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e857f97 | 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 | import logging
import os.path
import random
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from data.build_datasets import DataInfo
from open_clip import get_input_dtype, get_tokenizer
from open_clip.factory import HF_HUB_PREFIX
from data.process_audio import get_audio_transform, torchaudio_loader
class Audiocaps_dataset(Dataset):
def __init__(self, data_path, transform, loader, tokenizer):
super(Audiocaps_dataset, self).__init__()
self.audio_root = data_path
raw_meta = pd.read_csv(f'{self.audio_root}/audiocaps_test.tsv', delimiter='\t').values
audio_ids = list(set(raw_meta[:, 1].tolist()))
captions = {}
for i in raw_meta:
if captions.get(i[1], None) is None:
captions[i[1]] = [i[2]]
else:
captions[i[1]] = captions[i[1]] + [i[2]]
# captions = {i[:1][0]: i[1:].tolist() for i in raw_meta}
self.sample_len = 0
self.sentences_dict = {}
self.cut_off_points = []
for audio_id in audio_ids:
assert audio_id in captions
for cap in captions[audio_id]:
cap_txt = cap
self.sentences_dict[len(self.sentences_dict)] = (audio_id[10:], cap_txt)
self.cut_off_points.append(len(self.sentences_dict))
self.multi_sentence_per_audio = True # !!! important tag for eval
if self.multi_sentence_per_audio:
# if self.subset == "val" or self.subset == "test":
self.sentence_num = len(self.sentences_dict)
self.audio_num = len(audio_ids)
assert len(self.cut_off_points) == self.audio_num
print("Sentence number: {}".format(self.sentence_num))
print("Video number: {}".format(self.audio_num))
self.sample_len = len(self.sentences_dict)
self.transform = transform
self.torchaudio_loader = loader
self.tokenizer = tokenizer
def __len__(self):
return self.sample_len
def __getitem__(self, idx):
audiocap_id, caption = self.sentences_dict[idx]
audio_path = os.path.join(self.audio_root, audiocap_id)
audio = self.torchaudio_loader(audio_path)
audio_data = self.transform(audio)
input_ids, attention_mask = self.tokenizer(caption)
return audio_data, input_ids.squeeze(), attention_mask.squeeze()
class Clotho_dataset(Dataset):
def __init__(self, data_path, transform, loader, tokenizer):
super(Clotho_dataset, self).__init__()
self.audio_root = data_path
raw_meta = pd.read_csv(f'{self.audio_root}/CLOTHO_retrieval_dataset/clotho_captions_evaluation.csv').values
audio_ids = raw_meta[:, 0].tolist()
captions = {i[:1][0]: i[1:].tolist() for i in raw_meta}
# self.meta = pd.DataFrame(np.vstack([np.vstack([raw_meta[:, 0], raw_meta[:, i]]).T for i in range(1, 6)]),
# columns=['uniq_id', 'text'])
self.sample_len = 0
self.sentences_dict = {}
self.cut_off_points = []
for audio_id in audio_ids:
assert audio_id in captions
for cap in captions[audio_id]:
cap_txt = cap
self.sentences_dict[len(self.sentences_dict)] = (audio_id, cap_txt)
self.cut_off_points.append(len(self.sentences_dict))
self.multi_sentence_per_audio = True # !!! important tag for eval
if self.multi_sentence_per_audio:
# if self.subset == "val" or self.subset == "test":
self.sentence_num = len(self.sentences_dict)
self.audio_num = len(audio_ids)
assert len(self.cut_off_points) == self.audio_num
print("Sentence number: {}".format(self.sentence_num))
print("Video number: {}".format(self.audio_num))
self.sample_len = len(self.sentences_dict)
self.transform = transform
self.torchaudio_loader = loader
self.tokenizer = tokenizer
def __len__(self):
return self.sample_len
def __getitem__(self, idx):
audiocap_id, caption = self.sentences_dict[idx]
# audiocap_id = self.meta['uniq_id'][idx]
audio_path = os.path.join(self.audio_root, f'evaluation/{audiocap_id}')
audio = self.torchaudio_loader(audio_path)
audio_data = self.transform(audio)
# caption = self.meta['text'][idx]
input_ids, attention_mask = self.tokenizer(caption)
return audio_data, input_ids.squeeze(), attention_mask.squeeze()
def get_audio_dataset(args):
data_path = args.audio_data_path
transform = get_audio_transform(args)
tokenizer = get_tokenizer(HF_HUB_PREFIX+args.model, cache_dir=args.cache_dir)
if args.val_al_ret_data.lower() == 'audiocaps':
dataset = Audiocaps_dataset(data_path, transform=transform, loader=torchaudio_loader, tokenizer=tokenizer)
elif args.val_al_ret_data.lower() == 'clotho':
dataset = Clotho_dataset(data_path, transform=transform, loader=torchaudio_loader, tokenizer=tokenizer)
else:
raise ValueError(f'unsupport dataset {args.val_al_ret_data}')
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=False,
drop_last=False,
)
return dataloader
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