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# ------------------------------------------------------------------------
# Modified from OFA (https://github.com/OFA-Sys/OFA)
# Copyright 2022 The OFA-Sys Team.
# All rights reserved.
# This source code is licensed under the Apache 2.0 license
# found in the LICENSE file in the root directory.
# ------------------------------------------------------------------------
# Modifications Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
from io import BytesIO
import logging
import warnings
import numpy as np
import torch
import base64
import utils.transforms as T
import math
import os
from PIL import Image, ImageFile
from data import data_utils
from data.base_dataset import BaseDataset
from bert.tokenization_bert import BertTokenizer
ImageFile.LOAD_TRUNCATED_IMAGES = True
ImageFile.MAX_IMAGE_PIXELS = None
Image.MAX_IMAGE_PIXELS = None
logger = logging.getLogger(__name__)
warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
class RefcocoPretrainDataset(BaseDataset):
def __init__(
self,
split,
dataset,
bpe,
src_dict,
tgt_dict=None,
max_src_length=80,
max_tgt_length=30,
patch_image_size=512,
imagenet_default_mean_and_std=False,
num_bins=1000,
max_image_size=512,
image_path="../../datasets/images"
):
super().__init__(split, dataset, bpe, src_dict, tgt_dict)
self.max_src_length = max_src_length
self.max_tgt_length = max_tgt_length
self.patch_image_size = patch_image_size
self.num_bins = num_bins
self.image_path = image_path
if imagenet_default_mean_and_std:
mean = IMAGENET_DEFAULT_MEAN
std = IMAGENET_DEFAULT_STD
else:
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
# for positioning
self.positioning_transform = T.Compose([
T.RandomResize([patch_image_size], max_size=patch_image_size),
T.ToTensor(),
T.Normalize(mean=mean, std=std, max_image_size=max_image_size)
])
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
def __getitem__(self, index):
uniq_id, img_file, text, region_coord = self.dataset[index]
img_path = os.path.join(self.image_path, img_file)
image = Image.open(img_path).convert("RGB")
w, h = image.size
boxes_target = {"boxes": [], "labels": [], "area": [], "size": torch.tensor([h, w])}
x0, y0, x1, y1 = region_coord.strip().split(',')
region = torch.tensor([float(x0), float(y0), float(x1), float(y1)])
boxes_target["boxes"] = torch.tensor([[float(x0), float(y0), float(x1), float(y1)]])
boxes_target["labels"] = np.array([0])
boxes_target["area"] = torch.tensor([(float(x1) - float(x0)) * (float(y1) - float(y0))])
patch_image, patch_boxes = self.positioning_transform(image, boxes_target)
resize_h, resize_w = patch_boxes["size"][0], patch_boxes["size"][1]
patch_mask = torch.tensor([True])
quant_box = [patch_boxes["boxes"][0][i] * (self.num_bins - 1) for i in range(4)]
quant_box = np.array(quant_box).reshape(2, 2)
quant_box11 = [[math.floor(p[0]), math.floor(p[1])] for p in quant_box]
quant_box21 = [[math.ceil(p[0]), math.floor(p[1])] for p in quant_box]
quant_box12 = [[math.floor(p[0]), math.ceil(p[1])] for p in quant_box]
quant_box22 = [[math.ceil(p[0]), math.ceil(p[1])] for p in quant_box]
# compute linear interpolation coefficient (0 for bos token)
delta_x1 = torch.tensor([0] + [p[0] - math.floor(p[0]) for p in quant_box])
delta_y1 = torch.tensor([0] + [p[1] - math.floor(p[1]) for p in quant_box])
delta_x2 = 1 - delta_x1
delta_y2 = 1 - delta_y1
region_coord11 = " ".join([f"<bin_{int(p[0])}_{int(p[1])}>" for p in quant_box11])
region_coord21 = " ".join([f"<bin_{int(p[0])}_{int(p[1])}>" for p in quant_box21])
region_coord12 = " ".join([f"<bin_{int(p[0])}_{int(p[1])}>" for p in quant_box12])
region_coord22 = " ".join([f"<bin_{int(p[0])}_{int(p[1])}>" for p in quant_box22])
src_caption = self.pre_caption(text, self.max_src_length)
prompt = ' which region does the text " {} " describe?'.format(src_caption)
# tgt for input
tgt_item11 = self.encode_text(region_coord11, use_bpe=False)
tgt_item12 = self.encode_text(region_coord12, use_bpe=False)
tgt_item21 = self.encode_text(region_coord21, use_bpe=False)
tgt_item22 = self.encode_text(region_coord22, use_bpe=False)
# tgt for output
tgt_box = torch.reshape(patch_boxes["boxes"][0], (2, 2))
target_item = torch.cat([tgt_box, torch.tensor([[1, 1]])], dim=0) # [1, 1] is padding token for eos
#target_item = torch.cat([tgt_item, self.eos_item])
prev_output_item11 = torch.cat([self.bos_item, tgt_item11])
prev_output_item12 = torch.cat([self.bos_item, tgt_item12])
prev_output_item21 = torch.cat([self.bos_item, tgt_item21])
prev_output_item22 = torch.cat([self.bos_item, tgt_item22])
example = {
"id": uniq_id,
"source": prompt,
"patch_image": patch_image,
"patch_mask": patch_mask,
"target": target_item,
"prev_output_tokens_11": prev_output_item11,
"prev_output_tokens_12": prev_output_item12,
"prev_output_tokens_21": prev_output_item21,
"prev_output_tokens_22": prev_output_item22,
"delta_x1": delta_x1,
"delta_y1": delta_y1,
"delta_x2": delta_x2,
"delta_y2": delta_y2,
"w_resize_ratio": resize_w / w,
"h_resize_ratio": resize_h / h,
"region_coord": region,
"token_type": torch.tensor([0, 0, 2])
}
return example
def collate(self, samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}
def merge(key):
return data_utils.collate_tokens(
[s[key] for s in samples],
pad_idx,
eos_idx=eos_idx,
)
id = np.array([s["id"] for s in samples])
captions = [s["source"] for s in samples]
tokenized = self.tokenizer.batch_encode_plus(captions, padding="longest", return_tensors="pt")
src_tokens = tokenized["input_ids"]
att_masks = tokenized["attention_mask"]
src_lengths = torch.LongTensor(att_masks.ne(0).long().sum())
patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
w_resize_ratios = torch.stack([s["w_resize_ratio"] for s in samples], dim=0)
h_resize_ratios = torch.stack([s["h_resize_ratio"] for s in samples], dim=0)
delta_x1 = torch.stack([s["delta_x1"] for s in samples], dim=0)
delta_y1 = torch.stack([s["delta_y1"] for s in samples], dim=0)
delta_x2 = torch.stack([s["delta_x2"] for s in samples], dim=0)
delta_y2 = torch.stack([s["delta_y2"] for s in samples], dim=0)
region_coords = torch.stack([s['region_coord'] for s in samples], dim=0)
target = merge("target")
tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
ntokens = tgt_lengths.sum().item()
prev_output_tokens_11 = merge("prev_output_tokens_11")
prev_output_tokens_12 = merge("prev_output_tokens_12")
prev_output_tokens_21 = merge("prev_output_tokens_21")
prev_output_tokens_22 = merge("prev_output_tokens_22")
token_type = merge("token_type")
batch = {
"id": id,
"nsentences": len(samples),
"ntokens": ntokens,
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
"att_masks": att_masks,
"patch_images": patch_images,
"patch_masks": patch_masks,
"prev_output_tokens_11": prev_output_tokens_11,
"prev_output_tokens_12": prev_output_tokens_12,
"prev_output_tokens_21": prev_output_tokens_21,
"prev_output_tokens_22": prev_output_tokens_22,
"delta_x1": delta_x1,
"delta_y1": delta_y1,
"delta_x2": delta_x2,
"delta_y2": delta_y2
},
"target": target,
"token_type": token_type,
"w_resize_ratios": w_resize_ratios,
"h_resize_ratios": h_resize_ratios,
"region_coords": region_coords
}
return batch
def collater(self, samples, pad_to_length=None):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch containing the data of the task
"""
return self.collate(samples, pad_idx=self.pad, eos_idx=self.eos) |