Delete data/vqa_dataset.py

data/vqa_dataset.py

@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-import json
-import os
-from typing import Callable, List, Tuple, Union
-
-import torch
-
-from PIL import Image
-from torch import Tensor
-from torch.utils.data import Dataset
-
-
-class VQADataset(Dataset):
-    """
-    Create the dataset for VQA task.
-
-    Args:
-        ann_file (List[str]): The paths to annotation json files.
-        vqa_root (str): The path to vqa data directory.
-        vg_root (str): The path to vg data directory.
-        image_transform (Callable[[Image.Image], Tensor]): image data transform.
-        question_transform (Callable[[Union[List[str], str]], Tensor]): text data transform for questions.
-        answer_transform (Callable[[Union[List[str], str]], Tensor]): text data transform for answers.
-        split (str): Indicates train or test. Default is train.
-        answer_list (str): The path to the answers list. Required for test split.
-
-    Dataset Outputs:
-        if split is train:
-            image (Tensor): Transformed image input tensor of shape (C, W, H).
-            question (Tensor): Transformed question token input ids.
-            answers (List[Tensor]): List of transformed answers token input ids.
-            answer_weights (List[float]): List of answer weights.
-                answer_weights[i] is proportional to the number of occurences of answers[i]
-        if split is test:
-            image (Tensor): Transformed image input tensor of shape (C, W, H).
-            question (Tensor): Transformed text token input ids.
-            question_id (int): The question sample id.
-    """
-
-    def __init__(
-        self,
-        ann_file: List[str],
-        vqa_root: str,
-        vg_root: str,
-        image_transform: Callable[[Image.Image], Tensor],
-        question_transform: Callable[[Union[List[str], str]], Tensor],
-        answer_transform: Callable[[Union[List[str], str]], Tensor],
-        split: str = "train",
-        answer_list: str = None,
-    ) -> None:
-        self.ann = []
-        for f in ann_file:
-            self.ann += json.load(open(f, "r"))
-
-        self.vqa_root = vqa_root
-        self.vg_root = vg_root
-        self.image_transform = image_transform
-        self.question_transform = question_transform
-        self.answer_transform = answer_transform
-        self.split = split
-
-        if split == "test":
-            self.answer_list = json.load(open(answer_list, "r"))
-            self.answer_input_ids = self.answer_transform(self.answer_list)
-            self.answer_attention_mask = (self.answer_input_ids != 0).type(torch.long)
-
-    def __len__(self) -> int:
-        return len(self.ann)
-
-    def __getitem__(
-        self, index: int
-    ) -> Union[
-        Tuple[Tensor, Tensor, int], Tuple[Tensor, Tensor, List[Tensor], List[float]]
-    ]:
-        ann = self.ann[index]
-
-        image_root = self.vqa_root if ann["dataset"] == "vqa" else self.vg_root
-        image_path = os.path.join(image_root, ann["image"])
-        image = Image.open(image_path).convert("RGB")
-        image = self.image_transform(image)
-        question = self.question_transform(ann["question"])
-
-        if self.split == "test":
-            return image, question, ann["question_id"]
-
-        elif self.split == "train":
-            if ann["dataset"] == "vqa":
-                # Each VQA sample question has a list of answers (with potential repeats)
-                # answer_weight[answer] = count(answer) / len(answers for the question)
-                answer_weights = {}
-                for answer in ann["answer"]:
-                    if answer in answer_weights.keys():
-                        answer_weights[answer] += 1 / len(ann["answer"])
-                    else:
-                        answer_weights[answer] = 1 / len(ann["answer"])
-
-                answers = list(answer_weights.keys())
-                answer_weights = list(answer_weights.values())
-
-            elif ann["dataset"] == "vg":
-                # A VG sample question has one answer so assign it a constant weight (0.5)
-                answers = [ann["answer"]]
-                answer_weights = [0.5]
-
-            answers = list(self.answer_transform(answers))
-
-            return image, question, answers, answer_weights
-
-        else:
-            raise ValueError("dataset split should be train or test")