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- ''' Model training pipeline '''
- import os
- import logging
- import mindspore as ms
- import numpy as np
- from mindspore import nn, Tensor
- from mindspore import FixedLossScaleManager, Model
- from mindspore.communication import init, get_rank, get_group_size
- import mindspore.dataset.transforms as transforms
-
- from mindcv.models import create_model
- from mindcv.data import create_dataset, create_transforms, create_loader
- from mindcv.loss import create_loss
- from mindcv.optim import create_optimizer
- from mindcv.scheduler import create_scheduler
- from mindcv.utils import StateMonitor, Allreduce, TrainOneStepWithEMA
- from config import parse_args
-
- ms.set_seed(1)
- np.random.seed(1)
-
- logger = logging.getLogger('train')
- logger.setLevel(logging.INFO)
- h1 = logging.StreamHandler()
- formatter1 = logging.Formatter('%(message)s',)
- logger.addHandler(h1)
- h1.setFormatter(formatter1)
-
- def train(args):
- ''' main train function'''
- ms.set_context(mode=args.mode)
- if args.distribute:
- init()
- device_num = get_group_size()
- rank_id = get_rank()
- ms.set_auto_parallel_context(device_num=device_num,
- parallel_mode='data_parallel',
- gradients_mean=True)
- else:
- device_num = None
- rank_id = None
-
- # create dataset
- dataset_train = create_dataset(
- name=args.dataset,
- root=args.data_dir,
- split=args.train_split,
- shuffle=args.shuffle,
- num_samples=args.num_samples,
- num_shards=device_num,
- shard_id=rank_id,
- num_parallel_workers=args.num_parallel_workers,
- download=args.dataset_download,
- num_aug_repeats=args.aug_repeats)
-
- if args.num_classes is None:
- num_classes = dataset_train.num_classes()
- else:
- num_classes = args.num_classes
-
- # create transforms
- transform_list = create_transforms(
- dataset_name=args.dataset,
- is_training=True,
- image_resize=args.image_resize,
- scale=args.scale,
- ratio=args.ratio,
- hflip=args.hflip,
- vflip=args.vflip,
- color_jitter=args.color_jitter,
- interpolation=args.interpolation,
- auto_augment=args.auto_augment,
- mean=args.mean,
- std=args.std,
- re_prob=args.re_prob,
- re_scale=args.re_scale,
- re_ratio=args.re_ratio,
- re_value=args.re_value,
- re_max_attempts=args.re_max_attempts
- )
-
- target_transform = transforms.OneHot(num_classes) if args.loss == 'BCE' else None
-
- # load dataset
- loader_train = create_loader(
- dataset=dataset_train,
- batch_size=args.batch_size,
- drop_remainder=args.drop_remainder,
- is_training=True,
- mixup=args.mixup,
- cutmix=args.cutmix,
- cutmix_prob=args.cutmix_prob,
- num_classes=num_classes,
- transform=transform_list,
- target_transform=target_transform,
- num_parallel_workers=args.num_parallel_workers,
- )
-
- if args.val_while_train:
- dataset_eval = create_dataset(
- name=args.dataset,
- root=args.data_dir,
- split=args.val_split,
- num_shards=device_num,
- shard_id=rank_id,
- num_parallel_workers=args.num_parallel_workers,
- download=args.dataset_download)
-
- transform_list_eval = create_transforms(
- dataset_name=args.dataset,
- is_training=False,
- image_resize=args.image_resize,
- crop_pct=args.crop_pct,
- interpolation=args.interpolation,
- mean=args.mean,
- std=args.std
- )
-
- loader_eval = create_loader(
- dataset=dataset_eval,
- batch_size=args.batch_size,
- drop_remainder=False,
- is_training=False,
- transform=transform_list_eval,
- target_transform=target_transform,
- num_parallel_workers=args.num_parallel_workers,
- )
- # validation dataset count
- eval_count = dataset_eval.get_dataset_size()
- if args.distribute:
- all_reduce = Allreduce()
- eval_count = all_reduce(Tensor(eval_count, ms.int32))
- else:
- loader_eval = None
-
- num_batches = loader_train.get_dataset_size()
- # Train dataset count
- train_count = dataset_train.get_dataset_size()
- if args.distribute:
- all_reduce = Allreduce()
- train_count = all_reduce(Tensor(train_count, ms.int32))
-
- # create model
- network = create_model(model_name=args.model,
- num_classes=num_classes,
- in_channels=args.in_channels,
- drop_rate=args.drop_rate,
- drop_path_rate=args.drop_path_rate,
- pretrained=args.pretrained,
- checkpoint_path=args.ckpt_path,
- use_ema=args.use_ema)
-
- num_params = sum([param.size for param in network.get_parameters()])
-
- # create loss
- loss = create_loss(name=args.loss,
- reduction=args.reduction,
- label_smoothing=args.label_smoothing,
- aux_factor=args.aux_factor)
-
- # create learning rate schedule
- lr_scheduler = create_scheduler(num_batches,
- scheduler=args.scheduler,
- lr=args.lr,
- min_lr=args.min_lr,
- warmup_epochs=args.warmup_epochs,
- warmup_factor=args.warmup_factor,
- decay_epochs=args.decay_epochs,
- decay_rate=args.decay_rate,
- milestones=args.multi_step_decay_milestones,
- num_epochs=args.epoch_size,
- lr_epoch_stair=args.lr_epoch_stair,
- num_cycles=args.num_cycles,
- cycle_decay=args.cycle_decay)
-
- # resume training if ckpt_path is given
- if args.ckpt_path != '' and args.resume_opt:
- opt_ckpt_path = os.path.join(args.ckpt_save_dir, f'optim_{args.model}.ckpt')
- else:
- opt_ckpt_path = ''
-
- # create optimizer
- #TODO: consistent naming opt, name, dataset_name
- if args.use_ema:
- optimizer = create_optimizer(network.trainable_params(),
- opt=args.opt,
- lr=lr_scheduler,
- weight_decay=args.weight_decay,
- momentum=args.momentum,
- nesterov=args.use_nesterov,
- filter_bias_and_bn=args.filter_bias_and_bn,
- checkpoint_path=opt_ckpt_path,
- eps=args.eps)
- else:
- optimizer = create_optimizer(network.trainable_params(),
- opt=args.opt,
- lr=lr_scheduler,
- weight_decay=args.weight_decay,
- momentum=args.momentum,
- nesterov=args.use_nesterov,
- filter_bias_and_bn=args.filter_bias_and_bn,
- loss_scale=args.loss_scale,
- checkpoint_path=opt_ckpt_path,
- eps=args.eps)
-
- # Define eval metrics.
- if num_classes >= 5:
- eval_metrics = {'Top_1_Accuracy': nn.Top1CategoricalAccuracy(),
- 'Top_5_Accuracy': nn.Top5CategoricalAccuracy()}
- else:
- eval_metrics = {'Top_1_Accuracy': nn.Top1CategoricalAccuracy()}
-
- # init model
- if args.use_ema:
- net_with_loss = nn.WithLossCell(network, loss)
-
- if args.dynamic_loss_scale:
- loss_scale_manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=args.loss_scale, scale_factor=2,
- scale_window=1000)
- else:
- loss_scale_manager = nn.FixedLossScaleUpdateCell(loss_scale_value=args.loss_scale)
- ms.amp.auto_mixed_precision(net_with_loss, amp_level=args.amp_level)
- net_with_loss = TrainOneStepWithEMA(net_with_loss, optimizer, scale_sense=loss_scale_manager,
- use_ema=args.use_ema, ema_decay=args.ema_decay)
- eval_network = nn.WithEvalCell(network, loss, args.amp_level in ["O2", "O3", "auto"])
- model = Model(net_with_loss, eval_network=eval_network, metrics=eval_metrics, eval_indexes=[0, 1, 2])
- else:
- if args.dynamic_loss_scale:
- loss_scale_manager = ms.amp.DynamicLossScaleManager(init_loss_scale=args.loss_scale, scale_factor=2,
- scale_window=1000)
- else:
- loss_scale_manager = FixedLossScaleManager(loss_scale=args.loss_scale, drop_overflow_update=False)
- model = Model(network, loss_fn=loss, optimizer=optimizer, metrics=eval_metrics, amp_level=args.amp_level,
- loss_scale_manager=loss_scale_manager)
-
- # callback
- # save checkpoint, summary training loss
- # recorad val acc and do model selection if val dataset is availabe
- begin_epoch = 0
- if args.ckpt_path != '':
- if args.ckpt_path != '':
- begin_step = optimizer.global_step.asnumpy()[0]
- begin_epoch = args.ckpt_path.split('/')[-1].split('-')[1].split('_')[0]
- begin_epoch = int(begin_epoch)
-
- summary_dir = f"./{args.ckpt_save_dir}/summary"
- assert (args.ckpt_save_policy != 'top_k' or args.val_while_train == True), \
- "ckpt_save_policy is top_k, val_while_train must be True."
- state_cb = StateMonitor(model, summary_dir=summary_dir,
- dataset_val=loader_eval,
- val_interval=args.val_interval,
- metric_name=list(eval_metrics.keys()),
- ckpt_dir=args.ckpt_save_dir,
- ckpt_save_interval=args.ckpt_save_interval,
- best_ckpt_name=args.model + '_best.ckpt',
- rank_id=rank_id,
- device_num=device_num,
- log_interval=args.log_interval,
- keep_checkpoint_max=args.keep_checkpoint_max,
- model_name=args.model,
- last_epoch=begin_epoch,
- ckpt_save_policy=args.ckpt_save_policy)
-
- callbacks = [state_cb]
- # log
- if rank_id in [None, 0]:
- logger.info(f"-" * 40)
- logger.info(f"Num devices: {device_num if device_num is not None else 1} \n"
- f"Distributed mode: {args.distribute} \n"
- f"Num training samples: {train_count}")
- if args.val_while_train:
- logger.info(f"Num validation samples: {eval_count}")
- logger.info(f"Num classes: {num_classes} \n"
- f"Num batches: {num_batches} \n"
- f"Batch size: {args.batch_size} \n"
- f"Auto augment: {args.auto_augment} \n"
- f"Model: {args.model} \n"
- f"Model param: {num_params} \n"
- f"Num epochs: {args.epoch_size} \n"
- f"Optimizer: {args.opt} \n"
- f"LR: {args.lr} \n"
- f"LR Scheduler: {args.scheduler}")
- logger.info(f"-" * 40)
-
- if args.ckpt_path != '':
- logger.info(f"Resume training from {args.ckpt_path}, last step: {begin_step}, last epoch: {begin_epoch}")
- else:
- logger.info('Start training')
-
- model.train(args.epoch_size, loader_train, callbacks=callbacks, dataset_sink_mode=args.dataset_sink_mode)
-
- if __name__ == '__main__':
- args = parse_args()
-
- # data sync for cloud platform if enabled
- if args.enable_modelarts:
- import moxing as mox
- args.data_dir = f'/cache/{args.data_url}'
- mox.file.copy_parallel(src_url= os.path.join(args.data_url, args.dataset) , dst_url= args.data_dir)
-
- # core training
- train(args)
-
- if args.enable_modelarts:
- mox.file.copy_parallel(src_url= args.ckpt_save_dir, dst_url=args.train_url)
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