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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
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)
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
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
):
self.split = split
self.dataset = dataset
self.bpe = bpe
self.src_dict = src_dict
self.tgt_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.bos = src_dict.bos()
self.eos = src_dict.eos()
self.pad = src_dict.pad()
self.bos_item = torch.LongTensor([self.bos])
self.eos_item = torch.LongTensor([self.eos])
self.transtab = str.maketrans({key: None for key in string.punctuation})
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: image.convert("RGB"),
transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
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])
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(" what does the image describe?")
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 with the following keys:
"""
return collate(samples, pad_idx=self.pad, eos_idx=self.eos) |