forked from huawei/mindspore2022
200 lines
8.2 KiB
Python
200 lines
8.2 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 Deeptext and get checkpoint files."""
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import os
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import time
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import numpy as np
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from src.Deeptext.deeptext_vgg16 import Deeptext_VGG16
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from src.dataset import data_to_mindrecord_byte_image, create_deeptext_dataset
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from src.lr_schedule import dynamic_lr
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from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
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from model_utils.config import config
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from model_utils.moxing_adapter import moxing_wrapper
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from model_utils.device_adapter import get_device_id, get_device_num, get_rank_id
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import mindspore.common.dtype as mstype
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from mindspore import context, Tensor, Parameter
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from mindspore.common import set_seed
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from mindspore.communication.management import init, get_group_size, get_rank
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from mindspore.context import ParallelMode
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from mindspore.nn import Momentum
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from mindspore.train import Model
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from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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np.set_printoptions(threshold=np.inf)
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set_seed(1)
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context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
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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, config.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. unzip file num: {}".format(data_num), flush=True)
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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)), flush=True)
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print("Extract Done.", flush=True)
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else:
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print("This is not zip.", flush=True)
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else:
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print("Zip has been extracted.", flush=True)
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if config.need_modelarts_dataset_unzip:
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zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
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save_dir_1 = os.path.join(config.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, flush=True)
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print("Unzip file save dir: ", save_dir_1, flush=True)
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unzip(zip_file_1, save_dir_1)
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print("===Finish extract data synchronization===", flush=True)
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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 {}."
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.format(get_device_id(), zip_file_1, save_dir_1), flush=True)
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config.save_checkpoint_path = os.path.join(config.output_path, config.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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device_type = "Ascend" if context.get_context("device_target") == "Ascend" else "GPU"
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if config.run_distribute:
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init()
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if device_type == "Ascend":
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rank = get_rank_id()
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device_num = get_device_num()
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else:
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context.reset_auto_parallel_context()
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rank = get_rank()
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device_num = get_group_size()
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context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True)
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else:
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rank = get_rank_id()
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device_num = 1
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print("Start create dataset!", flush=True)
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# It will generate mindrecord file in config.mindrecord_dir,
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# and the file name is DeepText.mindrecord0, 1, ... file_num.
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prefix = config.mindrecord_prefix
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config.train_images = config.imgs_path
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config.train_txts = config.annos_path
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mindrecord_dir = config.mindrecord_dir
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mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
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print("CHECKING MINDRECORD FILES ...", flush=True)
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if rank == 0 and not os.path.exists(mindrecord_file):
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if not os.path.isdir(mindrecord_dir):
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os.makedirs(mindrecord_dir)
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if os.path.isdir(config.coco_root):
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if not os.path.exists(config.coco_root):
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print("Please make sure config:coco_root is valid.", flush=True)
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raise ValueError(config.coco_root)
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print("Create Mindrecord. It may take some time.", flush=True)
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data_to_mindrecord_byte_image(True, prefix)
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print("Create Mindrecord Done, at {}".format(mindrecord_dir), flush=True)
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else:
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print("coco_root not exits.", flush=True)
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while not os.path.exists(mindrecord_file + ".db"):
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time.sleep(5)
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print("CHECKING MINDRECORD FILES DONE!", flush=True)
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# When create MindDataset, using the fitst mindrecord file, such as FasterRcnn.mindrecord0.
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dataset = create_deeptext_dataset(mindrecord_file, repeat_num=1,
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batch_size=config.batch_size, device_num=device_num, rank_id=rank)
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dataset_size = dataset.get_dataset_size()
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print("Create dataset done! dataset_size = ", dataset_size, flush=True)
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net = Deeptext_VGG16(config=config)
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net = net.set_train()
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load_path = config.pre_trained
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if load_path != "":
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param_dict = load_checkpoint(load_path)
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if device_type == "GPU":
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print("Converting pretrained checkpoint from fp16 to fp32", flush=True)
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for key, value in param_dict.items():
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tensor = value.asnumpy().astype(np.float32)
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param_dict[key] = Parameter(tensor, key)
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load_param_into_net(net, param_dict)
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if device_type == "Ascend":
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net.to_float(mstype.float16)
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loss = LossNet()
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lr = Tensor(dynamic_lr(config, rank_size=device_num), mstype.float32)
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opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
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weight_decay=config.weight_decay, loss_scale=config.loss_scale)
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net_with_loss = WithLossCell(net, loss)
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if config.run_distribute:
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net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale, reduce_flag=True,
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mean=True, degree=device_num)
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else:
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net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale)
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time_cb = TimeMonitor(data_size=dataset_size)
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loss_cb = LossCallBack(rank_id=rank)
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cb = [time_cb, loss_cb]
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if config.save_checkpoint:
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ckptconfig = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * dataset_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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save_checkpoint_path = os.path.join(config.save_checkpoint_path, "ckpt_" + str(rank) + "/")
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ckpoint_cb = ModelCheckpoint(prefix='deeptext', directory=save_checkpoint_path, config=ckptconfig)
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cb += [ckpoint_cb]
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model = Model(net)
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model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=True)
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if __name__ == '__main__':
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run_train()
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