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- # Copyright 2021 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 resnet."""
- import os
- import time
- import argparse
- import ast
- import numpy as np
- 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
- from mindspore.train.loss_scale_manager import FixedLossScaleManager
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.common import set_seed
- from mindspore.communication.management import init
- from mindspore.train.callback import Callback
-
- from src.loss import Softmaxloss
- from src.loss import Tripletloss
- from src.loss import Quadrupletloss
- from src.lr_generator import get_lr
- from src.resnet import resnet50
- from src.utility import GetDatasetGenerator_softmax, GetDatasetGenerator_triplet, GetDatasetGenerator_quadruplet
-
- set_seed(1)
-
- parser = argparse.ArgumentParser(description='Image classification')
- # modelarts parameter
- parser.add_argument('--train_url', type=str, default=None, help='Train output path')
- parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
- parser.add_argument('--ckpt_url', type=str, default=None, help='Pretrained ckpt path')
- parser.add_argument('--checkpoint_name', type=str, default='resnet-120_625.ckpt', help='Checkpoint file')
- parser.add_argument('--loss_name', type=str, default='softmax',
- help='loss name: softmax(pretrained) triplet quadruplet')
- # Ascend parameter
- parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
- parser.add_argument('--ckpt_path', type=str, default=None, help='ckpt path name')
- parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
- parser.add_argument('--device_id', type=int, default=0, help='Device id')
- parser.add_argument('--run_modelarts', type=ast.literal_eval, default=False, help='Run distribute')
- args_opt = parser.parse_args()
-
- class Monitor(Callback):
- """Monitor"""
- def __init__(self, lr_init=None):
- super(Monitor, self).__init__()
- self.lr_init = lr_init
- self.lr_init_len = len(lr_init)
- def epoch_begin(self, run_context):
- self.losses = []
- self.epoch_time = time.time()
- dataset_generator.__init__(data_dir=DATA_DIR, train_list=TRAIN_LIST)
- def epoch_end(self, run_context):
- cb_params = run_context.original_args()
- epoch_mseconds = (time.time() - self.epoch_time) * 1000
- per_step_mseconds = epoch_mseconds / cb_params.batch_num
- print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:8.5f}"
- .format(epoch_mseconds, per_step_mseconds, np.mean(self.losses)))
- print('batch_size:', config.batch_size, 'epochs_size:', config.epoch_size,
- 'lr_model:', config.lr_decay_mode, 'lr:', config.lr_max, 'step_size:', step_size)
- def step_begin(self, run_context):
- self.step_time = time.time()
- def step_end(self, run_context):
- """step_end"""
- cb_params = run_context.original_args()
- step_mseconds = (time.time() - self.step_time) * 1000
- step_loss = cb_params.net_outputs
- if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
- step_loss = step_loss[0]
- if isinstance(step_loss, Tensor):
- step_loss = np.mean(step_loss.asnumpy())
- self.losses.append(step_loss)
- cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
- print("epochs: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:8.5f}/{:8.5f}], time:[{:5.3f}], lr:[{:8.5f}]".format(
- cb_params.cur_epoch_num, config.epoch_size, cur_step_in_epoch, cb_params.batch_num, step_loss,
- np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
-
- if __name__ == '__main__':
- if args_opt.loss_name == 'softmax':
- from src.config import config0 as config
- from src.dataset import create_dataset0 as create_dataset
- elif args_opt.loss_name == 'triplet':
- from src.config import config1 as config
- from src.dataset import create_dataset1 as create_dataset
- elif args_opt.loss_name == 'quadruplet':
- from src.config import config2 as config
- from src.dataset import create_dataset1 as create_dataset
- else:
- print('loss no')
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
- # init distributed
- if args_opt.run_modelarts:
- import moxing as mox
- device_id = int(os.getenv('DEVICE_ID'))
- device_num = int(os.getenv('RANK_SIZE'))
- context.set_context(device_id=device_id)
- local_data_url = '/cache/data'
- local_ckpt_url = '/cache/ckpt'
- local_train_url = '/cache/train'
- if device_num > 1:
- init()
- context.set_auto_parallel_context(device_num=device_num,
- parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- local_data_url = os.path.join(local_data_url, str(device_id))
- local_ckpt_url = os.path.join(local_ckpt_url, str(device_id))
- mox.file.copy_parallel(args_opt.data_url, local_data_url)
- mox.file.copy_parallel(args_opt.ckpt_url, local_ckpt_url)
- DATA_DIR = local_data_url + '/'
- else:
- if args_opt.run_distribute:
- device_id = int(os.getenv('DEVICE_ID'))
- device_num = int(os.getenv('RANK_SIZE'))
- context.set_context(device_id=device_id)
- init()
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(device_num=device_num,
- parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- else:
- context.set_context(device_id=args_opt.device_id)
- device_num = 1
- device_id = args_opt.device_id
- DATA_DIR = args_opt.dataset_path + '/'
-
- # create dataset
- TRAIN_LIST = DATA_DIR + 'train_half.txt'
- if args_opt.loss_name == 'softmax':
- dataset_generator = GetDatasetGenerator_softmax(data_dir=DATA_DIR,
- train_list=TRAIN_LIST)
- elif args_opt.loss_name == 'triplet':
- dataset_generator = GetDatasetGenerator_triplet(data_dir=DATA_DIR,
- train_list=TRAIN_LIST)
- elif args_opt.loss_name == 'quadruplet':
- dataset_generator = GetDatasetGenerator_quadruplet(data_dir=DATA_DIR,
- train_list=TRAIN_LIST)
- else:
- print('loss no')
- dataset = create_dataset(dataset_generator, do_train=True, batch_size=config.batch_size,
- device_num=device_num, rank_id=device_id)
- step_size = dataset.get_dataset_size()
-
- # define net
- net = resnet50(class_num=config.class_num)
-
- # init weight
- if args_opt.run_modelarts:
- checkpoint_path = os.path.join(local_ckpt_url, args_opt.checkpoint_name)
- else:
- checkpoint_path = args_opt.ckpt_path
- param_dict = load_checkpoint(checkpoint_path)
- load_param_into_net(net.backbone, param_dict)
-
- # init lr
- lr = Tensor(get_lr(lr_init=config.lr_init,
- lr_end=config.lr_end,
- lr_max=config.lr_max,
- warmup_epochs=config.warmup_epochs,
- total_epochs=config.epoch_size,
- steps_per_epoch=step_size,
- lr_decay_mode=config.lr_decay_mode))
-
- # define opt
- opt = Momentum(params=net.trainable_params(),
- learning_rate=lr,
- momentum=config.momentum,
- weight_decay=config.weight_decay,
- loss_scale=config.loss_scale)
-
- # define loss, model
- if args_opt.loss_name == 'softmax':
- loss = Softmaxloss(sparse=True, smooth_factor=0.1, num_classes=config.class_num)
- elif args_opt.loss_name == 'triplet':
- loss = Tripletloss(margin=0.1)
- elif args_opt.loss_name == 'quadruplet':
- loss = Quadrupletloss(train_batch_size=config.batch_size, samples_each_class=2, margin=0.1)
- else:
- print('loss no')
-
- loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
-
- if args_opt.loss_name == 'softmax':
- model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics=None,
- amp_level='O3', keep_batchnorm_fp32=False)
- else:
- model = Model(net.backbone, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics=None,
- amp_level='O3', keep_batchnorm_fp32=False)
-
- #define callback
- cb = []
- if config.save_checkpoint and (device_num == 1 or device_id == 0):
- config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
- keep_checkpoint_max=config.keep_checkpoint_max)
-
- check_name = 'ResNet50_' + args_opt.loss_name
- if args_opt.run_modelarts:
- ckpt_cb = ModelCheckpoint(prefix=check_name, directory=local_train_url, config=config_ck)
- else:
- save_ckpt_path = os.path.join(config.save_checkpoint_path, 'model_'+ str(device_id) +'/')
- ckpt_cb = ModelCheckpoint(prefix=check_name, directory=save_ckpt_path, config=config_ck)
- cb += [ckpt_cb]
- cb += [Monitor(lr_init=lr.asnumpy())]
-
- # train model
- model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb, dataset_sink_mode=True)
-
- if args_opt.run_modelarts and config.save_checkpoint and (device_num == 1 or device_id == 0):
- mox.file.copy_parallel(src_url=local_train_url, dst_url=args_opt.train_url)
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