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dataloader.py
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dataloader.py
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from PIL import Image
import torch
from utils import *
from torch.utils.data import Dataset
import preprocessor
import nltk
import json
preprocessor.set_options(preprocessor.OPT.URL, preprocessor.OPT.EMOJI, preprocessor.OPT.MENTION, preprocessor.OPT.HASHTAG)
import warnings
warnings.filterwarnings("ignore")
class ImageDataset(Dataset):
def __init__(self, args, annotation, root_dir, transform=None):
self.annotation = annotation
self.root_dir = root_dir
self.transform = transform
self.args = args
def __len__(self):
return len(self.annotation)
def __getitem__(self, idx):
img_id = str(self.annotation.loc[idx, 'tweet_id'])
img_path = os.path.join(self.root_dir, f'{img_id}.jpg')
try:
# corrupted image
image = Image.open(img_path).convert('RGB')
except:
print(f"{img_path} none!")
return None
if self.transform:
image = self.transform(image)
if self.args.exp_mode == 0:
label = self.annotation.loc[idx, 'stance']
label = encode_stance(label)
label = torch.FloatTensor([label])
else: # 1
label = self.annotation.loc[idx, 'persuasiveness']
label = encode_persuasiveness(label, self.args)
label = torch.FloatTensor([label])
return img_id, image, label
class ImageDataset(Dataset):
def __init__(self, args, annotation, root_dir, transform=None):
self.annotation = annotation
self.root_dir = root_dir
self.transform = transform
self.args = args
def __len__(self):
return len(self.annotation)
def __getitem__(self, idx):
img_id = str(self.annotation.loc[idx, 'tweet_id'])
img_path = os.path.join(self.root_dir, f'{img_id}.jpg')
try:
# corrupted image
image = Image.open(img_path).convert('RGB')
except:
print(f"{img_path} none!")
return None
if self.transform:
image = self.transform(image)
if self.args.exp_mode == 0:
label = self.annotation.loc[idx, 'stance']
label = encode_stance(label)
label = torch.FloatTensor([label])
else: # 1
label = self.annotation.loc[idx, 'persuasiveness']
label = encode_persuasiveness(label, self.args)
label = torch.FloatTensor([label])
return img_id, image, label
class TextDataset(Dataset):
def __init__(self, args, annotation, root_dir, transform=None):
self.annotation = annotation
self.transform = transform
annotation["tweet_text"] = annotation["tweet_text"].apply(lambda x: preprocessor.clean(x))
self.input_ids, self.attention_masks = bert_tokenizer(annotation["tweet_text"].tolist(), args)
self.args = args
self.root_dir = root_dir
def __len__(self):
return len(self.annotation)
def __getitem__(self, idx):
text_id = str(self.annotation.loc[idx, 'tweet_id'])
input_id = self.input_ids[idx]
attention_mask = self.attention_masks[idx]
img_id = str(self.annotation.loc[idx, 'tweet_id'])
img_path = os.path.join(self.root_dir, f'{img_id}.jpg')
try:
# corrupted image - even if for text only dataloader
image = Image.open(img_path).convert('RGB')
except:
print(f"{img_path} none!")
return None
if self.args.exp_mode == 0:
label = self.annotation.loc[idx, 'stance']
label = encode_stance(label)
label = torch.FloatTensor([label])
else: # 1
label = self.annotation.loc[idx, 'persuasiveness']
label = encode_persuasiveness(label, self.args)
label = torch.FloatTensor([label])
return text_id, input_id, attention_mask, label
class ImageTextDataset(Dataset):
def __init__(self, args, annotation, root_dir, transform=None):
self.annotation = annotation
annotation["tweet_text"] = annotation["tweet_text"].apply(lambda x: preprocessor.clean(x))
self.input_ids, self.attention_masks = bert_tokenizer(annotation["tweet_text"].tolist(), args)
self.root_dir = root_dir
self.transform = transform
self.args = args
def __len__(self):
return len(self.annotation)
def __getitem__(self, idx):
text_id = str(self.annotation.loc[idx, 'tweet_id'])
input_id = self.input_ids[idx]
attention_mask = self.attention_masks[idx]
img_id = str(self.annotation.loc[idx, 'tweet_id'])
img_path = os.path.join(self.root_dir, f'{img_id}.jpg')
try:
# corrupted image
image = Image.open(img_path).convert('RGB')
except:
print(f"{img_path} none!")
return None
if self.transform:
image = self.transform(image)
if self.args.exp_mode == 0:
label = self.annotation.loc[idx, 'stance']
label = encode_stance(label)
label = torch.FloatTensor([label])
else: # 1
label = self.annotation.loc[idx, 'persuasiveness']
label = encode_persuasiveness(label, self.args)
label = torch.FloatTensor([label])
return text_id, input_id, attention_mask, image, label
class TextTestDataset(Dataset):
def __init__(self, args, annotation, root_dir, transform=None):
self.annotation = annotation
self.transform = transform
annotation["tweet_text"] = annotation["tweet_text"].apply(lambda x: preprocessor.clean(x))
self.input_ids, self.attention_masks = bert_tokenizer(annotation["tweet_text"].tolist(), args)
self.args = args
self.root_dir = root_dir
def __len__(self):
return len(self.annotation)
def __getitem__(self, idx):
text_id = str(self.annotation.loc[idx, 'tweet_id'])
input_id = self.input_ids[idx]
attention_mask = self.attention_masks[idx]
img_id = str(self.annotation.loc[idx, 'tweet_id'])
img_path = os.path.join(self.root_dir, f'{img_id}.jpg')
try:
# corrupted image - even if for text only dataloader
image = Image.open(img_path).convert('RGB')
except:
print(f"{img_path} none!")
return None
return text_id, input_id, attention_mask
class ImageTestDataset(Dataset):
def __init__(self, args, annotation, root_dir, transform=None):
self.annotation = annotation
self.root_dir = root_dir
self.transform = transform
self.args = args
def __len__(self):
return len(self.annotation)
def __getitem__(self, idx):
img_id = str(self.annotation.loc[idx, 'tweet_id'])
img_path = os.path.join(self.root_dir, f'{img_id}.jpg')
try:
# corrupted image
image = Image.open(img_path).convert('RGB')
except:
print(f"{img_path} none!")
return None
if self.transform:
image = self.transform(image)
return img_id, image
class ImageTextTestDataset(Dataset):
def __init__(self, args, annotation, root_dir, transform=None):
self.annotation = annotation
annotation["tweet_text"] = annotation["tweet_text"].apply(lambda x: preprocessor.clean(x))
self.input_ids, self.attention_masks = bert_tokenizer(annotation["tweet_text"].tolist(), args)
self.root_dir = root_dir
self.transform = transform
self.args = args
def __len__(self):
return len(self.annotation)
def __getitem__(self, idx):
text_id = str(self.annotation.loc[idx, 'tweet_id'])
input_id = self.input_ids[idx]
attention_mask = self.attention_masks[idx]
img_id = str(self.annotation.loc[idx, 'tweet_id'])
img_path = os.path.join(self.root_dir, f'{img_id}.jpg')
try:
# corrupted image
image = Image.open(img_path).convert('RGB')
except:
print(f"{img_path} none!")
return None
if self.transform:
image = self.transform(image)
return text_id, input_id, attention_mask, image