# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """train squeezenet.""" import os import argparse from mindspore import context from mindspore import Tensor from mindspore.nn.optim.momentum import Momentum from mindspore.train.model import Model from mindspore.context import ParallelMode from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.communication.management import init, get_rank, get_group_size from mindspore.common import set_seed from src.lr_generator import get_lr from src.CrossEntropySmooth import CrossEntropySmooth parser = argparse.ArgumentParser(description='Image classification') parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'], help='Model.') parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.') parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute') parser.add_argument('--device_num', type=int, default=1, help='Device num.') parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') parser.add_argument('--device_target', type=str, default='Ascend', help='Device target') parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path') args_opt = parser.parse_args() set_seed(1) if args_opt.net == "squeezenet": from src.squeezenet import SqueezeNet as squeezenet if args_opt.dataset == "cifar10": from src.config import config1 as config from src.dataset import create_dataset_cifar as create_dataset else: from src.config import config2 as config from src.dataset import create_dataset_imagenet as create_dataset else: from src.squeezenet import SqueezeNet_Residual as squeezenet if args_opt.dataset == "cifar10": from src.config import config3 as config from src.dataset import create_dataset_cifar as create_dataset else: from src.config import config4 as config from src.dataset import create_dataset_imagenet as create_dataset if __name__ == '__main__': target = args_opt.device_target ckpt_save_dir = config.save_checkpoint_path # init context context.set_context(mode=context.GRAPH_MODE, device_target=target) if args_opt.run_distribute: if target == "Ascend": device_id = int(os.getenv('DEVICE_ID')) context.set_context(device_id=device_id, enable_auto_mixed_precision=True) context.set_auto_parallel_context( device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True) init() # GPU target else: init() context.set_auto_parallel_context( device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True) ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str( get_rank()) + "/" # create dataset dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1, batch_size=config.batch_size, target=target) step_size = dataset.get_dataset_size() # define net net = squeezenet(num_classes=config.class_num) # load checkpoint if args_opt.pre_trained: param_dict = load_checkpoint(args_opt.pre_trained) load_param_into_net(net, param_dict) # init lr lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max, total_epochs=config.epoch_size, warmup_epochs=config.warmup_epochs, pretrain_epochs=config.pretrain_epoch_size, steps_per_epoch=step_size, lr_decay_mode=config.lr_decay_mode) lr = Tensor(lr) # define loss if args_opt.dataset == "imagenet": if not config.use_label_smooth: config.label_smooth_factor = 0.0 loss = CrossEntropySmooth(sparse=True, reduction='mean', smooth_factor=config.label_smooth_factor, num_classes=config.class_num) else: loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') # define opt, model if target == "Ascend": loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False) opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale, use_nesterov=True) model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O2", keep_batchnorm_fp32=False) else: # GPU target opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, use_nesterov=True) model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) # define callbacks time_cb = TimeMonitor(data_size=step_size) loss_cb = LossMonitor() cb = [time_cb, loss_cb] if config.save_checkpoint: config_ck = CheckpointConfig( save_checkpoint_steps=config.save_checkpoint_epochs * step_size, keep_checkpoint_max=config.keep_checkpoint_max) ckpt_cb = ModelCheckpoint(prefix=args_opt.net + '_' + args_opt.dataset, directory=ckpt_save_dir, config=config_ck) cb += [ckpt_cb] # train model model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb)