Spaces:
Runtime error
Runtime error
File size: 6,312 Bytes
ee21b96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
# 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
import utils.transforms as T
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])
w_resize_ratios = torch.stack([s["w_resize_ratio"] for s in samples], dim=0)
h_resize_ratios = torch.stack([s["h_resize_ratio"] for s in samples], dim=0)
region_coords = torch.stack([s['region_coord'] for s in samples], dim=0)
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,
"w_resize_ratios": w_resize_ratios,
"h_resize_ratios": h_resize_ratios,
"region_coords": region_coords
}
return batch
class RefcocoDataset(OFADataset):
def __init__(
self,
split,
dataset,
bpe,
src_dict,
tgt_dict=None,
max_src_length=80,
max_tgt_length=30,
patch_image_size=512,
imagenet_default_mean_and_std=False,
num_bins=1000,
max_image_size=512
):
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.num_bins = num_bins
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]
# for positioning
self.positioning_transform = T.Compose([
T.RandomResize([patch_image_size], max_size=patch_image_size),
T.ToTensor(),
T.Normalize(mean=mean, std=std, max_image_size=max_image_size)
])
if type(bpe).__name__ == 'GPT2BPE':
self.prompt = ' which region does the text " {} " describe?'
elif type(bpe).__name__ == 'BertBPE':
self.prompt = '这段文字" {} "描述的是哪个区域？'
def __getitem__(self, index):
uniq_id, base64_str, text, region_coord = self.dataset[index]
image = Image.open(BytesIO(base64.urlsafe_b64decode(base64_str))).convert("RGB")
w, h = image.size
boxes_target = {"boxes": [], "labels": [], "area": [], "size": torch.tensor([h, w])}
x0, y0, x1, y1 = region_coord.strip().split(',')
region = torch.tensor([float(x0), float(y0), float(x1), float(y1)])
boxes_target["boxes"] = torch.tensor([[float(x0), float(y0), float(x1), float(y1)]])
boxes_target["labels"] = np.array([0])
boxes_target["area"] = torch.tensor([(float(x1) - float(x0)) * (float(y1) - float(y0))])
patch_image, patch_boxes = self.positioning_transform(image, boxes_target)
resize_h, resize_w = patch_boxes["size"][0], patch_boxes["size"][1]
patch_mask = torch.tensor([True])
quant_x0 = "<bin_{}>".format(int((patch_boxes["boxes"][0][0] * (self.num_bins - 1)).round()))
quant_y0 = "<bin_{}>".format(int((patch_boxes["boxes"][0][1] * (self.num_bins - 1)).round()))
quant_x1 = "<bin_{}>".format(int((patch_boxes["boxes"][0][2] * (self.num_bins - 1)).round()))
quant_y1 = "<bin_{}>".format(int((patch_boxes["boxes"][0][3] * (self.num_bins - 1)).round()))
region_coord = "{} {} {} {}".format(quant_x0, quant_y0, quant_x1, quant_y1)
src_caption = self.pre_caption(text, self.max_src_length)
src_item = self.encode_text(self.prompt.format(src_caption))
tgt_item = self.encode_text(region_coord, use_bpe=False)
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,
"w_resize_ratio": resize_w / w,
"h_resize_ratio": resize_h / h,
"region_coord": region
}
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)
|