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dataset_lstm.py
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import os
import torch.utils.data as data
from PIL import Image
import numpy as np
import random
#四种媒体数据共同加载
class CubDataset(data.Dataset):
def __init__(self, image_dir, list_path, input_transform=None):
super(CubDataset, self).__init__()
self.input_transform = input_transform
self.vocabulary = list(" abcdefghijklmnopqrstuvwxyz0123456789,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}")
self.max_length = 448
image_list = []
video_list = []
audio_list = []
text_list = []
label_list = []
with open(list_path, 'r') as f:
for line in f.readlines():
imagename, videoname, audioname, textname, class_label = line.split()
image_list.append(imagename)
video_list.append(videoname)
audio_list.append(audioname)
for line in open(os.path.join(image_dir, textname), encoding="utf-8"):
line = line.lower()
textname = line.split("\n")[0]
text_list.append(textname)
label_list.append(int(class_label))
self.image_filenames = [os.path.join(image_dir, x) for x in image_list]
self.video_filenames = [os.path.join(image_dir, x) for x in video_list]
self.audio_filenames = [os.path.join(image_dir, x) for x in audio_list]
self.text_filenames = text_list
# self.name_lists=name_list
self.label_list = label_list
def __getitem__(self, index):
data = []
input_image = Image.open(self.image_filenames[index]).convert('RGB')
input_video = Image.open(self.video_filenames[index]).convert('RGB')
input_audio = Image.open(self.audio_filenames[index]).convert('RGB')
if self.input_transform:
input_image = self.input_transform(input_image)
input_video = self.input_transform(input_video)
input_audio = self.input_transform(input_audio)
num = random.randrange(0, 10, 2) # 0, 2, 4, 8
data += [0] * num
data += [self.vocabulary.index(i) + 1 for i in list(raw_text) if i in self.vocabulary]
if len(data) > self.max_length:
data = data[:self.max_length]
elif len(data) < self.max_length:
data += [0] * (self.max_length - len(data))
input_text = np.array(data, dtype=np.int64)
class_label = self.label_list[index]
return input_image,input_video,input_audio, input_text, class_label
def __len__(self):
return len(self.image_filenames)
#加载音频和图片数据
class CubDataset1(data.Dataset):
def __init__(self, image_dir, list_path,input_transform=None):
super(CubDataset1, self).__init__()
self.input_transform = input_transform
name_list = []
label_list = []
with open(list_path, 'r') as f:
for line in f.readlines():
imagename, class_label = line.split()
name_list.append(imagename)
label_list.append(int(class_label))
self.image_filenames = [os.path.join(image_dir, x) for x in name_list]
self.label_list = label_list
def __getitem__(self, index):
input = Image.open(self.image_filenames[index]).convert('RGB')
if self.input_transform:
input = self.input_transform(input)
class_label = self.label_list[index]
return input, class_label
def __len__(self):
return len(self.image_filenames)
#加载文本数据
class CubTextDataset(data.Dataset):
def __init__(self, image_dir, list_path, split):
super(CubTextDataset, self).__init__()
self.split = split
self.vocabulary = list(" abcdefghijklmnopqrstuvwxyz0123456789,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}")
self.max_length = 448
texts, labels = [], []
with open(list_path, 'r') as f:
for line in f.readlines():
if self.split== 'train':
path = line.split()[3]
elif self.split== 'test':
path = line.split()[0]
label = int(line.split()[-1])
for line in open(os.path.join(image_dir, path), encoding="utf-8"):
line = line.lower()
text = line.split("\n")[0]
texts.append(text)
labels.append(label)
self.texts = texts
self.labels = labels
def __getitem__(self, index):
raw_text = self.texts[index]
data = []
if (self.split == 'train'):
num = random.randrange(0, 10, 2) # 0, 2, 4, 8
data += [0] * num
data += [self.vocabulary.index(i) + 1 for i in list(raw_text) if i in self.vocabulary]
else:
data = [self.vocabulary.index(i) + 1 for i in list(raw_text) if i in self.vocabulary]
if len(data) > self.max_length:
data = data[:self.max_length]
elif len(data) < self.max_length:
data += [0] * (self.max_length - len(data))
input = np.array(data, dtype=np.int64)
class_label = self.labels[index]
return input, class_label
def __len__(self):
return len(self.labels)
#加载视频数据
class CubDataset2(data.Dataset):
def __init__(self, image_dir, list_path,input_transform=None):
super(CubDataset2, self).__init__()
self.input_transform = input_transform
name_list = []
label_list = []
with open(list_path, 'r') as f:
for line in f.readlines():
imagename, class_label = line.split()
name_list.append(imagename)
label_list.append(int(class_label))
self.image_filenames = [os.path.join(image_dir, x) for x in name_list]
self.label_list = label_list
def __getitem__(self, index):
imagename = self.image_filenames[index]
input = Image.open(self.image_filenames[index]).convert('RGB')
if self.input_transform:
input = self.input_transform(input)
class_label = self.label_list[index]
return input, class_label,imagename
def __len__(self):
return len(self.image_filenames)