363 lines
17 KiB
Python
363 lines
17 KiB
Python
import os
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import math
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import argparse
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import random
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import logging
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import shutil
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from tqdm import tqdm
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import torch
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from data.data_sampler import DistIterSampler
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from trainer.eval.evaluator import create_evaluator
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from utils import util, options as option
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from data import create_dataloader, create_dataset, get_dataset_debugger
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from trainer.ExtensibleTrainer import ExtensibleTrainer
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from time import time
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from datetime import datetime
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from utils.util import opt_get, map_cuda_to_correct_device
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def init_dist(backend, **kwargs):
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# These packages have globals that screw with Windows, so only import them if needed.
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import torch.distributed as dist
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rank = int(os.environ['LOCAL_RANK'])
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assert rank < torch.cuda.device_count()
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torch.cuda.set_device(rank)
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dist.init_process_group(backend=backend, **kwargs)
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class Trainer:
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def init(self, opt_path, opt, launcher):
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self._profile = False
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self.val_compute_psnr = opt_get(opt, ['eval', 'compute_psnr'], False)
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self.val_compute_fea = opt_get(opt, ['eval', 'compute_fea'], False)
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self.current_step = 0
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self.total_training_data_encountered = 0
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#### loading resume state if exists
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if opt['path'].get('resume_state', None):
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# distributed resuming: all load into default GPU
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resume_state = torch.load(opt['path']['resume_state'], map_location=map_cuda_to_correct_device)
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else:
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resume_state = None
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#### mkdir and loggers
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if self.rank <= 0: # normal training (self.rank -1) OR distributed training (self.rank 0)
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if resume_state is None:
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util.mkdir_and_rename(
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opt['path']['experiments_root']) # rename experiment folder if exists
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util.mkdirs(
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(path for key, path in opt['path'].items() if not key == 'experiments_root' and path is not None
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and 'pretrain_model' not in key and 'resume' not in key))
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shutil.copy(opt_path, os.path.join(opt['path']['experiments_root'], f'{datetime.now().strftime("%d%m%Y_%H%M%S")}_{os.path.basename(opt_path)}'))
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# config loggers. Before it, the log will not work
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util.setup_logger('base', opt['path']['log'], 'train_' + opt['name'], level=logging.INFO,
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screen=True, tofile=True)
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self.logger = logging.getLogger('base')
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self.logger.info(option.dict2str(opt))
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# tensorboard logger
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if opt['use_tb_logger'] and 'debug' not in opt['name']:
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self.tb_logger_path = os.path.join(opt['path']['experiments_root'], 'tb_logger')
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from torch.utils.tensorboard import SummaryWriter
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self.tb_logger = SummaryWriter(log_dir=self.tb_logger_path)
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else:
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util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True)
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self.logger = logging.getLogger('base')
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if resume_state is not None:
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option.check_resume(opt, resume_state['iter']) # check resume options
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# convert to NoneDict, which returns None for missing keys
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opt = option.dict_to_nonedict(opt)
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self.opt = opt
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#### wandb init
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if opt['wandb'] and self.rank <= 0:
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import wandb
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os.makedirs(os.path.join(opt['path']['log'], 'wandb'), exist_ok=True)
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project_name = opt_get(opt, ['wandb_project_name'], opt['name'])
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run_name = opt_get(opt, ['wandb_run_name'], None)
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wandb.init(project=project_name, dir=opt['path']['log'], config=opt, name=run_name)
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#### random seed
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seed = opt['train']['manual_seed']
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if seed is None:
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seed = random.randint(1, 10000)
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if self.rank <= 0:
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self.logger.info('Random seed: {}'.format(seed))
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seed += self.rank # Different multiprocessing instances should behave differently.
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util.set_random_seed(seed)
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torch.backends.cudnn.benchmark = opt_get(opt, ['cuda_benchmarking_enabled'], True)
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# torch.backends.cudnn.deterministic = True
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if opt_get(opt, ['anomaly_detection'], False):
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torch.autograd.set_detect_anomaly(True)
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# Save the compiled opt dict to the global loaded_options variable.
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util.loaded_options = opt
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#### create train and val dataloader
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dataset_ratio = 1 # enlarge the size of each epoch
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for phase, dataset_opt in opt['datasets'].items():
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if phase == 'train':
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self.train_set, collate_fn = create_dataset(dataset_opt, return_collate=True)
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self.dataset_debugger = get_dataset_debugger(dataset_opt)
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if self.dataset_debugger is not None and resume_state is not None:
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self.dataset_debugger.load_state(opt_get(resume_state, ['dataset_debugger_state'], {}))
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train_size = int(math.ceil(len(self.train_set) / dataset_opt['batch_size']))
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total_iters = int(opt['train']['niter'])
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self.total_epochs = int(math.ceil(total_iters / train_size))
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if opt['dist']:
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self.train_sampler = DistIterSampler(self.train_set, self.world_size, self.rank, dataset_ratio)
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self.total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio)))
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shuffle = False
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else:
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self.train_sampler = None
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shuffle = True
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self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, self.train_sampler, collate_fn=collate_fn, shuffle=shuffle)
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if self.rank <= 0:
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self.logger.info('Number of training data elements: {:,d}, iters: {:,d}'.format(
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len(self.train_set), train_size))
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self.logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
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self.total_epochs, total_iters))
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elif phase == 'val':
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self.val_set, collate_fn = create_dataset(dataset_opt, return_collate=True)
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self.val_loader = create_dataloader(self.val_set, dataset_opt, opt, None, collate_fn=collate_fn)
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if self.rank <= 0:
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self.logger.info('Number of val images in [{:s}]: {:d}'.format(
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dataset_opt['name'], len(self.val_set)))
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else:
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raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
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assert self.train_loader is not None
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#### create model
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self.model = ExtensibleTrainer(opt)
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### Evaluators
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self.evaluators = []
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if 'eval' in opt.keys() and 'evaluators' in opt['eval'].keys():
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# In "pure" mode, we propagate through the normal training steps, but use validation data instead and average
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# the total loss. A validation dataloader is required.
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if opt_get(opt, ['eval', 'pure'], False):
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assert hasattr(self, 'val_loader')
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for ev_key, ev_opt in opt['eval']['evaluators'].items():
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self.evaluators.append(create_evaluator(self.model.networks[ev_opt['for']],
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ev_opt, self.model.env))
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#### resume training
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if resume_state:
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self.logger.info('Resuming training from epoch: {}, iter: {}.'.format(
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resume_state['epoch'], resume_state['iter']))
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self.start_epoch = resume_state['epoch']
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self.current_step = resume_state['iter']
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self.total_training_data_encountered = opt_get(resume_state, ['total_data_processed'], 0)
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if opt_get(opt, ['path', 'optimizer_reset'], False):
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print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
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print('!! RESETTING OPTIMIZER STATES')
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print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
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else:
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self.model.resume_training(resume_state, 'amp_opt_level' in opt.keys()) # handle optimizers and schedulers
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else:
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self.current_step = -1 if 'start_step' not in opt.keys() else opt['start_step']
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self.total_training_data_encountered = 0 if 'training_data_encountered' not in opt.keys() else opt['training_data_encountered']
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self.start_epoch = 0
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if 'force_start_step' in opt.keys():
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self.current_step = opt['force_start_step']
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self.total_training_data_encountered = self.current_step * opt['datasets']['train']['batch_size']
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opt['current_step'] = self.current_step
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#### validation
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if 'val_freq' in opt['train'].keys():
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self.val_freq = opt['train']['val_freq'] * opt['datasets']['train']['batch_size']
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else:
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self.val_freq = int(opt['train']['val_freq_megasamples'] * 1000000)
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self.next_eval_step = self.total_training_data_encountered + self.val_freq
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def do_step(self, train_data):
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if self._profile:
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print("Data fetch: %f" % (time() - _t))
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_t = time()
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opt = self.opt
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batch_size = self.opt['datasets']['train']['batch_size'] # It may seem weird to derive this from opt, rather than train_data. The reason this is done is
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# because train_data is process-local while the opt variant represents all of the data fed across all GPUs.
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self.current_step += 1
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self.total_training_data_encountered += batch_size
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will_log = self.current_step % opt['logger']['print_freq'] == 0
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#### update learning rate
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self.model.update_learning_rate(self.current_step, warmup_iter=opt['train']['warmup_iter'])
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#### training
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if self._profile:
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print("Update LR: %f" % (time() - _t))
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_t = time()
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self.model.feed_data(train_data, self.current_step)
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gradient_norms_dict = self.model.optimize_parameters(self.current_step, return_grad_norms=will_log)
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iteration_rate = (time() - _t) / batch_size
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if self._profile:
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print("Model feed + step: %f" % (time() - _t))
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_t = time()
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#### log
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if self.dataset_debugger is not None:
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self.dataset_debugger.update(train_data)
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if will_log:
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# Must be run by all instances to gather consensus.
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current_model_logs = self.model.get_current_log(self.current_step)
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if will_log and self.rank <= 0:
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logs = {'step': self.current_step,
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'samples': self.total_training_data_encountered,
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'megasamples': self.total_training_data_encountered / 1000000,
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'iteration_rate': iteration_rate}
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logs.update(current_model_logs)
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if self.dataset_debugger is not None:
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logs.update(self.dataset_debugger.get_debugging_map())
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logs.update(gradient_norms_dict)
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message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(self.epoch, self.current_step)
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for v in self.model.get_current_learning_rate():
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message += '{:.3e},'.format(v)
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message += ')] '
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for k, v in logs.items():
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if 'histogram' in k:
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self.tb_logger.add_histogram(k, v, self.current_step)
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elif isinstance(v, dict):
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self.tb_logger.add_scalars(k, v, self.current_step)
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else:
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message += '{:s}: {:.4e} '.format(k, v)
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# tensorboard logger
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if opt['use_tb_logger'] and 'debug' not in opt['name']:
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self.tb_logger.add_scalar(k, v, self.current_step)
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if opt['wandb'] and self.rank <= 0:
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import wandb
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if opt_get(opt, ['wandb_progress_use_raw_steps'], False):
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wandb.log(logs, step=self.current_step)
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else:
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wandb.log(logs, step=self.total_training_data_encountered)
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self.logger.info(message)
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#### save models and training states
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if self.current_step % opt['logger']['save_checkpoint_freq'] == 0:
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self.model.consolidate_state()
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if self.rank <= 0:
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self.logger.info('Saving models and training states.')
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self.model.save(self.current_step)
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state = {'epoch': self.epoch, 'iter': self.current_step, 'total_data_processed': self.total_training_data_encountered}
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if self.dataset_debugger is not None:
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state['dataset_debugger_state'] = self.dataset_debugger.get_state()
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self.model.save_training_state(state)
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if 'alt_path' in opt['path'].keys():
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import shutil
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print("Synchronizing tb_logger to alt_path..")
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alt_tblogger = os.path.join(opt['path']['alt_path'], "tb_logger")
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shutil.rmtree(alt_tblogger, ignore_errors=True)
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shutil.copytree(self.tb_logger_path, alt_tblogger)
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do_eval = self.total_training_data_encountered > self.next_eval_step
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if do_eval:
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self.next_eval_step = self.total_training_data_encountered + self.val_freq
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if opt_get(opt, ['eval', 'pure'], False) and do_eval:
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metrics = []
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for val_data in tqdm(self.val_loader):
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self.model.feed_data(val_data, self.current_step, perform_micro_batching=False)
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metrics.append(self.model.test())
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reduced_metrics = {}
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for metric in metrics:
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for k, v in metric.as_dict().items():
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if isinstance(v, torch.Tensor) and len(v.shape) == 0:
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if k in reduced_metrics.keys():
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reduced_metrics[k].append(v)
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else:
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reduced_metrics[k] = [v]
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if self.rank <= 0:
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for k, v in reduced_metrics.items():
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val = torch.stack(v).mean().item()
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self.tb_logger.add_scalar(f'val_{k}', val, self.current_step)
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print(f">>Eval {k}: {val}")
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if opt['wandb']:
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import wandb
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wandb.log({f'eval_{k}': torch.stack(v).mean().item() for k,v in reduced_metrics.items()})
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if len(self.evaluators) != 0 and do_eval:
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eval_dict = {}
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for eval in self.evaluators:
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if eval.uses_all_ddp or self.rank <= 0:
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eval_dict.update(eval.perform_eval())
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if self.rank <= 0:
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print("Evaluator results: ", eval_dict)
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for ek, ev in eval_dict.items():
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self.tb_logger.add_scalar(ek, ev, self.current_step)
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if opt['wandb']:
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import wandb
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wandb.log(eval_dict)
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# Should not be necessary, but make absolutely sure that there is no grad leakage from validation runs.
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for net in self.model.networks.values():
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net.zero_grad()
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def do_training(self):
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self.logger.info('Start training from epoch: {:d}, iter: {:d}'.format(self.start_epoch, self.current_step))
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for epoch in range(self.start_epoch, self.total_epochs + 1):
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self.epoch = epoch
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if self.opt['dist']:
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self.train_sampler.set_epoch(epoch)
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tq_ldr = tqdm(self.train_loader) if self.rank <= 0 else self.train_loader
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_t = time()
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for train_data in tq_ldr:
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self.do_step(train_data)
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def create_training_generator(self, index):
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self.logger.info('Start training from epoch: {:d}, iter: {:d}'.format(self.start_epoch, self.current_step))
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for epoch in range(self.start_epoch, self.total_epochs + 1):
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self.epoch = epoch
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if self.opt['dist']:
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self.train_sampler.set_epoch(epoch)
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tq_ldr = tqdm(self.train_loader, position=index)
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_t = time()
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for train_data in tq_ldr:
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yield self.model
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self.do_step(train_data)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_encoder_build_ctc_alignments.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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args = parser.parse_args()
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opt = option.parse(args.opt, is_train=True)
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if args.launcher != 'none':
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# export CUDA_VISIBLE_DEVICES for running in distributed mode.
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if 'gpu_ids' in opt.keys():
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gpu_list = ','.join(str(x) for x in opt['gpu_ids'])
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os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list
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print('export CUDA_VISIBLE_DEVICES=' + gpu_list)
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trainer = Trainer()
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#### distributed training settings
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if args.launcher == 'none': # disabled distributed training
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opt['dist'] = False
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trainer.rank = -1
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if len(opt['gpu_ids']) == 1:
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torch.cuda.set_device(opt['gpu_ids'][0])
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print('Disabled distributed training.')
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else:
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opt['dist'] = True
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init_dist('nccl')
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trainer.world_size = torch.distributed.get_world_size()
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trainer.rank = torch.distributed.get_rank()
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torch.cuda.set_device(torch.distributed.get_rank())
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trainer.init(args.opt, opt, args.launcher)
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trainer.do_training()
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