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import pdb
import torch
from torch.utils.data import DataLoader
from .dataset import IuxrayMultiImageDataset, MimiccxrSingleImageDataset
from medclip import MedCLIPProcessor
import numpy as np
class R2DataLoader(DataLoader):
def __init__(self, args, tokenizer, split, shuffle):
self.args = args
self.dataset_name = args.dataset
self.batch_size = args.bs
self.shuffle = shuffle
self.num_workers = args.num_workers
self.tokenizer = tokenizer
self.split = split
self.processor = MedCLIPProcessor()
if self.dataset_name == 'iu_xray':
self.dataset = IuxrayMultiImageDataset(self.args, self.tokenizer, self.split, self.processor)
else:
self.dataset = MimiccxrSingleImageDataset(self.args, self.tokenizer, self.split, self.processor)
self.init_kwargs = {
'dataset': self.dataset,
'batch_size': self.batch_size,
'shuffle': self.shuffle,
'collate_fn': self.collate_fn,
'num_workers': self.num_workers
}
super().__init__(**self.init_kwargs)
@staticmethod
def collate_fn(data):
image_id_batch, image_batch, report_ids_batch, report_masks_batch, processor_ids_batch, processor_mask_batch, seq_lengths_batch, processor_lenghts_batch = zip(*data)
image_batch = torch.stack(image_batch, 0)
max_seq_length = max(seq_lengths_batch)
target_batch = np.zeros((len(report_ids_batch), max_seq_length), dtype=int)
target_masks_batch = np.zeros((len(report_ids_batch), max_seq_length), dtype=int)
max_processor_length = max(processor_lenghts_batch)
target_processor_batch = np.zeros((len(processor_ids_batch), max_processor_length), dtype=int)
target_processor_mask_batch = np.zeros((len(processor_mask_batch), max_processor_length), dtype=int)
for i, report_ids in enumerate(report_ids_batch):
target_batch[i, :len(report_ids)] = report_ids
for i, report_masks in enumerate(report_masks_batch):
target_masks_batch[i, :len(report_masks)] = report_masks
for i, report_ids in enumerate(processor_ids_batch):
target_processor_batch[i, :len(report_ids)] = report_ids
for i, report_masks in enumerate(processor_mask_batch):
target_processor_mask_batch[i, :len(report_masks)] = report_masks
return image_id_batch, image_batch, torch.LongTensor(target_batch), torch.FloatTensor(target_masks_batch), torch.FloatTensor(target_processor_batch), torch.FloatTensor(target_processor_mask_batch)
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