# 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 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])
ref_dict = None
if samples[0].get("ref_dict", None) is not None:
ref_dict = np.array([s['ref_dict'] for s in samples])
constraint_masks = None
if samples[0].get("constraint_mask", None) is not None:
constraint_masks = merge("constraint_mask")
decoder_prompts = None
if samples[0].get("decoder_prompt", None) is not None:
decoder_prompts = np.array([s['decoder_prompt'].tolist() for s 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
},
"ref_dict": ref_dict,
"constraint_masks": constraint_masks,
"decoder_prompts": decoder_prompts,
"target": target
}
return batch
class SnliVeDataset(OFADataset):
def __init__(
self,
split,
dataset,
bpe,
src_dict,
tgt_dict=None,
max_src_length=80,
max_tgt_length=30,
patch_image_size=224,
add_caption=False,
constraint_trie=None,
imagenet_default_mean_and_std=False,
prompt_type="none"
):
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.add_caption = add_caption
self.constraint_trie = constraint_trie
self.prompt_type = prompt_type
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, hypothesis, caption, label = self.dataset[index]
if label == 'contradiction':
label = 'no'
elif label == 'entailment':
label = 'yes'
elif label == 'neutral':
label = 'maybe'
else:
raise NotImplementedError
image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
patch_image = self.patch_resize_transform(image)
patch_mask = torch.tensor([True])
hypothesis = self.pre_caption(hypothesis, self.max_src_length)
src_item = self.encode_text(' does the image describe " {} "?'.format(hypothesis))
tgt_item = self.encode_text(" {}".format(label))
ref_dict = {label: 1.0}
if self.add_caption:
caption = self.pre_caption(caption, self.max_src_length)
src_item = self.encode_text(' can image and text1 " {} " imply text2 " {} "?'.format(caption, hypothesis))
src_item = torch.cat([self.bos_item, src_item, self.eos_item])
if self.prompt_type == 'none':
prev_output_item = torch.cat([self.bos_item, tgt_item])
target_item = torch.cat([prev_output_item[1:], self.eos_item])
decoder_prompt = self.bos_item
elif self.prompt_type == 'src':
prev_output_item = torch.cat([src_item, tgt_item])
target_item = torch.cat([prev_output_item[1:], self.eos_item])
decoder_prompt = src_item
elif self.prompt_type == 'prev_output':
prev_output_item = torch.cat([src_item[:-1], tgt_item])
target_item = torch.cat([prev_output_item[1:], self.eos_item])
decoder_prompt = src_item[:-1]
else:
raise NotImplementedError
target_item[:-len(tgt_item)-1] = self.tgt_dict.pad()
example = {
"id": uniq_id,
"source": src_item,
"patch_image": patch_image,
"patch_mask": patch_mask,
"target": target_item,
"prev_output_tokens": prev_output_item,
"decoder_prompt": decoder_prompt,
"ref_dict": ref_dict,
}
if self.constraint_trie is not None:
constraint_mask = torch.zeros((len(target_item), len(self.tgt_dict))).bool()
start_idx = len(target_item) - len(tgt_item) - 1
for i in range(len(target_item)-len(tgt_item)-1, len(target_item)):
constraint_prefix_token = [self.tgt_dict.bos()] + target_item[start_idx:i].tolist()
constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
constraint_mask[i][constraint_nodes] = True
example["constraint_mask"] = constraint_mask
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)