Spaces:
Sleeping
Sleeping
File size: 9,630 Bytes
749745d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
"""
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
import torch
import json
from PIL import Image, ImageDraw
from .modulated_coco import ConvertCocoPolysToMask
from .tsv import ODTSVDataset
from pycocotools.coco import COCO
from maskrcnn_benchmark.structures.bounding_box import BoxList
import random
from .od_to_grounding import convert_object_detection_to_grounding_optimized_for_od, check_for_positive_overflow, sanity_check_target_after_processing, od_to_grounding_optimized_streamlined
from ._od_to_description import DescriptionConverter
import pdb
from collections import defaultdict
class CocoDetectionTSV(ODTSVDataset):
def __init__(
self,
name,
yaml_file,
transforms,
return_tokens,
tokenizer,
extra_fields,
random_sample_negative=-1,
add_detection_prompt=False,
add_detection_prompt_advanced=False,
use_od_data_aug=False,
control_probabilities={},
disable_shuffle=False,
prompt_engineer_version="v2",
prompt_limit_negative=-1,
positive_question_probability=0.6,
negative_question_probability=0.8,
full_question_probability=0.5,
disable_clip_to_image=False,
separation_tokens=" ",
no_mask_for_od=False,
max_num_labels=-1,
max_query_len=256,
od_to_grounding_version="legacy",
description_file = None,
similarity_file = None,
**kwargs
):
super(CocoDetectionTSV, self).__init__(yaml_file, extra_fields, **kwargs)
self._transforms = transforms
self.name = name
self.max_query_len = max_query_len
self.prepare = ConvertCocoPolysToMask(
return_masks=False, return_tokens=return_tokens, tokenizer=tokenizer, max_query_len=max_query_len
)
self.tokenizer = tokenizer
self.control_probabilities = control_probabilities
self.random_sample_negative = random_sample_negative
self.add_detection_prompt = add_detection_prompt
self.add_detection_prompt_advanced = add_detection_prompt_advanced
self.use_od_data_aug = use_od_data_aug
self.prompt_engineer_version = prompt_engineer_version
self.prompt_limit_negative = prompt_limit_negative
self.positive_question_probability = positive_question_probability
self.negative_question_probability = negative_question_probability
self.full_question_probability = full_question_probability
self.separation_tokens = separation_tokens
self.disable_clip_to_image = disable_clip_to_image
self.disable_shuffle = disable_shuffle
self.no_mask_for_od = no_mask_for_od
self.max_num_labels = max_num_labels
self.od_to_grounding_version = od_to_grounding_version
self.description_file = description_file
self.similarity_file = similarity_file
if "description" in self.od_to_grounding_version:
self.od_grounding_converter = DescriptionConverter(
self.description_file,
self.od_to_grounding_version,
[],
self.ind_to_class,
self.similarity_file,)
### stat
self.pos_rate = defaultdict(list)
def __len__(self):
return super(CocoDetectionTSV, self).__len__()
def categories(self, no_background=True):
categories = self.coco.dataset["categories"]
label_list = {}
for index, i in enumerate(categories):
# assert(index + 1 == i["id"])
if not no_background or (i["name"] != "__background__" and i["id"] != 0):
label_list[i["id"]] = i["name"]
return label_list
def __getitem__(self, idx):
# tgt is a BoxList
img, target, _, scale = super(CocoDetectionTSV, self).__getitem__(idx)
image_id = self.get_img_id(idx)
restricted_negative_list = None
if not self.disable_clip_to_image:
target = target.clip_to_image(remove_empty=True)
original_box_num = len(target)
target, positive_caption_length = check_for_positive_overflow(
target, self.ind_to_class, self.tokenizer, self.max_query_len - 2
) # leave some space for the special tokens
if len(target) < original_box_num:
print("WARNING: removed {} boxes due to positive caption overflow".format(original_box_num - len(target)))
if "mixed" in self.od_to_grounding_version: # 70% v.s. 30%
if random.random() < 0.7:
annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions, target = self.od_grounding_converter.train_od_to_grounding(
target=target,
image_id=image_id,
ind_to_class=self.ind_to_class,
tokenizer=self.tokenizer,
random_sample_negative=self.random_sample_negative,
)
else:
annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions = convert_object_detection_to_grounding_optimized_for_od(
target=target,
image_id=image_id,
ind_to_class=self.ind_to_class,
disable_shuffle=self.disable_shuffle,
add_detection_prompt=self.add_detection_prompt,
add_detection_prompt_advanced=self.add_detection_prompt_advanced,
random_sample_negative=self.random_sample_negative,
control_probabilities=self.control_probabilities,
restricted_negative_list=restricted_negative_list,
separation_tokens=self.separation_tokens,
max_num_labels=self.max_num_labels,
positive_caption_length=positive_caption_length,
tokenizer=self.tokenizer,
max_seq_length=self.max_query_len - 2,
)
elif "description" in self.od_to_grounding_version:
annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions, target = self.od_grounding_converter.train_od_to_grounding(
target=target,
image_id=image_id,
ind_to_class=self.ind_to_class,
tokenizer=self.tokenizer,
random_sample_negative=self.random_sample_negative,
)
elif self.od_to_grounding_version != "legacy":
annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions, target = od_to_grounding_optimized_streamlined(
target=target,
image_id=image_id,
ind_to_class=self.ind_to_class,
tokenizer=self.tokenizer,
od_to_grounding_version=self.od_to_grounding_version,
)
else:
annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions = convert_object_detection_to_grounding_optimized_for_od(
target=target,
image_id=image_id,
ind_to_class=self.ind_to_class,
disable_shuffle=self.disable_shuffle,
add_detection_prompt=self.add_detection_prompt,
add_detection_prompt_advanced=self.add_detection_prompt_advanced,
random_sample_negative=self.random_sample_negative,
control_probabilities=self.control_probabilities,
restricted_negative_list=restricted_negative_list,
separation_tokens=self.separation_tokens,
max_num_labels=self.max_num_labels,
positive_caption_length=positive_caption_length,
tokenizer=self.tokenizer,
max_seq_length=self.max_query_len - 2,
)
# assert(len(self.tokenizer.tokenize(caption)) <= self.max_query_len-2)
anno = {
"image_id": image_id,
"annotations": annotations,
"caption": caption,
"label_to_positions": label_to_positions,
}
if "spans" in target.extra_fields:
anno["spans"] = target.extra_fields["spans"]
if not isinstance(anno["spans"], list):
anno["spans"] = anno["spans"].tolist()
anno["greenlight_span_for_masked_lm_objective"] = greenlight_span_for_masked_lm_objective
if self.no_mask_for_od:
anno["greenlight_span_for_masked_lm_objective"].append((-1, -1, -1))
img, anno = self.prepare(img, anno, box_format="xyxy")
if self._transforms is not None:
img, target = self._transforms(img, target)
# add additional property
for ann in anno:
target.add_field(ann, anno[ann])
# sanity_check_target_after_processing(target)
return img, target, idx
def get_raw_image(self, idx):
image, *_ = super(CocoDetectionTSV, self).__getitem__(idx)
return image
def get_img_id(self, idx):
line_no = self.get_line_no(idx)
if self.label_tsv is not None:
row = self.label_tsv.seek(line_no)
img_id = row[0]
try:
return int(img_id)
except:
return idx
|