forked from huawei/mindspore2022
195 lines
8.5 KiB
Python
195 lines
8.5 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""train Xception."""
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import os
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import time
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from mindspore import context
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from mindspore import Tensor
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.context import ParallelMode
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from mindspore.train.model import Model
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor, LossMonitor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.communication.management import init
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.common import dtype as mstype
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from mindspore.common import set_seed
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from src.lr_generator import get_lr
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from src.Xception import xception
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from src.dataset import create_dataset
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from src.loss import CrossEntropySmooth
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from src.model_utils.config import config as args_opt, config_gpu, config_ascend
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from src.model_utils.moxing_adapter import moxing_wrapper
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from src.model_utils.device_adapter import get_device_id, get_rank_id, get_device_num
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set_seed(1)
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def modelarts_pre_process():
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'''modelarts pre process function.'''
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def unzip(zip_file, save_dir):
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import zipfile
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s_time = time.time()
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if not os.path.exists(os.path.join(save_dir, args_opt.modelarts_dataset_unzip_name)):
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zip_isexist = zipfile.is_zipfile(zip_file)
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if zip_isexist:
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fz = zipfile.ZipFile(zip_file, 'r')
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data_num = len(fz.namelist())
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print("Extract Start...")
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print("Unzip file num: {}".format(data_num))
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data_print = int(data_num / 100) if data_num > 100 else 1
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i = 0
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for file in fz.namelist():
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if i % data_print == 0:
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print("Unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
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i += 1
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fz.extract(file, save_dir)
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print("Cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
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int(int(time.time() - s_time) % 60)))
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print("Extract Done.")
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else:
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print("This is not zip.")
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else:
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print("Zip has been extracted.")
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if args_opt.modelarts_dataset_unzip_name:
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zip_file_1 = os.path.join(args_opt.data_path, args_opt.modelarts_dataset_unzip_name + ".zip")
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save_dir_1 = os.path.join(args_opt.data_path)
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sync_lock = "/tmp/unzip_sync.lock"
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# Each server contains 8 devices as most.
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if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
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print("Zip file path: ", zip_file_1)
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print("Unzip file save dir: ", save_dir_1)
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unzip(zip_file_1, save_dir_1)
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print("===Finish extract data synchronization===")
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try:
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os.mknod(sync_lock)
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except IOError:
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pass
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while True:
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if os.path.exists(sync_lock):
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break
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time.sleep(1)
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print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
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args_opt.train_data_dir = args_opt.data_path
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if args_opt.modelarts_dataset_unzip_name:
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args_opt.train_data_dir = os.path.join(args_opt.train_data_dir, args_opt.folder_name_under_zip_file)
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config_gpu.save_checkpoint_path = os.path.join(args_opt.output_path, config_gpu.save_checkpoint_path)
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config_ascend.save_checkpoint_path = os.path.join(args_opt.output_path, config_ascend.save_checkpoint_path)
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@moxing_wrapper(pre_process=modelarts_pre_process)
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def run_train():
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if args_opt.device_target == "Ascend":
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config = config_ascend
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elif args_opt.device_target == "GPU":
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config = config_gpu
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else:
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raise ValueError("Unsupported device_target.")
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# init distributed
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if args_opt.is_distributed:
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context.set_context(device_id=get_device_id(), mode=context.GRAPH_MODE, device_target=args_opt.device_target,
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save_graphs=False)
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init()
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rank = get_rank_id()
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group_size = get_device_num()
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parallel_mode = ParallelMode.DATA_PARALLEL
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context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=group_size, gradients_mean=True)
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else:
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rank = 0
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group_size = 1
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device_id = get_device_id()
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context.set_context(device_id=device_id, mode=context.GRAPH_MODE, device_target=args_opt.device_target,
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save_graphs=False)
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# define network
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net = xception(class_num=config.class_num)
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if args_opt.device_target == "Ascend":
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net.to_float(mstype.float16)
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# define loss
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if not config.use_label_smooth:
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config.label_smooth_factor = 0.0
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loss = CrossEntropySmooth(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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# define dataset
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dataset = create_dataset(args_opt.train_data_dir, do_train=True, batch_size=config.batch_size,
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device_num=group_size, rank=rank)
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step_size = dataset.get_dataset_size()
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# resume
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if args_opt.resume:
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ckpt = load_checkpoint(args_opt.resume)
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load_param_into_net(net, ckpt)
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# get learning rate
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loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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lr = Tensor(get_lr(lr_init=config.lr_init,
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lr_end=config.lr_end,
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lr_max=config.lr_max,
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warmup_epochs=config.warmup_epochs,
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total_epochs=config.epoch_size,
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steps_per_epoch=step_size,
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lr_decay_mode=config.lr_decay_mode,
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global_step=config.finish_epoch * step_size))
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# define optimization and model
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if args_opt.device_target == "Ascend":
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opt = Momentum(net.trainable_params(), lr, config.momentum, config.weight_decay, config.loss_scale)
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model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
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amp_level='O3', keep_batchnorm_fp32=True)
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elif args_opt.device_target == "GPU":
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if args_opt.is_fp32:
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opt = Momentum(net.trainable_params(), lr, config.momentum, config.weight_decay)
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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
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else:
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opt = Momentum(net.trainable_params(), lr, config.momentum, config.weight_decay, config.loss_scale)
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model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
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amp_level='O2', keep_batchnorm_fp32=True)
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# define callbacks
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cb = [TimeMonitor(), LossMonitor()]
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if config.save_checkpoint:
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if args_opt.device_target == "Ascend":
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save_ckpt_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/')
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elif args_opt.device_target == "GPU":
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if args_opt.is_fp32:
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save_ckpt_path = os.path.join(config.save_checkpoint_path, 'fp32/' + 'model_' + str(rank))
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else:
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save_ckpt_path = os.path.join(config.save_checkpoint_path, 'fp16/' + 'model_' + str(rank))
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(f"Xception-rank{rank}", directory=save_ckpt_path, config=config_ck)
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# begin train
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print("begin train")
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if args_opt.is_distributed:
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if rank == 0:
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cb += [ckpt_cb]
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model.train(config.epoch_size - config.finish_epoch, dataset, callbacks=cb, dataset_sink_mode=True)
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else:
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cb += [ckpt_cb]
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model.train(config.epoch_size - config.finish_epoch, dataset, callbacks=cb, dataset_sink_mode=True)
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print("train success")
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if __name__ == '__main__':
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run_train()
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