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import torch
from PIL import Image
import pandas as pd
class RetrievalDataset(torch.utils.data.Dataset):
def __init__(self, img_dir_path: str, annotations_file_path: str, split: str, transform=None, tokenizer=None) -> None:
self.img_dir_path = img_dir_path
self.transform = transform
self.tokenizer = tokenizer
self.split = split
self.annotations = self.split_data(
self.convert_image_names_to_path(
pd.read_csv(annotations_file_path)
)
)
def __len__(self) -> int:
return len(self.annotations)
def __getitem__(self, idx: int) -> tuple:
query_img_path = self.annotations.iloc[idx]['query_image']
query_text = self.annotations.iloc[idx]['query_text']
target_img_path = self.annotations.iloc[idx]['target_image']
query_img = Image.open(query_img_path).convert('RGB')
target_img = Image.open(target_img_path).convert('RGB')
# query_img = torchvision.io.read_image(path=query_img_path, mode=torchvision.io.image.ImageReadMode.RGB)
# target_img = torchvision.io.read_image(path=target_img_path, mode=torchvision.io.image.ImageReadMode.RGB)
if self.transform:
query_img = self.transform(query_img)
target_img = self.transform(target_img)
if self.tokenizer:
query_text = self.tokenizer(query_text).squeeze(0)
return query_img, query_text, target_img, self.annotations.iloc[idx]['query_text']
def split_data(self, annotations):
shuffled_df = annotations.sample(frac=1, random_state=42).reset_index(drop=True)
if self.split == "test":
return shuffled_df # sample test set
if self.split == "train":
return shuffled_df.iloc[:int(0.9 * len(shuffled_df))] # train set
if self.split == "validation":
return shuffled_df.iloc[int(0.9 * len(shuffled_df)):] # validation set
raise Exception("split is not valid")
def load_queries(self):
return self.annotations
def load_database(self):
return pd.DataFrame({'target_image': self.annotations["target_image"].unique()})
def convert_image_names_to_path(self, df):
df["query_image"] = self.img_dir_path + "/" + df["query_image"]
df["target_image"] = self.img_dir_path + "/" + df["target_image"]
return df |