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dataset.py
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dataset.py
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import pandas
import torch
from torch.utils.data import Dataset
from PIL import Image
import os
class KTRDataset(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",
# stride=32,
# truncation=True,
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
class HandWrittenDataset(Dataset):
def __init__(
self, root_dir, processor, train=True, max_target_length=128, test_split=0.2
):
self.root_dir = root_dir
self.processor = processor
# self.tokenizer = tokenizer
self.max_target_length = max_target_length
self.labels = os.listdir(self.root_dir)
self.images = []
self.targets = []
# print(f"Labels {self.labels[0]}")
for label in self.labels: # بەفرین
label_path = os.path.join(self.root_dir, label)
for img_filename in os.listdir(label_path):
img_path = os.path.join(label_path, img_filename)
self.images.append(img_path)
self.targets.append(label)
if train:
# print("Train split", int(len(self.images)*(1-test_split)))
self.images = self.images[: int(len(self.images) * (1 - test_split))]
self.targets = self.targets[: int(len(self.targets) * (1 - test_split))]
else:
# print("Test split", int(len(self.images)*(1 - test_split)))
self.images = self.images[int(len(self.images) * (1 - test_split)) :]
self.targets = self.targets[int(len(self.targets) * (1 - test_split)) :]
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
file_name = self.images[idx]
text = self.targets[idx]
# print(file_name, text)
img = Image.open(file_name).convert("RGB")
pixel_values = self.processor(img, return_tensors="pt").pixel_values
labels = self.processor.tokenizer(
text,
padding="max_length",
# stride=32,
# truncation=True,
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