Source code for mmdet.apis.train

import random
import warnings

import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
                         Fp16OptimizerHook, OptimizerHook, build_optimizer,
from mmcv.utils import build_from_cfg

from mmdet.core import DistEvalHook, EvalHook
from mmdet.datasets import (build_dataloader, build_dataset,
from mmdet.utils import get_root_logger

[docs]def set_random_seed(seed, deterministic=False): """Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` to True and `torch.backends.cudnn.benchmark` to False. Default: False. """ random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) if deterministic: torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False
def train_detector(model, dataset, cfg, distributed=False, validate=False, timestamp=None, meta=None): logger = get_root_logger(log_level=cfg.log_level) # prepare data loaders dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset] if 'imgs_per_gpu' in logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. ' 'Please use "samples_per_gpu" instead') if 'samples_per_gpu' in logger.warning( f'Got "imgs_per_gpu"={} and ' f'"samples_per_gpu"={}, "imgs_per_gpu"' f'={} is used in this experiments') else: logger.warning( 'Automatically set "samples_per_gpu"="imgs_per_gpu"=' f'{} in this experiments') = data_loaders = [ build_dataloader( ds,,, # cfg.gpus will be ignored if distributed len(cfg.gpu_ids), dist=distributed, seed=cfg.seed) for ds in dataset ] # put model on gpus if distributed: find_unused_parameters = cfg.get('find_unused_parameters', False) # Sets the `find_unused_parameters` parameter in # torch.nn.parallel.DistributedDataParallel model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) else: model = MMDataParallel( model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids) # build runner optimizer = build_optimizer(model, cfg.optimizer) if 'runner' not in cfg: cfg.runner = { 'type': 'EpochBasedRunner', 'max_epochs': cfg.total_epochs } warnings.warn( 'config is now expected to have a `runner` section, ' 'please set `runner` in your config.', UserWarning) else: if 'total_epochs' in cfg: assert cfg.total_epochs == cfg.runner.max_epochs runner = build_runner( cfg.runner, default_args=dict( model=model, optimizer=optimizer, work_dir=cfg.work_dir, logger=logger, meta=meta)) # an ugly workaround to make .log and .log.json filenames the same runner.timestamp = timestamp # fp16 setting fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: optimizer_config = Fp16OptimizerHook( **cfg.optimizer_config, **fp16_cfg, distributed=distributed) elif distributed and 'type' not in cfg.optimizer_config: optimizer_config = OptimizerHook(**cfg.optimizer_config) else: optimizer_config = cfg.optimizer_config # register hooks runner.register_training_hooks(cfg.lr_config, optimizer_config, cfg.checkpoint_config, cfg.log_config, cfg.get('momentum_config', None)) if distributed: if isinstance(runner, EpochBasedRunner): runner.register_hook(DistSamplerSeedHook()) # register eval hooks if validate: # Support batch_size > 1 in validation val_samples_per_gpu ='samples_per_gpu', 1) if val_samples_per_gpu > 1: # Replace 'ImageToTensor' to 'DefaultFormatBundle' = replace_ImageToTensor( val_dataset = build_dataset(, dict(test_mode=True)) val_dataloader = build_dataloader( val_dataset, samples_per_gpu=val_samples_per_gpu,, dist=distributed, shuffle=False) eval_cfg = cfg.get('evaluation', {}) eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner' eval_hook = DistEvalHook if distributed else EvalHook runner.register_hook(eval_hook(val_dataloader, **eval_cfg)) # user-defined hooks if cfg.get('custom_hooks', None): custom_hooks = cfg.custom_hooks assert isinstance(custom_hooks, list), \ f'custom_hooks expect list type, but got {type(custom_hooks)}' for hook_cfg in cfg.custom_hooks: assert isinstance(hook_cfg, dict), \ 'Each item in custom_hooks expects dict type, but got ' \ f'{type(hook_cfg)}' hook_cfg = hook_cfg.copy() priority = hook_cfg.pop('priority', 'NORMAL') hook = build_from_cfg(hook_cfg, HOOKS) runner.register_hook(hook, priority=priority) if cfg.resume_from: runner.resume(cfg.resume_from) elif cfg.load_from: runner.load_checkpoint(cfg.load_from), cfg.workflow)