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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 base64
import random
import numpy as np
import torch
from PIL import Image, ImageFile
from itertools import chain
from data.ofa_dataset import OFADataset
from data import data_utils
from PIL import Image
from io import BytesIO
import base64
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)
def collate(
samples,
pad_idx,
eos_idx,
left_pad_source=False,
left_pad_target=False,
):
if len(samples) == 0:
return {}
def merge(key, left_pad, move_eos_to_beginning=False):
return data_utils.collate_tokens(
[s[key] for s in samples],
pad_idx,
eos_idx,
left_pad,
move_eos_to_beginning,
)
id = np.array([s["id"] for s in samples])
src_tokens = merge("source", left_pad=left_pad_source)
# sort by descending source length
src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
code_images = np.array([s["code_image"] for s in samples])
code_masks = torch.cat([sample['code_mask'] for sample in samples])
prev_output_tokens = None
target = None
if samples[0].get("target", None) is not None:
target = merge("target", left_pad=left_pad_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", left_pad=left_pad_target)
else:
ntokens = src_lengths.sum().item()
batch = {
"id": id,
"nsentences": len(samples),
"ntokens": ntokens,
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
"code_masks": code_masks,
"prev_output_tokens": prev_output_tokens
},
"code_images": code_images,
"target": target
}
return batch
def preprocess_vqgan(x):
x = 2. * x - 1.
return x
class ImageGenDataset(OFADataset):
def __init__(
self,
split,
dataset,
bpe,
src_dict,
tgt_dict=None,
max_src_length=128,
code_dict_size=8192,
code_image_size=256,
num_bins=1000
):
super().__init__(split, dataset, bpe, src_dict, tgt_dict)
self.max_src_length = max_src_length
self.code_dict_size = code_dict_size
self.num_codes = (code_image_size // 8) ** 2
self.num_bins = num_bins
slice_id = self.dataset.slice_id
empty_img = Image.new('RGB', (code_image_size, code_image_size))
empty_img.save(f'temp_{slice_id}.png')
img = Image.open(f'temp_{slice_id}.png')
img_buffer = BytesIO()
img.save(img_buffer, format=img.format)
byte_data = img_buffer.getvalue()
self.empty_image_base64 = base64.urlsafe_b64encode(byte_data)
def __getitem__(self, index):
data = self.dataset[index]
if len(data) == 2:
uniq_id, text = data
image_code = [0] * 1024
image = self.empty_image_base64
elif len(data) == 3:
uniq_id, text, image_code = data
image_code = [int(num) for num in image_code.strip().split()]
image = self.empty_image_base64
elif len(data) == 4:
uniq_id, image, text, image_code = data
image_code = [int(num) for num in image_code.strip().split()]
else:
raise NotImplementedError
code_mask = torch.tensor([True])
image_code = torch.LongTensor(image_code)
tgt_item = image_code + len(self.src_dict) - self.code_dict_size - self.num_bins
target_item = torch.cat([tgt_item, self.eos_item])
prev_output_item = torch.cat([self.bos_item, tgt_item])
caption_token_list = text.strip().split()
caption = ' '.join(caption_token_list[:self.max_src_length])
src_item = self.encode_text(
" what is the complete image? caption: {}".format(caption),
append_bos=True,
append_eos=True
)
example = {
"id": uniq_id,
"source": src_item,
"code_mask": code_mask,
"code_image": image,
"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 of the task
"""
return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
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