johnlockejrr
commented
7 months ago Managed to get the data from the origin, now another problem, I follow your example and I get no model saved... do I do anything wrong?
import pandas as pd
from sklearn.model_selection import train_test_split
import torch
from torch.utils.data import Dataset
from PIL import Image
from transformers import TrOCRProcessor
from transformers import VisionEncoderDecoderModel
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments
from datasets import load_metric
from transformers import default_data_collator
from huggingface_hub import login
df = pd.read_fwf('./IAM/gt_test.txt', header=None)
df.rename(columns={0: "file_name", 1: "text"}, inplace=True)
print(df.head())
train_df, test_df = train_test_split(df, test_size=0.2)
# we reset the indices to start from zero
train_df.reset_index(drop=True, inplace=True)
test_df.reset_index(drop=True, inplace=True)
class IAMDataset(Dataset):
def __init__(self, root_dir, df, processor, max_target_length=128):
self.root_dir = root_dir
self.df = df
self.processor = processor
self.max_target_length = max_target_length
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
# get file name + text
file_name = self.df['file_name'][idx]
text = self.df['text'][idx]
# prepare image (i.e. resize + normalize)
image = Image.open(self.root_dir + file_name).convert("RGB")
pixel_values = self.processor(image, return_tensors="pt").pixel_values
# add labels (input_ids) by encoding the text
labels = self.processor.tokenizer(text,
padding="max_length",
max_length=self.max_target_length).input_ids
# important: make sure that PAD tokens are ignored by the loss function
labels = [label if label != self.processor.tokenizer.pad_token_id else -100 for label in labels]
encoding = {"pixel_values": pixel_values.squeeze(), "labels": torch.tensor(labels)}
return encoding
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
train_dataset = IAMDataset(root_dir='./IAM/',
df=train_df,
processor=processor)
eval_dataset = IAMDataset(root_dir='./IAM/',
df=test_df,
processor=processor)
print("Number of training examples:", len(train_dataset))
print("Number of validation examples:", len(eval_dataset))
encoding = train_dataset[0]
for k,v in encoding.items():
print(k, v.shape)
#labels = encoding['labels']
#labels[labels == -100] = processor.tokenizer.pad_token_id
#label_str = processor.decode(labels, skip_special_tokens=True)
#print(label_str)
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-stage1")
# set special tokens used for creating the decoder_input_ids from the labels
model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
model.config.pad_token_id = processor.tokenizer.pad_token_id
# make sure vocab size is set correctly
model.config.vocab_size = model.config.decoder.vocab_size
# set beam search parameters
model.config.eos_token_id = processor.tokenizer.sep_token_id
model.config.max_length = 64
model.config.early_stopping = True
model.config.no_repeat_ngram_size = 3
model.config.length_penalty = 2.0
model.config.num_beams = 4
training_args = Seq2SeqTrainingArguments(
predict_with_generate=True,
evaluation_strategy="steps",
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
fp16=True,
output_dir="./iam-train",
overwrite_output_dir=True,
learning_rate=2e-5,
weight_decay=0.01,
num_train_epochs=5,
push_to_hub=True,
hub_token="hf_XXXXXXXXXXXXXXXXXXXXXXXXX",
logging_steps=2,
save_steps=1000,
eval_steps=200,
load_best_model_at_end=True,
metric_for_best_model="cer",
)
cer_metric = load_metric("cer")
def compute_metrics(pred):
labels_ids = pred.label_ids
pred_ids = pred.predictions
pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)
labels_ids[labels_ids == -100] = processor.tokenizer.pad_token_id
label_str = processor.batch_decode(labels_ids, skip_special_tokens=True)
cer = cer_metric.compute(predictions=pred_str, references=label_str)
return {"cer": cer}
# instantiate trainer
trainer = Seq2SeqTrainer(
model=model,
tokenizer=processor.feature_extractor,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=default_data_collator,
)
trainer.train()(TrOCR-py3.10) incognito@DESKTOP-NHKR7QL:~/TrOCR-py3.10$ ls -al iam-train/
total 8
drwxr-xr-x 2 incognito incognito 4096 Apr 18 13:34 .
drwxr-xr-x 9 incognito incognito 4096 Apr 18 14:58 ..
NielsRogge
Seems the IAM dataset is not public anymore, any other location?
Trying to download, output: