dataset.py

#!/usr/bin/python
# encoding: utf-8
import os
import random
import torch
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
from utils import read_truths_args, read_truths

class listDataset(Dataset):

    def __init__(self, root, shape=None, shuffle=True, transform=None, target_transform=None, train=False, seen=0, batch_size=64, num_workers=4):
       with open(root, 'r') as file:
           self.lines = file.readlines()

       if shuffle:
           random.shuffle(self.lines)

       self.nSamples  = len(self.lines)
       self.transform = transform
       self.target_transform = target_transform
       self.train = train
       self.shape = shape
       self.seen = seen
       self.batch_size = batch_size
       self.num_workers = num_workers

    def __len__(self):
        return self.nSamples

    def __getitem__(self, index):
        assert index <= len(self), 'index range error'
        imgpath = self.lines[index].rstrip()

        if True:
            img = Image.open(imgpath).convert('RGB')
            if self.shape:
                img = img.resize(self.shape)
    
            labpath = imgpath.replace('images', 'labels').replace('JPEGImages', 'labels').replace('.jpg', '.txt').replace('.png','.txt')
            label = torch.zeros(50*5)
            if os.path.getsize(labpath):
                tmp = torch.from_numpy(np.loadtxt(labpath))
                #tmp = torch.from_numpy(read_truths_args(labpath, 8.0/img.width))
                #tmp = torch.from_numpy(read_truths(labpath))
                tmp = tmp.view(-1)
                tsz = tmp.numel()
                #print('labpath = %s , tsz = %d' % (labpath, tsz))
                if tsz > 50*5:
                    label = tmp[0:50*5]
                elif tsz > 0:
                    label[0:tsz] = tmp

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            label = self.target_transform(label)

        self.seen = self.seen + self.num_workers
        return (img, label)