videomind/dataset/hybrid.py

# 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