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82 lines
3.2 KiB
82 lines
3.2 KiB
# 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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"""model train script"""
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import os
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import shutil
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import numpy as np
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.train import Model
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from mindspore.nn.metrics import Accuracy
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore.common import set_seed
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from src.config import textrcnn_cfg as cfg
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from src.dataset import create_dataset
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from src.dataset import convert_to_mindrecord
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from src.textrcnn import textrcnn
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from src.utils import get_lr
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set_seed(0)
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if __name__ == '__main__':
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context.set_context(
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mode=context.GRAPH_MODE,
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save_graphs=False,
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device_target="Ascend")
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=device_id)
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if cfg.preprocess == 'true':
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print("============== Starting Data Pre-processing ==============")
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if os.path.exists(cfg.preprocess_path):
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shutil.rmtree(cfg.preprocess_path)
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os.mkdir(cfg.preprocess_path)
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convert_to_mindrecord(cfg.embed_size, cfg.data_path, cfg.preprocess_path, cfg.emb_path)
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if cfg.cell == "vanilla":
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print("============ Precision is lower than expected when using vanilla RNN architecture ===========")
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embedding_table = np.loadtxt(os.path.join(cfg.preprocess_path, "weight.txt")).astype(np.float32)
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network = textrcnn(weight=Tensor(embedding_table), vocab_size=embedding_table.shape[0],
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cell=cfg.cell, batch_size=cfg.batch_size)
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ds_train = create_dataset(cfg.preprocess_path, cfg.batch_size, True)
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step_size = ds_train.get_dataset_size()
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
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lr = get_lr(cfg, step_size)
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num_epochs = cfg.num_epochs
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if cfg.cell == "lstm":
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num_epochs = cfg.lstm_num_epochs
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opt = nn.Adam(params=network.trainable_params(), learning_rate=lr)
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loss_cb = LossMonitor()
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time_cb = TimeMonitor()
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model = Model(network, loss, opt, {'acc': Accuracy()}, amp_level="O3")
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print("============== Starting Training ==============")
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config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
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keep_checkpoint_max=cfg.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix=cfg.cell, directory=cfg.ckpt_folder_path, config=config_ck)
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model.train(num_epochs, ds_train, callbacks=[ckpoint_cb, loss_cb, time_cb])
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print("train success")
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