我需要 PyTorch 方面的帮助, 关于Dataloader和数据集 有人可以帮助/指导我
这是我的查询: 我正在尝试使用 https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/03-advanced/image_captioning 进行图像字幕.
这里他们使用了标准 COCO 数据集。
我的数据集为 images/和 Captions/目录。
示例
目录结构:
images/T001.jpg
images/T002.jpg
...
...
captions/T001.txt
captions/T002.txt
....
....
以上是关系。字幕文件的每行都有“n”个字幕。
我能够创建一个自定义数据集类,因为正在返回完整的标题文件内容。但我只想返回一根单独的气体。
关于如何实现这一目标的任何指导/建议。
++++++++++++++++++++++++++++++++++++++++++++++++++ + 这是我设计的类:
from __future__ import print_function
import torch
from torchvision import datasets, models, transforms
from torchvision import transforms
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence
import torch.optim as optim
import torch.nn as nn
#from torch import np
import numpy as np
import utils_c
from data_loader_c import get_cust_data_loader
from models import CNN, RNN
from vocab_custom import Vocabulary, load_vocab
import os
class ImageCaptionDataSet(data.Dataset):
def __init__(self, path, json, vocab=None, transform=None):
self.vocab = vocab
self.transform = transform
self.img_dir_path = path
self.cap_dir_path = json
self.all_imgs_path = glob.glob(os.path.join(self.img_dir_path,'*.jpg'))
self.all_caps_path = glob.glob(os.path.join(self.cap_dir_path,'*.txt'))
pass
def __getitem__(self,index):
vocab = self.vocab
img_path = self.all_imgs_path[index]
img_base_name = os.path.basename(img_path)
cap_base_name = img_base_name.replace(".jpg",".txt")
cap_path = os.path.join(self.cap_dir_path,cap_base_name)
caption_all_for_a_image = open(cap_path).read().split("\n")
image = Image.open(img_path)
image = image.convert('RGB')
if self.transform != None:
# apply image preprocessing
image = self.transform(image)
#captions_combined = []
#max_len = 0
#for caption in caption_all_for_a_image:
# caption_str = str(caption).lower()
# tokens = nltk.tokenize.word_tokenize(caption_str)
# m = len(tokens) + 2
# if m>max_len:
# max_len = m
# caption = torch.Tensor([vocab(vocab.start_token())] +
# [vocab(token) for token in tokens] +
# [vocab(vocab.end_token())])
# captions_combined.append(caption)
# #yield image, caption
#return image,torch.Tensor(captions_combined)
caption_str = str(caption_all_for_a_image).lower()
tokens = nltk.tokenize.word_tokenize(caption_str)
caption = torch.Tensor([vocab(vocab.start_token())] +
[vocab(token) for token in tokens] +
[vocab(vocab.end_token())])
return image,caption
def __len__(self):
return len(self.all_imgs_path)
++++++++++++++++++++++++++++++++++++
最佳答案
首先,使用 str()
将字幕列表转换为单个字符串 (caption_str = str(caption_all_for_a_image)
) 坏主意:
cap = ['a sentence', 'bla bla bla']
str(cap)
返回此刺:
"['a sentence', 'bla bla bla']"
请注意,['
和 ', '
是结果字符串的一部分!
您可以随机选择一个标题:
import random
...
cap_idx = random.randi(0, len(caption_all_for_a_image)-1) # pick one at random
caption_str = caption_all_for_a_image[cap_idx].lower() # actual selection
关于python - 如何处理 PyTorch 中图像字幕目录中的多标签数据集,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/53442510/