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- import torch
- from torch.utils.data import Dataset
- import os
- import numpy as np
- import random
- from torchvision import transforms
- from PIL import Image
- import cv2
- class FaceDataSet(Dataset):
- def __init__(self, dataset_path, batch_size):
- super(FaceDataSet, self).__init__()
- '''picture_dir_list = []
- for i in range(self.people_num):
- picture_dir_list.append('/data/home/renwangchen/vgg_align_224/'+self.people_list[i])
- self.people_pic_list = []
- for i in range(self.people_num):
- pic_list = os.listdir(picture_dir_list[i])
- person_pic_list = []
- for j in range(len(pic_list)):
- pic_dir = os.path.join(picture_dir_list[i], pic_list[j])
- person_pic_list.append(pic_dir)
- self.people_pic_list.append(person_pic_list)'''
- pic_dir = '/data/home/renwangchen/CelebA_224/'
- latent_dir = '/data/home/renwangchen/CelebA_latent/'
- tmp_list = os.listdir(pic_dir)
- self.pic_list = []
- self.latent_list = []
- for i in range(len(tmp_list)):
- self.pic_list.append(pic_dir + tmp_list[i])
- self.latent_list.append(latent_dir + tmp_list[i][:-3] + 'npy')
- self.pic_list = self.pic_list[:29984]
- '''for i in range(29984):
- print(self.pic_list[i])'''
- self.latent_list = self.latent_list[:29984]
- self.people_num = len(self.pic_list)
- self.type = 1
- self.bs = batch_size
- self.count = 0
- self.transformer = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
-
- def __getitem__(self, index):
- p1 = random.randint(0, self.people_num - 1)
- p2 = p1
- if self.type == 0:
- # load pictures from the same folder
- pass
- else:
- # load pictures from different folders
- p2 = p1
- while p2 == p1:
- p2 = random.randint(0, self.people_num - 1)
- pic_id_dir = self.pic_list[p1]
- pic_att_dir = self.pic_list[p2]
- latent_id_dir = self.latent_list[p1]
- latent_att_dir = self.latent_list[p2]
- img_id = Image.open(pic_id_dir).convert('RGB')
- img_id = self.transformer(img_id)
- latent_id = np.load(latent_id_dir)
- latent_id = latent_id / np.linalg.norm(latent_id)
- latent_id = torch.from_numpy(latent_id)
- img_att = Image.open(pic_att_dir).convert('RGB')
- img_att = self.transformer(img_att)
- latent_att = np.load(latent_att_dir)
- latent_att = latent_att / np.linalg.norm(latent_att)
- latent_att = torch.from_numpy(latent_att)
-
- self.count += 1
- data_type = self.type
- if self.count == self.bs:
- self.type = 1 - self.type
- self.count = 0
-
- return img_id, img_att, latent_id, latent_att, data_type
-
- def __len__(self):
- return len(self.pic_list)
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