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import torch
from torch.utils.data import Dataset
import json
import os
from PIL import Image
from utils import transform
class PascalVOCDataset(Dataset):
"""
A PyTorch Dataset class to be used in a PyTorch DataLoader to create batches.
"""
def __init__(self, data_folder, split, keep_difficult=False):
"""
:param data_folder: folder where data files are stored
:param split: split, one of 'TRAIN' or 'TEST'
:param keep_difficult: keep or discard objects that are considered difficult to detect?
"""
self.split = split.upper()
assert self.split in {'TRAIN', 'TEST'}
self.data_folder = data_folder
self.keep_difficult = keep_difficult
# Read data files
with open(os.path.join(data_folder, self.split + '_images.json'), 'r') as j:
self.images = json.load(j)
with open(os.path.join(data_folder, self.split + '_objects.json'), 'r') as j:
self.objects = json.load(j)
assert len(self.images) == len(self.objects)
def __getitem__(self, i):
# Read image
image = Image.open(self.images[i], mode='r')
image = image.convert('RGB')
# Read objects in this image (bounding boxes, labels, difficulties)
objects = self.objects[i]
boxes = torch.FloatTensor(objects['boxes']) # (n_objects, 4)
labels = torch.LongTensor(objects['labels']) # (n_objects)
difficulties = torch.ByteTensor(objects['difficulties']) # (n_objects)
# Discard difficult objects, if desired
if not self.keep_difficult:
boxes = boxes[1 - difficulties]
labels = labels[1 - difficulties]
difficulties = difficulties[1 - difficulties]
# Apply transformations
image, boxes, labels, difficulties = transform(image, boxes, labels, difficulties, split=self.split)
return image, boxes, labels, difficulties
def __len__(self):
return len(self.images)
def collate_fn(self, batch):
"""
Since each image may have a different number of objects, we need a collate function (to be passed to the DataLoader).
This describes how to combine these tensors of different sizes. We use lists.
Note: this need not be defined in this Class, can be standalone.
:param batch: an iterable of N sets from __getitem__()
:return: a tensor of images, lists of varying-size tensors of bounding boxes, labels, and difficulties
"""
images = list()
boxes = list()
labels = list()
difficulties = list()
for b in batch:
images.append(b[0])
boxes.append(b[1])
labels.append(b[2])
difficulties.append(b[3])
images = torch.stack(images, dim=0)
return images, boxes, labels, difficulties # tensor (N, 3, 300, 300), 3 lists of N tensors each
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