Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,759 Bytes
6073e55 23fdbc0 |
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 |
# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License.
import math
import random
from collections import defaultdict
from itertools import accumulate
import nncore
import numpy as np
import termplotlib as tpl
import torch
from tabulate import tabulate
from torch.utils.data import Dataset
from videomind.constants import IGNORE_INDEX
from videomind.dataset.utils import preprocess, process_vision_info
from videomind.utils.parser import parse_span
DATASETS = nncore.Registry('datasets')
class HybridDataset(Dataset):
def __init__(self, processor, model_config, model_args, data_args, training_args):
super().__init__()
datasets = []
for key in data_args.datasets.split(','):
datasets.append(DATASETS.get(key)(processor, model_args, data_args, training_args))
data_types = [a['data_type'] for d in datasets for a in d.annos]
cum_length = [0] + list(accumulate([len(d) for d in datasets]))
idx_ranges = [[cum_length[i], cum_length[i + 1]] for i in range(len(cum_length) - 1)]
if training_args.local_rank in (0, -1):
raw_length = sum(d.raw_length for d in datasets)
cur_length = idx_ranges[-1][-1]
ratio = round(cur_length / raw_length * 100, 2)
print(f'Number of samples: {raw_length} (original) -> {cur_length} (filtered) {ratio}%')
data_type_cnt = ' '.join([f'{data_types.count(t)} ({t})' for t in list(set(data_types))])
print(f'Data types: {data_type_cnt}')
tab = defaultdict(int)
for dataset in datasets:
for anno in dataset.annos:
tab[anno.get('source', 'unknown')] += 1
tab = [[k, v, round(v / cur_length, 3)] for k, v in tab.items()]
print(tabulate(tab, headers=['Source', '#Samples', 'Ratio'], tablefmt='pretty', stralign='left'))
d, _ = torch.Tensor([a['duration'] for d in datasets for a in d.annos if 'duration' in a]).sort()
if d.size(0) > 0:
n, r = min(d.size(0), 10), d.flip(0)
print(f'Top-{n} max video durations: {[round(r[i].item(), 1) for i in range(n)]}')
print(f'Top-{n} min video durations: {[round(d[i].item(), 1) for i in range(n)]}')
print(f'Average video duration ({d.size(0)} samples): {round(d.mean().item(), 1)}s')
print('Video duration histogram:')
counts, edges = np.histogram(d)
labels = [f'{edges[i]:.2f}s - {edges[i + 1]:.2f}s' for i in range(len(edges) - 1)]
fig = tpl.figure()
fig.barh(counts, labels)
fig.show()
d, _ = torch.Tensor([abs(b[0] - b[1]) for d in datasets for a in d.annos if 'span' in a
for b in a['span']]).sort()
if d.size(0) > 0:
n, r = min(d.size(0), 10), d.flip(0)
print(f'Top-{n} max span durations: {[round(r[i].item(), 1) for i in range(n)]}')
print(f'Top-{n} min span durations: {[round(d[i].item(), 1) for i in range(n)]}')
print(f'Average span duration ({d.size(0)} samples): {round(d.mean().item(), 1)}s')
print('Span duration histogram:')
counts, edges = np.histogram(d)
labels = [f'{edges[i]:.2f}s - {edges[i + 1]:.2f}s' for i in range(len(edges) - 1)]
fig = tpl.figure()
fig.barh(counts, labels)
fig.show()
self.datasets = datasets
self.data_types = data_types
self.idx_ranges = idx_ranges
self.processor = processor
self.model_config = model_config
self.model_args = model_args
self.data_args = data_args
self.training_args = training_args
def __len__(self):
return self.idx_ranges[-1][-1]
def __getitem__(self, idx):
for retry in range(self.data_args.max_retries + 1):
try:
return self.fetch_data(idx)
except Exception as e:
print(f'Error in loading {idx}: {type(e).__name__}({e})')
idx = random.choice([i for i, t in enumerate(self.data_types) if t == self.data_types[idx]])
raise RuntimeError(f'Data loading failed after {retry} retries')
def map(self, *args, **kwargs):
return self
def fetch_data(self, idx):
for (s, e), dataset in zip(self.idx_ranges, self.datasets):
if s <= idx < e:
meta = dataset[idx - s]
break
text = self.processor.apply_chat_template(meta['messages'])
text = [text.strip()]
images, videos = process_vision_info(meta['messages'], sanity_check=True)
data = self.processor(text=text, images=images, videos=videos, return_tensors='pt')
assert data['input_ids'].size(0) == 1
data['input_ids'] = data['input_ids'][0]
data['labels'] = preprocess(data['input_ids'], text[0], self.processor.tokenizer, self.model_args.conv_type)
# insert segment start/end tokens
if 'ss' in meta and 'se' in meta:
video_grid_thw = data['video_grid_thw'][0]
num_frames, window = int(video_grid_thw[0]), int(video_grid_thw[1] * video_grid_thw[2] / 4)
assert num_frames * window * 4 == data['pixel_values_videos'].size(0)
pos_s, pos_e = round(meta['ss'] * num_frames), round(meta['se'] * num_frames)
pos_s, pos_e = min(max(0, pos_s), num_frames), min(max(0, pos_e), num_frames)
assert pos_s <= pos_e, (num_frames, meta['ss'], meta['se'])
base_idx = torch.nonzero(data['input_ids'] == self.model_config.vision_start_token_id).item()
pos_s, pos_e = pos_s * window + base_idx + 1, pos_e * window + base_idx + 2
input_ids = data['input_ids'].tolist()
input_ids.insert(pos_s, self.model_config.seg_s_token_id)
input_ids.insert(pos_e, self.model_config.seg_e_token_id)
data['input_ids'] = torch.LongTensor(input_ids)
labels = data['labels'].tolist()
labels.insert(pos_s, IGNORE_INDEX)
labels.insert(pos_e, IGNORE_INDEX)
data['labels'] = torch.LongTensor(labels)
if 'span' in meta:
span, duration = meta['span'], meta['duration']
pixel_values_videos, video_grid_thw = data['pixel_values_videos'], data['video_grid_thw']
num_frames = int(video_grid_thw[0][0])
assert video_grid_thw.size(0) == 1
assert video_grid_thw.prod() == pixel_values_videos.size(0)
# actual fps would be 1/2 of config (temporal patch size = 2)
fps = num_frames / duration
safe_span = [parse_span(b, duration, 1 / fps) for b in span]
# num_reg_tokens -> num_bnds -> s & e
timestamps = [[[s / duration, e / duration] for s, e in safe_span]]
saliency, pos_inds = torch.zeros(num_frames), []
for s, e in safe_span:
span_ind = max(0, s * fps), min(e * fps, num_frames)
pos_inds = list(range(math.ceil(span_ind[0]), math.ceil(span_ind[1])))
assert len(pos_inds) > 0, f'empty pos_inds ({idx}): {fps} {num_frames} {duration} {span}'
saliency[pos_inds] = 1
assert saliency.any(), f'empty saliency ({idx}): {pos_inds} {fps} {num_frames} {duration} {span}'
pos_clip = random.sample(saliency.nonzero()[:, 0].tolist(), 1)
pos_clip = torch.LongTensor(pos_clip)
data['timestamps'] = timestamps
data['saliency'] = saliency
data['pos_clip'] = pos_clip
return data
|