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import torch
import numpy as np
from torchvision import transforms
from torch.utils.data import DataLoader
from .datasets import IuxrayMultiImageDataset, MimiccxrSingleImageDataset
class R2DataLoader(DataLoader):
def __init__(self, args, tokenizer, split, shuffle):
self.args = args
self.dataset_name = args.dataset_name
self.batch_size = args.batch_size
self.shuffle = shuffle
self.num_workers = args.num_workers
self.tokenizer = tokenizer
self.split = split
if split == 'train':
self.transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
else:
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
if self.dataset_name == 'iu_xray':
self.dataset = IuxrayMultiImageDataset(self.args, self.tokenizer, self.split, transform=self.transform)
else:
self.dataset = MimiccxrSingleImageDataset(self.args, self.tokenizer, self.split, transform=self.transform)
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):
images_id, images, reports_ids, reports_masks, seq_lengths = zip(*data)
images = torch.stack(images, 0)
max_seq_length = max(seq_lengths)
targets = np.zeros((len(reports_ids), max_seq_length), dtype=int)
targets_masks = np.zeros((len(reports_ids), max_seq_length), dtype=int)
for i, report_ids in enumerate(reports_ids):
targets[i, :len(report_ids)] = report_ids
for i, report_masks in enumerate(reports_masks):
targets_masks[i, :len(report_masks)] = report_masks
return images_id, images, torch.LongTensor(targets), torch.FloatTensor(targets_masks)
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