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import torch
import torch.distributed as dist
import time
from torchvision.ops import nms
import random
import numpy as np
from PIL import Image, ImageDraw
import pdb
from maskrcnn_benchmark.structures.bounding_box import BoxList
from .modulated_coco import ConvertCocoPolysToMask
from .tsv import ODTSVDataset, TSVYamlDataset
from .od_to_grounding import sanity_check_target_after_processing
class CaptionTSV(TSVYamlDataset):
def __init__(self,
yaml_file,
transforms,
return_tokens,
return_masks,
tokenizer,
caption_min_box=1,
replace_clean_label=False,
further_screen=False,
caption_conf=0.5,
caption_nms=-1,
pack_random_caption_number=0,
inference_caption=False,
sample_negative_for_grounding_data=-1,
random_pack_prob=-1.0,
no_random_pack_probability=0.0,
safeguard_positive_caption=True,
mlm_obj_for_only_positive=False,
caption_format_version="v1",
local_debug=False,
max_query_len=256,
**kwargs
):
super(CaptionTSV, self).__init__(yaml_file, None, replace_clean_label)
self.yaml_file = yaml_file
self._transforms = transforms
self.max_query_len = max_query_len
self.prepare = ConvertCocoPolysToMask(return_masks=return_masks,
return_tokens=return_tokens,
tokenizer=tokenizer,
max_query_len=max_query_len)
self.tokenizer = tokenizer
self.caption_min_box = caption_min_box
self.replace_clean_label = replace_clean_label
self.further_screen = further_screen
self.pack_random_caption_number = pack_random_caption_number
self.caption_format_version = caption_format_version
self.caption_conf = caption_conf
self.caption_nms = caption_nms
self.inference_caption = inference_caption
self.sample_negative_for_grounding_data = sample_negative_for_grounding_data
self.random_pack_prob = random_pack_prob
self.no_random_pack_probability = no_random_pack_probability
self.safeguard_positive_caption = safeguard_positive_caption
self.mlm_obj_for_only_positive = mlm_obj_for_only_positive
try:
self.rank = dist.get_rank()
except:
self.rank = 0
def __len__(self):
return super(CaptionTSV, self).__len__()
def pack_caption(self, positive_caption, negative_captions, original_tokens_positive):
if len(negative_captions) == 0:
return positive_caption, original_tokens_positive, [(0, len(positive_caption))]
if self.safeguard_positive_caption:
length_of_each_caption = []
for caption in negative_captions + [positive_caption]:
tokenized = self.tokenizer(caption, return_tensors="pt")
length_of_each_caption.append(tokenized.input_ids.size(-1))
max_length = self.max_query_len - length_of_each_caption[-1]
indexes = list(range(len(negative_captions)))
random.shuffle(indexes)
new_caption_list = [positive_caption]
for i in indexes:
if length_of_each_caption[i] < max_length:
new_caption_list.append(negative_captions[i])
max_length -= length_of_each_caption[i]
else:
new_caption_list = [positive_caption] + negative_captions
random.shuffle(new_caption_list)
new_caption = ''
for i in new_caption_list:
if i == positive_caption:
start_position = len(new_caption)
new_caption += i
if not i.endswith("."):
new_caption += "."
new_caption += " "
# shift the token positions the boxes are aligned to
for index, i in enumerate(original_tokens_positive):
original_tokens_positive[index] = [tuple(j) for j in i]
for i in original_tokens_positive:
for index, j in enumerate(i):
i[index] = (j[0] + start_position, j[1] + start_position)
return new_caption, original_tokens_positive, [(start_position, start_position + len(positive_caption))]
def __get_negative_captions__(self, idx, negative_size=7):
negative_captions = []
for i in range(negative_size):
img, anno, _, scale = super(CaptionTSV, self).__getitem__(np.random.choice(len(self)))
caption = anno["caption"]
negative_captions.append(caption)
return negative_captions
def __getitem__(self, idx):
try:
img, anno, _, scale = super(CaptionTSV, self).__getitem__(idx)
if self.inference_caption:
caption = None
if isinstance(anno, list):
caption = anno[0]["caption"] # inference mode for bing
anno = []
elif len(anno) == 1:
caption = anno["caption"] # inference mode for googlecc
anno = []
else:
caption = " ".join(anno["captions"])
anno = []
else:
'''
An example
{'img_h': 1154, 'img_w': 1600, 'caption': 'xxx', 'tokens_positive': [[[47, 50], [51, 53], [54, 59]], [[32, 35], [36, 41]], [[32, 35], [36, 41]], [[0, 3], [3, 6], [6, 10], [11, 16], [17, 19], [20, 23]], [[32, 35], [36, 41]], [[32, 35], [36, 41]]], 'bboxes': [[7.344961166381836, 10.479412078857422, 1592.2679443359375, 1090.0028076171875], [950.32861328125, 346.572021484375, 1333.2373046875, 679.3215942382812], [927.44140625, 342.7712707519531, 1389.833984375, 719.5758666992188], [90.48786163330078, 363.67572021484375, 1381.8631591796875, 1078.687744140625], [122.84217071533203, 422.6786193847656, 507.845703125, 667.2651977539062], [80.62384033203125, 416.500244140625, 563.1666259765625, 734.603271484375]], 'scores': [0.7966700196266174, 0.8952182531356812, 0.8186006546020508, 0.9995516538619995, 0.8021856546401978, 0.8923134803771973]}
'''
if len(anno["bboxes"]) < self.caption_min_box: # Retry triggered!
return self[np.random.choice(len(self))]
if self.caption_format_version == "v2":
anno = self.convert_anno_from_v2_to_v1(anno)
try:
if self.further_screen:
conf = self.caption_conf
nms_thre = self.caption_nms
bboxes = torch.as_tensor(anno["bboxes"]).float()
scores = torch.as_tensor(anno["scores"])
tokens_positive = anno["tokens_positive"]
# print("\n\n\n\n tokens_positive in original data", tokens_positive)
keep = scores > conf
scores = scores[keep]
bboxes = bboxes[keep]
tokens_positive = [i for index, i in enumerate(tokens_positive) if keep[index]]
assert (len(tokens_positive) == len(bboxes) == len(scores))
if len(bboxes) < self.caption_min_box: # Retry triggered!
return self[np.random.choice(len(self))]
if nms_thre > 0:
keep = nms(boxes=bboxes, scores=scores, iou_threshold=nms_thre)
scores = scores[keep]
bboxes = bboxes[keep]
tokens_positive = [tokens_positive[i] for i in keep]
assert (len(tokens_positive) == len(bboxes) == len(scores))
# Write back
anno["bboxes"] = bboxes.tolist()
anno["scores"] = scores.tolist()
anno["tokens_positive"] = tokens_positive
boxes = torch.as_tensor(anno["bboxes"])
if len(boxes) < self.caption_min_box: # Retry triggered!
return self[np.random.choice(len(self))]
target = BoxList(boxes, (anno["img_w"], anno["img_h"]), mode="xyxy")
target = target.clip_to_image(remove_empty=True)
caption = anno["caption"]
# print("original caption", caption)
empty_everything = False
if self.sample_negative_for_grounding_data != -1:
if random.random() < self.sample_negative_for_grounding_data:
empty_everything = True
if empty_everything:
caption = self.__get_negative_captions__(idx, negative_size=1)[0]
if self.pack_random_caption_number != 0:
if self.random_pack_prob != -1.0:
if random.random() < self.no_random_pack_probability:
negative_pack_number = 0
elif random.random() < self.random_pack_prob:
negative_pack_number = self.pack_random_caption_number
else:
negative_pack_number = np.random.choice(self.pack_random_caption_number)
else:
negative_pack_number = self.pack_random_caption_number
negative_captions = self.__get_negative_captions__(idx, negative_size=negative_pack_number)
caption, anno["tokens_positive"], greenlight_span_for_masked_lm_objective = self.pack_caption(
caption, negative_captions, anno["tokens_positive"])
else:
greenlight_span_for_masked_lm_objective = [(0, len(caption))]
if not self.mlm_obj_for_only_positive:
greenlight_span_for_masked_lm_objective = [(0, len(caption))]
new_anno = []
areas = target.area()
for i in range(len(target)):
new_anno_i = {}
new_anno_i["area"] = areas[i]
new_anno_i["iscrowd"] = 0
new_anno_i["image_id"] = idx
new_anno_i["category_id"] = 1 # following vg and others
new_anno_i["id"] = None
new_anno_i['bbox'] = target.bbox[i].numpy().tolist()
new_anno_i["tokens_positive"] = anno["tokens_positive"][i]
new_anno.append(new_anno_i)
except:
return self[np.random.choice(len(self))]
anno = new_anno
if empty_everything:
anno = []
annotations = {"image_id": idx, "annotations": anno, "caption": caption}
annotations["greenlight_span_for_masked_lm_objective"] = greenlight_span_for_masked_lm_objective
img, annotations = self.prepare(img, annotations, box_format="xyxy")
if self._transforms is not None:
img, target = self._transforms(img, target)
# add additional property
for ann in annotations:
target.add_field(ann, annotations[ann])
except:
print("Outter Retry triggered!!")
return self[np.random.choice(len(self))]
sanity_check_target_after_processing(target)
return img, target, idx
def convert_anno_from_v2_to_v1(self, anno):
flatterned_bboxes = []
flatterned_tokens_positive = []
flatterned_bboxes_scores = []
for i in range(len(anno["bboxes"])):
# i is the index for entity
for j in range(len(anno["bboxes"][i])):
# j is the index for each box
flatterned_bboxes.append(anno["bboxes"][i][j])
flatterned_tokens_positive.append(
anno["tokens_positive"][i]) # Assume this box corresponds to all the token_spans for this entity
flatterned_bboxes_scores.append(anno["scores"][i][j])
anno["bboxes"] = flatterned_bboxes
anno["tokens_positive"] = flatterned_tokens_positive
anno["scores"] = flatterned_bboxes_scores
return anno
def get_raw_image(self, idx):
image, *_ = super(CaptionTSV, 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]
return img_id
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