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import os
from PIL import Image
import json
from torch.utils.data import Dataset
import numpy as np
import torch
class BaseDataset(Dataset):
def __init__(self, args, tokenizer, split, processor):
self.image_dir = args.image_dir
self.ann_path = args.json_path
self.max_seq_length = args.max_seq_length
self.split = split
self.tokenizer = tokenizer
self.ann = json.loads(open(self.ann_path, 'r').read())
self.examples = self.ann[self.split]
self.processor = processor
def preprocess_text(self, text):
ids = self.tokenizer(text)[:self.max_seq_length]
mask = [1] * len(ids)
text_inputs = self.processor(text=text, return_tensors="pt",truncation=True, padding=False, max_length=self.max_seq_length)
processor_ids = text_inputs['input_ids'].squeeze(0).tolist()
processor_mask = text_inputs['attention_mask'].squeeze(0).tolist()
return ids, mask, processor_ids, processor_mask
def __len__(self):
return len(self.examples)
class IuxrayMultiImageDataset(BaseDataset):
def __getitem__(self, idx):
example = self.examples[idx]
report = example['report']
report_ids, report_masks, processor_ids, processor_mask = self.preprocess_text(report)
image_id = example['id']
image_path = example['image_path']
image_1 = Image.open(os.path.join(self.image_dir, image_path[0])).convert('RGB')
image_2 = Image.open(os.path.join(self.image_dir, image_path[1])).convert('RGB')
# MedCLIP processing
image_inputs_1 = self.processor(images=image_1, return_tensors="pt")
image_inputs_2 = self.processor(images=image_2, return_tensors="pt")
image = torch.stack((image_inputs_1.pixel_values[0], image_inputs_2.pixel_values[0]), 0)
seq_length = len(report_ids)
processor_length = len(processor_ids)
sample = (image_id, image, report_ids, report_masks, processor_ids, processor_mask, seq_length, processor_length)
return sample
class MimiccxrSingleImageDataset(BaseDataset):
def __getitem__(self, idx):
example = self.examples[idx]
report = example['report']
report_ids, report_masks, processor_ids, processor_mask = self.preprocess_text(report)
image_id = example['id']
image_path = example['image_path']
image = Image.open(os.path.join(self.image_dir, image_path[0])).convert('RGB')
image_inputs = self.processor(images=image, return_tensors="pt")
image = image_inputs.pixel_values[0]
seq_length = len(report_ids)
processor_length = len(processor_ids)
sample = (image_id, image, report_ids, report_masks, processor_ids, processor_mask, seq_length, processor_length)
return sample
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