utils/functional.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/ocr_model/utils/functional.py b/src/models/ocr_model/utils/functional.py
index 92f40c0..969a493 100644
--- a/src/models/ocr_model/utils/functional.py
+++ b/src/models/ocr_model/utils/functional.py
@@ -21,7 +21,7 @@ def left_move(x: torch.Tensor, pad_val):
 def tokenize_fn(samples: Dict[str, List[Any]], tokenizer=None) -> Dict[str, List[Any]]:
     assert tokenizer is not None, 'tokenizer should not be None'
     tokenized_formula = tokenizer(samples['latex_formula'], return_special_tokens_mask=True)
-    tokenized_formula['pixel_values'] = [np.array(sample) for sample in samples['image']]
+    tokenized_formula['pixel_values'] = samples['image']
     return tokenized_formula
 
 

From a65de1154e09139845fb77834a1d6bc40c993ee7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=89=E6=B4=8B=E4=B8=89=E6=B4=8B?= <1258009915@qq.com>
Date: Wed, 6 Mar 2024 04:57:43 +0000
Subject: [PATCH 2/5] =?UTF-8?q?=E5=AE=8C=E6=88=90=E4=BA=867M=E6=95=B0?=
 =?UTF-8?q?=E6=8D=AE=E9=9B=86tokenizer=E7=9A=84=E8=AE=AD=E7=BB=83?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/models/globals.py                         |     2 +-
 .../roberta-tokenizer-7Mformulas/merges.txt   | 14740 ++++++++
 .../special_tokens_map.json                   |    15 +
 .../tokenizer.json                            | 29830 ++++++++++++++++
 .../tokenizer_config.json                     |    57 +
 .../roberta-tokenizer-7Mformulas/vocab.json   |     1 +
 src/models/tokenizer/train/train.py           |     4 +-
 7 files changed, 44646 insertions(+), 3 deletions(-)
 create mode 100644 src/models/tokenizer/roberta-tokenizer-7Mformulas/merges.txt
 create mode 100644 src/models/tokenizer/roberta-tokenizer-7Mformulas/special_tokens_map.json
 create mode 100644 src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer.json
 create mode 100644 src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer_config.json
 create mode 100644 src/models/tokenizer/roberta-tokenizer-7Mformulas/vocab.json

diff --git a/src/models/globals.py b/src/models/globals.py
index 3f1ed56..c84f204 100644
--- a/src/models/globals.py
+++ b/src/models/globals.py
@@ -16,7 +16,7 @@ MAX_WIDTH  = 1280
 TEXIFY_INPUT_DENSITY = 100
 
 # ocr模型的tokenizer中的词典数量
-VOCAB_SIZE = 10000
+VOCAB_SIZE = 15000
 
 # ocr模型是否固定输入图片的大小
 OCR_FIX_SIZE = True
diff --git a/src/models/tokenizer/roberta-tokenizer-7Mformulas/merges.txt b/src/models/tokenizer/roberta-tokenizer-7Mformulas/merges.txt
new file mode 100644
index 0000000..42931b8
--- /dev/null
+++ b/src/models/tokenizer/roberta-tokenizer-7Mformulas/merges.txt
@@ -0,0 +1,14740 @@
+#version: 0.2
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diff --git a/src/models/tokenizer/roberta-tokenizer-7Mformulas/special_tokens_map.json b/src/models/tokenizer/roberta-tokenizer-7Mformulas/special_tokens_map.json
new file mode 100644
index 0000000..d569813
--- /dev/null
+++ b/src/models/tokenizer/roberta-tokenizer-7Mformulas/special_tokens_map.json
@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}
diff --git a/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer.json b/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer.json
new file mode 100644
index 0000000..aca96c2
--- /dev/null
+++ b/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer.json
@@ -0,0 +1,29830 @@
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+      "aF": 11291,
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+      "ĠDataset": 11310,
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+      "980": 11327,
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+      "486": 11448,
+      "}):|": 11449,
+      "_{{}_{[": 11450,
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+      "Ġ{*}}\\": 11452,
+      "port": 11453,
+      "INV": 11454,
+      "Ġconditions": 11455,
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+      "BY": 11457,
+      "RQ": 11458,
+      "oci": 11459,
+      "Ġ}}[": 11460,
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+      "wirt": 11464,
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+      "reen": 11466,
+      "ĠTran": 11467,
+      ")}(|\\": 11468,
+      ")|},\\]": 11469,
+      "}]\\}_{": 11470,
+      "\\!(\\": 11471,
+      ")}}{\\|\\": 11472,
+      "nput": 11473,
+      "Conj": 11474,
+      "}\\,(\\,": 11475,
+      "Index": 11476,
+      "&*&*": 11477,
+      "ĠCost": 11478,
+      "Ġcomplete": 11479,
+      "condition": 11480,
+      ",\\,\\,\\,\\,\\,\\,\\,\\": 11481,
+      "Ġsoft": 11482,
+      "4000": 11483,
+      "cE": 11484,
+      "Ġ)\\,.\\]": 11485,
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+      "egory": 11487,
+      "}]]=": 11488,
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+}
\ No newline at end of file
diff --git a/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer_config.json b/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer_config.json
new file mode 100644
index 0000000..c671e5a
--- /dev/null
+++ b/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer_config.json
@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}
diff --git a/src/models/tokenizer/roberta-tokenizer-7Mformulas/vocab.json b/src/models/tokenizer/roberta-tokenizer-7Mformulas/vocab.json
new file mode 100644
index 0000000..f2b812b
--- /dev/null
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diff --git a/src/models/tokenizer/train/train.py b/src/models/tokenizer/train/train.py
index ab37915..d2e51ae 100644
--- a/src/models/tokenizer/train/train.py
+++ b/src/models/tokenizer/train/train.py
@@ -4,8 +4,8 @@ from ...globals import VOCAB_SIZE
 
 
 if __name__ == '__main__':
-    tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-raw')
+    tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TexTeller/src/models/tokenizer/roberta-tokenizer-raw')
     dataset = load_dataset("/home/lhy/code/TexTeller/src/models/ocr_model/train/data/loader.py")['train']
     new_tokenizer = tokenizer.train_new_from_iterator(text_iterator=dataset['latex_formula'], vocab_size=VOCAB_SIZE)
-    new_tokenizer.save_pretrained('/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-550Kformulas')
+    new_tokenizer.save_pretrained('/home/lhy/code/TexTeller/src/models/tokenizer/roberta-tokenizer-7Mformulas')
     pause = 1

From 93979bddf64ca4a8a5e9c17e4e95daa106923b8a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=89=E6=B4=8B=E4=B8=89=E6=B4=8B?= <1258009915@qq.com>
Date: Wed, 6 Mar 2024 13:59:36 +0000
Subject: [PATCH 3/5] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86bug=EF=BC=9A?=
 =?UTF-8?q?=E5=BD=93=E6=A0=B7=E6=9C=AC=E4=B8=AD=E5=87=BA=E7=8E=B0=E9=9D=9E?=
 =?UTF-8?q?=E5=B8=B8=E9=95=BF=E7=9A=84=E5=85=AC=E5=BC=8F(=E5=AF=B9?=
 =?UTF-8?q?=E5=BA=94=E7=9A=84token=E6=95=B0=E5=8F=AF=E8=83=BD=E8=B6=85?=
 =?UTF-8?q?=E8=BF=872048)=EF=BC=8C=E4=BC=9A=E5=AF=BC=E8=87=B4=E7=BB=99labe?=
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MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/models/globals.py                       |  3 ++-
 src/models/ocr_model/train/train.py         | 27 +++++++++++++++------
 src/models/ocr_model/train/training_args.py | 11 +++++----
 src/models/ocr_model/utils/functional.py    | 11 +++++++--
 4 files changed, 37 insertions(+), 15 deletions(-)

diff --git a/src/models/globals.py b/src/models/globals.py
index c84f204..a797500 100644
--- a/src/models/globals.py
+++ b/src/models/globals.py
@@ -30,7 +30,8 @@ OCR_IMG_MAX_WIDTH  = 768
 OCR_IMG_CHANNELS = 1  # 灰度图
 
 # ocr模型训练数据集的最长token数
-MAX_TOKEN_SIZE = 2048     # 模型最长的embedding长度(默认512)
+MAX_TOKEN_SIZE = 1024     # 模型最长的embedding长度(默认512)
+# MAX_TOKEN_SIZE = 2048     # 模型最长的embedding长度(默认512)
 # MAX_TOKEN_SIZE = 600
 
 # ocr模型训练时随机缩放的比例
diff --git a/src/models/ocr_model/train/train.py b/src/models/ocr_model/train/train.py
index a4edc9c..40652ee 100644
--- a/src/models/ocr_model/train/train.py
+++ b/src/models/ocr_model/train/train.py
@@ -9,13 +9,24 @@ from transformers import Trainer, TrainingArguments, Seq2SeqTrainer, Seq2SeqTrai
 
 from .training_args import CONFIG
 from ..model.TexTeller import TexTeller
-from ..utils.functional import tokenize_fn, collate_fn, img_transform_fn
+from ..utils.functional import tokenize_fn, collate_fn, img_transform_fn, filter_fn
 from ..utils.metrics import bleu_metric
 from ...globals import MAX_TOKEN_SIZE, MIN_WIDTH, MIN_HEIGHT   
 
 
 def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer):
     training_args = TrainingArguments(**CONFIG)
+    debug_mode = True
+    if debug_mode:
+        training_args.auto_find_batch_size = False
+        training_args.num_train_epochs = 2
+        training_args.per_device_train_batch_size = 2
+        training_args.per_device_eval_batch_size = 2 * training_args.per_device_train_batch_size
+        training_args.jit_mode_eval = False
+        training_args.torch_compile = False
+        training_args.dataloader_num_workers = 1
+
+    
     trainer = Trainer(
         model,
         training_args,
@@ -34,7 +45,7 @@ def evaluate(model, tokenizer, eval_dataset, collate_fn):
     eval_config = CONFIG.copy()
     eval_config['predict_with_generate'] = True
     generate_config = GenerationConfig(
-        max_new_tokens=MAX_TOKEN_SIZE,
+        max_length=MAX_TOKEN_SIZE-200,
         num_beams=1,
         do_sample=False,
         pad_token_id=tokenizer.pad_token_id,
@@ -67,23 +78,25 @@ if __name__ == '__main__':
     dataset = load_dataset(
         '/home/lhy/code/TexTeller/src/models/ocr_model/train/data/loader.py'
     )['train']
-    dataset = dataset.filter(lambda x: x['image'].height > MIN_HEIGHT and x['image'].width > MIN_WIDTH)
+    tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TexTeller/src/models/tokenizer/roberta-tokenizer-7Mformulas')
+    filter_fn_with_tokenizer = partial(filter_fn, tokenizer=tokenizer)
+
+    # dataset = dataset.filter(lambda x: x['image'].height > MIN_HEIGHT and x['image'].width > MIN_WIDTH)
+    dataset = dataset.filter(filter_fn_with_tokenizer, num_proc=16)
     dataset = dataset.shuffle(seed=42)
     dataset = dataset.flatten_indices()
 
-    tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-550Kformulas')
-
     map_fn = partial(tokenize_fn, tokenizer=tokenizer)
     tokenized_dataset = dataset.map(map_fn, batched=True, remove_columns=dataset.column_names, num_proc=8, load_from_cache_file=True)
     tokenized_dataset = tokenized_dataset.with_transform(img_transform_fn)
 
-    split_dataset = tokenized_dataset.train_test_split(test_size=0.05, seed=42)    
+    split_dataset = tokenized_dataset.train_test_split(test_size=0.005, seed=42)    
     train_dataset, eval_dataset = split_dataset['train'], split_dataset['test']
     collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
     model = TexTeller()
     # model = TexTeller.from_pretrained('/home/lhy/code/TeXify/src/models/ocr_model/train/train_result/train_with_random_resize/checkpoint-80000')
 
-    enable_train = False
+    enable_train = True
     enable_evaluate = True
     if enable_train:
         train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)  
diff --git a/src/models/ocr_model/train/training_args.py b/src/models/ocr_model/train/training_args.py
index 4d818d9..e824459 100644
--- a/src/models/ocr_model/train/training_args.py
+++ b/src/models/ocr_model/train/training_args.py
@@ -4,7 +4,7 @@ CONFIG = {
     # "data_seed": 42,                     # data sampler的采样也固定
     # "full_determinism": True,            # 使整个训练完全固定（这个设置会有害于模型训练，只用于debug）
 
-    "output_dir": "train_result/train_with_random_resize",          # 输出目录
+    "output_dir": "train_result/TexTellerv2",          # 输出目录
     "overwrite_output_dir": False,         # 如果输出目录存在，不删除原先的内容
     "report_to": ["tensorboard"],          # 输出日志到TensorBoard，
                                            #+通过在命令行：tensorboard --logdir ./logs 来查看日志
@@ -12,7 +12,7 @@ CONFIG = {
     "logging_dir": None,                   # TensorBoard日志文件的存储目录(使用默认值)
     "log_level": "warning",                   # 其他可选:‘debug’, ‘info’, ‘warning’, ‘error’ and ‘critical’（由低级别到高级别）
     "logging_strategy": "steps",           # 每隔一定步数记录一次日志
-    "logging_steps": 500,                  # 记录日志的步数间隔，可以是int也可以是(0~1)的float，当是float时表示总的训练步数的ratio(比方说可以设置成1.0 / 2000)
+    "logging_steps": 4000,                  # 记录日志的步数间隔，可以是int也可以是(0~1)的float，当是float时表示总的训练步数的ratio(比方说可以设置成1.0 / 2000)
                                            #+通常与eval_steps一致
     "logging_nan_inf_filter": False,       # 对loss=nan或inf进行记录
 
@@ -25,6 +25,7 @@ CONFIG = {
     "per_device_train_batch_size": 64,    # 每个GPU的batch size
     "per_device_eval_batch_size": 16,      # 每个GPU的evaluation batch size
     "auto_find_batch_size": True,          # 自动搜索合适的batch size（指数decay）
+    # "auto_find_batch_size": False,          # 自动搜索合适的batch size（指数decay）
 
     "optim": "adamw_torch",                # 还提供了很多AdamW的变体（相较于经典的AdamW更加高效）
                                            #+当设置了optim后，就不需要在Trainer中传入optimizer
@@ -52,12 +53,12 @@ CONFIG = {
     "dataloader_drop_last": True,          # 丢掉最后一个minibatch，保证训练的梯度稳定
 
     "evaluation_strategy": "steps",        # 评估策略，可以是"steps"或"epoch"
-    "eval_steps": 500,                     # if evaluation_strategy="step"
+    "eval_steps": 4000,                     # if evaluation_strategy="step"
                                            #+默认情况下与logging_steps一样，可以是int也可以是(0~1)的float，当是float时表示总的训练步数的ratio(比方说可以设置成1.0 / 2000)
 
     "save_strategy": "steps",              # 保存checkpoint的策略
-    "save_steps": 500,                     # checkpoint保存的步数间隔，可以是int也可以是(0~1)的float，当是float时表示总的训练步数的ratio(比方说可以设置成1.0 / 2000)
-    "save_total_limit": 5,                 # 保存的模型的最大数量。如果超过这个数量，最旧的模型将被删除
+    "save_steps": 4000,                     # checkpoint保存的步数间隔，可以是int也可以是(0~1)的float，当是float时表示总的训练步数的ratio(比方说可以设置成1.0 / 2000)
+    "save_total_limit": 10,                 # 保存的模型的最大数量。如果超过这个数量，最旧的模型将被删除
 
     "load_best_model_at_end": True,        # 训练结束时是否加载最佳模型
                                            #+当设置True时，会保存训练时评估结果最好的checkpoint
diff --git a/src/models/ocr_model/utils/functional.py b/src/models/ocr_model/utils/functional.py
index 969a493..a2710aa 100644
--- a/src/models/ocr_model/utils/functional.py
+++ b/src/models/ocr_model/utils/functional.py
@@ -1,13 +1,13 @@
 import torch
-import numpy as np 
 
 from functools import partial
 from datasets import load_dataset
 
 from transformers import DataCollatorForLanguageModeling
 from typing import List, Dict, Any
-from ..model.TexTeller import TexTeller
 from .transforms import train_transform
+from ..model.TexTeller import TexTeller
+from ...globals import MIN_HEIGHT, MIN_WIDTH, MAX_TOKEN_SIZE
 
 
 def left_move(x: torch.Tensor, pad_val):
@@ -50,6 +50,13 @@ def img_transform_fn(samples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
     return samples
 
 
+def filter_fn(sample, tokenizer=None) -> bool:
+    return (
+        sample['image'].height > MIN_HEIGHT and sample['image'].width > MIN_WIDTH
+        and len(tokenizer(sample['latex_formula'])['input_ids']) < MAX_TOKEN_SIZE - 10
+    )
+
+
 if __name__ == '__main__':
     dataset = load_dataset(
         '/home/lhy/code/TeXify/src/models/ocr_model/train/dataset/latex-formulas/latex-formulas.py',

From a42df1510f0f016436e02a08a7aa91842511aa91 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=89=E6=B4=8B=E4=B8=89=E6=B4=8B?= <1258009915@qq.com>
Date: Wed, 13 Mar 2024 02:21:02 +0000
Subject: [PATCH 4/5] =?UTF-8?q?=E5=AE=8C=E6=88=90=E4=BA=86TexTellerv2?=
 =?UTF-8?q?=E7=9A=84=E8=AE=AD=E7=BB=83=EF=BC=88=E4=B8=8D=E6=94=AF=E6=8C=81?=
 =?UTF-8?q?=E8=87=AA=E7=84=B6=E5=9C=BA=E6=99=AF=EF=BC=89?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/models/ocr_model/train/train.py         | 21 ++++++++++-----------
 src/models/ocr_model/train/training_args.py |  8 ++++----
 2 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/src/models/ocr_model/train/train.py b/src/models/ocr_model/train/train.py
index 40652ee..6ab8814 100644
--- a/src/models/ocr_model/train/train.py
+++ b/src/models/ocr_model/train/train.py
@@ -1,5 +1,4 @@
 import os
-import numpy as np
 
 from functools import partial
 from pathlib import Path
@@ -16,16 +15,16 @@ from ...globals import MAX_TOKEN_SIZE, MIN_WIDTH, MIN_HEIGHT
 
 def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer):
     training_args = TrainingArguments(**CONFIG)
-    debug_mode = True
+    debug_mode = False
     if debug_mode:
         training_args.auto_find_batch_size = False
         training_args.num_train_epochs = 2
+        # training_args.per_device_train_batch_size = 3
         training_args.per_device_train_batch_size = 2
         training_args.per_device_eval_batch_size = 2 * training_args.per_device_train_batch_size
         training_args.jit_mode_eval = False
         training_args.torch_compile = False
         training_args.dataloader_num_workers = 1
-
     
     trainer = Trainer(
         model,
@@ -38,7 +37,8 @@ def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokeniz
         data_collator=collate_fn_with_tokenizer,
     )
 
-    trainer.train(resume_from_checkpoint=None)
+    # trainer.train(resume_from_checkpoint=None)
+    trainer.train(resume_from_checkpoint='/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv2/checkpoint-64000')
 
 
 def evaluate(model, tokenizer, eval_dataset, collate_fn):
@@ -90,18 +90,17 @@ if __name__ == '__main__':
     tokenized_dataset = dataset.map(map_fn, batched=True, remove_columns=dataset.column_names, num_proc=8, load_from_cache_file=True)
     tokenized_dataset = tokenized_dataset.with_transform(img_transform_fn)
 
-    split_dataset = tokenized_dataset.train_test_split(test_size=0.005, seed=42)    
+    split_dataset = tokenized_dataset.train_test_split(test_size=0.005, seed=42)
     train_dataset, eval_dataset = split_dataset['train'], split_dataset['test']
     collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
-    model = TexTeller()
-    # model = TexTeller.from_pretrained('/home/lhy/code/TeXify/src/models/ocr_model/train/train_result/train_with_random_resize/checkpoint-80000')
+    # model = TexTeller()
+    model = TexTeller.from_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv2/checkpoint-64000')
 
-    enable_train = True
+    enable_train    = True
     enable_evaluate = True
     if enable_train:
-        train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)  
+        train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)
     if enable_evaluate:
         evaluate(model, tokenizer, eval_dataset, collate_fn_with_tokenizer)
 
-
-    os.chdir(cur_path)
\ No newline at end of file
+    os.chdir(cur_path)
diff --git a/src/models/ocr_model/train/training_args.py b/src/models/ocr_model/train/training_args.py
index e824459..73055b7 100644
--- a/src/models/ocr_model/train/training_args.py
+++ b/src/models/ocr_model/train/training_args.py
@@ -16,16 +16,16 @@ CONFIG = {
                                            #+通常与eval_steps一致
     "logging_nan_inf_filter": False,       # 对loss=nan或inf进行记录
 
-    "num_train_epochs": 10,                # 总的训练轮数
+    "num_train_epochs": 2,                # 总的训练轮数
     # "max_steps": 3,                      # 训练的最大步骤数。如果设置了这个参数，
                                            #+那么num_train_epochs将被忽略（通常用于调试）
 
     # "label_names": ['your_label_name'],  # 指定data_loader中的标签名，如果不指定则默认为'labels'
 
-    "per_device_train_batch_size": 64,    # 每个GPU的batch size
-    "per_device_eval_batch_size": 16,      # 每个GPU的evaluation batch size
+    "per_device_train_batch_size": 3,    # 每个GPU的batch size
+    "per_device_eval_batch_size": 6,      # 每个GPU的evaluation batch size
+    # "auto_find_batch_size": True,          # 自动搜索合适的batch size（指数decay）
     "auto_find_batch_size": True,          # 自动搜索合适的batch size（指数decay）
-    # "auto_find_batch_size": False,          # 自动搜索合适的batch size（指数decay）
 
     "optim": "adamw_torch",                # 还提供了很多AdamW的变体（相较于经典的AdamW更加高效）
                                            #+当设置了optim后，就不需要在Trainer中传入optimizer

From ef7cccff03e4d8a6585457256a6c927956069488 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=89=E6=B4=8B=E4=B8=89=E6=B4=8B?= <1258009915@qq.com>
Date: Mon, 25 Mar 2024 11:46:43 +0000
Subject: [PATCH 5/5] TexTellerv2

---
 .gitignore                              |  3 ++-
 src/models/ocr_model/model/TexTeller.py | 34 ++++++++++++-------------
 src/models/ocr_model/train/train.py     |  6 ++---
 src/models/ocr_model/utils/metrics.py   |  2 +-
 4 files changed, 23 insertions(+), 22 deletions(-)

diff --git a/.gitignore b/.gitignore
index 787a849..789f903 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,4 +5,5 @@
 **/logs
 **/.cache
 **/tmp*
-**/data
\ No newline at end of file
+**/data
+**/ckpt
\ No newline at end of file
diff --git a/src/models/ocr_model/model/TexTeller.py b/src/models/ocr_model/model/TexTeller.py
index 1a521fa..adad913 100644
--- a/src/models/ocr_model/model/TexTeller.py
+++ b/src/models/ocr_model/model/TexTeller.py
@@ -1,4 +1,3 @@
-from PIL import Image
 from pathlib import Path
 
 from models.globals import (
@@ -43,22 +42,23 @@ class TexTeller(VisionEncoderDecoderModel):
 
 if __name__ == "__main__":
     # texteller = TexTeller()
-    from ..utils.inference import inference
-    model = TexTeller.from_pretrained('/home/lhy/code/TeXify/src/models/ocr_model/train/train_result/checkpoint-57500')
-    tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-550Kformulas')
+    # from ..utils.inference import inference
+    # model = TexTeller.from_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/model/ckpt')
+    # model.save_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/model/ckpt2', safe_serialization=False)
+    # tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-550Kformulas')
 
-    base = '/home/lhy/code/TeXify/src/models/ocr_model/model'
-    imgs_path = [
-        # base + '/1.jpg',
-        # base + '/2.jpg',
-        # base + '/3.jpg',
-        # base + '/4.jpg',
-        # base + '/5.jpg',
-        # base + '/6.jpg',
-        base + '/foo.jpg'
-    ]
+    # base = '/home/lhy/code/TeXify/src/models/ocr_model/model'
+    # imgs_path = [
+    #     # base + '/1.jpg',
+    #     # base + '/2.jpg',
+    #     # base + '/3.jpg',
+    #     # base + '/4.jpg',
+    #     # base + '/5.jpg',
+    #     # base + '/6.jpg',
+    #     base + '/foo.jpg'
+    # ]
 
-    # res = inference(model, [img1, img2, img3, img4, img5, img6, img7], tokenizer)
-    res = inference(model, imgs_path, tokenizer)
-    pause = 1
+    # # res = inference(model, [img1, img2, img3, img4, img5, img6, img7], tokenizer)
+    # res = inference(model, imgs_path, tokenizer)
+    # pause = 1
 
diff --git a/src/models/ocr_model/train/train.py b/src/models/ocr_model/train/train.py
index 6ab8814..e110c34 100644
--- a/src/models/ocr_model/train/train.py
+++ b/src/models/ocr_model/train/train.py
@@ -38,7 +38,7 @@ def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokeniz
     )
 
     # trainer.train(resume_from_checkpoint=None)
-    trainer.train(resume_from_checkpoint='/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv2/checkpoint-64000')
+    trainer.train(resume_from_checkpoint='/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv2/checkpoint-288000')
 
 
 def evaluate(model, tokenizer, eval_dataset, collate_fn):
@@ -94,9 +94,9 @@ if __name__ == '__main__':
     train_dataset, eval_dataset = split_dataset['train'], split_dataset['test']
     collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
     # model = TexTeller()
-    model = TexTeller.from_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv2/checkpoint-64000')
+    model = TexTeller.from_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv2/checkpoint-588000')
 
-    enable_train    = True
+    enable_train    = False
     enable_evaluate = True
     if enable_train:
         train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)
diff --git a/src/models/ocr_model/utils/metrics.py b/src/models/ocr_model/utils/metrics.py
index a21d131..c876af0 100644
--- a/src/models/ocr_model/utils/metrics.py
+++ b/src/models/ocr_model/utils/metrics.py
@@ -4,7 +4,7 @@ from transformers import EvalPrediction, RobertaTokenizer
 from typing import Dict
 
 def bleu_metric(eval_preds:EvalPrediction, tokenizer:RobertaTokenizer) -> Dict:
-    metric = evaluate.load('/home/lhy/code/TeXify/src/models/ocr_model/train/google_bleu/google_bleu.py')  # 这里需要联网，所以会卡住
+    metric = evaluate.load('/home/lhy/code/TexTeller/src/models/ocr_model/train/google_bleu')  # 这里需要联网，所以会卡住
     
     logits, labels = eval_preds.predictions, eval_preds.label_ids
     preds = logits