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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 random
import functools
import torch
import base64
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from torchvision.transforms import functional as F
from PIL import Image, ImageFile
from zhconv import convert
import unicodedata
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)
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])
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
},
"target": target,
}
return batch
def ocr_resize(img, patch_image_size, is_document=False):
img = img.convert("RGB")
width, height = img.size
if is_document:
new_height, new_width = 64, 1920
else:
if width >= height:
new_width = max(64, patch_image_size)
new_height = max(64, int(patch_image_size * (height / width)))
top = random.randint(0, patch_image_size - new_height)
bottom = patch_image_size - new_height - top
left, right = 0, 0
else:
new_height = max(64, patch_image_size)
new_width = max(64, int(patch_image_size * (width / height)))
left = random.randint(0, patch_image_size - new_width)
right = patch_image_size - new_width - left
top, bottom = 0, 0
img_new = F.resize(
img,
[new_height, new_width],
interpolation=InterpolationMode.BICUBIC,
)
if is_document:
img_split = transforms.ToTensor()(img_new).chunk(4, dim=-1)
img_new = transforms.ToPILImage()(torch.cat(img_split, dim=-2))
new_width, new_height = img_new.size
top = random.randint(0, patch_image_size - new_height)
bottom = patch_image_size - new_height - top
left, right = 0, 0
img_new = F.pad(img_new, padding=[left, top, right, bottom], padding_mode="edge")
assert img_new.size == (patch_image_size, patch_image_size)
return img_new
class OcrDataset(OFADataset):
def __init__(
self,
split,
dataset,
bpe,
src_dict,
tgt_dict=None,
max_src_length=80,
max_tgt_length=30,
patch_image_size=224,
imagenet_default_mean_and_std=False,
is_document=False,
):
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
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.patch_resize_transform = transforms.Compose(
[
lambda image: ocr_resize(
image, patch_image_size, is_document=is_document
),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
]
)
self.bpe = bpe
if type(bpe).__name__ == 'GPT2BPE':
self.prompt = " what are the texts on the image?"
elif type(bpe).__name__ == 'BertBPE':
self.prompt = "图片上的文字是什么?"
def __getitem__(self, index):
uniq_id, image, caption = self.dataset[index]
image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
patch_image = self.patch_resize_transform(image)
patch_mask = torch.tensor([True])
caption = unicodedata.normalize("NFKC", convert(caption, "zh-hans"))
if type(self.bpe).__name__ == 'GPT2BPE':
caption_token_list = caption.lower().strip().split()
tgt_caption = ' '.join(caption_token_list[:self.max_tgt_length])
elif type(self.bpe).__name__ == 'BertBPE':
tgt_caption = caption[: self.max_tgt_length].lower()
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])
example = {
"id": uniq_id,
"source": src_item,
"patch_image": patch_image,
"patch_mask": patch_mask,
"target": target_item,
"prev_output_tokens": prev_output_item,
}
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 required for the task
"""
return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
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