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from os.path import basename
import numpy as np
import logging
import torch
from dataset.base_dataset import BaseDataset
from dataset.utils import load_anno, pre_text
from dataset.video_utils import VIDEO_READER_FUNCS
from dataset.text_prompt import kinetics_templates_action_clip as kinetics_templates
logger = logging.getLogger(__name__)
class AudioTxtRetTrainDataset(BaseDataset):
media_type = "audio"
def __init__(
self, ann_file, transform, audio_sample_rate,
audio_reader_type='librosa', max_audio_length=0, num_tries=3):
super(AudioTxtRetTrainDataset, self).__init__()
self.anno_list = load_anno(ann_file)
self.transform = transform
self.audio_reader_type = audio_reader_type
self.num_tries = num_tries
self.has_multi_audio_gt = ann_file.get("has_multi_audio_gt", False)
self.trimmed30 = ann_file.get("trimmed30", False)
self.max_audio_length = max_audio_length
self.audio_sample_rate = audio_sample_rate
self.match_ids = {}
n = 0
for ann in self.anno_list:
key = ann["caption"] if self.has_multi_audio_gt else basename(ann["image"])
if key not in self.match_ids:
self.match_ids[key] = n
n += 1
def __len__(self):
return len(self.anno_list)
def __getitem__(self, index):
try:
ann = self.anno_list[index]
audio, index = self.load_and_transform_media_data(index, ann['image'])
caption = pre_text(ann["caption"])
key = ann["caption"] if self.has_multi_audio_gt else basename(ann["image"])
return audio, caption, self.match_ids[key]
except Exception as e:
logger.error(e)
print(e, flush=True)
index = np.random.randint(0, len(self))
return self.__getitem__(index)
class AudioTxtRetEvalDataset(BaseDataset):
media_type = "audio"
def __init__(
self, ann_file, transform, audio_sample_rate,
audio_reader_type='librosa', max_audio_length=0, num_tries=3):
super(AudioTxtRetEvalDataset, self).__init__()
self.anno_list = load_anno(ann_file)
self.transform = transform
self.audio_sample_rate = audio_sample_rate
self.max_audio_length = max_audio_length
self.audio_reader_type = audio_reader_type
self.num_tries = num_tries
self.has_multi_audio_gt = ann_file.get("has_multi_audio_gt", False)
self.trimmed30 = ann_file.get("trimmed30", False)
self.max_txt_l = ann_file.get("max_txt_l", 32)
self.text = None
self.audio = None
self.txt2img = None
self.img2txt = None
self.build_data()
def build_data(self):
self.text = []
self.audio = []
self.txt2img = {}
self.img2txt = {}
if self.has_multi_audio_gt:
self.build_data_multi_audio_gt()
else:
self.build_data_multi_txt_gt()
def build_data_multi_audio_gt(self):
"""each text may have multiple ground_truth audio, e.g., ssv2"""
audio_id = 0
for txt_id, ann in enumerate(self.anno_list):
self.text.append(pre_text(ann["caption"]))
self.txt2img[txt_id] = []
_audios = ann["image"] \
if isinstance(ann["image"], list) else [ann["image"], ]
for i, audio in enumerate(_audios):
self.audio.append(audio)
self.txt2img[txt_id].append(audio_id)
self.img2txt[audio_id] = txt_id
audio_id += 1
def build_data_multi_txt_gt(self):
"""each audio may have multiple ground_truth text, e.g., COCO and Flickr30K"""
txt_id = 0
for audio_id, ann in enumerate(self.anno_list):
self.audio.append(ann["image"])
self.img2txt[audio_id] = []
_captions = ann["caption"] \
if isinstance(ann["caption"], list) else [ann["caption"], ]
for i, caption in enumerate(_captions):
self.text.append(pre_text(caption))
self.img2txt[audio_id].append(txt_id)
self.txt2img[txt_id] = audio_id
txt_id += 1
def __len__(self):
return len(self.anno_list)
def __getitem__(self, index):
ann = self.anno_list[index]
audio, index = self.load_and_transform_media_data(index, ann["image"])
return audio, index
class ImgTxtRetTrainDataset(BaseDataset):
media_type = "image"
def __init__(self, ann_file, transform):
super(ImgTxtRetTrainDataset, self).__init__()
self.anno_list = load_anno(ann_file)
self.transform = transform
# each caption has multiple image as ground_truth, e.g., ssv2
self.has_multi_txt_gt = ann_file.get("has_multi_txt_gt", False)
self.has_multi_vision_gt = ann_file.get("has_multi_vision_gt", False)
if self.has_multi_txt_gt:
logger.info("The dataset has multiple ground truth for a image/video!")
tmp_anno_list = []
for ann in self.anno_list:
img_path = ann["image"]
for caption in ann["caption"]:
tmp_anno_list.append({
"image": img_path,
"caption": caption
})
self.anno_list = tmp_anno_list
self.match_ids = {}
n = 0
for ann in self.anno_list:
key = ann["caption"] if self.has_multi_vision_gt else basename(ann["image"])
if key not in self.match_ids:
self.match_ids[key] = n
n += 1
def __len__(self):
return len(self.anno_list)
def __getitem__(self, index):
try:
ann = self.anno_list[index]
image, index = self.load_and_transform_media_data(index, ann["image"])
caption = pre_text(ann["caption"])
key = ann["caption"] if self.has_multi_vision_gt else basename(ann["image"])
return image, caption, self.match_ids[key]
except Exception as e:
logger.error(e)
print(e, flush=True)
index = np.random.randint(0, len(self))
return self.__getitem__(index)
class VidTxtRetTrainDataset(ImgTxtRetTrainDataset):
media_type = "video"
def __init__(
self, ann_file, transform, num_frames=4,
video_reader_type="decord", sample_type="rand", num_tries=3):
super(VidTxtRetTrainDataset, self).__init__(ann_file, transform)
self.num_frames = num_frames
self.video_reader_type = video_reader_type
self.video_reader = VIDEO_READER_FUNCS[video_reader_type]
self.sample_type = sample_type
self.num_tries = num_tries
self.read_clip_from_video = ann_file.get("read_clip_from_video", False)
if self.read_clip_from_video:
raise NotImplementedError("key for match_ids is not implemented!")
self.is_paragraph_retrieval = ann_file.get("is_paragraph_retrieval", False)
if self.is_paragraph_retrieval:
self.anno_list = preprocess_para_retrieval_data(self.anno_list)
self.trimmed30 = ann_file.get("trimmed30", False)
if self.trimmed30:
logger.info("Trimming the video, only use the first 30s!")
class AudioVidTxtRetTrainDataset(VidTxtRetTrainDataset):
media_type = "audio_video"
def __init__(
self, ann_file, transform,
audio_sample_rate=16000,
audio_reader_type='torchaudio',
max_audio_length=10,
num_frames=4,
video_reader_type="decord", sample_type="rand", num_tries=3):
super(AudioVidTxtRetTrainDataset, self).__init__(ann_file, transform,
num_frames=num_frames, video_reader_type=video_reader_type, sample_type=sample_type, num_tries=num_tries)
assert self.media_type == 'audio_video', self.media_type
self.audio_sample_rate = audio_sample_rate
self.audio_reader_type = audio_reader_type
self.max_audio_length = max_audio_length
self.has_multi_audio_gt = ann_file.get("has_multi_audio_gt", False)
self.read_audio_from_video = ann_file.get("read_audio_from_video", False)
self.zero_audio_padding_for_video = ann_file.get("zero_audio_padding_for_video", False)
def __getitem__(self, index):
try:
ann = self.anno_list[index]
caption = pre_text(ann["caption"])
data_path = {'video': ann["image"]}
data_path["read_clip_from_video"] = self.read_clip_from_video
if "audio" in ann.keys():
data_path["read_audio_from_video"] = False
data_path["audio"] = ann["audio"]
else:
data_path["read_audio_from_video"] = self.read_audio_from_video
media, index = self.load_and_transform_media_data(index, data_path)
audio = media[0]
if audio is None and self.zero_audio_padding_for_video:
logger.warning(f"No audio in {data_path}")
media[0] = torch.zeros((998, 64), dtype=torch.float32)
key = ann["caption"] if self.has_multi_vision_gt else basename(ann["image"])
return media, caption, self.match_ids[key]
except Exception as e:
logger.error(e)
print(e, flush=True)
index = np.random.randint(0, len(self))
return self.__getitem__(index)
class ImgTxtRetEvalDataset(BaseDataset):
media_type = "image"
def __init__(self, ann_file, transform):
super(ImgTxtRetEvalDataset, self).__init__()
self.raw_anno_list = load_anno(ann_file)
self.transform = transform
self.has_multi_vision_gt = ann_file.get("has_multi_vision_gt", False) # each caption has multiple image as ground_truth
self.is_act_rec = ann_file.get("is_act_rec", False)
self.max_txt_l = ann_file.get("max_txt_l", 32) # NOTE
self.text = None
self.image = None
self.txt2img = None
self.img2txt = None
self.build_data()
def build_data(self):
self.text = []
self.image = []
self.txt2img = {}
self.img2txt = {}
if self.is_act_rec:
self.build_data_act_rec()
elif self.has_multi_vision_gt:
self.build_data_multi_img_gt()
else:
self.build_data_multi_txt_gt()
self.anno_list = [dict(image=e) for e in self.image]
def build_data_act_rec(self):
"""action recognition task, e.g., kinetics400"""
text = list(set([e["caption"] for e in self.raw_anno_list]))
text2label = {e: i for i, e in enumerate(text)}
text = [[t.format(e) for t in kinetics_templates] for e in text]
text = [e for l in text for e in l]
self.text = [pre_text(e) for e in text]
self.num_prompts = len(kinetics_templates)
self.img2txt = {i: text2label[e["caption"]] for i, e in enumerate(self.raw_anno_list)}
self.txt2img = [[] for _ in range(len(text) // len(kinetics_templates))]
for i, e in enumerate(self.raw_anno_list):
self.image.append(e["image"])
self.txt2img[text2label[e["caption"]]].append(i)
logger.info(f"Action recognition, number of prompts: {self.num_prompts}")
logger.info(f"Action recognition, number of classes: {len(self.text)}")
def build_data_multi_img_gt(self):
"""each text may have multiple ground_truth image, e.g., ssv2"""
img_id = 0
for txt_id, ann in enumerate(self.raw_anno_list):
self.text.append(pre_text(ann["caption"]))
self.txt2img[txt_id] = []
_images = ann["image"] \
if isinstance(ann["image"], list) else [ann["image"], ]
for i, image in enumerate(_images):
self.image.append(image)
self.txt2img[txt_id].append(img_id)
self.img2txt[img_id] = txt_id
img_id += 1
def build_data_multi_txt_gt(self):
"""each image may have multiple ground_truth text, e.g., COCO and Flickr30K"""
txt_id = 0
for img_id, ann in enumerate(self.raw_anno_list):
self.image.append(ann["image"])
self.img2txt[img_id] = []
_captions = ann["caption"] \
if isinstance(ann["caption"], list) else [ann["caption"], ]
for i, caption in enumerate(_captions):
self.text.append(pre_text(caption))
self.img2txt[img_id].append(txt_id)
self.txt2img[txt_id] = img_id
txt_id += 1
def __len__(self):
return len(self.anno_list)
def __getitem__(self, index):
ann = self.anno_list[index]
image, index = self.load_and_transform_media_data(index, ann["image"])
return image, index
class VidTxtRetEvalDataset(ImgTxtRetEvalDataset):
media_type = "video"
def __init__(
self, ann_file, transform, num_frames=4,
video_reader_type="decord", sample_type="rand", num_tries=1):
super(VidTxtRetEvalDataset, self).__init__(ann_file, transform)
self.num_frames = num_frames
self.video_reader_type = video_reader_type
self.video_reader = VIDEO_READER_FUNCS[video_reader_type]
self.sample_type = sample_type
self.num_tries = num_tries
self.is_paragraph_retrieval = ann_file.get("is_paragraph_retrieval", False)
if self.is_paragraph_retrieval:
logger.info("Preprocess paragraph retrieval data!!!")
self.anno_list = preprocess_para_retrieval_data(self.raw_anno_list)
self.trimmed30 = ann_file.get("trimmed30", False)
if self.trimmed30:
logger.info("Trimming the video, only use the first 30s!!!")
self.read_clip_from_video = ann_file.get("read_clip_from_video", False)
self.use_subtitle = ann_file.get("use_subtitle", False)
if self.use_subtitle:
if self.is_act_rec:
raise NotImplementedError
self.build_subtitle_data()
self.build_data()
def __getitem__(self, index):
ann = self.anno_list[index]
if self.read_clip_from_video:
raise NotImplementedError("key for match_ids is not implemented!")
else:
data_path = ann["image"]
image, index = self.load_and_transform_media_data(index, data_path)
return image, index
def build_subtitle_data(self):
self.subtitle = []
for _, ann in enumerate(self.raw_anno_list):
if self.trimmed30:
if "asr_trimmed_30" in ann.keys():
self.subtitle.append(pre_text(ann["asr_trimmed_30"]))
else:
self.subtitle.append("")
else:
if "asr" in ann.keys():
self.subtitle.append(pre_text(ann["asr"]))
else:
self.subtitle.append("")
def preprocess_para_retrieval_data(anno_list):
processed_anno_list = []
for d in anno_list:
d["caption"] = " ".join(d.pop("caption"))
processed_anno_list.append(d)
return processed_anno_list
class VidTxtRetMCEvalDataset(BaseDataset):
"""For MSRVTT-MC test task"""
media_type = "video"
def __init__(self, ann_file, transform, num_frames=4,
video_reader_type="decord", sample_type="rand", num_tries=1):
super(VidTxtRetMCEvalDataset, self).__init__()
self.anno_list = load_anno(ann_file)
self.transform = transform
# video args
self.num_frames = num_frames
self.video_reader_type = video_reader_type
self.video_reader = VIDEO_READER_FUNCS[video_reader_type]
self.sample_type = sample_type
self.num_tries = num_tries
def __len__(self):
return len(self.anno_list)
def __getitem__(self, index):
ann = self.anno_list[index]
image, index = self.load_and_transform_media_data(index, ann["image"])
caption = [pre_text(e) for e in ann["caption"]] # len=5
answer = ann["answer"]
return image, caption, answer, ann
class VidTxtRetMCNewEvalDataset(BaseDataset):
"""For SSV2-MC and Charades-MC test task"""
media_type = "video"
def __init__(self, ann_file, transform, num_frames=4,
video_reader_type="decord", sample_type="rand", num_tries=1):
super(VidTxtRetMCNewEvalDataset, self).__init__()
self.anno_list = load_anno(ann_file)
self.transform = transform
# video args
self.num_frames = num_frames
self.video_reader_type = video_reader_type
self.video_reader = VIDEO_READER_FUNCS[video_reader_type]
self.sample_type = sample_type
self.num_tries = num_tries
def __len__(self):
return len(self.anno_list)
def __getitem__(self, index):
ann = self.anno_list[index]
image, index = self.load_and_transform_media_data(index, ann["image"])
option = [pre_text(e) for e in ann["option"]] # len=174
answer = ann["answer"]
if isinstance(answer, list):
answer = torch.Tensor(answer)
return image, option, answer, ann
class AudioVidTxtRetEvalDataset(VidTxtRetEvalDataset):
media_type = "audio_video"
def __init__(
self, ann_file, transform, num_frames=4,
video_reader_type="decord", sample_type="rand", num_tries=1,
audio_sample_rate=16000,
audio_reader_type='torchaudio',
max_audio_length=10):
super(AudioVidTxtRetEvalDataset, self).__init__(ann_file, transform,
num_frames=num_frames, video_reader_type=video_reader_type,
sample_type=sample_type, num_tries=num_tries)
self.audio_sample_rate = audio_sample_rate
self.audio_reader_type = audio_reader_type
self.max_audio_length = max_audio_length
self.read_clip_from_video = ann_file.get("read_clip_from_video", False)
self.read_audio_from_video = ann_file.get("read_audio_from_video", False)
self.zero_audio_padding_for_video = ann_file.get("zero_audio_padding_for_video", False)
def __getitem__(self, index):
ann = self.anno_list[index]
data_path = {'video': ann["image"]}
if self.read_clip_from_video:
raise NotImplementedError("Need to modify load_anno!")
if not self.read_audio_from_video:
raise NotImplementedError("Need to modify load_anno!")
data_path["read_clip_from_video"] = self.read_clip_from_video
data_path["read_audio_from_video"] = self.read_audio_from_video
media, index = self.load_and_transform_media_data(index, data_path)
audio = media[0]
if audio is None and self.zero_audio_padding_for_video:
media[0] = torch.zeros((998, 64), dtype=torch.float32)
return media, index |