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# 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.
from io import BytesIO
import logging
import warnings
import string
import numpy as np
import torch
import base64
from torchvision import transforms
from PIL import Image, ImageFile
from data import data_utils
from data.ofa_dataset import OFADataset
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)
from utils.vision_helper import RandomAugment
import utils.transforms as T
import os
def collate(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])
src_tokens = merge("source")
src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
patch_types = torch.cat([sample['patch_type'] for sample in samples])
prev_output_tokens = None
target = None
if samples[0].get("target", None) is not None:
target = merge("target")
tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
ntokens = tgt_lengths.sum().item()
if samples[0].get("prev_output_tokens", None) is not None:
prev_output_tokens = merge("prev_output_tokens")
else:
ntokens = src_lengths.sum().item()
batch = {
"id": id,
"nsentences": len(samples),
"ntokens": ntokens,
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
"patch_images": patch_images,
"patch_masks": patch_masks,
"prev_output_tokens": prev_output_tokens,
"patch_types": patch_types,
},
"target": target,
}
return batch
class CaptionDataset(OFADataset):
def __init__(
self,
split,
dataset,
bpe,
src_dict,
tgt_dict=None,
max_src_length=128,
max_tgt_length=30,
patch_image_size=224,
imagenet_default_mean_and_std=False,
scst=False,
use_dataaug=False,
read_from_img_path=False,
image_dir='/gpfsscratch/rech/dyf/ugz83ue/data',
):
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.scst = scst
self.transtab = str.maketrans({key: None for key in string.punctuation})
self.read_from_img_path = read_from_img_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]
self.split = split
if self.split != 'train' or not use_dataaug:
self.patch_resize_transform = transforms.Compose([
lambda image: image.convert("RGB"),
transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
else:
scales = np.arange(patch_image_size, 481).tolist()
self.patch_resize_transform = transforms.Compose([
lambda image: image.convert("RGB"),
T.RandomResize(scales, max_size=672),
transforms.CenterCrop(patch_image_size),
RandomAugment(2, 7, isPIL=True, augs=['Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness',
'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
if type(bpe).__name__ == 'GPT2BPE':
self.prompt = " what does the image describe?"
elif type(bpe).__name__ == 'BertBPE':
self.prompt = "图片描述了什么内容?"
self.image_dir = image_dir
def __getitem__(self, index):
uniq_id, image, caption = self.dataset[index]
if self.read_from_img_path or '.jpg' in image:
image_path = os.path.join(self.image_dir, image)
image = Image.open(image_path).convert("RGB")
else:
image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
patch_image = self.patch_resize_transform(image)
patch_mask = torch.tensor([True])
if self.split == 'train' and not self.scst:
caption = caption.translate(self.transtab).strip()
caption_token_list = caption.strip().split()
tgt_caption = ' '.join(caption_token_list[:self.max_tgt_length])
else:
caption = ' '.join(caption.strip().split())
caption_list = [cap.translate(self.transtab).strip() for cap in caption.strip().split('&&')]
tgt_caption = '&&'.join(caption_list)
src_item = self.encode_text(self.prompt)
tgt_item = self.encode_text(" {}".format(tgt_caption))
src_item = torch.cat([self.bos_item, src_item, self.eos_item])
target_item = torch.cat([tgt_item, self.eos_item])
prev_output_item = torch.cat([self.bos_item, tgt_item])
patch_type = torch.tensor([0])
example = {
"id": uniq_id,
"source": src_item,
"patch_image": patch_image,
"patch_mask": patch_mask,
"target": target_item,
"prev_output_tokens": prev_output_item,
"patch_type": patch_type,
}
return example
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 collate(samples, pad_idx=self.pad, eos_idx=self.eos)
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