Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
from clrnet.utils import Registry, build_from_cfg
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import torch
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from functools import partial
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import numpy as np
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import random
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from mmcv.parallel import collate
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DATASETS = Registry('datasets')
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PROCESS = Registry('process')
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def build(cfg, registry, default_args=None):
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if isinstance(cfg, list):
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modules = [
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build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
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]
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return nn.Sequential(*modules)
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else:
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return build_from_cfg(cfg, registry, default_args)
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def build_dataset(split_cfg, cfg):
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return build(split_cfg, DATASETS, default_args=dict(cfg=cfg))
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def worker_init_fn(worker_id, seed):
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worker_seed = worker_id + seed
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np.random.seed(worker_seed)
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random.seed(worker_seed)
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def build_dataloader(split_cfg, cfg, is_train=True):
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if is_train:
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shuffle = True
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else:
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shuffle = False
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dataset = build_dataset(split_cfg, cfg)
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init_fn = partial(worker_init_fn, seed=cfg.seed)
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samples_per_gpu = cfg.batch_size // cfg.gpus
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data_loader = torch.utils.data.DataLoader(
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dataset,
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batch_size=cfg.batch_size,
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shuffle=shuffle,
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num_workers=cfg.workers,
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pin_memory=False,
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drop_last=False,
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collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
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worker_init_fn=init_fn)
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return data_loader
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